kim woods

Chapter 6_Managing Quality

8/20/12
1
6 – 1
6
PowerPoint presentation to accompany
Heizer and Render
Operations Management, 10e
Principles of Operations Management, 8e

PowerPoint slides by Jeff Heyl
MG 6303 Prof.Vivek Veeraiah
Managing Quality
6 – 2
Outline
MG 6303 Prof.Vivek Veeraiah
u  Global Company Profile: Arnold
Palmer Hospital
u  Quality and Strategy
u  Defining Quality
u  Implications of Quality
u  Malcolm Baldrige National Quality
Award
u  Cost of Quality (COQ)
u  Ethics and Quality Management
6 – 3
Outline – Continued
MG 6303 Prof.Vivek Veeraiah
u  International Quality Standards
u  ISO 9000
u  ISO14000
6 – 4
Outline – Continued
MG 6303 Prof.Vivek Veeraiah
u  Total Quality Management
u  Continuous Improvement
u  Six Sigma
u  Employee Empowerment
u  Benchmarking
u  Just-in-Time (JIT)
u  Taguchi Concepts
u  Knowledge of TQM Tools
6 – 5
Outline – Continued
MG 6303 Prof.Vivek Veeraiah
u  Tools of TQM
u  Check Sheets
u  Scatter Diagrams
u  Cause-and-Effect Diagrams
u  Pareto Charts
u  Flowcharts
u  Histograms
u  Statistical Process Control (SPC)
6 – 6
Outline – Continued
MG 6303 Prof.Vivek Veeraiah
u  The Role of Inspection
u  When and Where to Inspect
u  Source Inspection
u  Service Industry Inspection
u  Inspection of Attributes versus
Variables
u  TQM in Services

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6 – 7
Learning Objectives
When you complete this chapter you should be able to:
MG 6303 Prof.Vivek Veeraiah
1.  Define quality and TQM
2.  Describe the ISO international
quality standards
3.  Explain Six Sigma
4.  Explain how benchmarking is used
5.  Explain quality robust products and
Taguchi concepts
6.  Use the seven tools of TQM
6 – 8
Managing Quality
Provides a Competitive
Advantage
MG 6303 Prof.Vivek Veeraiah
Arnold Palmer Hospital
u  Deliver over 16,000 babies annually
u  Virtually every type of quality tool is
employed
u  Continuous improvement
u  Employee empowerment
u  Benchmarking
u  Just-in-time
u  Quality tools
6 – 9
Quality and Strategy
MG 6303 Prof.Vivek Veeraiah
An operations manager’s objective
is to build a total quality
management system that identifies
and satisfies customer needs
6 – 10
Quality and Strategy
MG 6303 Prof.Vivek Veeraiah
u  Managing quality supports
differentiation, low cost, and
response strategies
u  Quality helps firms increase
sales and reduce costs
u  Building a quality organization is
a demanding task
6 – 11
Two Ways Quality
Improves Profitability
MG 6303 Prof.Vivek Veeraiah
Improved
Quality
Increased
Profits
u  Increased productivity
u  Lower rework and scrap costs
u  Lower warranty costs
Reduced Costs via
u  Improved response
u  Flexible pricing
u  Improved reputation
Sales Gains via
Figure 6.1
6 – 12
The Flow of Activities
MG 6303 Prof.Vivek Veeraiah
Organizational Practices
Leadership, Mission statement, Effective operating
procedures, Staff support, Training
Yields: What is important and what is to be
accomplished
Quality Principles
Customer focus, Continuous improvement, Benchmarking,
Just-in-time, Tools of TQM
Yields: How to do what is important and to be
accomplished
Employee Fulfillment
Empowerment, Organizational commitment
Yields: Employee attitudes that can accomplish
what is important
Customer Satisfaction
Winning orders, Repeat customers
Yields: An effective organization with
a competitive advantage Figure 6.2

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6 – 13
Defining Quality
MG 6303 Prof.Vivek Veeraiah
The totality of features and
characteristics of a product or
service that bears on its ability to
satisfy stated or implied needs
American Society for Quality
6 – 14
Different Views
MG 6303 Prof.Vivek Veeraiah
u  User-based: better performance,
more features
u  Manufacturing-based:
conformance to standards,
making it right the first time
u  Product-based: specific and
measurable attributes of the
product
6 – 15
Implications of Quality
MG 6303 Prof.Vivek Veeraiah
1.  Company reputation
u  Perception of new products
u  Employment practices
u  Supplier relations
2.  Product liability
u  Reduce risk
3.  Global implications
u  Improved ability to compete
6 – 16
Key Dimensions of
Quality
u  Performance
u  Features
u  Reliability
u  Conformance
MG 6303 Prof.Vivek Veeraiah
u  Durability
u  Serviceability
u  Aesthetics
u  Perceived
quality
u  Value
6 – 17
Costs of Quality
MG 6303 Prof.Vivek Veeraiah
u  Prevention costs – reducing the
potential for defects
u  Appraisal costs – evaluating
products, parts, and services
u  Internal failure – producing defective
parts or service before delivery
u  External costs – defects discovered
after delivery
6 – 18
Costs of Quality
MG 6303 Prof.Vivek Veeraiah
External Failure
Internal Failure
Prevention
Appraisal
Total
Cost
Quality Improvement
Total Cost

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6 – 19
Ethics and Quality
Management
MG 6303 Prof.Vivek Veeraiah
u  Operations managers must deliver
healthy, safe, quality products and
services
u  Poor quality risks injuries, lawsuits,
recalls, and regulation
u  Organizations are judged by how
they respond to problems
u  All stakeholders much be
considered
6 – 20
International Quality
Standards
MG 6303 Prof.Vivek Veeraiah
u  ISO 9000 series (Europe/EC)
u  Common quality standards for products
sold in Europe (even if made in U.S.)
u  2008 update places greater emphasis on
leadership and customer requirements
and satisfaction
u  ISO 14000 series (Europe/EC)
6 – 21
ISO 14000
Environmental Standard
Core Elements:
MG 6303 Prof.Vivek Veeraiah
u  Environmental management
u  Auditing
u  Performance evaluation
u  Labeling
u  Life cycle assessment
6 – 22
ISO 14000
Environmental Standard
Advantages:
MG 6303 Prof.Vivek Veeraiah
u  Positive public image and reduced
exposure to liability
u  Systematic approach to pollution
prevention
u  Compliance with regulatory
requirements and opportunities for
competitive advantage
u  Reduction in multiple audits
6 – 23
TQM
Encompasses entire organization, from supplier to
customer
Stresses a commitment by management to have a
continuing, companywide drive toward excellence in all
aspects of products and services that are important to
the customer
MG 6303 Prof.Vivek Veeraiah 6 – 24
Deming’s Fourteen
Points
MG 6303 Prof.Vivek Veeraiah
Table 6.2
1.  Create consistency of purpose
2.  Lead to promote change
3.  Build quality into the product; stop
depending on inspections
4.  Build long-term relationships based on
performance instead of awarding
business on price
5.  Continuously improve product, quality,
and service

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6 – 25
Deming’s Fourteen
Points
MG 6303 Prof.Vivek Veeraiah
Table 6.2
6.  Start training
7.  Emphasize leadership
8.  Drive out fear
9.  Break down barriers between
departments
10.  Stop haranguing workers
11.  Support, help, and improve
6 – 26
Deming’s Fourteen
Points
MG 6303 Prof.Vivek Veeraiah
Table 6.2
12.  Remove barriers to pride in work
13.  Institute education and self-
improvement
14.  Put everyone to work on the
transformation
6 – 27
Seven Concepts of TQM
1.  Continuous improvement
2.  Six Sigma
3.  Employee empowerment
4.  Benchmarking
5.  Just-in-time (JIT)
6.  Taguchi concepts
7.  Knowledge of TQM tools
MG 6303 Prof.Vivek Veeraiah 6 – 28
Continuous
Improvement
MG 6303 Prof.Vivek Veeraiah
u Represents continual
improvement of all processes
u Involves all operations and work
centers including suppliers and
customers
u People, Equipment, Materials,
Procedures
6 – 29
Shewhart’s PDCA Model
MG 6303 Prof.Vivek Veeraiah
4. Act
Implement
the plan
document
2. Do
Test the
plan
3. Check
Is the plan
working?
1. Plan
Identify the
pattern and
make a plan
Figure 6.3
6 – 30
Six Sigma
MG 6303 Prof.Vivek Veeraiah
u  Two meanings
u  Statistical definition of a process that
is 99.9997% capable, 3.4 defects per
million opportunities (DPMO)
u  A program designed to reduce
defects, lower costs, and improve
customer satisfaction

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6 – 31
Six Sigma
MG 6303 Prof.Vivek Veeraiah
u  Two meanings
u  Statistical definition of a process that
is 99.9997% capable, 3.4 defects per
million opportunities (DPMO)
u  A program designed to reduce
defects, lower costs, and improve
customer satisfaction
Mean
Lower limits Upper limits
3.4 defects/million
±6σ
2,700 defects/million
±3σ
Figure 6.4
6 – 32
Six Sigma Program
MG 6303 Prof.Vivek Veeraiah
u  Originally developed by Motorola,
adopted and enhanced by
Honeywell and GE
u  Highly structured approach to
process improvement
u  A strategy
u  A discipline – DMAIC 6σ
6 – 33
Six Sigma
MG 6303 Prof.Vivek Veeraiah
1.  Define critical outputs
and identify gaps for
improvement
2.  Measure the work and
collect process data
3.  Analyze the data
4.  Improve the process
5.  Control the new process to
make sure new performance
is maintained
DMAIC Approach
6 – 34
Six Sigma
Implementation
MG 6303 Prof.Vivek Veeraiah
u  Emphasize defects per million
opportunities as a standard metric
u  Provide extensive training
u  Focus on corporate sponsor support
(Champions)
u  Create qualified process improvement
experts (Black Belts, Green Belts, etc.)
u  Set stretch objectives
This cannot be accomplished without a major
commitment from top level management
6 – 35
Employee
Empowerment
MG 6303 Prof.Vivek Veeraiah
u  Getting employees involved in product
and process improvements
u  85% of quality problems are due
to process and material
u  Techniques
u  Build communication networks
that include employees
u  Develop open, supportive supervisors
u  Move responsibility to employees
u  Build a high-morale organization
u  Create formal team structures
6 – 36
Quality Circles
MG 6303 Prof.Vivek Veeraiah
u  Group of employees who meet
regularly to solve problems
u  Trained in planning, problem
solving, and statistical methods
u  Often led by a facilitator
u  Very effective when done
properly

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6 – 37
Benchmarking
MG 6303 Prof.Vivek Veeraiah
Use
inte
rnal

benc
hma
rkin
g
if yo
u’re
big
enou
gh
Selecting best practices to use as a
standard for performance
1.  Determine what to
benchmark
2.  Form a benchmark team
3.  Identify benchmarking partners
4.  Collect and analyze benchmarking
information
5.  Take action to match or exceed the
benchmark
6 – 38
Best Practices for
Resolving Customer
Complaints
MG 6303 Prof.Vivek Veeraiah
Best Practice Justification
Make it easy for clients
to complain
It is free market research
Respond quickly to
complaints
It adds customers and loyalty
Resolve complaints on
first contact
It reduces cost
Use computers to
manage complaints
Discover trends, share them, and align
your services
Recruit the best for
customer service jobs
It should be part of formal training and
career advancement
Table 6.3
6 – 39
Just-in-Time (JIT)
Relationship to quality:
MG 6303 Prof.Vivek Veeraiah
u  JIT cuts the cost of quality
u  JIT improves quality
u  Better quality means less
inventory and better, easier-to-
employ JIT system
6 – 40
Just-in-Time (JIT)
MG 6303 Prof.Vivek Veeraiah
u  ‘Pull’ system of production scheduling
including supply management
u  Production only when signaled
u  Allows reduced inventory levels
u  Inventory costs money and hides process
and material problems
u  Encourages improved process and
product quality
6 – 41
Just-In-Time (JIT)
Example
MG 6303 Prof.Vivek Veeraiah
Scrap Unreliable Vendors
Capacity
Imbalances
Work in process
inventory level
(hides problems)
6 – 42
Just-In-Time (JIT)
Example
MG 6303 Prof.Vivek Veeraiah
Reducing inventory reveals
problems so they can be solved
Scrap Unreliable Vendors
Capacity
Imbalances

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6 – 43
Taguchi Concepts
MG 6303 Prof.Vivek Veeraiah
u  Engineering and experimental
design methods to improve product
and process design
u  Identify key component and process
variables affecting product variation
u  Taguchi Concepts
u  Quality robustness
u  Quality loss function
u  Target-oriented quality
6 – 44
Quality Robustness
MG 6303 Prof.Vivek Veeraiah
u  Ability to produce products
uniformly in adverse
manufacturing and environmental
conditions
u  Remove the effects of adverse
conditions
u  Small variations in materials and
process do not destroy product
quality
6 – 45
Quality Loss Function
MG 6303 Prof.Vivek Veeraiah
u  Shows that costs increase as the
product moves away from what
the customer wants
u  Costs include customer
dissatisfaction, warranty
and service, internal
scrap and repair, and costs to
society
u  Traditional conformance
specifications are too simplistic
Target-
oriented
quality
6 – 46
Quality Loss Function
MG 6303 Prof.Vivek Veeraiah
Unacceptable
Poor
Good
Best
Fair
High loss
Loss (to
producing
organization,
customer,
and society)
Low loss
Frequency
Lower Target Upper
Specification
Target-oriented quality
yields more product in
the “best” category
Target-oriented quality
brings product toward
the target value
Conformance-oriented
quality keeps products
within 3 standard
deviations
Figure 6.5
L = D2C
where
L = loss to society
D = distance from
target value
C = cost of deviation
6 – 47
Tools of TQM
MG 6303 Prof.Vivek Veeraiah
u  Tools for Generating Ideas
u  Check sheets
u  Scatter diagrams
u  Cause-and-effect diagrams
u  Tools to Organize the Data
u  Pareto charts
u  Flowcharts
6 – 48
Tools of TQM
MG 6303 Prof.Vivek Veeraiah
u  Tools for Identifying Problems
u  Histogram
u  Statistical process control chart

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6 – 49
Seven Tools of TQM
MG 6303 Prof.Vivek Veeraiah

/
/

/ / /// /
// ///
// ////

///
//
/
Hour
Defect 1 2 3 4 5 6 7 8
A
B
C

/
/
//

/

(a) Check Sheet: An organized method of
recording data
Figure 6.6
6 – 50
Seven Tools of TQM
MG 6303 Prof.Vivek Veeraiah
(b) Scatter Diagram: A graph of the value
of one variable vs. another variable
Absenteeism
P
ro
du
ct
iv
ity

Figure 6.6
6 – 51
Seven Tools of TQM
MG 6303 Prof.Vivek Veeraiah
(c) Cause-and-Effect Diagram: A tool that
identifies process elements (causes) that
might effect an outcome
Figure 6.6
Cause
Materials Methods
Manpower Machinery
Effect
6 – 52
Seven Tools of TQM
MG 6303 Prof.Vivek Veeraiah
(d) Pareto Chart: A graph to identify and plot
problems or defects in descending order of
frequency
Figure 6.6
Fr
eq
ue
nc
y
P
er
ce
nt

A B C D E
6 – 53
Seven Tools of TQM
MG 6303 Prof.Vivek Veeraiah
(e) Flowchart (Process Diagram): A chart that
describes the steps in a process
Figure 6.6
6 – 54
Seven Tools of TQM
MG 6303 Prof.Vivek Veeraiah
(f) Histogram: A distribution showing the
frequency of occurrences of a variable
Figure 6.6
Distribution
Repair time (minutes)
Fr
eq
ue
nc
y

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6 – 55
Seven Tools of TQM
MG 6303 Prof.Vivek Veeraiah
(g) Statistical Process Control Chart: A chart with
time on the horizontal axis to plot values of a
statistic
Figure 6.6
Upper control limit
Target value
Lower control limit
Time
6 – 56
Cause-and-Effect
Diagrams
MG 6303 Prof.Vivek Veeraiah
Material
(ball)
Method
(shooting process)
Machine
(hoop &
backboard)
Manpower
(shooter)
Missed
free-throws
Figure 6.7
Rim alignment
Rim size
Backboard
stability
Rim height
Follow-through
Hand position
Aiming point
Bend knees
Balance
Size of ball
Lopsidedness
Grain/Feel
(grip)
Air pressure
Training
Conditioning Motivation
Concentration
Consistency
6 – 57
Pareto Charts
MG 6303 Prof.Vivek Veeraiah
Number of
occurrences
Room svc Check-in Pool hours Minibar Misc.
72% 16% 5% 4% 3%
12
4 3 2
54
– 100 – 93 – 88 – 72
70 –
60 –
50 –
40 –
30 –
20 –
10 –
0 –
Fr
eq
ue
nc
y
(n
um
be
r)

Causes and percent of the total
C
um
ul
at
iv
e
pe
rc
en
t
Data for October
6 – 58
Flow Charts
MG 6303 Prof.Vivek Veeraiah
MRI Flowchart
1.  Physician schedules MRI
2.  Patient taken to MRI
3.  Patient signs in
4.  Patient is prepped
5.  Technician carries out MRI
6.  Technician inspects film
7.  If unsatisfactory, repeat
8.  Patient taken back to room
9.  MRI read by radiologist
10.  MRI report transferred to
physician
11.  Patient and physician discuss
11
10
20%
9
8
80%
1 2 3 4 5 6 7
6 – 59
Statistical Process
Control (SPC)
MG 6303 Prof.Vivek Veeraiah
u  Uses statistics and control charts to
tell when to take corrective action
u  Drives process improvement
u  Four key steps
u  Measure the process
u  When a change is indicated, find the
assignable cause
u  Eliminate or incorporate the cause
u  Restart the revised process
6 – 60
An SPC Chart
MG 6303 Prof.Vivek Veeraiah
Upper control limit
Coach’s target value
Lower control limit
Game number
| | | | | | | | |
1 2 3 4 5 6 7 8 9
20%
10%
0%
Plots the percent of free throws missed
Figure 6.8

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6 – 61
Inspection
MG 6303 Prof.Vivek Veeraiah
u  Involves examining items to see if
an item is good or defective
u  Detect a defective product
u  Does not correct deficiencies in
process or product
u  It is expensive
u  Issues
u  When to inspect
u  Where in process to inspect
6 – 62
When and Where to
Inspect
MG 6303 Prof.Vivek Veeraiah
1.  At the supplier’s plant while the supplier is
producing
2.  At your facility upon receipt of goods from
the supplier
3.  Before costly or irreversible processes
4.  During the step-by-step production process
5.  When production or service is complete
6.  Before delivery to your customer
7.  At the point of customer contact
6 – 63
Inspection
MG 6303 Prof.Vivek Veeraiah
u  Many problems
u  Worker fatigue
u  Measurement error
u  Process variability
u  Cannot inspect quality into a
product
u  Robust design, empowered
employees, and sound processes
are better solutions
6 – 64
Source Inspection
MG 6303 Prof.Vivek Veeraiah
u  Also known as source control
u  The next step in the process is
your customer
u  Ensure perfect product
to your customer
Poka-yoke is the concept of foolproof devices
or techniques designed to pass only
acceptable product
6 – 65
Service Industry
Inspection
MG 6303 Prof.Vivek Veeraiah
Organization What is Inspected Standard
Jones Law Office Receptionist
performance
Billing

Attorney
Is phone answered by the
second ring
Accurate, timely, and
correct format
Promptness in returning
calls
Table 6.4
6 – 66
Service Industry
Inspection
MG 6303 Prof.Vivek Veeraiah
Organization What is Inspected Standard
Hard Rock Hotel

Reception
desk
Doorman

Room

Minibar
Use customer’s name

Greet guest in less than 30
seconds
All lights working, spotless
bathroom
Restocked and charges
accurately posted to bill
Table 6.4

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12
6 – 67
Service Industry
Inspection
MG 6303 Prof.Vivek Veeraiah
Organization What is Inspected Standard
Arnold Palmer
Hospital
Billing

Pharmacy

Lab
Nurses

Admissions
Accurate, timely, and
correct format
Prescription accuracy,
inventory accuracy
Audit for lab-test accuracy
Charts immediately
updated
Data entered correctly and
completely
Table 6.4
6 – 68
Service Industry
Inspection
MG 6303 Prof.Vivek Veeraiah
Organization What is Inspected Standard
Olive Garden
Restaurant
Busboy

Busboy

Waiter
Serves water and bread
within 1 minute
Clears all entrée items and
crumbs prior to dessert
Knows and suggest
specials, desserts
Table 6.4
6 – 69
Service Industry
Inspection
MG 6303 Prof.Vivek Veeraiah
Organization What is Inspected Standard
Nordstrom
Department
Store

Display areas

Stockrooms

Salesclerks
Attractive, well-organized,
stocked, good lighting
Rotation of goods,
organized, clean
Neat, courteous, very
knowledgeable

Table 6.4
6 – 70
Attributes Versus
Variables
u  Attributes
u  Items are either good or bad, acceptable
or unacceptable
u  Does not address degree of failure
u  Variables
u  Measures dimensions such as weight,
speed, height, or strength
u  Falls within an acceptable range
u  Use different statistical techniques
MG 6303 Prof.Vivek Veeraiah
6 – 71
TQM In Services
MG 6303 Prof.Vivek Veeraiah
u  Service quality is more difficult to
measure than the quality of goods
u  Service quality perceptions depend
on
u  Intangible differences between
products
u  Intangible expectations customers
have of those products
6 – 72
Service Quality
MG 6303 Prof.Vivek Veeraiah
The Operations Manager must
recognize:
1.  The tangible component of
services is important
2.  The service process is important
3.  The service is judged against the
customer’s expectations
4.  Exceptions will occur

8/20/12
13
6 – 73
Service
Specifications
at UPS
MG 6303 Prof.Vivek Veeraiah 6 – 74
Determinants of Service
Quality
MG 6303 Prof.Vivek Veeraiah
Reliability Consistency of performance and dependability
Responsiveness Willingness or readiness of employees
Competence Required skills and knowledge
Access Approachability and ease of contact
Courtesy Politeness, respect, consideration, friendliness
Communication Keeping customers informed
Credibility Trustworthiness, believability, honesty
Security Freedom from danger, risk, or doubt
Understanding/
knowing the customer Understand the customer’s needs
Tangibles Physical evidence of the service
Table 6.5
6 – 75
Service Recovery
Strategy
u  Managers should have a plan for
when services fail
u  Marriott’s LEARN routine
u  Listen
u  Empathize
u  Apologize
u  React
u  Notify
MG 6303 Prof.Vivek Veeraiah

this is operations management book

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Jesse H. Jones Professor of Business Administration
Texas Lutheran University
Charles Harwood Professor of Operations Management
Crummer Graduate School of Business
Rollins College

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Library of congress cataloging-in-publication data
ISBN 10: 0-13-216392-6
ISBN 13: 978-0-13-216392-7
Heizer, Jay H.
Operations management / Jay Heizer, Barry Render. — 10th ed., Flexible version.
p. cm.
Includes bibliographical references and index.
ISBN-13: 978-0-13-216392-7 (alk. paper)
ISBN-10: 0-13-216392-6 (alk. paper)
1. Production management. I. Render, Barry. II. Title.
TS155.H3726 2010
658.5—dc22
2010039417

To: Tristan, Sebastian, Max, Kate, Owen, Clara;
the next generation is in good hands.
—JH
To: Suzan, Samantha, Dara, and Joey
—BR

Jay Heizer Professor Emeritus, the Jesse H. Jones Chair of Business Administration, Texas
Lutheran University, Seguin, Texas. He received his B.B.A. and M.B.A. from the University of
North Texas and his Ph.D. in Management and Statistics from Arizona State University. He was
previously a member of the faculty at the University of Memphis, the University of Oklahoma,
Virginia Commonwealth University, and the University of Richmond. He has also held visit-
ing positions at Boston University, George Mason University, the Czech Management Center,
and the Otto-Von-Guericka University, Magdeburg.
Dr. Heizer’s industrial experience is extensive. He learned the practical side of operations
management as a machinist apprentice at Foringer and Company, as a production planner
for Westinghouse Airbrake, and at General Dynamics, where he worked in engineering
administration. In addition, he has been actively involved in consulting in the OM and MIS
areas for a variety of organizations, including Philip Morris, Firestone, Dixie Container
Corporation, Columbia Industries, and Tenneco. He holds the CPIM certification from
APICS—the Association for Operations Management.
Professor Heizer has co-authored 5 books and has published more than 30 articles on a
variety of management topics. His papers have appeared in the Academy of Management
Journal, Journal of Purchasing, Personnel Psychology, Production & Inventory Control
Management, APICS—The Performance Advantage, Journal of Management History, IIE
Solutions and Engineering Management, among others. He has taught operations manage-
ment courses in undergraduate, graduate, and executive programs.
Barry Render Professor Emeritus, the Charles Harwood Professor of Operations
Management, Crummer Graduate School of Business, Rollins College, Winter Park, Florida.
He received his B.S. in Mathematics and Physics at Roosevelt University, and his M.S. in
Operations Research and Ph.D. in Quantitative Analysis at the University of Cincinnati. He
previously taught at George Washington University, University of New Orleans, Boston
University, and George Mason University, where he held the Mason Foundation Professorship
in Decision Sciences and was Chair of the Decision Science Department. Dr. Render has also
worked in the aerospace industry, for General Electric, McDonnell Douglas, and NASA.
Professor Render has co-authored 10 textbooks for Prentice Hall, including Managerial
Decision Modeling with Spreadsheets, Quantitative Analysis for Management, Service
Management, Introduction to Management Science, and Cases and Readings in
Management Science. Quantitative Analysis for Management, now in its 11th edition, is a
leading text in that discipline in the United States and globally. Dr. Render’s more than 100
articles on a variety of management topics have appeared in Decision Sciences, Production
and Operations Management, Interfaces, Information and Management, Journal of
Management Information Systems, Socio-Economic Planning Sciences, IIE Solutions, and
Operations Management Review, among others.
Dr. Render has been honored as an AACSB Fellow and was twice named a Senior Fulbright
Scholar. He was Vice President of the Decision Science Institute Southeast Region and served
as Software Review Editor for Decision Line for six years and as Editor of the New York Times
Operations Management special issues for five years. From 1984 to 1993, Dr. Render was
President of Management Service Associates of Virginia, Inc., whose technology clients
included the FBI; the U.S. Navy; Fairfax County, Virginia; and C&P Telephone.
Dr. Render has taught operations management courses in Rollins College’s MBA and
Executive MBA programs. He has received that school’s Welsh Award as leading Professor
and was selected by Roosevelt University as the 1996 recipient of the St. Claire Drake
Award for Outstanding Scholarship. In 2005, Dr. Render received the Rollins College MBA
Student Award for Best Overall Course, and in 2009 was named Professor of the Year by
full-time MBA students.
vi
About the Authors

PART ONE
Introduction to Operations Management 1
1. Operations and Productivity 1
2. Operations Strategy in a Global
Environment 23
3. Project Management 47
4. Forecasting 83
PART TWO
Designing Operations 123
5. Design of Goods and Services 123
6. Managing Quality 153
Supplement 6: Statistical Process Control 177
7. Process Strategy and Sustainability 201
Supplement 7: Capacity and Constraint
Management 227
8. Location Strategies 251
9. Layout Strategies 273
10. Human Resources, Job Design, and Work
Measurement 303
PART THREE
Managing Operations 333
11. Supply-Chain Management 333
Supplement 11: Outsourcing as a Supply-
Chain Strategy 359
12. Inventory Management 371
13. Aggregate Planning 407
14. Material Requirements Planning (MRP)
and ERP 433
15. Short-Term Scheduling 465
16. JIT and Lean Operations 495
17. Maintenance and Reliability 517
PART FOUR
Quantitative Modules 531
A. Decision-Making Tools 531
B. Linear Programming 547
C. Transportation Models 567
D. Waiting-Line Models 583
E. Learning Curves 605
F. Simulation 615
Online Tutorials
1. Statistical Tools for Managers T1-1
2. Acceptance Sampling T2-1
3. The Simplex Method of Linear
Programming T3-1
4. The MODI and VAM Methods
of Solving Transportation Problems T4-1
5. Vehicle Routing and Scheduling T5-1
Brief Table of Contents
vii

This page intentionally left blank

ix
About the Authors vi
Preface xvii
PART ONE
Introduction to Operations
Management 1
1. Operations and Productivity 1
Global Company Profile: Hard Rock Cafe 2
What Is Operations Management? 4
Organizing to Produce Goods and Services 4
Why Study OM? 6
What Operations Managers Do 7
The Heritage of Operations Management 8
Operations in the Service Sector 10
Differences Between Goods and Services 10
Growth of Services 11
Service Pay 11
Exciting New Trends in Operations
Management 12
The Productivity Challenge 13
Productivity Measurement 14
Productivity Variables 16
Productivity and the Service Sector 18
Ethics and Social Responsibility 19
Chapter Summary 19 • Key Terms 20 • Solved
Problems 20 • Bibliography 21
2. Operations Strategy in a Global
Environment 23
Global Company Profile: Boeing 24
A Global View of Operations 26
Cultural and Ethical Issues 29
Developing Missions and Strategies 30
Mission 30
Strategy 30
Achieving Competitive Advantage Through
Operations 31
Competing on Differentiation 31
Competing on Cost 32
Competing on Response 32
Ten Strategic OM Decisions 35
Issues in Operations Strategy 36
Strategy Development and Implementation 39
Key Success Factors and Core Competencies 39
Build and Staff the Organization 41
Integrate OM with Other Activities 41
Global Operations Strategy Options 42
International Strategy 42
Multidomestic Strategy 43
Global Strategy 43
Transnational Strategy 43
Chapter Summary 44 • Key Terms 44 • Solved
Problem 44 • Bibliography 45
3. Project Management 47
Global Company Profile: Bechtel Group 48
The Importance of Project Management 50
Project Planning 50
The Project Manager 51
Work Breakdown Structure 52
Project Scheduling 53
Project Controlling 54
Project Management Techniques: PERT
and CPM 55
The Framework of PERT and CPM 55
Network Diagrams and Approaches 55
Activity-on-Node Example 57
Activity-on-Arrow Example 60
Determining the Project Schedule 60
Forward Pass 61
Backward Pass 63
Calculating Slack Time and Identifying the
Critical Path(s) 64
Variability in Activity Times 65
Three Time Estimates in PERT 66
Probability of Project Completion 68
Cost–Time Trade-Offs and Project Crashing 71
A Critique of PERT and CPM 73
Using Microsoft Project to Manage Projects 74
Chapter Summary 77 • Key Terms 77 • Using
Software to Solve Project Management Problems
77 • Solved Problems 78 • Bibliography 81
Table of Contents

4. Forecasting 83
Global Company Profile: Walt Disney Parks &
Resorts 84
What Is Forecasting? 86
Forecasting Time Horizons 86
The Influence of Product Life Cycle 87
Types of Forecasts 87
The Strategic Importance of Forecasting 87
Human Resources 87
Capacity 87
Supply-Chain Management 88
Seven Steps in the Forecasting System 88
Forecasting Approaches 89
Overview of Qualitative Methods 89
Overview of Quantitative Methods 89
Time-Series Forecasting 90
Decomposition of a Time Series 90
Naive Approach 90
Moving Averages 91
Exponential Smoothing 94
Measuring Forecast Error 95
Exponential Smoothing with Trend Adjustment 98
Trend Projections 101
Seasonal Variations in Data 103
Cyclical Variations in Data 108
Associative Forecasting Methods: Regression
and Correlation Analysis 108
Using Regression Analysis for Forecasting 108
Standard Error of the Estimate 110
Correlation Coefficients for Regression Lines 111
Multiple-Regression Analysis 113
Monitoring and Controlling Forecasts 113
Adaptive Smoothing 115
Focus Forecasting 115
Forecasting in the Service Sector 116
Chapter Summary 117 • Key Terms 117 • Using
Software in Forecasting 118 • Solved Problems
119 • Bibliography 121
PART TWO
Designing Operations 123
5. Design of Goods and Services 123
Global Company Profile: Regal Marine 124
Goods and Services Selection 126
Product Strategy Options Support Competitive
Advantage 126
Product Life Cycles 127
Life Cycle and Strategy 128
Product-by-Value Analysis 128
Generating New Products 129
New Product Opportunities 129
Importance of New Products 129
Product Development 130
Product Development System 130
Quality Function Deployment (QFD) 131
Organizing for Product Development 133
Manufacturability and Value Engineering 134
Issues for Product Design 135
Robust Design 135
Modular Design 135
Computer-Aided Design (CAD) 136
Computer-Aided Manufacturing (CAM) 137
Virtual Reality Technology 137
Value Analysis 137
Ethics, Environmentally-Friendly Designs, and
Sustainability 138
Systems and Life Cycle Perspectives 138
Time-Based Competition 140
Purchasing Technology by Acquiring a Firm 141
Joint Ventures 142
Alliances 142
Defining a Product 142
Make-or-Buy Decisions 143
Group Technology 144
Documents for Production 144
Product Life-Cycle Management (PLM) 145
Service Design 146
Documents for Services 147
Application of Decision Trees to Product Design 149
Transition to Production 150
Chapter Summary 151 • Key Terms 151 • Solved
Problem 151 • Bibliography 152
6. Managing Quality 153
Global Company Profile: Arnold Palmer
Hospital 154
Quality and Strategy 156
Defining Quality 156
Implications of Quality 157
Malcolm Baldrige National Quality Award 158
Cost of Quality (COQ) 158
Ethics and Quality Management 158
International Quality Standards 159
ISO 9000 159
ISO 14000 159
Total Quality Management 160
Continuous Improvement 161
Six Sigma 161
x Table of Contents

Employee Empowerment 162
Benchmarking 163
Just-in-Time (JIT) 164
Taguchi Concepts 165
Knowledge of TQM Tools 166
Tools of TQM 166
Check Sheets 166
Scatter Diagrams 167
Cause-and-Effect Diagrams 167
Pareto Charts 167
Flowcharts 168
Histograms 169
Statistical Process Control (SPC) 169
The Role of Inspection 170
When and Where to Inspect 170
Source Inspection 171
Service Industry Inspection 171
Inspection of Attributes versus
Variables 171
TQM in Services 172
Chapter Summary 175 • Key Terms 175 •
Bibliography 175
Supplement 6: Statistical
Process Control 177
Statistical Process Control (SPC) 178
Control Charts for Variables 180
The Central Limit Theorem 180
Setting Mean Chart Limits ( -Charts) 181
Setting Range Chart Limits (R-Charts) 185
Using Mean and Range Charts 185
Control Charts for Attributes 186
Managerial Issues and Control Charts 190
Process Capability 191
Process Capability Ratio (Cp) 191
Process Capability Index 192
Acceptance Sampling 193
Operating Characteristic Curve 194
Average Outgoing Quality 195
Supplement Summary 196 • Key Terms 196 •
Using Software for SPC 196 • Solved
Problems 197 • Bibliography 199
7 Process Strategy and Sustainability 201
Global Company Profile: Harley-Davidson 202
Four Process Strategies 204
Process Focus 204
Repetitive Focus 205
Product Focus 206
Mass Customization Focus 206
Comparison of Process Choices 208
Process Analysis and Design 211
Flowchart 211
Time-Function Mapping 211
Value-Stream Mapping 212
Process Charts 213
Service Blueprinting 214
Special Considerations for Service Process
Design 214
Customer Interaction and Process Design 215
More Opportunities to Improve
Service Processes 217
Selection of Equipment and Technology 217
Production Technology 218
Machine Technology 218
Automatic Identification Systems (AISs)
and RFID 218
Process Control 219
Vision Systems 219
Robots 220
Automated Storage and Retrieval Systems
(ASRSs) 220
Automated Guided Vehicles (AGVs) 220
Flexible Manufacturing Systems (FMSs) 220
Computer-Integrated Manufacturing (CIM) 220
Technology in Services 221
Process Redesign 223
Sustainability 223
Resources 223
Recycle 223
Regulations 224
Reputation 224
Chapter Summary 225 • Key Terms 225 • Solved
Problem 225 • Bibliography 226
Supplement 7: Capacity and Constraint
Management 227
Capacity 228
Design and Effective Capacity 228
Capacity and Strategy 230
Capacity Considerations 231
Managing Demand 231
Demand and Capacity Management in the Service
Sector 233
Bottleneck Analysis and the Theory of Constraints 234
Process Times for Stations, Systems, and Cycles 235
Theory of Constraints 237
Bottleneck Management 237
Break-Even Analysis 238
Single-Product Case 240
1Cpk2
x
Table of Contents xi

xii Table of Contents
Multiproduct Case 240
Reducing Risk with Incremental Changes 242
Applying Expected Monetary Value (EMV) to
Capacity Decisions 243
Applying Investment Analysis to Strategy-Driven
Investments 244
Investment, Variable Cost, and Cash Flow 244
Net Present Value 244
Supplement Summary 247 • Key Terms 247 • Using
Software for Break-Even Analysis 247 • Solved
Problems 247 • Bibliography 250
8. Location Strategies 251
Global Company Profile: FedEx 252
The Strategic Importance of Location 254
Factors That Affect Location Decisions 255
Labor Productivity 256
Exchange Rates and Currency Risk 256
Costs 257
Political Risk, Values, and Culture 258
Proximity to Markets 258
Proximity to Suppliers 258
Proximity to Competitors (Clustering) 258
Methods of Evaluating Location Alternatives 259
The Factor-Rating Method 259
Locational Break-Even Analysis 260
Center-of-Gravity Method 262
Transportation Model 263
Service Location Strategy 264
How Hotel Chains Select Sites 265
The Call Center Industry 266
Geographic Information Systems 267
Chapter Summary 268 • Key Terms 268 • Using
Software to Solve Location Problems 269 • Solved
Problems 270 • Bibliography 271
9. Layout Strategies 273
Global Company Profile: McDonald’s 274
The Strategic Importance of Layout Decisions 276
Types of Layout 276
Office Layout 278
Retail Layout 279
Servicescapes 280
Warehousing and Storage Layouts 281
Cross-Docking 282
Random Stocking 282
Customizing 282
Fixed-Position Layout 282
Process-Oriented Layout 283
Computer Software for Process-Oriented Layouts 287
Work Cells 288
Requirements of Work Cells 288
Staffing and Balancing Work Cells 289
The Focused Work Center and the Focused
Factory 291
Repetitive and Product-Oriented Layout 292
Assembly-Line Balancing 293
Chapter Summary 298 • Key Terms 298 • Using
Software to Solve Layout Problems 298 • Solved
Problems 299 • Bibliography 302
10. Human Resources, Job Design, and Work
Measurement 303
Global Company Profile: Rusty Wallace’s NASCAR
Racing Team 304
Human Resource Strategy for Competitive
Advantage 306
Constraints on Human Resource Strategy 306
Labor Planning 307
Employment-Stability Policies 307
Work Schedules 307
Job Classifications and Work Rules 308
Job Design 308
Labor Specialization 308
Job Expansion 308
Psychological Components of Job Design 309
Self-Directed Teams 310
Motivation and Incentive Systems 311
Ergonomics and the Work Environment 311
Methods Analysis 314
The Visual Workplace 315
Labor Standards 317
Historical Experience 317
Time Studies 317
Predetermined Time Standards 322
Work Sampling 325
Ethics 328
Chapter Summary 328 • Key Terms 328 • Solved
Problems 329 • Bibliography 331
PART THREE
Managing Operations 333
11. Supply-Chain Management 333
Global Company Profile: Darden
Restaurants 334
The Supply Chain’s Strategic Importance 336
Supply-Chain Risk 337
Ethics and Sustainability 339

Table of Contents xiii
Supply-Chain Economics 340
Make-or-Buy Decisions 341
Outsourcing 341
Supply-Chain Strategies 341
Many Suppliers 341
Few Suppliers 341
Vertical Integration 342
Joint Ventures 343
Keiretsu Networks 343
Virtual Companies 343
Managing the Supply Chain 343
Issues in an Integrated Supply Chain 344
Opportunities in an Integrated Supply Chain 345
E-Procurement 347
Online Catalogs 347
Auctions 348
RFQs 348
Real-Time Inventory Tracking 348
Vendor Selection 349
Vendor Evaluation 349
Vendor Development 350
Negotiations 350
Logistics Management 350
Distribution Systems 351
Third-Party Logistics 351
Cost of Shipping Alternatives 352
Security and JIT 353
Measuring Supply-Chain Performance 354
The SCOR Model 356
Chapter Summary 357 • Key Terms 357 • Solved
Problems 357 • Bibliography 358
Supplement 11: Outsourcing as a
Supply-Chain Strategy 359
What Is Outsourcing? 360
Strategic Planning and Core Competencies 361
The Theory of Comparative Advantage 362
Risks of Outsourcing 363
Evaluating Outsourcing Risk with Factor Rating 365
Rating International Risk Factors 365
Rating Outsource Providers 366
Advantages and Disadvantages of
Outsourcing 367
Advantages of Outsourcing 367
Disadvantages of Outsourcing 367
Audits and Metrics to Evaluate Performance 368
Ethical Issues in Outsourcing 368
Supplement Summary 369 • Key Terms 369 •
Using Software to Solve Outsourcing
Problems 369 • Bibliography 369
12. Inventory Management 371
Global Company Profile: Amazon.com 372
The Importance of Inventory 374
Functions of Inventory 374
Types of Inventory 375
Managing Inventory 375
ABC Analysis 375
Record Accuracy 377
Cycle Counting 377
Control of Service Inventories 379
Inventory Models 380
Independent vs. Dependent Demands 380
Holding, Ordering, and Setup Costs 380
Inventory Models for Independent
Demand 380
The Basic Economic Order Quantity (EOQ)
Model 381
Minimizing Costs 381
Reorder Points 386
Production Order Quantity Model 387
Quantity Discount Models 390
Probabilistic Models and Safety Stock 393
Other Probabilistic Models 396
Single-Period Model 398
Fixed-Period (P) Systems 399
Chapter Summary 400 • Key Terms 401 • Using
Software to Solve Inventory Problems 401 • Solved
Problems 402 • Bibliography 405
13. Aggregate Planning 407
Global Company Profile: Frito-Lay 408
The Planning Process 410
Planning Horizons 410
The Nature of Aggregate Planning 411
Aggregate Planning Strategies 412
Capacity Options 413
Demand Options 414
Mixing Options to Develop a Plan 414
Methods for Aggregate Planning 415
Graphical Methods 415
Mathematical Approaches 420
Comparison of Aggregate Planning Methods 422
Aggregate Planning in Services 422
Restaurants 424
Hospitals 424
National Chains of Small Service Firms 424
Miscellaneous Services 424
Airline Industry 425
Yield Management 425

xiv Table of Contents
Chapter Summary 428 • Key Terms 429 • Using
Software for Aggregate Planning 429 • Solved
Problems 429 • Bibliography 431
14. Material Requirements Planning
(MRP) and ERP 433
Global Company Profile: Wheeled Coach 434
Dependent Demand 436
Dependent Inventory Model Requirements 436
Master Production Schedule 436
Bills of Material 438
Accurate Inventory Records 441
Purchase Orders Outstanding 441
Lead Times for Components 441
MRP Structure 441
MRP Management 446
MRP Dynamics 446
MRP and JIT 446
Lot-Sizing Techniques 447
Extensions of MRP 451
Material Requirements Planning II
(MRP II) 451
Closed-Loop MRP 452
Capacity Planning 453
MRP in Services 454
Distribution Resource Planning (DRP) 454
Enterprise Resource Planning (ERP) 455
Advantages and Disadvantages of ERP
Systems 458
ERP in the Service Sector 458
Chapter Summary 458 • Key Terms 458 • Using
Software to Solve MRP Problems 459 • Solved
Problems 460 • Bibliography 463
15. Short-Term Scheduling 465
Global Company Profile: Delta Air Lines 466
The Importance of Short-Term Scheduling 468
Scheduling Issues 468
Forward and Backward Scheduling 470
Scheduling Criteria 470
Scheduling Process-Focused Facilities 471
Loading Jobs 472
Input–Output Control 472
Gantt Charts 474
Assignment Method 475
Sequencing Jobs 478
Priority Rules for Dispatching Jobs 478
Critical Ratio 481
Sequencing N Jobs on Two Machines: Johnson’s
Rule 482
Limitations of Rule-Based Dispatching Systems 483
Finite Capacity Scheduling (FCS) 484
Scheduling Repetitive Facilities 485
Scheduling Services 486
Scheduling Service Employees with Cyclical
Scheduling 488
Chapter Summary 489 • Key Terms 489 • Using
Software for Short-Term Scheduling 489 • Solved
Problems 492 • Bibliography 494
16. JIT and Lean Operations 495
Global Company Profile: Toyota Motor
Corporation 496
Just-in-Time, the Toyota Production System,
and Lean Operations 498
Eliminate Waste 498
Remove Variability 499
Improve Throughput 500
Just-in-Time (JIT) 500
JIT Partnerships 501
Concerns of Suppliers 502
JIT Layout 503
Distance Reduction 503
Increased Flexibility 503
Impact on Employees 503
Reduced Space and Inventory 503
JIT Inventory 504
Reduce Inventory and Variability 504
Reduce Lot Sizes 504
Reduce Setup Costs 506
JIT Scheduling 506
Level Schedules 507
Kanban 508
JIT Quality 510
Toyota Production System 511
Continuous Improvement 511
Respect for People 511
Standard Work Practice 511
Lean Operations 512
Building a Lean Organization 512
Lean Operations in Services 513
Chapter Summary 515 • Key Terms 515 • Solved
Problems 515 • Bibliography 515
17. Maintenance and Reliability 517
Global Company Profile: Orlando Utilities
Commission 518
The Strategic Importance of Maintenance and
Reliability 520
Reliability 521
Improving Individual Components 521
Providing Redundancy 523

Table of Contents xv
Maintenance 524
Implementing Preventive Maintenance 524
Increasing Repair Capabilities 528
Autonomous Maintenance 528
Total Productive Maintenance 528
Techniques for Enhancing Maintenance 529
Chapter Summary 529 • Key Terms 529 • Using
Software to Solve Reliability Problems 530 •
Solved Problems 530 • Bibliography 530
PART FOUR
Quantitative Modules 531
A. Decision-Making Tools 531
The Decision Process in Operations 532
Fundamentals of Decision Making 533
Decision Tables 534
Types of Decision-Making Environments 534
Decision Making under Uncertainty 535
Decision Making under Risk 536
Decision Making under Certainty 537
Expected Value of Perfect Information
(EVPI) 537
Decision Trees 538
A More Complex Decision Tree 539
Using Decision Trees in Ethical Decision
Making 541
The Poker Decision Process 542
Module Summary 543 • Key Terms 543 • Using
Software for Decision Models 543 • Solved
Problems 545 • Bibliography 546
B. Linear Programming 547
Why Use Linear Programming? 548
Requirements of a Linear Programming
Problem 549
Formulating Linear Programming Problems 549
Shader Electronics Example 549
Graphical Solution to a Linear Programming
Problem 550
Graphical Representation of Constraints 550
Iso-Profit Line Solution Method 551
Corner-Point Solution Method 553
Sensitivity Analysis 555
Sensitivity Report 556
Changes in the Resources or Right-Hand-Side
Values 556
Changes in the Objective Function Coefficient 557
Solving Minimization Problems 557
Linear Programming Applications 559
Production-Mix Example 559
Diet Problem Example 560
Labor Scheduling Example 561
The Simplex Method of LP 562
Module Summary 563 • Key Terms 563 • Using
Software to Solve LP Problems 563 • Solved
Problems 564 • Bibliography 566
C. Transportation Models 567
Transportation Modeling 568
Developing an Initial Solution 570
The Northwest-Corner Rule 570
The Intuitive Lowest-Cost Method 571
The Stepping-Stone Method 572
Special Issues in Modeling 575
Demand Not Equal to Supply 575
Degeneracy 576
Module Summary 577 • Key Terms 577 • Using
Software to Solve Transportation Problems 578 •
Solved Problems 579 • Bibliography 581
D. Waiting-Line Models 583
Queuing Theory 584
Characteristics of a Waiting-Line System 585
Arrival Characteristics 585
Waiting-Line Characteristics 586
Service Characteristics 587
Measuring a Queue’s Performance 588
Queuing Costs 589
The Variety of Queuing Models 590
Model A (M/M/1): Single-Channel Queuing
Model with Poisson Arrivals and Exponential
Service Times 590
Model B (M/M/S): Multiple-Channel
Queuing Model 593
Model C (M/D/1): Constant-Service-Time
Model 597
Little’s Law 598
Model D: Limited-Population Model 599
Other Queuing Approaches 601
Module Summary 602 • Key Terms 602 • Using
Software to Solve Queuing Problems 602 • Solved
Problems 603 • Bibliography 604
E. Learning Curves 605
What Is a Learning Curve? 606
Learning Curves in Services and
Manufacturing 607
Applying the Learning Curve 608
Arithmetic Approach 608
Logarithmic Approach 609
Learning-Curve Coefficient Approach 609

xvi Table of Contents
Strategic Implications of Learning Curves 611
Limitations of Learning Curves 612
Module Summary 613 • Key Terms 613 • Using
Software for Learning Curves 613 • Solved
Problems 614 • Bibliography 614
F. Simulation 615
What Is Simulation? 616
Advantages and Disadvantages of Simulation 617
Monte Carlo Simulation 618
Simulation of a Queuing Problem 621
Simulation and Inventory Analysis 623
Module Summary 626 • Key Terms 626 • Using
Software in Simulation 626 • Solved Problems 628
• Bibliography 629
Appendices A1
Indices I1
Photo Credits P1
Online Tutorials
1. Statistical Tools for Managers T1-1
Discrete Probability Distributions T1-2
Expected Value of a Discrete Probability
Distribution T1-3
Variance of a Discrete Probability
Distribution T1-3
Continuous Probability Distributions T1-4
The Normal Distribution T1-4
Summary T1-7 • Key Terms T1-7 •
Discussion Questions T1-7 • Problems T1-7 •
Bibliography T1-7
2. Acceptance Sampling T2-1
Sampling Plans T2-2
Single Sampling T2-2
Double Sampling T2-2
Sequential Sampling T2-2
Operating Characteristic (OC) Curves T2-2
Producer’s and Consumer’s Risk T2-3
Average Outgoing Quality T2-5
Summary T2-6 • Key Terms T2-6 • Solved Problem
T2-7 • Discussion Questions T2-7 • Problems T2-7
3. The Simplex Method of Linear
Programming T3-1
Converting the Constraints to Equations T3-2
Setting Up the First Simplex Tableau T3-2
Simplex Solution Procedures T3-4
Summary of Simplex Steps for Maximization
Problems T3-6
Artificial and Surplus Variables T3-7
Solving Minimization Problems T3-7
Summary T3-8 • Key Terms T3-8 • Solved
Problem T3-8 • Discussion Questions T3-8 •
Problems T3-9
4. The MODI and VAM Methods of Solving
Transportation Problems T4-1
MODI Method T4-2
How to Use the MODI Method T4-2
Solving the Arizona Plumbing Problem with
MODI T4-2
Vogel’s Approximation Method: Another Way to
Find an Initial Solution T4-4
Discussion Questions T4-8 • Problems T4-8
5. Vehicle Routing and Scheduling T5-1
Introduction T5-2
Service Delivery Example: Meals-for-ME T5-2
Objectives of Routing and Scheduling
Problems T5-2
Characteristics of Routing and Scheduling
Problems T5-3
Classifying Routing and Scheduling
Problems T5-3
Solving Routing and Scheduling
Problems T5-4
Routing Service Vehicles T5-5
The Traveling Salesman Problem T5-5
Multiple Traveling Salesman Problem T5-8
The Vehicle Routing Problem T5-9
Cluster First, Route Second Approach T5-10
Scheduling Service Vehicles T5-11
The Concurrent Scheduler Approach T5-13
Other Routing and Scheduling Problems T5-13
Summary T5-14 • Key Terms T5-15 •
Discussion Questions T5-15 • Problems T5-15 •
Case Study: Routing and Scheduling of
Phlebotomists T5-17 • Bibliography T5-17

Welcome to your operations management (OM) course. In this book, we present a state-of-the-
art view of the activities of the operations function. Operations is an exciting area of manage-
ment that has a profound effect on the productivity of both manufacturing and services.
Indeed, few other activities have as much impact on the quality of our lives. The goal of this
text is to present a broad introduction to the field of operations in a realistic, practical manner.
OM includes a blend of topics from accounting, industrial engineering, management, manage-
ment science, and statistics. Even if you are not planning on a career in the operations area,
you will likely be working with people who are. Therefore, having a solid understanding of
the role of operations in an organization is of substantial benefit to you. This book will also
help you understand how OM affects society and your life. Certainly, you will better under-
stand what goes on behind the scenes when you purchase a bag of Frito-Lay potato chips; buy
a meal at an Olive Garden, a Red Lobster, or a Hard Rock Cafe; place an order through
Amazon.com; buy a customized Dell computer over the Internet; or enter Arnold Palmer
Hospital for medical care.
Although many of our readers are not OM majors, we know that marketing, finance, account-
ing, and MIS students across the globe will find the material both interesting and useful as we
develop a fundamental working knowledge of the operations side of the firm. More than 600,000
readers of our earlier editions seem to have endorsed this premise. We welcome comments by
email from our North American readers, from students using the EU edition, the Indian edition,
and our editions in Portuguese, Spanish, Turkish, Indonesian and Chinese. Our goal is to make
this material useful and interesting to each of you.
NEW TO THIS EDITION
Manufacturing Integration with Video Case Studies on Frito-Lay In this edi-
tion, we take you behind the scenes at one of the most exciting manufacturers in North America,
Frito-Lay, a subsidiary of PepsiCo. We provide five new Video Case Studies, photos, examples,
problems, and a Global Company Profile (Chapter 13). This multi-billion-dollar snack food pro-
ducer opened its doors so we could examine its use of statistical quality control (Supplement 6),
green manufacturing and sustainability (Chapter 7), inventory management (Chapter 12), and
maintenance (Chapter 17), as well as its overall OM strategy (Chapter 1) in a series of 8- to
14-minute videos.
Our prior editions focused on Darden Restaurants (Olive Garden/Red Lobster), Hard Rock
Cafe, Arnold Palmer Hospital, Wheeled Coach Ambulances, and Regal Marine. These videos and
cases appear in this edition as well, along with the five new ones for Frito-Lay.
xvii
Preface

Rapid Reviews In our never-ending quest to make this the most student-friendly text in our
field, we now include a two-page Rapid Review in the Lecture Guide & Activities Manual. This
detailed yet concise summary of the main points and equations in the chapter helps students pre-
pare for homework, exams, and lectures by capturing the essence of the material. Each Rapid
Review also includes a self-test, with questions linked to the learning objectives in that chapter.
Key terms introduced in the chapter are part of the Rapid Review.
xviii Preface
myomlab and the Learning Process This powerful tool ties together all elements in our
book into an innovative learning tool, an exam tool, a homework tool, and an assessment center.
myomlab’s new version 2.0 accompanies this edition of the text. By using myomlab, instructors can
assign thousands of problems from the text and/or problems/questions from the Test Item File for
their students to take online, in any time frame determined by the instructor. With many options for
randomizing the sequence, timing, and scoring, myomlab makes giving and grading homework and
exams easy. Most problems have also been converted to an algorithmic form, meaning that there are
numerous versions of each problem, with different data for each student. Solutions to each problem
and its data set are available, if instructors wish, to students immediately after they complete each
assignment. The program records grades into the instructor’s grade book. For help, students can
click directly to the relevant text page, watch the text authors solve a similar problem, walk through
other sample problems, or seek other useful forms of help. This new and innovative feature is truly a
wonderful teaching and learning aid. Visit www.myomlab.com for more information.

www.myomlab.com

Integration of Ethics Throughout the Book With this revision, we provide broad cov-
erage of ethics as an OM consideration. The topic is addressed in most chapters and in the Lecture
Guide & Activities Manual, we present an Ethical Dilemma that can be used for classroom discussion
or homework.
Expanded Treatment of the Theory of Constraints Supplement 7 now contains
expanded treatment on the theory of constraints, including the material previously covered in
Chapter 15. Theory of constraints and bottleneck analysis coverage includes examples, a solved
problem, and seven new homework problems.
Sustainability as an OM Responsibility Sustainability is now highlighted in several
chapters, especially Chapters 5 (“Design of Goods and Services”) and 7 (“Process Strategy and
Sustainability”). Chapter 7 also has two new case studies on the topic as it relates to Frito-Lay and
to Walmart.
Author Comments You will notice a new feature throughout every chapter that we call
Author Comments. Here we point out why a section, a figure, or a table is so important. The com-
ments are intended to be motivational to students, as well as educational.
Preface xix
Additional Homework Problems Our text already contains more homework problems
than any other text in the discipline. We have also added hundreds more problems to our Web site
for instructors who seek even more variety and freshness. These problems are available at
www.myomlab.com. Solutions to these additional problems appear along with regular text prob-
lems in our Instructor’s Solution Manual, which was created and proofed by the authors.
CHAPTER-BY-CHAPTER CHANGES
To highlight the extent of the revision from the ninth edition, here are a few of the changes, on a
chapter-by-chapter basis. We have added new material on the subject of the theory of constraints
(to Supplement 7); combined Chapter 10 and Supplement 10 into a new Chapter 10, called
“Human Resources, Job Design, and Work Measurement”; and have added extensive new mater-
ial on supply chains in Chapter 11 and Supplement 11. The Rapid Review section in the Lecture
Guide & Activities Manual is new to each chapter. Active Models, which were illustrated with
screen captures in most chapters, now appear in myomlab and the Companion Web site, www.
pearsonhighered.com/heizer.
Chapter 1. Operations and Productivity We include a heavily revised section to help
motivate students, called “Exciting New Trends in OM,” with a new emphasis on the environment
and ethics that runs throughout the book. Four homework problems have been expanded. The
Lecture Guide & Activities Manual includes the new Video Case Study “Frito-Lay: Operations
Management in Manufacturing.” This new case introduces the company that we refer to throughout
this edition and makes an excellent teaching comparison to the other Chapter 1 Video Case Study,
“Hard Rock Cafe: Operations Management in Services.” The Zychol Chemical Corporation case
has been moved to myomlab and the Companion Web site, www.pearsonhighered.com/heizer.
Chapter 2. Operations Strategy in a Global Environment This chapter has a
new figure (Figure 2.4) that relates OM to strategy and a new section called “Issues in Operations
Strategy,” which includes Porter’s value chain analysis and five forces model.
Chapter 3. Project Management We have revised our treatment of work breakdown struc-
ture with a more visual approach (Figure 3.3), added a new OM in Action box, “Prepping for the Miami
Heat Game,” and shortened coverage of Microsoft Project (from seven screen captures down to three).
Chapter 4. Forecasting We have revised the formula for tracking signals [see equation
(4.18) and Example 16] and moved the review of forecasting formulas from Table 4.1 into the
Rapid Review section in the Lecture Guide & Activities Manual.

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Chapter 5. Design of Goods and Services We have added a manufacturer, Cisco, to
our discussion of new product yields (Figure 5.6) and created a major new section titled “Ethics,
the Environment, and Sustainability.”
Chapter 6. Managing Quality This chapter contains expanded coverage of ISO 14000,
including a new OM in Action box, “Subaru’s Clean, Green Set of Wheels with ISO 14001,” and
a discussion of ISO 24700. We have also added “A Hospital Benchmarks Against the Ferrari
Racing Team” as another OM in Action box and have illustrated checklists as a way to improve
quality. A new homework problem uses data from The Economist’s poll of air travel dislikes.
Supplement 6. Statistical Process Control Frito-Lay’s use of SPC is featured in
both photographs and a new Video Case Study, “Frito-Lay’s Quality-Controlled Potato Chips.”
This video is not only an exploration of the firm’s quality program but a tutorial on how a real firm
creates control charts from scratch. There are seven expanded homework problems and one new
one, which is based on the former Alabama Air case study.
Chapter 7. Process Strategy and Sustainability The chapter begins with a new
Global Company Profile featuring Harley-Davidson’s repetitive manufacturing. We have exten-
sively revised our treatment of the four process strategies. It is shortened and concisely illustrated
in Figure 7.2. There is a new OM in Action box, “Mass Customization for Straight Teeth.” We have
added a major new section on sustainability, where we introduce the four Rs of sustainability. In the
Lecture Guide & Activities Manual, there are two new cases: “Environmental Sustainability at
Walmart” and the Video Case Study, “Green Manufacturing and Sustainability at Frito-Lay.”
Supplement 7. Capacity and Constraint Management This supplement has been
retitled and extensively revised to include coverage of constraint management and the theory of con-
straints. Material on the theory of constraints, formerly in Chapter 15, has been melded into this new
treatment. A new section, “Bottleneck Analysis and Theory of Constraints,” includes two examples
(S3 and S4), a solved problem (S7.5), an OM in Action box on banking and the theory of constraints,
and seven new homework problems (S7.9–S7.15). So as not to lengthen Supplement 7, we shortened
our coverage of multi-product break-even analysis, replaced decision trees with an EMV approach
to capacity decisions, and reduced our treatment of net present value analysis. Finally, the Video
Case Study, “Capacity Planning at Arnold Palmer Hospital” has been updated.
Chapter 8. Location Strategies The homework problem selection has been expanded,
and 11 problems have been revised to make them more challenging.
Chapter 9. Layout Strategies The main addition to this chapter is the new OM in Action
box “Work Cells Increase Productivity at Canon.”
Chapter 10. Human Resources, Job Design, and Work Measurement With
this edition, we have merged Supplement 10 (“Work Measurement”) into Chapter 10. This helps
make the coverage of this material more concise, while bringing more quantitative material into
the main chapter. Coverage of labor planning, job design, ergonomics, and the visual workplace
has been edited for brevity. Examples S1–S6 from Supplement 10 remain, now as Examples 1–6.
Solved Problems S10.1–S10.5 are now Solved Problems 10.2–10.6. There is a new OM in Action
box, “Saving Seconds at Retail Boosts Productivity.” The case study “The Fleet That Wanders”
has been moved to myomlab and the Companion Web site.
Chapter 11. Supply-Chain Management In keeping with the growing importance of
supply chains as an OM topic, we have rewritten this chapter (and Supplement 11) to keep readers
current in this dynamic field. There is new treatment of supply-chain risks, a new section on ethics
and sustainability, more coverage of joint ventures, a new section on CPFR, a revision of the mate-
rial on e-procurement, and new material explaining the SCOR model.
Supplement 11. Outsourcing as a Supply-Chain Strategy The material here
has been heavily edited to keep current with this important topic, including a new Table S11.1 on
ranking of outsourcing countries, NASA’s outsourcing shipments to the Space Station, and back-
sourcing. We have deleted the section on break-even analysis, which is a model discussed in other
chapters, and revised four of the homework problems to make them more challenging.
Chapter 12. Inventory Management The explanation of how to graph costs as a
function of order quantity has been expanded to help students better understand the concept
xx Preface

(Figure 12.4). We now cover the single-period model in Example 15 and in three new home-
work problems (12.36–12.38). Seven other homework problems have been revised and
expanded. Finally, a new Video Case Study is called “Managing Inventory at Frito-Lay.”
Chapter 13. Aggregate Planning The chapter begins with a new Global Company
Profile illustrating aggregate planning at Frito-Lay. We have also revised Examples 2–4 to make
them more current.
Chapter 14. Material Requirements Planning (MRP) and ERP MRP II and
its example in Table 14.4 have been rewritten, the order splitting discussion (and its Example 7)
has been revised, the ERP section has been shortened, and a new case study, “Hill’s Automotive,
Inc.” replaces the Ikon case, which now appears on myomlab and the Companion Web site, www.
pearsonhighered.com/heizer.
Chapter 15. Short-Term Scheduling We have expanded and moved the treatment of
the theory of constraints to Supplement 7 and rewritten Problem 15.17.
Chapter 16. Just-in-Time and Lean Operations The Global Company Profile on
Toyota has been revised; Figure 16.3, explaining hidden problems, is new; there is a revised kan-
ban figure (Figure 16.9); and kaizan is expanded both in the text and in the new OM in Action box
“Kaizen at Ducati.” There is also a new OM in Action box on TPS at the Los Angeles Police
Department, and there is a new case study, “JIT After a Catastrophe.”
Chapter 17. Maintenance and Reliability There are three new elements: a section
covering autonomous maintenance, an Ethical Dilemma regarding the Space Shuttle, and a Video
Case Study called “Maintenance Drives Profits at Frito-Lay.”
Module A. Decision-Making Tools This module now begins with an interesting
dilemma that involved a famous poker player in a Legends of Poker Tournament. We return to the
topic later in the module, with a section called “The Poker Decision Process,” Example 9, and
Problem A.24.
Modules B and C. Linear Programming and Transportation Models There
are no major changes in these two modules.
Module D. Waiting Line Models A new OM in Action box describes zero wait times at a
Michigan Hospital’s ER, and we have added examples of queuing at Costco and Alaska Airlines,
created a new section covering Little’s law, and expanded two of the homework problems.
Module E. Learning Curves We have updated Table E.1 and revamped the discussions on
the consequences and application of the learning curve.
Module F. Simulation We have added a new OM in Action box, “Simulation Software
Takes the Kinks Out of Starbucks’s Lines.”
STUDENT RESOURCES
To liven up the course and help students learn the content material, we have made available the
following resources:
• Student Study Guide (ISBN: 0-13-510725-3) created by Michael Donovan of Cedar Crest
College. Study Guide is designed to help students understand the concepts and quantitative
methods of operations management. Each chapter in the study guide consists nine basic com-
ponents: Summary; Learning Objectives; Skills to Develop; Annotated Outline; Hints and
Tips; Key Terms; Formulas; Self-test Questions; and Supplementary Materials.
• Thirty-one exciting video cases (Located on the Operations Management DVD Library,
ISBN: 0-13-611981-6, and at www.myomlab.com.) These Video Case Studies feature real
companies (Frito-Lay, Darden Restaurants, Regal Marine, Hard Rock Cafe, Ritz-Carlton,
Wheeled Coach, and Arnold Palmer Hospital) and allow students to watch short videos, read
about the key topics, and answer questions. These case studies can also be assigned without
using class time to show the videos. Each of them was developed and written by the text
authors to specifically supplement the book’s content.
• DVD video clips (Located on the Operations Management DVD Library, ISBN: 0-13-
611981-6, and at www.myomlab.com.) We have provided 37 one- to two-minute video clips
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to illustrate chapter-related topics with videos at Frito-Lay, Harley-Davidson, Ritz-Carlton,
Hard Rock Cafe, Olive Garden, and other firms.
• Virtual tours (Located on the Companion Web site, at www.pearsonhighered.com/heizer).
These company tours provide direct links to companies—ranging from a hospital to an auto
manufacturer—that practice key concepts. After touring each Web site, students are asked
questions directly related to the concepts discussed in the chapter.
• Self-study quizzes (Located on the Companion Web site, www.pearsonhighered.com/
heizer.) These quizzes allow students to test their understanding of each topic. These exten-
sive quizzes contain a broad assortment of questions, 20–25 per chapter, including multiple-
choice, true/false, and Internet essay questions. The quiz questions are graded and can be
transmitted to the instructor for extra credit or serve as practice exams.
• Active Models The 28 Active Models appear in files at www.myomlab.com and the
Companion Web site, www.pearsonhighered.com/heizer.
• Excel OM data files Examples in the text that can be solved with Excel OM appear on data
files on myomlab and the Companion Web site, www.pearsonhighered.com/heizer. They are
identified at the end of each example.
• POM for Windows software (Located at www.myomlab.com and the Companion Web site,
www.pearsonhighered.com/heizer.) POM for Windows is a powerful tool for easily solving
OM problems. Its 24 modules can be used to solve most of the homework problems in the text.
• Excel OM problem-solving software (Located at www.myomlab.com and the Companion
Web site, www.pearsonhighered.com/heizer.) Excel OM is our exclusive user-friendly Excel
add-in. Excel OM automatically creates worksheets to model and solve problems. Users select
a topic from the pull-down menu and fill in the data, and then Excel will display and graph
(where appropriate) the results. This software is great for student homework, what-if analysis
and classroom demonstrations. This edition includes a new version of Excel OM that’s com-
patible with Microsoft Excel 2007 as well as earlier versions of Excel.
• Online Tutorial Chapters (Located on myomlab and the Companion Web site, www.
pearsonhighered.com/heizer.) Statistical Tools for Managers, Acceptance Sampling, The Simplex
Method of Linear Programming, The MODI and VAM Methods of Solving Transportation
Problems, and Vehicle Routing and Scheduling are provided as additional material.
• Virtual office hours (Located at www.myomlab.com.) Professors Heizer and Render
appear on myomlab, walking students through 72 Solved Problems.
• Additional practice problems (Located at www.myomlab.com.) These problems provide
problem-solving experience. They supplement the examples and solved problems found in
each chapter.
• Additional case studies (Located at www.myomlab.com.) These additional case studies
supplement the ones in the text. Detailed solutions appear in the Solutions Manual.
• Microsoft Project 2007 (ISBN: 0-13-145421-8.) Microsoft Project, the most popular and
powerful project management package, is now available on an additional student CD-ROM.
This full version, documented in Chapter 3, is activated to work for 60 days.
INSTRUCTOR RESOURCES
Register, Redeem, Log in At www.pearsonhighered.com/irc instructors can register and
access a variety of print, media, and presentation resources that are available with this text in
downloadable digital format. For most texts, resources are also available for course management
platforms such as Blackboard, WebCT, and Course Compass.
It Gets Better Once you register, you will not have additional forms to fill out or multiple
usernames and passwords to remember to access new titles and/or editions. As a registered faculty
member, you can log in directly to download resource files and receive immediate access and
instructions for installing course management content to your campus server.
Need Help? Our dedicated technical support team is ready to answer instructors’ questions
about the media supplements that accompany this text. Visit http://247.prenhall.com for
answers to frequently asked questions and toll-free user support phone numbers. The supple-
ments are available to adopting instructors. Detailed descriptions are provided at the
Instructor’s Resource Center.
Instructor’s Resource Manual The Instructor’s Resource Manual, extensively updated
by Professor Charles Munson of Washington State University, contains many useful resources for
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instructors—course outlines, video notes, learning techniques, Internet exercises and sample
answers, case analysis ideas, additional teaching resources, and faculty notes. It also provides a
snapshot of the PowerPoint lecture slides. Instructors can download the Instructor’s Resource
Manual from the Instructor’s Resource Center, at www.pearsonhighered.com/heizer.
Instructor’s Solutions Manual The Instructor’s Solutions Manual, written by the
authors (and extensively proofed by Professor Annie Puciloski), contains the answers to all of the
discussion questions, Ethical Dilemmas, Active Models, and cases in the text, as well as worked-
out solutions to all the end-of-chapter problems, Internet problems, and Internet cases. Instructors
can download the Instructor’s Solutions Manual from the Instructor’s Resource Center, at www.
pearsonhighered.com/heizer.
PowerPoint Presentations An extensive set of PowerPoint presentations, created by
Professor Jeff Heyl of Lincoln University, is available for each chapter. Comprising well over
2,000 slides, this set has excellent color and clarity. These slides can also be downloaded from the
Instructor’s Resource Center, at www.pearsonhighered.com/heizer.
Test Item File The test item file, updated by Professor Greg Bier of University of
Missouri–Columbia, contains a variety of true/false, multiple-choice, fill-in-the-blank, short-
answer, and problem- and topic-integrating questions for each chapter. Instructors can download
the test item file from the Instructor’s Resource Center, at www.pearsonhighered.com/heizer.
TestGen The computerized TestGen package allows instructors to customize, save, and gener-
ate classroom tests. The test program permits instructors to edit, add, and delete questions from
the test bank; edit existing graphics and create new graphics; analyze test results; and organize a
database of test and student results. This software allows for extensive flexibility and ease of use.
It provides many options for organizing and displaying tests, along with search and sort features.
The software and the test banks can be downloaded from the Instructor’s Resource Center, at
www.pearsonhighered.com/heizer.
myomlab This powerful tool, noted on the inside front cover, ties together all elements in this
book into an innovative learning tool, an exam tool, a homework tool, and an assessment center.
By using myomlab, instructors can assign thousands of problems from the text and/or
problems/questions from the test item file for their students to take online at any time, as deter-
mined by the instructor. Visit www.myomlab.com for more information.
Video Package Designed and created by the authors specifically for their Heizer/Render
texts, the video package contains the following 31 videos:
Preface xxiii
• Frito-Lay: Operations Management in
Manufacturing (Ch. 1)
• Operations Management at Hard Rock
(Ch. 1)
• Regal Marine: Operations Strategy (Ch. 2)
• Hard Rock Cafe’s Global Strategy (Ch. 2)
• Project Management at Arnold Palmer
Hospital (Ch. 3)
• Managing Hard Rock’s Rockfest (Ch. 3)
• Forecasting at Hard Rock Cafe (Ch. 4)
• Regal Marine: Product Design (Ch. 5)
• The Culture of Quality at Arnold Palmer
Hospital (Ch. 6)
• Ritz-Carlton: Quality (Ch. 6)
• Frito-Lay’s Quality-Controlled Potato
Chips (Supp. 6)
• SPC and Quality at Darden Restaurants
(Supp. 6)
• Green Manufacturing and Sustainability at
Frito-Lay (Ch. 7)
• Wheeled Coach: Process Strategy (Ch. 7)
• Process Analysis at Arnold Palmer Hospital
(Ch. 7)
• Capacity Planning at Arnold Palmer
Hospital (Supp. 7)
• Locating the Next Red Lobster (Ch. 8)
• Where to Place the Hard Rock Cafe (Ch. 8)
• Wheeled Coach: Facility Layout (Ch. 9)
• Laying Out Arnold Palmer Hospital’s New
Facility (Ch. 9)
• Hard Rock Cafe’s Human Resource
Strategy (Ch. 10)
• Darden’s Global Supply Chains (Ch. 11)
• Regal Marine: Supply-Chain Management
(Ch. 11)
• Arnold Palmer Hospital’s Supply Chain
(Ch. 11)
• Darden’s Global Outsourcing (Supp. 11)
• Managing Inventory at Frito-Lay (Ch. 12)
• Wheeled Coach: Inventory Control (Ch. 12)
• Wheeled Coach: Materials Requirements
Planning (Ch. 14)
• Scheduling at Hard Rock Cafe (Ch. 15)
• JIT at Arnold Palmer Hospital (Ch. 16)
• Maintenance Drives Profits at Frito-Lay
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ACKNOWLEDGMENTS
We thank the many individuals who were kind enough to assist us in this endeavor. The
following professors provided insights that guided us in this edition (their names are in bold) and
in prior editions:
xxiv Preface
ALABAMA
Philip F. Musa
University of Alabama at
Birmingham
Doug Turner
Auburn University
ALASKA
Paul Jordan
University of Alaska
ARIZONA
Susan K. Norman
Northern Arizona University
Scott Roberts
Northern Arizona University
Vicki L. Smith-Daniels
Arizona State University
CALIFORNIA
Jean-Pierre Amor
University of San Diego
Moshen Attaran
California State
University–Bakersfield
Ali Behnezhad
California State
University–Northridge
Joe Biggs
California Polytechnic State
University
Lesley Buehler
Ohlone College
Richard Martin
California State
University–Long Beach
Zinovy Radovilsky
California State
University–Hayward
Robert J. Schlesinger
San Diego State University
V. Udayabhanu
San Francisco State
University
Rick Wing
San Francisco State
University
COLORADO
Peter Billington
Colorado State
University–Pueblo
CONNECTICUT
David Cadden
Quinnipiac University
Larry A. Flick
Norwalk Community
Technical College
FLORIDA
Rita Gibson
Embry-Riddle Aeronautical
University
Jim Gilbert
Rollins College
Donald Hammond
University of South Florida
Ronald K. Satterfield
University of South Florida
Theresa A. Shotwell
Florida A&M University
GEORGIA
John H. Blackstone
University of Georgia
Johnny Ho
Columbus State University
John Hoft
Columbus State University
John Miller
Mercer University
Spyros Reveliotis
Georgia Institute of
Technology
ILLINOIS
Suad Alwan
Chicago State University
Lori Cook
DePaul University
Zafar Malik
Governors State
University
INDIANA
Barbara Flynn
Indiana University
B.P. Lingeraj
Indiana University
Frank Pianki
Anderson University
Stan Stockton
Indiana University
Jianghua Wu
Purdue University
Xin Zhai
Purdue University
IOWA
Kevin Watson
Iowa State University
Lifang Wu
University of Iowa
KANSAS
William Barnes
Emporia State University
George Heinrich
Wichita State University
Sue Helms
Wichita State University
Hugh Leach
Washburn University
M.J. Riley
Kansas State University
Teresita S. Salinas
Washburn University
Avanti P. Sethi
Wichita State University
KENTUCKY
Wade Ferguson
Western Kentucky
University
Kambiz Tabibzadeh
Eastern Kentucky
University
LOUISIANA
Roy Clinton
University of Louisiana at
Monroe
L.Wayne Shell (retired)
Nicholls State University
MARYLAND
Eugene Hahn
Salisbury University
Samuel Y. Smith, Jr.
University of Baltimore
MASSACHUSETTS
Peter Ittig
University of Massachusetts

Jean Pierre Kuilboer
University of
Massachusetts–Boston
Dave Lewis
University of
Massachusetts–Lowell
Mike Maggard
Northeastern University
Peter Rourke
Wentworth Institute of
Technology
Daniel Shimshak
University of
Massachusetts–Boston
Ernest Silver
Curry College
MICHIGAN
Darlene Burk
Western Michigan
University
Damodar Golhar
Western Michigan
University
Dana Johnson
Michigan Technological
University
Doug Moodie
Michigan Technological
University
MINNESOTA
Rick Carlson
Metropolitan State
University
John Nicolay
University of Minnesota
Michael Pesch
St. Cloud State
University
MISSOURI
Shahid Ali
Rockhurst University
Stephen Allen
Truman State University
Sema Alptekin
University of
Missouri–Rolla
Gregory L. Bier
University of
Missouri–Columbia
James Campbell
University of Missouri–
St. Louis
Wooseung Jang
University of
Missouri–Columbia
Mary Marrs
University of
Missouri–Columbia
A. Lawrence Summers
University of Missouri
NEBRASKA
Zialu Hug
University of
Nebraska–Omaha
NEW JERSEY
Leon Bazil
Stevens Institute of
Technology
Mark Berenson
Montclair State
University
Joao Neves
The College of
New Jersey
Leonard Presby
William Paterson
University
NEW MEXICO
William Kime
University of
New Mexico
NEW YORK
Theodore Boreki
Hofstra University
John Drabouski
DeVry University
Richard E. Dulski
Daemen College
Beate Klingenberg
Marist College
Donna Mosier
SUNY Potsdam
Elizabeth Perry
SUNY Binghamton
William Reisel
St. John’s University
Kaushik Sengupta
Hofstra University
Girish Shambu
Canisius College
Rajendra Tibrewala
New York Institute of
Technology
NORTH CAROLINA
Ray Walters
Fayetteville Technical
Community College
OHIO
Victor Berardi
Kent State University
OKLAHOMA
Wen-Chyuan Chiang
University of Tulsa
OREGON
Anne Deidrich
Warner Pacific College
Gordon Miller
Portland State
University
PENNSYLVANIA
Henry Crouch
Pittsburgh State
University
Prafulla Oglekar
LaSalle University
David Pentico
Duquesne University
Stanford Rosenberg
LaRoche College
Edward Rosenthal
Temple University
Susan Sherer
Lehigh University
RHODE ISLAND
Laurie E. Macdonald
Bryant College
John Swearingen
Bryant College
Susan Sweeney
Providence College
SOUTH CAROLINA
Larry LaForge
Clemson University
Emma Jane Riddle
Winthrop University
TENNESSEE
Hugh Daniel
Lipscomb University
TEXAS
Warren W. Fisher
Stephen F. Austin State
University
Garland Hunnicutt
Texas State University
Gregg Lattier
Lee College
Preface xxv

Henry S. Maddux III
Sam Houston State
University
Arunachalam Narayanan
Texas A&M University
Ranga V. Ramasesh
Texas Christian
University
Victor Sower
San Houston State
University
Cecelia Temponi
Texas State University
John Visich-Disc
University of Houston
Bruce M. Woodworth
University of Texas–El Paso
UTAH
William Christensen
Dixie State College of
Utah
Shane J. Schvaneveldt
Weber State University
Madeline Thimmes (retired)
Utah State University
VIRGINIA
Andy Litteral
University of Richmond
Arthur C. Meiners, Jr.
Marymount University
Michael Plumb
Tidewater Community
College
WASHINGTON
Mark McKay
University of Washington
Chuck Munson
Washington State University
Chris Sandvig
Western Washington
University
John Stec
Oregon Institute of
Technology
WASHINGTON, DC
Narendrea K. Rustagi
Howard University
WEST VIRGINIA
Charles Englehardt
Salem International
University
Daesung Ha
Marshall University
John Harpell
West Virginia University
James S. Hawkes
University of Charleston
WISCONSIN
James R. Gross
University of
Wisconsin–Oshkosh
Marilyn K. Hart (retired)
University of
Wisconsin–Oshkosh
Niranjan Pati
University of Wisconsin–La
Crosse
X. M. Safford
Milwaukee Area Technical
College
Rao J. Taikonda
University of
Wisconsin–Oshkosh
WYOMING
Cliff Asay
University of Wyoming
INTERNATIONAL
Ronald Lau
Hong Kong University of
Science and Technology
xxvi Preface
In addition, we appreciate the wonderful people at Prentice Hall who provided both help and
advice: Eric Svendsen, our editor-in-chief; Chuck Synovec, our dynamic decision sciences edi-
tor; Anne Fahlgren, our marketing manager; Jason Calcano, our editorial assistant; Allison
Longley, our media project development manager; Courtney Kamauf for her dedicated work on
myomlab; Judy Leale, our senior managing editor; Becca Richter, our production project man-
ager; Mary Kate Murray, our editorial project manager, and Andrea Stefanowicz, our production
editor at PreMediaGlobal, Inc. Reva Shader developed the exemplary subject indexes for this
text. Donna Render and Kay Heizer provided the accurate typing and proofing so critical in a
rigorous textbook. We are truly blessed to have such a fantastic team of experts directing, guid-
ing, and assisting us.
In this edition we were thrilled to be able to include one of the country’s premiere manufac-
turers, Frito-Lay, in our ongoing video case series. This was possible because of the wonderful
efforts of Tom Rao, VP-Florida Operations and his superb management team, including Todd
Ehinger, Jim Wentzel, Angela McCormack, and Rod Hof. We are also particularly grateful to
Aurora Gonzalez in the Public Relations Department at Frito-Lay headquarters in Plano, Texas.
We also appreciate the efforts of colleagues who have helped to shape the entire learning
package that accompanies this text. Professor Howard Weiss (Temple University) developed the
Active Models, Excel OM, and POM for Windows microcomputer software; Professor Jeff Heyl
(Lincoln University) created the PowerPoints. Professor Chuck Munson (Washington State
University) created the Instructor’s Resource Manual and helped with the major rewrite of
Supplement 7; Professor Gregory L. Bier (University of Missouri–Columbia) prepared the Test
Bank; Professor Geoff Willis (University of Central Oklahoma) created the online study guide and
online virtual tours; Professor Michael Donovan (Cedar Crest College) prepared the study guide;
Beverly Amer (Northern Arizona University) produced and directed the videos and DVD Video
Case Study series; Professors Keith Willoughby (Bucknell University) and Ken Klassen (Brock
University) contributed the two Excel-based simulation games; Professor Gary LaPoint (Syracuse

University) developed the Microsoft Project crashing exercise and the dice game for SPC. Finally,
thanks to our accuracy checkers, Annie Puciloski and Vijay Gupta, for their attention to detail. We
have been fortunate to have been able to work with all these people.
We wish you a pleasant and productive introduction to operations management.
BARRY RENDER
GRADUATE SCHOOL OF BUSINESS
ROLLINS COLLEGE
WINTER PARK, FL 32789
EMAIL: BRENDER@ROLLINS.EDU
Preface xxvii
JAY HEIZER
TEXAS LUTHERAN UNIVERSITY
1000 W. COURT STREET
SEGUIN, TX 78155
EMAIL: JHEIZER@TLU.EDU
THREE VERSIONS ARE AVAILABLE
This text is available in three versions: Operations Management, tenth edition, a hardcover;
Principles of Operations Management, eighth edition, a paperback; and Operations Management,
Flexible Version, a package of a paperback text and the unique Lecture Guide & Activities Manual.
All three books include the identical core Chapters 1–17. However, Operations Management, tenth
edition, and the Flexible Version, also include six quantitative modules in Part IV.
OPERATIONS MANAGEMENT,
TENTH EDITION
ISBN: 0-13-611941-7
PART I INTRODUCTION TO
OPERATIONS MANAGEMENT
1. Operations and Productivity
2. Operations Strategy in a Global
Environment
3. Project Management
4. Forecasting
PART II DESIGNING OPERATIONS
5. Design of Goods and Services
6. Managing Quality
S6. Statistical Process Control
7. Process Strategy and Sustainability
S7. Capacity and Constraint Management
8. Location Strategies
9. Layout Strategies
10. Human Resources, Job Design, and Work
Measurement
PRINCIPLES OF OPERATIONS
MANAGEMENT, EIGHTH EDITION
ISBN: 0-13-611446-6
PART I INTRODUCTION TO
OPERATIONS MANAGEMENT
1. Operations and Productivity
2. Operations Strategy in a Global
Environment
3. Project Management
4. Forecasting
PART II DESIGNING OPERATIONS
5. Design of Goods and Services
6. Managing Quality
S6. Statistical Process Control
7. Process Strategy and Sustainability
S7. Capacity and Constraint Management
8. Location Strategies
9. Layout Strategies
10. Human Resources, Job Design, and Work
Measurement

PART III MANAGING OPERATIONS
11. Supply-Chain Management
S11. Outsourcing as a Supply-Chain Strategy
12. Inventory Management
13. Aggregate Planning
14. Material Requirements Planning (MRP) and
ERP
15. Short-Term Scheduling
16. Just-in-Time and Lean Operations
17. Maintenance and Reliability
PART IV QUANTITATIVE MODULES
A. Decision-Making Tools
B. Linear Programming
C. Transportation Models
D. Waiting-Line Models
E. Learning Curves
F. Simulation
ONLINE TUTORIALS
1. Statistical Tools for Managers
2. Acceptance Sampling
3. The Simplex Method of Linear
Programming
4. The MODI and VAM Methods
of Solving Transportation Problems
5. Vehicle Routing and Scheduling
xxviii Preface
PART III MANAGING OPERATIONS
11. Supply-Chain Management
S11. Outsourcing as a Supply-Chain Strategy
12. Inventory Management
13. Aggregate Planning
14. Material Requirements Planning (MRP) and
ERP
15. Short-Term Scheduling
16. Just-in-Time and Lean Operations
17. Maintenance and Reliability
ONLINE TUTORIALS
1. Statistical Tools for Managers
2. Acceptance Sampling
3. The Simplex Method of Linear
Programming
4. The MODI and VAM Methods
of Solving Transportation Problems
5. Vehicle Routing and Scheduling

This page intentionally left blank

Operations and Productivity
Chapter Outline
GLOBAL COMPANY PROFILE: HARD ROCK CAFE
What Is Operations Management? 4
Organizing to Produce Goods and Services 4
Why Study OM? 6
What Operations Managers Do 7
The Heritage of Operations Management 8
Operations in the Service Sector 10
Exciting New Trends in Operations
Management 12
The Productivity Challenge 13
Ethics and Social Responsibility 19
� Design of Goods and Services
� Managing Quality
� Process Strategy
� Location Strategies
� Layout Strategies
� Human Resources
� Supply-Chain Management
� Inventory Management
� Scheduling
� Maintenance
PART ONE
Introduction to Operations Management
(Chapters 1–4)
1

GLOBAL COMPANY PROFILE: HARD ROCK CAFE
OPERATIONS MANAGEMENT AT HARD ROCK CAFE
O
perations managers throughout the world are
producing products every day to provide for
the well-being of society. These products
take on a multitude of forms. They may be
washing machines at Whirlpool, motion pictures at
Dreamworks, rides at Disney World, or food at Hard
Rock Cafe. These firms produce thousands of complex
products every day—to be delivered as the customer
ordered them, when the customer wants them, and
where the customer wants them. Hard Rock does this
for over 35 million guests worldwide every year. This is
a challenging task, and the operations manager’s job,
2
Hard Rock Cafe in Orlando, Florida,
prepares over 3,500 meals each day.
Seating more than 1,500 people, it is
one of the largest restaurants in the
world. But Hard Rock’s operations
managers serve the hot food hot and
the cold food cold.
Operations managers are
interested in the attractiveness of
the layout, but they must be sure
that the facility contributes to the
efficient movement of people and
material with the necessary
controls to ensure that proper
portions are served.

whether at Whirlpool, Dreamworks, Disney, or Hard
Rock, is demanding.
Orlando-based Hard Rock Cafe opened its first
restaurant in London in 1971, making it over 39 years
old and the granddaddy of theme restaurants.
Although other theme restaurants have come and
gone, Hard Rock is still going strong, with 129
restaurants in more than 40 countries—and new
restaurants opening each year. Hard Rock made its
name with rock music memorabilia, having started
when Eric Clapton, a regular customer, marked his
favorite bar stool by hanging his guitar on the wall in
the London cafe. Now Hard Rock has millions of
dollars invested in memorabilia. To keep customers
coming back time and again, Hard Rock creates value
in the form of good food and entertainment.
The operations managers at Hard Rock Cafe at
Universal Studios in Orlando provide more than 3,500
custom products, in this case meals, every day. These
products are designed, tested, and then analyzed for
cost of ingredients, labor requirements, and customer
satisfaction. On approval, menu items are put into
production—and then only if the ingredients are available
from qualified suppliers. The production process, from
receiving, to cold storage, to grilling or baking or frying,
and a dozen other steps, is designed and maintained to
yield a quality meal. Operations managers, using the best
people they can recruit and train, also prepare effective
employee schedules and design efficient layouts.
Managers who successfully design and deliver goods
and services throughout the world understand operations.
In this text, we look not only at how Hard Rock’s managers
create value but also how operations managers in other
services, as well as in manufacturing, do so. Operations
management is demanding, challenging, and exciting.
It affects our lives every day. Ultimately, operations
managers determine how well we live.
Lots of work goes into designing, testing, and costing meals. Then suppliers deliver quality products on
time, every time, for well-trained cooks to prepare quality meals. But none of that matters unless an
enthusiastic wait staff, such as the one shown here, is doing its job.
3
Efficient kitchen layouts, motivated personnel, tight schedules,
and the right ingredients at the right place at the right time are
required to delight the customer.
HARD ROCK CAFE �

4 PART 1 Introduction to Operations Management
WHAT IS OPERATIONS MANAGEMENT?
Operations management (OM) is a discipline that applies to restaurants like Hard Rock Cafe as
well as to factories like Ford and Whirlpool. The techniques of OM apply throughout the world
to virtually all productive enterprises. It doesn’t matter if the application is in an office, a hospi-
tal, a restaurant, a department store, or a factory—the production of goods and services requires
operations management. And the efficient production of goods and services requires effective
applications of the concepts, tools, and techniques of OM that we introduce in this book.
As we progress through this text, we will discover how to manage operations in a changing
global economy. An array of informative examples, charts, text discussions, and pictures illus-
trates concepts and provides information. We will see how operations managers create the goods
and services that enrich our lives.
In this chapter, we first define operations management, explaining its heritage and exploring
the exciting role operations managers play in a huge variety of organizations. Then we discuss
production and productivity in both goods- and service-producing firms. This is followed by a
discussion of operations in the service sector and the challenge of managing an effective and effi-
cient production system.
Production is the creation of goods and services. Operations management (OM) is the
set of activities that creates value in the form of goods and services by transforming inputs
into outputs. Activities creating goods and services take place in all organizations. In
manufacturing firms, the production activities that create goods are usually quite obvious. In
them, we can see the creation of a tangible product such as a Sony TV or a Harley-Davidson
motorcycle.
In an organization that does not create a tangible good or product, the production function
may be less obvious. We often call these activities services. The services may be “hidden” from
the public and even from the customer. The product may take such forms as the transfer of funds
from a savings account to a checking account, the transplant of a liver, the filling of an empty
seat on an airplane, or the education of a student. Regardless of whether the end product is a
good or service, the production activities that go on in the organization are often referred to as
operations, or operations management.
ORGANIZING TO PRODUCE GOODS AND SERVICES
To create goods and services, all organizations perform three functions (see Figure 1.1). These
functions are the necessary ingredients not only for production but also for an organization’s sur-
vival. They are:
1. Marketing, which generates the demand, or at least takes the order for a product or service
(nothing happens until there is a sale).
2. Production/operations, which creates the product.
3. Finance/accounting, which tracks how well the organization is doing, pays the bills, and
collects the money.
Universities, churches or synagogues, and businesses all perform these functions. Even a volun-
teer group such as the Boy Scouts of America is organized to perform these three basic functions.
Figure 1.1 shows how a bank, an airline, and a manufacturing firm organize themselves to per-
form these functions. The blue-shaded areas of Figure 1.1 show the operations functions in these
firms.
Production
The creation of goods and
services.
Operations
management (OM)
Activities that relate to the
creation of goods and services
through the transformation of
inputs to outputs.
LO1: Define operations
management
Chapter 1 Learning Objectives
LO1: Define operations management 4
LO2: Explain the distinction between goods
and services 10
LO3: Explain the difference between
production and productivity 14
LO4: Compute single-factor productivity 14
LO5: Compute multifactor productivity 15
LO6: Identify the critical variables in
enhancing productivity 16
VIDEO 1.1
Operations Management
at Hard Rock
AUTHOR COMMENT
Let’s begin by defining what
this course is about.
AUTHOR COMMENT
Operations is one of the three
functions that every
organization performs.
VIDEO 1.2
Operations Management
at Frito-Lay

Chapter 1 Operations and Productivity 5
Manufacturing
Operations
Facilities
Construction; maintenance
Production and inventory control
Scheduling; materials control
Quality assurance and control
Manufacturing
Tooling; fabrication; assembly
Supply chain management
Design
Product development and design
Detailed product specifications
Industrial engineering
Efficient use of machines, space,
and personnel
Process analysis
Development and installation of
production tools and equipment
Finance/accounting
Disbursements/credits
Accounts receivable
Accounts payable
General ledger
Funds management
Money market
International exchange
Capital requirements
Stock issue
Bond issue and recall
Marketing
Sales promotion
Market research
Sales
Advertising
(C)
Commercial Bank
Operations
Teller scheduling
Check clearing
Collection
Transaction processing
Facilities design/layout
Vault operations
Maintenance
Security
Finance
Investments
Real estate
Securities
Accounting
Loans
Commercial
Industrial
Financial
Personal
Mortgage
Trust department
(A)
Auditing
Airline
Operations
Ground support equipment
Maintenance
Ground operations
Facility maintenance
Catering
Flight operations
Crew scheduling
Flying
Communications
Dispatching
Management science
Finance/accounting
Accounting
Accounts payable
Accounts receivable
General ledger
Finance
Cash control
International
exchange
Marketing
Marketing
Traffic administration
Reservations
Schedules
Tariffs (pricing)
Advertising
Sales
(B)
� FIGURE 1.1
Organization Charts for Two
Service Organizations and One
Manufacturing Organization
(A) a bank, (B) an airline, and
(C) a manufacturing
organization. The blue areas are
OM activities.
AUTHOR COMMENT
The areas in blue indicate the
significant role that OM plays
in both manufacturing and
service firms.

6 PART 1 Introduction to Operations Management
EXAMPLE 1 �
Examining the
options for
increasing
contribution
Fisher Technologies is a small firm that must double its dollar contribution to fixed cost and profit in
order to be profitable enough to purchase the next generation of production equipment. Management
has determined that if the firm fails to increase contribution, its bank will not make the loan and the
equipment cannot be purchased. If the firm cannot purchase the equipment, the limitations of the old
equipment will force Fisher to go out of business and, in doing so, put its employees out of work and
discontinue producing goods and services for its customers.
APPROACH � Table 1.1 shows a simple profit-and-loss statement and three strategic options
(marketing, finance/accounting, and operations) for the firm. The first option is a marketing option,
where good marketing management may increase sales by 50%. By increasing sales by 50%, contribu-
tion will in turn increase 71%. But increasing sales 50% may be difficult; it may even be impossible.
WHY STUDY OM?
We study OM for four reasons:
1. OM is one of the three major functions of any organization, and it is integrally related to all
the other business functions. All organizations market (sell), finance (account), and produce
(operate), and it is important to know how the OM activity functions. Therefore, we study
how people organize themselves for productive enterprise.
2. We study OM because we want to know how goods and services are produced. The produc-
tion function is the segment of our society that creates the products and services we use.
3. We study OM to understand what operations managers do. Regardless of your job in an
organization, you can perform better if you understand what operation managers do. In addi-
tion, understanding OM will help you explore the numerous and lucrative career opportuni-
ties in the field.
4. We study OM because it is such a costly part of an organization. A large percentage of the
revenue of most firms is spent in the OM function. Indeed, OM provides a major opportu-
nity for an organization to improve its profitability and enhance its service to society. Exam-
ple 1 considers how a firm might increase its profitability via the production function.
The second option is a finance/accounting option, where finance costs are cut in half through good
financial management. But even a reduction of 50% is still inadequate for generating the necessary
increase in contribution. Contribution is increased by only 21%.
The third option is an OM option, where management reduces production costs by 20% and
increases contribution by 114%.
SOLUTION � Given the conditions of our brief example, Fisher Technologies has increased con-
tribution from $10,500 to $22,500. It may now have a bank willing to lend it additional funds.
� TABLE 1.1
Options for Increasing
Contribution
AUTHOR COMMENT
Good OM managers are
scarce and, as a result,
career opportunities and
pay are excellent.
Marketing
Optiona
Finance/
Accounting
Optionb
OM
Optionc
Current
Increase Sales
Revenue 50%
Reduce Finance
Costs 50%
Reduce
Production
Costs 20%
Sales $100,000 $150,000 $100,000 $100,000
Costs of goods –80,000 –120,000 –80,000 –64,000
Gross margin 20,000 30,000 20,000 36,000
Finance costs –6,000 –6,000 –3,000 –6,000
Subtotal 14,000 24,000 17,000 30,000
Taxes at 25% –3,500 –6,000 –4,250 –7,500
Contributiond $ 10,500 $ 18,000 $ 12,750 $ 22,500
aIncreasing sales 50% increases contribution by $7,500, or 71% (7,500/10,500).
bReducing finance costs 50% increases contribution by $2,250, or 21% (2,250/10,500).
cReducing production costs 20% increases contribution by $12,000, or 114% (12,000/10,500).
dContribution to fixed cost (excluding finance costs) and profit.

Chapter 1 Operations and Productivity 7
Example 1 underscores the importance of an effective operations activity of a firm.
Development of increasingly effective operations is the approach taken by many companies as
they face growing global competition.
WHAT OPERATIONS MANAGERS DO
All good managers perform the basic functions of the management process. The management
process consists of planning, organizing, staffing, leading, and controlling. Operations managers
apply this management process to the decisions they make in the OM function. The 10 major
decisions of OM are shown in Table 1.2. Successfully addressing each of these decisions
requires planning, organizing, staffing, leading, and controlling. Typical issues relevant to these
decisions and the chapter where each is discussed are also shown.
Where Are the OM Jobs? How does one get started on a career in operations? The 10 OM
decisions identified in Table 1.2 are made by individuals who work in the disciplines shown in the
blue areas of Figure 1.1. Competent business students who know their accounting, statistics, finance,
and OM have an opportunity to assume entry-level positions in all of these areas. As you read this
text, identify disciplines that can assist you in making these decisions. Then take courses in those
INSIGHT � The OM option not only yields the greatest improvement in contribution but also may
be the only feasible option. Increasing sales by 50% and decreasing finance cost by 50% may both be
virtually impossible. Reducing operations cost by 20% may be difficult but feasible.
LEARNING EXERCISE � What is the impact of only a 15% decrease in costs in the OM
option? [Answer: A $19,500 contribution; an 86% increase.]
Management process
The application of planning,
organizing, staffing, leading,
and controlling to the
achievement of objectives.
AUTHOR COMMENT
An operations manager must
successfully address the
10 decisions around which
this text is organized.
AUTHOR COMMENT
Current OM emphasis on
quality and supply chain has
increased job opportunities
in these 10 areas.
Ten Decision Areas Issues Chapter(s)
1. Design of goods and services What good or service should we offer? 5
How should we design these products?
2. Managing quality How do we define the quality? 6, Supplement 6
Who is responsible for quality?
3. Process and capacity design What process and what capacity will these
products require?
7, Supplement 7
What equipment and technology is necessary for
these processes?
4. Location strategy Where should we put the facility? 8
On what criteria should we base the location
decision?
5. Layout strategy How should we arrange the facility? 9
How large must the facility be to meet our plan?
6. Human resources and job
design
How do we provide a reasonable work
environment?
10
How much can we expect our employees to
produce?
7. Supply-chain management Should we make or buy this component? 11, Supplement 11
Who should be our suppliers and how can we
integrate them into our strategy?
8. Inventory, material requirements
planning, and JIT (just-in-time)
How much inventory of each item should we
have?
12, 14, 16
When do we reorder?
9. Intermediate and short-term
scheduling
Are we better off keeping people on the payroll
during slowdowns?
13, 15
Which job do we perform next?
10. Maintenance How do we build reliability into our processes?
Who is responsible for maintenance?
17
� TABLE 1.2
Ten Critical Decisions of
Operations Management

8 PART 1 Introduction to Operations Management
areas. The more background an OM student has in accounting, statistics, information systems, and
mathematics, the more job opportunities will be available. About 40% of all jobs are in OM.
The following professional organizations provide various certifications that may enhance
your education and be of help in your career:
• APICS, the Association for Operations Management (www.apics.org)
• American Society for Quality (ASQ) (www.asq.org)
• Institute for Supply Management (ISM) (www.ism.ws)
• Project Management Institute (PMI) (www.pmi.org)
• Council of Supply Chain Management Professionals (www.cscmp.org)
Figure 1.2 shows some recent job opportunities.
THE HERITAGE OF OPERATIONS MANAGEMENT
The field of OM is relatively young, but its history is rich and interesting. Our lives and the OM
discipline have been enhanced by the innovations and contributions of numerous individuals. We
now introduce a few of these people, and we provide a summary of significant events in opera-
tions management in Figure 1.3.
1/15 Plant Manager
Division of Fortune 1000 company seeks plant manager for plant located in the upper Hudson Valley area. This
plant manufacturers loading dock equipment for commercial markets. The candidate must be experienced in plant
management including expertise in production planning, purchasing, and inventory management. Good written and
oral communication skills are a must, along with excellent application of skills in managing people.
2/23 Operations Analyst
Expanding national coffee shop: top 10 “Best Places to Work” wants junior level systems analyst to join our excel-
lent store improvement team. Business or I.E. degree, work methods, labor standards, ergonomics, cost accounting
knowledge a plus. This is a hands-on job and excellent opportunity for a team player with good
people skills. West coast location. Some travel required.
4/6 Supply Chain Manager and Planner
Responsibilities entail negotiating contracts and establishing long-term relationships with suppliers. We will
rely on the selected candidate to maintain accuracy in the purchasing system, invoices, and product returns. A
bachelor’s degree and up to 2 years related experience are required. Working knowledge of MRP, ability to use
feedback to master scheduling and suppliers and consolidate orders for best price and delivery are necessary.
Proficiency in all PC Windows applications, particularly Excel and Word, is essential. Knowledge of Oracle
business systems I is a plus. Effective verbal and written communication skills are essential.
3/18 Quality Manager
Several openings exist in our small package processing facilities in the Northeast, Florida, and Southern California
for quality managers. These highly visible positions require extensive use of statistical tools to monitor all aspects
of service, timeliness, and workload measurement. The work involves (1) a combination of hands-on applications
and detailed analysis using databases and spreadsheets. (2) process audits to identify areas for improvement and (3)
management of implementation of changes. Positions involve night hours and weekends. Send resume.
5/14 Process Improvement Consultants
An expanding consulting firm is seeking consultants to design and implement lean production and cycle time
reduction plans in both service and manufacturing processes. Our firm is currently working with an international
bank to improve its back office operations, as well as with several manufacturing firms. A business degree required;
APICS certification a plus.
� FIGURE 1.2 Many Opportunities Exist for Operations Managers

www.apics.org

www.asq.org

www.ism.ws

www.pmi.org

www.cscmp.org

Early Concepts
1776–1880
Labor Specialization
(Smith, Babbage)
Standardized Parts (Whitney)
Scientific Management Era
1880–1910
Gantt Charts (Gantt)
Motion & Time Studies
(Gilbreth)
Process Analysis (Taylor)
Queuing Theory (Erlang)
Mass Production Era
1910–1980
Moving Assembly Line
(Ford/Sorensen)
Statistical Sampling
(Shewhart)
Economic Order
Quantity (Harris)
Linear Programming
PERT/CPM (DuPont)
Material Requirements
Planning (MRP)
Mass Customization Era
1995–2015
Globalization
Internet/E-Commerce
Enterprise Resource Planning
International Quality Standards
(ISO)
Finite Scheduling
Supply-Chain Management
Mass Customization
Build-to-Order
Sustainability
Lean Production Era
1980–1995
Just-in-Time (JIT)
Computer-Aided Design
(CAD)
Electronic Data Interchange
(EDI)
Total Quality Management
(TQM)
Baldrige Award
Empowerment
Kanbans
Chapter 1 Operations and Productivity 9
Eli Whitney (1800) is credited for the early popularization of interchangeable parts, which
was achieved through standardization and quality control. Through a contract he signed with the
U.S. government for 10,000 muskets, he was able to command a premium price because of their
interchangeable parts.
Frederick W. Taylor (1881), known as the father of scientific management, contributed to per-
sonnel selection, planning and scheduling, motion study, and the now popular field of ergonom-
ics. One of his major contributions was his belief that management should be much more
resourceful and aggressive in the improvement of work methods. Taylor and his colleagues,
Henry L. Gantt and Frank and Lillian Gilbreth, were among the first to systematically seek the
best way to produce.
Another of Taylor’s contributions was the belief that management should assume more
responsibility for:
1. Matching employees to the right job.
2. Providing the proper training.
3. Providing proper work methods and tools.
4. Establishing legitimate incentives for work to be accomplished.
By 1913, Henry Ford and Charles Sorensen combined what they knew about standardized parts
with the quasi-assembly lines of the meatpacking and mail-order industries and added the revo-
lutionary concept of the assembly line, where men stood still and material moved.
Quality control is another historically significant contribution to the field of OM. Walter
Shewhart (1924) combined his knowledge of statistics with the need for quality control and pro-
vided the foundations for statistical sampling in quality control. W. Edwards Deming (1950)
� FIGURE 1.3 Significant Events in Operations Management

10 PART 1 Introduction to Operations Management
believed, as did Frederick Taylor, that management must do more to improve the work environ-
ment and processes so that quality can be improved.
Operations management will continue to progress with contributions from other disciplines,
including industrial engineering and management science. These disciplines, along with statis-
tics, management, and economics, contribute to improved models and decision making.
Innovations from the physical sciences (biology, anatomy, chemistry, physics) have also con-
tributed to advances in OM. These innovations include new adhesives, faster integrated circuits,
gamma rays to sanitize food products, and higher-quality glass for LCD and plasma TVs.
Innovation in products and processes often depends on advances in the physical sciences.
Especially important contributions to OM have come from information technology, which we
define as the systematic processing of data to yield information. Information technology—with
wireless links, Internet, and e-commerce—is reducing costs and accelerating communication.
Decisions in operations management require individuals who are well versed in management
science, in information technology, and often in one of the biological or physical sciences. In this
textbook, we look at the diverse ways a student can prepare for a career in operations management.
OPERATIONS IN THE SERVICE SECTOR
Manufacturers produce a tangible product, while service products are often intangible. But many
products are a combination of a good and a service, which complicates the definition of a ser-
vice. Even the U.S. government has trouble generating a consistent definition. Because defini-
tions vary, much of the data and statistics generated about the service sector are inconsistent.
However, we define services as including repair and maintenance, government, food and lodg-
ing, transportation, insurance, trade, financial, real estate, education, legal, medical, entertain-
ment, and other professional occupations.1
Differences Between Goods and Services
Let’s examine some of the differences between goods and services:
• Services are usually intangible (for example, your purchase of a ride in an empty airline seat
between two cities) as opposed to a tangible good.
• Services are often produced and consumed simultaneously; there is no stored inventory. For
instance, the beauty salon produces a haircut that is “consumed” simultaneously, or the doctor
produces an operation that is “consumed” as it is produced. We have not yet figured out how
to inventory haircuts or appendectomies.
• Services are often unique. Your mix of financial coverage, such as investments and insurance
policies, may not be the same as anyone else’s, just as the medical procedure or a haircut pro-
duced for you is not exactly like anyone else’s.
• Services have high customer interaction. Services are often difficult to standardize, automate,
and make as efficient as we would like because customer interaction demands uniqueness. In
fact, in many cases this uniqueness is what the customer is paying for; therefore, the opera-
tions manager must ensure that the product is designed (i.e., customized) so that it can be
delivered in the required unique manner.
• Services have inconsistent product definition. Product definition may be rigorous, as in the case
of an auto insurance policy, but inconsistent because policyholders change cars and mature.
• Services are often knowledge based, as in the case of educational, medical, and legal services,
and therefore hard to automate.
• Services are frequently dispersed. Dispersion occurs because services are frequently brought
to the client/customer via a local office, a retail outlet, or even a house call.
The activities of the operations function are often very similar for both goods and services.
For instance, both goods and services must have quality standards established, and both must be
designed and processed on a schedule in a facility where human resources are employed.
Having made the distinction between goods and services, we should point out that in many
cases, the distinction is not clear-cut. In reality, almost all services and almost all goods are a
mixture of a service and a tangible product. Even services such as consulting may require a tan-
gible report. Similarly, the sale of most goods includes a service. For instance, many products
Services
Economic activities that
typically produce an intangible
product (such as education,
entertainment, lodging,
government, financial,
and health services).
LO2: Explain the
distinction between
goods and services
1This definition is similar to the categories used by the U.S. Bureau of Labor Statistics.
AUTHOR COMMENT
Services are especially
important because almost
80% of all jobs are in
service firms.

Chapter 1 Operations and Productivity 11
� FIGURE 1.4 Development of the Service Economy and Manufacturing Productivity
Sources: U.S. Bureau of Labor Statistics; Federal Reserve Board, Industrial Production and Capacity Utilization (2009); and Statistical Abstract of the United States (2008).
Manufacturing
employment
(left scale)
0
E
m
p
lo
ym
e
n
t
(m
ill
io
n
s)
30
10
20
0
125
150
100
75
50
25
E
m
p
lo
ym
e
n
t
(m
ill
io
n
s
)
In
d
e
x
:
1
9
9
7
=
1
0
0
1960 1980 2000
1950 1970 1990 2010 (est.)
Industrial
production
(right scale)
Manufacturing
Services
1960 1980 2000
1950 1970 1990 2010 (est.)
40
United States
Canada
France
Italy
Britain
Japan
W. Germany
1970 2010 (est.)
Percent
40 50 60 70 80
(a) U.S. manufacturing and
service employment
(b) Number of people employed in
U.S. manufacturing has
decreased, but production
continues to increase. (c) Services as percentage of GDP
20
40
60
80
100
120
have the service components of financing and delivery (e.g., automobile sales). Many also
require after-sale training and maintenance (e.g., office copiers and machinery). “Service” activ-
ities may also be an integral part of production. Human resource activities, logistics, accounting,
training, field service, and repair are all service activities, but they take place within a manufac-
turing organization. Very few services are “pure,” meaning they have no tangible component.
Counseling may be one of the exceptions.
Growth of Services
Services constitute the largest economic sector in postindustrial societies. Until about 1900, most
Americans were employed in agriculture. Increased agricultural productivity allowed people to
leave the farm and seek employment in the city. Similarly, manufacturing employment has
decreased in the past 30 years. The changes in manufacturing and service employment, in mil-
lions, are shown in Figure 1.4(a). Interestingly, as Figure 1.4(b) indicates, the number of people
employed in manufacturing has decreased since 1950, but each person is now producing almost
20 times more than in 1950. Services became the dominant employer in the early 1920s, with
manufacturing employment peaking at about 32% in 1950. The huge productivity increases in
agriculture and manufacturing have allowed more of our economic resources to be devoted to
services, as shown in Figure 1.4(c). Consequently, much of the world can now enjoy the plea-
sures of education, health services, entertainment, and myriad other things that we call services.
Examples of firms and percentage of employment in the U.S. service sector are shown in Table
1.3. Table 1.3 also provides employment percentages for the nonservice sectors of manufactur-
ing, construction, agriculture, and mining on the bottom four lines.
Service Pay
Although there is a common perception that service industries are low paying, in fact, many service
jobs pay very well. Operations managers in the maintenance facility of an airline are very well paid,
as are the operations managers who supervise computer services to the financial community. About
42% of all service workers receive wages above the national average. However, the service-sector
average is driven down because 14 of the U.S. Department of Commerce categories of the 33 ser-
vice industries do indeed pay below the all-private industry average. Of these, retail trade, which
pays only 61% of the national private industry average, is large. But even considering the retail sec-
tor, the average wage of all service workers is about 96% of the average of all private industries.
Service sector
The segment of the economy
that includes trade, financial,
lodging, education, legal,
medical, and other professional
occupations.

12 PART 1 Introduction to Operations Management
AUTHOR COMMENT
One of the reasons OM is
such an exciting discipline is
that an operations manager
is confronted with ever-
changing issues, from
technology to sustainability.
EXCITING NEW TRENDS IN OPERATIONS MANAGEMENT
OM managers operate in an exciting and dynamic environment. This environment is the result of
a variety of challenging forces, from globalization of world trade to the transfer of ideas, prod-
ucts, and money at electronic speeds. The direction now being taken by OM—where it has been
and where it is going—is shown in Figure 1.5. Let’s look at some of these challenges:
• Ethics: Operations managers’ roles of buying from suppliers, transforming resources into
finished goods, and delivering to customers places them at critical junctures where they must
frequently make ethical decisions.
• Global focus: The rapid decline in communication and transportation costs has made mar-
kets global. Similarly, resources in the form of capital, materials, talent, and labor are now
also global. As a result, countries throughout the world are contributing to globalization as
they vie for economic growth. Operations managers are rapidly responding with creative
designs, efficient production, and quality goods.
• Rapid product development: Rapid international communication of news, entertainment, and
lifestyles is dramatically chopping away at the life span of products. Operations managers are
responding with management structures, technology, and alliances (partnerships) that are
more responsive and effective.
• Environmentally sensitive production: Operation managers’ continuing battle to improve
productivity is increasingly concerned with designing products and processes that are ecolog-
ically sustainable. That means designing products and packaging that minimize resource use,
are biodegradable, can be recycled, and are generally environmentally friendly.
• Mass customization: Once managers recognize the world as the marketplace, the cultural and
individual differences become quite obvious. In a world where consumers are increasingly
aware of innovation and options, substantial pressure is placed on firms to respond. And oper-
ations managers are responding with creative product designs and flexible production
processes that cater to the individual whims of consumers. The goal is to produce customized
products, whenever and wherever needed.
• Empowered employees: The knowledge explosion and more technical workplace have com-
bined to require more competence in the workplace. Operations managers are responding by
moving more decision making to individual workers.
• Supply-chain partnering: Shorter product life cycles, demanding customers, and fast
changes in technology, material, and processes require supply-chain partners to be more in
tune with the needs of end users. And because suppliers can contribute unique expertise, oper-
ations managers are outsourcing and building long-term partnerships with critical players in
the supply chain.
• Just-in-time performance: Inventory requires financial resources and impedes response to
rapid changes in the marketplace. These forces push operations managers to viciously cut
inventories at every level, from raw materials to finished goods.
These trends are part of the exciting OM challenges that are discussed in this text.
AUTHOR COMMENT
Service jobs with their
operations component are
growing as a percentage of
all jobs.
Sector Example
Percent of
All Jobs
Service Sector
Education, Legal, Medical, Other San Diego Zoo, Arnold Palmer Hospital 25.8
Trade (retail, wholesale) Walgreen’s, Wal-Mart, Nordstrom 14.4
Utilities, Transportation Pacific Gas & Electric, American Airlines 5.2
Professional and Business
Services
Snelling and Snelling, Waste Management,
Inc.
10.7
78.8
Finance, Information, Real Estate Citicorp, American Express, Prudential, Aetna 9.6
Food, Lodging, Entertainment Olive Garden, Motel 6, Walt Disney 8.5
Public Administration U.S., State of Alabama, Cook County 4.6
Manufacturing Sector General Electric, Ford, U.S. Steel, Intel 11.2
Construction Sector Bechtel, McDermott 8.1
Agriculture King Ranch 1.4
Mining Sector Homestake Mining .5
Grand Total 100.0
� TABLE 1.3
Examples of Organizations in
Each Sector
Sources: Statistical Abstract of the
United States (2008), Table 600,
and Bureau of Labor Statistics,
2008. x

Chapter 1 Operations and Productivity 13
Traditional Approach Reasons for Change Current Challenges
Ethics and regulation
not at the forefront
Local, regional,
national focus
Lengthy product
development
Low-cost production, with
little concern for environ-
ment; free resources (air,
water) ignored
Low-cost standard
products
Emphasis on specialized,
often manual tasks
Public concern over pollution, corruption,
child labor, etc.
Growth of reliable, low-cost communication
and transportation
Shorter life cycles; growth of global
communication; CAD; Internet
Public sensitivity to environment;
ISO 14000 standard; increasing
disposal costs
Rise of consumerism; increased
affluence; individualism
Recognizing the importance of the
employee’s total contribution;
knowledge society
High ethical and social responsibility;
increased legal and professional
standards (all chapters)
Global focus; international
collaboration (Chapters 2, 11)
Rapid product development;
design collaboration (Chapter 5)
Environmentally sensitive
production; green manufacturing;
sustainability (Chapters 5, 7)
Mass customization
(Chapters 5, 7)
Empowered employees;
enriched jobs
(Chapter 10)
“In-house” production;
low-bid purchasing
Rapid technology change; increasing
competitive forces
Supply-chain partnering;
joint ventures; alliances
(Chapter 11, Supplement 11)
Large lot production Shorter product life; increasing need to
reduce inventory
Just-in-time performance; lean;
continuous improvement
(Chapter 16)
� FIGURE 1.5 Changing Challenges for the Operations Manager
THE PRODUCTIVITY CHALLENGE
The creation of goods and services requires changing resources into goods and services. The
more efficiently we make this change, the more productive we are and the more value is added to
the good or service provided. Productivity is the ratio of outputs (goods and services) divided by
the inputs (resources, such as labor and capital) (see Figure 1.6). The operations manager’s job is
to enhance (improve) this ratio of outputs to inputs. Improving productivity means improving
efficiency.2
AUTHOR COMMENT
Why is productivity
important? Because it
determines our standard
of living.
� FIGURE 1.6
The Economic System Adds
Value by Transforming Inputs
to Outputs
An effective feedback loop
evaluates performance against a
strategy or standard. It also
evaluates customer satisfaction
and sends signals to managers
controlling the inputs and
transformation process.
Inputs Transformation Outputs
Feedback loop
Goods and
services
The U.S. economic system
transforms inputs
to outputs at about an
annual 2.5% increase in
productivity per year.
The productivity increase is
the result of a mix of
capital (38% of 2.5%),
labor (10% of 2.5%),
and management (52% of 2.5%).
Labor,
capital,
management
Productivity
The ratio of outputs (goods and
services) divided by one or
more inputs (such as labor,
capital, or management).
2Efficiency means doing the job well—with a minimum of resources and waste. Note the distinction between being
efficient, which implies doing the job well, and effective, which means doing the right thing. A job well done—say, by
applying the 10 decisions of operations management—helps us be efficient; developing and using the correct strategy
helps us be effective.

14 PART 1 Introduction to Operations Management
“This is a game of seconds . . .” says Silva Peterson, whom
Starbucks has put in charge of saving seconds. Her team
of 10 analysts is constantly asking themselves: “How can
we shave time off this?”
Peterson’s analysis suggested that there were some
obvious opportunities. First, stop requiring signatures on
credit card purchases under $25. This sliced 8 seconds off
the transaction time at the cash register.
Then analysts noticed that Starbucks’s largest cold
beverage, the Venti size, required two bending and digging
motions to scoop up enough ice. The scoop was too small.
Redesign of the scoop provided the proper amount in one
motion and cut 14 seconds off the average time of one
minute.
Third were new espresso machines; with the push of a
button, the machines grind coffee beans and brew. This
allowed the server, called
a “barista” in Starbucks’s
vocabulary, to do other
things. The savings: about
12 seconds per espresso
shot.
As a result, operations
improvements at Starbucks
outlets have increased the average yearly volume by nearly
$200,000, to about $940,000 in the past 6 years. This is a
27% improvement in productivity—about 4.5% per year. In
the service industry, a 4.5% per year increase is very tasty.
Sources: The Wall Street Journal (August 4, 2009): A1, A10 and (April 12,
2005): B2:B7; Industrial Engineer (January 2006): 66; and www.finfacts.
com, October 6, 2005.
OM in Action � Improving Productivity at Starbucks
This improvement can be achieved in two ways: reducing inputs while keeping output con-
stant or increasing output while keeping inputs constant. Both represent an improvement in pro-
ductivity. In an economic sense, inputs are labor, capital, and management, which are integrated
into a production system. Management creates this production system, which provides the con-
version of inputs to outputs. Outputs are goods and services, including such diverse items as
guns, butter, education, improved judicial systems, and ski resorts. Production is the making of
goods and services. High production may imply only that more people are working and that
employment levels are high (low unemployment), but it does not imply high productivity.
Measurement of productivity is an excellent way to evaluate a country’s ability to provide an
improving standard of living for its people. Only through increases in productivity can the standard
of living improve. Moreover, only through increases in productivity can labor, capital, and manage-
ment receive additional payments. If returns to labor, capital, or management are increased without
increased productivity, prices rise. On the other hand, downward pressure is placed on prices when
productivity increases, because more is being produced with the same resources.
The benefits of increased productivity are illustrated in the OM in Action box “Improving
Productivity at Starbucks.”
For well over a century (from about 1869), the U.S. has been able to increase productivity at an
average rate of almost 2.5% per year. Such growth has doubled U.S. wealth every 30 years. The
manufacturing sector, although a decreasing portion of the U.S. economy, has recently seen
annual productivity increases exceeding 4%, and the service sector, with increases of almost 1%,
has also shown some improvement. The combination has moved U.S. annual productivity growth
in this early part of the 21st century slightly above the 2.5% range for the economy as a whole.3
In this text, we examine how to improve productivity through operations management.
Productivity is a significant issue for the world and one that the operations manager is uniquely
qualified to address.
Productivity Measurement
The measurement of productivity can be quite direct. Such is the case when productivity is mea-
sured by labor-hours per ton of a specific type of steel. Although labor-hours is a common measure
of input, other measures such as capital (dollars invested), materials (tons of ore), or energy (kilo-
watts of electricity) can be used.4 An example of this can be summarized in the following equation:
(1-1)Productivity =
Units produced
Input used
LO4: Compute single-
factor productivity
4The quality and time period are assumed to remain constant.
3U.S. Dept. of Labor, July 2009: www.bls.gov/ipc/prodybar.html
LO3: Explain the difference
between production and
productivity

www.finfacts.com

www.finfacts.com

www.bls.gov/ipc/prodybar.html

Chapter 1 Operations and Productivity 15
For example, if units and labor-hours used is 250, then:
The use of just one resource input to measure productivity, as shown in Equation (1-1), is
known as single-factor productivity. However, a broader view of productivity is multifactor
productivity, which includes all inputs (e.g., capital, labor, material, energy). Multifactor pro-
ductivity is also known as total factor productivity. Multifactor productivity is calculated by
combining the input units as shown here:
(1-2)
To aid in the computation of multifactor productivity, the individual inputs (the denominator) can
be expressed in dollars and summed as shown in Example 2.
Productivity =
Output
Labor + Material + Energy + Capital + Miscellaneous
Productivity =
Units produced
Labor-hours used
=
1,000
250
= 4 units per labor-hour
produced = 1,000 Single-factor
productivity
Indicates the ratio of one
resource (input) to the goods
and services produced
(outputs).
Multifactor
productivity
Indicates the ratio of many or all
resources (inputs) to the goods
and services produced
(outputs).
LO5: Compute multifactor
productivity
� EXAMPLE 2
Computing single-
factor and
multifactor gains
in productivity
Collins Title wants to evaluate its labor and multifactor productivity with a new computerized title-
search system. The company has a staff of four, each working 8 hours per day (for a payroll cost of
$640/day) and overhead expenses of $400 per day. Collins processes and closes on 8 titles each day.
The new computerized title-search system will allow the processing of 14 titles per day. Although the
staff, their work hours, and pay are the same, the overhead expenses are now $800 per day.
APPROACH � Collins uses Equation (1-1) to compute labor productivity and Equation (1-2) to
compute multifactor productivity.
SOLUTION �
Labor productivity has increased from .25 to .4375. The change is or a
75% increase in labor productivity. Multifactor productivity has increased from .0077 to .0097. This
change is or a 26% increase in multifactor productivity.
INSIGHT � Both the labor (single-factor) and multifactor productivity measures show an increase
in productivity. However, the multifactor measure provides a better picture of the increase because it
includes all the costs connected with the increase in output.
LEARNING EXERCISE � If the overhead goes to $960 (rather than $800), what is the multi-
factor productivity? [Answer: .00875.]
RELATED PROBLEMS � 1.1, 1.2, 1.5, 1.6, 1.7, 1.8, 1.9, 1.11, 1.12, 1.14, 1.15
(.0097 – .0077)>.0077 = 0.26,
(.4375 – .25)>.25 = 0.75,
Multifactor productivity with the new system:
14 titles per day
$640 + 800
= .0097 titles per dollar
Multifactor productivity with the old system:
8 titles per day
$640 + 400
= .0077 titles per dollar
Labor productivity with the new system:
14 titles per day
32 labor-hours = .4375 titles per labor-hour
Labor productivity with the old system:
8 titles per day
32 labor-hours = .25 titles per labor-hour
Use of productivity measures aids managers in determining how well they are doing. But results
from the two measures can be expected to vary. If labor productivity growth is entirely the result
of capital spending, measuring just labor distorts the results. Multifactor productivity is usually
better, but more complicated. Labor productivity is the more popular measure. The multifactor-
productivity measures provide better information about the trade-offs among factors, but sub-
stantial measurement problems remain. Some of these measurement problems are:
1. Quality may change while the quantity of inputs and outputs remains constant. Compare an
HDTV of this decade with a black-and-white TV of the 1950s. Both are TVs, but few peo-
ple would deny that the quality has improved. The unit of measure—a TV—is the same, but
the quality has changed.
2. External elements may cause an increase or a decrease in productivity for which the system
under study may not be directly responsible. A more reliable electric power service may

16 PART 1 Introduction to Operations Management
Productivity variables
The three factors critical to
productivity improvement—
labor, capital, and the art and
science of management.
LO6: Identify the critical
variables in enhancing
productivity
5“Can’t Read, Can’t Count,” Scientific American (October 2001): 24; and “Economic Time Bomb: U.S. Teens Are
among Worst at Math,” The Wall Street Journal (December 7, 2004): B1.
Which of the following is true about
84% of 100?
It is greater than 100
It is less than 100
It is equal to 100
What is the area of this rectangle?
6 yds
4 yds
4 square yds
6 square yds
10 square yds
20 square yds
24 square yds
If 9y + 3 = 6y + 15 then y =
1
2
4
6
FIGURE 1.7 �
About Half of the 17-Year-Olds
in the U.S. Cannot Correctly
Answer Questions of This Type
greatly improve production, thereby improving the firm’s productivity because of this sup-
port system rather than because of managerial decisions made within the firm.
3. Precise units of measure may be lacking. Not all automobiles require the same inputs: Some
cars are subcompacts, others are 911 Turbo Porsches.
Productivity measurement is particularly difficult in the service sector, where the end product
can be hard to define. For example, economic statistics ignore the quality of your haircut, the
outcome of a court case, or service at a retail store. In some cases, adjustments are made for the
quality of the product sold but not the quality of the sales presentation or the advantage of a
broader product selection. Productivity measurements require specific inputs and outputs, but a
free economy is producing worth—what people want—which includes convenience, speed, and
safety. Traditional measures of outputs may be a very poor measure of these other measures of
worth. Note the quality-measurement problems in a law office, where each case is different,
altering the accuracy of the measure “cases per labor-hour” or “cases per employee.”
Productivity Variables
As we saw in Figure 1.6, productivity increases are dependent on three productivity variables:
1. Labor, which contributes about 10% of the annual increase.
2. Capital, which contributes about 38% of the annual increase.
3. Management, which contributes about 52% of the annual increase.
These three factors are critical to improved productivity. They represent the broad areas in which
managers can take action to improve productivity.
Labor Improvement in the contribution of labor to productivity is the result of a healthier,
better-educated, and better-nourished labor force. Some increase may also be attributed to a
shorter workweek. Historically, about 10% of the annual improvement in productivity is attributed
to improvement in the quality of labor. Three key variables for improved labor productivity are:
1. Basic education appropriate for an effective labor force.
2. Diet of the labor force.
3. Social overhead that makes labor available, such as transportation and sanitation.
Illiteracy and poor diets are a major impediment to productivity, costing countries up to 20% of
their productivity. Infrastructure that yields clean drinking water and sanitation is also an oppor-
tunity for improved productivity, as well as an opportunity for better health, in much of the
world.
In developed nations, the challenge becomes maintaining and enhancing the skills of labor
in the midst of rapidly expanding technology and knowledge. Recent data suggest that the
average American 17-year-old knows significantly less mathematics than the average Japanese
at the same age, and about half cannot answer the questions in Figure 1.7. Moreover, more
than 38% of American job applicants tested for basic skills were deficient in reading, writing,
or math.5
AUTHOR COMMENT
Perhaps as many as 25%
of U.S. workers lack the
basic skills needed for
their current job.

Chapter 1 Operations and Productivity 17
Overcoming shortcomings in the quality of labor while other countries have a better labor
force is a major challenge. Perhaps improvements can be found not only through increasing
competence of labor but also via better utilized labor with a stronger commitment. Training,
motivation, team building, and the human resource strategies discussed in Chapter 10, as well as
improved education, may be among the many techniques that will contribute to increased labor
productivity. Improvements in labor productivity are possible; however, they can be expected to
be increasingly difficult and expensive.
Capital Human beings are tool-using animals. Capital investment provides those tools.
Capital investment has increased in the U.S. every year except during a few very severe recession
periods. Annual capital investment in the U.S. has increased at an annual rate of 1.5% after
allowances for depreciation.
Inflation and taxes increase the cost of capital, making capital investment increasingly
expensive. When the capital invested per employee drops, we can expect a drop in productivity.
Using labor rather than capital may reduce unemployment in the short run, but it also makes
economies less productive and therefore lowers wages in the long run. Capital investment is
often a necessary, but seldom a sufficient ingredient in the battle for increased productivity.
The trade-off between capital and labor is continually in flux. The higher the cost of capital,
the more projects requiring capital are “squeezed out”: they are not pursued because the potential
return on investment for a given risk has been reduced. Managers adjust their investment plans to
changes in capital cost.
Management Management is a factor of production and an economic resource. Manage-
ment is responsible for ensuring that labor and capital are effectively used to increase produc-
tivity. Management accounts for over half of the annual increase in productivity. This increase
includes improvements made through the use of knowledge and the application of technology.
Using knowledge and technology is critical in postindustrial societies. Consequently, post-
industrial societies are also known as knowledge societies. Knowledge societies are those in
which much of the labor force has migrated from manual work to technical and information-
processing tasks requiring ongoing education. The required education and training are important
high-cost items that are the responsibility of operations managers as they build organizations and
workforces. The expanding knowledge base of contemporary society requires that managers use
technology and knowledge effectively.
More effective use of capital also contributes to productivity. It falls to the operations man-
ager, as a productivity catalyst, to select the best new capital investments as well as to improve
the productivity of existing investments.
Knowledge society
A society in which much of the
labor force has migrated from
manual work to work based on
knowledge.
The effective use of capital often means finding the proper trade-off between investment in capital assets (automation, left) and
human assets (a manual process, right). While there are risks connected with any investment, the cost of capital and physical
investments is fairly clear-cut, but the cost of employees has many hidden costs including fringe benefits, social insurance, and
legal constraints on hiring, employment, and termination.

18 PART 1 Introduction to Operations Management
The productivity challenge is difficult. A country cannot be a world-class competitor with
second-class inputs. Poorly educated labor, inadequate capital, and dated technology are
second-class inputs. High productivity and high-quality outputs require high-quality inputs,
including good operations managers.
Productivity and the Service Sector
The service sector provides a special challenge to the accurate measurement of productivity and
productivity improvement. The traditional analytical framework of economic theory is based pri-
marily on goods-producing activities. Consequently, most published economic data relate to
goods production. But the data do indicate that, as our contemporary service economy has
increased in size, we have had slower growth in productivity.
Productivity of the service sector has proven difficult to improve because service-sector work is:
1. Typically labor intensive (for example, counseling, teaching).
2. Frequently focused on unique individual attributes or desires (for example, investment
advice).
3. Often an intellectual task performed by professionals (for example, medical diagnosis).
4. Often difficult to mechanize and automate (for example, a haircut).
5. Often difficult to evaluate for quality (for example, performance of a law firm).
The more intellectual and personal the task, the more difficult it is to achieve increases in produc-
tivity. Low-productivity improvement in the service sector is also attributable to the growth of
low-productivity activities in the service sector. These include activities not previously a part of
the measured economy, such as child care, food preparation, house cleaning, and laundry ser-
vice. These activities have moved out of the home and into the measured economy as more and
more women have joined the workforce. Inclusion of these activities has probably resulted in
lower measured productivity for the service sector, although, in fact, actual productivity has
probably increased because these activities are now more efficiently produced than previously.
However, in spite of the difficulty of improving productivity in the service sector, improve-
ments are being made. And this text presents a multitude of ways to make these improvements.
Indeed, what can be done when management pays attention to how work actually gets done is
astonishing!
Although the evidence indicates that all industrialized countries have the same problem with
service productivity, the U.S. remains the world leader in overall productivity and service pro-
ductivity. Retailing is twice as productive in the U.S. as in Japan, where laws protect shopkeep-
ers from discount chains. The U.S. telephone industry is at least twice as productive as
Germany’s. The U.S. banking system is also 33% more efficient than Germany’s banking oligop-
olies. However, because productivity is central to the operations manager’s job and because the
service sector is so large, we take special note in this text of how to improve productivity in the
service sector. (See, for instance, the OM in Action box “Taco Bell Improves Productivity and
Goes Green to Lower Costs.”)
Siemens, the multi-billion-dollar
German conglomerate, has long
been known for its apprentice
programs in its home country.
Because education is often the
key to efficient operations in a
technological society, Siemens
has spread its apprentice-training
programs to its U.S. plants. These
programs are laying the
foundation for the highly skilled
workforce that is essential for
global competitiveness.

Chapter 1 Operations and Productivity 19
ETHICS AND SOCIAL RESPONSIBILITY
Operations managers are subjected to constant changes and challenges. The systems they build
to convert resources into goods and services are complex. The physical and social environment
changes, as do laws and values. These changes present a variety of challenges that come from the
conflicting perspectives of stakeholders such as customers, distributors, suppliers, owners,
lenders, and employees. These stakeholders, as well as government agencies at various levels,
require constant monitoring and thoughtful responses.
Identifying ethical and socially responsible responses while building productive systems is
not always clear-cut. Among the many ethical challenges facing operations managers are:
• Efficiently developing and producing safe, quality products.
• Maintaining a sustainable environment.
• Providing a safe workplace.
• Honoring stakeholder commitments.
Managers must do all of this in an ethical and socially responsible way while meeting the demands
of the marketplace. If operations managers have a moral awareness and focus on increasing
productivity in a system where all stakeholders have a voice, then many of the ethical challenges
will be successfully addressed. The organization will use fewer resources, the employees will be
committed, the market will be satisfied, and the ethical climate will be enhanced. Throughout this
text, we note ways in which operations managers can take ethical and socially responsible actions
while successfully addressing these challenges of the market. We also conclude each chapter with
an Ethical Dilemma exercise (see the Lecture Guide & Activities Manual).
Founded in 1962 by Glenn Bell, Taco Bell seeks competitive
advantage via low cost. Like many other services, Taco Bell
relies on its operations management to improve productivity
and reduce cost.
Its menu and meals are designed to be easy to
prepare. Taco Bell has shifted a substantial portion of
food preparation to suppliers who could perform food
processing more efficiently than a stand-alone restaurant.
Ground beef is precooked prior to arrival and then
reheated, as are many dishes that arrive in plastic boil
bags for easy sanitary reheating. Similarly, tortillas arrive
already fried and onions prediced. Efficient layout and
automation has cut to 8 seconds the time needed to
prepare tacos and burritos and has cut time in the
drive-thru lines by one minute. These advances have
been combined with training and empowerment to
increase the span of management from one supervisor
for 5 restaurants to one supervisor for 30 or more.
Operations managers at Taco Bell believe they have cut
in-store labor by 15 hours per day and reduced floor space
by more than 50%. The result is a store that can handle
twice the volume with half the labor.
In 2010, Taco Bell will have completed the rollout of its
new Grill-to-Order kitchens by installing water- and energy-
savings grills that conserve 300 million gallons of water
and 200 million KwH of electricity each year. This “green”-
inspired cooking method also saves the company’s 5,600
restaurants $17 million per year.
Effective operations management has resulted in
productivity increases that support Taco Bell’s low-cost
strategy. Taco Bell is now the fast-food low-cost leader
with a 73% share of the Mexican fast-food market.
Sources: Energy Business Journal (May 12, 2008): 111; Harvard Business
Review (July/August 2008): 118; and J. Hueter and W. Swart, Interfaces
(January–February 1998): 75–91.
OM in Action � Taco Bell Improves Productivity and Goes Green to Lower Costs
Operations, marketing, and finance/accounting are the
three functions basic to all organizations. The operations
function creates goods and services. Much of the progress
of operations management has been made in the twentieth
century, but since the beginning of time, humankind has
been attempting to improve its material well-being. Oper-
ations managers are key players in the battle to improve
productivity.
As societies become increasingly
affluent, more of their resources are
devoted to services. In the U.S., more
than three-quarters of the workforce is
employed in the service sector. Produc-
tivity improvements are difficult to achieve,
but operations managers are the primary vehicle for making
improvements.
CHAPTER SUMMARY
AUTHOR COMMENT
Ethics must drive
all of a manager’s
decisions.

20 PART 1 Introduction to Operations Management
Key Terms
Production (p. 4)
Operations management (OM) (p. 4)
Management process (p. 7)
Services (p. 10)
Service sector (p. 11)
Productivity (p. 13)
Single-factor productivity (p. 15)
Multifactor productivity (p. 15)
Productivity variables (p. 16)
Knowledge society (p. 17)
Solved Problems Virtual Office Hours help is available at www.myomlab.com
� SOLVED PROBLEM 1.1
Productivity can be measured in a variety of ways, such as by labor,
capital, energy, material usage, and so on. At Modern Lumber, Inc.,
Art Binley, president and producer of apple crates sold to growers,
has been able, with his current equipment, to produce 240 crates
per 100 logs. He currently purchases 100 logs per day, and each log
requires 3 labor-hours to process. He believes that he can hire a
professional buyer who can buy a better-quality log at the same
cost. If this is the case, he can increase his production to 260 crates
per 100 logs. His labor-hours will increase by 8 hours per day.
What will be the impact on productivity (measured in crates
per labor-hour) if the buyer is hired?
� SOLUTION
(a)
= .8 crates per labor-hour
=
240
300
Current labor productivity =
240 crates
100 logs * 3 hours>log
(b)
Using current productivity (.80 from [a]) as a base, the increase
will be 5.5% (.844/.8 = 1.055, or a 5.5% increase).
= .844 crates per labor-hour
=
260
308
Labor productivity
with buyer
=
260 crates
(100 logs * 3 hours>log) + 8 hours
Current System
Labor: 300 hrs. @10 = 3,000
Material: 100 logs/day 1,000
Capital: 350
Energy: 150
Total Cost: $4,500
Multifactor productivity of current system:
crates/dollar= 240 crates>4,500 = .0533
� SOLVED PROBLEM 1.2
Art Binley has decided to look at his productivity from a multifac-
tor (total factor productivity) perspective (refer to Solved Problem
1.1). To do so, he has determined his labor, capital, energy, and
material usage and decided to use dollars as the common denomi-
nator. His total labor-hours are now 300 per day and will increase
System with Professional Buyer
308 hrs. @10 = $3,080
1,000
350
150
$4,580
Multifactor productivity of proposed system:
crates/dollar= 260 crates>4,580 = .0568
to 308 per day. His capital and energy costs will remain constant at
$350 and $150 per day, respectively. Material costs for the 100
logs per day are $1,000 and will remain the same. Because he pays
an average of $10 per hour (with fringes), Binley determines his
productivity increase as follows:
� SOLUTION
Using current productivity (.0533) as a base, the increase will be .066. That is, or a 6.6% increase..0568>.0533 = 1.066,
� Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Zychol Chemicals Corp.: The production manager must prepare a productivity report, which includes multifactor analysis.

www.myomlab.com

www.myomlab.com

www.pearsonhighered.com/heizer

Chapter 1 Operations and Productivity 21
Bibliography
Broedner, P., S. Kinkel, and G. Lay. “Productivity Effects of
Outsourcing.” International Journal of Operations and
Production Management 29, no. 2 (2009): 127.
Hounshell, D. A. From the American System to Mass Production
1800–1932: The Development of Manufacturing. Baltimore:
Johns Hopkins University Press, 1985.
Lewis, William W. The Power of Productivity. Chicago: University
of Chicago Press, 2004.
Maroto, A., and L. Rubalcaba. “Services Productivity Revisited.”
The Service Industries Journal 28, no. 3 (April 2008): 337.
Sahay, B. S. “Multi-factor Productivity Measurement Model for
Service Organization.” International Journal of Productivity
and Performance Management 54, no. 1–2 (2005): 7–23.
San, G., T. Huang, and L. Huang. “Does Labor Quality Matter on
Productivity Growth?” Total Quality Management and
Business Excellence 19, no. 10 (October 2008): 1043.
Sprague, Linda G. “Evolution of the Field of Operations
Management,” Journal of Operations Management 25,
no. 2 (March 2007): 219–238.
Tangen, S. “Demystifying Productivity and Performance.”
International Journal of Productivity and Performance
Measurement 54, no. 1–2 (2005): 34–47.
Taylor, F. W. The Principles of Scientific Management. New York:
Harper & Brothers, 1911.
van Biema, Michael, and Bruce Greenwald. “Managing Our Way
to Higher Service-Sector Productivity.” Harvard Business
Review 75, no. 4 (July–August 1997): 87–95.
Wren, Daniel A. The Evolution of Management Thought,
New York: Wiley, 1994.

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Operations Strategy
in a Global Environment
Chapter Outline
GLOBAL COMPANY PROFILE: BOEING
A Global View of Operations 26
Developing Missions and Strategies 30
Achieving Competitive Advantage
Through Operations 31
Ten Strategic OM Decisions 35
Issues in Operations Strategy 36
Strategy Development and
Implementation 39
Global Operations Strategy Options 42
23
� Design of Goods and Services
� Managing Quality
� Process Strategy
� Location Strategies
� Layout Strategies
� Human Resources
� Supply-Chain Management
� Inventory Management
� Scheduling
� Maintenance

This state-of-the-art Boeing 787 is also global. Led
by Boeing at its Everett, Washington, facility, an
international team of aerospace companies developed
the airplane. New technologies, new design, new
manufacturing processes, and committed international
suppliers are helping Boeing and its partners achieve
unprecedented levels of performance in design,
manufacture, and operation.
The 787 is global not only because it has a range
of 8,300 miles but also because it is built all over the
world—with a huge financial risk of over $5 billion,
Boeing needed partners. The global nature of both
technology and the aircraft market meant finding
exceptional developers and suppliers, wherever they
might be. It also meant finding firms willing to step up
to the risk associated with a very expensive new
product. These partners not only spread the risk but
also bring commitment to the table. Countries that
have a stake in the 787 are more likely to buy from
Boeing than from the European competitor Airbus
Industries.
Boeing teamed with more than 20 international
systems suppliers to develop technologies and design
concepts for the 787. Boeing found its 787 partners in
over a dozen countries; a few of them are shown in the
table on the left.
With the 787’s state-of-the-art design, more spacious interior,
and global suppliers, Boeing has garnered record sales
worldwide.
GLOBAL COMPANY PROFILE: BOEING
BOEING’S GLOBAL STRATEGY YIELDS COMPETITIVE ADVANTAGE
B
oeing’s strategy for its 787 Dreamliner is
unique from both an engineering and global
perspective.
The Dreamliner incorporates the latest in a
wide range of aerospace technologies, from airframe
and engine design to superlightweight titanium graphite
laminate, carbon fiber and epoxy, and composites.
Another innovation is the electronic monitoring system
that allows the airplane to report maintenance
requirements to ground-based computer systems.
Boeing has also worked with General Electric and
Rolls-Royce to develop more efficient engines. The
advances in engine technology contribute as much as
8% of the increased fuel/payload efficiency of the new
airplane, representing a nearly two-generation jump in
technology.
Some of the International Suppliers of Boeing 787
Components
Latecoere France Passenger doors
Labinel France Wiring
Dassault France Design and PLM software
Messier-Bugatti France Electric brakes
Thales France Electrical power conversion
system and integrated
standby flight display
Messier-Dowty France Landing gear structure
Diehl Germany Interior lighting
Cobham UK Fuel pumps and valves
Rolls-Royce UK Engines
Smiths Aerospace UK Central computer system
BAE Systems UK Electronics
Alenia
Aeronautica
Italy Upper center fuselage and
horizontal stabilizer
Toray Industries Japan Carbon fiber for wing and
tail units
Fuji Heavy
Industries
Japan Center wing box
Kawasaki Heavy
Industries
Japan Forward fuselage, fixed
sections of wing, landing
gear wheel well
Teijin Seiki Japan Hydraulic actuators
Mitsubishi Heavy
Industries
Japan Wing box
Chengdu Aircraft
Group
China Rudder
Hafei Aviation China Parts
Korean Airlines South Korea Wingtips
Saab Sweden Cargo and access doors
24

Boeing’s collaborative technol-
ogy enables a “virtual work-
space” that allows engineers on
the 787, including partners in
Australia, Japan, Italy, Canada
and across the United States,
to make concurrent design
changes to the airplane in real
time. Designing, building, and
testing the 787 digitally before
production reduced design
errors and improved production
efficiencies.
State-of-the-art composite sections of the 787 are built
around the world and shipped to Boeing for final assembly.
Components from Boeing’s worldwide supply chain come
together on an assembly line in Everett, Washington. Although
components come from throughout the world, about 35% of
the 787 structure comes from Japanese companies.
The Japanese companies Toray, Teijin Seiki, Fuji,
Kawasaki, and Mitsubishi are producing over 35% of
the project, providing whole composite fuselage
sections. Italy’s Alenia Aeronautica is building an
additional 10% of the plane.
Many U.S. companies, including Crane Aerospace,
Fairchild Controls, Goodrich, General Dynamics,
Hamilton Sundstrand, Honeywell, Moog, Parker
Hannifin, Rockwell Collins, and Triumph Group are
also suppliers. Boeing has 70% to 80% of the
Dreamliner built by other companies. And even some
of the portion built by Boeing is produced at Boeing
facilities outside the United States, in Australia and
Canada.
The global Dreamliner is efficient, has a global
range, and is made from components produced
around the world. The result: a state-of-the-art airplane
reflecting the global nature of business in the 21st
century and one of the fastest-selling commercial
jets in history.
25
BOEING �

26 PART 1 Introduction to Operations Management
A GLOBAL VIEW OF OPERATIONS
Today’s operations manager must have a global view of operations strategy. Since the early
1990s, nearly 3 billion people in developing countries have overcome the cultural, religious, eth-
nic, and political barriers that constrain productivity and are now players on the global economic
stage. As these barriers disappear, simultaneous advances are being made in technology, reliable
shipping, and cheap communication. The unsurprising result is the growth of world trade (see
Figure 2.1), global capital markets, and the international movement of people; This means:
increasing economic integration and interdependence of countries—in a word, globalization. In
response, organizations are hastily extending their operations globally with innovative strategies.
For instance:
• Boeing is competitive because both its sales and production are worldwide.
• Italy’s Benetton moves inventory to stores around the world faster than its competition by
building flexibility into design, production, and distribution.
• Sony purchases components from suppliers in Thailand, Malaysia, and elsewhere around the
world for assembly in its electronic products.
• Volvo, considered a Swedish company, recently controlled by a U.S. company, Ford. But the
current Volvo S40 is built in Belgium on a platform shared with the Mazda 3 (built in Japan)
and the Ford Focus (built and sold in Europe.)
• China’s Haier (pronounced “higher”) is now producing compact refrigerators (it has one-third
of the U.S. market) and refrigerated wine cabinets (it has half of the U.S. market) in South
Carolina.
Globalization means that domestic production and exporting may no longer be a viable busi-
ness model; local production and exporting no longer guarantee success or even survival. There
are new standards of global competitiveness that impact quality, variety, customization, conve-
nience, timeliness, and cost. The globalization of strategy contributes efficiency and adds value
to products and services, but it also complicates the operations manager’s job. Complexity, risk
and competition are intensified; companies must carefully account for them.
LO1: Define mission and strategy 30
LO2: Identify and explain three strategic
approaches to competitive advantage 30
LO3: Identify and define the 10 decisions
of operations management 35
Chapter 2 Learning Objectives
LO4: Understand the significance
of key success factors and core
competencies 39
LO5: Identify and explain four global
operations strategy options 42
0
5
10
15
20
30
P
e
rc
e
n
t
1960
25
35
1970 1975 1980 1985 1990 1995 2000 20051965
Year
Collapse of the
Berlin Wall
2010 (est.*)
� FIGURE 2.1
Growth of World Trade
(world trade as a percentage
of world GDP)
AUTHOR COMMENT
As Prof. Thomas Sewell
observed, “No great
civilization has developed
in isolation.”
* Author estimate for 2010.
Source: Based on a speech by Mark A. Wynne, Federal Reserve Bank of Dallas, June 2009.

Chapter 2 Operations Strategy in a Global Environment 27
Maquiladoras
Mexican factories located along
the U.S.–Mexico border that
receive preferential tariff
treatment.
1The 27 members of the European Union (EU) as of 2010 were Austria, Belgium, Bulgaria, Cyprus, Czech Republic,
Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta,
the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, United Kingdom. Not all have adopted
the euro. In addition, Croatia, Macedonia, and Turkey are candidates for entry into the European Union.
We have identified six reasons why domestic business operations decide to change to some
form of international operation. They are:
1. Reduce costs (labor, taxes, tariffs, etc.).
2. Improve the supply chain.
3. Provide better goods and services.
4. Understand markets.
5. Learn to improve operations.
6. Attract and retain global talent.
Let us examine, in turn, each of the six reasons.
Reduce Costs Many international operations seek to take advantage of the tangible opportuni-
ties to reduce their costs. Foreign locations with lower wages can help lower both direct and indi-
rect costs. (See the OM in Action box “U.S. Cartoon Production at Home in Manila.”) Less
stringent government regulations on a wide variety of operation practices (e.g., environmental con-
trol, health and safety, etc.) reduce costs. Opportunities to cut the cost of taxes and tariffs also
encourage foreign operations. In Mexico, the creation of maquiladoras (free trade zones) allows
manufacturers to cut their costs of taxation by paying only on the value added by Mexican workers.
If a U.S. manufacturer, such as GM, brings a $500 engine to a maquiladora operation for assembly
work costing $25, tariff duties will be charged only on the $25 of work performed in Mexico.
Shifting low-skilled jobs to another country has several potential advantages. First, and most
obviously, the firm may reduce costs. Second, moving the lower skilled jobs to a lower cost loca-
tion frees higher cost workers for more valuable tasks. Third, reducing wage costs allows the
savings to be invested in improved products and facilities (and the retraining of existing workers,
if necessary) at the home location. The impact of this approach is shown in the OM in Action box
“Going Global to Compete” on the next page.
Trade agreements have also helped reduce tariffs and thereby reduce the cost of operating
facilities in foreign countries. The World Trade Organization (WTO) has helped reduce tariffs
from 40% in 1940 to less than 3% today. Another important trade agreement is the North
American Free Trade Agreement (NAFTA). NAFTA seeks to phase out all trade and tariff bar-
riers among Canada, Mexico, and the U.S. Other trade agreements that are accelerating global
trade include APEC (the Pacific Rim countries), SEATO (Australia, New Zealand, Japan, Hong
Kong, South Korea, New Guinea, and Chile), MERCOSUR (Argentina, Brazil, Paraguay, and
Uruguay), and CAFTA (Central America, Dominican Republic, and United States).
Another trading group is the European Union (EU).1 The European Union has reduced trade
barriers among the participating European nations through standardization and a common
Fred Flintstone is not from Bedrock. He is actually from
Manila, capital of the Philippines. So are Tom and Jerry,
Aladdin, and Donald Duck. More than 90% of American
television cartoons are produced in Asia and India, with the
Philippines leading the way. With their natural advantage of
English as an official language and a strong familiarity with
U.S. culture, animation companies in Manila now employ
more than 1,700 people. Filipinos understand Western
culture, and “you need to have a group of artists that can
understand the humor that goes with it,” says Bill Dennis,
a Hanna-Barbera executive.
Major studios like Disney, Marvel, Warner Brothers,
and Hanna-Barbera send storyboards—cartoon action
outlines—and voice tracks to the Philippines. Artists there
draw, paint, and film about
20,000 sketches for a 30-
minute episode. The cost of
$130,000 to produce an
episode in the Philippines
compares with $160,000 in
Korea and $500,000 in the
United States.
Sources: Journal of Global
Information Technology Management
(2007): 1–6; The New York Times
(February 26, 2004): A29; and The Wall
Street Journal (August 9, 2005): D8.
OM in Action � U.S. Cartoon Production at Home in Manila
World Trade
Organization (WTO)
An international organization
that promotes world trade by
lowering barriers to the free
flow of goods across borders.
North American Free
Trade Agreement
(NAFTA)
A free trade agreement between
Canada, Mexico, and the United
States.
European Union (EU)
A European trade group that has
27 member states.

currency, the euro. However, this major U.S. trading partner, with almost 500 million people, is
also placing some of the world’s most restrictive conditions on products sold in the EU.
Everything from recycling standards to automobile bumpers to hormone-free farm products must
meet EU standards, complicating international trade.
Improve the Supply Chain The supply chain can often be improved by locating facilities in
countries where unique resources are available. These resources may be expertise, labor, or raw
material. For example, auto-styling studios from throughout the world are migrating to the auto
mecca of southern California to ensure the necessary expertise in contemporary auto design.
Similarly, world athletic shoe production has migrated from South Korea to Guangzhou, China:
this location takes advantage of the low-cost labor and production competence in a city where
40,000 people work making athletic shoes for the world. And a perfume essence manufacturer
wants a presence in Grasse, France, where much of the world’s perfume essences are prepared
from the flowers of the Mediterranean.
Provide Better Goods and Services Although the characteristics of goods and services
can be objective and measurable (e.g., number of on-time deliveries), they can also be subjective
and less measurable (e.g., sensitivity to culture). We need an ever better understanding of differ-
ences in culture and of the way business is handled in different countries. Improved understand-
ing as the result of a local presence permits firms to customize products and services to meet
unique cultural needs in foreign markets.
Another reason to have international operations is to reduce response time to meet customers’
changing product and service requirements. Customers who purchase goods and services from
U.S. firms are increasingly located in foreign countries. Providing them with quick and adequate
service is often improved by locating facilities in their home countries.
Understand Markets Because international operations require interaction with foreign cus-
tomers, suppliers, and other competitive businesses, international firms inevitably learn about
opportunities for new products and services. Europe led the way with cell phone innovations, and
now the Japanese lead with the latest cell phone fads. Knowledge of these markets not only helps
firms understand where the market is going but also helps firms diversify their customer base,
add production flexibility, and smooth the business cycle.
Another reason to go into foreign markets is the opportunity to expand the life cycle (i.e.,
stages a product goes through; see Chapter 5) of an existing product. While some products in the
U.S. are in a “mature” stage of their product life cycle, they may represent state-of-the-art prod-
ucts in less developed countries. For example, the U.S. market for personal computers could be
characterized as “mature” but as in the “introductory” stage in many developing countries, such
as Albania, Vietnam, and Myanmar (Burma).
28 PART 1 Introduction to Operations Management
Wachovia Corp, the giant subsidiary of Wells Fargo, has
inked a $1.1 billion deal with India’s Genpact to outsource
finance and accounting jobs. Wachovia has also handed
over administration of its human resources programs to
Illinois-based Hewitt Associates. This is “what we need to
do to become a great customer-relationship company,”
says Wachovia executive P. J. Sidebottom. The expected
cost savings of $600 million to $1 billion over the next three
years will be invested in the U.S. to boost the core banking
business. These investments will be made in new ATMs,
branches, and personnel.
Similarly, Dana Corp. of Toledo, Ohio, is also taking
a global approach. Dana established a joint venture
with Cardanes S.A. to produce truck transmissions
in Queretaro, Mexico. Then Dana switched 288 U.S.
employees in its Jonesboro, Arkansas, plant from
producing truck transmissions at breakeven to axle
production at a profit. Productivity is up in Jonesboro,
and the Mexican joint venture is making money.
Employees in both Jonesboro and Queretaro, as well
as stockholders, came out ahead on the move. Dana
is also moving operations to China, India, Eastern
Europe, and South America.
Resourceful organizations like Wachovia and Dana use
a global perspective to become more efficient, which
allows them to develop new products, retrain employees,
and invest in new plant and equipment.
Sources: Business Week (January 30, 2006): 50–64; Forbes (May 8, 2006):
58; and www.dana.com/news/.
OM in Action � Going Global to Compete

www.dana.com/news/

Chapter 2 Operations Strategy in a Global Environment 29
Learn to Improve Operations Learning does not take place in isolation. Firms serve them-
selves and their customers well when they remain open to the free flow of ideas. For example,
GM found that it could improve operations by jointly building and running, with the Japanese, an
auto assembly plant in San Jose, California. This strategy allowed GM to contribute its capital
and knowledge of U.S. labor and environmental laws while the Japanese contributed production
and inventory ideas. Similarly, operations managers have improved equipment and layout by
learning from the ergonomic competence of the Scandinavians.
Attract and Retain Global Talent Global organizations can attract and retain better
employees by offering more employment opportunities. They need people in all functional areas
and areas of expertise worldwide. Global firms can recruit and retain good employees because
they provide both greater growth opportunities and insulation against unemployment during times
of economic downturn. During economic downturns in one country or continent, a global firm has
the means to relocate unneeded personnel to more prosperous locations.
So, to recap, successfully achieving a competitive advantage in our shrinking world means
maximizing all of the possible opportunities, from tangible to intangible, that international oper-
ations can offer.
Cultural and Ethical Issues
While there are great forces driving firms toward globalization, many challenges remain. One of
these challenges is reconciling differences in social and cultural behavior. With issues ranging
from bribery, to child labor, to the environment, managers sometimes do not know how to
respond when operating in a different culture. What one country’s culture deems acceptable may
be considered unacceptable or illegal in another. It is not by chance that there are fewer female
managers in the Middle East than in India.
In the last decade, changes in international laws, agreements, and codes of conduct have been
applied to define ethical behavior among managers around the world. The WTO, for example,
helps to make uniform the protection of both governments and industries from foreign firms that
engage in unethical conduct. Even on issues where significant differences between cultures exist,
as in the area of bribery or the protection of intellectual property, global uniformity is slowly
being accepted by most nations.
In spite of cultural and ethical differences, we live in a period of extraordinary mobility of
capital, information, goods, and even people. We can expect this to continue. The financial sec-
tor, the telecommunications sector, and the logistics infrastructure of the world are healthy insti-
tutions that foster efficient and effective use of capital, information, and goods. Globalization,
with all its opportunities and risks, is here and will continue. It must be embraced as managers
develop their missions and strategies.
A worldwide strategy places added burdens
on operations management. Because of
economic and lifestyle differences, designers
must target products to each market. For
instance, clothes washers sold in northern
countries must spin-dry clothes much
better than those in warmer climates,
where consumers are likely to line-dry
them. Similarly, as shown here, Whirlpool
refrigerators sold in Bangkok are
manufactured in bright colors because they
are often put in living rooms.
AUTHOR COMMENT
As the owner of a Guatemala
plant said, “The ethics of the
world markets is very clear:
Manufacturers will move
wherever it is cheapest
or most convenient to
their interests.”

30 PART 1 Introduction to Operations Management
DEVELOPING MISSIONS AND STRATEGIES
An effective operations management effort must have a mission so it knows where it is going and
a strategy so it knows how to get there. This is the case for a small domestic organization, as well
as a large international organization.
Mission
Economic success, indeed survival, is the result of identifying missions to satisfy a customer’s
needs and wants. We define the organization’s mission as its purpose—what it will contribute to
society. Mission statements provide boundaries and focus for organizations and the concept
around which the firm can rally. The mission states the rationale for the organization’s existence.
Developing a good strategy is difficult, but it is much easier if the mission has been well defined.
Figure 2.2 provides examples of mission statements.
Once an organization’s mission has been decided, each functional area within the firm determines
its supporting mission. By functional area we mean the major disciplines required by the firm, such
as marketing, finance/accounting, and production/operations. Missions for each function are devel-
oped to support the firm’s overall mission. Then within that function lower-level supporting missions
are established for the OM functions. Figure 2.3 provides such a hierarchy of sample missions.
Strategy
With the mission established, strategy and its implementation can begin. Strategy is an organi-
zation’s action plan to achieve the mission. Each functional area has a strategy for achieving its
mission and for helping the organization reach the overall mission. These strategies exploit
opportunities and strengths, neutralize threats, and avoid weaknesses. In the following sections,
we will describe how strategies are developed and implemented.
Firms achieve missions in three conceptual ways: (1) differentiation, (2) cost leadership, and
(3) response. This means operations managers are called on to deliver goods and services that are
(1) better, or at least different, (2) cheaper, and (3) more responsive. Operations managers trans-
late these strategic concepts into tangible tasks to be accomplished. Any one or combination of
these three strategic concepts can generate a system that has a unique advantage over competi-
tors. For example, Hunter Fan has differentiated itself as a premier maker of quality ceiling fans
that lower heating and cooling costs for its customers. Nucor Steel, on the other hand, satisfies
customers by being the lowest-cost steel producer in the world. And Dell achieves rapid response
by building personal computers with each customer’s requested software in a matter of hours.
Clearly, strategies differ. And each strategy puts different demands on operations manage-
ment. Hunter Fan’s strategy is one of differentiating itself via quality from others in the industry.
Nucor focuses on value at low cost, and Dell’s dominant strategy is quick, reliable response.
LO1: Define mission and
strategy
Mission
The purpose or rationale for an
organization’s existence.
Merck
The mission of Merck is to provide society with superior products and services—innova-
tions and solutions that improve the quality of life and satisfy customer needs—to provide
employees with meaningful work and advancement opportunities and investors with a
superior rate of return.
Hard Rock Cafe
Our Mission: To spread the spirit of Rock ’n’ Roll by delivering an exceptional entertainment
and dining experience. We are committed to being an important, contributing member of
our community and offering the Hard Rock family a fun, healthy, and nurturing work
environment while ensuring our long-term success.
Arnold Palmer Hospital
Arnold Palmer Hospital for Children provides state of the art, family-centered healthcare
focused on restoring the joy of childhood in an environment of compassion, healing and hope.
� FIGURE 2.2
Mission Statements for
Three Organizations
Sources: Annual reports: courtesy
of Merck, Hard Rock Cafe: Employee
Handbook, Arnold Palmer Childrens’
Care Team.
VIDEO 2.1
Operations Strategy at
Regal Marine
Strategy
How an organization expects to
achieve its missions and goals.
AUTHOR COMMENT
Getting an education and
managing an organization
both require a mission
and strategy.
LO2: Identify and explain
three strategic approaches
to competitive advantage

Chapter 2 Operations Strategy in a Global Environment 31
� FIGURE 2.3
Sample Missions for a
Company, the Operations
Function, and Major OM
Departments
Sample Company Mission
Sample OM Department Missions
To manufacture and service an innovative, growing, and profitable worldwide microwave
communications business that exceeds our customers’ expectations.
Sample Operations Management Mission
To produce products consistent with the company’s mission as the worldwide low-cost
manufacturer.
Process design To determine, design, and produce the production process
and equipment that will be compatible with low-cost product,
high quality, and a good quality of work life at economical cost.
Location To locate, design, and build efficient and economical facilities
that will yield high value to the company, its employees, and
the community.
Layout design To achieve, through skill, imagination, and resourcefulness in
layout and work methods, production effectiveness and
efficiency while supporting a high quality of work life.
Human resources To provide a good quality of work life, with well-designed, safe,
rewarding jobs, stable employment, and equitable pay, in
exchange for outstanding individual contribution from
employees at all levels.
Supply-chain management To collaborate with suppliers to develop innovative products
from stable, effective, and efficient sources of supply.
Inventory To achieve low investment in inventory consistent with high
customer service levels and high facility utilization.
Scheduling To achieve high levels of throughput and timely customer
delivery through effective scheduling.
Maintenance To achieve high utilization of facilities and equipment by
effective preventive maintenance and prompt repair of
facilities and equipment.
Product design To design and produce products and services with outstanding
Quality management To attain the exceptional value that is consistent with our
company mission and marketing objectives by close
attention to design, procurement, production, and field
service opportunities.
quality and inherent customer value.
ACHIEVING COMPETITIVE ADVANTAGE
THROUGH OPERATIONS
Each of the three strategies provides an opportunity for operations managers to achieve compet-
itive advantage. Competitive advantage implies the creation of a system that has a unique
advantage over competitors. The idea is to create customer value in an efficient and sustainable
way. Pure forms of these strategies may exist, but operations managers will more likely be called
on to implement some combination of them. Let us briefly look at how managers achieve com-
petitive advantage via differentiation, low cost, and response.
Competing on Differentiation
Safeskin Corporation is number one in latex exam gloves because it has differentiated itself and
its products. It did so by producing gloves that were designed to prevent allergic reactions about
which doctors were complaining. When other glove makers caught up, Safeskin developed
Competitive advantage
The creation of a unique
advantage over competitors.
AUTHOR COMMENT
For many organizations, the
operations function provides
the competitive advantage.

32 PART 1 Introduction to Operations Management
Differentiation
Distinguishing the offerings of
an organization in a way that the
customer perceives as adding
value.
hypoallergenic gloves. Then it added texture to its gloves. Then it developed a synthetic dispos-
able glove for those allergic to latex—always staying ahead of the competition. Safeskin’s strat-
egy is to develop a reputation for designing and producing reliable state-of-the-art gloves,
thereby differentiating itself.
Differentiation is concerned with providing uniqueness. A firm’s opportunities for creating
uniqueness are not located within a particular function or activity but can arise in virtually every-
thing the firm does. Moreover, because most products include some service, and most services
include some product, the opportunities for creating this uniqueness are limited only by imagina-
tion. Indeed, differentiation should be thought of as going beyond both physical characteristics
and service attributes to encompass everything about the product or service that influences the
value that the customers derive from it. Therefore, effective operations managers assist in defining
everything about a product or service that will influence the potential value to the customer. This
may be the convenience of a broad product line, product features, or a service related to the prod-
uct. Such services can manifest themselves through convenience (location of distribution centers,
stores, or branches), training, product delivery and installation, or repair and maintenance services.
In the service sector, one option for extending product differentiation is through an experience.
Differentiation by experience in services is a manifestation of the growing “experience economy.”
The idea of experience differentiation is to engage the customer—to use people’s five senses so
they become immersed, or even an active participant, in the product. Disney does this with the
Magic Kingdom. People no longer just go on a ride; they are immersed in the Magic Kingdom—
surrounded by a dynamic visual and sound experience that complements the physical ride. Some
rides further engage the customer by having them steer the ride or shoot targets or villains.
Theme restaurants, such as Hard Rock Cafe, likewise differentiate themselves by providing
an “experience.” Hard Rock engages the customer with classic rock music, big-screen rock
videos, memorabilia, and staff who can tell stories. In many instances, a full-time guide is avail-
able to explain the displays, and there is always a convenient retail store so the guest can take
home a tangible part of the experience. The result is a “dining experience” rather than just a
meal. In a less dramatic way, both Starbucks and your local supermarket deliver an experience
when they provide music and the aroma of fresh coffee or freshly baked bread.
Competing on Cost
Southwest Airlines has been a consistent moneymaker while other U.S. airlines have lost bil-
lions. Southwest has done this by fulfilling a need for low-cost and short-hop flights. Its opera-
tions strategy has included use of secondary airports and terminals, first-come, first-served
seating, few fare options, smaller crews flying more hours, snacks-only or no-meal flights, and
no downtown ticket offices.
Additionally, and less obviously, Southwest has very effectively matched capacity to demand
and effectively utilized this capacity. It has done this by designing a route structure that matches
the capacity of its Boeing 737, the only plane in its fleet. Second, it achieves more air miles than
other airlines through faster turnarounds—its planes are on the ground less.
One driver of a low-cost strategy is a facility that is effectively utilized. Southwest and others with
low-cost strategies understand this and utilize resources effectively. Identifying the optimum size
(and investment) allows firms to spread overhead costs, providing a cost advantage. For instance,
Wal-Mart continues to pursue its low-cost strategy with superstores, open 24 hours a day. For
20 years, it has successfully grabbed market share. Wal-Mart has driven down store overhead costs,
shrinkage, and distribution costs. Its rapid transportation of goods, reduced warehousing costs, and
direct shipment from manufacturers have resulted in high inventory turnover and made it a low-cost
leader. Franz Colruyt, as discussed in the OM in Action box, is also winning with a low-cost strategy.
Low-cost leadership entails achieving maximum value as defined by your customer. It requires
examining each of the 10 OM decisions in a relentless effort to drive down costs while meeting cus-
tomer expectations of value. A low-cost strategy does not imply low value or low quality.
Competing on Response
The third strategy option is response. Response is often thought of as flexible response, but it also
refers to reliable and quick response. Indeed, we define response as including the entire range of
values related to timely product development and delivery, as well as reliable scheduling and
flexible performance.
Experience
differentiation
Engaging a customer with a
product through imaginative
use of the five senses, so the
customer “experiences” the
product.
VIDEO 2.2
Hard Rock’s Global Strategy
Low-cost leadership
Achieving maximum value as
perceived by the customer.
Response
A set of values related to rapid,
flexible, and reliable
performance.

Chapter 2 Operations Strategy in a Global Environment 33
Belgian discount food retailer Franz Colruyt NV is so
obsessed with cutting costs that there are no shopping bags
at its checkout counters, the lighting at its stores is dimmed
to save money on electricity, and employees clock out when
they go on 5-minute coffee breaks. And to keep costs down
at the company’s spartan headquarters on the outskirts of
Brussels, employees don’t have voice mail on their phones.
Instead, two receptionists take messages for nearly 1,000
staffers. The messages are bellowed out every few minutes
from loudspeakers peppered throughout the building.
This same approach is evident at all 160 of Colruyt’s
shopping outlets, which are converted factory warehouses,
movie theaters, or garages, with black concrete floors,
exposed electrical wires, metal shelves, and discarded
boxes strewn about. There is no background music
(estimated annual cost saving: € 2 million, or $2.5 million),
nor are there bags for packing groceries (estimated annual
cost saving: € 5 million). And all the store’s freezers have
doors, so the company can save about € 3 million a year
on electricity for refrigeration.
The company also employs a team of 30 “work
simplifiers”—in Colruyt jargon—whose job is to come up
with new ways to improve productivity. One recently
discovered that 5 seconds could be shaved from every
minute it takes customers to check out if they paid at a
separate station from where groceries are scanned, so that
when one customer steps away from the scanner, another
can step up right away.
Chief Executive Rene De Wit says Colruyt’s strategy
is simple: cut costs at every turn and undersell your
competitors. In an industry where margins of 1% to 2%
are typical, Colruyt’s cost cutting is so effective that a profit
margin of 6.5% dwarfs those of rivals.
A low-cost strategy places significant demands on
operations management, but Franz Colruyt, like Wal-Mart,
makes it work.
Sources: The Wall Street Journal (January 5, 2005): 1 and (September 22,
2003): R3, R7.
OM in Action � Low-Cost Strategy Wins at Franz Colruyt
Flexible response may be thought of as the ability to match changes in a marketplace where
design innovations and volumes fluctuate substantially.
Hewlett-Packard is an exceptional example of a firm that has demonstrated flexibility in both
design and volume changes in the volatile world of personal computers. HP’s products often
have a life cycle of months, and volume and cost changes during that brief life cycle are dra-
matic. However, HP has been successful at institutionalizing the ability to change products and
volume to respond to dramatic changes in product design and costs—thus building a sustainable
competitive advantage.
The second aspect of response is the reliability of scheduling. One way the German machine
industry has maintained its competitiveness despite having the world’s highest labor costs is
through reliable response. This response manifests itself in reliable scheduling. German machine
firms have meaningful schedules—and they perform to these schedules. Moreover, the results of
Response strategy wins orders at Super
Fast Pizza. Using a wireless connection,
orders are transmitted to $20,000 kitchens
in vans. The driver, who works solo,
receives a printed order, goes to the
kitchen area, pulls premade pizzas from
the cooler, and places them in the oven—
it takes about 1 minute. The driver then
delivers the pizza—sometimes even
arriving before the pizza is ready.

34 PART 1 Introduction to Operations Management
these schedules are communicated to the customer and the customer can, in turn, rely on them.
Consequently, the competitive advantage generated through reliable response has value to the end
customer.
The third aspect of response is quickness. Johnson Electric, discussed in the OM in Action
box, competes on speed—speed in design, production, and delivery. Whether it is a production
system at Johnson Electric, a pizza delivered in 5 minutes by Pizza Hut, or customized phone
products delivered in three days from Motorola, the operations manager who develops systems
that respond quickly can have a competitive advantage.
In practice, differentiation, low cost, and response can increase productivity and generate a
sustainable competitive advantage (see Figure 2.4). Proper implementation of the following deci-
sions by operations managers will allow these advantages to be achieved.
Patrick Wang, managing director of Johnson Electric
Holdings, Ltd., walks through his Hong Kong headquarters
with a micromotor in his hand. This tiny motor, about twice
the size of his thumb, powers a Dodge Viper power door
lock. Although most people have never heard of Johnson
Electric, we all have several of its micromotors nearby. This
is because Johnson is the world’s leading producer of
micromotors for cordless tools, household appliances
(such as coffee grinders and food processors), personal
care items (such as hair dryers and electric shavers), and
cars. A luxury Mercedes, with its headlight wipers, power
windows, power seat adjustments, and power side mirrors,
may use 50 Johnson micromotors.
Like all truly global businesses, Johnson spends
liberally on communications to tie together its global
network of factories, R&D facilities, and design centers.
For example, Johnson Electric installed a $20 million
videoconferencing system that allows engineers in
Cleveland, Ohio, and Stuttgart, Germany, to monitor
trial production of their micromotors in China.
Johnson’s first strength is speed in product
development, speed in production, and speed in
delivering—13 million motors a month, mostly assembled
in China but delivered throughout the world. Its second
strength is the ability to stay close to its customers.
Johnson has design and technical centers scattered
across the United States, Europe, and Japan. “The
physical limitations of the past are gone” when it comes
to deciding where to locate a new center, says Patrick
Wang. “Customers talk to us where they feel most
comfortable, but products are made where they are
most competitive.”
Sources: Hoover’s Company Records (January 1, 2006): 58682; Far
Eastern Economic Review (May 16, 2002): 44–45; and Just Auto
(November 2008): 18–19.
OM in Action � Response Strategy at Hong Kong’s Johnson Electric
10 Operations
Decisions Approach Example
Competitive
Advantage
Product
Quality
Process
Location
Layout
Human resource
Supply chain
Inventory
Scheduling
Maintenance
Innovative design . . . . . . . . . . . . . . . . . . . . . . . Safeskin’s innovative gloves
Broad product line . . . . . . . . . . . . . . . . . . . . .Fidelity Security’s mutual funds
After-sales service . . . . . . . . . . . . . . . . Caterpillar’s heavy equipment service
Experience . . . . . . . . . . . . . . . . . . . . . . . . . Hard Rock Cafe’s dining experience
COST LEADERSHIP:
Low overhead . . . . . . . . . . . . . . . . . . . . . Franz-Colruyt’s warehouse-type stores
Effective capacity use . . . . . . . . . . . . Southwest Airlines’s high aircraft utilization
Inventory management . . . . . . . . . . Wal-Mart’s sophisticated distribution system
RESPONSE:
Flexibility . . . . . . . . . . . . . Hewlett-Packard’s response to volatile world market
Reliability . . . . . . . . . . . . . . . . . . . . . . . FedEx’s “absolutely, positively on time”
Quickness . . . . . . . . . . . . . Pizza Hut’s five-minute guarantee at lunchtime
DIFFERENTIATION:
Differentiation
(better)
Cost
leadership
(cheaper)
Response
(faster)
� FIGURE 2.4 Achieving Competitive Advantage Through Operations
AUTHOR COMMENT
These 10 decisions are used
to implement a specific
strategy and yield a
competitive advantage.

Chapter 2 Operations Strategy in a Global Environment 35
TEN STRATEGIC OM DECISIONS
Differentiation, low cost, and response can be achieved when managers make effective decisions
in 10 areas of OM. These are collectively known as operations decisions. The 10 decisions of
OM that support missions and implement strategies are:
1. Goods and service design: Designing goods and services defines much of the transfor-
mation process. Costs, quality, and human resource decisions are often determined by
design decisions. Designs usually determine the lower limits of cost and the upper limits
of quality.
2. Quality: The customer’s quality expectations must be determined and policies and proce-
dures established to identify and achieve that quality.
3. Process and capacity design: Process options are available for products and services.
Process decisions commit management to specific technology, quality, human resource use,
and maintenance. These expenses and capital commitments determine much of the firm’s
basic cost structure.
4. Location selection: Facility location decisions for both manufacturing and service organi-
zations may determine the firm’s ultimate success. Errors made at this juncture may over-
whelm other efficiencies.
5. Layout design: Material flows, capacity needs, personnel levels, technology decisions, and
inventory requirements influence layout.
6. Human resources and job design: People are an integral and expensive part of the total sys-
tem design. Therefore, the quality of work life provided, the talent and skills required, and
their costs must be determined.
7. Supply-chain management: These decisions determine what is to be made and what is to be
purchased. Consideration is also given to quality, delivery, and innovation, all at a satisfactory
price. Mutual trust between buyer and supplier is necessary for effective purchasing.
8. Inventory: Inventory decisions can be optimized only when customer satisfaction, suppli-
ers, production schedules, and human resource planning are considered.
9. Scheduling: Feasible and efficient schedules of production must be developed; the demands
on human resources and facilities must be determined and controlled.
10. Maintenance: Decisions must be made regarding desired levels of reliability and stability,
and systems must be established to maintain that reliability and stability.
Operations managers implement these 10 decisions by identifying key tasks and the staffing
needed to achieve them. However, the implementation of decisions is influenced by a variety of
issues, including a product’s proportion of goods and services (see Table 2.1 on page 37). Few
products are either all goods or all services. Although the 10 decisions remain the same for both
goods and services, their relative importance and method of implementation depend on this ratio
of goods and services. Throughout this text, we discuss how strategy is selected and imple-
mented for both goods and services through these 10 operations management decisions.
Let’s look at an example of strategy development through one of the 10 decisions.
Pierre Alexander has just completed culinary school and is ready to open his own restaurant. After
examining both the external environment and his prospective strengths and weaknesses, he makes a
decision on the mission for his restaurant, which he defines as “To provide outstanding French fine dining
for the people of Chicago.”
APPROACH � Alexander’s supporting operations strategy is to ignore the options of cost leader-
ship and quick response and focus on differentiation. Consequently, his operations strategy requires
him to evaluate product designs (menus and meals) and selection of process, layout, and location. He
must also evaluate the human resources, suppliers, inventory, scheduling, and maintenance that will
support his mission and a differentiation strategy.
SOLUTION � Examining just one of these 10 decisions, process design, requires that Alexander
consider the issues presented in the following figure.
(Continued)
� EXAMPLE 1
Strategy
development
Operations decisions
The strategic decisions of OM
are goods and service design,
quality, process design, location
selection, layout design, human
resources and job design,
supply-chain management,
inventory, scheduling, and
maintenance.
LO3: Identify and define
the 10 decisions of
operations management
AUTHOR COMMENT
This text is structured
around these 10 decisions.

36 PART 1 Introduction to Operations Management
The 10 decisions of operations management are implemented in ways that provide competi-
tive advantage, not just for fine-dining restaurants, but for all the goods and services that enrich
our lives. How this might be done for two drug companies, one seeking a competitive advantage
via differentiation, and the other via low cost, is shown in Table 2.2.
ISSUES IN OPERATIONS STRATEGY
Whether the OM strategy is differentiation, cost, or response (as shown earlier in Figure 2.4), OM is
a critical player. Therefore, prior to establishing and attempting to implement a strategy, some alter-
nate perspectives may be helpful. One perspective is to take a resources view. This means think-
ing in terms of the financial, physical, human, and technological resources available and ensuring
The first option is to operate in the lower right corner of the preceding figure, where he could produce
high volumes of food with a limited variety, much as in an institutional kitchen. Such a process could
produce large volumes of standard items such as baked goods and mashed potatoes prepared with state-
of-the-art automated equipment. Alexander concludes that this is not an acceptable process option.
Alternatively, he can move to the middle of the figure, where he could produce more variety and
lower volumes. Here he would have less automation and use prepared modular components for meals,
much as a fast-food restaurant does. Again, he deems such process designs inappropriate for his mission.
Another option is to move to the upper right corner and produce a high volume of customized
meals, but neither Alexander nor anyone else knows how to do this with gourmet meals.
Finally, Alexander can design a process that operates in the upper left corner of the figure, which
requires little automation but lends itself to high variety. This process option suggests that he build an
extremely flexible kitchen suitable for a wide variety of custom meals catering to the whims of each
customer. With little automation, such a process would be suitable for a huge variety. This process
strategy will support his mission and desired product differentiation. Only with a process such as this
can he provide the fine French-style gourmet dining that he has in mind.
INSIGHT � By considering the options inherent in each of the 10 OM decisions, managers—
Alexander, in this case—can make decisions that support the mission.
LEARNING EXERCISE � If Alexander’s mission were to offer less expensive meals and
reduce the variety offered but still do so with a French flair, what might his process strategy be?
[Answer: Alexander might try a repetitive (modular) strategy and mimic the La Madeleine cafeteria-
style restaurants.]
V
a
ri
e
ty
o
f
p
ro
d
u
c
ts
Moderate
Moderate
Volume
Low
Low
High
High
(Print shop, emergency
room, machine shop,
fine-dining
restaurant)
JOB SHOPS
Process focused
(Cars, appliances, TVs,
fast-food restaurants)
ASSEMBLY LINE
Repetitive (modular)
focus
(Steel, beer, paper, bread,
institutional kitchen)
CONTINUOUS
Product focused
Customization at
High Volume
(Dell Computer’s PC,
cafeteria)
Mass Customization
Resources view
A method managers use to
evaluate the resources at their
disposal and manage or alter them
to achieve competitive advantage.
AUTHOR COMMENT
An effective strategy finds
the optimum fit for the firm’s
resources in the dynamic
environment.

� TABLE 2.1
The Differences between Goods and Services Influence How the 10 Operations Management Decisions Are Applied
Operations Decisions Goods Services
Goods and service design Product is usually tangible (a computer). Product is not tangible. A new range of product
attributes (a smile).
Quality Many objective quality standards (battery life). Many subjective quality standards (nice color).
Process and capacity
design
Customer is not involved in most of the process
(auto assembly).
Customer may be directly involved in the process
(a haircut).
Capacity must match demand to avoid lost sales
(customers often avoid waiting).
Location selection May need to be near raw materials or labor force
(steel plant near ore).
May need to be near customer (car rental).
Layout design Layout can enhance production efficiency
(assembly line).
Can enhance product as well as production (layout
of a classroom or a fine-dining restaurant).
Human resources and
job design
Workforce focused on technical skills (stone
mason). Labor standards can be consistent
(assembly line employee). Output-based wage
system possible (garment sewing).
Direct workforce usually needs to be able to
interact well with customer (bank teller); labor
standards vary depending on customer
requirements (legal cases).
Supply-chain
management
Supply chain relationships critical to final
product.
Supply chain relationships important but may not
be critical
Inventory Raw materials, work-in-process, and finished
goods may be inventoried (beer).
Most services cannot be stored; so other ways must
be found to accommodate fluctuations in demand
(can’t store haircuts, but even the barber shop has
an inventory of supplies).
Scheduling Ability to inventory may allow leveling of
production rates (lawn mowers).
Often concerned with meeting the customer’s
immediate schedule with human resources.
Maintenance Maintenance is often preventive and takes
place at the production site.
Maintenance is often “repair” and takes place at
the customer’s site.
� TABLE 2.2
Operations Strategies of Two Drug Companies
Brand Name Drugs, Inc. Generic Drug Corp.
Competitive Advantage Product Differentiation Low Cost
Product Selection
and Design
Heavy R&D investment; extensive labs; focus on
development in a broad range of drug categories
Low R&D investment; focus on development of
generic drugs
Quality Quality is major priority, standards exceed
regulatory requirements
Meets regulatory requirements on a country-by-
country basis, as necessary
Process Product and modular production process; tries to
have long product runs in specialized facilities;
builds capacity ahead of demand
Process focused; general production processes;
“job shop” approach, short-run production; focus
on high utilization
Location Still located in city where it was founded Recently moved to low-tax, low-labor-cost
environment
Layout Layout supports automated product-focused
production
Layout supports process-focused “job shop”
practices
Human Resources Hire the best; nationwide searches Very experienced top executives provide direction;
other personnel paid below industry average
Supply Chain Long-term supplier relationships Tends to purchase competitively to find bargains
Inventory Maintains high finished goods inventory primarily
to ensure all demands are met
Process focus drives up work-in-process inventory;
finished goods inventory tends to be low
Scheduling Centralized production planning Many short-run products complicate scheduling
Maintenance Highly trained staff; extensive parts inventory Highly trained staff to meet changing demands
AUTHOR COMMENT
The production of both goods and
services requires execution of the
10 OM decisions.
AUTHOR COMMENT
Notice how the 10 decisions are
altered to build two distinct strategies
in the same industry.

that the potential strategy is compatible with those resources. Another perspective is Porter’s
value-chain analysis.2 Value-chain analysis is used to identify activities that represent strengths,
or potential strengths, and may be opportunities for developing competitive advantage. These are
areas where the firm adds its unique value through product research, design, human resources,
supply-chain management, process innovation, or quality management. Porter also suggests
analysis of competitors via what he calls his five forces model.3 These potential competing forces
are immediate rivals, potential entrants, customers, suppliers, and substitute products.
In addition to the competitive environment, the operations manager needs to understand that
the firm is operating in a system with many other external factors. These factors range from polit-
ical, to legal, to cultural. They influence strategy development and execution and require con-
stant scanning of the environment.
The firm itself is also undergoing constant change. Everything from resources, to technology,
to product life cycles is in flux. Consider the significant changes required within the firm as its
products move from introduction, to growth, to maturity, and to decline (see Figure 2.5). These
internal changes, combined with external changes, require strategies that are dynamic.
In this chapter’s Global Company Profile, Boeing provides an example of how strategy must
change as technology and the environment change. Boeing can now build planes from carbon
38 PART 1 Introduction to Operations Management
Value-chain analysis
A way to identify those elements
in the product/service chain that
uniquely add value.
Five forces analysis
A method of analyzing the five
forces in the competitive
environment.
2M. E. Porter, Competitive Advantage: Creating and Sustaining Superior Performance. New York: The Free Press,
1985.
3Michael E. Porter, Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York: The Free
Press, 1980, 1998.
Product design and
development critical
Frequent product
and process
design changes
Short production
runs
High production
costs
Limited models
Attention to quality
Practical to change
price or quality
image
Strengthen niche
Poor time to change
image, price, or quality
Competitive costs
become critical
Defend market position
Cost control
critical
Forecasting critical
Product and
process reliability
Competitive product
improvements and
options
Increase capacity
Shift toward product
focus
Enhance distribution
Standardization
Fewer rapid product
changes, more
minor changes
Optimum capacity
Increasing stability
of process
Long production
runs
Product improvement
and cost cutting
Little product
differentiation
Cost minimization
Overcapacity in
the industry
Prune line to
eliminate items
not returning
good margin
Reduce capacity
Best period to
increase market
share
R&D engineering
is critical
Introduction Growth DeclineMaturity
O
M
S
tr
a
te
g
y
/
I
s
s
u
e
s
C
o
m
p
a
n
y
S
tr
a
te
g
y
/
I
s
s
u
e
s
Sales
Drive-thru restaurantsInternet search engines
Analog
TVs
CD-ROMs
Avatars
Boeing 787
Twitter
LCD & plasma TVs
iPods
Xbox 360
� FIGURE 2.5 Strategy and Issues During a Product’s Life

fiber, using a global supply chain. Like many other OM strategies, Boeing’s strategy has changed
with technology and globalization. Microsoft has also had to adapt quickly to a changing envi-
ronment. Faster processors, new computer languages, changing customer preferences, increased
security issues, the Internet, and Google have all driven changes at Microsoft. These forces have
moved Microsoft’s product strategy from operating systems to office products, to Internet ser-
vice provider, and now to integrator of computers, cell phones, games, and television.
The more thorough the analysis and understanding of both the external and internal factors,
the more likely that a firm can find the optimum use of its resources. Once a firm understands
itself and the environment, a SWOT analysis, which we discuss next, is in order.
STRATEGY DEVELOPMENT AND IMPLEMENTATION
A SWOT analysis is a formal review of the internal Strengths and Weakness and the external
Opportunity and Threats. Beginning with SWOT analyses, organizations position themselves,
through their strategy, to have a competitive advantage. A firm may have excellent design skills or
great talent at identifying outstanding locations. However, it may recognize limitations of its manufac-
turing process or in finding good suppliers. The idea is to maximize opportunities and minimize
threats in the environment while maximizing the advantages of the organization’s strengths and mini-
mizing the weaknesses. Any preconceived ideas about mission are then reevaluated to ensure they are
consistent with the SWOT analysis. Subsequently, a strategy for achieving the mission is developed.
This strategy is continually evaluated against the value provided customers and competitive realities.
The process is shown in Figure 2.6. From this process, key success factors are identified.
Key Success Factors and Core Competencies
Because no firm does everything exceptionally well, a successful strategy requires determining the
firm’s critical success factors and core competencies. Key success factors (KSFs) are those activi-
ties that are necessary for a firm to achieve its goals. Key success factors can be so significant that
a firm must get them right to survive in the industry. A KSF for McDonald’s, for example, is layout.
Without a play area, an effective drive-thru, and an efficient kitchen, McDonald’s cannot be suc-
cessful. KSFs are often necessary, but not sufficient for competitive advantage. On the other hand,
core competencies are the set of unique skills, talents, and capabilities that a firm does at a world-
class standard. They allow a firm to set itself apart and develop a competitive advantage. Organizations
that prosper identify their core competencies and nurture them. While McDonald’s KSFs may include
layout, its core competency may be consistency and quality. Honda Motors’s core competence is gas-
powered engines—engines for automobiles, motorcycles, lawn mowers, generators, snow blowers,
and more. The idea is to build KSFs and core competencies that provide a competitive advantage
and support a successful strategy and mission. A core competence may be a subset of KSFs or a
combination of KSFs. The operations manager begins this inquiry by asking:
• “What tasks must be done particularly well for a given strategy to succeed?”
• “Which activities will help the OM function provide a competitive advantage?”
• “Which elements contain the highest likelihood of failure, and which require additional com-
mitment of managerial, monetary, technological, and human resources?”
Chapter 2 Operations Strategy in a Global Environment 39
Key success factors
(KSFs)
Activities or factors that are
key to achieving competitive
advantage.
Core competencies
A set of skills, talents, and
activities in which a firm is
particularly strong.
SWOT analysis
A method of determining
internal strengths and
weaknesses and external
opportunities and threats.
Analyze the Environment
Determine the Corporate Mission
State the reason for the firm’s existence and identify the value it wishes to create.
Form a Strategy
Build a competitive advantage, such as low price, design or volume flexibility,
quality, quick delivery, dependability, after-sale services, or broad product lines.
Identify the strengths, weaknesses, opportunities, and threats.
Understand the environment, customers, industry, and competitors.
� FIGURE 2.6
Strategy Development
Process
AUTHOR COMMENT
A SWOT analysis provides
an excellent model for
evaluating a strategy.
LO4: Understand the
significance of key success
factors and core
competencies

Only by identifying and strengthening key success factors and core competencies can an organi-
zation achieve sustainable competitive advantage.
In this text we focus on the 10 OM decisions that typically include the KSFs. Potential KSFs for
marketing, finance, and operations are shown in Figure 2.7. The 10 OM decisions we develop in this
text provide an excellent initial checklist for determining KSFs and identifying core competencies
within the operations function. For instance, the 10 decisions, related KSFs, and core competencies
can allow a firm to differentiate its product or service. That differentiation may be via a core compe-
tence of innovation and new products, where the KSFs are product design and speed to market, as is
the case for 3M and Rubbermaid. Similarly, differentiation may be via quality, where the core com-
petence is institutionalizing quality, as at Toyota. Differentiation may also be via maintenance, where
the KSFs are product reliability and after-sale service, as is the case at IBM and Canon.
40 PART 1 Introduction to Operations Management
Service
Distribution
Promotion
Price
Channels of distribution
Product positioning
(image, functions)
Leverage
Cost of capital
Working capital
Receivables
Payables
Financial control
Lines of credit
Product
Quality
Process
Location
Layout
Human resource
Supply chain
Inventory
Schedule
Maintenance
Marketing Finance/Accounting Operations
Decisions Sample Options Chapter
Customized or standardized
Define customer expectations and how to
achieve them
Facility design, capacity
Near supplier or near customer
Work cells or assembly line
Specialized or enriched jobs
Single or multiple suppliers
When to reorder; how much to keep on hand
Stable or fluctuating production rate
Repair as required or preventive maintenance
5
6,S6
7,S7
8
9
10
11, S11
12,14,16
13,15
17
Support a Core Competence and
Implement Strategy by Identifying and Executing
the Key Success Factors in the Functional Areas
� FIGURE 2.7
Implement Strategy by
Identifying and Executing
Key Success Factors That
Support Core Competences
Activity map
A graphical link of competitive
advantage, KSFs, and
supporting activities.
Generators Automobiles 4-Wheel Scooters Water Pumps
Marine Motors Race Cars Motorcycles Snow Blowers
Honda’s core competence
is the design and
manufacture of gas-
powered engines. This
competence has allowed
Honda to become a
leader in the design and
manufacture of a wide
range of gas-powered
products. Tens of millions
of these products are
produced and shipped
around the world.
Whatever the KSFs and core competences, they must be supported by the related activities.
One approach to identifying the activities is an activity map, which links competitive advantage,
KSFs, and supporting activities. For example, Figure 2.8 shows how Southwest Airlines, whose
core competence is operations, built a set of integrated activities to support its low-cost compet-
itive advantage. Notice how the KSFs support operations and in turn are supported by other
activities. The activities fit together and reinforce each other. And the better they fit and reinforce
each other, the more sustainable the competitive advantage. By focusing on enhancing its core

competence and KSFs with a supporting set of activities, Southwest Airlines has become one of
the great airline success stories.
Build and Staff the Organization
The operations manager’s job is a three-step process. Once a strategy and key success factors
have been identified, the second step is to group the necessary activities into an organizational
structure. The third step is to staff it with personnel who will get the job done. The manager
works with subordinate managers to build plans, budgets, and programs that will successfully
implement strategies that achieve missions. Firms tackle this organization of the operations
function in a variety of ways. The organization charts shown in Chapter 1 (Figure 1.1) indicate the
way some firms have organized to perform the required activities.
Integrate OM with Other Activities
The organization of the operations function and its relationship to other parts of the organization
vary with the OM mission. Moreover, the operations function is most likely to be successful
when the operations strategy is integrated with other functional areas of the firm, such as market-
ing, finance, information technology, and human resources. In this way, all of the areas support
the company’s objectives. For example, short-term scheduling in the airline industry is domi-
nated by volatile customer travel patterns. Day-of-week preference, holidays, seasonality, col-
lege schedules, and so on, all play a role in changing flight schedules. Consequently, airline
scheduling, although an OM activity, can be a part of marketing. Effective scheduling in the
trucking industry is reflected in the amount of time trucks travel loaded. However, scheduling of
trucks requires information from delivery and pickup points, drivers, and other parts of the orga-
nization. When the OM function results in effective scheduling in the air passenger and commer-
cial trucking industries, a competitive advantage can exist.
The operations manager transforms inputs into outputs. The transformations may be in terms
of storage, transportation, manufacturing, dissemination of information, and utility of the prod-
uct or service. The operations manager’s job is to implement an OM strategy, provide competi-
tive advantage, and increase productivity.
Chapter 2 Operations Strategy in a Global Environment 41
Courteous but
Limited Passenger
Service
Short Haul, Point-to-
Point Routes, Often to
Secondary Airports
Frequent, Reliable
Schedules
Standardized Fleet
of Boeing 737
Aircraft
High Aircraft
Utilization
Lean, Productive
Employees
No baggage
transfers
No seat
assignmentsAutomated
ticketing machines
Empowered
employees
High employee
compensation
Hire for attitude,
then train
20-minute gate
turnarounds
High level of stock
ownership
Maintenance
personnel trained
on only one type
of aircraft
Flexible employees/unions
and standard planes
aid scheduling
Excellent supplier
relations with
Boeing has aided
financing
Pilot training
required on only
one type of aircraft
Reduced maintenance
inventory required
because only one
type of aircraft is used
Saturate a city with
flights, lowering
administrative costs
(advertising, HR, etc.)
per passenger
for that city
High number of
flights reduces
employee idle time
between flights
Lower gate costs
at secondary airports
Competitive Advantage:
Low Cost
No meals
(peanuts)
� FIGURE 2.8 Activity Mapping of Southwest Airlines’s Low-Cost Competitive Advantage
To achieve a low-cost competitive advantage, Southwest has identified a number of key success factors (connected by red arrows) and support
activities (shown by blue arrows). As this figure indicates, a low-cost advantage is highly dependent on a very well run operations function.

GLOBAL OPERATIONS STRATEGY OPTIONS
As we suggested early in this chapter, many operations strategies now require an international
dimension. We tend to call a firm with an international dimension an international business or a
multinational corporation. An international business is any firm that engages in international
trade or investment. This is a very broad category and is the opposite of a domestic, or local, firm.
A multinational corporation (MNC) is a firm with extensive international business involve-
ment. MNCs buy resources, create goods or services, and sell goods or services in a variety of
countries. The term multinational corporation applies to most of the world’s large, well-known
businesses. Certainly IBM is a good example of an MNC. It imports electronics components
to the U.S. from over 50 countries, exports computers to over 130 countries, has facilities in
45 countries, and earns more than half its sales and profits abroad.
Operations managers of international and multinational firms approach global opportunities
with one of four operations strategies: international, multidomestic, global, or transnational
(see Figure 2.9). The matrix of Figure 2.9 has a vertical axis of cost reduction and a horizontal
axis of local responsiveness. Local responsiveness implies quick response and/or the differentia-
tion necessary for the local market. The operations manager must know how to position the firm
in this matrix. Let us briefly examine each of the four strategies.
International Strategy
An international strategy uses exports and licenses to penetrate the global arena. As Figure 2.9
suggests, the international strategy is the least advantageous, with little local responsiveness and
little cost advantage. There is little responsiveness because we are exporting or licensing goods
from the home country. And the cost advantages may be few because we are using the existing
production process at some distance from the new market. However, an international strategy is
often the easiest, as exports can require little change in existing operations, and licensing agree-
ments often leave much of the risk to the licensee.
42 PART 1 Introduction to Operations Management
International business
A firm that engages in cross-
border transactions.
Multinational
corporation (MNC)
A firm that has extensive
involvement in international
business, owning or controlling
facilities in more than one
country.
International strategy
A strategy in which global
markets are penetrated using
exports and licenses.
Low
HighLow
High
Local Responsiveness
(Quick Response and/or Differentiation)
C
o
s
t
R
e
d
u
c
ti
o
n
• Use existing
domestic model globally
• Franchise, joint
ventures, subsidiaries
Examples:
Heinz
McDonald’s
The Body Shop
Hard Rock Cafe
Multidomestic
strategy
• Import/export
or license existing
product
Examples:
U.S. Steel
Harley-Davidson
International
strategy
Global
strategy
• Standardized
product
• Economies of scale
• Cross-cultural
learning
Examples:
Texas Instruments
Caterpillar
Otis Elevator
Transnational
strategy
Move material,
people, or ideas
across national
boundaries
Economies of scale
Cross-cultural
learning
Examples:
Coca-Cola
Nestlé
•
•
•
� FIGURE 2.9
Four International Operations
Strategies
Sources: See a similar presentation in
M. Hitt, R. D. Ireland, and R. E. Hoskisson,
Strategic Management, Competitiveness
and Globalization, 7th ed. (Cincinnati:
Southwestern College Publishing, 2009).
AUTHOR COMMENT
Firms that ignore the global
economy will not survive.
LO5: Identify and explain
four global operations
strategy options

Multidomestic Strategy
The multidomestic strategy has decentralized authority with substantial autonomy at each business.
Organizationally these are typically subsidiaries, franchises, or joint ventures with substantial inde-
pendence. The advantage of this strategy is maximizing a competitive response for the local market;
however, the strategy has little or no cost advantage. Many food producers, such as Heinz, use a mul-
tidomestic strategy to accommodate local tastes because global integration of the production process
is not critical. The concept is one of “we were successful in the home market; let’s export the manage-
ment talent and processes, not necessarily the product, to accommodate another market.” McDonald’s
is operating primarily as a multidomestic, which gives it the local responsiveness needed to modify its
menu country by country. McDonald’s can then serve beer in Germany, wine in France, McHuevo
(poached egg hamburger) in Uruguay, and hamburgers without beef in India. With over 2,000 restau-
rants in Japan and a presence for more than a generation, the average Japanese family thinks Japan
invented McDonald’s. Interestingly, McDonald’s prefers to call itself multilocal.4
Global Strategy
A global strategy has a high degree of centralization, with headquarters coordinating the organi-
zation to seek out standardization and learning between plants, thus generating economies of
scale. This strategy is appropriate when the strategic focus is cost reduction but has little to rec-
ommend it when the demand for local responsiveness is high. Caterpillar, the world leader in
earth-moving equipment, and Texas Instruments, a world leader in semiconductors, pursue
global strategies. Caterpillar and Texas Instruments find this strategy advantageous because the
end products are similar throughout the world. Earth-moving equipment is the same in Nigeria as
in Iowa, which allows Caterpillar to have individual factories focus on a limited line of products
to be shipped worldwide. This results in economies of scale and learning within each facility.
A global strategy also allows Texas Instruments to build optimum-size plants with similar
processes and to then maximize learning by aggressive communication between plants. The
result is an effective cost reduction advantage for Texas Instruments.
Transnational Strategy
A transnational strategy exploits the economies of scale and learning, as well as pressure for
responsiveness, by recognizing that core competence does not reside in just the “home” country but
can exist anywhere in the organization. Transnational describes a condition in which material,
people, and ideas cross—or transgress—national boundaries. These firms have the potential to pur-
sue all three operations strategies (i.e., differentiation, low cost, and response). Such firms can be
Chapter 2 Operations Strategy in a Global Environment 43
Multidomestic strategy
A strategy in which operating
decisions are decentralized to
each country to enhance local
responsiveness.
4James L. Watson, ed., Golden Arches East: McDonald’s in East Asia (Stanford University Press, 1997): 12. Note:
McDonald’s also operates with some of the advantages of a global organization. By using very similar product lines
throughout the world, McDonald’s obtains some of the standardization advantages of a global strategy. However, it
manages to retain the advantages of a multidomestic strategy.
In a continuing fierce worldwide battle, both Komatsu and Caterpillar seek global advantage in the heavy equipment market. As
Komatsu (left) moved west to the UK, Caterpillar (right) moved east, with 13 facilities and joint ventures in China. Both firms are
building equipment throughout the world as cost and logistics dictate. Their global strategies allow production to move as
markets, risk, and exchange rates dictate.
Global strategy
A strategy in which operating
decisions are centralized and
headquarters coordinates the
standardization and learning
between facilities.
Transnational strategy
A strategy that combines the
benefits of global-scale
efficiencies with the benefits of
local responsiveness.

44 PART 1 Introduction to Operations Management
Global operations provide an increase in both the challenges
and opportunities for operations managers. Although the task
is challenging, operations managers can and do improve pro-
ductivity. They can build and manage OM functions that con-
tribute in a significant way to competitiveness. Organizations
identify their strengths and weaknesses. They then develop
effective missions and strategies that account for these
strengths and weaknesses and complement the opportunities
and threats in the environment. If this procedure is performed
well, the organization can have competitive advantage
through some combination of product differentiation, low
cost, and response. This competitive
advantage is often achieved via a
move to international, multidomestic,
global, or transnational strategies.
Effective use of resources, whether
domestic or international, is the respon-
sibility of the professional manager,
and professional managers are among the few in our society
who can achieve this performance. The challenge is great, and
the rewards to the manager and to society substantial.
Key Terms
Maquiladoras (p. 27)
World Trade Organization (WTO) (p. 27)
North American Free Trade Agreement
(NAFTA) (p. 27)
European Union (EU) (p. 27)
Mission (p. 30)
Strategy (p. 30)
Competitive advantage (p. 31)
Differentiation (p. 32)
Experience differentiation (p. 32)
Low-cost leadership (p. 32)
Response (p. 32)
Operations decisions (p. 35)
Resources view (p. 36)
Value-chain analysis (p. 38)
Five forces analysis (p. 38)
SWOT analysis (p. 39)
Key success factors (KSFs) (p. 39)
Core competencies (p. 39)
Activity map (p. 40)
International business (p. 42)
Multinational corporation
(MNC) (p. 42)
International strategy (p. 42)
Multidomestic strategy (p. 43)
Global strategy (p. 43)
Transnational strategy (p. 43)
thought of as “world companies” whose country identity is not as important as its interdependent
network of worldwide operations. Key activities in a transnational company are neither centralized
in the parent company nor decentralized so that each subsidiary can carry out its own tasks on a
local basis. Instead, the resources and activities are dispersed, but specialized, so as to be both effi-
cient and flexible in an interdependent network. Nestlé is a good example of such a company.
Although it is legally Swiss, 95% of its assets are held and 98% of its sales are made outside
Switzerland. Fewer than 10% of its workers are Swiss. Similarly, service firms such as Asea Brown
Boveri (an engineering firm that is Swedish but headquartered in Switzerland), Reuters (a news
agency), Bertelsmann (a publisher), and Citicorp (a banking corporation) can be viewed as transna-
tionals. We can expect the national identities of these transnationals to continue to fade.
Solved Problem Virtual Office Hours help is available at www.myomlab.com
� SOLVED PROBLEM 2.1
The global tire industry continues to consolidate. Michelin buys
Goodrich and Uniroyal and builds plants throughout the world.
Bridgestone buys Firestone, expands its research budget, and
focuses on world markets. Goodyear spends almost 4% of its sales
revenue on research. These three aggressive firms have come to
dominate the world tire market, with total market share approach-
ing 60%. And the German tire maker Continental AG has strength-
ened its position as fourth in the world, with a dominant presence
in Germany. Against this formidable array, the old-line Italian tire
company Pirelli SpA found it difficult to respond effectively.
Although Pirelli still had 5% of the market, it was losing millions a
year while the competition was getting stronger. Tires are a tough,
competitive business that rewards companies having strong market
shares and long production runs. Pirelli has some strengths: an out-
standing reputation for excellent high-performance tires and an
innovative manufacturing function.
Use a SWOT analysis to establish a feasible strategy for
Pirelli.
� SOLUTION
First, find an opportunity in the world tire market that avoids the
threat of the mass-market onslaught by the big three tire makers.
Second, utilize the internal marketing strength represented by
Pirelli’s strong brand name and history of winning World Rally
Championships. Third, maximize the internal innovative capabili-
ties of the operations function.
To achieve these goals, Pirelli made a strategic shift out of
low-margin standard tires and into higher-margin performance
tires. Pirelli established deals with luxury brands Jaguar, BMW,
Maserati, Ferrari, Bentley, and Lotus Elise and established itself
as a provider of a large share of tires on new Porsches, S-class
Mercedes, and Saabs. As a result, more than 70% of the com-
pany’s tire production is now high-performance tires. People are
willing to pay a premium for Pirellis.
The operations function continued to focus its design efforts
on performance tires and developing a system of modular tire man-
ufacture that allows much faster switching between models. This
modular system, combined with investments in new manufacturing
CHAPTER SUMMARY

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Chapter 2 Operations Strategy in a Global Environment 45
Bibliography
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�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Motorola’s Global Strategy: Focuses on Motorola’s international strategy.
flexibility, has driven batch sizes down to as small as 150 to 200,
making small-lot performance tires economically feasible. Manu-
facturing innovations at Pirelli have streamlined the production
process, moving it from a 14-step process to a 3-step process. A
threat from the big three going after the performance market
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www.pirelli.com/web/investors

www.pirelli.com/web/investors

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Project Management
Chapter Outline
GLOBAL COMPANY PROFILE: BECHTEL GROUP
The Importance of Project Management 50
Project Planning 50
Project Scheduling 53
Project Controlling 54
Project Management Techniques:
PERT and CPM 55
Determining the Project Schedule 60
Variability in Activity Times 65
Cost–Time Trade-Offs and Project
Crashing 71
A Critique of PERT and CPM 73
Using Microsoft Project
to Manage Projects 74
47

GLOBAL COMPANY PROFILE: BECHTEL GROUP
PROJECT MANAGEMENT PROVIDES A COMPETITIVE
ADVANTAGE FOR BECHTEL
N
ow in its 112th year, the San Francisco–
based Bechtel Group (www.bechtel.com)
is the world’s premier manager of massive
construction and engineering projects.
Known for billion-dollar projects, Bechtel is famous
for its construction feats on the Hoover Dam, the
Boston Central Artery/Tunnel project, and rebuilding
of Kuwait’s oil and gas infrastructure after the invasion
by Iraq in 1990.
Conditions weren’t what Bechtel expected when it
won a series of billion-dollar contracts from the U.S.
government to help reconstruct Iraq in 2003–2006.
48
Workers wrestle with a 1,500-ton boring machine, measuring
25 feet in diameter, that was used to dig the Eurotunnel
between England and France in the early 1990s. With over-
runs that boosted the cost of the project to $13 billion,
a Bechtel Group VP was brought in to head operations.
Saddam Hussein’s defeat by Allied forces hadn’t caused
much war damage. Instead, what Bechtel found was a
country that had been crumbling for years. None of the
sewage plants in Baghdad worked. Power flicked on and
off. Towns and cities in the anti-Hussein south had been
left to decay as punishment. And to complicate matters
even more, scavengers were stealing everything from
museum artifacts to electric power lines. Bechtel’s job
was to oversee electric power, sewage, transportation,
and airport repairs.
Bechtel’s crews travelled under armed escort and
slept in trailers surrounded by razor wire. But the
company’s efforts have paid off. Iraq’s main seaport,
Umm Qasr, was reopened when Bechtel dredged the
water and repaired the grain elevators. Electrical
generation was back to prewar levels in 10 months.
Bechtel refurbished more than 1,200 schools.
With a global procurement program, Bechtel easily
tapped the company’s network of suppliers and buyers
A massive dredge hired by Bechtel removes silt from
Iraq’s port at Umm Qasr. This paved the way for large-scale
deliveries of U.S. food and the return of commercial
shipping.

www.bechtel.com

49
worldwide to help rebuild Iraq’s infrastructure. Other
interesting recent Bechtel projects include:
• Building 26 massive distribution centers, in just 2 years,
for the Internet company Webvan Group ($1 billion).
• Constructing 30 high-security data centers
worldwide for Equinix, Inc. ($1.2 billion).
• Building and running a rail line between London and
the Channel Tunnel ($4.6 billion).
• Developing an oil pipeline from the Caspian Sea
region to Russia ($850 million).
• Expanding the Dubai Airport in the United Arab
Emirates ($600 million) and the Miami International
Airport ($2 billion).
• Building liquefied natural gas plants in Trinidad,
West Indies ($1 billion).
• Building a new subway for Athens, Greece
($2.6 billion).
• Constructing a natural gas pipeline in Thailand
($700 million).
• Building 30 plants for iMotors.com, a company that
sells refurbished autos online ($300 million).
• Building a highway to link the north and south of
Croatia ($303 million).
When companies or countries seek out firms to
manage massive projects, they go to Bechtel, which, again
and again, through outstanding project management, has
demonstrated its competitive advantage.
Managing massive con-
struction projects such as
this is the strength of
Bechtel. With large penal-
ties for late completion and
incentives for early comple-
tion, a good project man-
ager is worth his or her
weight in gold.
Reconstructed terminal at Baghdad International Airport
Bechtel was the construction contractor for the Hoover Dam.
This dam, on the Colorado River, is the highest in the Western
Hemisphere.
BECHTEL GROUP �

50 PART 1 Introduction to Operations Management
Chapter 3 Learning Objectives
LO1: Use a Gantt chart for scheduling 53
LO2: Draw AOA and AON networks 57
LO3: Complete forward and backward
passes for a project 61
LO4: Determine a critical path 65
LO5: Calculate the variance of activity times 67
LO6: Crash a project 71
THE IMPORTANCE OF PROJECT MANAGEMENT
When Bechtel, the subject of the opening Global Company Profile, entered Iraq after the 2003
war, it quickly had to mobilize an international force of manual workers, construction profes-
sionals, cooks, medical personnel, and security forces. Its project management team had to
access millions of tons of supplies to rebuild ports, roads, schools, and electrical systems.
Similarly, when Hard Rock Cafe sponsors Rockfest, hosting 100,000 plus fans at its annual con-
cert, the project manager begins planning some 9 months earlier. Using the software package
Microsoft Project, described in this chapter, each of the hundreds of details can be monitored and
controlled. When a band can’t reach the Rockfest site by bus because of massive traffic jams,
Hard Rock’s project manager is ready with a helicopter backup.
Bechtel and Hard Rock are just two examples of firms that face modern phenomena: growing
project complexity and collapsing product/service life cycles. This change stems from awareness
of the strategic value of time-based competition and a quality mandate for continuous improve-
ment. Each new product/service introduction is a unique event—a project. In addition, projects
are a common part of our everyday life. We may be planning a wedding or a surprise birthday
party, remodeling a house, or preparing a semester-long class project.
Scheduling projects is a difficult challenge for operations managers. The stakes in project
management are high. Cost overruns and unnecessary delays occur due to poor scheduling and
poor controls.
Projects that take months or years to complete are usually developed outside the normal pro-
duction system. Project organizations within the firm may be set up to handle such jobs and are
often disbanded when the project is complete. On other occasions, managers find projects just a
part of their job. The management of projects involves three phases (see Figure 3.1):
1. Planning: This phase includes goal setting, defining the project, and team organization.
2. Scheduling: This phase relates people, money, and supplies to specific activities and relates
activities to each other.
3. Controlling: Here the firm monitors resources, costs, quality, and budgets. It also revises or
changes plans and shifts resources to meet time and cost demands.
We begin this chapter with a brief overview of these functions. Three popular techniques to allow
managers to plan, schedule, and control—Gantt charts, PERT, and CPM—are also described.
PROJECT PLANNING
Projects can be defined as a series of related tasks directed toward a major output. In some firms
a project organization is developed to make sure existing programs continue to run smoothly on
a day-to-day basis while new projects are successfully completed.
For companies with multiple large projects, such as a construction firm, a project organization
is an effective way of assigning the people and physical resources needed. It is a temporary orga-
nization structure designed to achieve results by using specialists from throughout the firm.
NASA and many other organizations use the project approach. You may recall Project Gemini
and Project Apollo. These terms were used to describe teams that NASA organized to reach
space exploration objectives.
The project organization works best when:
1. Work can be defined with a specific goal and deadline.
2. The job is unique or somewhat unfamiliar to the existing organization.
3. The work contains complex interrelated tasks requiring specialized skills.
4. The project is temporary but critical to the organization.
5. The project cuts across organizational lines.
VIDEO 3.1
Project Management at Hard
Rock’s Rockfest
Project organization
An organization formed to
ensure that programs (projects)
receive the proper management
and attention.
AUTHOR COMMENT
Wherever your career takes
you, one of the most useful
tools you can have, as a
manager, is the ability to
manage a project.

Chapter 3 Project Management 51
Planning the Project
Scheduling the Project
Controlling the Project
Set the goals
Performance
1.1
1.2
2.0
2.1
2.11
Define the project
Develop work
breakdown
structure
Identify team/
resources
Sequence activities Assign people
Schedule deliverables Schedule resources
T
im
e Cost
1.1
1.2
2.0
2.1
2.11
Monitor resources,
costs, quality
June
Adams
Smith
Jones
S M T W T F S
1 2 3 4 5 6
7 8 9 10 11 12 13
Shift resources
Revise and
change plans
Adams
Smith
Jones
Before
project
Start of project During
project
Timeline
� FIGURE 3.1 Project Planning, Scheduling, and Controlling
Project
No.1
Project
No. 2
Mechanical
Engineer
Test
Engineer
Production
Project
Manager
Technician
Electrical
Engineer
Computer
Engineer
Project
Manager
Technician
Quality
Mgt.
Human
Resources Marketing Finance Design
President
The Project Manager
An example of a project organization is shown in Figure 3.2. Project team members are temporar-
ily assigned to a project and report to the project manager. The manager heading the project coor-
dinates activities with other departments and reports directly to top management. Project managers
receive high visibility in a firm and are responsible for making sure that (1) all necessary activities
are finished in proper sequence and on time; (2) the project comes in within budget; (3) the project
meets its quality goals; and (4) the people assigned to the project receive the motivation, direction,
AUTHOR COMMENT
Managers must “make
the plan and then work the
plan.” Project planning helps
you do that.
AUTHOR COMMENT
Project organizations can be
temporary or permanent.
A permanent organization is
usually called a “matrix
organization.”
� FIGURE 3.2
A Sample Project
Organization

52 PART 1 Introduction to Operations Management
and information needed to do their jobs. This means that project managers should be good coaches
and communicators, and be able to organize activities from a variety of disciplines.
Ethical Issues Faced in Project Management Project managers not only have high visi-
bility but they also face ethical decisions on a daily basis. How they act establishes the code of
conduct for the project. Project managers often deal with (1) offers of gifts from contractors,
(2) pressure to alter status reports to mask the reality of delays, (3) false reports for charges of
time and expenses, and (4) pressures to compromise quality to meet bonus or penalty schedules.
Using the Project Management Institute’s (www.pmi.org) ethical codes is one means of try-
ing to establish standards. Research has shown that without good leadership and a strong organi-
zational culture, most people follow their own set of ethical standards and values.1
Work Breakdown Structure
The project management team begins its task well in advance of project execution so that a plan
can be developed. One of its first steps is to carefully establish the project’s objectives, then
break the project down into manageable parts. This work breakdown structure (WBS) defines
the project by dividing it into its major subcomponents (or tasks), which are then subdivided into
more detailed components, and finally into a set of activities and their related costs. The division
of the project into smaller and smaller tasks can be difficult, but is critical to managing the proj-
ect and to scheduling success. Gross requirements for people, supplies, and equipment are also
estimated in this planning phase.
The work breakdown structure typically decreases in size from top to bottom and is indented
like this:
Level
1 Project
2 Major tasks in the project
3 Subtasks in major tasks
4 Activities (or “work packages”) to be completed
This hierarchical framework can be illustrated with the development of Microsoft’s operating sys-
tem Windows 7. As we see in Figure 3.3, the project, creating a new operating system, is labeled
1.0. The first step is to identify the major tasks in the project (level 2). Three examples would be
software design (1.1), project management (1.2), and system testing (1.3). Two major subtasks
for 1.1 are development of graphical user interfaces (GUIs) (1.1.1) and creating compatibility
with previous versions of Windows (1.1.2). The major subtasks for 1.1.2 are level-4 activities,
Work breakdown
structure (WBS)
A hierarchical description of a
project into more and more
detailed components.
1See Hilder Helgadottir, “The Ethical Dimension of Project Management,” International Journal of Project
Management 26, no. 7 (October 2008): 743.
Level 2
Level 3
Level 4
Level 1 Develop Windows 7
Operating System
Software
Design
Project
Management
System
Testing
Develop
GUIs
Planning
1.0
1.1 1.2 1.3
1.1.1
1.1.2
1.1.2.1
1.1.2.2
1.1.2.3
1.2.2 1.3.2
1.2.1 1.3.1
Module
Testing
Ensure Compatibility
with Earlier Versions
Defect
Tracking
Cost/Schedule
Management
Compatible
with Windows ME(Work packages)
Compatible
with Windows Vista
Compatible
with Windows XP
� FIGURE 3.3
Work Breakdown Structure

www.pmi.org

Chapter 3 Project Management 53
such as creating a team to handle compatibility with Windows ME (1.1.2.1), creating a team for
Windows Vista (1.1.2.2), and creating a team for Windows XP (1.1.2.3). There are usually many
level-4 activities.
PROJECT SCHEDULING
Project scheduling involves sequencing and allotting time to all project activities. At this stage,
managers decide how long each activity will take and compute how many people and materials
will be needed at each stage of production. Managers also chart separate schedules for personnel
needs by type of skill (management, engineering, or pouring concrete, for example). Charts also
can be developed for scheduling materials.
One popular project scheduling approach is the Gantt chart. Gantt charts are low-cost means
of helping managers make sure that (1) activities are planned, (2) order of performance is docu-
mented, (3) activity time estimates are recorded, and (4) overall project time is developed. As
Figure 3.4 shows, Gantt charts are easy to understand. Horizontal bars are drawn for each project
activity along a time line. This illustration of a routine servicing of a Delta jetliner during a
40-minute layover shows that Gantt charts also can be used for scheduling repetitive
operations. In this case, the chart helps point out potential delays. The OM in Action box on
Delta provides additional insights. (A second illustration of a Gantt chart is also provided in
Chapter 15, Figure 15.4.)
On simple projects, scheduling charts such as these permit managers to observe the progress
of each activity and to spot and tackle problem areas. Gantt charts, though, do not adequately
illustrate the interrelationships between the activities and the resources.
PERT and CPM, the two widely used network techniques that we shall discuss shortly, do
have the ability to consider precedence relationships and interdependency of activities. On com-
plex projects, the scheduling of which is almost always computerized, PERT and CPM thus have
an edge over the simpler Gantt charts. Even on huge projects, though, Gantt charts can be used as
summaries of project status and may complement the other network approaches.
To summarize, whatever the approach taken by a project manager, project scheduling serves
several purposes:
1. It shows the relationship of each activity to others and to the whole project.
2. It identifies the precedence relationships among activities.
3. It encourages the setting of realistic time and cost estimates for each activity.
4. It helps make better use of people, money, and material resources by identifying critical bot-
tlenecks in the project.
Gantt charts
Planning charts used to schedule
resources and allocate time.
LO1: Use a Gantt chart for
scheduling
0 10 20 30 40
Time, minutes
Passengers
Baggage
Fueling
Lavatory servicing
Galley servicing
Cargo and mail
Drinking water
Flight service
Cabin cleaning
Cargo and mail
Operating crew
Baggage
Passengers
Baggage claim
Deplaning
Container offload
Engine injection water
Pumping
Container offload
Main cabin door
Aft cabin door
Loading
Aft, center, forward
Economy section
First-class section
Container/bulk loading
Galley/cabin check
Aircraft check
Receive passengers
Loading
Boarding
� FIGURE 3.4
Gantt Chart of Service
Activities for a Delta Jet
during a 40-Minute Layover
Delta hopes to save $50
million a year with this
turnaround time, which is a
reduction from its traditional
60-minute routine.
AUTHOR COMMENT
Gantt charts are simple
and visual, making them
widely used.

54 PART 1 Introduction to Operations Management
Flight 574’s engines screech its arrival as the jet lumbers
down Richmond’s taxiway with 140 passengers arriving from
Atlanta. In 40 minutes, the plane is to be airborne again.
However, before this jet can depart, there is business
to attend to: passengers, luggage, and cargo to unload and
load; thousands of gallons of jet fuel and countless drinks
to restock; cabin and restrooms to clean; toilet holding
tanks to drain; and engines, wings, and landing gear to
inspect.
The 10-person ground crew knows that a miscue
anywhere—a broken cargo loader, lost baggage,
misdirected passengers—can mean a late departure and
trigger a chain reaction of headaches from Richmond to
Atlanta to every destination of a connecting flight.
Carla Sutera, the operations manager for Delta’s
Richmond International Airport, views the turnaround
operation like a pit boss awaiting a race car. Trained crews
are in place for Flight 574 with baggage carts and tractors,
hydraulic cargo loaders, a truck to load food and drinks,
another to lift the cleanup crew, another to put fuel on,
and a fourth to take water
off. The “pit crew” usually
performs so smoothly that
most passengers never
suspect the proportions of
the effort. Gantt charts,
such as the one in Figure
3.4, aid Delta and other
airlines with the staffing and
scheduling that are needed
for this task.
Sources: Knight Ridder Tribune Business News (July 16, 2005): 1 and
(November 21, 2002): 1.
OM in Action � Delta’s Ground Crew Orchestrates a Smooth Takeoff
PROJECT CONTROLLING
The control of projects, like the control of any management system, involves close monitoring of
resources, costs, quality, and budgets. Control also means using a feedback loop to revise the
project plan and having the ability to shift resources to where they are needed most.
Computerized PERT/CPM reports and charts are widely available today on personal computers.
Some of the more popular of these programs are Primavera (by Primavera Systems, Inc.),
MacProject (by Apple Computer Corp.), Pertmaster (by Westminster Software, Inc.),
VisiSchedule (by Paladin Software Corp.), Time Line (by Symantec Corp.), and Microsoft
Project (by Microsoft Corp.), which we illustrate in this chapter.
These programs produce a broad variety of reports, including (1) detailed cost breakdowns for
each task, (2) total program labor curves, (3) cost distribution tables, (4) functional cost and hour
summaries, (5) raw material and expenditure forecasts, (6) variance reports, (7) time analysis
reports, and (8) work status reports.
Construction of the new 11-story building at Arnold Palmer Hospital in Orlando, Florida, was an enormous project for the hospital
administration. The photo on the left shows the first six floors under construction. The photo on the right shows the building as
completed two years later. Prior to beginning actual construction, regulatory and funding issues added, as they do with most
projects; substantial time to the overall project. Cities have zoning and parking issues; the EPA has drainage and waste issues;
and regulatory authorities have their own requirements; as do issuers of bonds. The $100 million, 4-year project at Arnold Palmer
Hospital is discussed in the Video Case Study in the Lecture Guide & Activities Manual.
VIDEO 3.2
Project Management at Arnold
Palmer Hospital
AUTHOR COMMENT
Software has revolutionized
project control.

Chapter 3 Project Management 55
PROJECT MANAGEMENT TECHNIQUES: PERT AND CPM
Program evaluation and review technique (PERT) and the critical path method (CPM)
were both developed in the 1950s to help managers schedule, monitor, and control large and
complex projects. CPM arrived first, in 1957, as a tool developed by J. E. Kelly of Remington
Rand and M. R. Walker of duPont to assist in the building and maintenance of chemical plants
at duPont. Independently, PERT was developed in 1958 by Booz, Allen, and Hamilton for the
U.S. Navy.
The Framework of PERT and CPM
PERT and CPM both follow six basic steps:
1. Define the project and prepare the work breakdown structure.
2. Develop the relationships among the activities. Decide which activities must precede and
which must follow others.
3. Draw the network connecting all the activities.
4. Assign time and/or cost estimates to each activity.
5. Compute the longest time path through the network. This is called the critical path.
6. Use the network to help plan, schedule, monitor, and control the project.
Step 5, finding the critical path, is a major part of controlling a project. The activities on the crit-
ical path represent tasks that will delay the entire project if they are not completed on time.
Managers can gain the flexibility needed to complete critical tasks by identifying noncritical
activities and replanning, rescheduling, and reallocating labor and financial resources.
Although PERT and CPM differ to some extent in terminology and in the construction of the
network, their objectives are the same. Furthermore, the analysis used in both techniques is very
similar. The major difference is that PERT employs three time estimates for each activity. These
time estimates are used to compute expected values and standard deviations for the activity. CPM
makes the assumption that activity times are known with certainty and hence requires only one
time factor for each activity.
For purposes of illustration, the rest of this section concentrates on a discussion of PERT.
Most of the comments and procedures described, however, apply just as well to CPM.
PERT and CPM are important because they can help answer questions such as the following
about projects with thousands of activities:
1. When will the entire project be completed?
2. What are the critical activities or tasks in the project—that is, which activities will delay the
entire project if they are late?
3. Which are the noncritical activities—the ones that can run late without delaying the whole
project’s completion?
4. What is the probability that the project will be completed by a specific date?
5. At any particular date, is the project on schedule, behind schedule, or ahead of schedule?
6. On any given date, is the money spent equal to, less than, or greater than the budgeted
amount?
7. Are there enough resources available to finish the project on time?
8. If the project is to be finished in a shorter amount of time, what is the best way to accomplish
this goal at the least cost?
Network Diagrams and Approaches
The first step in a PERT or CPM network is to divide the entire project into significant activities
in accordance with the work breakdown structure. There are two approaches for drawing a proj-
ect network: activity-on-node (AON) and activity-on-arrow (AOA). Under the AON conven-
tion, nodes designate activities. Under AOA, arrows represent activities. Activities consume time
and resources. The basic difference between AON and AOA is that the nodes in an AON diagram
represent activities. In an AOA network, the nodes represent the starting and finishing times of an
activity and are also called events. So nodes in AOA consume neither time nor resources.
Figure 3.5 illustrates both conventions for a small portion of the airline turnaround Gantt chart
(in Figure 3.4). The examples provide some background for understanding six common activity
Program evaluation
and review technique
(PERT)
A project management
technique that employs three
time estimates for each activity.
Critical path method
(CPM)
A project management
technique that uses only one
time factor per activity.
Critical path
The computed longest time
path(s) through a network.
Activity-on-node
(AON)
A network diagram in which
nodes designate activities.
Activity-on-arrow
(AOA)
A network diagram in which
arrows designate activities.
AUTHOR COMMENT
To use project management
software, you first need to
understand the next two
sections in this chapter.

56 PART 1 Introduction to Operations Management
B C
A
B
C
A
B
C
A
B
C
B
C
A
B CA
B DA
C
CA
DB
A
B
D
A C
B
C
DA
CA
DB
CA
DB
A comes before B,
which comes before C.
A and B must both
be completed
before C can start.
B and C cannot begin
until A is completed.
C and D cannot begin
until both A and B
are completed.
C cannot begin until both
A and B are completed;
D cannot begin until B is
completed. A dummy activity
is introduced in AOA.
B and C cannot begin until
A is completed. D cannot
begin until both B and C
are completed. A dummy
activity is again introduced
in AOA.
Dummy
activity
Dummy
activity
Activity-on-Node (AON) Activity Meaning Activity-on-Arrow (AOA)
(a)
(b)
(c)
(d)
(e)
(f)
� FIGURE 3.5 A Comparison of AON and AOA Network Conventions
relationships in networks. In Figure 3.5(a), activity A must be finished before activity B is
started, and B must, in turn, be completed before C begins. Activity A might represent “deplan-
ing passengers,” while B is “cabin cleaning,” and C is “boarding new passengers.”
Figures 3.5(e) and 3.5(f) illustrate that the AOA approach sometimes needs the addition of a
dummy activity to clarify relationships. A dummy activity consumes no time or resources, but
is required when a network has two activities with identical starting and ending events, or when
two or more follow some, but not all, “preceding” activities. The use of dummy activities is also
important when computer software is employed to determine project completion time. A dummy
activity has a completion time of zero and is shown graphically with a dashed line.
Although both AON and AOA are popular in practice, many of the project management soft-
ware packages, including Microsoft Project, use AON networks. For this reason, although we
illustrate both types of networks in the next examples, we focus on AON networks in subsequent
discussions in this chapter.
Dummy activity
An activity having no time that
is inserted into a network to
maintain the logic of the
network.

Chapter 3 Project Management 57
Activity-on-Node Example
� EXAMPLE 1
Activity-on-node for
EPA problem at
Milwaukee Paper
Milwaukee Paper Manufacturing, Inc., located near downtown Milwaukee, has long been delaying the
expense of installing air pollution control equipment in its facility. The Environmental Protection
Agency (EPA) has recently given the manufacturer 16 weeks to install a complex air filter system.
Milwaukee Paper has been warned that it may be forced to close the facility unless the device is
installed in the allotted time. Joni Steinberg, the plant manager, wants to make sure that installation of
the filtering system progresses smoothly and on time.
Given the following information, develop a table showing activity precedence relationships.
APPROACH � Milwaukee Paper has identified the eight activities that need to be performed in
order for the project to be completed. When the project begins, two activities can be simultaneously
started: building the internal components for the device (activity A) and the modifications necessary for
the floor and roof (activity B). The construction of the collection stack (activity C) can begin when the
internal components are completed. Pouring the concrete floor and installation of the frame (activity D)
can be started as soon as the internal components are completed and the roof and floor have been
modified.
After the collection stack has been constructed, two activities can begin: building the high-temperature
burner (activity E) and installing the pollution control system (activity F). The air pollution device can be
installed (activity G) after the concrete floor has been poured, the frame has been installed, and the high-
temperature burner has been built. Finally, after the control system and pollution device have been
installed, the system can be inspected and tested (activity H).
SOLUTION � Activities and precedence relationships may seem rather confusing when they are
presented in this descriptive form. It is therefore convenient to list all the activity information in a table,
as shown in Table 3.1. We see in the table that activity A is listed as an immediate predecessor of activ-
ity C. Likewise, both activities D and E must be performed prior to starting activity G.
INSIGHT � To complete a network, all predecessors must be clearly defined.
LEARNING EXERCISE � What is the impact on the sequence of activities if EPA approval is
required after Inspect and Test? [Answer: The immediate predecessor for the new activity would be H,
Inspect and Test, with EPA approval as the last activity.]
LO2: Draw AOA and AON
networks
Activity Description
Immediate
Predecessors
A Build internal components —
B Modify roof and floor —
C Construct collection stack A
D Pour concrete and install frame A, B
E Build high-temperature burner C
F Install pollution control system C
G Install air pollution device D, E
H Inspect and test F, G
� TABLE 3.1
Milwaukee Paper
Manufacturing’s Activities
and Predecessors
Note that in Example 1, it is enough to list just the immediate predecessors for each activity.
For instance, in Table 3.1, since activity A precedes activity C, and activity C precedes activity E,
the fact that activity A precedes activity E is implicit. This relationship need not be explicitly
shown in the activity precedence relationships.
When there are many activities in a project with fairly complicated precedence relationships,
it is difficult for an individual to comprehend the complexity of the project from just the tabular
information. In such cases, a visual representation of the project, using a project network, is con-
venient and useful. A project network is a diagram of all the activities and the precedence rela-
tionships that exist between these activities in a project. Example 2 illustrates how to construct a
project network for Milwaukee Paper Manufacturing.

58 PART 1 Introduction to Operations Management
Monday. It is time for John Nicely to make a grocery list. He’s
serving dinner on Saturday, so he’ll need a few things . . . 150
pounds of steak and chicken, ingredients for 48 gallons of
shrimp bisque, 400 sushi rolls, and 25 pounds of jambalaya.
Plus a couple hundred pizzas and a couple thousand hot
dogs—just enough to feed the Miami Heat basketball players
and the 19,600 guests expected. You see, Nicely is the
executive chef at American Airlines Arena in Miami, and on
Saturday the Heat are hosting the L.A. Lakers.
How do you feed huge crowds good food in a short time?
It takes good project management, combined with creativity
and improvisation. With 250 facilities serving food and
beverage, “The Arena,” Nicely says, “is its own beast.”
Tuesday. Shopping day.
Wednesday–Friday. The staff prepares whatever it can,
chopping vegetables, marinating meats, mixing salad
dressings—everything but cooking the food. Nicely also
begins his shopping lists for next Monday’s game against
Toronto and for a Queen concert three days later.
Saturday. 3:55 P.M. Clutch time. Suddenly the kitchen is
a joke-free zone. In five minutes, Nicely’s first clients, 200
elite season ticket holders, expect their meals—from a
unique menu created for each game.
5:00 P.M. As the Heat and Lakers
start warming up, the chefs
move their operation in a brisk
procession of hot boxes and
cold-food racks to the satellite
kitchens.
6:00 P.M. Nicely and team face
surprises at concession stands:
a shortage of cashiers and a
broken cash register.
Halftime. There is a run on
roasted potatoes in the
Flagship restaurant. But Nicely has thought ahead
and anticipated. The backup potatoes arrive before
customers even notice.
For John Nicely, successful project management means
happy guests as the result of a thousand details that have
been identified, planned, and executed. Just another night
of delivering restaurant-quality meals and top-grade fast food
to a sold-out stadium crowd in a span of a couple hours.
Sources: Fast Company (May, 2006): 52–57; and Knight Ridder Tribune
Business News (March 9, 2006): 1.
OM in Action � Prepping for the Miami Heat Game
EXAMPLE 2 �
AON graph for
Milwaukee Paper
Draw the AON network for Milwaukee Paper, using the data in Example 1.
APPROACH � In the AON approach, we denote each activity by a node. The lines, or arrows, rep-
resent the precedence relationships between the activities.
SOLUTION � In this example, there are two activities (A and B) that do not have any predeces-
sors. We draw separate nodes for each of these activities, as shown in Figure 3.6. Although not
required, it is usually convenient to have a unique starting activity for a project. We have therefore
included a dummy activity called Start in Figure 3.6. This dummy activity does not really exist and
takes up zero time and resources. Activity Start is an immediate predecessor for both activities A and
B, and serves as the unique starting activity for the entire project.
A
B
Activity A
(Build Internal Components)
Activity B
(Modify Roof and Floor)
Start
Start
Activity
� FIGURE 3.6
Beginning AON Network for
Milwaukee Paper
We now show the precedence relationships using lines with arrow symbols. For example, an arrow
from activity Start to activity A indicates that Start is a predecessor for activity A. In a similar fashion,
we draw an arrow from Start to B.
Next, we add a new node for activity C. Since activity A precedes activity C, we draw an arrow from
node A to node C (see Figure 3.7). Likewise, we first draw a node to represent activity D. Then, since
activities A and B both precede activity D, we draw arrows from A to D and from B to D (see Figure 3.7).

Chapter 3 Project Management 59
D
C
Activity A Precedes Activity C
Activities A and B
Precede Activity D
Start
B
A
� FIGURE 3.7
Intermediate AON Network
for Milwaukee Paper
D G
H
Arrows Show
Precedence
Relationships
Start
B
A
F
E
C
� FIGURE 3.8
Complete AON Network for
Milwaukee Paper
We proceed in this fashion, adding a separate node for each activity and a separate line for each
precedence relationship that exists. The complete AON project network for the Milwaukee Paper
Manufacturing project is shown in Figure 3.8.
INSIGHT � Drawing a project network properly takes some time and experience. We would like
the lines to be straight and arrows to move to the right when possible.
LEARNING EXERCISE � If EPA Approval occurs after Inspect and Test, what is the impact on
the graph? [Answer: A straight line is extended to the right beyond H to reflect the additional activity.]
RELATED PROBLEMS � 3.3, 3.6, 3.7, 3.9a, 3.10, 3.12, 3.15a
When we first draw a project network, it is not unusual that we place our nodes (activities) in
the network in such a fashion that the arrows (precedence relationships) are not straight lines.
That is, the lines could be intersecting each other, and even facing in opposite directions. For
example, if we had switched the location of the nodes for activities E and F in Figure 3.8, the
lines from F to H and E to G would have intersected. Although such a project network is per-
fectly valid, it is good practice to have a well-drawn network. One rule that we especially recom-
mend is to place the nodes in such a fashion that all arrows point in the same direction. To
achieve this, we suggest that you first draw a rough draft of the network, making sure all the rela-
tionships are shown. Then you can redraw the network to make appropriate changes in the loca-
tion of the nodes.
As with the unique starting node, it is convenient to have the project network finish with a
unique ending node. In the Milwaukee Paper example, it turns out that a unique activity, H, is the
last activity in the project. We therefore automatically have a unique ending node.
In situations in which a project has multiple ending activities, we include a “dummy” ending
activity. This dummy activity has all the multiple ending activities in the project as immediate
predecessors. We illustrate this type of situation in Solved Problem 3.2 at the end of this chapter.

60 PART 1 Introduction to Operations Management
Activity-on-Arrow Example
We saw earlier that in an AOA project network we can represent activities by arrows. A node rep-
resents an event, which marks the start or completion time of an activity. We usually identify an
event (node) by a number.
EXAMPLE 3 �
Activity-on-arrow for
Milwaukee Paper
Draw the complete AOA project network for Milwaukee Paper’s problem.
APPROACH � Using the data from Table 3.1 in Example 1, draw one activity at a time, starting
with A.
SOLUTION � We see that activity A starts at event 1 and ends at event 2. Likewise, activity B
starts at event 1 and ends at event 3. Activity C, whose only immediate predecessor is activity A, starts at
node 2 and ends at node 4. Activity D, however, has two predecessors (i.e., A and B). Hence, we need
both activities A and B to end at event 3, so that activity D can start at that event. However, we cannot
have multiple activities with common starting and ending nodes in an AOA network. To overcome this
difficulty, in such cases, we may need to add a dummy line (activity) to enforce the precedence relation-
ship. The dummy activity, shown in Figure 3.9 as a dashed line, is inserted between events 2 and 3 to
make the diagram reflect the precedence between A and D. The remainder of the AOA project network
for Milwaukee Paper’s example is also shown.
DETERMINING THE PROJECT SCHEDULE
Look back at Figure 3.8 (in Example 2) for a moment to see Milwaukee Paper’s completed AON
project network. Once this project network has been drawn to show all the activities and their
precedence relationships, the next step is to determine the project schedule. That is, we need to
identify the planned starting and ending time for each activity.
Let us assume Milwaukee Paper estimates the time required for each activity, in weeks, as
shown in Table 3.2. The table indicates that the total time for all eight of the company’s activities
is 25 weeks. However, since several activities can take place simultaneously, it is clear that the
total project completion time may be less than 25 weeks. To find out just how long the project
will take, we perform the critical path analysis for the network.
C
(Construct Stack)
(Pour Concrete/Install Frame)
D
(B
u
ild
B
u
rn
e
r)
E
A
(B
uil
d
In
te
rn
al
Co
m
po
ne
nt
s)
B
(M
odify Roof/Floor)
(Install Controls)
F
G
(In
st
al
l P
ol
lu
tio
n
De
vic
e)
(Inspect/Test)
HDummy Activity
2 4
5
6 7
3
1
� FIGURE 3.9
Complete AOA Network (with
Dummy Activity) for
Milwaukee Paper
Critical path analysis
A process that helps determine
a project schedule.
INSIGHT � Dummy activities are common in AOA networks. They do not really exist in the proj-
ect and take zero time.
LEARNING EXERCISE � A new activity, EPA Approval, follows activity H. Add it to Figure 3.9.
[Answer: Insert an arrowed line from node 7, which ends at a new node 8, and is labeled I (EPA
Approval).]
RELATED PROBLEMS � 3.4, 3.5, 3.9b
AUTHOR COMMENT
The dummy activity
consumes no time, but note
how it changes precedence.
Now activity D cannot begin
until both B and the dummy
are complete.
AUTHOR COMMENT
We now add times to
complete each activity. This
lets us find the critical path.

0
A
2
2
0
B
3
3 8
G
5
13
13
H
2
15
4
F
3
7
2
C
2
4
3
D
4
7
4
E
4
80
Start
0
0
0 0
0 2
LS
LF = Min(2,4)
= 2
2 4
10 13
LF = Min(LS of E, LS of F)
= Min(4,10) = 4
4 8
1 4 4 8
LS = LF – 4
8 13
LF = EF
of Project
13 15
Overlay 1: Latest Start and Finish Times Are Now Added

Overlay 2: Slack Times Are Now Computed and Added
0
A
2
2
0
B
3
3 8
G
5
13
13
H
2
15
4
F
3
7
2
C
2
4
3
D
4
7
4
E
4
80
Start
0
0
0 0
0 2 2 4
10 13
4 8
1 4 4 8 8 13
13 15
Slack = 0
Slack = 0
Slack = 6
Slack = 1 Slack = 1
Slack = 0Slack = 0
Slack = 0

0
A
2
2
0
B
3
3 8
G
5
13
13
H
2
15
4
F
3
7
2
C
2
4
3
D
4
7
4
E
4
80
Start
0
0
0 0
0 2 2 4
10 13
4 8
1 4 4 8 8 13
13 15
Overlay 3: The Critical Path Is Now Shown in Five Thick Blue Lines

Chapter 3 Project Management 61
Activity Description Time (weeks)
A Build internal components 2
B Modify roof and floor 3
C Construct collection stack 2
D Pour concrete and install frame 4
E Build high-temperature burner 4
F Install pollution control system 3
G Install air pollution device 5
H Inspect and test 2
Total time (weeks) 25
� TABLE 3.2
Time Estimates for Milwaukee
Paper Manufacturing
As mentioned earlier, the critical path is the longest time path through the network. To find the
critical path, we calculate two distinct starting and ending times for each activity. These are
defined as follows:
We use a two-pass process, consisting of a forward pass and a backward pass, to determine
these time schedules for each activity. The early start and finish times (ES and EF) are determined
during the forward pass. The late start and finish times (LS and LF) are determined during the
backward pass.
Forward Pass
To clearly show the activity schedules on the project network, we use the notation shown in
Figure 3.10. The ES of an activity is shown in the top left corner of the node denoting that activ-
ity. The EF is shown in the top right corner. The latest times, LS and LF, are shown in the bottom-
left and bottom-right corners, respectively.
Earliest Start Time Rule Before an activity can start, all its immediate predecessors must
be finished:
• If an activity has only a single immediate predecessor, its ES equals the EF of the predecessor.
• If an activity has multiple immediate predecessors, its ES is the maximum of all EF values of
its predecessors. That is,
(3-1)
Earliest Finish Rule The earliest finish time (EF) of an activity is the sum of its earliest start
time (ES) and its activity time. That is,
(3-2)EF = ES + Activity time
ES = Max {EF of all immediate predecessors}
the completion time of the entire project
Latest finish (LF) = latest time by which an activity has to finish so as to not delay
the completion time of the entire project
Latest start (LS) = latest time at which an activity can start so as to not delay
Earliest finish (EF) = earliest time at which an activity can be finished
predecessors have been completed
Earliest start (ES) = earliest time at which an activity can start, assuming all
Forward pass
A process that identifies all the
early times.
LO3: Complete forward
and backward passes for a
project
ES
Earliest
Start
Earliest
Finish
Activity Name
or Symbol
Activity Duration
Latest
Start
Latest
Finish
A
2
LS
EF
LF
� FIGURE 3.10
Notation Used in Nodes for
Forward and Backward Pass
AUTHOR COMMENT
Does this mean the project
will take 25 weeks to
complete? No. Don’t forget
that several of the activities
are being performed at the
same time. It would take 25
weeks if they were done
sequentially.
AUTHOR COMMENT
All predecessor activities
must be completed before an
acitivity can begin.

62 PART 1 Introduction to Operations Management
0
A
ES
of A
ES EF
= Max(2,3)
LS LF
Activity
Name
Activity
Duration
ES = Max(EF of D, EF of E)
= Max(7, 8) = 8
EF of A =
ES of A + 2
ES of C =
EF of A
2
2
0
B
3
3 8
G
5
13
13
H
2
15
4
F
3
7
2
C
2
4
3
D
4
7
4
E
4
80
Start
0
0
� FIGURE 3.11 Earliest Start and Earliest Finish Times for Milwaukee Paper
Calculate the earliest start and finish times for the activities in the Milwaukee Paper Manufacturing project.
APPROACH � Use Table 3.2, which contains the activity times. Complete the project network for
the company’s project, along with the ES and EF values for all activities.
SOLUTION � With the help of Figure 3.11, we describe how these values are calculated.
Since activity Start has no predecessors, we begin by setting its ES to 0. That is, activity Start can
begin at time 0, which is the same as the beginning of week 1. If activity Start has an ES of 0, its EF is
also 0, since its activity time is 0.
Next, we consider activities A and B, both of which have only Start as an immediate predecessor. Using
the earliest start time rule, the ES for both activities A and B equals zero, which is the EF of activity Start.
Now, using the earliest finish time rule, the EF for A is 2 (= 0 + 2), and the EF for B is 3 (= 0 + 3).
Since activity A precedes activity C, the ES of C equals the EF of A (= 2). The EF of C is therefore
4 (= 2 + 2).
We now come to activity D. Both activities A and B are immediate predecessors for B. Whereas A
has an EF of 2, activity B has an EF of 3. Using the earliest start time rule, we compute the ES of
activity D as follows:
The EF of D equals 7 (= 3 + 4). Next, both activities E and F have activity C as their only immediate
predecessor. Therefore, the ES for both E and F equals 4 (= EF of C). The EF of E is 8 (= 4 + 4), and
the EF of F is 7 (= 4 + 3).
Activity G has both activities D and E as predecessors. Using the earliest start time rule, its ES is
therefore the maximum of the EF of D and the EF of E. Hence, the ES of activity G equals 8 (= maxi-
mum of 7 and 8), and its EF equals 13 (= 8 + 5).
ES of D = Max(EF of A, EF of B) = Max(2, 3) = 3
EXAMPLE 4 �
Computing earliest
start and finish
times for Milwaukee
Paper

Chapter 3 Project Management 63
Backward pass
An activity that finds all the
late start and late finish times.
Finally, we come to activity H. Since it also has two predecessors, F and G, the ES of H is the max-
imum EF of these two activities. That is, the ES of H equals 13 (= maximum of 13 and 7). This implies
that the EF of H is 15 (= 13 + 2). Since H is the last activity in the project, this also implies that the ear-
liest time in which the entire project can be completed is 15 weeks.
INSIGHT � The ES of an activity that has only one predecessor is simply the EF of that predeces-
sor. For an activity with more than one predecessor, we must carefully examine the EFs of all immedi-
ate predecessors and choose the largest one.
LEARNING EXERCISE � A new activity I, EPA Approval, takes 1 week. Its predecessor is
activity H. What are I’s ES and EF? [Answer: 15, 16]
RELATED PROBLEMS � 3.11, 3.14c
EXCEL OM Data File Ch03Ex4.xls can be found at www.pearsonhighered.com/heizer.
Although the forward pass allows us to determine the earliest project completion time, it does
not identify the critical path. To identify this path, we need to now conduct the backward pass to
determine the LS and LF values for all activities.
Backward Pass
Just as the forward pass began with the first activity in the project, the backward pass begins
with the last activity in the project. For each activity, we first determine its LF value, followed by
its LS value. The following two rules are used in this process.
Latest Finish Time Rule This rule is again based on the fact that before an activity can start,
all its immediate predecessors must be finished:
• If an activity is an immediate predecessor for just a single activity, its LF equals the LS of the
activity that immediately follows it.
• If an activity is an immediate predecessor to more than one activity, its LF is the minimum of
all LS values of all activities that immediately follow it. That is:
(3-3)
Latest Start Time Rule The latest start time (LS) of an activity is the difference of its latest
finish time (LF) and its activity time. That is:
(3-4)LS = LF – Activity time
LF = Min{LS of all immediate following activities}
� EXAMPLE 5
Computing latest
start and finish
times for Milwaukee
Paper
Calculate the latest start and finish times for each activity in Milwaukee Paper’s pollution project.
APPROACH � Use Figure 3.11 as a beginning point. Overlay 1 of Figure 3.11 shows the com-
plete project network for Milwaukee Paper, along with LS and LF values for all activities. In what fol-
lows, we see how these values were calculated.
SOLUTION � We begin by assigning an LF value of 15 weeks for activity H. That is, we specify
that the latest finish time for the entire project is the same as its earliest finish time. Using the latest
start time rule, the LS of activity H is equal to 13 (� 15 – 2).
Since activity H is the lone succeeding activity for both activities F and G, the LF for both F and G
equals 13. This implies that the LS of G is 8 (� 13 – 5), and the LS of F is 10 (� 13 – 3).
Proceeding in this fashion, we see that the LF of E is 8 (� LS of G), and its LS is 4 (� 8 – 4).
Likewise, the LF of D is 8 (� LS of G), and its LS is 4 (� 8 � 4).
We now consider activity C, which is an immediate predecessor to two activities: E and F. Using the
latest finish time rule, we compute the LF of activity C as follows:
The LS of C is computed as 2 (= 4 – 2). Next, we compute the LF of B as 4 (= LS of D), and its LS as
1 (� 4 � 3).
LF of C = Min(LS of E, LS of F) = Min(4, 10) = 4

www.pearsonhighered.com/heizer

64 PART 1 Introduction to Operations Management
Calculating Slack Time and Identifying
the Critical Path(s)
After we have computed the earliest and latest times for all activities, it is a simple matter to find
the amount of slack time2 that each activity has. Slack is the length of time an activity can be
delayed without delaying the entire project. Mathematically:
(3-5)Slack = LS – ES or Slack = LF – EF
Slack time
Free time for an activity.
EXAMPLE 6 �
Calculating slack times
for Milwaukee Paper
Activity
Earliest
Start
ES
Earliest
Finish
EF
Latest
Start
LS
Latest
Finish
LF
Slack
LS – ES
On
Critical
Path
A 0 2 0 2 0 Yes
B 0 3 1 4 1 No
C 2 4 2 4 0 Yes
D 3 7 4 8 1 No
E 4 8 4 8 0 Yes
F 4 7 10 13 6 No
G 8 13 8 13 0 Yes
H 13 15 13 15 0 Yes
� TABLE 3.3
Milwaukee Paper’s Schedule
and Slack Times
Calculate the slack for the activities in the Milwaukee Paper project.
APPROACH � Start with the data in Overlay 1 of Figure 3.11 in Example 5 and develop Table 3.3
one line at a time.
SOLUTION � Table 3.3 summarizes the ES, EF, LS, LF, and slack time for all of the firm’s activi-
ties. Activity B, for example, has 1 week of slack time since its LS is 1 and its ES is 0 (alternatively, its
LF is 4 and its EF is 3). This means that activity B can be delayed by up to 1 week, and the whole proj-
ect can still be finished in 15 weeks.
2Slack time may also be referred to as free time, free float, or free slack.
We now consider activity A. We compute its LF as 2 (= minimum of LS of C and LS
of D). Hence, the LS of activity A is 0 (� 2 – 2). Finally, both the LF and LS of activity Start are
equal to 0.
INSIGHT � The LF of an activity that is the predecessor of only one activity is just the LS of that
following activity. If the activity is the predecessor to more than one activity, its LF is the smallest LS
value of all activities that follow immediately.
LEARNING EXERCISE � A new activity I, EPA Approval, takes 1 week. Its predecessor is
activity H. What are I’s LS and LF? [Answer: 15, 16]
RELATED PROBLEMS � 3.11, 3.14c.
On the other hand, activities A, C, E, G, and H have no slack time. This means that none of
them can be delayed without delaying the entire project. Conversely, if plant manager Joni
Steinberg wants to reduce the total project times, she will have to reduce the length of one of these
activities.
Overlay 2 of Figure 3.11 shows the slack computed for each activity.
INSIGHT � Slack may be computed from either early/late starts or early/late finishes. The key is
to find which activities have zero slack.
LEARNING EXERCISE � A new activity I, EPA Approval, follows activity H and takes
1 week. Is it on the critical path? [Answer: Yes, it’s LS – ES = 0]
RELATED PROBLEMS � 3.6, 3.11, 3.27
ACTIVE MODEL 3.1 This example is further illustrated in Active Model 3.1 at www.pearsonhighered.com/heizer.

www.pearsonhighered.com/heizer

Chapter 3 Project Management 65
The activities with zero slack are called critical activities and are said to be on the critical path.
The critical path is a continuous path through the project network that:
• Starts at the first activity in the project (Start in our example).
• Terminates at the last activity in the project (H in our example).
• Includes only critical activities (i.e., activities with no slack time).
LO4: Determine a critical
path
� EXAMPLE 7
Showing critical
path with blue
arrows
Show Milwaukee Paper’s critical path and find the project completion time.
APPROACH � We use Table 3.3 and Overlay 3 of Figure 3.11. Overlay 3 of Figure 3.11 indicates
that the total project completion time of 15 weeks corresponds to the longest path in the network. That
path is Start-A-C-E-G-H in network form. It is shown with thick blue arrows.
INSIGHT � The critical path follows the activities with slack = 0. This is considered the longest
path through the network.
LEARNING EXERCISE � Why are activities B, D, and F not on the path with the thick blue
line? [Answer: They are not critical and have slack values of 1, 1, and 6 weeks, respectively.]
RELATED PROBLEMS � 3.3, 3.4, 3.5, 3.6, 3.7, 3.12, 3.14b, 3.15, 3.17, 3.20a, 3.22a, 3.23, 3.26
Total Slack Time Look again at the project network in Overlay 3 of Figure 3.11. Consider
activities B and D, which have slack of 1 week each. Does it mean that we can delay each activ-
ity by 1 week, and still complete the project in 15 weeks? The answer is no.
Let’s assume that activity B is delayed by 1 week. It has used up its slack of 1 week and now
has an EF of 4. This implies that activity D now has an ES of 4 and an EF of 8. Note that these
are also its LS and LF values, respectively. That is, activity D also has no slack time now.
Essentially, the slack of 1 week that activities B and D had is, for that path, shared between them.
Delaying either activity by 1 week causes not only that activity, but also the other activity, to lose
its slack. This type of a slack time is referred to as total slack. Typically, when two or more non-
critical activities appear successively in a path, they share total slack.
VARIABILITY IN ACTIVITY TIMES
In identifying all earliest and latest times so far, and the associated critical path(s), we have
adopted the CPM approach of assuming that all activity times are known and fixed constants.
That is, there is no variability in activity times. However, in practice, it is likely that activity com-
pletion times vary depending on various factors.
Total slack
Time shared among more than
one activity.
To plan, monitor, and
control the huge number
of details involved in
sponsoring a rock festival
attended by more than
100,000 fans, Hard Rock
Cafe uses Microsoft
Project and the tools
discussed in this chapter.
The Video Case Study
“Managing Hard Rock’s
Rockfest,” in the Lecture
Guide & Activities Manual,
provides more details of
the management task.
AUTHOR COMMENT
PERT’s ability to handle three
time estimates for each
activity enables us to
compute the probability that
we can complete the project
by a target date.

66 PART 1 Introduction to Operations Management
For example, building internal components (activity A) for Milwaukee Paper Manufacturing is
estimated to finish in 2 weeks. Clearly, factors such as late arrival of raw materials, absence of key
personnel, and so on, could delay this activity. Suppose activity A actually ends up taking
3 weeks. Since A is on the critical path, the entire project will now be delayed by 1 week to
16 weeks. If we had anticipated completion of this project in 15 weeks, we would obviously miss
our deadline.
Although some activities may be relatively less prone to delays, others could be extremely
susceptible to delays. For example, activity B (modify roof and floor) could be heavily
dependent on weather conditions. A spell of bad weather could significantly affect its com-
pletion time.
This means that we cannot ignore the impact of variability in activity times when deciding the
schedule for a project. PERT addresses this issue.
Three Time Estimates in PERT
In PERT, we employ a probability distribution based on three time estimates for each activity, as
follows:
Optimistic time (a) � time an activity will take if everything goes as planned.
In estimating this value, there should be only a small probability
(say, 1/100) that the activity time will be < a. Pessimistic time (b) � time an activity will take assuming very unfavorable conditions. In estimating this value, there should also be only a small probability (also, 1/100) that the activity time will be > b.
Most likely time (m) � most realistic estimate of the time required to complete an
activity.
When using PERT, we often assume that activity time estimates follow the beta probability dis-
tribution (see Figure 3.12). This continuous distribution is often appropriate for determining the
expected value and variance for activity completion times.
To find the expected activity time, t, the beta distribution weights the three time estimates as
follows:
(3-6)
That is, the most likely time (m) is given four times the weight as the optimistic time (a) and pes-
simistic time (b). The time estimate t computed using Equation (3-6) for each activity is used in
the project network to compute all earliest and latest times.
To compute the dispersion or variance of activity completion time, we use the
formula3:
(3-7)Variance = [(b – a)>6]2
t = (a + 4m + b)>6
Optimistic
Time (a)
Most Likely
Time (m)
Pessimistic
Time (b)
Activity Time
P
ro
b
a
b
ili
ty
Probability of 1 in 100
of occurring< a Probability of 1 in 100 of occurring> b
� FIGURE 3.12
Beta Probability Distribution
with Three Time Estimates
Optimistic time
The “best” activity completion
time that could be obtained in a
PERT network.
Pessimistic time
The “worst” activity time that
could be expected in a PERT
network.
Most likely time
The most probable time to
complete an activity in a PERT
network.
3This formula is based on the statistical concept that from one end of the beta distribution to the other is 6 standard
deviations (±3 standard deviations from the mean). Since (b – a) is 6 standard deviations, the variance is [(b – a)/6]2.

Chapter 3 Project Management 67
� EXAMPLE 8
Expected times
and variances for
Milwaukee Paper
LO5: Calculate the
variance of activity times
Activity
Optimistic
a
Most
Likely
m
Pessimistic
b
Expected Time
t = (a + 4m + b)/6
Variance
[(b � a)/6]2
A 1 2 3 2 [(3 – 1)/6]2 = 4/36 = .11
B 2 3 4 3 [(4 – 2)/6]2 = 4/36 = .11
C 1 2 3 2 [(3 – 1)/6]2 = 4/36 = .11
D 2 4 6 4 [(6 – 2)/6]2 = 16/36 = .44
E 1 4 7 4 [(7 – 1)/6]2 = 36/36 = 1.00
F 1 2 9 3 [(9 – 1)/6]2 = 64/36 = 1.78
G 3 4 11 5 [(11 – 3)/6]2 = 64/36 = 1.78
H 1 2 3 2 [(3 – 1)/6]2 = 4/36 = .11
� TABLE 3.4
Time Estimates (in weeks)
for Milwaukee Paper’s Project
RELATED PROBLEMS � 3.13, 3.14a, 3.17a,b, 3.21a
EXCEL OM Data File Ch03Ex8.xls can be found at www.pearsonhighered.com/heizer.
Joni Steinberg and the project management team at Milwaukee Paper want an expected time and vari-
ance for Activity F (Installing the Pollution Control System) where:
APPROACH � Use Equations (3-6) and (3-7) to compute the expected time and variance for F.
SOLUTION � The expected time for Activity F is:
The variance for Activity F is:
INSIGHT � Steinberg now has information that allows her to understand and manage Activity F.
The expected time is, in fact, the activity time used in our earlier computation and identification of the
critical path.
LEARNING EXERCISE � Review the expected times and variances for all of the other activi-
ties in the project. These are shown in Table 3.4.
Variance = c
(b – a)
6
d
2
= c
(9 – 1)
6
d
2
= a
8
6
b
2
=
64
36
= 1.78
t =
a + 4m + b
6
=
1 + 4(2) + 9
6
=
18
6
= 3 weeks
a = 1 week, m = 2 weeks, b = 9 weeks
AUTHOR COMMENT
Can you see why the variance
is higher in some activities
than in others? Note the
spread between the optimistic
and pessimistic times.
We see here a ship being
built at the Hyundai
shipyard, Asia’s largest
shipbuilder, in Korea.
Managing this project uses
the same techniques as
managing the remodeling
of a store or installing a
new production line.

www.pearsonhighered.com/heizer

68 PART 1 Introduction to Operations Management
Probability of Project Completion
The critical path analysis helped us determine that Milwaukee Paper’s expected project comple-
tion time is 15 weeks. Joni Steinberg knows, however, that there is significant variation in the
time estimates for several activities. Variation in activities that are on the critical path can affect
the overall project completion time—possibly delaying it. This is one occurrence that worries the
plant manager considerably.
PERT uses the variance of critical path activities to help determine the variance of the overall
project. Project variance is computed by summing variances of critical activities:
(3-8)s2p = Project variance = ©(variances of activities on critical path)
EXAMPLE 9 �
Computing project
variance and standard
deviation for
Milwaukee Paper
Milwaukee Paper’s managers now wish to know the project’s variance and standard deviation.
APPROACH � Because the activities are independent, we can add the variances of the activities
on the critical path and then take the square root to determine the project’s standard deviation.
SOLUTION � From Example 8 (Table 3.4), we have the variances of all of the activities on the
critical path. Specifically, we know that the variance of activity A is 0.11, variance of activity C is 0.11,
variance of activity E is 1.00, variance of activity G is 1.78, and variance of activity H is 0.11.
Compute the total project variance and project standard deviation:
which implies:
INSIGHT � Management now has an estimate not only of expected completion time for the proj-
ect but also of the standard deviation of that estimate.
LEARNING EXERCISE � If the variance for activity A is actually 0.30 (instead of 0.11), what
is the new project standard deviation? [Answer: 1.817.]
RELATED PROBLEM � 3.17e
Project standard deviation (sp) = 2Project variance = 23.11 = 1.76 weeks
Project variance (s2p) = 0.11 + 0.11 + 1.00 + 1.78 + 0.11 = 3.11
How can this information be used to help answer questions regarding the probability of finishing
the project on time? PERT makes two more assumptions: (1) total project completion times
follow a normal probability distribution, and (2) activity times are statistically independent. With
these assumptions, the bell-shaped normal curve shown in Figure 3.13 can be used to represent
project completion dates. This normal curve implies that there is a 50% chance that the manufac-
turer’s project completion time will be less than 15 weeks and a 50% chance that it will exceed
15 weeks.
Standard Deviation = 1.76 Weeks
15 Weeks
(Expected Completion Time)
� FIGURE 3.13
Probability Distribution for
Project Completion Times
at Milwaukee Paper

Chapter 3 Project Management 69
� EXAMPLE 10
Probability of
completing a
project on time
Joni Steinberg would like to find the probability that her project will be finished on or before the 16-
week EPA deadline.
APPROACH � To do so, she needs to determine the appropriate area under the normal curve. This
is the area to the left of the 16th week.
SOLUTION � The standard normal equation can be applied as follows:
(3-9)
where Z is the number of standard deviations the due date or target date lies from the mean or expected
date.
Referring to the Normal Table in Appendix I, we find a Z value of 0.57 to the right of the mean indi-
cates a probability of 0.7157. Thus, there is a 71.57% chance that the pollution control equipment can
be put in place in 16 weeks or less. This is shown in Figure 3.14.
= (16 weeks – 15 weeks)>1.76 weeks = 0.57
Z = (Due date – Expected date of completion)>sp
15
Weeks
16
Weeks
0.57 Standard Deviations
Time
Probability
(T ≤ 16 Weeks)
is 71.57%
� FIGURE 3.14
Probability That Milwaukee
Paper will Meet the 16-Week
Deadline
Determining Project Completion Time for a Given Confidence Level Let’s say Joni
Steinberg is worried that there is only a 71.57% chance that the pollution control equipment can
be put in place in 16 weeks or less. She thinks that it may be possible to plead with the environ-
mental group for more time. However, before she approaches the group, she wants to arm herself
with sufficient information about the project. Specifically, she wants to find the deadline by
which she has a 99% chance of completing the project. She hopes to use her analysis to convince
the group to agree to this extended deadline.
Clearly, this due date would be greater than 16 weeks. However, what is the exact value of this
new due date? To answer this question, we again use the assumption that Milwaukee Paper’s
project completion time follows a normal probability distribution with a mean of 15 weeks and a
standard deviation of 1.76 weeks.
� EXAMPLE 11
Computing
probability for any
completion date
INSIGHT � The shaded area to the left of the 16th week (71.57%) represents the probability that
the project will be completed in less than 16 weeks.
LEARNING EXERCISE � What is the probability that the project will be completed on or
before the 17th week? [Answer: About 87.2%.]
RELATED PROBLEMS � 3.14d, 3.17f, 3.21d,e, 3.22b, 3.24
Joni Steinberg wants to find the due date that gives her company’s project a 99% chance of on-time
completion.
APPROACH � She first needs to compute the Z-value corresponding to 99%, as shown in
Figure 3.15. Mathematically, this is similar to Example 10, except the unknown is now Z rather than the
due date.
AUTHOR COMMENT
Here is a chance to review
your statistical skills and use
of a normal distribution table
(Appendix I).

70 PART 1 Introduction to Operations Management
SOLUTION � Referring again to the Normal Table in Appendix I, we identify a Z-value of 2.33 as
being closest to the probability of 0.99. That is, Joni Steinberg’s due date should be 2.33 standard devi-
ations above the mean project completion time. Starting with the standard normal equation (see Equa-
tion [3-9]), we can solve for the due date and rewrite the equation as:
(3-10)
INSIGHT � If Steinberg can get the environmental group to agree to give her a new deadline of
19.1 weeks (or more), she can be 99% sure of finishing the project on time.
LEARNING EXERCISE � What due date gives the project a 95% chance of on-time comple-
tion? [Answer: About 17.9 weeks.]
RELATED PROBLEMS � 3.22c, 3.24e
= 15 + (2.33 * 1.76) = 19.1 weeks
Due date = Expected completion time + (Z * sp)
0 2.33
Z2.33 Standard
Deviations
Probability
of 0.99
Probability
of 0.01
� FIGURE 3.15
Z-Value for 99% Probability
of Project Completion at
Milwaukee Paper
Variability in Completion Time of Noncritical Paths In our discussion so far, we have
focused exclusively on the variability in the completion times of activities on the critical path.
This seems logical since these activities are, by definition, the more important activities in a proj-
ect network. However, when there is variability in activity times, it is important that we also
investigate the variability in the completion times of activities on noncritical paths.
Consider, for example, activity D in Milwaukee Paper’s project. Recall from Overlay 3 in
Figure 3.11 (in Example 7) that this is a noncritical activity, with a slack time of 1 week. We have
therefore not considered the variability in D’s time in computing the probabilities of project com-
pletion times. We observe, however, that D has a variance of 0.44 (see Table 3.4 in Example 8).
In fact, the pessimistic completion time for D is 6 weeks. This means that if D ends up taking its
pessimistic time to finish, the project will not finish in 15 weeks, even though D is not a critical
activity.
For this reason, when we find probabilities of project completion times, it may be necessary
for us to not focus only on the critical path(s). Indeed, some research has suggested that expend-
ing project resources to reduce the variability of activities not on the critical path can be an effec-
tive element in project management.4 We may need also to compute these probabilities for
noncritical paths, especially those that have relatively large variances. It is possible for a noncrit-
ical path to have a smaller probability of completion within a due date, when compared with the
critical path. Determining the variance and probability of completion for a noncritical path is
done in the same manner as Examples 9 and 10.
What Project Management Has Provided So Far Project management techniques
have thus far been able to provide Joni Steinberg with several valuable pieces of management
information:
1. The project’s expected completion date is 15 weeks.
2. There is a 71.57% chance that the equipment will be in place within the 16-week deadline.
PERT analysis can easily find the probability of finishing by any date Steinberg is interested in.
4F. M. Pokladnik, T. F. Anthony, R. R. Hill, G. Ulrich, “A Fresh Look at Estimated Project Duration: Noncritical Path
Activity Contribution to Project Variance in PERT/CPM,” Proceedings of the 2003 Southwest Decision Science
Conference, Houston.

Chapter 3 Project Management 71
3. Five activities (A, C, E, G, and H) are on the critical path. If any one of these is delayed for
any reason, the entire project will be delayed.
4. Three activities (B, D, F) are not critical and have some slack time built in. This means that
Steinberg can borrow from their resources, and, if necessary, she may be able to speed up the
whole project.
5. A detailed schedule of activity starting and ending dates, slack, and critical path activities
has been made available (see Table 3.3 in Example 6).
COST–TIME TRADE-OFFS AND PROJECT CRASHING
While managing a project, it is not uncommon for a project manager to be faced with either (or
both) of the following situations: (1) the project is behind schedule, and (2) the scheduled proj-
ect completion time has been moved forward. In either situation, some or all of the remaining
activities need to be speeded up (usually by adding resources) to finish the project by the desired
due date. The process by which we shorten the duration of a project in the cheapest manner pos-
sible is called project crashing.
CPM is a technique in which each activity has a normal or standard time that we use in our
computations. Associated with this normal time is the normal cost of the activity. However,
another time in project management is the crash time, which is defined as the shortest duration
required to complete an activity. Associated with this crash time is the crash cost of the activity.
Usually, we can shorten an activity by adding extra resources (e.g., equipment, people) to it.
Hence, it is logical for the crash cost of an activity to be higher than its normal cost.
The amount by which an activity can be shortened (i.e., the difference between its normal
time and crash time) depends on the activity in question. We may not be able to shorten some
activities at all. For example, if a casting needs to be heat-treated in the furnace for 48 hours,
adding more resources does not help shorten the time. In contrast, we may be able to shorten
some activities significantly (e.g., frame a house in 3 days instead of 10 days by using three times
as many workers).
Likewise, the cost of crashing (or shortening) an activity depends on the nature of the activity.
Managers are usually interested in speeding up a project at the least additional cost. Hence, when
choosing which activities to crash, and by how much, we need to ensure the following:
• The amount by which an activity is crashed is, in fact, permissible
• Taken together, the shortened activity durations will enable us to finish the project by the due
date
• The total cost of crashing is as small as possible
Crashing a project involves four steps:
STEP 1: Compute the crash cost per week (or other time period) for each activity in the net-
work. If crash costs are linear over time, the following formula can be used:
(3-11)
STEP 2: Using the current activity times, find the critical path(s) in the project network.
Identify the critical activities.
STEP 3: If there is only one critical path, then select the activity on this critical path that
(a) can still be crashed and (b) has the smallest crash cost per period. Crash this
activity by one period.
If there is more than one critical path, then select one activity from each critical
path such that (a) each selected activity can still be crashed and (b) the total crash
cost per period of all selected activities is the smallest. Crash each activity by one
period. Note that the same activity may be common to more than one critical path.
STEP 4: Update all activity times. If the desired due date has been reached, stop. If not, return
to Step 2.
We illustrate project crashing in Example 12.
Crash cost per period =
(Crash cost – Normal cost)
(Normal time – Crash time)
Crashing
Shortening activity time in a
network to reduce time on the
critical path so total completion
time is reduced.
LO6: Crash a project
AUTHOR COMMENT
When a project needs to
be shortened, we want to find
the most economical way
of “crashing” it.

72 PART 1 Introduction to Operations Management
EXAMPLE 12 �
Project crashing to
meet a deadline at
Milwaukee Paper
Suppose that Milwaukee Paper Manufacturing has been given only 13 weeks (instead of 16 weeks) to
install the new pollution control equipment or face a court-ordered shutdown. As you recall, the length
of Joni Steinberg’s critical path was 15 weeks, but she must now complete the project in 13 weeks.
APPROACH � Steinberg needs to determine which activities to crash, and by how much, to meet
this 13-week due date. Naturally, Steinberg is interested in speeding up the project by 2 weeks, at the
least additional cost.
SOLUTION � The company’s normal and crash times, and normal and crash costs, are shown in
Table 3.5. Note, for example, that activity B’s normal time is 3 weeks (the estimate used in computing
the critical path), and its crash time is 1 week. This means that activity B can be shortened by up to
2 weeks if extra resources are provided. The cost of these additional resources is $4,000 (= difference
between the crash cost of $34,000 and the normal cost of $30,000). If we assume that the crashing cost
is linear over time (i.e., the cost is the same each week), activity B’s crash cost per week is $2,000 (=
$4,000/2).
Time (Weeks) Cost ($)
Activity Normal Crash Normal Crash
Crash Cost
per Week ($)
Critical
Path?
A 2 1 22,000 22,750 750 Yes
B 3 1 30,000 34,000 2,000 No
C 2 1 26,000 27,000 1,000 Yes
D 4 3 48,000 49,000 1,000 No
E 4 2 56,000 58,000 1,000 Yes
F 3 2 30,000 30,500 500 No
G 5 2 80,000 84,500 1,500 Yes
H 2 1 16,000 19,000 3,000 Yes
� TABLE 3.5
Normal and Crash Data
for Milwaukee Paper
Manufacturing
This calculation for Activity B is shown in Figure 3.16. Crash costs for all other activities can be
computed in a similar fashion.
1
$30,000
Activity
Cost
$31,000
$32,000
$33,000
$34,000
2 3
Crash
Normal
Crash Cost/Week = Crash Cost – Normal Cost
Normal Time – Crash Time
= $34,000 – $30,000
3 – 1
= $4,000
2 Weeks
= $2,000/Week
Time (Weeks)
Crash Time Normal Time
Normal
Cost
Crash
Cost
� FIGURE 3.16
Crash and Normal Times
and Costs for Activity B
Steps 2, 3, and 4 can now be applied to reduce Milwaukee Paper’s project completion time at a min-
imum cost. We show the project network for Milwaukee Paper again in Figure 3.17.

Chapter 3 Project Management 73
0
A
Activity
Name
2
2
0
B
3
3 8
G
5
13
4
E
4
8 13
H
2
15
4
F
3
7
2
C
2
4
3
D
4
7
0 2
EFES
2 4
10 13
4 8
1 4 4 8 8 13
13 15
LS
Slack = 0
LF
Slack = 0
Slack = 6
Activity
Duration
Slack = 1 Slack = 1 Slack = 0
Slack = 0 Slack = 0
0
Start
0
0
0 0
� FIGURE 3.17
Critical Path and Slack Times
for Milwaukee Paper
The current critical path (using normal times) is Start-A-C-E-G-H, in which Start is just a dummy
starting activity. Of these critical activities, activity A has the lowest crash cost per week of $750. Joni
Steinberg should therefore crash activity A by 1 week to reduce the project completion time to 14
weeks. The cost is an additional $750. Note that activity A cannot be crashed any further, since it has
reached its crash limit of 1 week.
At this stage, the original path Start-A-C-E-G-H remains critical with a completion time of 14
weeks. However, a new path Start-B-D-G-H is also critical now, with a completion time of 14 weeks.
Hence, any further crashing must be done to both critical paths.
On each of these critical paths, we need to identify one activity that can still be crashed. We also
want the total cost of crashing an activity on each path to be the smallest. We might be tempted to sim-
ply pick the activities with the smallest crash cost per period in each path. If we did this, we would
select activity C from the first path and activity D from the second path. The total crash cost would then
be $2,000 (= $1,000 + $1,000).
But we spot that activity G is common to both paths. That is, by crashing activity G, we will simul-
taneously reduce the completion time of both paths. Even though the $1,500 crash cost for activity G is
higher than that for activities C and D, we would still prefer crashing G, since the total crashing cost
will now be only $1,500 (compared with the $2,000 if we crash C and D).
INSIGHT � To crash the project down to 13 weeks, Steinberg should crash activity A by 1 week,
and activity G by 1 week. The total additional cost will be $2,250 (= $750 + $1,500). This is important
because many contracts for projects include bonuses or penalties for early or late finishes.
LEARNING EXERCISE � Say the crash cost for activity B is $31,000 instead of $34,000. How
does this change the answer? [Answer: no change.]
RELATED PROBLEMS � 3.16, 3.18, 3.19, 3.20, 3.25
EXCEL OM Data File Ch03Ex12.xls can be found at www.pearsonhighered.com/heizer.
A CRITIQUE OF PERT AND CPM
As a critique of our discussions of PERT, here are some of its features about which operations
managers need to be aware:
Advantages
1. Especially useful when scheduling and controlling large projects.
2. Straightforward concept and not mathematically complex.
3. Graphical networks help highlight relationships among project activities.
4. Critical path and slack time analyses help pinpoint activities that need to be closely watched.
5. Project documentation and graphs point out who is responsible for various activities.
6. Applicable to a wide variety of projects.
7. Useful in monitoring not only schedules but costs as well.
AUTHOR COMMENT
Every technique has shortfalls
as well as strengths. It is
important to know both.

www.pearsonhighered.com/heizer

74 PART 1 Introduction to Operations Management
Limitations
1. Project activities have to be clearly defined, independent, and stable in their relationships.
2. Precedence relationships must be specified and networked together.
3. Time estimates tend to be subjective and are subject to fudging by managers who fear the
dangers of being overly optimistic or not pessimistic enough.
4. There is the inherent danger of placing too much emphasis on the longest, or critical, path.
Near-critical paths need to be monitored closely as well.
USING MICROSOFT PROJECT TO MANAGE PROJECTS
The approaches discussed so far are effective for managing small projects. However, for large
or complex projects, specialized project management software is much preferred. In this sec-
tion, we provide a brief introduction to the most popular example of such specialized soft-
ware, Microsoft Project. A time-limited version of Microsoft Project may be requested with
this text.
Microsoft Project is extremely useful in drawing project networks, identifying the project
schedule, and managing project costs and other resources.
Entering Data Let us again consider the Milwaukee Paper Manufacturing project. Recall that
this project has eight activities (repeated in the margin). The first step is to define the activities and
their precedence relationships. To do so, we select File|New to open a blank project. We type the
project start date (as July 1), then enter all activity information (see Program 3.1). For each activ-
ity (or task, as Microsoft Project calls it), we fill in the name and duration. The description of the
activity is also placed in the Task Name column in Program 3.1. As we enter activities and dura-
tions, the software automatically inserts start and finish dates.
The next step is to define precedence relationships between these activities. To do so, we enter
the relevant activity numbers (e.g., 1, 2) in the Predecessors column.
On September 11, 2001, American Airlines Flight 77
slammed into the Pentagon. The world was shocked
by this and the other terrorist attacks on the Twin
Towers in New York City. One hundred and twenty-five
people died when a large portion of the Pentagon was
severely damaged. Among the first to react were
construction workers renovating another portion of the
Pentagon. Their heroism saved lives and eased suffering.
Within hours of the disaster, heavy equipment began
arriving on the site, accompanied by hundreds of
volunteer construction workers driven by patriotism
and pride.
Just four days after the attack, Walker Evey, named
program manager for “Project Phoenix,” promised to rebuild
the damaged portions of the Pentagon “faster than anyone
has a right to expect . . . and to have people back in the
damaged portion of the building, right where the plane hit,
by September 11, 2002.”
Preliminary construction reports estimated it would
take 3 to 4 years and $3/4 billion to rebuild. By directing the
project with teamwork, handshake contracts, creativity,
and ingenuity—not to mention emotional 20-hour days
6 to 7 days a week—Evey’s Project Phoenix met its
psychological and physical goal. In less than 11 months,
and for only $501 million, workers demolished and rebuilt
the damaged sections—
400,000 square feet of
structure, 2 million square
feet of offices, 50,000 tons
of debris—using 1,000
construction workers from
80 companies. By
September 9, 2002, over
600 military and civilian
personnel were sitting at
their desks in rebuilt
Pentagon offices.
Outside, the blackened gash is long gone. Instead,
some 4,000 pieces of limestone—mined from the
same Indiana vein that the Pentagon’s original stone
came from 65 years ago—have been placed on the
building’s façade. For this impressive accomplishment,
the Pentagon and Walker Evey were nominated for the
Project Management Institute’s 2003 Project of the
Year Award.
Sources: Knight-Ridder Tribune Business News (February 1, 2004): 1;
ENR (September 2, 2002): 6; U.S. News & World Report (September 16,
2002): 35.
Milwaukee Paper Co.
Activities
Time Prede-
Activity (wks) cessors
A 2 —
B 3 —
C 2 A
D 4 A, B
E 4 C
F 3 C
G 5 D, E
H 2 F, G
AUTHOR COMMENT
Now that you understand the
workings of PERT and CPM,
you are ready to master this
useful program. Knowing
such software gives you
an edge over others in the job
market.
OM in Action � Rebuilding the Pentagon after 9/11

Chapter 3 Project Management 75
Project will finish on
Friday, 10/14.
View has been
zoomed out to
show weeks.
Click here to select
different views.
Gantt chart
view.
� PROGRAM 3.1 Gantt Chart in Microsoft Project for Milwaukee Paper Manufacturing
Viewing the Project Schedule When all links have been defined, the complete project
schedule can be viewed as a Gantt chart. We can also select View|Network Diagram to view
the schedule as a project network (shown in Program 3.2). The critical path is shown in red on
the screen in the network diagram. We can click on any of the activities in the project network
to view details of the activities. Likewise, we can easily add or remove activities from the proj-
ect network. Each time we do so, Microsoft Project automatically updates all start dates, finish
dates, and the critical path(s). If desired, we can manually change the layout of the network
(e.g., reposition activities) by changing the options in Format|Layout.
Critical path and
activities (A, C, E,
G, and H) are shown
in red.
Click activity to see
details regarding the activity.
Project network
view.
� PROGRAM 3.2 Project Network in Microsoft Project for Milwaukee Paper Manufacturing

76 PART 1 Introduction to Operations Management
Using PERT/CPM, Taco
Bell built and opened this
fast-food restaurant in
Compton, California, in
just 2 days! Typically,
2 months are needed
to accomplish such
a task. Good project
management means a
faster revenue stream
instead of money tied up
in construction.
Programs 3.1 and 3.2 show that if Milwaukee Paper’s project starts July 1, it can be finished
on October 14. The start and finish dates for all activities are also clearly identified. Project man-
agement software, we see, can greatly simplify the scheduling procedures discussed earlier in
this chapter.
PERT Analysis Microsoft Project does not perform the PERT probability calculations
discussed in Examples 10 and 11. However, by clicking View|Toolbars|PERT Analysis, we
can get Microsoft Project to allow us to enter optimistic, most likely, and pessimistic times for
each activity. We can then choose to view Gantt charts based on any of these three times for
each activity.
Tracking the Time Status of a Project Perhaps the biggest advantage of using software to
manage projects is that it can track the progress of the project. In this regard, Microsoft Project
has many features available to track individual activities in terms of time, cost, resource usage,
and so on.
An easy way to track the time progress of tasks is to enter the percent of work completed for
each task. One way to do so is to double-click on any activity in the Task Name column in
Program 3.1. A window is displayed that allows us to enter the percent of work completed for
each task.
The table in the margin provides data regarding the percent of each of Milwaukee Paper’s
activities as of today. (Assume that today is Friday, August 12, i.e., the end of the sixth week of
the project schedule.)
As shown in Program 3.3, the Gantt chart immediately reflects this updated information by
drawing a thick line within each activity’s bar. The length of this line is proportional to the per-
cent of that activity’s work that has been completed.
How do we know if we are on schedule? Notice that there is a vertical line shown on the
Gantt chart corresponding to today’s date. Microsoft Project will automatically move this line
to correspond with the current date. If the project is on schedule, we should see all bars to the
left of today’s line indicate that they have been completed. For example, Program 3.3 shows that
activities A, B, and C are on schedule. In contrast, activities D, E, and F appear to be behind
schedule. These activities need to be investigated further to determine the reason for the delay.
This type of easy visual information is what makes such software so useful in practice for proj-
ect management.
We encourage you to load the copy of Microsoft Project that may be ordered with your text
and to create a project network for work you are currently doing.
Pollution Project
Percentage Completed
on Aug. 12
Activity Completed
A 100
B 100
C 100
D 10
E 20
F 20
G 0
H 0

Chapter 3 Project Management 77
Activity F is behind
schedule, as are
activities D and E.
Check mark indicates
activity is 100% complete.
This is the indicator for
today’s date (Aug. 12).
Bar indicates
activity process.
� PROGRAM 3.3 Tracking Project Progress in Microsoft Project
PERT, CPM, and other scheduling techniques have proven to
be valuable tools in controlling large and complex projects.
With these tools, managers understand the status of each activ-
ity and know which activities are critical and which have slack;
in addition, they know where crashing makes the most sense.
Projects are segmented into discrete activities, and specific
resources are identified. This allows project managers to
respond aggressively to global competition. Effective project
management also allows firms to create products and services
for global markets. As with Microsoft Project illustrated in this
chapter, a wide variety of software pack-
ages are available to help managers han-
dle network modeling problems.
PERT and CPM do not, however,
solve all the project scheduling and man-
agement problems. Good management prac-
tices, clear responsibilities for tasks, and straightforward and
timely reporting systems are also needed. It is important to
remember that the models we described in this chapter are
only tools to help managers make better decisions.
CHAPTER SUMMARY
Key Terms
Project organization (p. 50)
Work breakdown structure (WBS) (p. 52)
Gantt charts (p. 53)
Program evaluation and review technique
(PERT) (p. 55)
Critical path method (CPM) (p. 55)
Critical path (p. 55)
Activity-on-node (AON) (p. 55)
Activity-on-arrow (AOA) (p. 55)
Dummy activity (p. 56)
Critical path analysis (p. 60)
Forward pass (p. 61)
Backward pass (p. 63)
Slack time (p. 64)
Total slack (p. 65)
Optimistic time (p. 66)
Pessimistic time (p. 66)
Most likely time (p. 66)
Crashing (p. 71)
Using Software to Solve Project Management Problems
In addition to the Microsoft Project software just illustrated, both Excel OM and POM for Windows are
available to readers of this text as project management tools.
X Using Excel OM
Excel OM has a Project Scheduling module. Program 3.4 uses the data from the Milwaukee Paper
Manufacturing example in this chapter (see Examples 4 and 5). The PERT/CPM analysis also handles
activities with three time estimates.

78 PART 1 Introduction to Operations Management
Early start is the maximum of the
computations below.
Late finishes depend on the tasks that
precede the given task. The late finish is
the earliest of the dependencies.
Enter the task names, times, and the names of the
precedences. Be careful that the precedence
names match the task names.
EF = ES + task time.
Late start is the late finish (from below)
minus the task time.
� PROGRAM 3.4
Excel OM’s Use of Milwaukee
Paper Manufacturing’s Data
from Examples 4 and 5
Solved Problems Virtual Office Hours help is available at www.myomlab.com
� SOLVED PROBLEM 3.1
Construct an AON network based on the following:
Immediate
Activity Predecessor(s)
A —
B —
C —
D A, B
E C
� SOLUTION
End
D
E
B
A
C
Start
� SOLVED PROBLEM 3.2
Insert a dummy activity and event to correct the following AOA
network:
� SOLUTION
Since we cannot have two activities starting and ending at the
same node, we add the following dummy activity and dummy
event to obtain the correct AOA network:
5
Dummy
activity
2 3
4
1
Dummy
event
(0 days)
3 d
ays
5 days
3 days
5 days
2
3
4
51
P Using POM for Windows
POM for Window’s Project Scheduling module can also find the expected project completion time for
a CPM and PERT network with either one or three time estimates. POM for Windows also performs
project crashing. For further details refer to Appendix IV.

www.myomlab.com

Chapter 3 Project Management 79
� SOLVED PROBLEM 3.3
Calculate the critical path, project completion time T, and project
variance based on the following AON network information:
Activity Time Variance ES EF LS LF Slack
A 2 0 2 0 2 0
B 3 0 3 1 4 1
C 2 2 4 2 4 0
D 4 3 7 4 8 1
E 4 4 8 4 8 0
F 3 4 7 10 13 6
G 5 8 13 8 13 0
1
6
1
6
2
6
4
6
4
6
2
6
2
6
s2p,
� SOLUTION
We conclude that the critical path is Start-A-C-E-G-End:
and
Variances on the critical path =
2
6
+
4
6
+
2
6
+
1
6
=
9
6
= 1.5s2p = ©
Total project time = T = 2 + 2 + 4 + 5 = 13
Start End E G
A
B
FC
D
Expected Time
Activity (in weeks) Variance
A 2
B 3
C 5
D 9
E 5 1
F 5
G 2
1
9
1
9
1
9
1
9
1
9
1
9
� SOLVED PROBLEM 3.4
To complete the wing assembly for an experimental aircraft, Jim
Gilbert has laid out the seven major activities involved. These
activities have been labeled A through G in the following table,
which also shows their estimated completion times (in weeks) and
immediate predecessors. Determine the expected time and vari-
ance for each activity:
Immediate
Activity a m b Predecessors
A 1 2 3 —
B 2 3 4 —
C 4 5 6 A
D 8 9 10 B
E 2 5 8 C, D
F 4 5 6 D
G 1 2 3 E
� SOLUTION
Expected times and variances can be computed using Equations
(3-6) and (3-7) presented on page 66 in this chapter. The results
are summarized in the following table:

80 PART 1 Introduction to Operations Management
� SOLVED PROBLEM 3.5
Referring to Solved Problem 3.4, now Jim Gilbert would like to
determine the critical path for the entire wing assembly project as
well as the expected completion time for the total project. In addi-
tion, he would like to determine the earliest and latest start and fin-
ish times for all activities.
� SOLUTION
The AON network for Gilbert’s project is shown in Figure 3.18.
Note that this project has multiple activities (A and B) with no
immediate predecessors, and multiple activities (F and G) with
no successors. Hence, in addition to a unique starting activity
(Start), we have included a unique finishing activity (End) for the
project.
Figure 3.18 shows the earliest and latest times for all activi-
ties. The results are also summarized in the following table:
Activity Time
Activity ES EF LS LF Slack
A 0 2 5 7 5
B 0 3 0 3 0
C 2 7 7 12 5
D 3 12 3 12 0
E 12 17 12 17 0
F 12 17 14 19 2
G 17 19 17 19 0
Expected project length � 19 weeks
Variance of the critical path � 1.333
Standard deviation of the critical path � 1.155 weeks
The activities along the critical path are B, D, E, and G. These
activities have zero slack as shown in the table.
Activity
Duration
Dummy Ending
Activity
17
G
2
19
17 190
Start
0
0
0
A
Activity
Name
2
2
0
B
3
3 12
F
5
17
19
End
0
19
12
E
5
172
C
5
7
3
D
9
12
0 0
5 7 7 12 12 17
0 3 3 12 14 19
19 19
Dummy
Starting
Activity
ES EF
LS LF
� FIGURE 3.18
Critical Path for Solved
Problem 3.5
T = 62Due date = 44
� SOLVED PROBLEM 3.6
The following information has been computed from a project:
What is the probability that the project will be completed 18 weeks
before its expected completion date?
� SOLUTION
The desired completion date is 18 weeks before the expected com-
pletion date, 62 weeks. The desired completion date is 44 (or
62 – 18) weeks:
The normal curve appears as follows:
=
44 – 62
9
=
– 18
9
= – 2.0
Z =
Due date – Expected completion date
sp
sp = 2Project variance
Project variance (s2p) = 81
Expected total project time = T = 62 weeks
Because the normal curve is symmetrical and table values are
calculated for positive values of Z, the area desired is equal to 1 –
(table value). For the area from the table is .97725.
Thus, the area corresponding to a Z value of –2.0 is .02275 (or 1 –
.97725). Hence, the probability of completing the project 18
weeks before the expected completion date is approximately .023,
or 2.3%.
Z = + 2.0,

Chapter 3 Project Management 81
� SOLVED PROBLEM 3.7
Determine the least cost of reducing the project completion date
by 3 months based on the following information:
� SOLUTION
The first step in this problem is to compute ES, EF, LS, LF, and
slack for each activity:
Activity ES EF LS LF Slack
A 0 6 9 15 9
B 0 7 0 7 0
C 6 13 15 22 9
D 7 13 7 13 0
E 13 22 13 22 0
Start
B
C
D E
End
A
Normal Crash
Time Time Normal Crash
Activity (months) (months) Cost Cost
A 6 4 $2,000 $2,400
B 7 5 3,000 3,500
C 7 6 1,000 1,300
D 6 4 2,000 2,600
E 9 8 8,800 9,000
Normal Crash Cost – Crash
Time – Normal Cost/ Critical
Activity Crash Time Cost Month Path?
A 2 $400 $200/month No
B 2 500 250/month Yes
C 1 300 300/month No
D 2 600 300/month Yes
E 1 200 200/month Yes
Finally, we will select that activity on the critical path with the
smallest crash cost/month. This is activity E. Thus, we can reduce
the total project completion date by 1 month for an additional cost
of $200. We still need to reduce the project completion date by 2
more months. This reduction can be achieved at least cost along the
critical path by reducing activity B by 2 months for an additional
cost of $500. Neither reduction has an effect on noncritical activi-
ties. This solution is summarized in the following table:
Activity Months Reduced Cost
E 1 $200
B 2 500
Total: $700
The critical path consists of activities B, D, and E.
Next, crash cost/month must be computed for each activity:
�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Shale Oil Company: This oil refinery must shut down for maintenance of a major piece of equipment.
Bibliography
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Helgadottir, Hilder. “The Ethical Dimension of Project
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Oates, David. “Understanding and Solving the Causes of Project
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(2009).
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Wiley (2009).

www.myomlab.com

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Forecasting
83
Chapter Outline
GLOBAL COMPANY PROFILE: WALT DISNEY PARKS
& RESORTS
What Is Forecasting? 86
The Strategic Importance of Forecasting 87
Seven Steps in the Forecasting System 88
Forecasting Approaches 89
Time-Series Forecasting 90
Associative Forecasting Methods:
Regression and Correlation Analysis 108
Monitoring and Controlling Forecasts 113
Forecasting in the Service Sector 116

GLOBAL COMPANY PROFILE: WALT DISNEY PARKS & RESORTS
FORECASTING PROVIDES A COMPETITIVE ADVANTAGE FOR DISNEY
W
hen it comes to the world’s most
respected global brands, Walt Disney
Parks & Resorts is a visible leader.
Although the monarch of this magic
kingdom is no man but a mouse—Mickey Mouse—it’s
CEO Robert Iger who daily manages the
entertainment giant.
Disney’s global portfolio includes Hong Kong
Disneyland (opened 2005), Disneyland Paris (1992),
and Tokyo Disneyland (1983). But it is Walt Disney
World Resort (in Florida) and Disneyland Resort (in
California) that drive profits in this $43 billion
corporation, which is ranked 54th in the Fortune 500
and 79th in the Financial Times Global 500.
Revenues at Disney are all about people—how many
visit the parks and how they spend money while there.
When Iger receives a daily report from his four theme
parks near Orlando, the report contains only two numbers:
the forecast of yesterday’s attendance at the parks
(Magic Kingdom, Epcot, Disney’s Animal Kingdom,
Disney-MGM Studios, Typhoon Lagoon, and Blizzard
Beach) and the actual attendance. An error close to zero
is expected. Iger takes his forecasts very seriously.
The giant sphere is the symbol of Epcot,
one of Disney’s four Orlando parks, for
which forecasts of meals, lodging,
entertainment, and transportation must be
made. This Disney monorail moves guests
among parks and the 20 hotels on the
massive 47-square-mile property (about
the size of San Francisco and twice the
size of Manhattan).
Mickey and Minnie Mouse, and other Disney characters, with
Cinderella Castle in the background, provide the public image
of Disney to the world. Forecasts drive the work schedules of
58,000 cast members working at Walt Disney World Resort
near Orlando.
84

The forecasting team at Walt Disney World Resort
doesn’t just do a daily prediction, however, and Iger is
not its only customer. The team also provides daily,
weekly, monthly, annual, and 5-year forecasts to the
labor management, maintenance, operations, finance,
and park scheduling departments. Forecasters use
judgmental models, econometric models, moving-
average models, and regression analysis.
With 20% of Walt Disney World Resort’s customers
coming from outside the United States, its economic
model includes such variables as gross domestic
product (GDP), cross-exchange rates, and arrivals into
the U.S. Disney also uses 35 analysts and 70 field
people to survey 1 million people each year. The
surveys, administered to guests at the parks and its
20 hotels, to employees, and to travel industry
professionals, examine future travel plans and
experiences at the parks. This helps forecast not only
attendance but behavior at each ride (e.g., how long
people will wait, how many times they will ride). Inputs to
the monthly forecasting model include airline specials,
speeches by the chair of the Federal Reserve, and
Wall Street trends. Disney even monitors 3,000 school
districts inside and outside the U.S. for holiday/vacation
schedules. With this approach, Disney’s 5-year
attendance forecast yields just a 5% error on average.
Its annual forecasts have a 0% to 3% error.
Attendance forecasts for the parks drive a whole slew
of management decisions. For example, capacity on any
day can be increased by opening at 8 A.M. instead of the
usual 9 A.M., by opening more shows or rides, by adding
more food/beverage carts (9 million hamburgers and 50
million Cokes are sold per year!), and by bringing in more
employees (called “cast members”). Cast members are
scheduled in 15-minute intervals throughout the parks for
flexibility. Demand can be managed by limiting the
number of guests admitted to the parks, with the “FAST
PASS” reservation system, and by shifting crowds from
rides to more street parades.
At Disney, forecasting is a key driver in the
company’s success and competitive advantage.
WALT DISNEY PARKS & RESORTS �
� Disney uses characters such as Minnie Mouse to entertain
guests when lines are forecast to be long. On slow days,
Disney calls fewer cast members to work.
� A daily forecast of attendance is made by adjusting
Disney’s annual operating plan for weather forecasts, the
previous day’s crowds, conventions, and seasonal variations.
One of the two water parks at Walt Disney World Resort,
Typhoon Lagoon, is shown here.
� Forecasts are critical to making sure rides are not
overcrowded. Disney is good at “managing demand” with
techniques such as adding more street activities to reduce
long lines for rides.
85

86 PART 1 Introduction to Operations Management
WHAT IS FORECASTING?
Every day, managers like those at Disney make decisions without knowing what will
happen in the future. They order inventory without knowing what sales will be, purchase new
equipment despite uncertainty about demand for products, and make investments without
knowing what profits will be. Managers are always trying to make better estimates of what
will happen in the future in the face of uncertainty. Making good estimates is the main pur-
pose of forecasting.
In this chapter, we examine different types of forecasts and present a variety of forecasting
models. Our purpose is to show that there are many ways for managers to forecast. We also pro-
vide an overview of business sales forecasting and describe how to prepare, monitor, and judge
the accuracy of a forecast. Good forecasts are an essential part of efficient service and manufac-
turing operations.
Forecasting is the art and science of predicting future events. Forecasting may involve taking
historical data and projecting them into the future with some sort of mathematical model. It may
be a subjective or intuitive prediction. Or it may involve a combination of these—that is, a math-
ematical model adjusted by a manager’s good judgment.
As we introduce different forecasting techniques in this chapter, you will see that there is sel-
dom one superior method. What works best in one firm under one set of conditions may be a
complete disaster in another organization, or even in a different department of the same firm. In
addition, you will see that there are limits as to what can be expected from forecasts. They are
seldom, if ever, perfect. They are also costly and time-consuming to prepare and monitor.
Few businesses, however, can afford to avoid the process of forecasting by just waiting to see
what happens and then taking their chances. Effective planning in both the short run and long run
depends on a forecast of demand for the company’s products.
Forecasting Time Horizons
A forecast is usually classified by the future time horizon that it covers. Time horizons fall into
three categories:
1. Short-range forecast: This forecast has a time span of up to 1 year but is generally less than
3 months. It is used for planning purchasing, job scheduling, workforce levels, job assign-
ments, and production levels.
2. Medium-range forecast: A medium-range, or intermediate, forecast generally spans from
3 months to 3 years. It is useful in sales planning, production planning and budgeting, cash
budgeting, and analysis of various operating plans.
3. Long-range forecast: Generally 3 years or more in time span, long-range forecasts are used
in planning for new products, capital expenditures, facility location or expansion, and
research and development.
Medium and long-range forecasts are distinguished from short-range forecasts by three features:
1. First, intermediate and long-run forecasts deal with more comprehensive issues and support
management decisions regarding planning and products, plants, and processes. Implement-
ing some facility decisions, such as GM’s decision to open a new Brazilian manufacturing
plant, can take 5 to 8 years from inception to completion.
2. Second, short-term forecasting usually employs different methodologies than longer-term
forecasting. Mathematical techniques, such as moving averages, exponential smoothing,
LO1: Understand the three time horizons and
which models apply for each 86
LO2: Explain when to use each of the four
qualitative models 89
LO3: Apply the naive, moving-average,
exponential smoothing, and trend
methods 92
Chapter 4 Learning Objectives
LO4: Compute three measures of forecast
accuracy 95
LO5: Develop seasonal indices 104
LO6: Conduct a regression and correlation
analysis 108
LO7: Use a tracking signal 114
Forecasting
The art and science of
predicting future events.
LO1: Understand the three
time horizons and which
models apply for each
AUTHOR COMMENT
An increasingly complex
world economy makes
forecasting challenging.

Chapter 4 Forecasting 87
and trend extrapolation (all of which we shall examine shortly), are common to short-run
projections. Broader, less quantitative methods are useful in predicting such issues as
whether a new product, like the optical disk recorder, should be introduced into a company’s
product line.
3. Finally, as you would expect, short-range forecasts tend to be more accurate than longer-
range forecasts. Factors that influence demand change every day. Thus, as the time horizon
lengthens, it is likely that forecast accuracy will diminish. It almost goes without saying,
then, that sales forecasts must be updated regularly to maintain their value and integrity.
After each sales period, forecasts should be reviewed and revised.
The Influence of Product Life Cycle
Another factor to consider when developing sales forecasts, especially longer ones, is product
life cycle. Products, and even services, do not sell at a constant level throughout their lives. Most
successful products pass through four stages: (1) introduction, (2) growth, (3) maturity, and
(4) decline.
Products in the first two stages of the life cycle (such as virtual reality and the Boeing
787 Dreamliner) need longer forecasts than those in the maturity and decline stages (such
as large SUVs and skateboards). Forecasts that reflect life cycle are useful in projecting dif-
ferent staffing levels, inventory levels, and factory capacity as the product passes from the
first to the last stage. The challenge of introducing new products is treated in more detail in
Chapter 5.
Types of Forecasts
Organizations use three major types of forecasts in planning future operations:
1. Economic forecasts address the business cycle by predicting inflation rates, money sup-
plies, housing starts, and other planning indicators.
2. Technological forecasts are concerned with rates of technological progress, which can
result in the birth of exciting new products, requiring new plants and equipment.
3. Demand forecasts are projections of demand for a company’s products or services. These
forecasts, also called sales forecasts, drive a company’s production, capacity, and schedul-
ing systems and serve as inputs to financial, marketing, and personnel planning.
Economic and technological forecasting are specialized techniques that may fall outside the role
of the operations manager. The emphasis in this book will therefore be on demand forecasting.
THE STRATEGIC IMPORTANCE OF FORECASTING
Good forecasts are of critical importance in all aspects of a business: The forecast is the only esti-
mate of demand until actual demand becomes known. Forecasts of demand therefore drive deci-
sions in many areas. Let’s look at the impact of product demand forecast on three activities:
(1) human resources, (2) capacity, and (3) supply-chain management.
Human Resources
Hiring, training, and laying off workers all depend on anticipated demand. If the human
resources department must hire additional workers without warning, the amount of training
declines and the quality of the workforce suffers. A large Louisiana chemical firm almost lost its
biggest customer when a quick expansion to around-the-clock shifts led to a total breakdown in
quality control on the second and third shifts.
Capacity
When capacity is inadequate, the resulting shortages can lead to loss of customers and market
share. This is exactly what happened to Nabisco when it underestimated the huge demand for its
new low-fat Snackwell Devil’s Food Cookies. Even with production lines working overtime,
Nabisco could not keep up with demand, and it lost customers. As the photo on the next page
shows, Amazon made the same error with its Kindle. On the other hand, when excess capacity
exists, costs can skyrocket.
Economic forecasts
Planning indicators that
are valuable in helping
organizations prepare medium-
to long-range forecasts.
Technological forecasts
Long-term forecasts concerned
with the rates of technological
progress.
Demand forecasts
Projections of a company’s
sales for each time period in
the planning horizon.
VIDEO 4.1
Forecasting at Hard Rock Cafe

88 PART 1 Introduction to Operations Management
Supply-Chain Management
Good supplier relations and the ensuing price advantages for materials and parts depend on accu-
rate forecasts. In the global marketplace, where expensive components for Boeing 787 jets are
manufactured in dozens of countries, coordination driven by forecasts is critical. Scheduling
transportation to Seattle for final assembly at the lowest possible cost means no last-minute sur-
prises that can harm already-low profit margins.
SEVEN STEPS IN THE FORECASTING SYSTEM
Forecasting follows seven basic steps. We use Disney World, the focus of this chapter’s Global
Company Profile, as an example of each step:
1. Determine the use of the forecast: Disney uses park attendance forecasts to drive decisions
about staffing, opening times, ride availability, and food supplies.
2. Select the items to be forecasted: For Disney World, there are six main parks. A forecast of daily
attendance at each is the main number that determines labor, maintenance, and scheduling.
3. Determine the time horizon of the forecast: Is it short, medium, or long term? Disney develops
daily, weekly, monthly, annual, and 5-year forecasts.
4. Select the forecasting model(s): Disney uses a variety of statistical models that we shall dis-
cuss, including moving averages, econometrics, and regression analysis. It also employs
judgmental, or nonquantitative, models.
5. Gather the data needed to make the forecast: Disney’s forecasting team employs 35 analysts
and 70 field personnel to survey 1 million people/businesses every year. Disney also uses a
firm called Global Insights for travel industry forecasts and gathers data on exchange rates,
arrivals into the U.S., airline specials, Wall Street trends, and school vacation schedules.
6. Make the forecast.
7. Validate and implement the results: At Disney, forecasts are reviewed daily at the highest
levels to make sure that the model, assumptions, and data are valid. Error measures are
applied; then the forecasts are used to schedule personnel down to 15-minute intervals.
These seven steps present a systematic way of initiating, designing, and implementing a forecast-
ing system. When the system is to be used to generate forecasts regularly over time, data must be
routinely collected. Then actual computations are usually made by computer.
Regardless of the system that firms like Disney use, each company faces several realities:
• Forecasts are seldom perfect. This means that outside factors that we cannot predict or control
often impact the forecast. Companies need to allow for this reality.
• Most forecasting techniques assume that there is some underlying stability in the system.
Consequently, some firms automate their predictions using computerized forecasting soft-
ware, then closely monitor only the product items whose demand is erratic.
• Both product family and aggregated forecasts are more accurate than individual product fore-
casts. Disney, for example, aggregates daily attendance forecasts by park. This approach helps
balance the over- and underpredictions of each of the six attractions.
Even vaunted Amazon can make a major forecasting
error, as it did in the case of its much-hyped Kindle
e-book reader. With the holiday shopping season at
hand, Amazon’s Web page announced “Due to heavy
customer demand, Kindle is sold out . . . ships in
11 to 13 weeks.” Underforecasting demand for the
product was the culprit, according to the Taiwanese
manufacturer Prime View, which has since ramped
up production.

Chapter 4 Forecasting 89
FORECASTING APPROACHES
There are two general approaches to forecasting, just as there are two ways to tackle all decision
modeling. One is a quantitative analysis; the other is a qualitative approach. Quantitative fore-
casts use a variety of mathematical models that rely on historical data and/or associative
variables to forecast demand. Subjective or qualitative forecasts incorporate such factors as the
decision maker’s intuition, emotions, personal experiences, and value system in reaching a fore-
cast. Some firms use one approach and some use the other. In practice, a combination of the two
is usually most effective.
Overview of Qualitative Methods
In this section, we consider four different qualitative forecasting techniques:
1. Jury of executive opinion: Under this method, the opinions of a group of high-level experts
or managers, often in combination with statistical models, are pooled to arrive at a group
estimate of demand. Bristol-Myers Squibb Company, for example, uses 220 well-known
research scientists as its jury of executive opinion to get a grasp on future trends in the world
of medical research.
2. Delphi method: There are three different types of participants in the Delphi method: decision
makers, staff personnel, and respondents. Decision makers usually consist of a group of 5 to 10
experts who will be making the actual forecast. Staff personnel assist decision makers by
preparing, distributing, collecting, and summarizing a series of questionnaires and survey
results. The respondents are a group of people, often located in different places, whose judg-
ments are valued. This group provides inputs to the decision makers before the forecast is made.
The state of Alaska, for example, has used the Delphi method to develop its long-range eco-
nomic forecast. An amazing 90% of the state’s budget is derived from 1.5 million barrels of oil
pumped daily through a pipeline at Prudhoe Bay. The large Delphi panel of experts had to rep-
resent all groups and opinions in the state and all geographic areas. Delphi was the perfect
forecasting tool because panelist travel could be avoided. It also meant that leading Alaskans
could participate because their schedules were not affected by meetings and distances.
3. Sales force composite: In this approach, each salesperson estimates what sales will be in his
or her region. These forecasts are then reviewed to ensure that they are realistic. Then they
are combined at the district and national levels to reach an overall forecast. A variation of
this approach occurs at Lexus, where every quarter Lexus dealers have a “make meeting.” At
this meeting, they talk about what is selling, in what colors, and with what options, so the
factory knows what to build.
4. Consumer market survey: This method solicits input from customers or potential cus-
tomers regarding future purchasing plans. It can help not only in preparing a forecast but
also in improving product design and planning for new products. The consumer market
survey and sales force composite methods can, however, suffer from overly optimistic
forecasts that arise from customer input. The 2001 crash of the telecommunication indus-
try was the result of overexpansion to meet “explosive customer demand.” Where did
these data come from? Oplink Communications, a Nortel Networks supplier, says its
“company forecasts over the last few years were based mainly on informal conversations
with customers.”1
Overview of Quantitative Methods
Five quantitative forecasting methods, all of which use historical data, are described in this chap-
ter. They fall into two categories:
1. Naive approach
2. Moving averages
3. Exponential smoothing time-series models
4. Trend projection
5. Linear regression associative model
Quantitative forecasts
Forecasts that employ
mathematical modeling to
forecast demand.
Qualitative forecasts
Forecasts that incorporate such
factors as the decision maker’s
intuition, emotions, personal
experiences, and value system.
Jury of executive
opinion
A forecasting technique that
uses the opinion of a small
group of high-level managers
to form a group estimate of
demand.
1“Lousy Sales Forecasts Helped Fuel the Telecom Mess,” The Wall Street Journal (July 9, 2001): B1–B4.
LO2: Explain when to use
each of the four qualitative
models
Delphi method
A forecasting technique using a
group process that allows
experts to make forecasts.
Sales force composite
A forecasting technique based
on salespersons’ estimates of
expected sales.
Consumer market
survey
A forecasting method that
solicits input from customers or
potential customers regarding
future purchasing plans.
AUTHOR COMMENT
Forecasting is part science
and part art.

90 PART 1 Introduction to Operations Management
Time-Series Models Time-series models predict on the assumption that the future is a func-
tion of the past. In other words, they look at what has happened over a period of time and use a
series of past data to make a forecast. If we are predicting sales of lawn mowers, we use the past
sales for lawn mowers to make the forecasts.
Associative Models Associative models, such as linear regression, incorporate the variables
or factors that might influence the quantity being forecast. For example, an associative model for
lawn mower sales might use factors such as new housing starts, advertising budget, and competi-
tors’ prices.
TIME-SERIES FORECASTING
A time series is based on a sequence of evenly spaced (weekly, monthly, quarterly, and so on)
data points. Examples include weekly sales of Nike Air Jordans, quarterly earnings reports of
Microsoft stock, daily shipments of Coors beer, and annual consumer price indices. Forecasting
time-series data implies that future values are predicted only from past values and that other vari-
ables, no matter how potentially valuable, may be ignored.
Decomposition of a Time Series
Analyzing time series means breaking down past data into components and then projecting them
forward. A time series has four components:
1. Trend is the gradual upward or downward movement of the data over time. Changes in
income, population, age distribution, or cultural views may account for movement in trend.
2. Seasonality is a data pattern that repeats itself after a period of days, weeks, months, or quar-
ters. There are six common seasonality patterns:
Time series
A forecasting technique that
uses a series of past data points
to make a forecast.
Naive approach
A forecasting technique which
assumes that demand in the
next period is equal to demand
in the most recent period.
Number of
Period of Pattern “Season” Length “Seasons” in Pattern
Week Day 7
Month Week 4–4
Month Day 28–31
Year Quarter 4
Year Month 12
Year Week 52
1
2
Restaurants and barber shops, for example, experience weekly seasons, with Saturday
being the peak of business. See the OM in Action box “Forecasting at Olive Garden and
Red Lobster.” Beer distributors forecast yearly patterns, with monthly seasons. Three
“seasons”—May, July, and September—each contain a big beer-drinking holiday.
3. Cycles are patterns in the data that occur every several years. They are usually tied into the
business cycle and are of major importance in short-term business analysis and planning.
Predicting business cycles is difficult because they may be affected by political events or by
international turmoil.
4. Random variations are “blips” in the data caused by chance and unusual situations. They
follow no discernible pattern, so they cannot be predicted.
Figure 4.1 illustrates a demand over a 4-year period. It shows the average, trend, seasonal com-
ponents, and random variations around the demand curve. The average demand is the sum of the
demand for each period divided by the number of data periods.
Naive Approach
The simplest way to forecast is to assume that demand in the next period will be equal to demand
in the most recent period. In other words, if sales of a product—say, Nokia cell phones—were
68 units in January, we can forecast that February’s sales will also be 68 phones. Does this make
any sense? It turns out that for some product lines, this naive approach is the most cost-effective
AUTHOR COMMENT
Here is the meat of this
chapter. We now show you a
wide variety of models that
use time-series data.
AUTHOR COMMENT
The peak “seasons” for sales
of Frito-Lay chips are the
Super Bowl, Memorial Day,
Labor Day, and the Fourth
of July.

Chapter 4 Forecasting 91
and efficient objective forecasting model. At least it provides a starting point against which more
sophisticated models that follow can be compared.
Moving Averages
A moving-average forecast uses a number of historical actual data values to generate a forecast.
Moving averages are useful if we can assume that market demands will stay fairly steady over
time. A 4-month moving average is found by simply summing the demand during the past
4 months and dividing by 4. With each passing month, the most recent month’s data are added
to the sum of the previous 3 months’ data, and the earliest month is dropped. This practice tends
to smooth out short-term irregularities in the data series.
Mathematically, the simple moving average (which serves as an estimate of the next period’s
demand) is expressed as
(4-1)
where n is the number of periods in the moving average—for example, 4, 5, or 6 months, respec-
tively, for a 4-, 5-, or 6-period moving average.
Moving average =
© demand in previous n periods
n
Moving averages
A forecasting method that uses
an average of the n most recent
periods of data to forecast the
next period.
Seasonal peaks
Random variation
Actual demand
line
Average demand
over 4 years
Trend
component
1
D
e
m
a
n
d
f
o
r
p
ro
d
u
c
t
o
r
s
e
rv
ic
e
Time (years)
2 3 4
� FIGURE 4.1
Demand Charted over 4
Years with a Growth Trend
and Seasonality Indicated
It’s Friday night in the college town of Gainesville, Florida,
and the local Olive Garden restaurant is humming.
Customers may wait an average of 30 minutes for a table,
but they can sample new wines and cheeses and admire
scenic paintings of Italian villages on the Tuscan-style
restaurant’s walls. Then comes dinner with portions so
huge that many people take home a doggie bag. The
typical bill: under $15 per person.
Crowds flock to the Darden restaurant chain’s Olive
Garden, Red Lobster, Seasons 52, and Bahama Breeze
for value and consistency—and they get it.
Every night, Darden’s computers crank out forecasts
that tell store managers what demand to anticipate the
next day. The forecasting software generates a total meal
forecast and breaks that down into specific menu items.
The system tells a manager, for instance, that if 625 meals
will be served the next day, “you will serve these items in
these quantities. So before you go home, pull 25 pounds of
shrimp and 30 pounds of crab out, and tell your operations
people to prepare 42
portion packs of chicken,
75 scampi dishes, 8 stuffed
flounders, and so on.”
Managers often fine tune
the quantities based on
local conditions, such as
weather or a convention,
but they know what their customers are going to order.
By relying on demand history, the forecasting system
has cut millions of dollars of waste out of the system.
The forecast also reduces labor costs by providing the
necessary information for improved scheduling. Labor
costs decreased almost a full percent in the first year,
translating into additional millions in savings for the Darden
chain. In the low-margin restaurant business, every dollar
counts.
Source: Interviews with Darden executives.
OM in Action � Forecasting at Olive Garden and Red Lobster
AUTHOR COMMENT
Forecasting is easy when
demand is stable. But with
trend, seasonality, and cycles
considered, the job is a lot
more interesting.

EXAMPLE 1 �
Determining the
moving average
Donna’s Garden Supply wants a 3-month moving-average forecast, including a forecast for next
January, for shed sales.
APPROACH � Storage shed sales are shown in the middle column of the table below. A 3-month
moving average appears on the right.
92 PART 1 Introduction to Operations Management
When a detectable trend or pattern is present, weights can be used to place more emphasis on
recent values. This practice makes forecasting techniques more responsive to changes
because more recent periods may be more heavily weighted. Choice of weights is somewhat
arbitrary because there is no set formula to determine them. Therefore, deciding which
weights to use requires some experience. For example, if the latest month or period is
weighted too heavily, the forecast may reflect a large unusual change in the demand or sales
pattern too quickly.
A weighted moving average may be expressed mathematically as:
(4-2)
Example 2 shows how to calculate a weighted moving average.
Weighted moving average =
© (Weight for period n)(Demand in period n)
© Weights
SOLUTION � The forecast for December is . To project the demand for sheds in the coming
January, we sum the October, November, and December sales and divide by 3: January forecast
INSIGHT � Management now has a forecast that averages sales for the last 3 months. It is easy to
use and understand.
LEARNING EXERCISE � If actual sales in December were 18 (rather than 14), what is the new
January forecast? [Answer: ]
RELATED PROBLEMS � 4.1a, 4.2b, 4.5a, 4.6, 4.8a,b, 4.10a, 4.13b, 4.15, 4.47
EXCEL OM Data File Ch04Ex1.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 4.1 This example is further illustrated in Active Model 4.1 at www.pearsonhighered.com/heizer.
1713.
= (18 + 16 + 14)/3 = 16.
20 23
Example 1 shows how moving averages are calculated.
Month Actual Shed Sales 3-Month Moving Average
January 10
February 12
March 13
April 16 (10 + 12 + 13)/3 =
May 19 (12 + 13 + 16)/3 =
June 23 (13 + 16 + 19)/3 = 16
July 26 (16 + 19 + 23)/3 =
August 30 (19 + 23 + 26)/3 =
September 28 (23 + 26 + 30)/3 =
October 18 (26 + 30 + 28)/3 = 28
November 16 (30 + 28 + 18)/3 =
December 14 (28 + 18 + 16)/3 = 2023
2513
2613
2223
1913
1323
1123
LO3: Apply the naive,
moving-average, exponential
smoothing, and trend
methods

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Chapter 4 Forecasting 93
� EXAMPLE 2
Determining the
weighted moving
average
Donna’s Garden Supply (see Example 1) wants to forecast storage shed sales by weighting the past 3
months, with more weight given to recent data to make them more significant.
APPROACH � Assign more weight to recent data, as follows:
SOLUTION � The results of this weighted-average forecast are as follows:
INSIGHT � In this particular forecasting situation, you can see that more heavily weighting the lat-
est month provides a much more accurate projection.
LEARNING EXERCISE � If the assigned weights were 0.50, 0.33, and 0.17 (instead of 3, 2, and 1)
what is the forecast for January’s weighted moving average? Why? [Answer: There is no change. These
are the same relative weights. Note that weights = 1 now, so there is no need for a denominator. When
the weights sum to 1, calculations tend to be simpler.
RELATED PROBLEMS � 4.1b, 4.2c, 4.5c, 4.6, 4.7, 4.10b
EXCEL OM Data File Ch04Ex2.xls can be found at www.pearsonhighered.com/heizer.
©
Both simple and weighted moving averages are effective in smoothing out sudden fluctuations in the
demand pattern to provide stable estimates. Moving averages do, however, present three problems:
1. Increasing the size of n (the number of periods averaged) does smooth out fluctuations bet-
ter, but it makes the method less sensitive to real changes in the data.
2. Moving averages cannot pick up trends very well. Because they are averages, they will
always stay within past levels and will not predict changes to either higher or lower levels.
That is, they lag the actual values.
3. Moving averages require extensive records of past data.
Figure 4.2, a plot of the data in Examples 1 and 2, illustrates the lag effect of the moving-average
models. Note that both the moving-average and weighted-moving-average lines lag the actual
demand. The weighted moving average, however, usually reacts more quickly to demand
Weights Applied Period
3 Last month
2 Two months ago
1 Three months ago
6 Sum of weights
Forecast for this month =
3 * Sales last mo. + 2 * Sales 2 mos. ago + 1 * Sales 3 mos. ago
Sum of the weights
3-Month Weighted
Month Actual Shed Sales Moving Average
January 10
February 12
March 13
April 16 [(3 × 13) + (2 × 12) + (10)]/6 =
May 19 [(3 × 16) + (2 × 13) + (12)]/6 =
June 23 [(3 × 19) + (2 × 16) + (13)]/6 = 17
July 26 [(3 × 23) + (2 × 19) + (16)]/6 =
August 30 [(3 × 26) + (2 × 23) + (19)]/6 =
September 28 [(3 × 30) + (2 × 26) + (23)]/6 =
October 18 [(3 × 28) + (2 × 30) + (26)]/6 =
November 16 [(3 × 18) + (2 × 28) + (30)]/6 =
December 14 [(3 × 16) + (2 × 18) + (28)]/6 = 1823
2313
2813
2712
2356
20 12
1413
1216

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94 PART 1 Introduction to Operations Management
changes. Even in periods of downturn (see November and December), it more closely tracks
the demand.
Exponential Smoothing
Exponential smoothing is a sophisticated weighted-moving-average forecasting method that is
still fairly easy to use. It involves very little record keeping of past data. The basic exponential
smoothing formula can be shown as follows:
(4-3)
where α is a weight, or smoothing constant, chosen by the forecaster, that has a value between
0 and 1. Equation (4-3) can also be written mathematically as:
(4-4)
where Ft � new forecast
Ft–1 � previous period’s forecast
α � smoothing (or weighting) constant (0 ≤ α ≤ 1)
At–1 � previous period’s actual demand
The concept is not complex. The latest estimate of demand is equal to the old estimate adjusted
by a fraction of the difference between the last period’s actual demand and the old estimate.
Example 3 shows how to use exponential smoothing to derive a forecast.
F t = F t –1 + a(At–1 – F t–1)
+ � (Last period’s actual demand – Last period’s forecast)
New forecast = Last period’s forecast
Exponential smoothing
A weighted-moving-average
forecasting technique in which
data points are weighted by an
exponential function.
Smoothing constant
The weighting factor used in an
exponential smoothing forecast,
a number between 0 and 1.
EXAMPLE 3 �
Determining a
forecast via
exponential
smoothing
Weighted moving average
Actual sales
Moving average
Jan. Feb. Mar. Apr. May June
Month
July Aug. Sept. Oct. Nov. Dec.
20
S
a
le
s
d
e
m
a
n
d
15
10
5
25
30
� FIGURE 4.2
Actual Demand vs. Moving-
Average and Weighted-
Moving-Average Methods for
Donna’s Garden Supply
In January, a car dealer predicted February demand for 142 Ford Mustangs. Actual February demand
was 153 autos. Using a smoothing constant chosen by management of α = .20, the dealer wants to fore-
cast March demand using the exponential smoothing model.
APPROACH � The exponential smoothing model in Equations (4-3) and (4-4) can be applied.
SOLUTION � Substituting the sample data into the formula, we obtain:
Thus, the March demand forecast for Ford Mustangs is rounded to 144.
= 144.2
New forecast (for Marche demand) = 142 + .2(153 – 142) = 142 + 2.2
AUTHOR COMMENT
Moving average methods
always lag behind when there
is a trend present, as shown
by the blue line (actual sales)
for January through August.

Chapter 4 Forecasting 95
INSIGHT � Using just two pieces of data, the forecast and the actual demand, plus a smoothing
constant, we developed a forecast of 144 Ford Mustangs for March.
LEARNING EXERCISE � If the smoothing constant is changed to .30, what is the new fore-
cast? [Answer: 145.3]
RELATED PROBLEMS � 4.1c, 4.3, 4.4, 4.5d, 4.6, 4.9d, 4.11, 4.12, 4.13a, 4.17, 4.18, 4.37,
4.43, 4.47, 4.49
LO4: Compute three
measures of forecast
accuracy
The smoothing constant, α, is generally in the range from .05 to .50 for business applications.
It can be changed to give more weight to recent data (when α is high) or more weight to past data
(when α is low). When α reaches the extreme of 1.0, then in Equation (4-4), Ft = 1.0At–1. All the
older values drop out, and the forecast becomes identical to the naive model mentioned earlier in
this chapter. That is, the forecast for the next period is just the same as this period’s demand.
The following table helps illustrate this concept. For example, when α = .5, we can see that the
new forecast is based almost entirely on demand in the last three or four periods. When α = .1, the
forecast places little weight on recent demand and takes many periods (about 19) of historical
values into account.
Weight Assigned to
Most 2nd Most 3rd Most 4th Most 5th Most
Recent Recent Recent Recent Recent
Smoothing Period Period Period Period Period
Constant (α) α(1 − α) α(1 − α)2 α(1 − α)3 α(1 − α)4
α = .1 .1 .09 .081 .073 .066
α = .5 .5 .25 .125 .063 .031
Selecting the Smoothing Constant The exponential smoothing approach is easy to use,
and it has been successfully applied in virtually every type of business. However, the appropriate
value of the smoothing constant, α, can make the difference between an accurate forecast and an
inaccurate forecast. High values of α are chosen when the underlying average is likely to change.
Low values of α are used when the underlying average is fairly stable. In picking a value for the
smoothing constant, the objective is to obtain the most accurate forecast.
Measuring Forecast Error
The overall accuracy of any forecasting model—moving average, exponential smoothing, or
other—can be determined by comparing the forecasted values with the actual or observed values.
If Ft denotes the forecast in period t, and At denotes the actual demand in period t, the forecast
error (or deviation) is defined as:
Several measures are used in practice to calculate the overall forecast error. These measures can
be used to compare different forecasting models, as well as to monitor forecasts to ensure they
are performing well. Three of the most popular measures are mean absolute deviation (MAD),
mean squared error (MSE), and mean absolute percent error (MAPE). We now describe and give
an example of each.
Mean Absolute Deviation The first measure of the overall forecast error for a model
is the mean absolute deviation (MAD). This value is computed by taking the sum of
the absolute values of the individual forecast errors (deviations) and dividing by the number
of periods of data (n):
(4-5)MAD =
© ƒ Actual – Forecast ƒ
n
= At – Ft
Forecast error = Actual demand – Forecast value
Mean absolute
deviation (MAD)
A measure of the overall
forecast error for a model.
AUTHOR COMMENT
The forecast error tells
us how well the model
performed against itself
using past data.

96 PART 1 Introduction to Operations Management
Example 4 applies MAD, as a measure of overall forecast error, by testing two values of α.
EXAMPLE 4 �
Determining the
mean absolute
deviation (MAD)
During the past 8 quarters, the Port of Baltimore has unloaded large quantities of grain from ships. The
port’s operations manager wants to test the use of exponential smoothing to see how well the technique
works in predicting tonnage unloaded. He guesses that the forecast of grain unloaded in the first quar-
ter was 175 tons. Two values of α are to be examined: α = .10 and α = .50.
APPROACH � Compare the actual data with the data we forecast (using each of the two α values)
and then find the absolute deviation and MADs.
SOLUTION � The following table shows the detailed calculations for α = .10 only:
To evaluate the accuracy of each smoothing constant, we can compute forecast errors in terms of
absolute deviations and MADs:
INSIGHT � On the basis of this comparison of the two MADs, a smoothing constant of α = .10 is
preferred to α = .50 because its MAD is smaller.
LEARNING EXERCISE � If the smoothing constant is changed from α = .10 to α = .20, what
is the new MAD? [Answer: 10.21.]
RELATED PROBLEMS � 4.5b, 4.8c, 4.9c, 4.14, 4.23, 4.37a
EXCEL OM Data File Ch04Ex4a.xls and Ch04Ex4b.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 4.2 This example is further illustrated in Active Model 4.2 at www.pearsonhighered.com/heizer.
Most computerized forecasting software includes a feature that automatically finds the smooth-
ing constant with the lowest forecast error. Some software modifies the α value if errors become
larger than acceptable.
Actual
Tonnage Forecast Forecast
Quarter Unloaded with α = .10 with α = .50
1 180 175 175
2 168 175.50 = 175.00 + .10(180 − 175) 177.50
3 159 172.75
4 175 165.88
5 190 170.44
6 205 180.22
7 180 192.61
8 182 186.30
9 ? 184.15178.59 = 178.22 + .10(182 – 178.22)
178.22 = 178.02 + .10(180 – 178.02)
178.02 = 175.02 + .10(205 – 175.02)
175.02 = 173.36 + .10(190 – 173.36)
173.36 = 173.18 + .10(175 – 173.18)
173.18 = 174.75 + .10(159 – 174.75)
174.75 = 175.50 + .10(168 – 175.50)
Absolute Absolute
Actual Forecast Deviation Forecast Deviation
Tonnage with for with for
Quarter Unloaded α = .10 α = .10 α = .50 α = .50
1 180 175 5.00 175 5.00
2 168 175.50 7.50 177.50 9.50
3 159 174.75 15.75 172.75 13.75
4 175 173.18 1.82 165.88 9.12
5 190 173.36 16.64 170.44 19.56
6 205 175.02 29.98 180.22 24.78
7 180 178.02 1.98 192.61 12.61
8 182 178.22 3.78 186.30 4.30
Sum of absolute deviations: 82.45 98.62
10.31 12.33 MAD =
©|Deviations|
n

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Chapter 4 Forecasting 97
Mean Squared Error The mean squared error (MSE) is a second way of measuring over-
all forecast error. MSE is the average of the squared differences between the forecasted and
observed values. Its formula is:
(4-6)
Example 5 finds the MSE for the Port of Baltimore introduced in Example 4.
MSE =
©(Forecast errors)2
n
� EXAMPLE 5
Determining the
mean squared
error (MSE)
The operations manager for the Port of Baltimore now wants to compute MSE for α = .10.
APPROACH � Use the same forecast data for α = .10 from Example 4, then compute the MSE
using Equation (4-6).
SOLUTION �
INSIGHT � Is this MSE = 190.8 good or bad? It all depends on the MSEs for other forecasting
approaches. A low MSE is better because we want to minimize MSE. MSE exaggerates errors because
it squares them.
LEARNING EXERCISE � Find the MSE for α = .50. [Answer: MSE = 195.24. The result indi-
cates that α = .10 is a better choice because we seek a lower MSE. Coincidentally, this is the same con-
clusion we reached using MAD in Example 4.]
RELATED PROBLEMS � 4.8d, 4.14, 4.20
MSE =
©(Forecast errors)2
n
= 1,526.54>8 = 190.8
A drawback of using the MSE is that it tends to accentuate large deviations due to the squared
term. For example, if the forecast error for period 1 is twice as large as the error for period 2, the
squared error in period 1 is four times as large as that for period 2. Hence, using MSE as the mea-
sure of forecast error typically indicates that we prefer to have several smaller deviations rather
than even one large deviation.
Mean Absolute Percent Error A problem with both the MAD and MSE is that their
values depend on the magnitude of the item being forecast. If the forecast item is measured
in thousands, the MAD and MSE values can be very large. To avoid this problem, we can
use the mean absolute percent error (MAPE). This is computed as the average of the
absolute difference between the forecasted and actual values, expressed as a percentage of the
actual values. That is, if we have forecasted and actual values for n periods, the MAPE is
calculated as:
(4-7)
Example 6 illustrates the calculations using the data from Examples 4 and 5.
MAPE =
a
n
i= 1
100 ƒ Actuali – Forecasti ƒ>Actuali
n
Mean squared
error (MSE)
The average of the squared
differences between the
forecasted and observed values.
Mean absolute percent
error (MAPE)
The average of the absolute
differences between the forecast
and actual values, expressed as
a percent of actual values.
Actual Tonnage Forecast for
Quarter Unloaded α = .10 (Error)2
1 180 175
2 168 175.50
3 159 174.75
4 175 173.18
5 190 173.36
6 205 175.02
7 180 178.02
8 182 178.22
Sum of errors squared � 1,526.46
(3.78)2 = 14.31
(1.98)2 = 3.92
(29.98)2 = 898.70
(16.64)2 = 276.89
(1.82)2 = 3.33
( – 15.75)2 = 248.06
(—7.5)2 = 56.25
52 = 25

98 PART 1 Introduction to Operations Management
EXAMPLE 6 �
Determining the
mean absolute
percent error
(MAPE)
The Port of Baltimore wants to now calculate the MAPE when α = .10.
APPROACH � Equation (4-7) is applied to the forecast data computed in Example 4.
SOLUTION �
INSIGHT � MAPE expresses the error as a percent of the actual values, undistorted by a single
large value.
LEARNING EXERCISE � What is MAPE when α is .50? [Answer: MAPE = 6.75%. As was
the case with MAD and MSE, the α = .1 was preferable for this series of data.]
RELATED PROBLEMS � 4.8e, 4.33c
MAPE =
© absolute percent errors
n
=
44.75%
8
= 5.59%
The MAPE is perhaps the easiest measure to interpret. For example, a result that the
MAPE is 6% is a clear statement that is not dependent on issues such as the magnitude of the
input data.
Exponential Smoothing with Trend Adjustment
Simple exponential smoothing, the technique we just illustrated in Examples 3 to 6, is like any
other moving-average technique: It fails to respond to trends. Other forecasting techniques that
can deal with trends are certainly available. However, because exponential smoothing is such a
popular modeling approach in business, let us look at it in more detail.
Here is why exponential smoothing must be modified when a trend is present. Assume that
demand for our product or service has been increasing by 100 units per month and that we
have been forecasting with α = 0.4 in our exponential smoothing model. The following table
shows a severe lag in the 2nd, 3rd, 4th, and 5th months, even when our initial estimate for
month 1 is perfect:
Month Actual Demand Forecast for Month T(FT)
1 100
2 200
3 300
4 400
5 500 F5 = F4 + a (A4 – F4) = 204 + .4(400 – 204) = 282
F4 = F3 + a (A3 – F3) = 140 + .4(300 – 140) = 204
F3 = F2 + a (A2 – F2) = 100 + .4(200 – 100) = 140
F2 = F1 + a (A1 – F1) = 100 + .4(100 – 100) = 100
F1 = 100 (given)
To improve our forecast, let us illustrate a more complex exponential smoothing model, one that
adjusts for trend. The idea is to compute an exponentially smoothed average of the data and then
adjust for positive or negative lag in trend. The new formula is:
(4-8)+ Exponentially smoothed trend(Tt)
Forecast including trend(FITt) = Exponentially smoothed forecast(Ft)
Actual Tonnage Forecast for Absolute Percent Error
Quarter Unloaded α = .10 100 (|error|/actual)
1 180 175.00 100(5/180) = 2.78%
2 168 175.50 100(7.5/168) = 4.46%
3 159 174.75 100(15.75/159) = 9.90%
4 175 173.18 100(1.82/175) = 1.05%
5 190 173.36 100(16.64/190) = 8.76%
6 205 175.02 100(29.98/205) = 14.62%
7 180 178.02 100(1.98/180) = 1.10%
8 182 178.22 100(3.78/182) = 2.08%
Sum of % errors = 44.75%

Chapter 4 Forecasting 99
With trend-adjusted exponential smoothing, estimates for both the average and the trend are
smoothed. This procedure requires two smoothing constants: α for the average and β for the
trend. We then compute the average and trend each period:
Ft = α(Actual demand last period) + (1 − α)(Forecast last period + Trend estimate last period)
or:
(4-9)
or:
(4-10)
where Ft � exponentially smoothed forecast of the data series in period t
Tt � exponentially smoothed trend in period t
At � actual demand in period t
α � smoothing constant for the average (0 ≤ α ≤ 1)
β � smoothing constant for the trend (0 ≤ β ≤ 1)
So the three steps to compute a trend-adjusted forecast are:
Step 1: Compute Ft, the exponentially smoothed forecast for period t, using Equation (4-9).
Step 2: Compute the smoothed trend, Tt, using Equation (4-10).
Step 3: Calculate the forecast including trend, FITt, by the formula FITt = Ft + Tt (from
Equation [4-8]).
Example 7 shows how to use trend-adjusted exponential smoothing.
Tt = �(Ft – Ft – 1) + (1 – �)Tt – 1
Tt = b(Forecast this period – Forecast last period) + (1 – b)(Trend estimate last period)
Ft = a(At – 1) + (1 – a)(Ft – 1 + Tt – 1)
� EXAMPLE 7
Computing a
trend-adjusted
exponential
smoothing
forecast
A large Portland manufacturer wants to forecast demand for a piece of pollution-control equipment. A
review of past sales, as shown below, indicates that an increasing trend is present:
Smoothing constants are assigned the values of α = .2 and β = .4. The firm assumes the initial forecast
for month 1 (F1) was 11 units and the trend over that period (T1) was 2 units.
APPROACH � A trend-adjusted exponential smoothing model, using Equations (4-9), (4-10), and
(4-8) and the three steps above, is employed.
SOLUTION �
Step 1: Forecast for month 2:
Step 2: Compute the trend in period 2:
Step 3: Compute the forecast including trend (FITt):
= 14.72 units
= 12.8 + 1.92
FIT2 = F2 + T2
= (.4)(1.8) + (.6)(2) = .72 + 1.2 = 1.92
= .4(12.8 – 11) + (1 – .4)(2)
T2 = b(F2 – F1) + (1 – b)T1
= 2.4 + (.8)(13) = 2.4 + 10.4 = 12.8 units
F2 = (.2)(12) + (1 – .2)(11 + 2)
F2 = aA1 + (1 – a)(F1 + T1)
Month (t) Actual Demand (At) Month (t) Actual Demand (At)
1 12 6 21
2 17 7 31
3 20 8 28
4 19 9 36
5 24 10 ?

100 PART 1 Introduction to Operations Management
We will also do the same calculations for the third month:
Step 1:
Step 2:
Step 3:
Table 4.1 completes the forecasts for the 10-month period.
= 15.18 + 2.10 = 17.28.
FIT3 = F3 + T3
= (.4)(2.38) + (.6)(1.92) = .952 + 1.152 = 2.10
T3 = b(F3 – F2) + (1 – b)T2 = (.4)(15.18 – 12.8) + (1 – .4)(1.92)
= 3.4 + (.8)(14.72) = 3.4 + 11.78 = 15.18
F3 = aA2 + (1 – a)(F2 + T2) = (.2)(17) + (1 – .2)(12.8 + 1.92)
Forecast
Actual Smoothed Smoothed Including Trend,
Month Demand Forecast, Ft Trend, Tt FITt
1 12 11 2 13.00
2 17 12.80 1.92 14.72
3 20 15.18 2.10 17.28
4 19 17.82 2.32 20.14
5 24 19.91 2.23 22.14
6 21 22.51 2.38 24.89
7 31 24.11 2.07 26.18
8 28 27.14 2.45 29.59
9 36 29.28 2.32 31.60
10 — 32.48 2.68 35.16
�TABLE 4.1
Forecast with = .2 and = .4BA
40
35
30
25
20
15
10
5
0
1 2 3 4 5
Time (months)
P
ro
d
u
c
t
d
e
m
a
n
d
6 7 8 9
Actual demand (At )
Forecast including trend (FITt )
with α = .2 and β = .4
� FIGURE 4.3
Exponential Smoothing
with Trend-Adjustment
Forecasts Compared to
Actual Demand Data
INSIGHT � Figure 4.3 compares actual demand (At) to an exponential smoothing forecast that
includes trend (FITt). FIT picks up the trend in actual demand. A simple exponential smoothing model
(as we saw in Examples 3 and 4) trails far behind.
LEARNING EXERCISE � Using the data for actual demand for the 9 months, compute the
exponentially smoothed forecast without trend (using Equation [4-4] as we did earlier in Examples 3 and
4). Apply α = .2 and assume an initial forecast for month 1 of 11 units. Then plot the months 2–10 fore-
cast values on Figure 4.3. What do you notice? [Answer: Month 10 forecast = 24.65. All the points are
below and lag the trend-adjusted forecast.]
RELATED PROBLEMS � 4.19, 4.20, 4.21, 4.22, 4.44
EXCEL OM Data File Ch04Ex7.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 4.3 This example is further illustrated in Active Model 4.3 at www.pearsonhighered.com/heizer.

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The value of the trend-smoothing constant, β, resembles the α constant because a high β is more
responsive to recent changes in trend. A low β gives less weight to the most recent trends and
tends to smooth out the present trend. Values of β can be found by the trial-and-error approach or
by using sophisticated commercial forecasting software, with the MAD used as a measure of
comparison.
Simple exponential smoothing is often referred to as first-order smoothing, and trend-
adjusted smoothing is called second-order, or double smoothing. Other advanced exponential-
smoothing models are also used, including seasonal-adjusted and triple smoothing, but these are
beyond the scope of this book.2
Trend Projections
The last time-series forecasting method we will discuss is trend projection. This technique fits a
trend line to a series of historical data points and then projects the line into the future for medium to
long-range forecasts. Several mathematical trend equations can be developed (for example, expo-
nential and quadratic), but in this section, we will look at linear (straight-line) trends only.
If we decide to develop a linear trend line by a precise statistical method, we can apply the
least-squares method. This approach results in a straight line that minimizes the sum of the
squares of the vertical differences or deviations from the line to each of the actual observations.
Figure 4.4 illustrates the least-squares approach.
A least-squares line is described in terms of its y-intercept (the height at which it intercepts
the y-axis) and its expected change (slope). If we can compute the y-intercept and slope, we can
express the line with the following equation:
(4-11)
where (called “y hat”) = computed value of the variable to be predicted (called the
dependent variable)
a = y-axis intercept
b = slope of the regression line (or the rate of change in y for given
changes in x)
x = the independent variable (which in this case is time)
Statisticians have developed equations that we can use to find the values of a and b for any
regression line. The slope b is found by:
(4-12)b =
©xy – nxy
©x2 – nx2
yN
yN = a + bx
Time period
îTrend line, y = a + bx
V
a
lu
e
s
o
f
d
e
p
e
n
d
e
n
t
v
a
ri
a
b
le
(
y
-v
a
lu
e
s
)
⎧
⎨
⎩
Deviation 3
Deviation
(error)
1
Deviation
Deviation 5 Deviation 6
Deviation 7
Deviation 2
Actual observation (y -value)
4
⎧
⎪
⎨
⎪
⎩
⎧
⎪
⎨
⎪
⎩
⎧
⎨
⎩
⎧
⎪
⎨
⎪
⎩
⎧
⎨
⎩
1 2 3 4 5 6 7
� FIGURE 4.4
The Least-Squares Method
for Finding the Best-Fitting
Straight Line, Where the
Asterisks Are the Locations
of the Seven Actual
Observations or Data Points
2For more details, see D. Groebner, P. Shannon, P. Fry, and K. Smith, Business Statistics, 8th ed. (Upper Saddle River,
NJ: Prentice Hall, 2011).
Trend projection
A time-series forecasting
method that fits a trend line to a
series of historical data points
and then projects the line into
the future for forecasts.
Chapter 4 Forecasting 101

102 PART 1 Introduction to Operations Management
where b = slope of the regression line
Σ = summation sign
x = known values of the independent variable
y = known values of the dependent variable
= average of the x-values
= average of the y-values
n = number of data points or observations
We can compute the y-intercept a as follows:
(4-13)
Example 8 shows how to apply these concepts.
a = y – bx
y
x
EXAMPLE 8 �
Forecasting with
least squares
The demand for electric power at N.Y. Edison over the period 2003 to 2009 is shown in the following
table, in megawatts. The firm wants to forecast 2010 demand by fitting a straight-line trend to these data.
Electrical Electrical
Year Power Demand Year Power Demand
2003 74 2007 105
2004 79 2008 142
2005 80 2009 122
2006 90
APPROACH � With a series of data over time, we can minimize the computations by transform-
ing the values of x (time) to simpler numbers. Thus, in this case, we can designate 2003 as year 1,
2004 as year 2, and so on. Then Equations (4-12) and (4-13) can be used to create the trend projection
model.
SOLUTION �
Time Electric Power
Year Period (x) Demand (y) x2 xy
2003 1 74 1 74
2004 2 79 4 158
2005 3 80 9 240
2006 4 90 16 360
2007 5 105 25 525
2008 6 142 36 852
2009 7 122 49 854
©xy = 3,063©x2 = 140©y = 692©x = 28
Thus, the least squares trend equation is To project demand in 2010, we first denote
the year 2010 in our new coding system as x = 8:
INSIGHT � To evaluate the model, we plot both the historical demand and the trend line in
Figure 4.5. In this case, we may wish to be cautious and try to understand the 2008 to 2009 swing in
demand.
= 141.02, or 141 megawatts
Demand in 2010 = 56.70 + 10.54(8)
yN = 56.70 + 10.54x.
a = y – bx = 98.86 – 10.54(4) = 56.70
b =
©xy – nxy
©x2 – nx2
=
3,063 – (7)(4)(98.86)
140 – (7)(42)
=
295
28
= 10.54
x =
©x
n
=
28
7
= 4 y = ©y
n
=
692
7
= 98.86

LEARNING EXERCISE � Estimate demand for 2011. [Answer: 151.56 or 152 megawatts.]
RELATED PROBLEMS � 4.6, 4.13c, 4.16, 4.25, 4.39, 4.49
EXCEL OM Data File Ch04Ex8.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 4.4 This example is further illustrated in Active Model 4.4 at www.pearsonhighered.com/heizer.
î
2003 2004 2005 2006 2007 2008 2009 2010 2011
160
150
140
130
120
110
100
90
80
70
60
50
Year
P
o
w
e
r
d
e
m
a
n
d
(
m
e
g
a
w
a
tt
s
)
Trend line,
y = 56.70 + 10.54x
� FIGURE 4.5
Electrical Power and the
Computed Trend Line
Notes on the Use of the Least-Squares Method Using the least-squares method
implies that we have met three requirements:
1. We always plot the data because least-squares data assume a linear relationship. If a curve
appears to be present, curvilinear analysis is probably needed.
2. We do not predict time periods far beyond our given database. For example, if we have
20 months’ worth of average prices of Microsoft stock, we can forecast only 3 or 4 months
into the future. Forecasts beyond that have little statistical validity. Thus, you cannot take
5 years’ worth of sales data and project 10 years into the future. The world is too uncertain.
3. Deviations around the least-squares line (see Figure 4.4) are assumed to be random. They
are normally distributed, with most observations close to the line and only a smaller number
farther out.
Seasonal Variations in Data
Seasonal variations in data are regular up-and-down movements in a time series that relate to
recurring events such as weather or holidays. Demand for coal and fuel oil, for example, peaks
during cold winter months. Demand for golf clubs or sunscreen may be highest in summer.
Seasonality may be applied to hourly, daily, weekly, monthly, or other recurring patterns.
Fast-food restaurants experience daily surges at noon and again at 5 P.M. Movie theaters see
higher demand on Friday and Saturday evenings. The post office, Toys “ ” Us, The Christmas
Store, and Hallmark Card Shops also exhibit seasonal variation in customer traffic and sales.
Similarly, understanding seasonal variations is important for capacity planning in organizations
that handle peak loads. These include electric power companies during extreme cold and warm peri-
ods, banks on Friday afternoons, and buses and subways during the morning and evening rush hours.
Time-series forecasts like those in Example 8 involve reviewing the trend of data over a series
of time periods. The presence of seasonality makes adjustments in trend-line forecasts necessary.
Seasonality is expressed in terms of the amount that actual values differ from average values in the
time series. Analyzing data in monthly or quarterly terms usually makes it easy for a statistician to
spot seasonal patterns. Seasonal indices can then be developed by several common methods.
In what is called a multiplicative seasonal model, seasonal factors are multiplied by an esti-
mate of average demand to produce a seasonal forecast. Our assumption in this section is that
R
Seasonal variations
Regular upward or downward
movements in a time series that
tie to recurring events.
AUTHOR COMMENT
John Deere understands
seasonal variations: It has
been able to obtain 70% of
its orders in advance of
seasonal use so it can
smooth production.
Chapter 4 Forecasting 103

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104 PART 1 Introduction to Operations Management
Demand for many prod-
ucts is seasonal. Yamaha,
the manufacturer of these
jet skis and snowmobiles,
produces products with
complementary demands
to address seasonal
fluctuations.
EXAMPLE 9 �
Determining
seasonal indices
A Des Moines distributor of Sony laptop computers wants to develop monthly indices for sales. Data
from 2007–2009, by month, are available.
APPROACH � Follow the five steps listed above.
SOLUTION �
Demand
Average Average
2007–2009 Monthly Seasonal
Month 2007 2008 2009 Demand Demanda Indexb
Jan. 80 85 105 90 94
Feb. 70 85 85 80 94
Mar. 80 93 82 85 94
Apr. 90 95 115 100 94
May 113 125 131 123 94
June 110 115 120 115 94
July 100 102 113 105 94
Aug. 88 102 110 100 94
Sept. 85 90 95 90 94
Oct. 77 78 85 80 94
Nov. 75 82 83 80 94
Dec. 82 78 80 80 94
Total average annual demand = 1,128
aAverage monthly demand bSeasonal index =
Average 2007- 2009 monthly demand
Average monthly demand
.=
1,128
12 months
= 94.
.851 (= 80>94)
.851 (= 80>94)
.851 (= 80>94)
.957 (= 90>94)
1.064 (= 100>94)
1.117 ( = 105>94)
1.223 (= 115>94)
1.309 (= 123>94)
1.064 (= 100>94)
.904 ( = 85>94)
.851 (= 80>94)
.957 ( = 90>94)
trend has been removed from the data. Otherwise, the magnitude of the seasonal data will be dis-
torted by the trend.
Here are the steps we will follow for a company that has “seasons” of 1 month:
1. Find the average historical demand each season (or month in this case) by summing the
demand for that month in each year and dividing by the number of years of data available.
For example, if, in January, we have seen sales of 8, 6, and 10 over the past 3 years, average
January demand equals (8 + 6 + 10)/3 = 8 units.
2. Compute the average demand over all months by dividing the total average annual demand
by the number of seasons. For example, if the total average demand for a year is 120 units
and there are 12 seasons (each month), the average monthly demand is 120/12 = 10 units.
3. Compute a seasonal index for each season by dividing that month’s actual historical demand
(from step 1) by the average demand over all months (from step 2). For example, if the aver-
age historical January demand over the past 3 years is 8 units and the average demand over
all months is 10 units, the seasonal index for January is 8/10 = .80. Likewise, a seasonal
index of 1.20 for February would mean that February’s demand is 20% larger than the aver-
age demand over all months.
4. Estimate next year’s total annual demand.
5. Divide this estimate of total annual demand by the number of seasons, then multiply it by
the seasonal index for that month. This provides the seasonal forecast.
Example 9 illustrates this procedure as it computes seasonal indices from historical data.
LO5: Develop seasonal
indices

If we expected the 2010 annual demand for computers to be 1,200 units, we would use these seasonal
indices to forecast the monthly demand as follows:
Month Demand Month Demand
Jan. July
Feb. Aug.
Mar. Sept.
Apr. Oct.
May Nov.
June Dec.
1,200
12
* .851 = 85
1,200
12
* 1.223 = 122
1,200
12
* .851 = 85
1,200
12
* 1.309 = 131
1,200
12
* .851 = 85
1,200
12
* 1.064 = 106
1,200
12
* .957 = 96
1,200
12
* .904 = 90
1,200
12
* 1.064 = 106
1,200
12
* .851 = 85
1,200
12
* 1.117 = 112
1,200
12
* .957 = 96
INSIGHT � Think of these indices as percentages of average sales. The average sales (without
seasonality) would be 94, but with seasonality, sales fluctuate from 85% to 131% of average.
LEARNING EXERCISE � If 2010 annual demand is 1,150 laptops (instead of 1,200), what will
the January, February, and March forecasts be? [Answer: 91.7, 81.5, and 86.6, which can be rounded to
92, 82, and 87]
RELATED PROBLEMS � 4.27, 4.28
EXCEL OM Data File Ch04Ex9.xls can be found at www.pearsonhighered.com/heizer.
For simplicity, only 3 periods are used for each monthly index in the preceding example.
Example 10 illustrates how indices that have already been prepared can be applied to adjust
trend-line forecasts for seasonality.
� EXAMPLE 10
Applying both
trend and seasonal
indices
San Diego Hospital wants to improve its forecasting by applying both trend and seasonal indices to 66
months of data it has collected. It will then forecast “patient-days” over the coming year.
APPROACH � A trend line is created; then monthly seasonal indices are computed. Finally, a
multiplicative seasonal model is used to forecast months 67 to 78.
SOLUTION � Using 66 months of adult inpatient hospital days, the following equation was
computed:
where � patient days
x � time, in months
Based on this model, which reflects only trend data, the hospital forecasts patient days for the next
month (period 67) to be:
Patient days = 8,090 + (21.5)(67) = 9,530 (trend only)
yN
yN = 8,090 + 21.5x
Chapter 4 Forecasting 105

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106 PART 1 Introduction to Operations Management
Jan.
67
Feb.
68
9,000
9,600
9,800
10,000
10,200
9,400
9,200
In
p
a
ti
e
n
t
d
a
y
s
Mar.
69
Apr.
70
9,594
9,530
9,551
9,573
May
71
9,616
June
72
9,637
July
73
9,659
Aug.
74
Sept.
75
9,680
9,702
Oct.
76
9,724
Dec.
78
9,766
Nov.
77
9,745
Month
(period = 67 for Jan. through 78 for Dec.)
� FIGURE 4.6
Trend Data for San Diego
Hospital
Source: From “Modern Methods Improve
Hospital Forecasting” by W. E. Sterk and
E. G. Shryock from Healthcare Financial
Management, Vol. 41, no. 3, p. 97.
Reprinted by permission of Healthcare
Financial Management Association.
The following table provides seasonal indices based on the same 66 months. Such seasonal data, by the
way, were found to be typical of hospitals nationwide.
Seasonality Indices for Adult Inpatient Days at San Diego Hospital
Month Seasonality Index Month Seasonality Index
January 1.04 July 1.03
February 0.97 August 1.04
March 1.02 September 0.97
April 1.01 October 1.00
May 0.99 November 0.96
June 0.99 December 0.98
These seasonal indices are graphed in Figure 4.7. Note that January, March, July, and August seem to
exhibit significantly higher patient days on average, while February, September, November, and
December experience lower patient days.
However, neither the trend data nor the seasonal data alone provide a reasonable forecast for the
hospital. Only when the hospital multiplied the trend-adjusted data times the appropriate seasonal
index did it obtain good forecasts. Thus, for period 67 (January):
The patient days for each month are:
Patient days = (Trend-adjusted forecast) (Monthly seasonal index) = (9,530)(1.04) = 9,911
Period 67 68 69 70 71 72 73 74 75 76 77 78
Month Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec.
Forecast 9,911 9,265 9,764 9,691 9,520 9,542 9,949 10,068 9,411 9,724 9,355 9,572
with Trend
& Seasonality
While this model, as plotted in Figure 4.6, recognized the upward trend line in the demand for inpatient
services, it ignored the seasonality that the administration knew to be present.

INSIGHT � Notice that with trend only, the September forecast is 9,702, but with both trend and
seasonal adjustments, the forecast is 9,411. By combining trend and seasonal data, the hospital was bet-
ter able to forecast inpatient days and the related staffing and budgeting vital to effective operations.
LEARNING EXERCISE � If the slope of the trend line for patient-days is 22.0 (rather than
21.5) and the index for December is .99 (instead of .98), what is the new forecast for December inpa-
tient days? [Answer: 9,708.]
RELATED PROBLEMS � 4.26, 4.29
Example 11 further illustrates seasonality for quarterly data at a department store.
Jan.
67
Feb.
68
0.94
0.96
0.92
0.98
1.00
1.02
1.04
1.06
Month
(period = 67 for Jan. through 78 for Dec.)
In
d
e
x
f
o
r
in
p
a
ti
e
n
t
d
a
y
s
Mar.
69
Apr.
70
1.01
1.04
0.97
1.02
May
71
0.99
June
72
0.99
July
73
1.03
Aug.
74
Sept.
75
1.04
0.97
Oct.
76
1.00
Dec.
78
0.98
Nov.
77
0.96
� FIGURE 4.7
Seasonal Index for San
Diego Hospital
A graph showing the forecast that combines both trend and seasonality appears in Figure 4.8.
Jan.
67
Feb.
68
9,400
9,200
9,800
9,000
10,200
10,000
9,600
Month
(period = 67 for Jan. through 78 for Dec.)
In
p
a
ti
e
n
t
d
a
y
s
Mar.
69
Apr.
70
May
71
June
72
July
73
Aug.
74
Sept.
75
Oct.
76
Dec.
78
Nov.
77
9,691
9,911
9,265
9,764
9,520
9,542
9,949
10,068
9,411
9,724
9,572
9,355
� FIGURE 4.8
Combined Trend and
Seasonal Forecast
� EXAMPLE 11
Adjusting trend
data with seasonal
indices
Management at Davis’s Department Store has used time-series regression to forecast retail sales for the
next 4 quarters. Sales estimates are $100,000, $120,000, $140,000, and $160,000 for the respective
quarters. Seasonal indices for the 4 quarters have been found to be 1.30, .90, .70, and 1.10, respectively.
APPROACH � To compute a seasonalized or adjusted sales forecast, we just multiply each sea-
sonal index by the appropriate trend forecast:
yN seasonal = Index * yN trend forecast
Chapter 4 Forecasting 107

108 PART 1 Introduction to Operations Management
Cyclical Variations in Data
Cycles are like seasonal variations in data but occur every several years, not weeks, months, or
quarters. Forecasting cyclical variations in a time series is difficult. This is because cycles
include a wide variety of factors that cause the economy to go from recession to expansion to
recession over a period of years. These factors include national or industrywide overexpansion
in times of euphoria and contraction in times of concern. Forecasting demand for individual
products can also be driven by product life cycles—the stages products go through from intro-
duction through decline. Life cycles exist for virtually all products; striking examples include
floppy disks, video recorders, and the original Game Boy. We leave cyclical analysis to fore-
casting texts.
Developing associative techniques of variables that affect one another is our next topic.
ASSOCIATIVE FORECASTING METHODS:
REGRESSION AND CORRELATION ANALYSIS
Unlike time-series forecasting, associative forecasting models usually consider several vari-
ables that are related to the quantity being predicted. Once these related variables have been
found, a statistical model is built and used to forecast the item of interest. This approach is
more powerful than the time-series methods that use only the historical values for the fore-
casted variable.
Many factors can be considered in an associative analysis. For example, the sales of Dell PCs
may be related to Dell’s advertising budget, the company’s prices, competitors’ prices and pro-
motional strategies, and even the nation’s economy and unemployment rates. In this case, PC
sales would be called the dependent variable, and the other variables would be called
independent variables. The manager’s job is to develop the best statistical relationship between
PC sales and the independent variables. The most common quantitative associative forecasting
model is linear-regression analysis.
Using Regression Analysis for Forecasting
We can use the same mathematical model that we employed in the least-squares method of
trend projection to perform a linear-regression analysis. The dependent variables that we want
to forecast will still be . But now the independent variable, x, need no longer be time. We use
the equation:
where value of the dependent variable (in our example, sales)
a � y-axis intercept
b � slope of the regression line
x � independent variable
Example 12 shows how to use linear regression.
yN =
yN = a + bx
yN
Cycles
Patterns in the data that occur
every several years.
Linear-regression
analysis
A straight-line mathematical
model to describe the functional
relationships between
independent and dependent
variables.
LO6: Conduct a regression
and correlation analysis
SOLUTION �
INSIGHT � The straight-line trend forecast is now adjusted to reflect the seasonal changes.
LEARNING EXERCISE � If the sales forecast for Quarter IV was 180,000 (rather than
160,000), what would be the seasonally adjusted forecast? [Answer: $198,000.]
RELATED PROBLEMS � 4.26, 4.29
Quarter IV: yNIV = (1.10)($160,000) = $176,000
Quarter III: yNIII = (.70)($140,000) = $98,000
Quarter II: yNII = (.90)($120,000) = $108,000
Quarter I: yN I = (1.30)($100,000) = $130,000
AUTHOR COMMENT
We now deal with the
same mathematical model
that we saw earlier, the
least-squares method. But
we use any potential “cause-
and-effect” variable as x.

� EXAMPLE 12
Computing a
linear regression
equation
Nodel Construction Company renovates old homes in West Bloomfield, Michigan. Over time, the com-
pany has found that its dollar volume of renovation work is dependent on the West Bloomfield area
payroll. Management wants to establish a mathematical relationship to help predict sales.
APPROACH � Nodel’s VP of operations has prepared the following table, which lists company
revenues and the amount of money earned by wage earners in West Bloomfield during the past 6 years:
The VP needs to determine whether there is a straight-line (linear) relationship between area payroll
and sales. He plots the known data on a scatter diagram:
Area payroll (in $ billions)
0
1.0
2.0
3.0
4.0
1 2 3 54 6 7
N
o
d
e
l’
s
s
a
le
s
(i
n
$
m
il
li
o
n
s
)
From the six data points, there appears to be a slight positive relationship between the independent vari-
able (payroll) and the dependent variable (sales): As payroll increases, Nodel’s sales tend to be higher.
SOLUTION � We can find a mathematical equation by using the least-squares regression
approach:
The estimated regression equation, therefore, is:
or:
Sales = 1.75 + .25 (payroll)
yN = 1.75 + .25x
a = y – bx = 2.5 – (.25)(3) = 1.75
b =
©xy – nx y
©x2 – nx 2
=
51.5 – (6)(3)(2.5)
80 – (6)(32)
= .25
y =
©y
6
=
15
6
= 2.5
x =
©x
6
=
18
6
= 3
Nodel’s Sales Area Payroll Nodel’s Sales Area Payroll
(in $ millions), y (in $ billions), x (in $ millions), y (in $ billions), x
2.0 1 2.0 2
3.0 3 2.0 1
2.5 4 3.5 7
Sales, y Payroll, x x2 xy
2.0 1 1 2.0
3.0 3 9 9.0
2.5 4 16 10.0
2.0 2 4 4.0
2.0 1 1 2.0
3.5 7 49 24.5
Σy = 15.0 Σx = 18 Σx2 = 80 Σxy = 51.5
AUTHOR COMMENT
A scatter diagram is a
powerful data analysis tool.
It helps quickly size up the
relationship between two
variables.
Chapter 4 Forecasting 109

110 PART 1 Introduction to Operations Management
If the local chamber of commerce predicts that the West Bloomfield area payroll will be $6 billion next
year, we can estimate sales for Nodel with the regression equation:
or:
INSIGHT � Given our assumptions of a straight-line relationship between payroll and sales, we
now have an indication of the slope of that relationship: on average, sales increase at the rate of a mil-
lion dollars for every quarter billion dollars in the local area payroll. This is because b = .25.
LEARNING EXERCISE � What are Nodel’s sales when the local payroll is $8 billion?
[Answer: $3.75 million.]
RELATED PROBLEMS � 4.24, 4.30, 4.31, 4.32, 4.33, 4.35, 4.38, 4.40, 4.41, 4.46, 4.48, 4.49
EXCEL OM Data File Ch04Ex12.xls can be found at www.pearsonhighered.com/heizer.
Sales = $3,250,000
= 1.75 + 1.50 = 3.25
Sales (in $ millions) = 1.75 + .25(6)
The final part of Example 12 shows a central weakness of associative forecasting methods like
regression. Even when we have computed a regression equation, we must provide a forecast of
the independent variable x—in this case, payroll—before estimating the dependent variable y for
the next time period. Although this is not a problem for all forecasts, you can imagine the diffi-
culty of determining future values of some common independent variables (such as unemploy-
ment rates, gross national product, price indices, and so on).
Standard Error of the Estimate
The forecast of $3,250,000 for Nodel’s sales in Example 12 is called a point estimate of y. The
point estimate is really the mean, or expected value, of a distribution of possible values of sales.
Figure 4.9 illustrates this concept.
To measure the accuracy of the regression estimates, we must compute the standard error of
the estimate, Sy, x. This computation is called the standard deviation of the regression: It mea-
sures the error from the dependent variable, y, to the regression line, rather than to the mean.
Equation (4-14) is a similar expression to that found in most statistics books for computing the
standard deviation of an arithmetic mean:
(4-14)
where y = y-value of each data point
yc = computed value of the dependent variable, from the regression equation
n = number of data points
Sy,x = A
©(y – yc)2
n – 2
Standard error of the
estimate
A measure of variability around
the regression line—its
standard deviation.
1 2 3 4 5 6 7
3.25
4.0
3.0
2.0
1.0
Area payroll
(in $ billions)
N
o
d
e
l’
s
s
a
le
s
(i
n
$
m
il
li
o
n
s
)
Regression line,
y = 1.75 + .25x
x
y� FIGURE 4.9
Distribution about the Point
Estimate of $3.25 Million
Sales

www.pearsonhighered.com/heizer

Glidden Paints’ assembly lines fill
thousands of cans per hour. To pre-
dict demand, the firm uses associa-
tive forecasting methods such as
linear regression, with independent
variables such as disposable per-
sonal income and GNP. Although
housing starts would be a natural
variable, Glidden found that it corre-
lated poorly with past sales. It turns
out that most Glidden paint is sold
through retailers to customers who
already own homes or businesses.
Equation (4-15) may look more complex, but it is actually an easier-to-use version of Equation
(4-14). Both formulas provide the same answer and can be used in setting up prediction intervals
around the point estimate3:
(4-15)
Example 13 shows how we would calculate the standard error of the estimate in Example 12.
Sy, x = A
©y2 – a©y – b©xy
n – 2
� EXAMPLE 13
Computing the
standard error of
the estimate
Nodel’s VP of operations now wants to know the error associated with the regression line computed in
Example 12.
APPROACH � Compute the standard error of the estimate, Sy,x, using Equation (4-15).
SOLUTION � The only number we need that is not available to solve for Sy,x is Some quick
addition reveals Therefore:
The standard error of the estimate is then $306,000 in sales.
INSIGHT � The interpretation of the standard error of the estimate is similar to the standard devia-
tion; namely, ±1 standard deviation = .6827. So there is a 68.27% chance of sales being ±$306,000 from
the point estimate of $3,250,000.
LEARNING EXERCISE � What is the probability sales will exceed $3,556,000? [Answer:
About 16%.]
RELATED PROBLEMS � 4.41e, 4.48b
= 2.09375 = .306 (in $ millions)
=
A
39.5 – 1.75(15.0) – .25(51.5)
6 – 2
Sy,x = A
©y2 – a©y – b©xy
n – 2
©y2 = 39.5.
©y2.
Correlation Coefficients for Regression Lines
The regression equation is one way of expressing the nature of the relationship between two vari-
ables. Regression lines are not “cause-and-effect” relationships. They merely describe the rela-
tionships among variables. The regression equation shows how one variable relates to the value
and changes in another variable.
Another way to evaluate the relationship between two variables is to compute the coefficient
of correlation. This measure expresses the degree or strength of the linear relationship. Usually
Coefficient of
correlation
A measure of the strength of the
relationship between two
variables.
3When the sample size is large (n > 30), the prediction interval value of y can be computed using normal tables. When
the number of observations is small, the t-distribution is appropriate. See D. Groebner et al., Business Statistics, 8th ed.
(Upper Saddle River, NJ: Prentice Hall, 2011).
Chapter 4 Forecasting 111

112 PART 1 Introduction to Operations Management
identified as r, the coefficient of correlation can be any number between �1 and �1. Figure 4.10
illustrates what different values of r might look like.
To compute r, we use much of the same data needed earlier to calculate a and b for the regres-
sion line. The rather lengthy equation for r is:
(4-16)
Example 14 shows how to calculate the coefficient of correlation for the data given in Examples
12 and 13.
r =
n©xy – ©x©y
2[n©x2 – (©x)2][n©y2 – (©y)2]
EXAMPLE 14 �
Determining the
coefficient of
correlation
(a) Perfect positive
correlation:
r = +1
(b) Positive correlation:
0 < r < 1 (c) No correlation: r = 0 (d) Perfect negative correlation: r = –1 x y x y x y x y� FIGURE 4.10 Four Values of the Correlation Coefficient In Example 12, we looked at the relationship between Nodel Construction Company’s renovation sales and payroll in its hometown of West Bloomfield. The VP now wants to know the strength of the asso- ciation between area payroll and sales. APPROACH � We compute the r value using Equation (4-16). We need to first add one more col- umn of calculations—for y2. SOLUTION � The data, including the column for y2 and the calculations, are shown here: INSIGHT � This r of .901 appears to be a significant correlation and helps confirm the closeness of the relationship between the two variables. LEARNING EXERCISE � If the coefficient of correlation was –.901 rather than +.901, what would this tell you? [Answer: The negative correlation would tell you that as payroll went up, Nodel’s sales went down—a rather unlikely occurrence that would suggest you recheck your math.] RELATED PROBLEMS � 4.24d, 4.35d, 4.38c, 4.41f, 4.48b = 39 43.3 = .901 = 309 - 270 2(156)(12) = 39 21,872 r = (6)(51.5) - (18)(15.0) 2[(6)(80) - (18)2][(6)(39.5) - (15.0)2] Although the coefficient of correlation is the measure most commonly used to describe the relationship between two variables, another measure does exist. It is called the coefficient of determination and is simply the square of the coefficient of correlation—namely, r 2. The value of r 2 will always be a positive number in the range The coefficient of determination0 … r 2 … 1. Coefficient of determination A measure of the amount of variation in the dependent variable about its mean that is explained by the regression equation. y x x2 xy y2 2.0 1 1 2.0 4.0 3.0 3 9 9.0 9.0 2.5 4 16 10.0 6.25 2.0 2 4 4.0 4.0 2.0 1 1 2.0 4.0 3.5 7 49 24.5 12.25 Σy = 15.0 Σx = 18 Σx2 = 80 Σ xy = 51.5 Σy2 = 39.5 is the percent of variation in the dependent variable (y) that is explained by the regression equa- tion. In Nodel’s case, the value of r 2 is .81, indicating that 81% of the total variation is explained by the regression equation. Multiple-Regression Analysis Multiple regression is a practical extension of the simple regression model we just explored. It allows us to build a model with several independent variables instead of just one variable. For example, if Nodel Construction wanted to include average annual interest rates in its model for forecasting renovation sales, the proper equation would be: (4-17) where � dependent variable, salesyN yN = a + b1x1 + b2x2 Multiple regression An associative forecasting method with more than one independent variable. � EXAMPLE 15 Using a multiple- regression equation Nodel Construction wants to see the impact of a second independent variable, interest rates, on its sales. APPROACH � The new multiple-regression line for Nodel Construction, calculated by computer software, is: We also find that the new coefficient of correlation is .96, implying the inclusion of the variable x2, interest rates, adds even more strength to the linear relationship. SOLUTION � We can now estimate Nodel’s sales if we substitute values for next year’s payroll and interest rate. If West Bloomfield’s payroll will be $6 billion and the interest rate will be .12 (12%), sales will be forecast as: or: INSIGHT � By using both variables, payroll and interest rates, Nodel now has a sales forecast of $3 million and a higher coefficient of correlation. This suggests a stronger relationship between the two variables and a more accurate estimate of sales. LEARNING EXERCISE � If interest rates were only 6%, what would be the sales forecast? [Answer: or $3,300,000.] RELATED PROBLEMS � 4.34, 4.36 1.8 + 1.8 - 5.0(.06) = 3.3, Sales = $3,000,000 = 3.00 = 1.8 + 1.8 - .6 Sales($ millions) = 1.80 + .30(6) - 5.0(.12) yN = 1.80 + .30x1 - 5.0x2 MONITORING AND CONTROLLING FORECASTS Once a forecast has been completed, it should not be forgotten. No manager wants to be reminded that his or her forecast is horribly inaccurate, but a firm needs to determine why actual demand (or whatever variable is being examined) differed significantly from that projected. If the forecaster is accurate, that individual usually makes sure that everyone is aware of his or her talents. Very seldom does one read articles in Fortune, Forbes, or The Wall Street Journal, how- ever, about money managers who are consistently off by 25% in their stock market forecasts. AUTHOR COMMENT Using a tracking signal is a good way to make sure the forecasting system is continuing to do a good job. a � a constant, the y intercept x1 and x2 � values of the two independent variables, area payroll and interest rates, respectively b1 and b2 = coefficients for the two independent variables The mathematics of multiple regression becomes quite complex (and is usually tackled by com- puter), so we leave the formulas for a, b1, and b2 to statistics textbooks. However, Example 15 shows how to interpret Equation (4-17) in forecasting Nodel’s sales. Chapter 4 Forecasting 113 114 PART 1 Introduction to Operations Management Tracking signal A measurement of how well a forecast is predicting actual values. One way to monitor forecasts to ensure that they are performing well is to use a tracking sig- nal. A tracking signal is a measurement of how well a forecast is predicting actual values. As forecasts are updated every week, month, or quarter, the newly available demand data are com- pared to the forecast values. The tracking signal is computed as the cumulative error divided by the mean absolute devia- tion (MAD): (4-18) where as seen earlier, in Equation (4-5). Positive tracking signals indicate that demand is greater than forecast. Negative signals mean that demand is less than forecast. A good tracking signal—that is, one with a low cumu- lative error—has about as much positive error as it has negative error. In other words, small deviations are okay, but positive and negative errors should balance one another so that the tracking signal centers closely around zero. A consistent tendency for forecasts to be greater or less than the actual values (that is, for a high absolute cumulative error) is called a bias error. Bias can occur if, for example, the wrong variables or trend line are used or if a sea- sonal index is misapplied. Once tracking signals are calculated, they are compared with predetermined control limits. When a tracking signal exceeds an upper or lower limit, there is a problem with the forecast- ing method, and management may want to reevaluate the way it forecasts demand. Figure 4.11 shows the graph of a tracking signal that is exceeding the range of acceptable variation. If the model being used is exponential smoothing, perhaps the smoothing constant needs to be readjusted. How do firms decide what the upper and lower tracking limits should be? There is no single answer, but they try to find reasonable values—in other words, limits not so low as to be triggered with every small forecast error and not so high as to allow bad forecasts to be regularly overlooked. One MAD is equivalent to approximately .8 standard deviation, ±2 MADs = ±1.6 standard deviations, ±3 MADs = ±2.4 standard deviations, and ±4 MADs = ±3.2 standard deviations. This fact suggests that for a forecast to be “in control,” 89% of the errors are expected to fall within ±2 MADs, 98% within ±3 MADs, or 99.9% within ±4 MADs.4 Example 16 shows how the tracking signal and cumulative error can be computed. (MAD) = © ƒ Actual - Forecast ƒ n = ©(Actual demand in period i - Forecast demand in period i) MAD (Tracking signal) = Cumulative error MAD + – 0 MADs Upper control limit Lower control limit Time Signal exceeded limit Tracking signal Acceptable range * � FIGURE 4.11 A Plot of Tracking Signals 4To prove these three percentages to yourself, just set up a normal curve for ±1.6 standard deviations (z-values). Using the normal table in Appendix I, you find that the area under the curve is .89. This represents ±2 MADs. Likewise, ±3 MADs = ±2.4 standard deviations encompass 98% of the area, and so on for ±4 MADs. Bias A forecast that is consistently higher or consistently lower than actual values of a time series. LO7: Use a tracking signal � EXAMPLE 16 Computing the tracking signal at Carlson’s Bakery Carlson’s Bakery wants to evaluate performance of its croissant forecast. APPROACH � Develop a tracking signal for the forecast and see if it stays within acceptable lim- its, which we define as ±4 MADs. SOLUTION � Using the forecast and demand data for the past 6 quarters for croissant sales, we develop a tracking signal in the table below: INSIGHT � Because the tracking signal drifted from –2 MAD to +2.5 MAD (between 1.6 and 2.0 standard deviations), we can conclude that it is within acceptable limits. LEARNING EXERCISE � If actual demand in quarter 6 was 130 (rather than 140), what would be the MAD and resulting tracking signal? [Answer: MAD for quarter 6 would be 12.5, and the track- ing signal for period 6 would be 2 MADs.] RELATED PROBLEMS � 4.37, 4.45 and Tracking signal = Cumulative error MAD = 35 14.2 = 2.5 MADs At the end of quarter 6, MAD = ©|Forecast errors| n = 85 6 = 14.2 Cumulative Tracking Absolute Absolute Signal Actual Forecast Cumulative Forecast Forecast (cumulative Quarter Demand Demand Error Error Error Error MAD error/MAD) 1 90 100 –10 –10 10 10 10.0 –10/10 = –1 2 95 100 –5 –15 5 15 7.5 –15/7.5 = –2 3 115 100 +15 0 15 30 10.0 0/10 = 0 4 100 110 –10 –10 10 40 10.0 –10/10 = –1 5 125 110 +15 +5 15 55 11.0 +5/11 = +0.5 6 140 110 +30 +35 30 85 14.2 +35/14.2 = +2.5 Adaptive Smoothing Adaptive forecasting refers to computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit. For example, when applied to exponential smoothing, the α and β coefficients are first selected on the basis of values that minimize error forecasts and then adjusted accordingly whenever the computer notes an errant tracking signal. This process is called adaptive smoothing. Focus Forecasting Rather than adapt by choosing a smoothing constant, computers allow us to try a variety of fore- casting models. Such an approach is called focus forecasting. Focus forecasting is based on two principles: 1. Sophisticated forecasting models are not always better than simple ones. 2. There is no single technique that should be used for all products or services. Bernard Smith, inventory manager for American Hardware Supply, coined the term focus fore- casting. Smith’s job was to forecast quantities for 100,000 hardware products purchased by American’s 21 buyers.5 He found that buyers neither trusted nor understood the exponential smoothing model then in use. Instead, they used very simple approaches of their own. So Smith developed his new computerized system for selecting forecasting methods. Smith chose to test seven forecasting methods. They ranged from the simple ones that buyers used (such as the naive approach) to statistical models. Every month, Smith applied the forecasts of all seven models to each item in stock. In these simulated trials, the forecast values were sub- tracted from the most recent actual demands, giving a simulated forecast error. The forecast Adaptive smoothing An approach to exponential smoothing forecasting in which the smoothing constant is automatically changed to keep errors to a minimum. Focus forecasting Forecasting that tries a variety of computer models and selects the best one for a particular application. 5Bernard T. Smith, Focus Forecasting: Computer Techniques for Inventory Control (Boston: CBI Publishing, 1978). Chapter 4 Forecasting 115 116 PART 1 Introduction to Operations Management method yielding the least error is selected by the computer, which then uses it to make next month’s forecast. Although buyers still have an override capability, American Hardware finds that focus forecasting provides excellent results. FORECASTING IN THE SERVICE SECTOR Forecasting in the service sector presents some unusual challenges. A major technique in the retail sector is tracking demand by maintaining good short-term records. For instance, a barber- shop catering to men expects peak flows on Fridays and Saturdays. Indeed, most barbershops are closed on Sunday and Monday, and many call in extra help on Friday and Saturday. A downtown restaurant, on the other hand, may need to track conventions and holidays for effective short-term forecasting. The OM in Action box “Forecasting at FedEx’s Customer Service Centers” provides an example of a major service-sector industry, the call center. Specialty Retail Shops Specialty retail facilities, such as flower shops, may have other unusual demand patterns, and those patterns will differ depending on the holiday. When Valentine’s Day falls on a weekend, for example, flowers can’t be delivered to offices, and those romantically inclined are likely to celebrate with outings rather than flowers. If a holiday falls on a Monday, some of the celebration may also take place on the weekend, reducing flower sales. However, when Valentine’s Day falls in midweek, busy midweek schedules often make flowers the optimal way to celebrate. Because flowers for Mother’s Day are to be deliv- ered on Saturday or Sunday, this holiday forecast varies less. Due to special demand patterns, many service firms maintain records of sales, noting not only the day of the week but also unusual events, including the weather, so that patterns and correlations that influence demand can be developed. Fast-Food Restaurants Fast-food restaurants are well aware not only of weekly, daily, and hourly but even 15-minute variations in demands that influence sales. Therefore, detailed fore- casts of demand are needed. Figure 4.12(a) shows the hourly forecast for a typical fast-food restaurant. Note the lunchtime and dinnertime peaks. This contrasts to the mid-morning and mid- afternoon peaks at FedEx’s call center in Figure 14.12(b). Firms like Taco Bell now use point-of-sale computers that track sales every quarter hour. Taco Bell found that a 6-week moving average was the forecasting technique that minimized its mean squared error (MSE) of these quarter-hour forecasts. Building this forecasting methodology into each of Taco Bell’s 6,500 stores’ computers, the model makes weekly projections of customer 11–12 5% Hour of day P e rc e n t o f s a le s b y h o u r o f d a y 12–1 (Lunchtime) 1–2 2–3 3–4 4–5 5–6 6–7 7–8 8–9 9–10 10% 15% 20% 10–11 (Dinnertime) Hourly sales at a fast-food restaurant (a) 1 0% Hour of day 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 1210 11 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% Monday calls at a FedEx call center* (b) A.M. P.M. � FIGURE 4.12 Forecasts Are Unique: Note the Variations between (a) Hourly Sales at a Fast-Food Restaurant and (b) Hourly Call Volume at FedEx *Based on historical data: see Journal of Business Forecasting (Winter 1999–2000): 6–11. AUTHOR COMMENT Forecasting at McDonald’s, FedEx, and Walmart is as important and complex as it is for manufacturers such as Toyota and Dell. The world’s largest express shipping company, FedEx, generates $38 billion in revenues, using 675 planes, 44,000 trucks, and a workforce of 145,000 in 220 countries. To support this global network, the company has 51 customer service call centers, whose service goal is to answer 90% of all calls within 20 seconds. With a half- million daily calls just in the U.S., FedEx makes extensive use of forecasting models for staffing decisions and to ensure that customer satisfaction levels stay the highest in the industry. FedEx’s Forecasting & Modeling department makes several different forecasts. One-year and five-year models predict number of calls, average handle time, and staffing needs. They break forecasts into weekday, Saturday, and Sunday and then use the Delphi method and time-series analysis. FedEx’s tactical forecasts are monthly and use 8 years of historical daily data. This time-series model addresses month, day of week, and day of month to predict caller volume. Finally, the operational forecast uses a weighted moving average and 6 weeks of data to project the number of calls on a half-hourly basis. FedEx’s forecasts are consistently accurate to within 1% to 2% of actual call volumes. This means coverage needs are met, service levels are maintained, and costs are controlled. Sources: Hoover’s Company Records (July 1, 2009): 10552; Baseline (January 2005): 54; and Journal of Business Forecasting (Winter 1999–2000): 7–11. OM in Action � Forecasting at FedEx’s Customer Service Centers Forecasts are a critical part of the operations manager’s func- tion. Demand forecasts drive a firm’s production, capacity, and scheduling systems and affect the financial, marketing, and personnel planning functions. There are a variety of qualitative and quantitative fore- casting techniques. Qualitative approaches employ judg- ment, experience, intuition, and a host of other factors that are difficult to quantify. Quantitative forecasting uses histor- ical data and causal, or associative, relations to project future demands. The Rapid Review for this chapter (found in the Lecture Guide & Activities Manual) summarizes the formulas we introduced in quantitative forecasting. Forecast calculations are seldom performed by hand. Most oper- ations managers turn to software pack- ages such as Forecast PRO, SAP, AFS, SAS, SPSS, or Excel. No forecasting method is perfect under all conditions. And even once management has found a satisfactory approach, it must still monitor and control forecasts to make sure errors do not get out of hand. Forecasting can often be a very challeng- ing, but rewarding, part of managing. CHAPTER SUMMARY 6J. Hueter and W. Swart, “An Integrated Labor Management System for Taco Bell,” Interfaces 28, no. 1 (January–February 1998): 75–91. transactions. These in turn are used by store managers to schedule staff, who begin in 15-minute increments, not 1-hour blocks as in other industries. The forecasting model has been so successful that Taco Bell has increased customer service while documenting more than $50 million in labor cost savings in 4 years of use.6 Key Terms Forecasting (p. 86) Economic forecasts (p. 87) Technological forecasts (p. 87) Demand forecasts (p. 87) Quantitative forecasts (p. 89) Qualitative forecasts (p. 89) Jury of executive opinion (p. 89) Delphi method (p. 89) Sales force composite (p. 89) Consumer market survey (p. 89) Time series (p. 90) Naive approach (p. 90) Moving averages (p. 91) Exponential smoothing (p. 94) Smoothing constant (p. 94) Mean absolute deviation (MAD) (p. 95) Mean squared error (MSE) (p. 97) Mean absolute percent error (MAPE) (p. 97) Trend projection (p. 101) Seasonal variations (p. 103) Cycles (p. 108) Linear-regression analysis (p. 108) Standard error of the estimate (p. 110) Coefficient of correlation (p. 111) Coefficient of determination (p. 112) Multiple regression (p. 113) Tracking signal (p. 114) Bias (p. 114) Adaptive smoothing (p. 115) Focus forecasting (p. 115) Chapter 4 Forecasting 117 � PROGRAM 4.1 Using Excel to Develop Your Own Forecast, with Data from Example 8 As an alternative, you may want to experiment with Excel’s built-in regression analysis. To do so, under the Data menu bar selection choose Data Analysis, then Regression. Enter your Y and X data into two columns (say B and C). When the regression window appears, enter the Y and X ranges, then select OK. Excel offers several plots and tables to those interested in more rigorous analysis of regression problems. X Using Excel OM Excel OM’s forecasting module has five components: (1) moving averages, (2) weighted moving aver- ages, (3) exponential smoothing, (4) regression (with one variable only), and (5) decomposition. Excel OM’s error analysis is much more complete than that available with the Excel add-in. Program 4.2 illustrates Excel OM’s input and output, using Example 2’s weighted-moving-average data. P Using POM for Windows POM for Windows can project moving averages (both simple and weighted), handle exponential smoothing (both simple and trend adjusted), forecast with least squares trend projection, and solve linear-regression (associative) models. A summary screen of error analysis and a graph of the data can also be generated. As a special example of exponential smoothing adaptive forecasting, when using an α of 0, POM for Windows will find the α value that yields the minimum MAD. Appendix IV provides further details. Computations Value Cell Excel Formula Action Trend line column D4 =$B$16+$B$17*C4 Copy to D5:D14 (or =TREND($B$4:$B$10,$C$4:$C$10,C4)) Intercept B16 =INTERCEPT(B4:B10, C4:C10) Slope (trend) B17 =SLOPE(B4:B10, C4:C10) Standard error B19 =STEYX(B4:B10, C4:C10) Correlation B20 =CORREL(B4:B10, C4:C10) 118 PART 1 Introduction to Operations Management Using Software in Forecasting This section presents three ways to solve forecasting problems with computer software. First, you can create your own Excel spreadsheets to develop forecasts. Second, you can use the Excel OM software that comes with the text and is found on our text web site. Third, POM for Windows is another program that is located on our web site at www.pearsonhighered.com/heizer. Creating Your Own Excel Spreadsheets Excel spreadsheets (and spreadsheets in general) are frequently used in forecasting. Exponential smooth- ing, trend analysis, and regression analysis (simple and multiple) are supported by built-in Excel functions. Program 4.1 illustrates how to build an Excel forecast for the data in Example 8. The goal for N.Y. Edison is to create a trend analysis of the 2003–2009 data. Note that in cell D4 you can enter either = $B$16 + $B$17 * C4 or = TREND ($B$4: $B$10, $C$4: $C$10, C4). www.pearsonhighered.com/heizer Chapter 4 Forecasting 119 � PROGRAM 4.2 Analysis of Excel OM’s Weighted-Moving-Average Program, Using Data from Example 2 as Input Enter the weights to be placed on each of the last three periods at the top of column C: Weights must be entered from oldest to most recent. Forecast is the weighted sum of past sales (SUMPRODUCT) divided by the sum of the weights (SUM) because weights do not sum to 1. Error (B11 – E11) is the difference between the demand and the forecast. = AVERAGE(H11: H19) The standard error is given by the square root of the total error divided by n – 2 , where n is the number of periods for which forecasts exist, i.e., 9. = SUMPRODUCT(B17:B19, $C$8:$C$10)/SUM($C$8:$C$10) Solved Problems Virtual Office Hours help is available at www.myomlab.com � SOLVED PROBLEM 4.1 Sales of Volkswagen’s popular Beetle have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). The sales manager had predicted in 2004 that 2005 sales would be 410 VWs. Using exponential smoothing with a weight of α = .30, develop forecasts for 2006 through 2010. � SOLUTION � SOLUTION = 20.14 FIT4 = 17.82 + 2.32 = 2.32 = 1.056 + 1.26 = (.4)(2.64) + (.6)(2.10) = (.4)(17.82 - 15.18) + (1 - .4)(2.10) T4 = b(F4 - F3) + (1 - b)T3 = 17.82 = 4.0 + 13.82 = 4.0 + (.8)(17.28) = (.2)(20) + (1 - .2)(15.18 + 2.10) F4 = aA3 + (1 - a)(F3 + T3) Year Sales Forecast 2005 450 410 2006 495 2007 518 2008 563 2009 584 2010 ? Year Forecast 2005 410.0 2006 2007 2008 2009 2010 521.8 = 495.2 + .3 (584 - 495.2) 495.2 = 466.1 + .3 (563 - 466.1) 466.1 = 443.9 + .3 (518 - 443.9) 443.9 = 422 + .3 (495 - 422) 422.0 = 410 + .3 (450 - 410) � SOLVED PROBLEM 4.2 In Example 7, we applied trend-adjusted exponential smoothing to forecast demand for a piece of pollution-control equipment for months 2 and 3 (out of 9 months of data provided). Let us now continue this process for month 4. We want to confirm the forecast for month 4 shown in Table 4.1 (p. 100) and Figure 4.3 (p. 100). For month 4, A4 = 19, with α = .2, and β = .4. www.myomlab.com � SOLVED PROBLEM 4.3 Room registrations in the Toronto Towers Plaza Hotel have been recorded for the past 9 years. To project future occupancy, man- agement would like to determine the mathematical trend of guest registration. This estimate will help the hotel determine whether future expansion will be needed. Given the following time-series data, develop a regression equation relating registrations to time (e.g., a trend equation). Then forecast 2011 registrations. Room registrations are in the thousands: 2001: 17 2002: 16 2003: 16 2004: 21 2005: 20 2006: 20 2007: 23 2008: 25 2009: 24 � SOLUTION Transformed Registrants, y Year Year, x (in thousands) x2 xy 2001 1 17 1 17 2002 2 16 4 32 2003 3 16 9 48 2004 4 21 16 84 2005 5 20 25 100 2006 6 20 36 120 2007 7 23 49 161 2008 8 25 64 200 2009 9 24 81 216 Σx = 45 Σy = 182 Σx2 = 285 Σxy = 978 The projection of registrations in the year 2011 (which is x = 11 in the coding system used) is: or 27,030 guests in 2011 yN = 14.545 + (1.135)(11) = 27.03 yN (registrations) = 14.545 + 1.135x a = y- bx = 20.22 - (1.135)(5) = 20.22 - 5.675 = 14.545 b = ©xy - nxy ©x2 - nx2 = 978 - (9)(5)(20.22) 285 - (9)(25) = 978 - 909.9 285 - 225 = 68.1 60 = 1.135 � SOLVED PROBLEM 4.4 Quarterly demand for Ford F150 pickups at a New York auto dealer is forecast with the equation: where x = quarters, and: Quarter I of 2008 = 0 Quarter II of 2008 = 1 Quarter III of 2008 = 2 Quarter IV of 2008 = 3 Quarter I of 2009 = 4 and so on and: The demand for trucks is seasonal, and the indices for Quarters I, II, III, and IV are 0.80, 1.00, 1.30, and 0.90, respectively. Forecast demand for each quarter of 2010. Then, seasonalize each forecast to adjust for quarterly variations. yN = quarterly demand yN = 10 + 3x � SOLUTION Quarter II of 2009 is coded x = 5; Quarter III of 2009, x = 6; and Quarter IV of 2009, x = 7. Hence, Quarter I of 2010 is coded x = 8; Quarter II, x = 9; and so on. Adjusted forecast = (.90)(43) = 38.7 Adjusted forecast = (1.30)(40) = 52 Adjusted forecast = (1.00)(37) = 37 Adjusted forecast = (.80)(34) = 27.2 yN (2010 Quarter IV) = 10 + 3(11) = 43 yN (2010 Quarter III) = 10 + 3(10) = 40 yN (2010 Quarter II) = 10 + 3(9) = 37 yN (2010 Quarter I) = 10 + 3(8) = 34 120 PART 1 Introduction to Operations Management Chapter 4 Forecasting 121 �Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study: North-South Airlines: Reflects the merger of two airlines and addresses their maintenance costs. Bibliography Balakrishnan, R., B. Render, and R. M. Stair. Managerial Decision Modeling with Spreadsheets, 2nd ed. Upper Saddle River, NJ: Prentice Hall, 2007. Berenson, Mark, Tim Krehbiel, and David Levine. Basic Business Statistics, 11th ed. Upper Saddle River, NJ: Prentice Hall, 2009. Campbell, Omar. “Forecasting in Direct Selling Business: Tupperware’s Experience.” The Journal of Business Forecasting 27, no. 2 (Summer 2008): 18–19. Diebold, F. X. Elements of Forecasting, 5th ed. Cincinnati: South-Western College Publishing, 2010. Fildes, Robert, and Paul Goodwin. “Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting.” Decision Sciences 37, no. 6 (November–December 2007): 570–576. Georgoff, D. M., and R. G. Murdick. “Manager’s Guide to Forecasting.” Harvard Business Review 64 (January–February 1986): 110–120. Gilliland, M., and M. Leonard. “Forecasting Software—The Past and the Future.” The Journal of Business Forecasting 25, no. 1 (Spring 2006): 33–36. Hanke, J. E. and D. W. Wichern. Business Forecasting, 9th ed. Upper Saddle River, NJ: Prentice Hall, 2009. Heizer, Jay. “Forecasting with Stagger Charts.” IIE Solutions 34 (June 2002): 46–49. Jain, Chaman L. “Benchmarking Forecasting Software and Systems.” The Journal of Business Forecasting 26, no. 4 (Winter 2007/2008): 30–34. Onkal, D., M. S. Gonul, and M. Lawrence. “Judgmental Adjustments of Previously Adjusted Forecasts.” Decision Sciences 39, no. 2 (May 2008): 213–238. Render, B., R. M. Stair, and M. Hanna. Quantitative Analysis for Management, 10th ed. Upper Saddle River, NJ: Prentice Hall, 2009. Shah, Piyush. “Techniques to Support Better Forecasting.” APICS Magazine (November/December 2008): 49–50. Tabatabai, Bijan. “Improving Forecasting.” Financial Management (October 2008): 48–49. Urs, Rajiv. “How to Use a Demand Planning System for Best Forecasting and Planning Results.” The Journal of Business Forecasting 27, no. 2 (Summer 2008): 22–25. Wilson, J. H., B. Keating, and J. Galt. Business Forecasting, 6th ed. New York: McGraw-Hill, 2009. Yurklewicz, Jack. “Forecasting at Steady State.” Analytics (Summer 2008): 42–45. www.myomlab.com www.pearsonhighered.com/heizer This page intentionally left blank Design of Goods and Services Chapter Outline GLOBAL COMPANY PROFILE: REGAL MARINE Goods and Services Selection 126 Generating New Products 129 Product Development 130 Issues for Product Design 135 Ethics, Environmentally-Friendly Designs, and Sustainability 138 Time-Based Competition 140 Defining a Product 142 Documents for Production 144 Service Design 146 Application of Decision Trees to Product Design 149 Transition to Production 150 � Design of Goods and Services � Managing Quality � Process Strategy � Location Strategies � Layout Strategies � Human Resources � Supply-Chain Management � Inventory Management � Scheduling � Maintenance PART TWO Designing Operations (Chapters 5–10) 123 T hirty years after its founding by potato farmer Paul Kuck, Regal Marine has become a powerful force on the waters of the world. The world’s third-largest boat manufacturer (by global sales), Regal exports to 30 countries, including Russia and China. Almost one-third of its sales are overseas. Product design is critical in the highly competitive pleasure boat business: “We keep in touch with our customers and we respond to the marketplace,” says Kuck. “We’re introducing six new models this year alone. I’d say we’re definitely on the aggressive end of the spectrum.” With changing consumer tastes, compounded by material changes and ever-improving marine engineering, the design function is under constant pressure. Added to these pressures is the constant issue of cost competitiveness combined with the need to provide good value for customers. Consequently, Regal Marine is a frequent user of computer-aided design (CAD). New designs come to life via Regal’s three-dimensional CAD system, borrowed from automotive technology. Regal’s naval architects’ goal is to continue to reduce the time from concept to prototype to production. The sophisticated CAD system not only has reduced GLOBAL COMPANY PROFILE: REGAL MARINE PRODUCT STRATEGY PROVIDES COMPETITIVE ADVANTAGE AT REGAL MARINE CAD/CAM is used to design the hull of a new product. This process results in faster and more efficient design and production. Once a hull has been pulled from the mold, it travels down a monorail assembly path. JIT inventory delivers engines, wiring, seats, flooring, and interiors when needed. product development time but also has reduced problems with tooling and production, resulting in a superior product. All of Regal’s products, from its $14,000 19-foot boat to the $500,000 44-foot Commodore yacht, follow a similar production process. Hulls and decks are separately hand-produced by spraying preformed molds with three to five layers of a fiberglass laminate. The hulls and decks harden and are 124 removed to become the lower and upper structure of the boat. As they move to the assembly line, they are joined and components added at each workstation. Wooden components, precut in-house by computer-driven routers, are delivered on a just-in- time basis for installation at one station. Engines— one of the few purchased components—are installed at another. Racks of electrical wiring harnesses, engineered and rigged in-house, are then installed. An in-house upholstery department delivers customized seats, beds, dashboards, or other cushioned components. Finally, chrome fixtures are put in place, and the boat is sent to Regal’s test tank for watertight, gauge, and system inspection. Larger boats, such as this luxurious Commodore 4260 Express, are water tested on a lake or ocean. Regal is one of the few boat builders in the world to earn the ISO 9001:2000 quality certification. At the final stage, smaller boats, such as this one, are placed in this test tank, where a rain machine ensures watertight fits. 125 REGAL MARINE � Here the deck, suspended from ceiling cranes, is being finished prior to being moved to join the hull. 126 PART 2 Designing Operations GOODS AND SERVICES SELECTION Global firms like Regal Marine know that the basis for an organization’s existence is the good or service it provides society. Great products are the keys to success. Anything less than an excel- lent product strategy can be devastating to a firm. To maximize the potential for success, top companies focus on only a few products and then concentrate on those products. For instance, Honda’s focus is engines. Virtually all of Honda’s sales (autos, motorcycles, generators, lawn mowers) are based on its outstanding engine technology. Likewise, Intel’s focus is on micro- processors, and Michelin’s is on tires. However, because most products have a limited and even predictable life cycle, companies must constantly be looking for new products to design, develop, and take to market. Good operations managers insist on strong communication among customer, product, processes, and suppliers that results in a high success rate for their new prod- ucts. 3M’s goal is to produce 30% of its profit from products introduced in the last 4 years. Benchmarks, of course, vary by industry; Regal introduces six new boats a year, and Rubbermaid introduces a new product each day! One product strategy is to build particular competence in customizing an established family of goods or services. This approach allows the customer to choose product variations while rein- forcing the organization’s strength. Dell Computer, for example, has built a huge market by de- livering computers with the exact hardware and software desired by end users. And Dell does it fast—it understands that speed to market is imperative to gain a competitive edge. Note that many service firms also refer to their offerings as products. For instance, when Allstate Insurance offers a new homeowner’s policy, it is referred to as a new “product.” Simi- larly, when Citicorp opens a mortgage department, it offers a number of new mortgage “prod- ucts.” Although the term products may often refer to tangible goods, it also refers to offerings by service organizations. An effective product strategy links product decisions with investment, market share, and product life cycle, and defines the breadth of the product line. The objective of the product deci- sion is to develop and implement a product strategy that meets the demands of the marketplace with a competitive advantage. As one of the 10 decisions of OM, product strategy may focus on developing a competitive advantage via differentiation, low cost, rapid response, or a combina- tion of these. Product Strategy Options Support Competitive Advantage A world of options exists in the selection, definition, and design of products. Product selection is choosing the good or service to provide customers or clients. For instance, hospitals specialize in various types of patients and medical procedures. A hospital’s management may decide to oper- ate a general-purpose hospital or a maternity hospital or, as in the case of the Canadian hospital Shouldice, to specialize in hernias. Hospitals select their products when they decide what kind of hospital to be. Numerous other options exist for hospitals, just as they exist for Taco Bell and Toyota. Service organizations like Shouldice Hospital differentiate themselves through their prod- uct. Shouldice differentiates itself by offering a distinctly unique and high-quality product. Its Product decision The selection, definition, and design of products. VIDEO 5.1 Product Strategy at Regal Marine Chapter 5 Learning Objectives LO1: Define product life cycle 127 LO2: Describe a product development system 130 LO3: Build a house of quality 132 LO4: Describe how time-based competition is implemented by OM 140 LO5: Describe how products and services are defined by OM 142 LO6: Describe the documents needed for production 145 LO7: Describe customer participation in the design and production of services 146 LO8: Apply decision trees to product issues 149 AUTHOR COMMENT Product strategy is critical to achieving competitive advantage. Chapter 5 Design of Goods and Services 127 LO1: Define product life cycle (b) Technology: Michelin’s latest technology: radical new tires that don’t go flat. (c) Packaging: Sherwin Williams’s Dutch Boy has revolutionized the paint industry with its square Twist & Pour paint container. Product Design Can Manifest Itself in Concepts, Technology, and Packaging. Whether it is a design focused on style at Nike (a), the application of technology at Michelin (b), or a new container at Sherwin-Williams (c), operations managers need to remind themselves that the creative process is ongoing with major implications for production. (a) Concepts: Nike, in its creative way, has moved athletic shoes from utilitarian necessities into glamorous accessories and in the process is constantly reinventing all parts of the shoe, including the heel. world-renowned specialization in hernia-repair service is so effective it allows patients to return to normal living in 8 days as opposed to the average 2 weeks—and with very few complications. The entire production system is designed for this one product. Local anesthetics are used; patients enter and leave the operating room on their own; rooms are spartan, and meals are served in a common dining room, encouraging patients to get out of bed for meals and join fellow patients in the lounge. As Shouldice has demonstrated, product selection affects the entire production system. Taco Bell has developed and executed a low-cost strategy through product design. By design- ing a product (its menu) that can be produced with a minimum of labor in small kitchens, Taco Bell has developed a product line that is both low cost and high value. Successful product design has allowed Taco Bell to increase the food content of its products from 27¢ to 45¢ of each sales dollar. Toyota’s strategy is rapid response to changing consumer demand. By executing the fastest automobile design in the industry, Toyota has driven the speed of product development down to well under 2 years in an industry whose standard is still over 2 years. The shorter design time allows Toyota to get a car to market before consumer tastes change and to do so with the latest technology and innovations. Product decisions are fundamental to an organization’s strategy and have major implications throughout the operations function. For instance, GM’s steering columns are a good example of the strong role product design plays in both quality and efficiency. The redesigned steering col- umn has a simpler design, with about 30% fewer parts than its predecessor. The result: Assembly time is one-third that of the older column, and the new column’s quality is about seven times higher. As an added bonus, machinery on the new line costs a third less than that on the old line. Product Life Cycles Products are born. They live and they die. They are cast aside by a changing society. It may be helpful to think of a product’s life as divided into four phases. Those phases are introduction, growth, maturity, and decline. Product life cycles may be a matter of a few hours (a newspaper), months (seasonal fashions and personal computers), years (video cassette tapes), or decades (Volkswagen Beetle). Regard- less of the length of the cycle, the task for the operations manager is the same: to design a sys- tem that helps introduce new products successfully. If the operations function cannot perform 128 PART 2 Designing Operations Product-by-value analysis A list of products, in descending order of their individual dollar contribution to the firm, as well as the total annual dollar contribution of the product. 1Contribution is defined as the difference between direct cost and selling price. Direct costs are labor and material that go into the product. effectively at this stage, the firm may be saddled with losers—products that cannot be produced efficiently and perhaps not at all. Figure 5.1 shows the four life cycle stages and the relationship of product sales, cash flow, and profit over the life cycle of a product. Note that typically a firm has a negative cash flow while it develops a product. When the product is successful, those losses may be recovered. Eventually, the successful product may yield a profit prior to its decline. However, the profit is fleeting— hence, the constant demand for new products. Life Cycle and Strategy Just as operations managers must be prepared to develop new products, they must also be pre- pared to develop strategies for new and existing products. Periodic examination of products is appropriate because strategies change as products move through their life cycle. Successful product strategies require determining the best strategy for each product based on its position in its life cycle. A firm, therefore, identifies products or families of products and their position in the life cycle. Let us review some strategy options as products move through their life cycles. Introductory Phase Because products in the introductory phase are still being “fine-tuned” for the market, as are their production techniques, they may warrant unusual expenditures for (1) research, (2) product development, (3) process modification and enhancement, and (4) supplier development. For example, when cellular phones were first introduced, the features desired by the public were still being determined. At the same time, operations managers were still groping for the best manufacturing techniques. Growth Phase In the growth phase, product design has begun to stabilize, and effective fore- casting of capacity requirements is necessary. Adding capacity or enhancing existing capacity to accommodate the increase in product demand may be necessary. Maturity Phase By the time a product is mature, competitors are established. So high-volume, innovative production may be appropriate. Improved cost control, reduction in options, and a par- ing down of the product line may be effective or necessary for profitability and market share. Decline Phase Management may need to be ruthless with those products whose life cycle is at an end. Dying products are typically poor products in which to invest resources and managerial tal- ent. Unless dying products make some unique contribution to the firm’s reputation or its product line or can be sold with an unusually high contribution, their production should be terminated.1 Product-by-Value Analysis The effective operations manager selects items that show the greatest promise. This is the Pareto principle (i.e., focus on the critical few, not the trivial many) applied to product mix: Resources are to be invested in the critical few and not the trivial many. Product-by-value analysis lists products in descending order of their individual dollar contribution to the firm. It also lists the total annual dollar contribution of the product. Low contribution on a per-unit basis by a particu- lar product may look substantially different if it represents a large portion of the company’s sales. A product-by-value report allows management to evaluate possible strategies for each prod- uct. These may include increasing cash flow (e.g., increasing contribution by raising selling price Introduction Growth Maturity Decline Cost of development and production S a le s, c o st , a n d c a sh f lo w Sales revenue Loss Net revenue (profit) Cash flow Negative cash flow � FIGURE 5.1 Product Life Cycle, Sales, Cost, and Profit or lowering cost), increasing market penetration (improving quality and/or reducing cost or price), or reducing costs (improving the production process). The report may also tell manage- ment which product offerings should be eliminated and which fail to justify further investment in research and development or capital equipment. Product-by-value analysis focuses manage- ment’s attention on the strategic direction for each product. GENERATING NEW PRODUCTS Because products die; because products must be weeded out and replaced; because firms gener- ate most of their revenue and profit from new products—product selection, definition, and de- sign take place on a continuing basis. Consider recent product changes: TV to HDTV, radio to satellite radio, coffee shops to Starbucks lifestyle coffee, traveling circuses to Cirque du Soleil, land lines to cell phones, cell phone to Blackberry, Walkman to iPod, mops to Swiffers—and the list goes on. Knowing how to successfully find and develop new products is a requirement. New Product Opportunities Aggressive new product development requires that organizations build structures internally that have open communication with customers, innovative organizational cultures, aggressive R&D, strong leadership, formal incentives, and training. Only then can a firm profitably and energeti- cally focus on specific opportunities such as the following: 1. Understanding the customer is the premier issue in new-product development. Many com- mercially important products are initially thought of and even prototyped by users rather than producers. Such products tend to be developed by “lead users”—companies, organiza- tions, or individuals that are well ahead of market trends and have needs that go far beyond those of average users. The operations manager must be “tuned in” to the market and partic- ularly these innovative lead users. 2. Economic change brings increasing levels of affluence in the long run but economic cycles and price changes in the short run. In the long run, for instance, more and more people can afford automobiles, but in the short run, a recession may weaken the demand for automobiles. 3. Sociological and demographic change may appear in such factors as decreasing family size. This trend alters the size preference for homes, apartments, and automobiles. 4. Technological change makes possible everything from cell phones to iPods to artificial hearts. 5. Political/legal change brings about new trade agreements, tariffs, and government requirements. 6. Other changes may be brought about through market practice, professional standards, suppliers, and distributors. Operations managers must be aware of these dynamics and be able to anticipate changes in prod- uct opportunities, the products themselves, product volume, and product mix. Importance of New Products The importance of new products cannot be overestimated. As Figure 5.2(a) shows, leading com- panies generate a substantial portion of their sales from products less than 5 years old. Even Disney (Figure 5.2(b)) needs new theme parks to boost attendance. And giant Cisco Systems is expanding from its core business of making routers and switches into building its own computer servers (Figure 5.2(c)). The need for new products is why Gillette developed its multi-blade razors, in spite of continuing high sales of its phenomenally successful Sensor razor and why Disney innovates in spite of being the leading family entertainment company in the world. Despite constant efforts to introduce viable new products, many new products do not succeed. Indeed, for General Mills to come up with a winner in the breakfast cereal market—defined as a cereal that gets a scant half of 1% of the market—isn’t easy. Among the top 10 brands of cereal, the youngest, Honey Nut Cheerios, was created in 1979. DuPont estimates that it takes 250 ideas to yield one marketable product.2 Chapter 5 Design of Goods and Services 129 AUTHOR COMMENT Societies reward those who supply new products that reflect their needs. 2Rosabeth Kanter, John Kao, and Fred Wiersema, Innovation Breakthrough Thinking at 3M, DuPont, GE, Pfizer, and Rubbermaid (New York: HarperBusiness, 1997). 130 PART 2 Designing Operations As one can see, product selection, definition, and design occur frequently—perhaps hundreds of times for each financially successful product. Operations managers and their organizations must be able to accept risk and tolerate failure. They must accommodate a high volume of new product ideas while maintaining the activities to which they are already committed. PRODUCT DEVELOPMENT Product Development System An effective product strategy links product decisions with cash flow, market dynamics, product life cycle, and the organization’s capabilities. A firm must have the cash for product develop- ment, understand the changes constantly taking place in the marketplace, and have the necessary talents and resources available. The product development system may well determine not only product success but also the firm’s future. Figure 5.3 shows the stages of product development. In this system, product options go through a series of steps, each having its own screening and evaluation criteria, but providing a continuing flow of information to prior steps. The screening process extends to the operations function. Optimum product development de- pends not only on support from other parts of the firm but also on the successful integration of all 10 of the OM decisions, from product design to maintenance. Identifying products that appear LO2: Describe a product development system In m il li o n s o f v is it o rs 0 10 20 30 40 50 ’93 ’95 ’97 ’99 ’01 ’03 ’05 ’07 Animal Kingdom Disney-Hollywood Studios Epcot Magic Kingdom P e rc e n t o f s a le s f ro m n e w p ro d u c ts Position of firm in its industry Disney attendance by year (a) (b) B il li o n s o f d o ll a rs 0 10 5 15 25 20 30 35 ’02 ’03 ’04 ’05 ’06 ’07 ’08 Routers Switches Other Cisco product revenue by year (c) 50% Industry leader The higher the percentage of sales from the last 5 years, the more likely the firm is to be a leader. Disney World innovates with new parks, rides, and attractions to boost attendance. Much of Cisco’s growth has come from new non-networking products. Top third Middle third Bottom third 40% 30% 20% 10% 0% � FIGURE 5.2 Innovation and New Products Yield Results for Both Manufacturing and Services AUTHOR COMMENT Motorola went through 3,000 working models before it developed its first pocket cell phone. Source: The Orlando Sentinel and trade magazines. Source: Cisco Systems, Inc. Lightweight Easy to use Reliable Easy to hold steady Color correction Lo w e le ct ric ity re qu ire m en ts A lu m in u m c o m p o n e n ts A u to f o cu s A u to e xp o su re P a in t p a lle t E rg o n o m ic d e si g n Overlay 1. First, through market research, Great Cameras, Inc., determined what the customer wants. Those wants are shown on the left of the house of quality. Second, the product development team determined how the organization is going to translate those customer wants into product design and process attribute targets. These hows are entered across the top portion of the house of quality. Our importance ratings 22 9 27 27 32 25 3 4 5 2 1 of 25 = (1 � 3) + (3 � 4) + (2 � 5) High relationship (5) Medium relationship (3) Low relationship (1) Overlay 2. Third, the team evaluated each of the customer wants against the hows. In the relationship matrix of the house, the team evaluated how well its design meets customer needs. Fourth, the “roof” of the house, indicates the relationship between the attributes. Fifth, the team developed importance ratings for its design attributes on the bottom row of the table. This was done by assigning values (5 for high, 3 for medium, and 1 for low) to each entry in the relationship matrix, and then multiplying each of these values by the customer’s importance rating. The values in |the “Our importance ratings” row provide a ranking of how to proceed with product and process design, with the highest values being the most critical to a successful product. Overlay 3. Sixth, the house of quality is also used for the evaluation of competitors. The two columns on the right indicate how market research thinks competitors, A and B, satisfy customer wants (Good, Fair, or Poor). Products from other firms and even the proposed product can be added next to company B. G G F G P P P G P P C o m p a n y A C o m p a n y B 0. 5 A 7 5 % 2' t o ∞ 2 c ir cu its F a ilu re 1 p e r 1 0 ,0 0 0 P a n e l r a n ki n g 0.7 0.6 0.5 60% 50% 75% yes yes yes 1 2 2 ok ok ok G F G Overlay 4. Seventh, the team identifies the technical attributes and evaluates how well Great Cameras, Inc., and its competitors address these attributes. Here the team decided on the noted technical attributes. Chapter 5 Design of Goods and Services 131 likely to capture market share, be cost effective, and profitable, but are in fact very difficult to produce, may lead to failure rather than success. Quality Function Deployment (QFD) Quality function deployment (QFD) refers to both (1) determining what will satisfy the cus- tomer and (2) translating those customer desires into the target design. The idea is to capture a rich understanding of customer wants and to identify alternative process solutions. This informa- tion is then integrated into the evolving product design. QFD is used early in the design process to help determine what will satisfy the customer and where to deploy quality efforts. One of the tools of QFD is the house of quality. The house of quality is a graphic technique for defining the relationship between customer desires and product (or service). Only by defining this relationship in a rigorous way can operations managers design products and processes with features desired by customers. Defining this relationship is the first step in building a world-class production system. To build the house of quality, we perform seven basic steps: 1. Identify customer wants. (What do prospective customers want in this product?) 2. Identify how the good/service will satisfy customer wants. (Identify specific product charac- teristics, features, or attributes and show how they will satisfy customer wants.) 3. Relate customer wants to product hows. (Build a matrix, as in Example 1, that shows this relationship.) 4. Identify relationships between the firm’s hows. (How do our hows tie together? For instance, in the following example, there is a high relationship between low electricity requirements and auto focus, auto exposure, and a paint pallet because they all require electricity. This relationship is shown in the “roof” of the house in Example 1.) Quality function deployment (QFD) A process for determining customer requirements (customer “wants”) and translating them into the attributes (the “hows”) that each functional area can understand and act on. House of quality A part of the quality function deployment process that utilizes a planning matrix to relate cus- tomer “wants” to “how” the firm is going to meet those “wants.” Scope for design and engineering teams Scope of product development team Design review: Are these product specifications the best way to meet customer requirements? Functional specifications: How the product will work Customer requirements to win orders Does firm have ability to carry out idea? Ideas from many sources Product specifications: How the product will be made Test market: Does product meet customer expectations? Introduction to market Evaluation (success?) � FIGURE 5.3 Product Development Stages Product concepts are developed from a variety of sources, both external and internal to the firm. Concepts that survive the product idea stage progress through various stages, with nearly constant review, feedback, and evaluation in a highly participative environment to minimize failure. 132 PART 2 Designing Operations 5. Develop importance ratings. (Using the customer’s importance ratings and weights for the relationships shown in the matrix, compute our importance ratings, as in Example 1.) 6. Evaluate competing products. (How well do competing products meet customer wants? Such an evaluation, as shown in the two columns on the right of the figure in Example 1, would be based on market research.) 7. Determine the desirable technical attributes, your performance, and the competitor’s per- formance against these attributes. (This is done at the bottom of the figure in Example 1). The following series of overlays for Example 1 show how to construct a house of quality. EXAMPLE 1 � Constructing a house of quality Great Cameras, Inc., wants a methodology that strengthens its ability to meet customer desires with its new digital camera. APPROACH � Use QFD’s house of quality. SOLUTION � Build the house of quality for Great Cameras, Inc. We do so here using Overlays 1, 2, 3, and 4. Quality Function Deployment’s (QFD) House of Quality Relationship between the things we can do What we can do (how the organization is going to translate customer wants into product and process attributes and design targets) G = good F = fair P = poor How well what we do meets the customer’s wants (relationship matrix) Customer importance ratings (5 = highest) What the customer wants Weighted rating Competitive assessment Target values (technical attributes) Technical evaluation LO3: Build a house of quality Chapter 5 Design of Goods and Services 133 INSIGHT � QFD provides an analytical tool that structures design features and technical issues, as well as providing importance rankings and competitor comparison. LEARNING EXERCISE � If the market research for another country indicates that “light- weight” has the most important customer ranking (5), and reliability a 3, what is the new total impor- tance ranking for low electricity requirements, aluminum components, and ergonomic design? [Answer: 18, 15, 27, respectively.] RELATED PROBLEMS � 5.1, 5.2, 5.3, 5.4 Another use of quality function deployment (QFD) is to show how the quality effort will be deployed. As Figure 5.4 shows, design characteristics of House 1 become the inputs to House 2, which are satisfied by specific components of the product. Similarly, the concept is carried to House 3, where the specific components are to be satisfied through particular production processes. Once those production processes are defined, they become requirements of House 4 to be satisfied by a quality plan that will ensure conformance of those processes. The quality plan is a set of specific tolerances, procedures, methods, and sampling techniques that will ensure that the production process meets the customer requirements. Much of the QFD effort is devoted to meeting customer requirements with design characteris- tics (House 1 in Figure 5.4), and its importance is not to be underestimated. However, the sequence of houses is a very effective way of identifying, communicating, and allocating resources throughout the system. The series of houses helps operations managers determine where to deploy quality resources. In this way we meet customer requirements, produce quality products, and win orders. Organizing for Product Development Let’s look at four approaches to organizing for product development. First, the traditional U.S. approach to product development is an organization with distinct departments: a research and development department to do the necessary research; an engineering department to design the product; a manufacturing engineering department to design a product that can be produced; and a production department that produces the product. The distinct advantage of this approach is that fixed duties and responsibilities exist. The distinct disadvantage is lack of forward thinking: How will downstream departments in the process deal with the concepts, ideas, and designs pre- sented to them, and ultimately what will the customer think of the product? A second and popular approach is to assign a product manager to “champion” the product through the product development system and related organizations. However, a third, and per- haps the best, product development approach used in the U.S. seems to be the use of teams. Such teams are known variously as product development teams, design for manufacturability teams, and value engineering teams. Design characteristics C u st o m e r re q u ir e m e n ts Specific components D e si g n ch a ra ct e ri st ic s Production process S p e ci fic co m p o n e n ts House 4 Quality plan P ro d u c ti o n p ro c e s s House 1 House 2 House 3 � FIGURE 5.4 House of Quality Sequence Indicates How to Deploy Resources to Achieve Customer Requirements 134 PART 2 Designing Operations The Japanese use a fourth approach. They bypass the team issue by not subdividing organiza- tions into research and development, engineering, production, and so forth. Consistent with the Japanese style of group effort and teamwork, these activities are all in one organization. Japanese culture and management style are more collegial and the organization less structured than in most Western countries. Therefore, the Japanese find it unnecessary to have “teams” provide the necessary communication and coordination. However, the typical Western style, and the conven- tional wisdom, is to use teams. Product development teams are charged with the responsibility of moving from market requirements for a product to achieving a product success (refer to Figure 5.3 on page 131). Such teams often include representatives from marketing, manufacturing, purchasing, quality assurance, and field service personnel. Many teams also include representatives from ven- dors. Regardless of the formal nature of the product development effort, research suggests that success is more likely in an open, highly participative environment where those with potential contributions are allowed to make them. The objective of a product development team is to make the good or service a success. This includes marketability, manufacturability, and serviceability. Use of such teams is also called concurrent engineering and implies a team representing all affected areas (known as a cross-functional team). Concurrent engineering also implies speedier product development through simultaneous performance of various aspects of product develop- ment.3 The team approach is the dominant structure for product development by leading organi- zations in the U.S. Manufacturability and Value Engineering Manufacturability and value engineering activities are concerned with improvement of design and specifications at the research, development, design, and production stages of product devel- opment. (See the OM in Action box “Design Challenges with Trident’s Splash.”) In addition to immediate, obvious cost reduction, design for manufacturability and value engineering may pro- duce other benefits. These include: 1. Reduced complexity of the product. 2. Reduction of environmental impact. 3. Additional standardization of components. 4. Improvement of functional aspects of the product. Cadbury Schweppes PLC sells a lot of gum—Dentyne, Bubbaloo, and Trident—some $4.2 billion of a $15.4 billion market that is growing about 6% per year. However, Cadbury perceived a niche for a new gum that would be a low calorie substitute for unhealthy snacks. Cadbury wanted the new product to compete with the creamy or crunchy mouth experience one gets from snacks other than gum. The R&D team eventually designed a unique three-layer pellet with a candy shell over sugarless gum with a liquid center. For the liquid center Cadbury scientists evaluated scores of long lasting flavors before settling on two unusual blends: strawberry-lime and peppermint-vanilla. Development wasn’t easy; neither was designing a product that could be produced. Although Cadbury acquired the liquid center technology from Pfizer, some of the flavors were too water-soluble—making the gum soft. Early formulations leaked during production. Others survived production only to fail when subjected to the punishment of transportation. Adding to production problems was the lack of sugar in the gum. Sugar traditionally adds strength and bulk to aid the production process, but with artificial sweeteners, the centers were not strong enough for the application of the candy coating. The machinery crushed the weakened pellets and the liquid flavors oozed out. This in turn contributed to some messy production equipment. It took two years and millions of dollars, but Cadbury’s biggest ever new-product development effort, Trident Splash, is now on the market. Sources: The Wall Street Journal (January 12, 2006): A1, A8; Fortune (April 3, 2006): 33 OM in Action �Design Challenges with Trident’s Splash Product development teams Teams charged with moving from market requirements for a product to achieving product success. Concurrent engineering Use of participating teams in design and engineering activities. Manufacturability and value engineering Activities that help improve a product’s design, production, maintainability, and use. 3Firms that have high technological or product change in their competitive environment tend to use more concurrent engineering practices. See X. Koufteros, M. Vonderembse, and W. Doll, “Concurrent Engineering and Its Consequences,” Journal of Operations Management 19, no. 1 (January 2001): 97–115. Chapter 5 Design of Goods and Services 135 5. Improved job design and job safety. 6. Improved maintainability (serviceability) of the product. 7. Robust design. Manufacturability and value engineering activities may be the best cost-avoidance technique available to operations management. They yield value improvement by focusing on achieving the functional specifications necessary to meet customer requirements in an optimal way. Value engineering programs, when effectively managed, typically reduce costs between 15% and 70% without reducing quality. Some studies have indicated that for every dollar spent on value engi- neering, $10 to $25 in savings can be realized. Product design affects virtually all aspects of operating expense and sustainability. Consequently, the development process needs to ensure a thorough evaluation of design prior to a commitment to produce. The cost reduction achieved for a specific bracket via value engineer- ing is shown in Figure 5.5 ISSUES FOR PRODUCT DESIGN In addition to developing an effective system and organization structure for product develop- ment, several techniques are important to the design of a product. We will now review six of these: (1) robust design, (2) modular design, (3) computer-aided design (CAD), (4) computer- aided manufacturing (CAM), (5) virtual reality technology, and (6) value analysis. Robust Design Robust design means that the product is designed so that small variations in production or assembly do not adversely affect the product. For instance, Lucent developed an integrated cir- cuit that could be used in many products to amplify voice signals. As originally designed, the cir- cuit had to be manufactured very expensively to avoid variations in the strength of the signal. But after testing and analyzing the design, Lucent engineers realized that if the resistance of the cir- cuit was reduced—a minor change with no associated costs—the circuit would be far less sensi- tive to manufacturing variations. The result was a 40% improvement in quality. Modular Design Products designed in easily segmented components are known as modular designs. Modular designs offer flexibility to both production and marketing. Operations managers find modularity helpful because it makes product development, production, and subsequent changes easier. Moreover, marketing may like modularity because it adds flexibility to the ways customers can be satisfied. For instance, virtually all premium high-fidelity sound systems are produced and sold this way. The customization provided by modularity allows customers to mix and match to their own taste. This is also the approach taken by Harley-Davidson, where relatively few different engines, chassis, gas tanks, and suspension systems are mixed to produce a huge variety of motor- cycles. It has been estimated that many automobile manufacturers can, by mixing the available modules, never make two cars alike. This same concept of modularity is carried over to many industries, from airframe manufacturers to fast-food restaurants. Airbus uses the same wing mod- ules on several planes, just as McDonald’s and Burger King use relatively few modules (cheese, lettuce, buns, sauces, pickles, meat patties, french fries, etc.) to make a variety of meals. 1 3 $3.50 $2.00 2 $.80 � FIGURE 5.5 Cost Reduction of a Bracket via Value Engineering AUTHOR COMMENT Each time the bracket is redesigned and simplified, we are able to produce it for less. Robust design A design that can be produced to requirements even with unfavorable conditions in the production process. Modular design A design in which parts or components of a product are subdivided into modules that are easily interchanged or replaced. Computer-Aided Design (CAD) Computer-aided design (CAD) is the use of computers to interactively design products and pre- pare engineering documentation. Use and variety of CAD software is extensive and is rapidly expanding. CAD software allows designers to use three-dimensional drawings to save time and money by shortening development cycles for virtually all products (see the 3-D design photos below). The speed and ease with which sophisticated designs can be manipulated, analyzed, and modified with CAD makes review of numerous options possible before final commitments are made. Faster development, better products, accurate flow of information to other departments—all contribute to a tremendous payoff for CAD. The payoff is particularly significant because most product costs are determined at the design stage. One extension of CAD is design for manufacture and assembly (DFMA) software, which focuses on the effect of design on assembly. It allows designers to examine the integration of product designs before the product is manufactured. For instance, DFMA allows automobile designers to examine how a transmission will be placed in a car on the production line, even while both the transmission and the car are still in the design stage. A second CAD extension is 3-D object modeling. The technology is particularly useful for small prototype development (as shown in the photo on page 137). 3-D object modeling rapidly builds up a model in very thin layers of synthetic materials for evaluation. This technology speeds development by avoiding a more lengthy and formal manufacturing process. 3-D printers, costing as little as $5,000, are also now available. Shoemaker Timberland, Inc., uses theirs to allow footwear designers to see their constructions overnight rather than waiting a week for model-mak- ers to carve them. Some CAD systems have moved to the Internet through e-commerce, where they link com- puterized design with purchasing, outsourcing, manufacturing, and long-term maintenance. This move supports rapid product change and the growing trend toward “mass customization.” With CAD on the Internet, customers can enter a supplier’s design libraries and make design changes. The supplier’s software can then automatically generate the drawings, update the bill of material, and prepare instructions for the supplier’s production process. The result is customized products produced faster and at less expense. As product life cycles shorten and design becomes more complex, collaboration among depart- ments, facilities and suppliers throughout the world becomes critical. The potential of such collab- oration has proven so important that a standard for its exchange has been developed, known as the standard for the exchange of product data (STEP). STEP permits manufacturers to express 136 PART 2 Designing Operations Computer-aided design (CAD) Interactive use of a computer to develop and document a product. Design for manufacture and assembly (DFMA) Software that allows designers to look at the effect of design on manufacturing of the product. 3-D object modeling An extension of CAD that builds small prototypes. Standard for the exchange of product data (STEP) A standard that provides a format allowing the electronic transmittal of three-dimensional data. The increasing sophistication of CAD software provides (a) 3D solid design, (b) integrated assembly, and (c) analysis of stress, pressure, and thermal issues, which improves design, speeds the design process, and provides computer code for CAM equipment while reducing costs. (a) (b) (c) Chapter 5 Design of Goods and Services 137 3-D product information in a standard format so it can be exchanged internationally, allowing geo- graphically dispersed manufacturers to integrate design, manufacture, and support processes.4 Computer-Aided Manufacturing (CAM) Computer-aided manufacturing (CAM) refers to the use of specialized computer programs to direct and control manufacturing equipment. When computer-aided design (CAD) information is translated into instructions for computer-aided manufacturing (CAM), the result of these two technologies is CAD/CAM. The benefits of CAD and CAM include: 1. Product quality: CAD permits the designer to investigate more alternatives, potential prob- lems, and dangers. 2. Shorter design time: A shorter design phase lowers cost and allows a more rapid response to the market. 3. Production cost reductions: Reduced inventory, more efficient use of personnel through improved scheduling, and faster implementation of design changes lower costs. 4. Database availability: Provides information for other manufacturing software and accurate prod- uct data so everyone is operating from the same information, resulting in dramatic cost reductions. 5. New range of capabilities: For instance, the abilities to rotate and depict objects in three- dimensional form, to check clearances, to relate parts and attachments, and to improve the use of numerically controlled machine tools—all provide new capability for manufacturing. CAD/CAM removes substantial detail work, allowing designers to concentrate on the con- ceptual and imaginative aspects of their task. Virtual Reality Technology Virtual reality is a visual form of communication in which images substitute for the real thing but still allow the user to respond interactively. The roots of virtual reality technology in opera- tions are in computer-aided design. Once design information is in a CAD system, it is also in electronic digital form for other uses, such as developing 3-D layouts of everything from restau- rants to amusement parks. Changes to mechanical design, restaurant layouts, or amusement park rides are much less expensive at the design stage than later. Value Analysis Although value engineering (discussed on page 134) focuses on preproduction design improve- ment, value analysis, a related technique, takes place during the production process, when it is clear that a new product is a success. Value analysis seeks improvements that lead to either a better product, or a product made more economically, or a product with less environmental impact. The techniques and advantages for value analysis are the same as for value engineering, although minor changes in implementation may be necessary because value analysis is taking place while the product is being produced. Computer-aided manufacturing (CAM) The use of information technology to control machinery. Virtual reality A visual form of communication in which images substitute for reality and typically allow the user to respond interactively. Value analysis A review of successful products that takes place during the production process. 4The STEP format is documented in the European Community’s standard ISO 10303. This prototype wheel for a tire (at the left of the photo) is being built using 3-D System’s Stereolithography technology, a 3-D object modeling system. This technology uses data from CAD and builds structures layer by layer in .001-inch increments. The technique reduces the time it takes to create a sample from weeks to hours while also reducing costs. The technique is also known as rapid prototyping. ETHICS, ENVIRONMENTALLY-FRIENDLY DESIGNS, AND SUSTAINABILITY An operations manager’s task is to enhance productivity while delivering desired goods and ser- vices in an ethical, environmentally sound, and sustainable way. In an OM context, sustainability means ecological stability. This means operating a production system in a way that supports conservation and renewal of resources. The entire product life cycle—from design, to production, to final destruction or recycling—provides an opportunity to preserve resources. Planet Earth is finite; managers who squeeze more out of its resources are its heroes. The good news is that operations managers have tools that can drive down costs or improve margins while preserving resources. Here are examples of how firms do so: • At the design stage: DuPont developed a polyester film stronger and thinner so it uses less material and costs less to make. Also, because the film performs better, customers are willing to pay more for it. Similarly, Nike’s new Air Jordan shoe contains very little chemical-based glue and an outsole made of recycled material, yielding lower manufacturing cost and less impact on the environment. • At the production stage: Bristol-Myers Squibb established an environmental and pollution prevention program designed to address environmental, health, and safety issues at all stages of the product life cycle. Ban Roll-On was one of the first products studied and an early suc- cess. Repackaging Ban in smaller cartons resulted in a reduction of 600 tons of recycled paperboard. The product then required 55% less shelf space for display. As a result, not only is pollution prevented but store operating costs are also reduced. • At the destruction stage: The automobile industry has been very successful: The industry now recycles more than 84% of the material by weight of 13 million cars scrapped each year. Much of this success results from care at the design stage. For instance, BMW, with environ- mentally friendly designs, recycles most of a car, including many plastic components (see the photo). These efforts are consistent with the environmental issues raised by the ISO 14000 standard, a topic we address in Chapter 6. Systems and Life Cycle Perspectives One way to accomplish programs like those at DuPont, Bristol-Myers Squibb, and BMW is to add an ethical and environmental charge to the job of operations managers and their value engi- neering/analysis teams. Team members from different functional areas working together can pre- sent a wide range of environmental perspectives and approaches. Managers and teams should consider two issues. First, they need to view products from a “systems” perspective—that is, view a product in terms of its impact on sustainability—ecological stability. This means taking a comprehensive look at the inputs to the firm, the processes, and the outputs, recognizing that some of the resources, long considered free, are in fact not free. Particulates and sulfur in the air are pollution for someone else; similarly, bacteria and phosphates in the water going downstream become 138 PART 2 Designing Operations Sustainability A production system that supports conservation and renewal of resources. AUTHOR COMMENT OM can do a lot to save our planet. Saving the planet is good business and good ethics. BMW uses parts made of recycled plastics (blue) and parts that can be recycled (green). “Green manufacturing” means companies can reuse, refurbish, or dispose of a product’s components safely and reduce total life cycle product costs. Chapter 5 Design of Goods and Services 139 someone else’s problem. In the case of the battle between styrofoam and paper containers, which one is really “better,” and by what criteria? We may know which is more economical for the firm, but is that one also most economical for society? Second, operations managers must consider the life cycle of the product, that is, from design, through production, to final disposition. This can be done via value engineering, as noted earlier, or as a part of a life cycle assessment (LCA) initiative. LCA is part of the ISO 14000 environ- mental management standard. The goal is to reduce the environmental impact of a product throughout its life—a challenging task. The likelihood that ethical decisions will be made is enhanced when managers maintain these two perspectives and maintain an open dialogue among all stakeholders. Goals Consistent with the two issues above, goals for ethical, environment-friendly designs are: 1. Develop safe and more environmentally sound products. 2. Minimize waste of raw materials and energy. 3. Reduce environmental liabilities. 4. Increase cost-effectiveness of complying with environmental regulations. 5. Be recognized as a good corporate citizen. Guidelines The following six guidelines may help operations managers achieve ethical and environmentally-friendly designs: 1. Make products recyclable: Many firms are doing this on their own, but the U.S. and the EU now have take-back laws that affect a variety of products, from automobiles and tires to computers. Not only is most of a car recycled but so are over half the aluminum cans and a large portion of paper, plastic, and glass. In some cases, as with tires, the manufacturer is responsible for 100% disposal. 2. Use recycled materials: Scotch-Brite soap pads at 3M are designed to use recycled plastics, as are the park benches and other products made by Plastic Recycling Corporation. Recycled plastics and old clothing are making their way into seat upholstery for the Ford Escape hybrid sport-utility. This application has added benefits: it’s waterproof and it will save 600,000 gallons of water, 1.8 million pounds of carbon doxide, and more than 7 million kilowatt hours of electricity per year.5 3. Use less harmful ingredients: Standard Register, like most of the printing industry, has replaced environmentally dangerous inks with soy-based inks that reduce air and water pollution. 4. Use lighter components: The auto and truck industries continue to expand the use of alu- minum and plastic components to reduce weight. Mercedes is even building car exteriors from a banana plant fiber that is both biodegradable and lightweight. Similarly, Boeing is using carbon fiber, epoxy composites, and titanium graphite laminate to reduce weight in its new 787 Dreamliner. These changes can be expensive, but they make autos, trucks, and air- craft more environmentally friendly by improving payload and fuel efficiency. 5. Use less energy: While the auto, truck, and airframe industries are redesigning to improve mileage, General Electric is designing a new generation of refrigerators that requires sub- stantially less electricity during their lifetime. DuPont is so good at energy efficiency that it has turned its expertise into a consulting business. 6. Use less material: Organizations fight to drive down material use—in the plant and in the packaging. An employee team at a Sony semiconductor plant achieved a 50% reduction in the amount of chemicals used in the silicon wafer etching process. And Frito-Lay’s U.S. plants have driven down water consumption over 31% in the past 10 years, with a goal of 75% reduction by 2017. These and similar successes reduce both production costs and envi- ronmental concerns. To conserve packaging, Boston’s Park Plaza Hotel eliminated bars of soap and bottles of shampoo by installing pump dispensers in its bathrooms, saving the need for a million plastic containers a year. Laws and Industry Standards Laws and industry standards can help operations managers make ethical and socially responsible decisions. In the last 100 years we have seen development of legal and industry standards to guide managers in product design, manufacture/assembly, and disassembly/disposal. Life cycle assessment (LCA) Part of ISO 14000; assesses the environmental impact of a product, from material and energy inputs to disposal and environmental releases. 5“Vehicles That Use Recycled Material,” The Wall Street Journal (January 25, 2007): D6. Design: On the legal side, U.S. laws and regulations such as those promulgated by the Food and Drug Administration, Consumer Product Safety Commission, National Highway Safety Administration, and Children’s Product Safety Act provide guidance, if not explicit law, to aid deci- sion making. Guidance is also provided by phrases in case law like “design for foreseeable misuse” and in regard to children’s toys, “The concept of a prudent child is . . . a grotesque combination.” Manufacture/assembly: The manufacture and assembly of products has standards and guide- lines from the Occupational Safety and Health Administration (OSHA), Environmental Protection Agency (EPA), professional ergonomic standards, and a wide range of state and fed- eral laws that deal with employment standards, disabilities, discrimination, and the like. Disassembly/disposal: Product disassembly and disposal in the U.S., Canada, and the EU are governed by increasingly rigid laws. In the U.S., the Vehicle Recycling Partnership, supported by the auto industry, provides Design for Disassembly Standards for auto disassembly and disposal. However, in the fragmented electronics industry, safe disposal of TVs, computers, and cell phones is much more difficult and dangerous (see the photos above). Ethical, socially responsible decisions can be difficult and complex—often with no easy answers—but such decisions are appreciated by the public, and they can save money, material, and the environment. These are the types of win–win situations that operations managers seek. TIME-BASED COMPETITION As product life cycles shorten, the need for faster product development increases. Additionally, as technological sophistication of new products increases, so do the expense and risk. For instance, drug firms invest an average of 12 to 15 years and $600 million before receiving regulatory approval of each new drug. And even then, only 1 of 5 will actually be a success. Those operations managers who master this art of product development continually gain on slower product develop- ers. To the swift goes the competitive advantage. This concept is called time-based competition. Often, the first company into production may have its product adopted for use in a variety of applications that will generate sales for years. It may become the “standard.” Consequently, there is often more concern with getting the product to market than with optimum product design or process efficiency. Even so, rapid introduction to the market may be good management because until competition begins to introduce copies or improved versions, the product can sometimes be priced high enough to justify somewhat inefficient production design and methods. For example, when Kodak first introduced its Ektar film, it sold for 10% to 15% more than conventional film and Apple’s innovative iPod and new versions have a 75% market share even after 5 years. Because time-based competition is so important, instead of developing new products from scratch (which has been the focus thus far in this chapter) a number of other strategies can be used. Figure 5.6 shows a continuum that goes from new, internally developed products (on the lower left) to “alliances.” Enhancements and migrations use the organization’s existing product strengths for innovation and therefore are typically faster while at the same time being less risky than developing entirely new products. Enhancements may be changes in color, size, weight, or features, such as are taking place in cellular phones (see OM in Action box “Chasing Fads in the Cell Phone Industry”), or even changes in commercial aircraft. Boeing’s enhancements of the 737 since its introduction in 1967 has made the 737 the largest-selling commercial aircraft in 140 PART 2 Designing Operations With increasing restrictions on disposal of TVs, cell phones, computers, and other electronic waste, much of such waste (left) ends its life in Guangdong province on China’s southern coast (right). Here, under less- than-ideal conditions, Chinese women strip old circuit boards to salvage the chips. AUTHOR COMMENT Fast communication, rapid technological change, and short product life cycles push product development. Time-based competition Competition based on time; rapidly developing products and moving them to market. LO4: Describe how time-based competition is implemented by OM Chapter 5 Design of Goods and Services 141 history. Boeing also uses its engineering prowess in air frames to migrate from one model to the next. This allows Boeing to speed development while reducing both cost and risk for new designs. This approach is also referred to as building on product platforms. Black & Decker has used its “platform” expertise in hand-powered tools to build a leading position in that market. Similarly, Hewlett-Packard has done the same in the printer business. Enhancements and migra- tions are a way of building on existing expertise and extending a product’s life cycle. The product development strategies on the lower left of Figure 5.6 are internal development strategies, while the three approaches we now introduce can be thought of as external devel- opment strategies. Firms use both. The external strategies are (1) purchase the technology, (2) establish joint ventures, and (3) develop alliances. Purchasing Technology by Acquiring a Firm Microsoft and Cisco Systems are examples of companies on the cutting edge of technology that often speed development by acquiring entrepreneurial firms that have already developed the technology that fits their mission. The issue then becomes fitting the purchased organization, its Internal Lengthy High Shared Rapid and/or Existing Shared External development strategies Product Development Continuum Internal development strategies Migrations of existing products Enhancements to existing products New internally developed products Alliances Joint ventures Purchase technology or expertise by acquiring the developer Cost of product development Speed of product development Risk of product development � FIGURE 5.6 Product Development Continuum In the shrinking world marketplace, innovations that appeal to customers in one region rapidly become global trends. The process shakes up the structure of one industry after another, from computers to automobiles to consumer electronics. Nowhere has this impact been greater in recent years than in the cell phone industry. The industry sells about 1.3 billion phones each year, but product life cycle is short. Competition is intense. Higher margins go to the innovator— and manufacturers that jump on an emerging trend early can reap substantial rewards. The swiftest Chinese manufacturers, such as Ningbo Bird and TCL, now replace some phone models after just 6 months. In the past, Motorola, Nokia, and other industry veterans enjoyed what are now considered long life cycles—2 years. New styles and technological advances in cell phones constantly appear somewhere in the world. Wired, well-traveled consumers seek the latest innovation; local retailers rush to offer it; and telecommunication providers order it. Contemporary cell phones may be a curvy, boxy, or a clamshell fashion item; have a tiny keyboard for quick and easy typing or a more limited number pad for a phone; have a built-in radio or a digital music player; have a camera, Internet access, or TV clips; function on cellular or wireless (Wi-Fi) networks; or have games or personal organizers. Mattel and Nokia even have Barbie phones for preteen girls, complete with prepaid minutes, customized ringtones, and faceplates. The rapid changes in features and demand are forcing manufacturers into a frenzied race to keep up or simply to pull out. “We got out of the handset business because we couldn’t keep up with the cycle times,” says Jeffrey Belk, Marketing V.P. for Qualcomm Inc., the San Diego company that now focuses on making handset chips. Developing new products is always a challenge, but in the dynamic global market place of cell phones, product development takes on new technology and new markets at breakneck speed. Sources: Supply Chain Management Review (October, 2007): 28; The Wall Street Journal (October 30, 2003): A1 and (Sept. 8, 2004): D5; and International Business Times (March 3,2009). OM in Action � Chasing Fads in the Cell Phone Industry AUTHOR COMMENT Managers seek a variety of approaches to obtain speed to market. The president of one U.S. firm says: “If I miss one product cycle, I’m dead.” 142 PART 2 Designing Operations technology, its product lines, and its culture into the buying firm, rather than a product develop- ment issue. Joint Ventures Joint ventures are combined ownership, usually between just two firms, to form a new entity. Ownership can be 50–50, or one owner can assume a larger portion to ensure tighter control. Joint ventures are often appropriate for exploiting specific product opportunities that may not be central to the firm’s mission. Such ventures are more likely to work when the risks are known and can be equitably shared. For instance, GM and Toyota formed a joint venture to pro- duce the GM Prism and the Toyota Corolla. Both companies saw a learning opportunity as well as a product they both needed in the North American market. Toyota wanted to learn about building and managing a plant in North America, and GM wanted to learn about manufacturing a small car with Toyota’s manufacturing techniques. The risks were well understood, as were the respective commitments. Similarly, Fuji-Xerox, a manufacturer and marketer of photo- copiers, is a joint venture of Xerox, the U.S. maker of photocopiers, and Fuji, Japan’s largest manufacturer of film. Alliances Alliances are cooperative agreements that allow firms to remain independent but use comple- menting strengths to pursue strategies consistent with their individual missions. When new prod- ucts are central to the mission, but substantial resources are required and sizable risk is present, then alliances may be a good strategy for product development. Alliances are particularly benefi- cial when the products to be developed also have technologies that are in ferment. For example, Microsoft is pursuing a number of alliances with a variety of companies to deal with the conver- gence of computing, the Internet, and television broadcasting. Alliances in this case are appropri- ate because the technological unknowns, capital demands, and risks are significant. Similarly, three firms, Mercedes Benz, Ford Motor, and Ballard Power Systems, have formed an alliance to develop “green” cars powered by fuel cells. However, alliances are much more difficult to achieve and maintain than joint ventures because of the ambiguities associated with them. It may be help- ful to think of an alliance as an incomplete contract between the firms. The firms remain separate. Enhancements, migration, acquisitions, joint ventures, and alliances are all strategies for speeding product development. Moreover, they typically reduce the risk associated with product development while enhancing the human and capital resources available. DEFINING A PRODUCT Once new goods or services are selected for introduction, they must be defined. First, a good or service is defined in terms of its functions—that is, what it is to do. The product is then designed, and the firm determines how the functions are to be achieved. Management typically has a vari- ety of options as to how a product should achieve its functional purpose. For instance, when an alarm clock is produced, aspects of design such as the color, size, or location of buttons may make substantial differences in ease of manufacture, quality, and market acceptance. Rigorous specifications of a product are necessary to assure efficient production. Equipment, layout, and human resources cannot be determined until the product is defined, designed, and documented. Therefore, every organization needs documents to define its products. This is true of everything from meat patties, to cheese, to computers, to a medical procedure. In the case of cheese, a written specification is typical. Indeed, written specifications or standard grades exist and provide the definition for many products. For instance, Monterey Jack cheese has a written description that specifies the characteristics necessary for each Department of Agriculture grade. A portion of the Department of Agriculture grade for Monterey Jack Grade AA is shown in Figure 5.7. Similarly, McDonald’s Corp. has 60 specifications for potatoes that are to be made into french fries. Most manufactured items as well as their components are defined by a drawing, usually referred to as an engineering drawing. An engineering drawing shows the dimensions, toler- ances, materials, and finishes of a component. The engineering drawing will be an item on a bill of material. An engineering drawing is shown in Figure 5.8. The bill of material (BOM) lists the components, their description, and the quantity of each required to make one unit of a product. Joint ventures Firms establishing joint ownership to pursue new products or markets. Alliances Cooperative agreements that allow firms to remain independent, but pursue strategies consistent with their individual missions. LO5: Describe how products and services are defined by OM AUTHOR COMMENT Before anything can be produced, a product’s functions and attributes must be defined. Engineering drawing A drawing that shows the dimensions, tolerances, materials, and finishes of a component. Bill of material (BOM) A list of the components, their description, and the quantity of each required to make one unit of a product. Chapter 5 Design of Goods and Services 143 A bill of material for a manufactured item is shown in Figure 5.9(a). Note that subassemblies and components (lower-level items) are indented at each level to indicate their subordinate position. An engineering drawing shows how to make one item on the bill of material. In the food-service industry, bills of material manifest themselves in portion-control standards. The portion-control standard for Hard Rock Cafe’s hickory BBQ bacon cheeseburger is shown in Figure 5.9(b). In a more complex product, a bill of material is referenced on other bills of mate- rial of which they are a part. In this manner, subunits (subassemblies) are part of the next higher unit (their parent bill of material) that ultimately makes a final product. In addition to being defined by written specifications, portion-control documents, or bills of material, products can be defined in other ways. For example, products such as chemicals, paints, and petroleums may be defined by formulas or proportions that describe how they are to be made. Movies are defined by scripts, and insurance coverage by legal documents known as policies. Make-or-Buy Decisions For many components of products, firms have the option of producing the components them- selves or purchasing them from outside sources. Choosing between these options is known as the make-or-buy decision. The make-or-buy decision distinguishes between what the firm wants to produce and what it wants to purchase. Because of variations in quality, cost, and delivery sched- ules, the make-or-buy decision is critical to product definition. Many items can be purchased as a “standard item” produced by someone else. Examples are the standard bolts listed on the bill of material shown in Figure 5.9(a), for which there will be SAE (Society of Automotive Engineers) specifications. Therefore, there typically is no need for the firm to duplicate this specification in another document. We discuss the make-or-buy decision in more detail in Chapter 11. § 58.2469 Specifications for U.S. grades of Monterey (Monterey Jack) cheese (2) Body and texture. A plug drawn from the cheese shall be reasonably firm. It shall have numerous small mechanical openings evenly distributed throughout the plug. It shall not possess sweet holes, yeast holes, or other gas holes. (4) Finish and appearance —bandaged and paraffin-dipped. The rind shall be sound, firm, and smooth, providing a good protection to the cheese. (a) U.S. grade AA. Monterey Cheese shall conform to the following requirements: (1) Flavor. Is fine and highly pleasing, free from undesirable flavors and odors. May possess a very slight acid or feed flavor. (3) Color. Shall have a natural, uniform, bright, attractive appearance. Code of Federal Regulation, Parts 53 to 109, General Service Administration. � FIGURE 5.7 Monterey Jack A portion of the general requirements for the U.S. grades of Monterey cheese is shown here. .250 .251 DIA. THRU FINE KNURL .250 .093 5-40 TAP THRU 1/64 R X .010 DP. AFTER KNURL .050 .055. 3 7 5 .6 2 4 .6 2 5 AUX. VIEW MARK PART NO. REVISIONS Tolerance Unless Specified: DRIVE ROLLER FULL D. PHILLIPS Material Heat Treat Finish Scale: Checked: Drawn: Date: A- Bryce D. Jewett Machine Mfg. Co., Inc. A 2 58-60 RC Fractional: Decimal: 1— 64 +– +– .005 No. By Date � FIGURE 5.8 Engineering Drawings Such as This One Show Dimensions, Tolerances, Materials, and Finishes Make-or-buy decision The choice between producing a component or a service and purchasing it from an outside source. 144 PART 2 Designing Operations Group Technology Engineering drawings may also include codes to facilitate group technology. Group technology requires that components be identified by a coding scheme that specifies the type of processing (such as drilling) and the parameters of the processing (such as size). This facilitates standardiza- tion of materials, components, and processes as well as the identification of families of parts. As families of parts are identified, activities and machines can be grouped to minimize setups, rout- ings, and material handling. An example of how families of parts may be grouped is shown in Figure 5.10. Group technology provides a systematic way to review a family of components to see if an existing component might suffice on a new project. Using existing or standard compo- nents eliminates all the costs connected with the design and development of the new part, which is a major cost reduction. For these reasons, successful implementation of group technology leads to the following advantages: 1. Improved design (because more design time can be devoted to fewer components). 2. Reduced raw material and purchases. 3. Simplified production planning and control. 4. Improved layout, routing, and machine loading. 5. Reduced tooling setup time, and work-in-process and production time. The application of group technology helps the entire organization, as many costs are reduced. DOCUMENTS FOR PRODUCTION Once a product is selected, designed, and ready for production, production is assisted by a vari- ety of documents. We will briefly review some of these. An assembly drawing simply shows an exploded view of the product. An assembly drawing is usually a three-dimensional drawing, known as an isometric drawing; the relative locations of com- ponents are drawn in relation to each other to show how to assemble the unit (see Figure 5.11[a]). The assembly chart shows in schematic form how a product is assembled. Manufactured com- ponents, purchased components, or a combination of both may be shown on an assembly chart. The assembly chart identifies the point of production at which components flow into subassemblies and ultimately into a final product. An example of an assembly chart is shown in Figure 5.11(b). The route sheet lists the operations necessary to produce the component with the material specified in the bill of material. The route sheet for an item will have one entry for each operation to be performed on the item. When route sheets include specific methods of operation and labor standards, they are often known as process sheets. The work order is an instruction to make a given quantity of a particular item, usually to a given schedule. The ticket that a waiter writes in your favorite restaurant is a work order. In a hospital or factory, the work order is a more formal document that provides authorization to draw Bill of Material for a Panel Weldment A 60-7 R 60-17 R 60-428 P 60-2 A 60-72 R 60-57-1 A 60-4 02-50-1150 A 60-73 A 60-74 R 60-99 02-50-1150 LOWER ROLLER ASSM. ROLLER PIN LOCKNUT GUIDE ASSM. REAR SUPPORT ANGLE ROLLER ASSEM. BOLT GUIDE ASSM. FRONT SUPPORT WELDM’T WEAR PLATE BOLT 1 1 1 1 1 1 1 1 1 1 1 1 (a) Hard Rock Cafe’s Hickory BBQ Bacon Cheeseburger Bun Hamburger patty Cheddar cheese Bacon BBQ onions Hickory BBQ sauce Burger set Lettuce Tomato Red onion Pickle French fries Seasoned salt 11- inch plate HRC flag 1 8 oz. 2 slices 2 strips 1/2 cup 1 oz. 1 leaf 1 slice 4 rings 1 slice 5 oz. 1 tsp. 1 1 (b) PANEL WELDM’T 1A 60-71 NUMBER DESCRIPTION QTY DESCRIPTION QTY � FIGURE 5.9 Bills of Material Take Different Forms in a (a) Manufacturing Plant and a (b) Restaurant, but in Both Cases, the Product Must Be Defined Group technology A product and component coding system that specifies the type of processing and the parameters of the processing; it allows similar products to be grouped. AUTHOR COMMENT Production personnel need clear, specific documents to help them make the product. Assembly drawing An exploded view of the product. Assembly chart A graphic means of identifying how components flow into subassemblies and final products. Route sheet A listing of the operations necessary to produce a component with the material specified in the bill of material. Work order An instruction to make a given quantity of a particular item. AUTHOR COMMENT Hard Rock’s recipe here serves the same purpose as a bill of material in a factory: It defines the product for production. Chapter 5 Design of Goods and Services 145 various pharmaceuticals or items from inventory, to perform various functions, and to assign per- sonnel to perform those functions. Engineering change notices (ECNs) change some aspect of the product’s definition or doc- umentation, such as an engineering drawing or a bill of material. For a complex product that has a long manufacturing cycle, such as a Boeing 777, the changes may be so numerous that no two 777s are built exactly alike—which is indeed the case. Such dynamic design change has fostered the development of a discipline known as configuration management, which is concerned with product identification, control, and documentation. Configuration management is the system by which a product’s planned and changing configurations are accurately identified and for which control and accountability of change are maintained. Product Life-Cycle Management (PLM) Product life-cycle management (PLM) is an umbrella of software programs that attempts to bring together phases of product design and manufacture—including tying together many of the tech- niques discussed in the prior two sections, Defining a Product and Documents for Production. The idea behind PLM software is that product design and manufacture decisions can be performed more creatively, faster, and more economically when the data are integrated and consistent. Although there is not one standard, PLM products often start with product design (CAD/CAM); move on to design for manufacture and assembly (DFMA); and then into product LO6: Describe the documents needed for production (a) Ungrouped Parts (b) Grouped Cylindrical Parts (families of parts) Grooved Slotted Threaded Drilled Machined � FIGURE 5.10 A Variety of Group Technology Coding Schemes Move Manufactured Components from (a) Ungrouped to (b) Grouped (families of parts) R 209 Angle R 207 Angle Bolts w/nuts (2) Left bracket assembly R 209 Angle R 207 Angle Bolts w/nuts (2) Right bracket assembly Bolt w/nut Part number tag R 404 Roller Lock washer Box w/packing material Poka-yoke inspection A1 A2 A3 A5 A4 (b) Assembly Chart R 207 31/2"� 3/8" Hex head bolt 3/8" Hex nut R 404 R 209 11/2" � 3/8" Hex head bolt R 207 3/8" Lock washer 3/8" Hex nut (a) Assembly Drawing 1 2 3 4 5 6 7 8 9 10 11 SA 2 SA 1 � FIGURE 5.11 Assembly Drawing and Assembly Chart Engineering change notice (ECN) A correction or modification of an engineering drawing or bill of material. Configuration management A system by which a product’s planned and changing components are accurately identified. Product life-cycle management (PLM) Software programs that tie together many phases of product design and manufacture. 146 PART 2 Designing Operations routing, materials, layout, assembly, maintenance and even environmental issues.6 Integration of these tasks makes sense because many of these decisions areas require overlapping pieces of data. PLM software is now a tool of many large organizations, including Lockheed Martin, GE, Procter & Gamble, Toyota, and Boeing. Boeing estimates that PLM will cut final assembly of its 787 jet from 2 weeks to 3 days. PLM is now finding its way into medium and small manufacture as well. Shorter life cycles, more technologically challenging products, more regulations about mate- rials and manufacturing processes, and more environmental issues all make PLM an appealing tool for operations managers. SERVICE DESIGN Much of our discussion so far has focused on what we can call tangible products, that is, goods. On the other side of the product coin are, of course, services. Service industries include banking, finance, insurance, transportation, and communications. The products offered by service firms range from a medical procedure that leaves only the tiniest scar after an appendectomy, to a shampoo and cut at a hair salon, to a great movie. Designing services is challenging because they often have unique characteristics. One reason productivity improvements in services are so low is because both the design and delivery of ser- vice products include customer interaction. When the customer participates in the design process, the service supplier may have a menu of services from which the customer selects options (see Figure 5.12a). At this point, the customer may even participate in the design of the service. Design specifications may take the form of a contract or a narrative description with photos (such as for cosmetic surgery or a hairstyle). Similarly, the customer may be involved in the delivery of a service (see Figure 5.12b) or in both design and delivery, a situation that maxi- mizes the product design challenge (see Figure 5.12c). However, as with goods, a large part of cost and quality of a service is defined at the design stage. Also as with goods, a number of techniques can both reduce costs and enhance the product. One technique is to design the product so that customization is delayed as late in the process as pos- sible. This is the way a hair salon operates: Although shampoo and rinse are done in a standard way with lower-cost labor, the tint and styling (customizing) are done last. It is also the way most restau- rants operate: How would you like that cooked? Which dressing would you prefer with your salad? The second approach is to modularize the product so that customization takes the form of changing modules. This strategy allows modules to be designed as “fixed,” standard entities. The modular approach to product design has applications in both manufacturing and service. Just as modular design allows you to buy a Harley-Davidson motorcycle or a high-fidelity sound system Each year the JR Simplot potato- processing facility in Caldwell, Idaho, produces billions of french fries for McDonald’s (left photo). Sixty specifications (including a special blend of frying oil, a unique steaming process, and exact time and temperature for prefrying and drying) define how these potatoes become french fries. Further, 40% of all french fries must be 2 to 3 inches long, 40% must be over 3 inches long, and a few stubby ones constitute the final 20%. Quality control personnel use a micrometer to measure the fries (right photo). AUTHOR COMMENT Services also need to be defined and documented. LO7: Describe customer participation in the design and production of services 6Some PLM vendors include supply chain elements such as sourcing, material management, and vendor evaluation in their packages, but in most instances, these are considered part of the ERP systems discussed along with MRP in Chapter 14. See, for instance, SAP PLM (www.mySAP.com), Parametric Technology Corp. (www.ptc.com), UGS Corp. (www.ugs.com), and Proplanner (www.proplanner.com). www.mySAP.com www.proplanner.com www.ptc.com www.ugs.com Chapter 5 Design of Goods and Services 147 with just the features you want, modular flexibility also lets you buy meals, clothes, and insurance on a mix-and-match (modular) basis. Similarly, investment portfolios are put together on a mod- ular basis, as are college curricula. Both are examples of how the modular approach can be used to customize a service. A third approach to the design of services is to divide the service into small parts and identify those parts that lend themselves to automation or reduced customer interaction. For instance, by isolating check-cashing activity via ATM machines, banks have been very effective at designing a product that both increases customer service and reduces costs. Similarly, airlines are moving to ticketless service. Because airlines spend $15 to $30 to produce a single ticket (including labor, printing, and travel agent’s commission), ticketless systems save the industry a billion dollars a year. Reducing both costs and lines at airports—and thereby increasing customer satisfaction— provides a win–win “product” design. Because of the high customer interaction in many service industries, a fourth technique is to focus design on the so-called moment of truth. Jan Carlzon, former president of Scandinavian Airways, believes that in the service industry there is a moment of truth when the relationship between the provider and the customer is crucial. At that moment, the customer’s satisfaction with the service is defined. The moment of truth is the moment that exemplifies, enhances, or detracts from the customer’s expectations. That moment may be as simple as a smile or having the checkout clerk focus on you rather than talking over his shoulder to the clerk at the next counter. Moments of truth can occur when you order at McDonald’s, get a haircut, or register for college courses. Figure 5.13 shows a moment-of-truth analysis for a computer company’s cus- tomer service hotline. The operations manager’s task is to identify moments of truth and design operations that meet or exceed the customer’s expectations. Documents for Services Because of the high customer interaction of most services, the documents for moving the prod- uct to production are different from those used in goods-producing operations. The documenta- tion for a service will often take the form of explicit job instructions that specify what is to happen at the moment of truth. For instance, regardless of how good a bank’s products may be in State College Registration Customer Delivery (a) Customer participation in design (b) Customer participation in delivery (c) Customer participation in design and delivery Design Customer Delivery Design Customer Delivery (prearranged funeral services or cosmetic surgery) (stress test for cardiac exam or delivery of a baby) (counseling, college education, financial management of personal affairs, or interior decorating) Design � FIGURE 5.12 Customer Participation in the Design of Services 148 PART 2 Designing Operations terms of checking, savings, trusts, loans, mortgages, and so forth, if the moment of truth is not done well, the product may be poorly received. Example 2 shows the kind of documentation a bank may use to move a product (drive-up window banking) to “production.” In a telemarketing service, the product design is communicated to production personnel in the form of telephone script, while a storyboard is used for movie and TV production. EXAMPLE 2 � Service documentation for production Experience Enhancers Standard Expectations Experience Detractors Better Best I had to call more than once to get through. A recording spoke to me rather than a person. While on hold, I get silence, and I wonder if I am disconnected. The technician sounded like he was reading a form of routine questions. The technician sounded uninterested. The technician rushed me. Only one local number needs to be dialed. I never get a busy signal. I get a human being to answer my call quickly, and he or she is pleasant and responsive to my problem. A timely resolution to my problem is offered. The technician is able to explain to me what I can expect to happen next. The technician was sin- cerely concerned and apologetic about my problem. The technician asked intelligent questions that allowed me to feel confident in his abilities. The technician offered various times to have work done to suit my schedule. Ways to avoid future problems were suggested. � FIGURE 5.13 Moment of Truth: Customer Contacts at a Computer Company’s Service Hotline Improve As We Move from Left to Right. First Bank Corp. wants to ensure effective delivery of service to its drive-up customers. APPROACH � Develop a “production” document for the tellers at the drive-up window that pro- vides the information necessary to do an effective job. SOLUTION � Documentation for Tellers at Drive-Up Windows Customers who use the drive-up teller windows rather than walk-in lobbies require a different customer relations technique. The distance and machinery between the teller and the customer raises communi- cation barriers. Guidelines to ensure good customer relations at the drive-up window are: • Be especially discreet when talking to the customer through the microphone. • Provide written instructions for customers who must fill out forms you provide. • Mark lines to be completed or attach a note with instructions. • Always say “please” and “thank you” when speaking through the microphone. • Establish eye contact with the customer if the distance allows it. • If a transaction requires that the customer park the car and come into the lobby, apologize for the inconvenience. Source: Adapted with permission from Teller Operations (Chicago, IL: The Institute of Financial Education, 1999): 32. INSIGHT � By providing documentation in the form of a script/guideline for tellers, the likeli- hood of effective communication and a good product/service is improved. LEARNING EXERCISE � Modify the guidelines above to show how they would be different for a drive-through restaurant. [Answer: Written instructions, marking lines to be completed, or coming into the store are seldom necessary, but techniques for making change, and proper transfer of the order should be included.] RELATED PROBLEM � 5.7 Chapter 5 Design of Goods and Services 149 APPLICATION OF DECISION TREES TO PRODUCT DESIGN Decision trees can be used for new-product decisions as well as for a wide variety of other man- agement problems. They are particularly helpful when there are a series of decisions and various outcomes that lead to subsequent decisions followed by other outcomes. To form a decision tree, we use the following procedure: 1. Be sure that all possible alternatives and states of nature are included in the tree. This includes an alternative of “doing nothing.” 2. Payoffs are entered at the end of the appropriate branch. This is the place to develop the pay- off of achieving this branch. 3. The objective is to determine the expected value of each course of action. We accomplish this by starting at the end of the tree (the right-hand side) and working toward the beginning of the tree (the left), calculating values at each step and “pruning” alternatives that are not as good as others from the same node. Example 3 shows the use of a decision tree applied to product design. � EXAMPLE 3 Decision tree applied to product design Silicon, Inc., a semiconductor manufacturer, is investigating the possibility of producing and marketing a microprocessor. Undertaking this project will require either purchasing a sophisticated CAD system or hiring and training several additional engineers. The market for the product could be either favorable or unfavorable. Silicon, Inc., of course, has the option of not developing the new product at all. With favorable acceptance by the market, sales would be 25,000 processors selling for $100 each. With unfavorable acceptance, sales would be only 8,000 processors selling for $100 each. The cost of CAD equipment is $500,000, but that of hiring and training three new engineers is only $375,000. However, manufacturing costs should drop from $50 each when manufacturing without CAD, to $40 each when manufacturing with CAD. The probability of favorable acceptance of the new microprocessor is .40; the probability of unfa- vorable acceptance is .60. ⎧ ⎪ ⎨ ⎪ ⎩ $2,500,000 –1,000,000 – 500,000 ––––––––– $1,000,000 Revenue Mfg. cost ($40 � 25,000) CAD cost Net (.4) High sales ⎧ ⎪ ⎨ ⎪ ⎩ $800,000 –320,000 –500,000 ––––––– –$20,000 Revenue Mfg. cost ($40 � 8,000) CAD cost Net loss (.6) Low sales ⎧ ⎪ ⎨ ⎪ ⎩ $2,500,000 –1,250,000 – 375,000 ––––––––– $875,000 Revenue Mfg. cost ($50 � 25,000) Hire and train cost Net (.4) High sales ⎧ ⎪ ⎨ ⎪ ⎩ $800,000 –400,000 –375,000 ––––––– $25,000 Revenue Mfg. cost ($50 � 8,000) Hire and train cost Net (.6) Low sales $0 Net⎧⎨ ⎩ Do nothing $0 Hire and train engineers $365,000 Purchase CAD $388,000 � FIGURE 5.14 Decision Tree for Development of a New Product LO8: Apply decision trees to product issues AUTHOR COMMENT The manager’s options are to purchase CAD, hire/train engineers, or do nothing. Purchasing CAD has the highest EMV. AUTHOR COMMENT A decision tree is a great tool for thinking through a problem. 150 PART 2 Designing Operations APPROACH � Use of a decision tree seems appropriate as Silicon, Inc., has the basic ingredients: a choice of decisions, probabilities, and payoffs. SOLUTION � In Figure 5.14 we draw a decision tree with a branch for each of the three deci- sions, assign the respective probabilities payoff for each branch, and then compute the respective EMVs. The expected monetary values (EMVs) have been circled at each step of the decision tree. For the top branch: This figure represents the results that will occur if Silicon, Inc., purchases CAD. The expected value of hiring and training engineers is the second series of branches: The EMV of doing nothing is $0. Because the top branch has the highest expected monetary value (an EMV of $388,000 vs. $365,000 vs. $0), it represents the best decision. Management should purchase the CAD system. INSIGHT � Use of the decision tree provides both objectivity and structure to our analysis of the Silicon, Inc., decision. LEARNING EXERCISE � If Silicon, Inc., thinks the probabilities of high sales and low sales may be equal, at .5 each, what is the best decision? [Answer: Purchase CAD remains the best decision, but with an EMV of $490,000.] RELATED PROBLEMS � 5.10, 5.11, 5.12, 5.13, 5.14, 5.15, 5.16, 5.18 ACTIVE MODEL 5.1 This example is further illustrated in Active Model 5.1 at www.pearsonhighered.com/heizer. = $365,000 EMV 1Hire>train engineers2 = 1.421$875,0002 + 1.621$25,0002
= $388,000
EMV 1purchase CAD syatem2 = 1.421$1,000,0002 + 1.621- $20,0002
TRANSITION TO PRODUCTION
Eventually, a product, whether a good or service, has been selected, designed, and defined. It
has progressed from an idea to a functional definition, and then perhaps to a design. Now,
management must make a decision as to further development and production or termination
of the product idea. One of the arts of modern management is knowing when to move a prod-
uct from development to production; this move is known as transition to production. The
product development staff is always interested in making improvements in a product.
Because this staff tends to see product development as evolutionary, they may never have a
completed product, but as we noted earlier, the cost of late product introduction is high.
Although these conflicting pressures exist, management must make a decision—more devel-
opment or production.
Once this decision is made, there is usually a period of trial production to ensure that
the design is indeed producible. This is the manufacturability test. This trial also gives
the operations staff the opportunity to develop proper tooling, quality control procedures,
and training of personnel to ensure that production can be initiated successfully. Finally,
when the product is deemed both marketable and producible, line management will assume
responsibility.
Some companies appoint a project manager; others use product development teams to ensure
that the transition from development to production is successful. Both approaches allow a wide
range of resources and talents to be brought to bear to ensure satisfactory production of a prod-
uct that is still in flux. A third approach is integration of the product development and manufac-
turing organizations. This approach allows for easy shifting of resources between the two
organizations as needs change. The operations manager’s job is to make the transition from R&D
to production seamless.
AUTHOR COMMENT
One of the arts of
management is knowing
when a product should
move from development
to production.

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Chapter 5 Design of Goods and Services 151
Effective product strategy requires selecting, designing, and
defining a product and then transitioning that product to pro-
duction. Only when this strategy is carried out effectively
can the production function contribute its maximum to the
organization. The operations manager must build a product
development system that has the ability to conceive, design,
and produce products that will yield a competitive advan-
tage for the firm. As products move through their life cycle
(introduction, growth, maturity, and decline), the options
that the operations manager should pursue change. Both man-
ufactured and service products have a variety of techniques
available to aid in performing this
activity efficiently.
Written specifications, bills of mate-
rial, and engineering drawings aid in
defining products. Similarly, assembly
drawings, assembly charts, route sheets, and
work orders are often used to assist in the actual production of
the product. Once a product is in production, value analysis is
appropriate to ensure maximum product value. Engineering
change notices and configuration management provide prod-
uct documentation.
CHAPTER SUMMARY
Key Terms
Product decision (p. 126)
Product-by-value analysis (p. 128)
Quality function deployment (QFD) (p. 131)
House of quality (p. 131)
Product development teams (p. 134)
Concurrent engineering (p. 134)
Manufacturability and value engineering
(p. 134)
Robust design (p. 135)
Modular design (p. 135)
Computer-aided design (CAD) (p. 136)
Design for manufacture and assembly
(DFMA) (p. 136)
3-D object modeling (p. 136)
Standard for the Exchange of Product
Data (STEP) (p. 136)
Computer-aided manufacturing
(CAM) (p. 137)
Virtual reality (p. 137)
Value analysis (p. 137)
Sustainability (p. 138)
Life cycle assessment (LCA) (p. 139)
Time-based competition (p. 140)
Joint ventures (p. 142)
Alliances (p. 142)
Engineering drawing (p. 142)
Bill of material (BOM) (p. 142)
Make-or-buy decision (p. 143)
Group technology (p. 144)
Assembly drawing (p. 144)
Assembly chart (p. 144)
Route sheet (p. 144)
Work order (p. 144)
Engineering change notice (ECN)
(p. 145)
Configuration management (p. 145)
Product life-cycle management (PLM)
(p. 145)
Solved Problem Virtual Office Hours help is available at www.myomlab.com
� SOLUTION
We draw the decision tree to reflect the two decisions and the
probabilities associated with each decision. We then determine the
payoff associated with each branch. The resulting tree is shown in
Figure 5.15.
For design A:
For design B:
The highest payoff is design option B, at $600,000.
= $600,000
EMV1design B2 = 1.821$750,0002 + 1.221$02
= $425,000
EMV1design A2 = 1.921$350,0002 + 1.121$1,100,0002
� SOLVED PROBLEM 5.1
Sarah King, president of King Electronics, Inc., has two design
options for her new line of high-resolution cathode-ray tubes
(CRTs) for CAD workstations. The life cycle sales forecast for the
CRT is 100,000 units.
Design option A has a .90 probability of yielding 59 good
CRTs per 100 and a .10 probability of yielding 64 good CRTs per
100. This design will cost $1,000,000.
Design option B has a .80 probability of yielding 64 good units
per 100 and a .20 probability of yielding 59 good units per 100. This
design will cost $1,350,000.
Good or bad, each CRT will cost $75. Each good CRT will sell
for $150. Bad CRTs are destroyed and have no salvage value. We
ignore any disposal costs in this problem.

www.myomlab.com

152 PART 2 Designing Operations
EMV = $425,000
(.9)
(.1)
$8,850,000
–7,500,000
–1,000,000
–––––––––
Sales 59,000 at $150
Mfg. cost 100,000 at $75
Design cost
$350,000
⎧
⎪
⎨
⎪
⎩
Yield 59
Yield 64
EMV = $600,000
(.8)
(.2)
Yield 64
Yield 59
$9,600,000
–7,500,000
–1,000,000
–––––––––
Sales 64,000 at $150
Mfg. cost 100,000 at $75
Design cost
$1,100,000
⎧
⎪
⎨
⎪
⎩
$9,600,000
–7,500,000
–1,350,000
–––––––––
Sales 64,000 at $150
Mfg. cost 100,000 at $75
Design cost
$750,000
⎧
⎪
⎨
⎪
⎩
$8,850,000
–7,500,000
–1,350,000
–––––––––
Sales 59,000 at $150
Mfg. cost 100,000 at $75
Design cost
0
⎧
⎪
⎨
⎪
⎩
Design A
Design B
FIGURE 5.15 �
Decision Tree for
Solved Problem 5.1
Bibliography
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133–141.
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4th ed. New York: McGraw-Hill, 2008.

Managing Quality
Chapter Outline
GLOBAL COMPANY PROFILE: ARNOLD PALMER
HOSPITAL
Quality and Strategy 156
Defining Quality 156
International Quality Standards 159
Total Quality Management 160
Tools of TQM 166
The Role of Inspection 170
TQM in Services 172
� Design of Goods and Services
� Managing Quality
� Process Strategy
� Location Strategies
� Layout Strategies
� Human Resources
� Supply-Chain Management
� Inventory Management
� Scheduling
� Maintenance
153

GLOBAL COMPANY PROFILE: ARNOLD PALMER HOSPITAL
MANAGING QUALITY PROVIDES A COMPETITIVE ADVANTAGE
AT ARNOLD PALMER HOSPITAL
S
ince 1989, the Arnold Palmer Hospital, named
after its famous golfing benefactor, has
touched the lives of over 7 million children
and women and their families. Its patients
come not only from its Orlando location but from all
50 states and around the world. More than 16,000
babies are delivered every year at Arnold Palmer, and
its huge neonatal intensive care unit boasts one of
the highest survival rates in the U.S.
Every hospital professes quality health care, but at
Arnold Palmer quality is the mantra—practiced in a
fashion like the Ritz-Carlton practices it in the hotel
industry. The hospital typically scores in the top 10%
of national benchmark studies in terms of patient
satisfaction. And its managers follow patient
questionnaire results daily. If anything is amiss,
corrective action takes place immediately.
Virtually every quality management technique we
present in this chapter is employed at Arnold Palmer
Hospital:
• Continuous improvement: The hospital constantly
seeks new ways to lower infection rates, readmission
rates, deaths, costs, and hospital stay times.
• Employee empowerment: When employees see a
problem, they are trained to take care of it; staff are
empowered to give gifts to patients displeased with
some aspect of service.
• Benchmarking: The hospital belongs to a 2,000-
member organization that monitors standards in
many areas and provides monthly feedback to the
hospital.
• Just-in-time: Supplies are delivered to Arnold Palmer
on a JIT basis. This keeps inventory costs low and
keeps quality problems from hiding.
The Storkboard is a visible chart of the status of each baby
about to be delivered, so all nurses and doctors are kept up-
to-date at a glance.
The lobby of Arnold Palmer Hospital, with its 20-foot-high Genie,
is clearly intended as a warm and friendly place for children.
154

The Mark Twain quote on the board reads
“Always Do Right. This will gratify some
people and astonish most.” The hospital has
redesigned its neonatal rooms. In the old
system, there were 16 neonatal beds in an
often noisy and large room. The new rooms
are semiprivate, with a quiet simulated-night
atmosphere. These rooms have proven to help
babies develop and improve more quickly.
This PYXIS inventory station gives nurses quick access to medicines
and supplies needed in their departments. When the nurse removes an
item for patient use, the item is automatically billed to that account, and
usage is noted at the main supply area.
When Arnold Palmer Hospital began planning for a new
11-story hospital across the street from its existing
building, it decided on a circular pod design, creating a
patient-centered environment. Rooms use warm colors,
have pull-down Murphy beds for family members, 14-foot
ceilings, and natural lighting with oversized windows.
The pod concept also means there is a nursing station
within a few feet of each 10-bed pod, saving much wasted
walking time by nurses to reach the patient. The Video
Case Study in Chapter 9 of the Lecture Guide & Activities
Manual examines this layout in detail.
• Tools such as Pareto charts and flowcharts: These
tools monitor processes and help the staff
graphically spot problem areas and suggest
ways they can be improved.
From their first day of orientation, employees from
janitors to nurses learn that the patient comes first.
Staff standing in hallways will never be heard
discussing their personal lives or commenting on
confidential issues of health care. This culture of
quality at Arnold Palmer Hospital makes a hospital
visit, often traumatic to children and their parents, a
warmer and more comforting experience.
ARNOLD PALMER HOSPITAL �
155

156 PART 2 Designing Operations
QUALITY AND STRATEGY
As Arnold Palmer Hospital and many other organizations have found, quality is a wonderful tonic
for improving operations. Managing quality helps build successful strategies of differentiation,
low cost, and response. For instance, defining customer quality expectations has helped Bose
Corp. successfully differentiate its stereo speakers as among the best in the world. Nucor has
learned to produce quality steel at low cost by developing efficient processes that produce consis-
tent quality. And Dell Computers rapidly responds to customer orders because quality systems,
with little rework, have allowed it to achieve rapid throughput in its plants. Indeed, quality may
be the critical success factor for these firms just as it is at Arnold Palmer Hospital.
As Figure 6.1 suggests, improvements in quality help firms increase sales and reduce costs,
both of which can increase profitability. Increases in sales often occur as firms speed response,
increase or lower selling prices, and improve their reputation for quality products. Similarly,
improved quality allows costs to drop as firms increase productivity and lower rework, scrap, and
warranty costs. One study found that companies with the highest quality were five times as
productive (as measured by units produced per labor-hour) as companies with the poorest qual-
ity. Indeed, when the implications of an organization’s long-term costs and the potential for
increased sales are considered, total costs may well be at a minimum when 100% of the goods or
services are perfect and defect free.
Quality, or the lack of quality, affects the entire organization from supplier to customer and
from product design to maintenance. Perhaps more importantly, building an organization that can
achieve quality is a demanding task. Figure 6.2 lays out the flow of activities for an organization
to use to achieve total quality management (TQM). A successful quality strategy begins with an
organizational culture that fosters quality, followed by an understanding of the principles of qual-
ity, and then engaging employees in the necessary activities to implement quality. When these
things are done well, the organization typically satisfies its customers and obtains a competitive
advantage. The ultimate goal is to win customers. Because quality causes so many other good
things to happen, it is a great place to start.
DEFINING QUALITY
An operations manager’s objective is to build a total quality management system that identifies
and satisfies customer needs. Total quality management takes care of the customer.
Consequently, we accept the definition of quality as adopted by the American Society for
Quality (ASQ, at www.asq.org): “The totality of features and characteristics of a product or ser-
vice that bears on its ability to satisfy stated or implied needs.”
Quality
The ability of a product or
service to meet customer needs.
LO1: Define quality and TQM 157
LO2: Describe the ISO international
quality standards 159
LO3: Explain what Six Sigma is 161
LO4: Explain how benchmarking is used
in TQM 163
Chapter 6 Learning Objectives
LO5: Explain quality robust products and
Taguchi concepts 165
LO6: Use the seven tools of TQM 167
AUTHOR COMMENT
Quality is an issue that affects
an entire organization.
AUTHOR COMMENT
To create a quality good or
service, operations managers
need to know what the
customer expects.
AUTHOR COMMENT
High-quality products and
services are the most
profitable.
VIDEO 6.1
The Culture of Quality at
Arnold Palmer Hospital
Improved Quality Increased Profits
Reduced Costs via
Increased productivity
Lower rework and scrap costs
Lower warranty costs
Sales Gains via
Two Ways Quality
Improves Profitability
Improved response
Flexible pricing
Improved reputation
� FIGURE 6.1
Ways Quality Improves
Profitability

www.asq.org

Chapter 6 Managing Quality 157
LO1: Define quality
and TQM
Others, however, believe that definitions of quality fall into several categories. Some defini-
tions are user based. They propose that quality “lies in the eyes of the beholder.” Marketing peo-
ple like this approach and so do customers. To them, higher quality means better performance,
nicer features, and other (sometimes costly) improvements. To production managers, quality is
manufacturing based. They believe that quality means conforming to standards and “making it
right the first time.” Yet a third approach is product based, which views quality as a precise and
measurable variable. In this view, for example, really good ice cream has high butterfat levels.
This text develops approaches and techniques to address all three categories of quality. The
characteristics that connote quality must first be identified through research (a user-based
approach to quality). These characteristics are then translated into specific product attributes
(a product-based approach to quality). Then, the manufacturing process is organized to ensure
that products are made precisely to specifications (a manufacturing-based approach to quality).
A process that ignores any one of these steps will not result in a quality product.
Implications of Quality
In addition to being a critical element in operations, quality has other implications. Here are three
other reasons why quality is important:
1. Company reputation: An organization can expect its reputation for quality—be it good or
bad—to follow it. Quality will show up in perceptions about the firm’s new products,
employment practices, and supplier relations. Self-promotion is not a substitute for quality
products.
2. Product liability: The courts increasingly hold organizations that design, produce, or dis-
tribute faulty products or services liable for damages or injuries resulting from their use.
Legislation such as the Consumer Product Safety Act sets and enforces product standards by
banning products that do not reach those standards. Impure foods that cause illness, night-
gowns that burn, tires that fall apart, or auto fuel tanks that explode on impact can all lead to
huge legal expenses, large settlements or losses, and terrible publicity.
3. Global implications: In this technological age, quality is an international, as well as OM,
concern. For both a company and a country to compete effectively in the global economy,
products must meet global quality, design, and price expectations. Inferior products harm a
firm’s profitability and a nation’s balance of payments.
Organizational practices
Leadership, Mission statement, Effective operating procedures,
Staff support, Training
Yields: What is important and what is to be accomplished.
Quality principles
Customer focus, Continuous improvement, Benchmarking,
Just-in-time, Tools of TQM
Yields: How to do what is important and to be accomplished.
Employee fulfillment
Empowerment, Organizational commitment
Yields: Employee attitudes that can accomplish
what is important.
Customer satisfaction
Winning orders, Repeat customers
Yields: An effective organization with
a competitive advantage.
� FIGURE 6.2 The Flow of Activities that Are Necessary to Achieve Total Quality Management

158 PART 2 Designing Operations
Malcolm Baldrige National Quality Award
The global implications of quality are so important that the U.S. has established the Malcolm
Baldrige National Quality Award for quality achievement. The award is named for former
Secretary of Commerce Malcolm Baldrige. Winners include such firms as Motorola, Milliken,
Xerox, FedEx, Ritz-Carlton Hotels, AT&T, Cadillac, and Texas Instruments. (For details about
the Baldrige Award and its 1,000-point scoring system, visit www.quality.nist.gov.)
The Japanese have a similar award, the Deming Prize, named after an American,
Dr. W. Edwards Deming.
Cost of Quality (COQ)
Four major categories of costs are associated with quality. Called the cost of quality (COQ), they are:
• Prevention costs: costs associated with reducing the potential for defective parts or services
(e.g., training, quality improvement programs).
• Appraisal costs: costs related to evaluating products, processes, parts, and services (e.g., test-
ing, labs, inspectors).
• Internal failure: costs that result from production of defective parts or services before deliv-
ery to customers (e.g., rework, scrap, downtime).
• External costs: costs that occur after delivery of defective parts or services (e.g., rework,
returned goods, liabilities, lost goodwill, costs to society).
The first three costs can be reasonably estimated, but external costs are very hard to quantify.
When GE had to recall 3.1 million dishwashers recently (because of a defective switch alleged to
have started seven fires), the cost of repairs exceeded the value of all the machines. This leads to
the belief by many experts that the cost of poor quality is consistently underestimated.
Observers of quality management believe that, on balance, the cost of quality products is only
a fraction of the benefits. They think the real losers are organizations that fail to work aggres-
sively at quality. For instance, Philip Crosby stated that quality is free. “What costs money are
the unquality things—all the actions that involve not doing it right the first time.”1
Leaders in Quality Besides Crosby there are several other giants in the field of quality man-
agement, including Deming, Feigenbaum, and Juran. Table 6.1 summarizes their philosophies
and contributions.
Ethics and Quality Management
For operations managers, one of the most important jobs is to deliver healthy, safe, and quality
products and services to customers. The development of poor-quality products, because of inade-
quate design and production processes, results not only in higher production costs but also leads to
injuries, lawsuits, and increased government regulation.
If a firm believes that it has introduced a questionable product, ethical conduct must dictate
the responsible action. This may be a worldwide recall, as conducted by both Johnson & Johnson
(for Tylenol) and Perrier (for sparkling water), when each of these products was found to be con-
taminated. A manufacturer must accept responsibility for any poor-quality product released to
the public. In recent years, Ford (the Explorer SUV maker) and Firestone (the radial tire maker)
have been accused of failing to issue product recalls, of withholding damaging information, and
of handling complaints on an individual basis.2
There are many stakeholders involved in the production and marketing of poor-quality prod-
ucts, including stockholders, employees, customers, suppliers, distributors, and creditors. As a
matter of ethics, management must ask if any of these stakeholders are being wronged. Every
company needs to develop core values that become day-to-day guidelines for everyone from the
CEO to production-line employees.
Cost of quality (COQ)
The cost of doing things
wrong—that is, the price of
nonconformance.
Takumi is a Japanese
character that symbolizes a
broader dimension than
quality, a deeper process than
education, and a more perfect
method than persistence.
1Philip B. Crosby, Quality Is Free (New York: McGraw-Hill, 1979). Further, J. M. Juran states, in his book Juran on
Quality by Design (The Free Press 1992, p. 119), that costs of poor quality “are huge, but the amounts are not known with
precision. In most companies the accounting system provides only a minority of the information needed to quantify this
cost of poor quality. It takes a great deal of time and effort to extend the accounting system so as to provide full coverage.”
2For further reading, see O. Fisscher and A. Nijhof, “Implications of Business Ethics for Quality Management,” TQM
Magazine 17 (2005): 150–161; and M. R. Nayebpour and D. Koehn, “The Ethics of Quality: Problems and
Preconditions,” Journal of Business Ethics 44 (April, 2003): 37–48.

www.quality.nist.gov

Chapter 6 Managing Quality 159
� TABLE 6.1 Leaders in the Field of Quality Management
Leader Philosophy/Contribution
W. Edwards Deming Deming insisted management accept responsibility for building good systems. The employee cannot
produce products that on average exceed the quality of what the process is capable of producing. His
14 points for implementing quality improvement are presented in this chapter.
Joseph M. Juran A pioneer in teaching the Japanese how to improve quality, Juran believed strongly in top-management
commitment, support, and involvement in the quality effort. He was also a believer in teams that
continually seek to raise quality standards. Juran varies from Deming somewhat in focusing on the
customer and defining quality as fitness for use, not necessarily the written specifications.
Armand Feigenbaum His 1961 book, Total Quality Control, laid out 40 steps to quality improvement processes. He viewed
quality not as a set of tools but as a total field that integrated the processes of a company. His work in
how people learn from each other’s successes led to the field of cross-functional teamwork.
Philip B. Crosby Quality is Free was Crosby’s attention-getting book published in 1979. Crosby believed that in the
traditional trade-off between the cost of improving quality and the cost of poor quality, the cost of poor
quality is understated. The cost of poor quality should include all of the things that are involved in not
doing the job right the first time. Crosby coined the term zero defects and stated, “There is absolutely no
reason for having errors or defects in any product or service.”
INTERNATIONAL QUALITY STANDARDS
ISO 9000
Quality is so important globally that the world is uniting around a single quality standard, ISO
9000. ISO 9000 is the only quality standard with international recognition. In 1987, 91 member
nations (including the U.S.) published a series of quality assurance standards, known collectively
as ISO 9000. The U.S., through the American National Standards Institute (ANSI), has adopted
the ISO 9000 series as the ANSI/ASQ Q9000 series. The focus of the standards is to establish
quality management procedures, through leadership, detailed documentation, work instructions,
and recordkeeping. These procedures, we should note, say nothing about the actual quality of the
product—they deal entirely with standards to be followed.
To become ISO 9000 certified, organizations go through a 9- to 18-month process that
involves documenting quality procedures, an on-site assessment, and an ongoing series of audits
of their products or services. To do business globally being listed in the ISO directory is critical.
As of 2009, there were over 1 million certifications awarded to firms in 175 countries. About
40,000 U.S. firms are ISO 9000 certified. Over 200,000 Chinese firms have received certificates.
ISO upgraded its standards in 2008 into more of a quality management system, which is
detailed in its ISO 9001: 2008 component. Leadership by top management and customer
requirements and satisfaction play a much larger role, while documented procedures receive less
emphasis under ISO 9001: 2008.
ISO 14000
The continuing internationalization of quality is evident with the development of ISO 14000.
ISO 14000 is a series of environmental management standards that contain five core elements:
(1) environmental management, (2) auditing, (3) performance evaluation, (4) labeling, and
(5) life cycle assessment. The new standard could have several advantages:
• Positive public image and reduced exposure to liability.
• Good systematic approach to pollution prevention through the minimization of ecological
impact of products and activities.
• Compliance with regulatory requirements and opportunities for competitive advantage.
• Reduction in need for multiple audits.
This standard is being accepted worldwide, with ISO 14001, which addresses environmental impacts
of activities systematically, receiving great attention. The OM in Action box “Subaru’s Clean, Green
Set of Wheels with ISO 14001” illustrates the growing application of the ISO 14000 series.
As a follow-on to ISO 14000, ISO 24700 reflects the business world’s current approach to
reusing recovered components from many products. These components must be “qualified as
AUTHOR COMMENT
International quality
standards grow in
prominence every year.
See www.iso.ch and
www.asq.org to learn
more about them.
LO2: Describe the ISO
international quality
standards
ISO 14000
A series of environmental
management standards
established by the
International Organization
for Standardization (ISO).
ISO 9000
A set of quality standards
developed by the International
Organization for Standardization
(ISO).

www.iso.ch

www.asq.org

160 PART 2 Designing Operations
good as new” and meet all safety and environmental criteria. Xerox was one of the companies
that helped write ISO 24700 and an early applicant for certification.
TOTAL QUALITY MANAGEMENT
Total quality management (TQM) refers to a quality emphasis that encompasses the entire
organization, from supplier to customer. TQM stresses a commitment by management to have
a continuing companywide drive toward excellence in all aspects of products and services that
are important to the customer. Each of the 10 decisions made by operations managers deals
with some aspect of identifying and meeting customer expectations. Meeting those expecta-
tions requires an emphasis on TQM if a firm is to compete as a leader in world markets.
Quality expert W. Edwards Deming used 14 points (see Table 6.2) to indicate how he imple-
mented TQM. We develop these into seven concepts for an effective TQM program: (1) continu-
ous improvement, (2) Six Sigma, (3) employee empowerment, (4) benchmarking, (5) just-in-time
(JIT), (6) Taguchi concepts, and (7) knowledge of TQM tools.
Total quality
management (TQM)
Management of an entire
organization so that it excels in
all aspects of products and
services that are important to
the customer.
AUTHOR COMMENT
The 7 concepts that make
up TQM are part of the
lexicon of business.
Going green had a humble beginning. First, it was
newspapers, soda cans and bottles, and corrugated
packaging—the things you typically throw into your own
recycling bins. Similarly, at Subaru’s Lafayette, Indiana, plant,
the process of becoming the first completely waste-free auto
plant in North America began with employees dropping these
things in containers throughout the plant. Then came
employee empowerment. “We had 268 suggestions for
different things to improve our recycling efforts,” said Denise
Coogan, plant ISO 14001 environmental compliance leader.
Some ideas were easy to handle. “With plastic shrink
wrap, we found some (recyclers) wouldn’t take colored
shrink wrap. So we went back to our vendors and asked for
only clear shrink wrap,” Coogan said. Some suggestions
were a lot more dirty. “We went dumpster diving to see what
we were throwing away and see what we could do with it.”
The last load of waste generated by Subaru made its way
to a landfill four years ago. Since then, everything that enters
the plant eventually exits as a usable product. Coogan adds,
“We didn’t redefine ‘zero.’ Zero means zero. Nothing from our
manufacturing process goes to the landfill.”
Last year alone, the Subaru plant recycled 13,142 tons
of steel, 1,448 tons of paper products, 194 tons of plastics,
10 tons of solvent-soaked rags, and 4 tons of light bulbs. It
thereby conserved 29,200 trees, 670,000 gallons of oil,
34,700 gallons of gas, 10 million gallons of water, and
53,000 million watts of electricity.
Going green isn’t easy, but it can be done!
Sources: The Wall Street Journal (March 23, 2009): R4; Industry Week
(July 2008): 36–41; and Industrial Engineer (April 2006): 26–29.
OM in Action � Subaru’s Clean, Green Set of Wheels with ISO 14001
1. Create consistency of purpose.
2. Lead to promote change.
3. Build quality into the product; stop depending on inspections to catch problems.
4. Build long-term relationships based on performance instead of awarding business on the basis of price.
5. Continuously improve product, quality, and service.
6. Start training.
7. Emphasize leadership.
8. Drive out fear.
9. Break down barriers between departments.
10. Stop haranguing workers.
11. Support, help, and improve.
12. Remove barriers to pride in work.
13. Institute a vigorous program of education and self-improvement.
14. Put everybody in the company to work on the transformation.
Source: Deming, W. Edwards. Out of the Crisis, pp. 23–24, © 2000 W. Edwards Deming Institute, published by
The MIT Press. Reprinted by permission.
� TABLE 6.2
Deming’s 14 Points for
Implementing Quality
Improvement

Chapter 6 Managing Quality 161
Continuous Improvement
Total quality management requires a never-ending process of continuous improvement that cov-
ers people, equipment, suppliers, materials, and procedures. The basis of the philosophy is that
every aspect of an operation can be improved. The end goal is perfection, which is never
achieved but always sought.
Plan-Do-Check-Act Walter Shewhart, another pioneer in quality management, developed a
circular model known as PDCA (plan, do, check, act) as his version of continuous improvement.
Deming later took this concept to Japan during his work there after World War II.3 The PDCA
cycle is shown in Figure 6.3 as a circle to stress the continuous nature of the improvement process.
The Japanese use the word kaizen to describe this ongoing process of unending improvement—
the setting and achieving of ever-higher goals. In the U.S., TQM and zero defects are also used to
describe continuous improvement efforts. But whether it’s PDCA, kaizen, TQM, or zero defects, the
operations manager is a key player in building a work culture that endorses continuous improvement.
Six Sigma
The term Six Sigma, popularized by Motorola, Honeywell, and General Electric, has two
meanings in TQM. In a statistical sense, it describes a process, product, or service with an
extremely high capability (99.9997% accuracy). For example, if 1 million passengers pass
through the St. Louis Airport with checked baggage each month, a Six Sigma program for bag-
gage handling will result in only 3.4 passengers with misplaced luggage. The more common
three-sigma program (which we address in the supplement to this chapter) would result in 2,700
passengers with misplaced bags every month. See Figure 6.4.
The second TQM definition of Six Sigma is a program designed to reduce defects to help lower
costs, save time, and improve customer satisfaction. Six Sigma is a comprehensive system—a
strategy, a discipline, and a set of tools—for achieving and sustaining business success:
• It is a strategy because it focuses on total customer satisfaction.
• It is a discipline because it follows the formal Six Sigma Improvement Model known as
DMAIC. This five-step process improvement model (1) Defines the project’s purpose, scope,
and outputs and then identifies the required process information, keeping in mind the customer’s
PDCA
A continuous improvement
model of plan, do, check. act.
LO3: Explain what
Six Sigma is
Six Sigma
A program to save time,
improve quality, and lower
costs.
3. Check
Is the plan
working?
2. Do
Test the
plan.
4. Act
Implement
the plan,
document.
1. Plan
Identify the
problem and
make a plan.
Upper
limits
Lower
limits
2,700 defects/million
Mean
±3σ
±6σ
3.4 defects/million
� FIGURE 6.4
Defects per million
for vs. —6S—3S
AUTHOR COMMENT
Recall that provides
99.73% accuracy, while
is 99.9997%.
; 6s
; 3s
3As a result, the Japanese refer to the PDCA cycle as a Deming circle, while others call it a Shewhart circle.
� FIGURE 6.3
PDCA cycle

162 PART 2 Designing Operations
definition of quality; (2) Measures the process and collects data; (3) Analyzes the data, ensur-
ing repeatability (the results can be duplicated), and reproducibility (others get the same result);
(4) Improves, by modifying or redesigning, existing processes and procedures; and (5) Controls
the new process to make sure performance levels are maintained.
• It is a set of seven tools that we introduce shortly in this chapter: check sheets, scatter diagrams,
cause-and-effect diagrams, Pareto charts, flowcharts, histograms, and statistical process control.
Motorola developed Six Sigma in the 1980s in response to customer complaints about its
products, and to stiff competition. The company first set a goal of reducing defects by 90%.
Within 1 year it had achieved such impressive results—through benchmarking competitors,
soliciting new ideas from employees, changing reward plans, adding training, and revamping
critical processes—that it documented the procedures into what it called Six Sigma. Although
the concept was rooted in manufacturing, GE later expanded Six Sigma into services, including
human resources, sales, customer services, and financial/credit services. The concept of wiping
out defects turns out to be the same in both manufacturing and services.
Implementing Six Sigma Implementing Six Sigma “is a big commitment,” says the head
of that program at Praxair, a major industrial gas company. “We’re asking our executives to
spend upward of 15% of their time on Six Sigma. If you don’t spend the time, you don’t get the
results.” Indeed, successful Six Sigma programs in every firm, from GE to Motorola to DuPont
to Texas Instruments require a major time commitment, especially from top management. These
leaders have to formulate the plan, communicate their buy-in and the firm’s objectives, and take
a visible role in setting the example for others.
Successful Six Sigma projects are clearly related to the strategic direction of a company. It is
a management-directed, team-based, and expert-led approach.4
Employee Empowerment
Employee empowerment means involving employees in every step of the production process.
Consistently, business literature suggests that some 85% of quality problems have to do with
materials and processes, not with employee performance. Therefore, the task is to design equip-
ment and processes that produce the desired quality. This is best done with a high degree of
involvement by those who understand the shortcomings of the system. Those dealing with the
system on a daily basis understand it better than anyone else. One study indicated that TQM pro-
grams that delegate responsibility for quality to shop-floor employees tend to be twice as likely
to succeed as those implemented with “top-down” directives.5
When nonconformance occurs, the worker is seldom wrong. Either the product was designed
wrong, the system that makes the product was designed wrong, or the employee was improperly
trained. Although the employee may be able to help solve the problem, the employee rarely causes it.
Techniques for building employee empowerment include (1) building communication net-
works that include employees; (2) developing open, supportive supervisors; (3) moving respon-
sibility from both managers and staff to production employees; (4) building high-morale
organizations; and (5) creating such formal organization structures as teams and quality circles.
Teams can be built to address a variety of issues. One popular focus of teams is quality. Such
teams are often known as quality circles. A quality circle is a group of employees who meet reg-
ularly to solve work-related problems. The members receive training in group planning, problem
solving, and statistical quality control. They generally meet once a week (usually after work but
sometimes on company time). Although the members are not rewarded financially, they
do receive recognition from the firm. A specially trained team member, called the facilitator,
usually helps train the members and keeps the meetings running smoothly. Teams with a quality
focus have proven to be a cost-effective way to increase productivity as well as quality.
4To train employees in how to improve quality and its relationship to customers, there are three other key players in the Six
Sigma program: Master Black Belts, Black Belts, and Green Belts. Master Black Belts are full-time teachers who have
extensive training in statistics, quality tools, and leadership. They mentor Black Belts, who in turn are project team leaders,
directing perhaps a half-dozen projects per year. Dow Chemical and DuPont have more than 1,000 Black Belts each in their
global operations. DuPont also has 160 Master Black Belts and introduces over 2,000 Green Belts per year into its ranks.
5“The Straining of Quality,” The Economist (January 14, 1995): 55. We also see that this is one of the strengths of
Southwest Airlines, which offers bare-bones domestic service but whose friendly and humorous employees help it
obtain number one ranking for quality. (See Fortune [March 6, 2006]: 65–69.)
Employee empowerment
Enlarging employee jobs so that
the added responsibility and
authority is moved to the
lowest level possible in the
organization.
Quality circle
A group of employees meeting
regularly with a facilitator to
solve work-related problems
in their work area.

Chapter 6 Managing Quality 163
Benchmarking
Benchmarking is another ingredient in an organization’s TQM program. Benchmarking
involves selecting a demonstrated standard of products, services, costs, or practices that repre-
sent the very best performance for processes or activities very similar to your own. The idea is to
develop a target at which to shoot and then to develop a standard or benchmark against which to
compare your performance. The steps for developing benchmarks are:
1. Determine what to benchmark.
2. Form a benchmark team.
3. Identify benchmarking partners.
4. Collect and analyze benchmarking information.
5. Take action to match or exceed the benchmark.
Typical performance measures used in benchmarking include percentage of defects, cost per
unit or per order, processing time per unit, service response time, return on investment, customer
satisfaction rates, and customer retention rates.
In the ideal situation, you find one or more similar organizations that are leaders in the partic-
ular areas you want to study. Then you compare yourself (benchmark yourself) against them.
The company need not be in your industry. Indeed, to establish world-class standards, it may be
best to look outside your industry. If one industry has learned how to compete via rapid product
development while yours has not, it does no good to study your industry.
This is exactly what Xerox and Mercedes Benz did when they went to L.L. Bean for order-filling
and warehousing benchmarks. Xerox noticed that L.L. Bean was able to “pick” orders three times as
fast as it could. After benchmarking, it was immediately able to pare warehouse costs by 10%.
Mercedes Benz observed that L.L. Bean warehouse employees used flowcharts to spot wasted
motions. The auto giant followed suit and now relies more on problem solving at the worker level.
Benchmarks often take the form of “best practices” found in other firms or in other divisions.
Table 6.3 illustrates best practices for resolving customer complaints.
Likewise, Britain’s Great Ormond Street Hospital benchmarked the Ferrari Racing Team’s pit
stops to improve one aspect of medical care. (See the OM in Action box on the next page.)
Internal Benchmarking When an organization is large enough to have many divisions or busi-
ness units, a natural approach is the internal benchmark. Data are usually much more accessible
than from outside firms. Typically, one internal unit has superior performance worth learning from.
Workers at this TRW airbag manufacturing plant
in Marshall, Illinois, are their own inspectors.
Empowerment is an essential part of TQM. This
man is checking the quality of a crash sensor
he built.
Benchmarking
Selecting a demonstrated
standard of performance that
represents the very best
performance for a process or an
activity.
LO4: Explain how
benchmarking is used
in TQM
Best Practice Justification
Make it easy for clients to complain.
Respond quickly to complaints.
Resolve complaints on the first contact.
Use computers to manage complaints.
Recruit the best for customer service jobs.
It is free market research.
It adds customers and loyalty.
It reduces cost
Discover trends, share them, and align your services.
It should be part of formal training and career
advancement.
Source: Canadian Government Guide on Complaint Mechanism.
� TABLE 6.3
Best Practices for Resolving
Customer Complaints

164 PART 2 Designing Operations
6Note that benchmarking is good for evaluating how well you are doing the thing you are doing compared with the
industry, but the more imaginative approach to process improvement is to ask, Should we be doing this at all?
Comparing your warehousing operations to the marvelous job that L.L. Bean does is fine, but maybe you should be out-
sourcing the warehousing function (see Supplement 11).
After surgeons successfully completed a 6-hour operation
to fix a hole in a 3-year-old boy’s heart, Dr. Angus McEwan
supervised one of the most dangerous phases of the
procedure: the boy’s transfer from surgery to the intensive
care unit.
Thousands of such “handoffs” occur in hospitals every
day, and devastating mistakes can happen during them. In
fact, at least 35% of preventable hospital mishaps take
place because of handoff problems. Risks come from
many sources: using temporary nursing staff, frequent
shift changes for interns, surgeons working in larger
teams, and an ever-growing tangle of wires and tubes
connected to patients.
In one of the most unlikely benchmarks in modern
medicine, Britain’s largest children’s hospital turned to
Italy’s Formula One Ferrari racing team for help in
revamping patient handoff techniques. Armed with videos
and slides, the racing team described how they analyze
pit crew performance. It also explained how its system for
recording errors stressed the small ones that go
unnoticed in pit-stop handoffs.
To move forward, Ferrari invited a team of doctors to
attend practice sessions at the British Grand Prix in order
to get closer looks at pit stops. Ferrari’s technical director,
Nigel Stepney, then watched a video of a hospital handoff.
Stepney was not impressed. “In fact, he was amazed at
how clumsy, chaotic, and informal the process appeared,”
said one hospital official. At that meeting, Stepney
described how each Ferrari crew member is required
to do a specific job, in a specific sequence, and in
silence. The hospital handoff, in contrast, had several
conversations going on at once, while different members
of its team disconnected or reconnected patient
equipment, but in no particular order.
Results of the benchmarking process: handoff errors fell
42% to 49%, with a bonus of faster handoff time.
Sources: The Wall Street Journal (December 3, 2007): B11 and (November 14,
2006): A1, A8.
OM in Action � A Hospital Benchmarks against the Ferrari Racing Team?
Xerox’s almost religious belief in benchmarking has paid off not only by looking outward to
L.L. Bean but by examining the operations of its various country divisions. For example, Xerox
Europe, a $6 billion subsidiary of Xerox Corp., formed teams to see how better sales could result
through internal benchmarking. Somehow, France sold five times as many color copiers as did
other divisions in Europe. By copying France’s approach, namely, better sales training and use of
dealer channels to supplement direct sales, Norway increased sales by 152%, Holland by 300%,
and Switzerland by 328%!
Benchmarks can and should be established in a variety of areas. Total quality management
requires no less.6
Just-in-Time (JIT)
The philosophy behind just-in-time (JIT) is one of continuing improvement and enforced prob-
lem solving. JIT systems are designed to produce or deliver goods just as they are needed. JIT is
related to quality in three ways:
• JIT cuts the cost of quality: This occurs because scrap, rework, inventory investment, and
damage costs are directly related to inventory on hand. Because there is less inventory on
hand with JIT, costs are lower. In addition, inventory hides bad quality, whereas JIT immedi-
ately exposes bad quality.
• JIT improves quality: As JIT shrinks lead time it keeps evidence of errors fresh and limits the
number of potential sources of error. JIT creates, in effect, an early warning system for quality
problems, both within the firm and with vendors.

Chapter 6 Managing Quality 165
• Better quality means less inventory and a better, easier-to-employ JIT system: Often the purpose of
keeping inventory is to protect against poor production performance resulting from unreliable qual-
ity. If consistent quality exists, JIT allows firms to reduce all the costs associated with inventory.
Taguchi Concepts
Most quality problems are the result of poor product and process design. Genichi Taguchi has
provided us with three concepts aimed at improving both product and process quality: quality
robustness, quality loss function, and target-oriented quality.7
Quality robust products are products that can be produced uniformly and consistently in ad-
verse manufacturing and environmental conditions. Taguchi’s idea is to remove the effects of
adverse conditions instead of removing the causes. Taguchi suggests that removing the effects is
often cheaper than removing the causes and more effective in producing a robust product. In this
way, small variations in materials and process do not destroy product quality.
A quality loss function (QLF) identifies all costs connected with poor quality and shows
how these costs increase as the product moves away from being exactly what the customer
wants. These costs include not only customer dissatisfaction but also warranty and service costs;
internal inspection, repair, and scrap costs; and costs that can best be described as costs to soci-
ety. Notice that Figure 6.5(a) shows the quality loss function as a curve that increases at an
increasing rate. It takes the general form of a simple quadratic formula:
All the losses to society due to poor performance are included in the loss function. The smaller
the loss, the more desirable the product. The farther the product is from the target value, the more
severe the loss.
Taguchi observed that traditional conformance-oriented specifications (i.e., the product is
good as long as it falls within the tolerance limits) are too simplistic. As shown in Figure 6.5(b),
conformance-oriented quality accepts all products that fall within the tolerance limits, producing
more units farther from the target. Therefore, the loss (cost) is higher in terms of customer satis-
faction and benefits to society. Target-oriented quality, on the other hand, strives to keep the prod-
uct at the desired specification, producing more (and better) units near the target. Target-oriented
quality is a philosophy of continuous improvement to bring the product exactly on target.
C = cost of the deviation at the specification limit
D2 = square of the distance from the target value
where L = loss to society
L = D2C
LO5: Explain quality
robust products and Taguchi
concepts
Quality robust
Products that are consistently
built to meet customer needs in
spite of adverse conditions in
the production process.
Quality loss function
(QLF)
A mathematical function that
identifies all costs connected
with poor quality and shows
how these costs increase as
product quality moves from
what the customer wants.
Target-oriented quality
brings products toward
the target value.
Conformance-oriented
quality keeps products
within 3 standard
deviations.
Distribution of
Specifications for
Products Produced
(b)
Quality Loss Function
(a)
High loss
Low loss
Loss (to
producing
organization,
customer,
and society)
Frequency
Lower
Specification
Target Upper
Poor
Fair
Good
Best
Unacceptable
Target-oriented quality
yields more product in
the “best” category.
� FIGURE 6.5
(a) Quality Loss Function and
(b) Distribution of Products
Produced
Taguchi aims for the target
because products produced near
the upper and lower acceptable
specifications result in higher
quality loss function.
7G. Taguchi, S. Chowdhury, and Y. Wu, Taguchi’s Quality Engineering Handbook (New York: Wiley, 2004).
Target-oriented quality
A philosophy of continuous
improvement to bring a product
exactly on target.

166 PART 2 Designing Operations
Knowledge of TQM Tools
To empower employees and implement TQM as a continuing effort, everyone in the organization
must be trained in the techniques of TQM. In the following section, we focus on some of the di-
verse and expanding tools that are used in the TQM crusade.
TOOLS OF TQM
Seven tools that are particularly helpful in the TQM effort are shown in Figure 6.6. We will now
introduce these tools.
Check Sheets
A check sheet is any kind of a form that is designed for recording data. In many cases, the re-
cording is done so the patterns are easily seen while the data are being taken (see Figure 6.6[a]).
Check sheets help analysts find the facts or patterns that may aid subsequent analysis. An
AUTHOR COMMENT
These 7 tools will prove
useful in many of your
courses and throughout
your career.
Tools for Generating Ideas
Tools for Organizing the Data
Tools for Identifying Problems
(f) Histogram: A distribution that shows the frequency of
occurrences of a variable
(g) Statistical Process Control Chart: A chart with time on the
horizontal axis for plotting values of a statistic
(b) Scatter Diagram: A graph of the value
of one variable vs. another variable
(a) Check Sheet: An organized method of
recording data
(c) Cause-and-Effect Diagram: A tool that
identifies process elements (causes)
that may affect an outcome
Defect 1
l l l
l l
l l l l l l l l l
l l l l l l l l
l l l l l l l l
2 3 4 5 6 7 8
A
B
C
Hour
Absenteeism
P
ro
d
u
c
ti
v
it
y
Effect
MethodsMaterials
MachineryManpower
Cause
P
e
rc
e
n
t
F
re
q
u
e
n
c
y
A B C D E
Repair time (minutes)
Distribution
F
re
q
u
e
n
cy
Upper control limit
Lower control limit
Time
Target value
(d) Pareto Chart: A graph that identifies and plots problems or
defects in descending order of frequency
(e) Flowchart (Process Diagram): A chart that describes the
steps in a process
� FIGURE 6.6 Seven Tools of TQM

Chapter 6 Managing Quality 167
example might be a drawing that shows a tally of the areas where defects are occurring or a
check sheet showing the type of customer complaints.
Scatter Diagrams
Scatter diagrams show the relationship between two measurements. An example is the positive
relationship between length of a service call and the number of trips a repairperson makes back
to the truck for parts. Another example might be a plot of productivity and absenteeism, as
shown in Figure 6.6(b). If the two items are closely related, the data points will form a tight band.
If a random pattern results, the items are unrelated.
Cause-and-Effect Diagrams
Another tool for identifying quality issues and inspection points is the cause-and-effect diagram,
also known as an Ishikawa diagram or a fish-bone chart. Figure 6.7 illustrates a chart (note the
shape resembling the bones of a fish) for a basketball quality control problem—missed free-
throws. Each “bone” represents a possible source of error.
The operations manager starts with four categories: material, machinery/equipment, man-
power, and methods. These four Ms are the “causes.” They provide a good checklist for initial
analysis. Individual causes associated with each category are tied in as separate bones along that
branch, often through a brainstorming process. For example, the method branch in Figure 6.7 has
problems caused by hand position, follow-through, aiming point, bent knees, and balance. When
a fish-bone chart is systematically developed, possible quality problems and inspection points
are highlighted.
Pareto Charts
Pareto charts are a method of organizing errors, problems, or defects to help focus on problem-
solving efforts. They are based on the work of Vilfredo Pareto, a 19th-century economist. Joseph
M. Juran popularized Pareto’s work when he suggested that 80% of a firm’s problems are a result
of only 20% of the causes.
Example 1 indicates that of the five types of complaints identified, the vast majority were of
one type—poor room service.
LO6: Use the seven tools
of TQM
Cause-and-effect
diagram
A schematic technique used to
discover possible locations of
quality problems.
Material (ball)
Rim alignment
Size of ball
Lopsidedness
Method (shooting process)
Hand position
Follow-through
Missed
free-throws
Conditioning
Consistency
Manpower (shooter)
Rim size
Machine (hoop & backboard)
Concentration
Motivation
Training
Balance
Bend knees
Aiming pointGrain/feel (grip)
Air pressure
Rim height
Backboard stability
Pareto charts
Graphics that identify the few
critical items as opposed to
many less important ones.
Source: Adapted from MoreSteam.com, 2007.
� FIGURE 6.7 Fish-Bone Chart (or Cause-and-Effect Diagram) for Problems with Missed Free-throws

168 PART 2 Designing Operations
EXAMPLE 1 �
A Pareto chart at the
Hard Rock Hotel
The Hard Rock Hotel in Bali has just collected the data from 75 complaint calls to the general manager
during the month of October. The manager wants to prepare an analysis of the complaints. The data
provided are room service, 54; check-in delays, 12; hours the pool is open, 4; minibar prices, 3; and
miscellaneous, 2.
APPROACH � A Pareto chart is an excellent choice for this analysis.
SOLUTION � The Pareto chart shown below indicates that 72% of the calls were the result of one
cause: room service. The majority of complaints will be eliminated when this one cause is corrected.
Pareto analysis indicates which problems may yield the greatest payoff. Pacific Bell discov-
ered this when it tried to find a way to reduce damage to buried phone cable, the number-one
cause of phone outages. Pareto analysis showed that 41% of cable damage was caused by con-
struction work. Armed with this information, Pacific Bell was able to devise a plan to reduce ca-
ble cuts by 24% in one year, saving $6 million.
Likewise, Japan’s Ricoh Corp., a copier maker, used the Pareto principle to tackle the “call-
back” problem. Callbacks meant the job was not done right the first time and that a second visit,
at Ricoh’s expense, was needed. Identifying and retraining only the 11% of the customer engi-
neers with the most callbacks resulted in a 19% drop in return visits.
Flowcharts
Flowcharts graphically present a process or system using annotated boxes and interconnected lines
(see Figure 6.6[e]). They are a simple, but great tool for trying to make sense of a process or explain
a process. Example 2 uses a flowchart to show the process of completing an MRI at a hospital.
Room service Check-in Pool hours Minibar Misc.
F
re
q
u
e
n
c
y
(
n
u
m
b
e
r)
0
10
20
30
40
50
60
70
Causes as a percentage of the total
72% 16% 5% 4% 3%
54
12
4 3 2
C
u
m
u
la
ti
v
e
p
e
rc
e
n
ta
g
e
72
88
93
100
Data for October
Pareto Analysis of Hotel Complaints
Number of
occurrences
INSIGHT � This visual means of summarizing data is very helpful—particularly with large
amounts of data, as in the Southwestern University case study in the Lecture Guide & Activities Manual.
We can immediately spot the top problems and prepare a plan to address them.
LEARNING EXERCISE � Hard Rock’s bar manager decides to do a similar analysis on com-
plaints she has collected over the past year: too expensive, 22; weak drinks, 15; slow service, 65; short
hours, 8; unfriendly bartender, 12. Prepare a Pareto chart. [Answer: slow service, 53%; expensive,
18%; drinks, 12%; bartender, 10%; hours, 7%.]
RELATED PROBLEMS � 6.1, 6.3, 6.7b, 6.12, 6.13, 6.16c
ACTIVE MODEL 6.1 This example is further illustrated in Active Model 6.1 at www.pearsonhighered.com/heizer.
Flowcharts
Block diagrams that graphically
describe a process or system.

www.pearsonhighered.com/heizer

Chapter 6 Managing Quality 169
� EXAMPLE 2
A flowchart for
hospital MRI
service
Arnold Palmer Hospital has undertaken a series of process improvement initiatives. One of these is to
make the MRI service efficient for patient, doctor, and hospital. The first step, the administrator
believes, is to develop a flowchart for this process.
APPROACH � A process improvement staffer observed a number of patients and followed them
(and information flow) from start to end. Here are the 11 steps:
1. Physician schedules MRI after examining patient (START).
2. Patient taken to the MRI lab with test order and copy of medical records.
3. Patient signs in, completes required paperwork.
4. Patient is prepped by technician for scan.
5. Technician carries out the MRI scan.
6. Technician inspects film for clarity.
7. If MRI not satisfactory (20% of time), steps 5 and 6 are repeated.
8. Patient taken back to hospital room.
9. MRI is read by radiologist and report is prepared.
10. MRI and report are transferred electronically to physician.
11. Patient and physician discuss report (END).
SOLUTION � Here is the flowchart:
1 112 3 4 5 6
9
8
10
7
80%
20%
INSIGHT � With the flowchart in hand, the hospital can analyze each step and identify value-
added activities and activities that can be improved or eliminated.
LEARNING EXERCISE � If the patient’s blood pressure is over 200/120 when being prepped
for the MRI, she is taken back to her room for 2 hours and the process returns to Step 2. How does the
flowchart change? Answer:
2 3 4
RELATED PROBLEMS � 6.6, 6.15
AUTHOR COMMENT
Flowcharting any process
is an excellent way to
understand and then try to
improve that process.
Histograms
Histograms show the range of values of a measurement and the frequency with which each value
occurs (see Figure 6.6[f]). They show the most frequently occurring readings as well as the vari-
ations in the measurements. Descriptive statistics, such as the average and standard deviation,
may be calculated to describe the distribution. However, the data should always be plotted so the
shape of the distribution can be “seen.” A visual presentation of the distribution may also provide
insight into the cause of the variation.
Statistical Process Control (SPC)
Statistical process control monitors standards, makes measurements, and takes corrective
action as a product or service is being produced. Samples of process outputs are examined; if
they are within acceptable limits, the process is permitted to continue. If they fall outside certain
specific ranges, the process is stopped and, typically, the assignable cause located and removed.
Control charts are graphic presentations of data over time that show upper and lower limits
for the process we want to control (see Figure 6.6[g]). Control charts are constructed in such a
Statistical process
control (SPC)
A process used to monitor
standards, make
measurements, and take
corrective action as a product
or service is being produced.
Control charts
Graphic presentations of
process data over time, with
predetermined control limits.

170 PART 2 Designing Operations
way that new data can be quickly compared with past performance data. We take samples of the
process output and plot the average of each of these samples on a chart that has the limits on it.
The up-per and lower limits in a control chart can be in units of temperature, pressure, weight,
length, and so on.
Figure 6.8 shows the plot of the average percentages of samples in a control chart. When the
average of the samples falls within the upper and lower control limits and no discernible pattern
is present, the process is said to be in control with only natural variation present. Otherwise, the
process is out of control or out of adjustment.
The supplement to this chapter details how control charts of different types are developed. It
also deals with the statistical foundation underlying the use of this important tool.
THE ROLE OF INSPECTION
To make sure a system is producing at the expected quality level, control of the process is
needed. The best processes have little variation from the standard expected. The operations man-
ager’s task is to build such systems and to verify, often by inspection, that they are performing to
standard. This inspection can involve measurement, tasting, touching, weighing, or testing of the
product (sometimes even destroying it when doing so). Its goal is to detect a bad process imme-
diately. Inspection does not correct deficiencies in the system or defects in the products; nor does
it change a product or increase its value. Inspection only finds deficiencies and defects.
Moreover, inspections are expensive and do not add value to the product.
Inspection should be thought of as a vehicle for improving the system. Operations managers
need to know critical points in the system: (1) when to inspect and (2) where to inspect.
When and Where to Inspect
Deciding when and where to inspect depends on the type of process and the value added at each
stage. Inspections can take place at any of the following points:
1. At your supplier’s plant while the supplier is producing.
2. At your facility upon receipt of goods from your supplier.
3. Before costly or irreversible processes.
4. During the step-by-step production process.
5. When production or service is complete.
6. Before delivery to your customer.
7. At the point of customer contact.
The seven tools of TQM discussed in the previous section aid in this “when and where to in-
spect” decision. However, inspection is not a substitute for a robust product produced by well-
trained employees in a good process. In one well-known experiment conducted by an
independent research firm, 100 defective pieces were added to a “perfect” lot of items and then
subjected to 100% inspection.8 The inspectors found only 68 of the defective pieces in their first
inspection. It took another three passes by the inspectors to find the next 30 defects. The last
two defects were never found. So the bottom line is that there is variability in the inspection
process. Additionally, inspectors are only human: They become bored, they become tired, and
1
Game number
0%
10%
20%
2 3 4 5 6 7 8 9
Upper control limit
Plot of the percentage of free-throws missed
Lower control limit
Coach’s target value
� FIGURE 6.8
Control Chart for Percentage
of Free-throws Missed by the
Chicago Bulls in Their First
Nine Games of the New
Season
AUTHOR COMMENT
One of the themes of quality
is that “quality cannot be
inspected into a product.”
Inspection
A means of ensuring that an
operation is producing at the
quality level expected.
8Statistical Quality Control (Springfield, MA: Monsanto Chemical Company, n.d.): 19.

Chapter 6 Managing Quality 171
the inspection equipment itself has variability. Even with 100% inspection, inspectors cannot
guarantee perfection. Therefore, good processes, employee empowerment, and source control
are a better solution than trying to find defects by inspection. You cannot inspect quality into the
product.
For example, at Velcro Industries, as in many organizations, quality was viewed by machine
operators as the job of “those quality people.” Inspections were based on random sampling, and
if a part showed up bad, it was thrown out. The company decided to pay more attention to the
system (operators, machine repair and design, measurement methods, communications, and
responsibilities), and to invest more money in training. Over time as defects declined, Velcro was
able to pull half its quality control people out of the process.
Source Inspection
The best inspection can be thought of as no inspection at all; this “inspection” is always done
at the source—it is just doing the job properly with the operator ensuring that this is so. This
may be called source inspection (or source control) and is consistent with the concept of
employee empowerment, where individual employees self-check their own work. The idea is
that each supplier, process, and employee treats the next step in the process as the customer,
ensuring perfect product to the next “customer.” This inspection may be assisted by the use of
checklists and controls such as a fail-safe device called a poka-yoke, a name borrowed from
the Japanese.
A poka-yoke is a foolproof device or technique that ensures production of good units
every time. These special devices avoid errors and provide quick feedback of problems. A
simple example of a poka-yoke device is the diesel gas pump nozzle that will not fit into the
“unleaded” gas tank opening on your car. In McDonald’s, the french fry scoop and standard-
size bag used to measure the correct quantity are poka-yokes. Similarly, in a hospital, the
prepackaged surgical coverings that contain exactly the items needed for a medical procedure
are poka-yokes. Checklists are another type of poka-yoke. The idea of source inspection and
poka-yokes is to ensure that 100% good product or service is provided at each step in the
process.
Service Industry Inspection
In service-oriented organizations, inspection points can be assigned at a wide range of locations,
as illustrated in Table 6.4. Again, the operations manager must decide where inspections are jus-
tified and may find the seven tools of TQM useful when making these judgments.
Inspection of Attributes versus Variables
When inspections take place, quality characteristics may be measured as either attributes or
variables. Attribute inspection classifies items as being either good or defective. It does not
address the degree of failure. For example, the lightbulb burns or it does not. Variable inspection
Good methods analysis and the proper tools can result in
poka-yokes that improve both quality and speed. Here, two
poka-yokes are demonstrated. First, the aluminum scoop
automatically positions the french fries vertically, and second,
the properly sized container ensures that the portion served is
correct. McDonald’s thrives by bringing rigor and consistency
to the restaurant business.
Source inspection
Controlling or monitoring at
the point of production or
purchase—at the source.
Poka-yoke
Literally translated, “foolproof”;
it has come to mean a device
or technique that ensures the
production of a good unit
every time.
Attribute inspection
An inspection that classifies
items as being either good or
defective.
Variable inspection
Classifications of inspected
items as falling on a continuum
scale, such as dimension,
or strength.

172 PART 2 Designing Operations
measures such dimensions as weight, speed, size, or strength to see if an item falls within an
acceptable range. If a piece of electrical wire is supposed to be 0.01 inch in diameter, a microme-
ter can be used to see if the product is close enough to pass inspection.
Knowing whether attributes or variables are being inspected helps us decide which statistical
quality control approach to take, as we will see in the supplement to this chapter.
TQM IN SERVICES
The personal component of services is more difficult to measure than the quality of the tangible
component. Generally, the user of a service, like the user of a good, has features in mind that
form a basis for comparison among alternatives. Lack of any one feature may eliminate the ser-
vice from further consideration. Quality also may be perceived as a bundle of attributes in which
many lesser characteristics are superior to those of competitors. This approach to product com-
parison differs little between goods and services. However, what is very different about the se-
lection of services is the poor definition of the (1) intangible differences between products and
(2) the intangible expectations customers have of those products. Indeed, the intangible attributes
AUTHOR COMMENT
The personal component
of a service can make
quality measurement
difficult.
Using checklists, as simple as they are, is a powerful way to improve quality. Everyone from airline pilots to physicians use them.
Johns Hopkins Hospital uses checklists to monitor patients, and the Michigan Health and Hospital Association is using them with
great success to reduce infections.
� TABLE 6.4
Examples of Inspection
in Services
Organization What Is Inspected Standard
Jones Law Offices Receptionist performance Phone answered by the second ring
Billing Accurate, timely, and correct format
Attorney Promptness in returning calls
Hard Rock Hotel Reception desk Use customer’s name
Doorman Greet guest in less than 30 seconds
Room All lights working, spotless bathroom
Minibar Restocked and charges accurately posted to bill
Arnold Palmer Billing Accurate, timely, and correct format
Hospital Pharmacy Prescription accuracy, inventory accuracy
Lab Audit for lab-test accuracy
Nurses Charts immediately updated
Admissions Data entered correctly and completely
Olive Garden Busboy Serves water and bread within 1 minute
Restaurant Busboy Clears all entrée items and crumbs prior to dessert
Waiter Knows and suggests specials, desserts
Nordstrom Department Display areas Attractive, well organized, stocked, good lighting
Store Stockrooms Rotation of goods, organized, clean
Salesclerks Neat, courteous, very knowledgeable

Chapter 6 Managing Quality 173
Hair can’t grow below
shirt collar
Turn in sales leads
No smoking
in front of
customers
No beards
Use DIAD to log
everything from
driver’s miles per
gallon to tracking
data on parcels
Key ring held
on the pinky
finger
“All Good Kids Love Milk”: the five
seeing habits of drivers: Aim high in
steering, Get the big picture, Keep
your eyes moving, Leave yourself
an out, Make sure they see you
Sideburns can’t
grow below the
bottom of the ear
Undershirts must
be either white
or brown
Shirts can’t be unbuttoned
below the first button
Load boxes neatly and
evenly like a stack of bricks
Walk briskly. No running
allowed
Sport clean uniform
every day
Black or brown polishable
shoes, nonslip soles
Toot horn
when arriving
at business
or residence
Present parcels for
five stops ahead
UPS drivers are taught
340 precise methods of
how to correctly deliver
a package. Regimented?
Absolutely. But UPS
credits its uniformity and
efficiency with laying the
foundation for its high-
quality service.
may not be defined at all. They are often unspoken images in the purchaser’s mind. This is why
all of those marketing issues such as advertising, image, and promotion can make a difference
(see the photo of the UPS driver).
The operations manager plays a significant role in addressing several major aspects of service
quality. First, the tangible component of many services is important. How well the service is
designed and produced does make a difference. This might be how accurate, clear, and complete
your checkout bill at the hotel is, how warm the food is at Taco Bell, or how well your car runs
after you pick it up at the repair shop.
Second, another aspect of service and service quality is the process. Notice in Table 6.5 that
9 out of 10 of the determinants of service quality are related to the service process. Such things
as reliability and courtesy are part of the process. An operations manager can design processes
(service products) that have these attributes and can ensure their quality through the TQM
techniques discussed in this chapter.
Third, the operations manager should realize that the customer’s expectations are the standard
against which the service is judged. Customers’ perceptions of service quality result from a com-
parison of their before-service expectations with their actual-service experience. In other words,
service quality is judged on the basis of whether it meets expectations. The manager may be able
to influence both the quality of the service and the expectation. Don’t promise more than you can
deliver.
Fourth, the manager must expect exceptions. There is a standard quality level at which
the regular service is delivered, such as the bank teller’s handling of a transaction. However,
there are “exceptions” or “problems” initiated by the customer or by less-than-optimal
operating conditions (e.g., the computer “crashed”). This implies that the quality control
system must recognize and have a set of alternative plans for less-than-optimal operating
conditions.
Well-run companies have service recovery strategies. This means they train and empower
frontline employees to immediately solve a problem. For instance, staff at Marriott Hotels are
VIDEO 6.2
TQM at Ritz-Carlton Hotels
Service recovery
Training and empowering
frontline workers to solve a
problem immediately.

174 PART 2 Designing Operations
drilled in the LEARN routine—Listen, Empathize, Apologize, React, Notify—with the final step
ensuring that the complaint is fed back into the system. And at the Ritz-Carlton, staff members
are trained not to say merely “sorry” but “please accept my apology.” The Ritz gives them a
budget for reimbursing upset guests.
Designing the product, managing the service process, matching customer expectations to the
product, and preparing for the exceptions are keys to quality services. The OM in Action box
“Richey International’s Spies” provides another glimpse of how OM managers improve quality
in services.
How do luxury hotels maintain quality? They inspect.
But when the product is one-on-one service, largely
dependent on personal behavior, how do you inspect?
You hire spies!
Richey International is the spy. Preferred Hotels and
Resorts Worldwide and Intercontinental Hotels have both
hired Richey to do quality evaluations via spying. Richey
employees posing as customers perform the inspections.
However, even then management must have established
what the customer expects and specific services that yield
customer satisfaction. Only then do managers know
where and how to inspect. Aggressive training and
objective inspections reinforce behavior that will meet
those customer expectations.
The hotels use Richey’s undercover inspectors to
ensure performance to exacting standards. The hotels
do not know when the evaluators will arrive. Nor what
aliases they will use. Over 50 different standards are
evaluated before the inspectors even check in at a
luxury hotel. Over the next 24 hours, using checklists,
tape recordings, and photos, written reports are
prepared. The reports include evaluation of standards
such as:
• Does the doorman greet each guest in less than
30 seconds?
• Does the front-desk clerk use the guest’s name during
check-in?
• Is the bathroom tub and shower spotlessly clean?
• How many minutes does it take to get coffee after the
guest sits down for breakfast?
• Did the waiter make eye contact?
• Were minibar charges posted correctly on the bill?
Established standards, aggressive training, and
inspections are part of the TQM effort at these hotels.
Quality does not happen by accident.
Sources: Hotel and Motel Management (August 2002): 128; The Wall
Street Journal (May 12, 1999): B1, B12; and Forbes (October 5, 1998):
88–89.
OM in Action � Richey International’s Spies
� TABLE 6.5
Determinants of
Service Quality
Reliability involves consistency of performance and dependability. It means that the firm performs
the service right the first time and that the firm honors its promises.
Responsiveness concerns the willingness or readiness of employees to provide service. It involves
timeliness of service.
Competence means possession of the required skills and knowledge to perform the service.
Access involves approachability and ease of contact.
Courtesy involves politeness, respect, consideration, and friendliness of contact personnel (including
receptionists, telephone operators, etc.).
Communication means keeping customers informed in language they can understand and listening
to them. It may mean that the company has to adjust its language for different consumers—increasing
the level of sophistication with a well-educated customer and speaking simply and plainly with a
novice.
Credibility involves trustworthiness, believability, and honesty. It involves having the customer’s best
interests at heart.
Security is the freedom from danger, risk, or doubt.
Understanding/knowing the customer involves making the effort to understand the customer’s needs.
Tangibles include the physical evidence of the service.
Source: Adapted from A. Parasuranam, Valarie A. Zeithaml, and Leonard L. Berry, “A Conceptual Model of Service Quality and its
Implications for Future Research,” Journal of Marketing (Fall 1985): 44; Journal of Marketing, 58, no. 1 (January 1994): 111–125;
Journal of Retailing 70 (Fall 1994): 201–230.

Chapter 6 Managing Quality 175
CHAPTER SUMMARY
Quality is a term that means different things to different peo-
ple. We define quality as “the totality of features and charac-
teristics of a product or service that bears on its ability to
satisfy stated or implied needs.” Defining quality expectations
is critical to effective and efficient operations.
Quality requires building a total quality management (TQM)
environment because quality cannot be inspected into a product.
The chapter also addresses seven TQM concepts: continuous
improvement, Six Sigma, employee
empowerment, benchmarking, just-in-
time, Taguchi concepts, and knowl-
edge of TQM tools. The seven TQM
tools introduced in this chapter are
check sheets, scatter diagrams, cause-and-
effect diagrams, Pareto charts, flowcharts, his-
tograms, and statistical process control (SPC).
Key Terms
Quality (p. 156)
Cost of quality (COQ) (p. 158)
ISO 9000 (p. 159)
ISO 14000 (p. 159)
Total quality management (TQM) (p. 160)
PDCA (p. 161)
Six Sigma (p. 161)
Employee empowerment (p. 162)
Quality circle (p. 162)
Benchmarking (p. 163)
Quality robust (p. 165)
Quality loss function (QLF) (p. 165)
Target-oriented quality (p. 165)
Cause-and-effect diagram, Ishikawa
diagram, or fish-bone chart (p. 167)
Pareto charts (p. 167)
Flowcharts (p. 168)
Statistical process control (SPC) (p. 169)
Control charts (p. 169)
Inspection (p. 170)
Source inspection (p. 171)
Poka-yoke (p. 171)
Attribute inspection (p. 171)
Variable inspection (p. 171)
Service recovery (p. 173)
�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Westover Electrical, Inc.: This electric motor manufacturer has a large log of defects in its wiring process.
Bibliography
Besterfield, Dale H. Quality Control, 8th ed. Upper Saddle River,
NJ: Prentice Hall, 2009.
Brown, Mark G. Baldrige Award Winning Quality, 19th ed.
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Crosby, P. B. Quality Is Still Free. New York: McGraw-Hill,
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Evans, J. R., and W. M. Lindsay. Managing for Quality and
Performance Excellence. 7th ed. Mason, OH: Thompson-
Southwestern, 2008.
Feigenbaum, A. V. “Raising the Bar.” Quality Progress 41, no. 7
(July 2008): 22–28.
Gitlow, Howard S. A Guide to Lean Six Sigma Management Skills.
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Gonzalez-Benito, J., and O. Gonzalez-Benito. “Operations
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Harrington, D. R., M. Khanna, and G. Deltas. “Striving to Be
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Mitra, Amit. Fundamentals of Quality Control and Improvement.
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Pande, P. S., R. P. Neuman, R. R. Cavanagh. What Is Design for
Six Sigma? New York: McGraw-Hill, 2005.
Schroeder, Roger G., et al. “Six Sigma: Definition and Underlying
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536–554.
Soltani, E., P. Lai, and P. Phillips. “A New Look at Factors
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2008): 125.
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www.pearsonhighered.com/heizer

www.myomlab.com

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Statistical
Process Control
Supplement Outline
Statistical Process Control (SPC) 178
Process Capability 191
Acceptance Sampling 193
177
SUPPLEMENTSUPPLEMENT

BetzDearborn, A Division of
Hercules Incorporated, is
headquartered in Trevose,
Pennsylvania. It is a global
supplier of specialty chemicals for
the treatment of industrial water,
wastewater, and process systems.
The company uses statistical
process control to monitor the
performance of treatment
programs in a wide variety of
industries throughout the world.
BetzDearborn’s quality assurance
laboratory (shown here) also uses
statistical sampling techniques to
monitor manufacturing processes
at all of the company’s production
plants.
STATISTICAL PROCESS CONTROL (SPC)
In this supplement, we address statistical process control—the same techniques used at
BetzDearborn, at IBM, at GE, and at Motorola to achieve quality standards. We also introduce
acceptance sampling. Statistical process control is the application of statistical techniques to
the control of processes. Acceptance sampling is used to determine acceptance or rejection of
material evaluated by a sample.
Statistical process control (SPC) is a statistical technique that is widely used to ensure that
processes meet standards. All processes are subject to a certain degree of variability. While
studying process data in the 1920s, Walter Shewhart of Bell Laboratories made the distinction
between the common and special causes of variation. Many people now refer to these variations
as natural and assignable causes. He developed a simple but powerful tool to separate the two—
the control chart.
We use statistical process control to measure performance of a process. A process is said to be
operating in statistical control when the only source of variation is common (natural) causes.
The process must first be brought into statistical control by detecting and eliminating special
(assignable) causes of variation.1 Then its performance is predictable, and its ability to meet cus-
tomer expectations can be assessed. The objective of a process control system is to provide a sta-
tistical signal when assignable causes of variation are present. Such a signal can quicken
appropriate action to eliminate assignable causes.
LO1: Explain the purpose of a
control chart 180
LO2: Explain the role of the central
limit theorem in SPC 181
LO3: Build -charts and R-charts 181
LO4: List the five steps involved in building
control charts 185
x
AUTHOR COMMENT
In this supplement we
show you how to set up a
control chart.
Supplement 6 Learning Objectives
LO5: Build p-charts and c-charts 186
LO6: Explain process capability and
compute Cp and Cpk 191
LO7: Explain acceptance sampling 193
LO8: Compute the AOQ 195
Statistical process
control (SPC)
A process used to monitor
standards by taking
measurements and corrective
action as a product or service is
being produced.
Control chart
A graphical presentation of
process data over time.
1Removing assignable causes is work. Quality expert W. Edwards Deming observed that a state of statistical control is
not a natural state for a manufacturing process. Deming instead viewed it as an achievement, arrived at by elimination,
one by one, by determined effort, of special causes of excessive variation. See J. R. Thompson and J. Koronacki,
Statistical Process Control, The Deming Paradigm and Beyond. Boca Raton, FL: Chapman and Hall, 2002.
178 PART 2 Designing Operations

Supplement 6 Statistical Process Control 179
Natural variations
Variability that affects every
production process to some
degree and is to be expected;
also known as common cause.
Natural Variations Natural variations affect almost every production process and are to be
expected. Natural variations are the many sources of variation that occur within a process that
is in statistical control. Natural variations behave like a constant system of chance causes.
Although individual values are all different, as a group they form a pattern that can be described
as a distribution. When these distributions are normal, they are characterized by two parameters:
• Mean, (the measure of central tendency—in this case, the average value)
• Standard deviation, (the measure of dispersion)
As long as the distribution (output measurements) remains within specified limits, the process is
said to be “in control,” and natural variations are tolerated.
Assignable Variations Assignable variation in a process can be traced to a specific reason.
Factors such as machine wear, misadjusted equipment, fatigued or untrained workers, or new
batches of raw material are all potential sources of assignable variations.
Natural and assignable variations distinguish two tasks for the operations manager. The first is
to ensure that the process is capable of operating under control with only natural variation. The
second is, of course, to identify and eliminate assignable variations so that the processes will
remain under control.
Samples Because of natural and assignable variation, statistical process control uses averages
of small samples (often of four to eight items) as opposed to data on individual parts. Individual
pieces tend to be too erratic to make trends quickly visible.
Figure S6.1 provides a detailed look at the important steps in determining process variation.
The horizontal scale can be weight (as in the number of ounces in boxes of cereal) or length (as
in fence posts) or any physical measure. The vertical scale is frequency. The samples of five
boxes of cereal in Figure S6.1 (a) are weighed; (b) form a distribution, and (c) can vary. The dis-
tributions formed in (b) and (c) will fall in a predictable pattern (d) if only natural variation is
present. If assignable causes of variation are present, then we can expect either the mean to vary
or the dispersion to vary, as is the case in (e).
s
m
Assignable variation
Variation in a production
process that can be traced to
specific causes.
F
re
q
u
e
n
cy
Weight
(a)
(b)
(c)
(e)
(d)
Samples of the product, say five
boxes of cereal taken off the filling
machine line, vary from one
another in weight.
After enough sample means are
taken from a stable process, they
form a pattern called a distribution.
There are many types of
distributions, including the normal
(bell-shaped) distribution, but
distributions do differ in terms of
central tendency (mean), standard
deviation or variance, and shape.
If only natural causes of variation
are present, the output of a
process forms a distribution that is
stable over time and is predictable.
If assignable causes of variation
are present, the process output is
not stable over time and is not
predictable. That is, when causes
that are not an expected part of
the process occur, the samples will
yield unexpected distributions that
vary by central tendency, standard
deviation, and shape.
F
re
q
u
e
n
cy
Weight
F
re
q
u
e
n
cy
Weight
Measure of central
tendency (mean)
Variation
(std. deviation)
Each of these
represents one
sample of five boxes
of cereal.
The solid line
represents
the distribution.
Shape
F
re
q
u
e
n
cy
Weight
Prediction
Weight Weight
Time
F
re
q
u
e
n
cy
Weight
Prediction
??
?
?
?
??
????
????
Time
� FIGURE S6.1
Natural and Assignable
Variation

180 PART 2 Designing Operations
Control Charts The process of building control charts is based on the concepts presented in
Figure S6.2. This figure shows three distributions that are the result of outputs from three types of
processes. We plot small samples and then examine characteristics of the resulting data to see if
the process is within “control limits.” The purpose of control charts is to help distinguish between
natural variations and variations due to assignable causes. As seen in Figure S6.2, a process is (a)
in control and the process is capable of producing within established control limits, (b) in control
but the process is not capable of producing within established limits, or (c) out of control. We now
look at ways to build control charts that help the operations manager keep a process under control.
Control Charts for Variables
The variables of interest here are those that have continuous dimensions. They have an infinite
number of possibilities. Examples are weight, speed, length, or strength. Control charts for the
mean, or x-bar, and the range, R, are used to monitor processes that have continuous dimen-
sions. The -chart tells us whether changes have occurred in the central tendency (the mean, in
this case) of a process. These changes might be due to such factors as tool wear, a gradual
increase in temperature, a different method used on the second shift, or new and stronger materi-
als. The R-chart values indicate that a gain or loss in dispersion has occurred. Such a change
may be due to worn bearings, a loose tool, an erratic flow of lubricants to a machine, or to slop-
piness on the part of a machine operator. The two types of charts go hand in hand when monitor-
ing variables because they measure the two critical parameters: central tendency and dispersion.
The Central Limit Theorem
The theoretical foundation for -charts is the central limit theorem. This theorem states that
regardless of the distribution of the population, the distribution of s (each of which is a mean of
a sample drawn from the population) will tend to follow a normal curve as the number of sam-
ples increases. Fortunately, even if the sample (n) is fairly small (say, 4 or 5), the distributions of
the averages will still roughly follow a normal curve. The theorem also states that: (1) the mean
of the distribution of the s (called ) will equal the mean of the overall population (called );
and (2) the standard deviation of the sampling distribution, , will be the population standard
deviation, divided by the square root of the sample size, n. In other words:
(S6-1)x = m
sx
mxx
x
x
x
x
-chart
A quality control chart for
variables that indicates when
changes occur in the central
tendency of a production
process.
x
R-chart
A control chart that tracks the
“range” within a sample; it
indicates that a gain or loss in
uniformity has occurred in
dispersion of a production
process.
LO1: Explain the purpose
of a control chart
Frequency
Size
(weight, length, speed, etc.)
Upper control limitLower control limit
⎧
⎪
⎨
⎪
⎩
⎧
⎪
⎨
⎪
⎩
A process with only
natural causes of
variation and capable of
producing within the
specified control limits
(a) In statistical control
and capable of producing
within control limits
(b) In statistical control
but not capable of producing
within control limits
(c) Out of control
⎧
⎪
⎨
⎪
⎩ A process in control (only natural
causes of variation are present)
but not capable of producing
within the specified control
limits
⎧
⎪
⎨
⎪
⎩
A process out of control having
assignable causes of variation
� FIGURE S6.2
Process Control: Three Types
of Process Outputs
2The standard deviation is easily calculated as s =
Q
a
n
i= 1
1xi – x22
n – 1
.
Central limit theorem
The theoretical foundation for
-charts, which states that
regardless of the distribution
of the population of all parts
or services, the distribution
of s tends to follow a normal
curve as the number of samples
increases.
x
x
2

Supplement 6 Statistical Process Control 181
and
(S6-2)
Figure S6.3 shows three possible population distributions, each with its own mean, and stan-
dard deviation, If a series of random samples ( and so on), each of size n, is
drawn from any population distribution (which could be normal, beta, uniform, and so on), the
resulting distribution of will appear as they do in Figure S6.3.
Moreover, the sampling distribution, as is shown in Figure S6.4, will have less variability than
the process distribution. Because the sampling distribution is normal, we can state that:
• 95.45% of the time, the sample averages will fall within if the process has only natural
variations.
• 99.73% of the time, the sample averages will fall within if the process has only natural
variations.
If a point on the control chart falls outside of the control limits, then we are 99.73% sure
the process has changed. This is the theory behind control charts.
Setting Mean Chart Limits ( -Charts)
If we know, through past data, the standard deviation of the process population, we can set
upper and lower control limits by using these formulas:
(S6-3)
(S6-4)
where � mean of the sample means or a target value set for the process
z � number of normal standard deviations (2 for 95.45% confidence, 3 for 99.73%)
� standard deviation of the sample means = s>1nsx
x
Lower control limit 1LCL2 = x – zsx
Upper control limit 1UCL2 = x + zsx
s,
x
; 3sx
; 3sx
; 2sx
xis
x1, x2, x3, x4,s.
m,
sx =
s
1n
LO2: Explain the role of
the central limit theorem
in SPC
LO3: Build -charts and
R-charts
x
95.45% fall
within ±2σx
99.73% of all x ´s
fall within ±3σx
(mean)
Beta
Normal
Uniform
Population
distributions
Distribution of
sample means
+2σx +3σx+1σx–1σx–2σx–3σx x
Mean of sample means = x
Standard deviation of
the sample means =σx
σ
√n
=
� FIGURE S6.3
The Relationship between
Population and Sampling
Distributions
Even though the population
distributions will differ (e.g.,
normal, beta, uniform), each
with its own mean and
standard deviation the
distribution of sample means
always approaches a normal
distribution.
1s2,
1m2
s � population (process) standard deviation
n � sample size
Example S1 shows how to set control limits for sample means using standard deviations.

182 PART 2 Designing Operations
EXAMPLE S1 �
Setting control limits
using samples
(mean)
x = μ
Sampling
distribution of means
Process
distribution of means
� FIGURE S6.4
The Sampling Distribution of
Means Is Normal and Has
Less Variability Than the
Process Distribution
In this figure, the process
distribution from which the
sample was drawn was also
normal, but it could have been
any distribution.
The weights of boxes of Oat Flakes within a large production lot are sampled each hour. Managers
want to set control limits that include 99.73% of the sample means.
APPROACH � Randomly select and weigh nine boxes each hour. Then find the overall
mean and use Equations (S6–3) and (S6–4) to compute the control limits. Here are the nine boxes
chosen for Hour 1:
1n = 92
17 oz.
Oat
Flakes
13 oz. 16 oz. 18 oz. 17 oz. 16 oz. 15 oz. 17 oz. 16 oz.
Oat
Flakes
Oat
Flakes
Oat
Flakes
Oat
Flakes
Oat
Flakes
Oat
Flakes
Oat
Flakes
Oat
Flakes
SOLUTION �
Also, the population standard deviation is known to be 1 ounce. We do not show each of the
boxes randomly selected in hours 2 through 12, but here are all 12 hourly samples:
1s2
= 16.1 ounces.
The average weight in the first sample =
17 + 13 + 16 + 18 + 17 + 16 + 15 + 17 + 16
9
Weight of Sample Weight of Sample Weight of Sample
Hour (Avg. of 9 Boxes) Hour (Avg. of 9 Boxes) Hour (Avg. of 9 Boxes)
1 16.1 5 16.5 9 16.3
2 16.8 6 16.4 10 14.8
3 15.5 7 15.2 11 14.2
4 16.5 8 16.4 12 17.3
The average mean of the 12 samples is calculated to be exactly 16 ounces. We therefore have
ounces, ounce, and The control limits are:
The 12 samples are then plotted on the following control chart:
LCLx = x – zsx = 16 – 3¢ 1
19
≤ = 16 – 3¢1
3
≤ = 15 ounces UCLx = x + zsx = 16 + 3¢ 119≤ = 16 + 3¢13≤ = 17 ounces
z = 3.n = 9,� = 1
x = 16
AUTHOR COMMENT
If you want to see an example
of such variability in your
supermarket, go to the soft
drink section and line up a
few 2-liter bottles of Coke,
Pepsi, or any other brand.
Notice that the liquids are not
the same measurement.

Supplement 6 Statistical Process Control 183
Because process standard deviations are either not available or difficult to compute, we usually
calculate control limits based on the average range values rather than on standard deviations.
Table S6.1 provides the necessary conversion for us to do so. The range is defined as the differ-
ence between the largest and smallest items in one sample. For example, the heaviest box of Oat
Flakes in Hour 1 of Example S1 was 18 ounces and the lightest was 13 ounces, so the range for
that hour is 5 ounces. We use Table S6.1 and the equations:
(S6-5)
and:
(S6-6)
average range of the samples
value found in Table S6.1
mean of the sample means
Example S2 shows how to set control limits for sample means by using Table S6.1 and the
average range.
x =
A2 =
where R =
LCLx = x – A2R
UCLx = x + A2R
Sample Size, n Mean Factor, A2 Upper Range, D4 Lower Range, D3
2 1.880 3.268 0
3 1.023 2.574 0
4 .729 2.282 0
5 .577 2.115 0
6 .483 2.004 0
7 .419 1.924 0.076
8 .373 1.864 0.136
9 .337 1.816 0.184
10 .308 1.777 0.223
12 .266 1.716 0.284
Source: Reprinted by permission of American Society for Testing Materials. Copyright 1951. Taken from Special Technical Publication
15–C, “Quality Control of Materials,” pp. 63 and 72. Copyright ASTM INTERNATIONAL. Reprinted with permission.
INSIGHT � Because the means of recent sample averages fall outside the upper and lower control
limits of 17 and 15, we can conclude that the process is becoming erratic and is not in control.
LEARNING EXERCISE � If Oat Flakes’s population standard deviation is 2 (instead of 1),
what is your conclusion? [Answer: the process would be in control.]
RELATED PROBLEMS � S6.1, S6.2, S6.4, S6.8, S6.10a,b
EXCEL OM Data File Ch06SExS1.xls can be found at www.pearsonhighered.com/heizer.
LCL = 14, UCL = 18;
1s2
Control Chart
for samples of
9 boxes
17 = UCL
Out of
control
Out of
controlSample number
1 2 3 4 5 6 7 8 9 10 11 12
Variation
due to assignable
causes
Variation
due to assignable
causes
Variation due to
natural causes16 = Mean
15 = LCL
� TABLE S6.1
Factors for Computing Control
Chart Limits (3 sigma)

www.pearsonhighered.com/heizer

184 PART 2 Designing Operations
EXAMPLE S2 �
Setting mean limits
using table values
Super Cola bottles soft drinks labeled “net weight 12 ounces.” Indeed, an overall process average of 12
ounces has been found by taking many samples, in which each sample contained 5 bottles. The average
range of the process is .25 ounce. The OM team wants to determine the upper and lower control limits
for averages in this process.
APPROACH � Super Cola applies Equations (S6-5) and (S6-6) and uses the column of Table S6.1.
SOLUTION � Looking in Table S6.1 for a sample size of 5 in the mean factor column, we find
the value .577. Thus, the upper and lower control chart limits are:
INSIGHT � The advantage of using this range approach, instead of the standard deviation, is that
it is easy to apply and may be less confusing.
LEARNING EXERCISE � If the sample size was and the average range ounces,
what are the revised and ? [Answer: 12.146, 11.854.]
RELATED PROBLEMS � S6.3a, S6.5, S6.6, S6.7, S6.9, S6.10b,c,d S6.11, S6.34
EXCEL OM Data File Ch06SExS2.xls can be found at www.pearsonhighered.com/heizer.
LCLxUCLx
= .20n = 4
= 11.856 ounces
= 12 – .144
LCLx = x – A2R
= 12.144 ounces
= 12 + .144
= 12 + 1.57721.252
UCLx = x + A2R
A2
A2
VIDEO S6.1
Farm to Fork: Quality
of Darden Restaurants
11.5 UCL=11.524
UCL=0.6943
R=0.2125
LCL=10.394
LCL=0
USL
LSL 10
USL 12
Specifications
LSL
x=10.959
1 3 5 7 9 11 13 15 17
10.5
11.0
x Bar Chart
S
a
m
p
le
M
e
a
n
0.8
1 3 5
10
.2
10
.5
10
.8
11
.1
11
.4
11
.7
12
.0
7 9 11 13 15 17
0.0
0.4
Range Chart
Capability Histogram
S
a
m
p
le
R
a
n
g
e
Mean = 10.959
Std.dev = 1.88
Cp = 1.77
Cpk = 1.7
Capability
AUTHOR COMMENT
Here the restaurant chain
uses weight (11 oz)
as a measure of SPC for
salmon filets.
AUTHOR COMMENT
The range here is the
difference between the
heaviest and the lightest
salmon filets weighed in each
sample. A range chart shows
changes in dispersion.
Salmon filets are monitored by Darden Restaurant’s SPC software, which includes and R-charts
and a process capability histogram. The video case study “Farm to Fork,” in the Lecture Guide &
Activities Manual, asks you to interpret these figures.
x-

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Supplement 6 Statistical Process Control 185
Setting Range Chart Limits (R-Charts)
In Examples S1 and S2, we determined the upper and lower control limits for the process
average. In addition to being concerned with the process average, operations managers are inter-
ested in the process dispersion, or range. Even though the process average is under control, the
dispersion of the process may not be. For example, something may have worked itself loose in a
piece of equipment that fills boxes of Oat Flakes. As a result, the average of the samples may
remain the same, but the variation within the samples could be entirely too large. For this reason,
operations managers use control charts for ranges to monitor the process variability, as well as
control charts for averages, which monitor the process central tendency. The theory behind the
control charts for ranges is the same as that for process average control charts. Limits are estab-
lished that contain standard deviations of the distribution for the average range We can use
the following equations to set the upper and lower control limits for ranges:
(S6-7)
(S6-8)
where upper control chart limit for the range
lower control chart limit for the range
values from Table S6.1
Example S3 shows how to set control limits for sample ranges using Table S6.1 and the average
range.
D4 and D3 =
LCLR =
UCLR =
LCLR = D3R
UCLR = D4R
R.; 3
� EXAMPLE S3
Setting range
limits using table
values
The average range of a product at Clinton Manufacturing is 5.3 pounds. With a sample size of 5, owner
Roy Clinton wants to determine the upper and lower control chart limits.
APPROACH � Looking in Table S6.1 for a sample size of 5, he finds that and
SOLUTION � The range control limits are:
INSIGHT � Computing ranges with Table S6.1 is straightforward and an easy way to evaluate
dispersion.
LEARNING EXERCISE � Clinton decides to increase the sample size to What are the
new and values? [Answer: 10.197, 0.403]
RELATED PROBLEMS � S6.3b, S6.5, S6.6, S6.7, S6.9, S6.10c, S6.11, S6.12, S6.34
LCLRUCLR
n = 7.
LCLR = D3R = 10215.3 pounds2 = 0
UCLR = D4R = 12.115215.3 pounds2 = 11.2 pounds
D3 = 0.D4 = 2.115
Using Mean and Range Charts
The normal distribution is defined by two parameters, the mean and standard deviation. The
(mean)-chart and the R-chart mimic these two parameters. The -chart is sensitive to shifts in the
process mean, whereas the R-chart is sensitive to shifts in the process standard deviation.
Consequently, by using both charts we can track changes in the process distribution.
For instance, the samples and the resulting -chart in Figure S6.5(a) show the shift in the process
mean, but because the dispersion is constant, no change is detected by the R-chart. Conversely, the
samples and the -chart in Figure S6.5(b) detect no shift (because none is present), but the R-chart
does detect the shift in the dispersion. Both charts are required to track the process accurately.
Steps to Follow When Using Control Charts There are five steps that are generally
followed in using and R-charts:
1. Collect 20 to 25 samples, often of or observations each, from a stable process
and compute the mean and range of each.
2. Compute the overall means ( and ), set appropriate control limits, usually at the
99.73% level, and calculate the preliminary upper and lower control limits. Refer to
Rx
n = 5n = 4
x-
x
x
x
x
LO4: List the five steps
involved in building control
charts

186 PART 2 Designing Operations
UCL
UCL
LCL
LCL
These
sampling
distributions
result in
the charts
below.
These
sampling
distributions
result in
the charts
below.
(a)
(b)
UCL
UCL
LCL
LCL
x-chart (x-chart detects shift in central
tendency
(Sampling mean is
shifting upward, but
range is consistent.)
(Sampling mean is
constant, but
dispersion is increasing.)
(R-chart does not detect
change in mean.)
(x-chart does not detect the
increase in dispersion.)
(R-chart detects increase in
dispersion.)
x-chart
R-chart
R-chart
.)
� Figure S6.5
Mean and Range Charts
Complement Each Other by
Showing the Mean and
Dispersion of the Normal
Distribution
Table S6.2 for other control limits. If the process is not currently stable and in control, use
the desired mean, instead of to calculate limits.
3. Graph the sample means and ranges on their respective control charts and determine
whether they fall outside the acceptable limits.
4. Investigate points or patterns that indicate the process is out of control. Try to assign causes
for the variation, address the causes, and then resume the process.
5. Collect additional samples and, if necessary, revalidate the control limits using the new data.
Control Charts for Attributes
Control charts for and R do not apply when we are sampling attributes, which are typically
classified as defective or nondefective. Measuring defectives involves counting them (for exam-
ple, number of bad lightbulbs in a given lot, or number of letters or data entry records typed with
errors), whereas variables are usually measured for length or weight. There are two kinds of
attribute control charts: (1) those that measure the percent defective in a sample—called
p-charts—and (2) those that count the number of defects—called c-charts.
p-Charts Using p-charts is the chief way to control attributes. Although attributes that are
either good or bad follow the binomial distribution, the normal distribution can be used to calcu-
late p-chart limits when sample sizes are large. The procedure resembles the -chart approach,
which is also based on the central limit theorem.
The formulas for p-chart upper and lower control limits follow:
(S6-9)
(S6-10)LCLp = p – zspN
UCLp = p + zspN
x
x
xm,
LO5: Build p-charts and
c-charts
p-chart
A quality control chart that is
used to control attributes.
AUTHOR COMMENT
Mean ( ) charts are a
measure of central tendency,
while range (R) charts are a
measure of dispersion. SPC
requires both charts for a
complete assessment
because a sample mean
could be out of control while
the range is in control, and
vice versa.
x
� TABLE S6.2
Common z Values
Desired
Control Limit
(%)
z-Value
(standard
deviation
required
for desired
level of
confidence)
90.0 1.65
95.0 1.96
95.45 2.00
99.0 2.58
99.73 3.00

Supplement 6 Statistical Process Control 187
VIDEO S6.2
Frito-Lay’s Quality-Controlled
Potato Chips
where = mean fraction defective in the samples
z = number of standard deviations ( for 95.45% limits; for 99.73% limits)
= standard deviation of the sampling distribution
is estimated by the formula:
(S6-11)
where = number of observations in each samples
Example S4 shows how to set control limits for p-charts for these standard deviations.
n
spN = A
p11 – p2
n
spN
spN
z = 3z = 2
p
Clerks at Mosier Data Systems key in thousands of insurance records each day for a variety of client
firms. CEO Donna Mosier wants to set control limits to include 99.73% of the random variation in the
data entry process when it is in control.
APPROACH � Samples of the work of 20 clerks are gathered (and shown in the table). Mosier carefully
examines 100 records entered by each clerk and counts the number of errors. She also computes the fraction
defective in each sample. Equations (S6-9), (S6-10), and (S6-11) are then used to set the control limits.
� EXAMPLE S4
Setting control
limits for percent
defective
Sample
Number
Number
of Errors
Fraction
Defective
Sample
Number
Number
of Errors
Fraction
Defective
1 6 .06 11 6 .06
2 5 .05 12 1 .01
3 0 .00 13 8 .08
4 1 .01 14 7 .07
5 4 .04 15 5 .05
6 2 .02 16 4 .04
7 5 .05 17 11 .11
8 3 .03 18 3 .03
9 3 .03 19 0 .00
10 2 .02 20 4 .04
80
SOLUTION �
(Note: 100 is the size of each sample n.)=
spN = A
1.04211 – .042
100
= .02 1rounded up from .01962
p =
Total number of errors
Total number of records examined
=
80
110021202
= .04
Frito-Lay uses charts to control production quality at critical points in the process. Each half-hour, three batches of chips are
taken from the conveyor (on the left) and analyzed electronically to get an average salt content which is plotted on an -chart
(on the right). Points plotted in the green zone are “in control,” while those in the yellow zone are “out of control.” The SPC chart
is displayed, where all production employees can monitor process stability.
x
x

188 PART 2 Designing Operations
(because we cannot have a negative percentage defective)
INSIGHT � When we plot the control limits and the sample fraction defectives, we find that only
one data-entry clerk (number 17) is out of control. The firm may wish to examine that individual’s
work a bit more closely to see if a serious problem exists (see Figure S6.6).
LCLp = p – zspN = .04 – 31.022 = 0
UCLp = p + zspN = .04 + 31.022 = .10
.11
.10
.09
.08
.07
.06
.05
.04
.03
.02
.01
.00
1 2 3 4 5 6 7 8 9 10 11 12
Sample number
13 14 15 16 17 18 19 20
F
ra
ct
io
n
d
e
fe
ct
iv
e
UCLp = 0.10
p = 0.04–
LCLp = 0.00
� FIGURE S6.6
p-Chart for Data Entry for
Example S4
LEARNING EXERCISE � Mosier decides to set control limits at 95.45% instead. What are the
new and [Answer: 0.08, 0]
RELATED PROBLEMS � S6.13, S6.14, S6.15, S6.16, S6.17, S6.18, S6.19, S6.20, S6.25, S6.35
EXCEL OM Data File Ch06SExS4.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL S6.1 This example is further illustrated in Active Model S6.1 at www.pearsonhighered.com/heizer.
LCLp?UCLp
AUTHOR COMMENT
We are always pleased to
be at zero or below the
center line in a p-chart.
The OM in Action box “Unisys Corp.’s Costly Experiment in Health Care Services” provides
a real-world follow-up to Example S4.
c-Charts In Example S4, we counted the number of defective records entered. A defective
record was one that was not exactly correct because it contained at least one defect. However, a
bad record may contain more than one defect. We use c-charts to control the number of defects
per unit of output (or per insurance record, in the preceding case).
c-chart
A quality control chart used to
control the number of defects
per unit of output.
When Unisys Corp. expanded into the computerized health
care service business things looked rosy. It had just beat out
Blue Cross/Blue Shield of Florida for an $86 million contract
to serve Florida’s state employee health-insurance services.
Its job was to handle the 215,000 Florida employees’ claims
processing—a seemingly simple and lucrative growth area
for an old-line computer company like Unisys.
But 1 year later the contract was not only torn up,
Unisys was fined more than $500,000 for not meeting
quality standards. Here are two of the measures of quality,
both attributes (that is, either “defective” or “not defective”)
on which the firm was out of control:
1. Percentage of claims processed with errors: An audit
over a 3-month period, by Coopers & Lybrand, found
that Unisys made errors in 8.5% of claims processed.
The industry standard is 3.5% “defectives.”
2. Percentage of claims processed within 30 days:
For this attribute measure, a “defect” is a processing
time longer than the contract’s time allowance. In
one month’s sample, 13% of the claims exceeded the
30-day limit, far above the 5% allowed by the state of
Florida.
The Florida contract was a migraine for Unisys, which
underestimated the labor-intensiveness of health claims.
CEO James Unruh pulled the plug on future ambitions in
health care. Meanwhile, the State of Florida’s Ron Poppel
says, “We really need somebody that’s in the insurance
business.”
Sources: Knight Ridder Tribune Business News (October 20, 2004): 1 and
(February 7, 2002): 1; and BusinessWeek (June 16, 1997): 6.
OM in Action � Unisys Corp.’s Costly Experiment in Health Care Services

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Supplement 6 Statistical Process Control 189
3A Poisson probability distribution is a discrete distribution commonly used when the items of interest (in this case,
defects) are infrequent or occur in time and space.
Sampling wine from these
wooden barrels, to make
sure it is aging properly,
uses both SPC (for
alcohol content and
acidity) and subjective
measures (for taste).
Control charts for defects are helpful for monitoring processes in which a large number of
potential errors can occur, but the actual number that do occur is relatively small. Defects may be
errors in newspaper words, bad circuits in a microchip, blemishes on a table, or missing pickles
on a fast-food hamburger.
The Poisson probability distribution,3 which has a variance equal to its mean, is the basis for
c-charts. Because is the mean number of defects per unit, the standard deviation is equal to .
To compute 99.73% control limits for , we use the formula:
(S6-12)
Example S5 shows how to set control limits for a -chart.c
Control limits = c ; 32c
c
2cc
� EXAMPLE S5
Setting control
limits for number
defective
Red Top Cab Company receives several complaints per day about the behavior of its drivers. Over a
9-day period (where days are the units of measure), the owner, Gordon Hoft, received the following
numbers of calls from irate passengers: 3, 0, 8, 9, 6, 7, 4, 9, 8, for a total of 54 complaints. Hoft wants
to compute 99.73% control limits.
APPROACH � He applies Equation (S6–12).
SOLUTION �
Thus:
INSIGHT � After Hoft plotted a control chart summarizing these data and posted it prominently in
the drivers’ locker room, the number of calls received dropped to an average of three per day. Can you
explain why this occurred?
LEARNING EXERCISE � Hoft collects 3 more days’ worth of complaints (10, 12, and 8 com-
plaints) and wants to combine them with the original 9 days to compute updated control limits. What
are the revised and ? [Answer: 14.94, 0.]
RELATED PROBLEMS � S6.21, S6.22, S6.23, S6.24
EXCEL OM Data File Ch06SExS5.xls can be found at www.pearsonhighered.com/heizer.
LCLcUCLc
LCLc = c – 32c = 6 – 316 = 6 – 312.452 = 0 ; 1since it cannot be negative2
UCLc = c + 32c = 6 + 316 = 6 + 312.452 = 13.35, or 13
c =
54
9
= 6 complaints per day

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190 PART 2 Designing Operations
Managerial Issues and Control Charts
In an ideal world, there is no need for control charts. Quality is uniform and so high that employ-
ees need not waste time and money sampling and monitoring variables and attributes. But
because most processes have not reached perfection, managers must make three major decisions
regarding control charts.
First, managers must select the points in their process that need SPC. They may ask “Which
parts of the job are critical to success?” or “Which parts of the job have a tendency to become out
of control?”
Second, managers need to decide if variable charts (i.e., and R) or attribute charts (i.e., p and c)
are appropriate. Variable charts monitor weights or dimensions. Attribute charts are more of a
“yes–no” or “go–no go” gauge and tend to be less costly to implement. Table S6.3 can help you
understand when to use each of these types of control charts.
Third, the company must set clear and specific SPC policies for employees to follow. For exam-
ple, should the data-entry process be halted if a trend is appearing in percent defective records being
keyed? Should an assembly line be stopped if the average length of five successive samples is above
the centerline? Figure S6.7 illustrates some of the patterns to look for over time in a process.
x
Variable Data
Using an -Chart and an R-Chart
1. Observations are variables, which are usually products measured for size or weight. Examples are
the width or length of a wire being cut and the weight of a can of Campbell’s soup.
2. Collect 20 to 25 samples, usually of or more, each from a stable process, and compute
the means for an -chart and the ranges for an R-chart.
3. We track samples of n observations each, as in Example S1.
x
n = 4, n = 5,
x
Attribute Data
Using a p-Chart
1. Observations are attributes that can be categorized as good or bad (or pass–fail, or functional–
broken), that is, in two states.
2. We deal with fraction, proportion, or percent defectives.
3. There are several samples, with many observations in each. For example, 20 samples of n = 100
observations in each, as in Example S4.
Using a c-Chart
1. Observations are attributes whose defects per unit of output can be counted.
2. We deal with the number counted, which is a small part of the possible occurrences.
3. Defects may be: number of blemishes on a desk; complaints in a day; crimes in a year; broken seats
in a stadium; typos in a chapter of this text; or flaws in a bolt of cloth, as is shown in Example S5.
� TABLE S6.3
Helping You Decide Which
Control Chart to Use
Upper control limit
Target
Lower control limit
Upper control limit
Target
Lower control limit
Normal behavior.
Process is “in control.”
One point out above (or
below). Investigate for
cause. Process is “out
of control.”
Run of 5 points above
(or below) central line.
Investigate for cause.
Two points very near
lower (or upper) control.
Investigate for cause.
Trends in either direction,
5 points. Investigate for
cause of progressive
change. This could be the
result of gradual tool wear.
Erratic behavior.
Investigate.
� FIGURE S6.7
Patterns to Look
for on Control Charts
Source: Adapted from Bertrand L.
Hansen, Quality Control: Theory and
Applications (1991): 65. Reprinted by
permission of Prentice Hall, Upper
Saddle River, New Jersey.
AUTHOR COMMENT
This is a really useful table.
When you are not sure which
control chart to use, turn
here for clarification.
AUTHOR COMMENT
Workers in companies such
as Frito-Lay are trained to
follow rules like these.

Supplement 6 Statistical Process Control 191
A tool called a run test is available to help identify the kind of abnormalities in a process that we
see in Figure S6.7. In general, a run of 5 points above or below the target or centerline may suggest
that an assignable, or nonrandom, variation is present. When this occurs, even though all the points
may fall inside the control limits, a flag has been raised. This means the process may not be statisti-
cally in control. A variety of run tests are described in books on the subject of quality methods.4
PROCESS CAPABILITY
Statistical process control means keeping a process in control. This means that the natural varia-
tion of the process must be stable. But a process that is in statistical control may not yield goods
or services that meet their design specifications (tolerances). The ability of a process to meet
design specifications, which are set by engineering design or customer requirements, is called
process capability. Even though that process may be statistically in control (stable), the output
of that process may not conform to specifications.
For example, let’s say the time a customer expects to wait for the completion of a lube job at
Quik Lube is 12 minutes, with an acceptable tolerance of minutes. This tolerance gives an
upper specification of 14 minutes and a lower specification of 10 minutes. The lube process has
to be capable of operating within these design specifications—if not, some customers will not
have their requirements met. As a manufacturing example, the tolerances for Harley-Davidson
cam gears are extremely low, only 0.0005 inch—and a process must be designed that is capable
of achieving this tolerance.
There are two popular measures for quantitatively determining if a process is capable: process
capability ratio ( ) and process capability index ( ).
Process Capability Ratio (Cp)
For a process to be capable, its values must fall within upper and lower specifications. This typi-
cally means the process capability is within standard deviations from the process mean.
Since this range of values is 6 standard deviations, a capable process tolerance, which is the dif-
ference between the upper and lower specifications, must be greater than or equal to 6.
The process capability ratio, is computed as:
(S6-13)
Example S6 shows the computation of Cp.
Cp =
Upper specification – Lower specification
6s
Cp,
; 3
CpkCp
; 2
Run test
A test used to examine the
points in a control chart to see if
nonrandom variation is present.
Process capability
The ability to meet design
specifications.
LO6: Explain process
capability and compute Cp
and Cpk
AUTHOR COMMENT
Here we deal with whether
a process meets the
specification it was
designed to yield.
Cp
A ratio for determining whether
a process meets design
specifications; a ratio of the
specification to the process
variation.
4See Gerald Smith, Statistical Process Control and Process Improvement, 7th ed. (Upper Saddle River, NJ: Prentice
Hall, 2010).
� EXAMPLE S6
Process capability
ratio (Cp)
In a GE insurance claims process, minutes, and minutes.
The design specification to meet customer expectations is minutes. So the Upper
Specification is 213 minutes and the lower specification is 207 minutes. The OM manager wants to
compute the process capability ratio.
APPROACH � GE applies Equation (S6-13).
SOLUTION �
INSIGHT � Since a ratio of 1.00 means that 99.73% of a process’s outputs are within specifica-
tions, this ratio suggests a very capable process, with nonconformance of less than 4 claims per million.
LEARNING EXERCISE � If (instead of .516), what is the new ? [Answer: 1.667, a
very capable process still.]
RELATED PROBLEMS � S6.26, S6.27
EXCEL OM Data File Ch06SExS6.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL S6.2 This example is further illustrated in Active Model S6.2 at www.pearsonhighered.com/heizer.
Cp� = .60
Cp =
Upper specification – Lower specification
6s
=
213 – 207
61.5162
= 1.938
210 ; 3
� = .516x = 210.0
A capable process has a of at least 1.0. If the is less than 1.0, the process yields products or
services that are outside their allowable tolerance. With a of 1.0, 2.7 parts in 1,000 can beCp
CpCp

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192 PART 2 Designing Operations
Cpk
A proportion of variation ( )
between the center of the
process and the nearest
specification limit.
3s
5This is because a of 1.0 has 99.73% of outputs within specifications. So with 1,000 parts,
there are
For a of 2.0, 99.99966% of outputs are “within spec.” So with 1 million parts,
there are 3.4 defects.
1.00 – .9999966 = .0000034;Cp
.0027 * 1,000 = 2.7 defects.
1.00 – .9973 = .0027;Cp
EXAMPLE S7 �
Process capability
index (Cpk)
You are the process improvement manager and have developed a new machine to cut insoles for the
company’s top-of-the-line running shoes. You are excited because the company’s goal is no more than
3.4 defects per million and this machine may be the innovation you need. The insoles cannot be more
than of an inch from the required thickness of .250�. You want to know if you should replace the
existing machine, which has a of 1.0.
APPROACH � You decide to determine the , using Equation (S6-14), for the new machine
and make a decision on that basis.
SOLUTION �
Mean of the new process
Estimated standard deviation of the new process
Both calculations result in:
INSIGHT � Because the new machine has a of only 0.67, the new machine should not replace
the existing machine.
LEARNING EXERCISE � If the insoles can be (instead of ) from the required
, what is the new ? [Answer: 1.33 and the new machine should replace the existing one.]
RELATED PROBLEMS � S6.27, S6.28, S6.29, S6.30, S6.31
EXCEL OM Data File Ch06SExS7.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL S6.2 This example is further illustrated in Active Model S6.2 at www.pearsonhighered.com/heizer.
Cpk.250–
.001–; .002–
Cpk
.001
.0015
= .67.
Cpk = Minimum of B1.2512 – .250
132.0005
,
.250 – 1.2492
132.0005
R Cpk = Minimum of BUpper specification limit – X3s , X – Lower specification limit3s R
= � = .0005 inch.
X = .250 inch.
Lower specification limit = .249 inch
Upper specification limit = .251 inch
Cpk
Cpk
; .001
expected to be “out of spec.”5 The higher the process capability ratio, the greater the likelihood
the process will be within design specifications. Many firms have chosen a Cp of 1.33 (a 4-sigma
standard) as a target for reducing process variability. This means that only 64 parts per million
can be expected to be out of specification.
Recall that in Chapter 6 we mentioned the concept of Six Sigma quality, championed by GE
and Motorola. This standard equates to a of 2.0, with only 3.4 defective parts per million
(very close to zero defects) instead of the 2.7 parts per 1,000 with 3-sigma limits.
Although relates to the spread (dispersion) of the process output relative to its tolerance, it
does not look at how well the process average is centered on the target value.
Process Capability Index (Cpk)
The process capability index, Cpk, measures the difference between the desired and actual
dimensions of goods or services produced.
The formula for is:
(S6-14)
where
When the index for both the upper and lower specification limits equals 1.0, the process
variation is centered and the process is capable of producing within standard deviations
(fewer than 2,700 defects per million). A Cpk of 2.0 means the process is capable of producing
fewer than 3.4 defects per million. For Cpk to exceed 1, must be less than of the difference between
the specification and the process mean ( ). Figure S6.8 shows the meaning of various mea-
sures of Cpk, and Example S7 shows an application of Cpk.
X
1
3�
; 3
Cpk
� = standard deviation of the process population
X = process mean
Cpk = Minimum of BUpper specification limit – X3s , X – Lower specification limit3s RCpk
Cp
Cp

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Supplement 6 Statistical Process Control 193
Cpk = negative number
(Process does not
meet specifications.)
Cpk = zero
(Process does not
meet specifications.)
Cpk = between 0 and 1
(Process does not
meet specifications.)
Cpk = 1
(Process meets
specifications.)
Cpk greater than 1
(Process is better than
the specification
requires.) Lower
specification
limit
Upper
specification
limit
� FIGURE S6.8
Meanings of Cpk Measures
A Cpk index of 1.0 for both the
upper and lower control limits
indicates that the process
variation is within the upper
and lower control limits. As
the Cpk index goes above
1.0, the process becomes
increasingly target oriented,
with fewer defects. If the Cpk
is less than 1.0, the process
will not produce within the
specified tolerance. Because a
process may not be centered,
or may “drift,” a Cpk above 1
is desired.
Note that and will be the same when the process is centered. However, if the mean of the
process is not centered on the desired (specified) mean, then the smaller numerator in Equation
(S6-14) is used (the minimum of the difference between the upper specification limit and the
mean or the lower specification limit and the mean). This application of is shown in Solved
Problem S6.4. is the standard criterion used to express process performance.
ACCEPTANCE SAMPLING6
Acceptance sampling is a form of testing that involves taking random samples of “lots,” or
batches, of finished products and measuring them against predetermined standards. Sampling is
more economical than 100% inspection. The quality of the sample is used to judge the quality of
all items in the lot. Although both attributes and variables can be inspected by acceptance sam-
pling, attribute inspection is more commonly used, as illustrated in this section.
Cpk
Cpk
CpkCp
Flowers Bakery in Villa
Rica, Georgia, uses a
digital camera to inspect
just-baked sandwich buns
as they move along the
production line. Items that
don’t measure up in terms
of color, shape, seed
distribution, or size are
identified and removed
automatically from the
conveyor.
Acceptance sampling
A method of measuring random
samples of lots or batches of
products against predetermined
standards.
LO7: Explain acceptance
sampling
6Refer to Tutorial 2 on our free website www.pearsonhighered.com/heizer for an extended
discussion of acceptance sampling.

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194 PART 2 Designing Operations
Operating characteristic
(OC) curve
A graph that describes how well
an acceptance plan discriminates
between good and bad lots.
Producer’s risk
The mistake of having a
producer’s good lot rejected
through sampling.
7Note that sampling always runs the danger of leading to an erroneous conclusion. Let us say in one company that the
total population under scrutiny is a load of 1,000 computer chips, of which in reality only 30 (or 3%) are defective. This
means that we would want to accept the shipment of chips, because for this particular firm 4% is the allowable defect
rate. However, if a random sample of chips was drawn, we could conceivably end up with 0 defects and accept
that shipment (that is, it is okay), or we could find all 30 defects in the sample. If the latter happened, we could wrongly
conclude that the whole population was 60% defective and reject them all.
n = 50
0
Indifference zone
100
95
75
50
25
10
0
2 4 6 81 3 5 7
Bad lotsGood
lots
Consumer’s
risk
for LTPD
AQL LTPD
Probability
of
acceptance
β = .10
Percentage
defective
α = .05 Producer’s risk for AQL
� FIGURE S6.9
An Operating Characteristic
(OC) Curve Showing
Producer’s and
Consumer’s Risks
A good lot for this particular
acceptance plan has less than
or equal to 2% defectives. A
bad lot has 7% or more
defectives.
Consumer’s risk
The mistake of a customer’s
acceptance of a bad lot
overlooked through sampling.
Acceptable quality level
(AQL)
The quality level of a lot
considered good.
Acceptance sampling can be applied either when materials arrive at a plant or at final inspection,
but it is usually used to control incoming lots of purchased products. A lot of items rejected, based on
an unacceptable level of defects found in the sample, can (1) be returned to the supplier or (2) be 100%
inspected to cull out all defects, with the cost of this screening usually billed to the supplier. However,
acceptance sampling is not a substitute for adequate process controls. In fact, the current approach is to
build statistical quality controls at suppliers so that acceptance sampling can be eliminated.
Operating Characteristic Curve
The operating characteristic (OC) curve describes how well an acceptance plan discriminates
between good and bad lots. A curve pertains to a specific plan—that is, to a combination of n
(sample size) and c (acceptance level). It is intended to show the probability that the plan will
accept lots of various quality levels.
With acceptance sampling, two parties are usually involved: the producer of the product and the
consumer of the product. In specifying a sampling plan, each party wants to avoid costly mistakes in
accepting or rejecting a lot. The producer usually has the responsibility of replacing all defects in the
rejected lot or of paying for a new lot to be shipped to the customer. The producer, therefore, wants to
avoid the mistake of having a good lot rejected (producer’s risk). On the other hand, the customer or
consumer wants to avoid the mistake of accepting a bad lot because defects found in a lot that has
already been accepted are usually the responsibility of the customer (consumer’s risk). The OC curve
shows the features of a particular sampling plan, including the risks of making a wrong decision.7
Figure S6.9 can be used to illustrate one sampling plan in more detail. Four concepts are illus-
trated in this figure.
The acceptable quality level (AQL) is the poorest level of quality that we are willing to
accept. In other words, we wish to accept lots that have this or a better level of quality, but no
lower. If an acceptable quality level is 20 defects in a lot of 1,000 items or parts, then AQL is
defectives.20>1,000 = 2%
AUTHOR COMMENT
Figure S6.9 is further
illustrated in Active Model
S6.3 on our website, www.
pearsonhighered.com/heizer

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Supplement 6 Statistical Process Control 195
The lot tolerance percentage defective (LTPD) is the quality level of a lot that we consider
bad. We wish to reject lots that have this or a poorer level of quality. If it is agreed that an unac-
ceptable quality level is 70 defects in a lot of 1,000, then the LTPD is defective.
To derive a sampling plan, producer and consumer must define not only “good lots” and “bad
lots” through the AQL and LTPD, but they must also specify risk levels.
Producer’s risk ( ) is the probability that a “good” lot will be rejected. This is the risk that a
random sample might result in a much higher proportion of defects than the population of all
items. A lot with an acceptable quality level of AQL still has an � chance of being rejected.
Sampling plans are often designed to have the producer’s risk set at or 5%.
Consumer’s risk ( ) is the probability that a “bad” lot will be accepted. This is the risk that a
random sample may result in a lower proportion of defects than the overall population of items.
A common value for consumer’s risk in sampling plans is
The probability of rejecting a good lot is called a type I error. The probability of accepting a
bad lot is a type II error.
Sampling plans and OC curves may be developed by computer (as seen in the software avail-
able with this text), by published tables, or by calculation, using binomial or Poisson distributions.
Average Outgoing Quality
In most sampling plans, when a lot is rejected, the entire lot is inspected and all defective items
replaced. Use of this replacement technique improves the average outgoing quality in terms of
percent defective. In fact, given (1) any sampling plan that replaces all defective items encountered
and (2) the true incoming percent defective for the lot, it is possible to determine the average
outgoing quality (AOQ) in percentage defective. The equation for AOQ is:
(S6-15)
where true percentage defective of the lot
probability of accepting the lot for a given sample size and quantity defective
number of items in the lot
number of items in the sample
The maximum value of AOQ corresponds to the highest average percentage defective or the low-
est average quality for the sampling plan. It is called the average outgoing quality limit (AOQL).
Acceptance sampling is useful for screening incoming lots. When the defective parts are
replaced with good parts, acceptance sampling helps to increase the quality of the lots by reduc-
ing the outgoing percent defective.
Figure S6.10 compares acceptance sampling, SPC, and . As Figure S6.10 shows,
(a) acceptance sampling by definition accepts some bad units, (b) control charts try to keep the
process in control, but (c) the Cpk index places the focus on improving the process. As operations
managers, that is what we want to do—improve the process.
Cpk
n =
N =
Pa =
Pd =
AOQ =
1Pd21Pa21N – n2
N
b = .10, or 10%.
�
� = .05,
�
70>1,000 = 7%
Lot tolerance
percentage defective
(LTPD)
The quality level of a lot
considered bad.
This laser tracking device,
by Faro Technologies,
enables quality control
personnel to measure and
inspect parts and tools
during production. The
tracker can measure
objects from 100 feet
away and takes up to
1,000 readings per
second.
Type I error
Statistically, the probability of
rejecting a good lot.
Type II error
Statistically, the probability of
accepting a bad lot.
Average outgoing
quality (AOQ)
The percentage defective in an
average lot of goods inspected
through acceptance sampling
LO8: Compute the AOQ

196 PART 2 Designing Operations
SUPPLEMENT SUMMARY
Statistical process control is a major statistical tool of quality
control. Control charts for SPC help operations managers distin-
guish between natural and assignable variations. The -chart and
the R-chart are used for variable sampling, and the p-chart and
x
the c-chart for attribute sampling. The index is a way to
express process capability. Operating characteristic (OC) curves
facilitate acceptance sampling and provide the manager with
tools to evaluate the quality of a production run or shipment.
Cpk
Key Terms
Statistical process control (SPC) (p. 178)
Control chart (p. 178)
Natural variations (p. 179)
Assignable variation (p. 179)
-chart (p. 180)
R-chart (p. 180)
Central limit theorem (p. 180)
p-chart (p. 186)
x
c-chart (p. 188)
Run test (p. 191)
Process capability (p. 191)
Cp (p. 191)
Cpk (p. 192)
Acceptance sampling (p. 193)
Operating characteristic (OC) curve (p. 194)
Producer’s risk (p. 194)
Consumer’s risk (p. 194)
Acceptable quality level (AQL) (p. 194)
Lot tolerance percentage defective
(LTPD) (p. 195)
Type I error (p. 195)
Type II error (p. 195)
Average outgoing quality (AOQ) (p. 195)
Lower
specification
limit
Process mean, μ
Upper
specification
limit (a) Acceptance
sampling
(Some bad units
accepted; the “lot”
is good or bad.)
(b) Statistical
process control
(Keep the process
“in control.”)
(c) Cpk > 1
(Design a process
that is in control.)
� FIGURE S6.10
The Application of
Statistical Process
Techniques Contributes to
the Identification and
Systematic Reduction of
Process Variability
Using Software for SPC
Excel, Excel OM, and POM for Windows may be used to develop control charts for most of the prob-
lems in this chapter.
X Creating Excel Spreadsheets to Determine Control Limits for a c-Chart
Excel and other spreadsheets are extensively used in industry to maintain control charts. Program S6.1
is an example of how to use Excel to determine the control limits for a c-chart. c-charts are used when
the number of defects per unit of output is known. The data from Example S5 are used. In this example,
54 complaints occurred over 9 days. Excel also contains a built-in graphing ability with Chart Wizard.
PROGRAM S6.1 �
An Excel Spreadsheet
for Creating a c-Chart
for Example S5

Supplement 6 Statistical Process Control 197
X Using Excel OM
Excel OM’s Quality Control module has the ability to develop -charts, p-charts, and c-charts. It also
handles OC curves, acceptance sampling, and process capability. Program S6.2 illustrates Excel OM’s
spreadsheet approach to computing the control limits for the Oat Flakes company in Example S1.x
x
Value Cell Excel Formula
Total Defects E6 =SUM(B6:B14)
Defect rate, l E7 =E6/B3
Standard deviation E8 =SQRT(E7)
Upper Control Limit E11 =E7+E9*E8
Center Line E12 =E7
Lower Control Limit E13 =IF(E7-E9*E8>0,E7-E9*E8,0)
Enter the desired
number of standard
deviations.
Do not change this cell without changing
the number of rows in the data table.
Enter the mean
weight for each
of the 12
samples.
Calculate x-bar-bar-the overall average weight
of all the samples = AVERAGE (B10:B21).
Use the overall average as the center line; add and subtract the product
of the desired number of standard deviations and sigma x-bar in order
to create upper and lower control limits (e.g., LCL = F10 – F11*F12).
= B7/SQRT(B6)
= B22
Enter the size for each of the
hourly samples taken.
� PROGRAM S6.2
Excel OM Input and Selected Formulas for the Oat Flakes Example S1
P Using POM for Windows
The POM for Windows Quality Control module has the ability to compute all the SPC control charts we
introduced in this supplement, as well as OC curves, acceptance sampling, and process capability. See
Appendix IV for further details.
Solved Problems Virtual Office Hours help is available at www.myomlab.com
� SOLVED PROBLEM S6.1
A manufacturer of precision machine parts produces round
shafts for use in the construction of drill presses. The average
diameter of a shaft is .56 inch. Inspection samples contain 6
shafts each. The average range of these samples is .006 inch.
Determine the upper and lower control chart limits.x
� SOLUTION
The mean factor from Table S6.1 where the sample size is 6,
is seen to be .483. With this factor, you can obtain the upper and
lower control limits:
= .5571 inch
LCLx = .56 – .0029
= .5629 inch
= .56 + .0029
UCLx = .56 + 1.48321.0062
A2

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198 PART 2 Designing Operations
� SOLVED PROBLEM S6.2
Nocaf Drinks, Inc., a producer of decaffeinated coffee, bottles
Nocaf. Each bottle should have a net weight of 4 ounces. The
machine that fills the bottles with coffee is new, and the operations
manager wants to make sure that it is properly adjusted. Bonnie
Crutcher, the operations manager, randomly selects and weighs
bottles and records the average and range in ounces for
each sample. The data for several samples is given in the follow-
ing table. Note that every sample consists of 8 bottles.
n = 8
� SOLUTION
We first find that and Then, using Table S6.1,
we find:
It appears that the process average and range are both in statistical
control.
The operations manager needs to determine if a process with
a mean (4.03) slightly above the desired mean of 4.00 is satisfac-
tory; if it is not, the process will need to be changed.
LCLR = D3R = 1.13621.5052 = .07
UCLR = D4R = 11.86421.5052 = .94
LCLx = x – A2R = 4.03 – 1.37321.5052 = 3.84
UCLx = x + A2R = 4.03 + 1.37321.5052 = 4.22
R = .505.x = 4.03
Sample
Sample
Range
Sample
Average Sample
Sample
Range
Sample
Average
A .41 4.00 E .56 4.17
B .55 4.16 F .62 3.93
C .44 3.99 G .54 3.98
D .48 4.00 H .44 4.01
Is the machine properly adjusted and in control?
� SOLVED PROBLEM S6.3
Altman Distributors, Inc., fills catalog orders. Samples of size orders have been taken each day over the past six weeks. The
average defect rate was .05. Determine the upper and lower limits for this process for 99.73% confidence.
� SOLUTION
Using Equations (S6-9), (S6-10), and (S6-11),
(because percentage defective cannot be negative)= .05 – .0654 = 0
LCLp = p – 3A
p11 – p2
n
= .05 – 310.02182
= .05 + 310.02182 = .1154
UCLp = p + 3A
p11 – p2
n
= .05 + 3A
1.05211 – .052
100
z = 3, p = .05.
n = 100
� SOLVED PROBLEM S6.4
Ettlie Engineering has a new catalyst injection system for your countertop production line. Your process engineering department has
conducted experiments and determined that the mean is 8.01 grams with a standard deviation of .03. Your specifications are:
and which means an upper specification limit of 8.12 and a lower specification limit of 7.88
What is the performance of the injection system?
� SOLUTION
Using Equation (S6-14):
where
The minimum is 1.22, so the is within specifications and has an implied error rate of less than 2,700 defects per million.Cpk
B .11
.09
= 1.22 ,
.13
.09
= 1.44R Cpk = minimum of B8.12 – 8.01(3)(.03) , 8.01 – 7.88(3)(.03) R
s = standard deviation of the process population
X = process mean
Cpk = Minimum of BUpper specification limit – X3s , X – Lower specification limit3s R
Cpk
3= 8.0 – 31.0424.
[= 8.0 + 31.0424s = .04,m = 8.0

Supplement 6 Statistical Process Control 199
Bibliography
Bakir, S. T. “A Quality Control Chart for Work Performance
Appraisal.” Quality Engineering 17, no. 3 (2005): 429.
Besterfield, Dale H. Quality Control, 8th ed. Upper Saddle River,
NJ: Prentice Hall, 2009.
Elg, M., J. Olsson, and J. J. Dahlgaard. “Implementing Statistical
Process Control.” The International Journal of Quality and
Reliability Management 25, no. 6 (2008): 545.
Goetsch, David L., and Stanley B. Davis. Quality Management,
5th ed. Upper Saddle River, NJ: Prentice Hall, 2006.
Gryna, F. M., R. C. H. Chua, and J. A. DeFeo. Juran’s Quality
Planning and Analysis, 5th ed. New York: McGraw-Hill,
2007.
Lin, H., and G. Sheen. “Practical Implementation of the Capability
Index Cpk Based on Control Chart Data.” Quality Engineering
17, no. 3 (2005): 371.
Matthes, N., et al. “Statistical Process Control for Hospitals.”
Quality Management in Health Care 16, no. 3
(July–September 2007): 205.
Mitra, Amit. Fundamentals of Quality Control and Improvement,
3rd ed. New York: Wiley, 2008.
Montgomery, D. C. Introduction to Statistical Quality Control,
6th ed. New York: Wiley, 2008.
Roth, H. P. “How SPC Can Help Cut Costs.” Journal of Corporate
Accounting and Finance 16, no. 3 (March–April 2005):
21–30.
Summers, Donna. Quality Management, 2nd ed. Upper Saddle
River, NJ: Prentice Hall, 2009.
�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Green River Chemical Company: Involves a company that needs to set up a control chart to monitor sulfate content because of
customer complaints.

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Process Strategy
and Sustainability
Chapter Outline
GLOBAL COMPANY PROFILE: HARLEY-DAVIDSON
Four Process Strategies 204
Process Analysis and Design 211
Special Considerations for Service
Process Design 214
Selection of Equipment and Technology 217
Production Technology 218
Technology in Services 221
Process Redesign 223
Sustainability 223
� Design of Goods and Services
� Managing Quality
� Process Strategy
� Location Strategies
� Layout Strategies
� Human Resources
� Supply-Chain Management
� Inventory Management
� Scheduling
� Maintenance
201

GLOBAL COMPANY PROFILE: HARLEY-DAVIDSON
REPETITIVE MANUFACTURING WORKS AT HARLEY-DAVIDSON
S
ince Harley-Davidson’s founding in Milwaukee
in 1903, it has competed with hundreds of
manufacturers, foreign and domestic. The
competition has been tough. Recent
competitive battles have been with the Japanese,
and earlier battles were with the German, English,
and Italian manufacturers. But after over 100 years,
Harley is the only major U.S. motorcycle company.
The company now has five U.S. facilities and an
assembly plant in Brazil. The Sportster powertrain is
manufactured in Wauwatosa, Wisconsin, and the
sidecars, saddlebags, windshields, and other specialty
items are produced in Tomahawk, Wisconsin. The
families of Touring and Softail bikes are assembled in
York, Pennsylvania, while the Sportster models, Dyna
models, and VRSC models of motorcycles are produced
in Kansas City, Missouri.
As a part of management’s lean manufacturing
effort, Harley groups production of parts that require
similar processes together. The result is work cells.
Using the latest technology, work cells perform in
one location all the operations necessary for
production of a specific module. Raw materials
are moved to the work cells and then the modules
proceed to the assembly line. As a double check
on quality, Harley has also installed “light curtain”
technology which uses an infrared sensor to verify
the bin from which an operator is taking parts.
Materials go to the assembly line on a just-in-time
basis, or as Harley calls it, using a Materials as
Needed (MAN) system.
The 12.5-million-square-foot York facility includes
manufacturing cells that perform tube bending, frame-
building, machining, painting, and polishing. Innovative
Frame tube
bending
Frame-building
work cells
Frame
machining
28 tests
THE ASSEMBLY LINE
TESTING
Incoming parts
From Milwaukee
on a JIT arrival
schedule
Hot-paint
frame painting
Roller testing
Oil tank work cell
Shocks and forks
Crating
Handlebars
Fender work cell
Air cleaners
Fluids and mufflers
Fuel tank work cell
Wheel work cell
Engines and
transmissions
Flow Diagram Showing the Production
Process at Harley-Davidson’s York,
Pennsylvania, Assembly Plant
202

Wheel assembly modules are prepared in a work cell for JIT
delivery to the assembly line.
For manufacturers like Harley-Davidson, which produces a large
number of end products from a relatively small number of options,
modular bills of material provide an effective solution.
It all comes together on the line. Any employee who
spots a problem has the authority to stop the line
until the problem in corrected. The multicolored
“andon” light above the motorcycle on the frame of
the carrier signals the severity of the problem.
motorcycles available which allows customers to
individualize their purchase. The Harley-Davidson
production system works because high-quality modules
are brought together on a tightly scheduled repetitive
production line.
manufacturing techniques use robots to load machines
and highly automated production to reduce machining
time. Automation and precision sensors play a key role
in maintaining tolerances and producing a quality
product. Each day the York facility produces up to 600
heavy-duty factory-custom motorcycles. Bikes are
assembled with different engine displacements,
multiple wheel options, colors, and accessories.
The result is a huge number of variations in the
HARLEY-DAVIDSON �
203
Engines, having arrived just-in-time from the
Milwaukee engine plant in their own protective
shipping containers, are placed on an overhead
conveyor for movement to the assembly line.

204 PART 2 Designing Operations
LO1: Describe four production processes 204
LO2: Compute crossover points for
different processes 210
LO3: Use the tools of process analysis 211
LO4: Describe customer interaction in
process design 215
Chapter 7 Learning Objectives
LO5: Identify recent advances in
production technology 218
LO6: Discuss the four R’s of sustainability 223
FOUR PROCESS STRATEGIES
In Chapter 5, we examined the need for the selection, definition, and design of goods and services.
Our purpose was to create environmentally-friendly designs that could be delivered in an ethical,
sustainable manner. We now turn to their production. A major decision for an operations manager
is finding the best way to produce so as not to waste our planet’s resources. Let’s look at ways to
help managers design a process for achieving this goal.
A process (or transformation) strategy is an organization’s approach to transforming
resources into goods and services. The objective of a process strategy is to build a production
process that meets customer requirements and product specifications within cost and other man-
agerial constraints. The process selected will have a long-term effect on efficiency and flexibil-
ity of production, as well as on cost and quality of the goods produced. Therefore, the limitations
of a firm’s operations strategy are determined at the time of the process decision.
Virtually every good or service is made by using some variation of one of four process strate-
gies: (1) process focus, (2) repetitive focus, (3) product focus, and (4) mass customization. The
relationship of these four strategies to volume and variety is shown in Figure 7.1. We examine
Arnold Palmer Hospital as an example of a process-focused firm, Harley-Davidson as a repeti-
tive producer, Frito-Lay as a product-focused operation, and Dell as a mass customizer.
Process Focus
The vast majority of global production is devoted to making low-volume, high-variety products
in places called “job shops.” Such facilities are organized around specific activities or processes.
In a factory, these processes might be departments devoted to welding, grinding, and painting. In
an office, the processes might be accounts payable, sales, and payroll. In a restaurant, they might
be bar, grill, and bakery. Such facilities are process focused in terms of equipment, layout, and
supervision. They provide a high degree of product flexibility as products move between
processes. Each process is designed to perform a wide variety of activities and handle frequent
changes. Consequently, they are also called intermittent processes.
Process strategy
An organization’s approach to
transforming resources into
goods and services.
AUTHOR COMMENT
Production processes
provide an excellent way
to think about how we
organize to produce
goods and services.
V
a
ri
e
ty
(
fle
xi
b
ili
ty
)
Changes in Modules
modest runs,
standardized modules
Repetitive Process
Volume
Changes in Attributes
(such as grade, quality,
size, thickness, etc.)
long runs only
Low Volume
High Variety
one or few
units per run
(allows customization)
High Volume
Process Focus
projects, job shops
(machine, print,
hospitals,restaurants)
Arnold Palmer Hospital
Repetitive
(autos, motorcycles,
home appliances)
Harley-Davidson
Poor Strategy
(Both fixed and
variable costs
are high.)
Product Focus
(commercial baked goods,
steel, glass, beer)
Frito-Lay
Mass Customization
(difficult to achieve,
but huge rewards)
Dell Computer
� FIGURE 7.1
Process Selected Must
Fit with Volume and
Variety
Process focus
A production facility organized
around processes to facilitate
low-volume, high-variety
production.
LO1: Describe four
production processes

Chapter 7 Process Strategy and Sustainability 205
Many inputs
Process Focus
(low-volume, high-variety,
intermittent process)
Arnold Palmer Hospital
Many different outputs
(uniquely treated patients)
Many departments and
many routings
Many departments and
many routings
Raw material
and module inputs
Repetitive Focus
(modular)
Harley-Davidson
Modules combined
for many outputs
(many combinations of
motorcycles)
Few
modules
Many part and
component inputs
Mass Customization
(high-volume, high-variety)
Dell Computer
Many output versions
(custom PCs and notebooks)
Many modules
Few inputs
(surgeries, sick patients,
baby deliveries, emergencies)
(multiple engines and
wheel modules)
(chips, hard drives,
software, cases)
(corn, potatoes, water,
seasoning)
Product Focus
(high-volume, low-variety,
continuous process)
Frito-Lay
(a) (b) (d)(c)
Output variations in size,
shape, and packaging
(3-oz, 5-oz, 24-oz packages
labeled for each market)
� FIGURE 7.2 Process Options
Referring to Figure 7.2(a), imagine a diverse group of patients entering Arnold Palmer
Hospital, a process-focused facility, to be routed to specialized departments, treated in a distinct
way, and then exiting as uniquely cared for individuals.
Process-focused facilities have high variable costs with extremely low utilization of facili-
ties, as low as 5%. This is the case for many restaurants, hospitals, and machine shops.
However, some facilities that lend themselves to electronic controls do somewhat better. With
computer-controlled machines, it is possible to program machine tools, piece movement, tool
changing, placement of the parts on the machine, and even the movement of materials between
machines.
Repetitive Focus
A repetitive process falls between the product and process focuses seen in Figures 7.1 and 7.2(b).
Repetitive processes, as we saw in the Global Company Profile on Harley-Davidson, use modules.
Modules are parts or components previously prepared, often in a continuous process.
The repetitive process is the classic assembly line. Widely used in the assembly of virtually
all automobiles and household appliances, it has more structure and consequently less flexibility
than a process-focused facility.
Fast-food firms are another example of a repetitive process using modules. This type of
production allows more customizing than a product-focused facility; modules (for example,
meat, cheese, sauce, tomatoes, onions) are assembled to get a quasi-custom product, a cheese-
burger. In this manner, the firm obtains both the economic advantages of the continuous model
Repetitive process
A product-oriented production
process that uses modules.
Modules
Parts or components of a
product previously prepared,
often in a continuous process.
AUTHOR COMMENT
Here we show 4 process
options, with an example
of each.

206 PART 2 Designing Operations
Product focus
A facility organized around
products; a product-oriented,
high-volume, low-variety
process.
(where many of the modules are prepared) and the custom advantage of the low-volume, high-
variety model.
Product Focus
High-volume, low-variety processes are product focused. The facilities are organized around
products. They are also called continuous processes, because they have very long, continuous
production runs. Products such as glass, paper, tin sheets, lightbulbs, beer, and potato chips are
made via a continuous process. Some products, such as lightbulbs, are discrete; others, such as
rolls of paper, are nondiscrete. Still others, such as repaired hernias at Shouldice Hospital, are
services. It is only with standardization and effective quality control that firms have estab-
lished product-focused facilities. An organization producing the same lightbulb or hot dog bun
day after day can organize around a product. Such an organization has an inherent ability to set
standards and maintain a given quality, as opposed to an organization that is producing unique
products every day, such as a print shop or general-purpose hospital. For example, Frito-Lay’s
family of products is also produced in a product-focused facility (see Figure 7.2[c]). At Frito-
Lay, corn, potatoes, water, and seasoning are the relatively few inputs, but outputs (like
Cheetos, Ruffles, Tostitos, and Fritos) vary in seasoning and packaging within the product
family.
A product-focused facility produces high volume and low variety. The specialized nature of
the facility requires high fixed cost, but low variable costs reward high facility utilization.
Mass Customization Focus
Our increasingly wealthy and sophisticated world demands individualized goods and services. A
peek at the rich variety of goods and services that operations managers are called on to supply is
shown in Table 7.1. The explosion of variety has taken place in automobiles, movies, breakfast
cereals, and thousands of other areas. In spite of this proliferation of products, operations man-
agers have improved product quality while reducing costs. Consequently, the variety of products
continues to grow. Operations managers use mass customization to produce this vast array of
goods and services. Mass customization is the rapid, low-cost production of goods and services
that fulfill increasingly unique customer desires. But mass customization (see the upper-right
section of Figure 7.1) is not just about variety; it is about making precisely what the customer
wants when the customer wants it economically.
Mass customization brings us the variety of products traditionally provided by low-volume
manufacture (a process focus) at the cost of standardized high-volume (product-focused) produc-
tion. However, achieving mass customization is a challenge that requires sophisticated opera-
tional capabilities. Building agile processes that rapidly and inexpensively produce custom
products requires imaginative and aggressive use of organizational resources. And the link
between sales, design, production, supply chain, and logistics must be tight.
Mass customization
Rapid, low-cost production that
caters to constantly changing
unique customer desires.
Number of Choicesa
Item 1970s 21st Century
Vehicle models 140 286
Vehicle styles 18 1,212
Bicycle types 8 211,000c
Software titles 0 400,000
Web sites 0 162,000,000d
Movie releases per year 267 765e
New book titles 40,530 300,000+
Houston TV channels 5 185
Breakfast cereals 160 340
Items (SKUs) in supermarkets 14,000b 150,000f
LCD TVs 0 102
aVariety available in America; worldwide the variety increases even more. b1989.
cPossible combinations for one manufacturer. dRoyal Pingdom Estimate (2008).
ewww.movieweb.com (2009). fSKUs managed by H. E. Butts grocery chain.
� TABLE 7.1
Mass Customization Provides
More Choices Than Ever
Source: Various; however, many
of the data are from the Federal
Reserve Bank of Dallas.

www.movieweb.com

Chapter 7 Process Strategy and Sustainability 207
Dell Computer (see Figure 7.2[d]) has demonstrated that the payoff for mass customization
can be substantial. More traditional manufacturers include Toyota, which recently announced
delivery of custom-ordered cars in 5 days. Similarly, electronic controls allow designers in the
textile industry to rapidly revamp their lines and respond to changes.
The service industry is also moving toward mass customization. For instance, not very many
years ago, most people had the same telephone service. Now, not only is the phone service full of
options, from caller ID to voice mail, but contemporary phones are hardly phones. They may also
be part camera, computer, game player, GPS, and Web browser. Insurance companies are adding
and tailoring new products with shortened development times to meet the unique needs of their
customers. And firms like iTunes, Napster, and emusic maintain a music inventory on the Internet
that allow customers to select a dozen songs of their choosing and have them made into a custom
CD. Similarly, the number of new books and movies increases each year. Mass customization
places new demands on operations managers who must build the processes that provide this
expanding variety of goods and services.
One of the essential ingredients in mass customization is a reliance on modular design. In all
the examples cited, as well as those in the OM in Action box “Mass Customization at Borders
Books and at Smooth FM Radio,” modular design is the key. However, as Figure 7.3 shows, very
effective scheduling, personnel and facility flexibility, supportive supply chains, and rapid
throughput are also required. These items influence all 10 of the OM decisions and therefore
require excellent operations management.
Making Mass Customization Work Mass customization suggests a high-volume system
in which products are built-to-order.1 Build-to-order (BTO) means producing to customer
orders, not forecasts. Build-to-order can be a successful order-winning strategy when executed
successfully. But high-volume build-to-order is difficult. Some major challenges are:
• Product design must be imaginative and fast. Successful build-to-order designs often use mod-
ules. Ping Inc., the premier golf club manufacturer, uses different combinations of club heads,
grips, shafts, and angles to make 20,000 variations of its golf clubs.
• Process design must be flexible and able to accommodate changes in both design and tech-
nology. For instance, postponement allows for customization late in the production process.
Toyota installs unique interior modules very late in production for its popular Scion, a process
also typical with customized vans. Postponement is further discussed in Chapter 11.
1Build-to-order (BTO) may be referred to and refined as engineer-to-order (ETO) and design-to-order (DTO), depending on the extent of the customization.
So you want a hard-to-get, high-quality paperback book in
15 minutes? Borders can take care of you—even if you
want a book that the store does not carry or have in stock.
First, a Borders employee checks the digital database of
titles that have been licensed from publishers. If the title is
available, a digital file of the book is downloaded to two
printers from a central server in Atlanta. One printer
makes the book cover and the other the pages. Then the
employee puts the two pieces together in a bookbinding
machine. A separate machine cuts the book to size. And
your book is ready. You get the book you want now, and
Borders gets a sale. Books sold this way also avoid both
inventory and incoming shipping cost, as well as the cost
of returning books that do not sell.
Smooth FM provides a “customized” radio broadcast for
Houston, Boston, Milwaukee, Albany, and Jacksonville
from its midtown Manhattan station. Here is how it works.
During Smooth FM’s 40-minute music blocks, an
announcer in Manhattan busily records 30-second blocks
of local weather and traffic, commercials, promotions, and
5-second station IDs. Then the recorded material is
transmitted to the affiliate stations. When the music block is
over, the Manhattan announcer hits a button that signals
computers at all the affiliates to simultaneously air the
prerecorded “local” segments. Any “national” news or
“national” ads can also be added from Manhattan. The
result is the economy of mass production and a
customized product for the local market. Radio people
call it “local customization.”
Sources: Hoover’s Company Records (March 15, 2009): 101773; The New
York Times (February 16, 2004): C3; The Wall Street Journal (June 1, 1999):
B1, B4.
OM in Action �Mass Customization at Borders Books and at Smooth FM Radio
Postponement
The delay of any modifications
or customization to a product as
long as possible in the
production process.
Build-to-order (BTO)
Produce to customer order
rather than to a forecast.

208 PART 2 Designing Operations
• Inventory management requires tight control. To be successful with build-to-order, a firm must
avoid being stuck with unpopular or obsolete components. With virtually no raw material, Dell
puts custom computers together in less than a day.
• Tight schedules that track orders and material from design through delivery are another
requirement of mass customization. Align Technology, a well-known name in orthodontics,
figured out how to achieve competitive advantage by delivering custom-made clear plastic
aligners within three weeks of your first visit to the dentist’s office.
• Responsive partners in the supply chain can yield effective collaboration. Forecasting, inven-
tory management, and ordering for JCPenney shirts are all handled for the retailer by its sup-
plier in Hong Kong.
Mass customization/build-to-order is difficult, but is the new imperative for operations. There are
advantages to mass customization and building to order: first, by meeting the demands of the market
place, firms win orders and stay in business; in addition, they trim costs (from personnel to inventory
to facilities) that exist because of inaccurate sales forecasting. Mass customization and build-to-
order can be done—and operations managers in leading organizations are accepting the challenge.
Comparison of Process Choices
The characteristics of the four processes are shown in Table 7.2 and Figure 7.2 (on page 205).
Advantages exist across the continuum of processes, and firms may find strategic advantage in any
process. Each of the processes, when properly matched to volume and variety, can produce a low-
cost advantage. For instance, unit costs will be less in the continuous-process case when high vol-
ume (and high utilization) exists. However, we do not always use the continuous-process (that is,
specialized equipment and facilities) because it is too expensive when volumes are low or flexibil-
ity is required. A low-volume, unique, highly differentiated good or service is more economical
when produced under process focus; this is the way fine-dining restaurants and general-purpose
hospitals are organized. Just as all four processes, when appropriately selected and well managed,
can yield low cost, so too can all four be responsive and produce differentiated products.
Figure 7.3 indicated that equipment utilization in a process-focused facility is often in the
range of 5% to 25%. When utilization goes above 15%, moving toward a repetitive or product
focus, or even mass customization, may be advantageous. A cost advantage usually exists by
improving utilization, provided the necessary flexibility is maintained. McDonald’s started an
entirely new industry by moving its limited menu from process focus to repetitive focus.
McDonald’s is now trying to add more variety and move toward mass customization.
Much of what is produced in the world is still produced in very small lots—often as small
as one. This is true for most legal services, medical services, dental services, and restaurants. An
X-ray machine in a dentist’s office and much of the equipment in a fine-dining restaurant have
low utilization. Hospitals, too, have low utilization, which suggests why their costs are consid-
ered high. Why such low utilization? In part because excess capacity for peak loads is desirable.
Hospital administrators, as well as managers of other service facilities and their patients and cus-
tomers, expect equipment to be available as needed. Another reason is poor scheduling (although
Repetitive Focus
Flexible people
and equipment
Process Focused
High variety, low volume
Low utilization (5% to 25%)
General-purpose equipment
Product Focused
Low variety, high volume
High utilization (70% to 90%)
Specialized equipment
Modular
techniques
Responsive
supply chains
Accommodating
product and process
design
Rapid
throughput
techniques
Effective
scheduling
techniques
Mass Customization
� FIGURE 7.3
Requirements to Achieve
Mass Customization
AUTHOR COMMENT
OM must align a variety of
factors to make mass
customization work.

Chapter 7 Process Strategy and Sustainability 209
substantial efforts have been made to forecast demand in the service industry) and the resulting
imbalance in the use of facilities.
Crossover Charts The comparison of processes can be further enhanced by looking at the
point where the total cost of the processes changes. For instance, Figure 7.4 shows three alterna-
tive processes compared on a single chart. Such a chart is sometimes called a crossover chart.
Process A has the lowest cost for volumes below process B has the lowest cost between
and and process C has the lowest cost at volumes above
Example 1 illustrates how to determine the exact volume where one process becomes more
expensive than another.
V2.V2,
V1V1,
� TABLE 7.2
Comparison of the Characteristics of Four Types of Processes
Process Focus
(low volume, high variety)
(e.g., Arnold Palmer Hospital)
Repetitive Focus
(modular)
(e.g., Harley-Davidson)
Product Focus
(high volume, low variety)
(e.g., Frito-Lay)
Mass Customization
(high volume, high variety)
(e.g., Dell Computer)
1. Small quantity and large
variety of products are
produced.
1. Long runs, usually a
standardized product with
options, are produced from
modules.
1. Large quantity and small
variety of products are
produced.
1. Large quantity and large
variety of products are
produced.
2. Equipment used is general
purpose.
2. Special equipment aids in
use of an assembly line.
2. Equipment used is special
purpose.
2. Rapid changeover on
flexible equipment.
3. Operators are broadly
skilled.
3. Employees are modestly
trained.
3. Operators are less broadly
skilled.
3. Flexible operators are
trained for the necessary
customization.
4. There are many job
instructions because each
job changes.
4. Repetitive operations
reduce training and
changes in job instructions.
4. Work orders and job
instructions are few because
they are standardized.
4. Custom orders require
many job instructions.
5. Raw-material inventories are
high relative to the value of
the product.
5. Just-in-time procurement
techniques are used.
5. Raw material inventories are
low relative to the value of
the product.
5. Raw material inventories
are low relative to the
value of the product.
6. Work-in-process is high
compared to output.
6. Just-in-time inventory
techniques are used.
6. Work-in-process inventory
is low compared to output.
6. Work-in-process inventory
is driven down by JIT,
kanban, lean production.
7. Units move slowly through
the facility.
7. Assembly is measured in
hours and days.
7. Swift movement of units
through the facility is
typical.
7. Goods move swiftly
through the facility.
8. Finished goods are usually
made to order and not stored.
8. Finished goods are made
to frequent forecasts.
8. Finished goods are usually
made to a forecast and stored.
8. Finished goods are often
build-to-order (BTO).
9. Scheduling is complex and
concerned with the trade-off
between inventory availability,
capacity, and customer
service.
9. Scheduling is based on
building various models
from a variety of modules
to forecasts.
9. Scheduling is relatively
simple and concerned with
establishing a rate of output
sufficient to meet sales
forecasts.
9. Sophisticated scheduling is
required to accommodate
custom orders.
10. Fixed costs tend to be low
and variable costs high.
10. Fixed costs are dependent
on flexibility of the facility.
10. Fixed costs tend to be high
and variable costs low.
10. Fixed costs tend to be high,
but variable costs must be low.
Kleber Enterprises would like to evaluate three accounting software products (A, B, and C) to support
changes in its internal accounting processes. The resulting processes will have cost structures similar to
those shown in Figure 7.4. The costs of the software for these processes are:
� EXAMPLE 1
Crossover chart
Total Dollars Required
Fixed Cost per Accounting Report
Software A $200,000 $60
Software B $300,000 $25
Software C $400,000 $10
Crossover chart
A chart of costs at the possible
volumes for more than one
process.

210 PART 2 Designing Operations
APPROACH � Solve for the crossover point for software A and B and then the crossover point for
software B and C.
SOLUTION � Software A yields a process that is most economical up to but to exactly what
number of reports (volume)? To determine the volume at we set the cost of software A equal to the
cost of software B. is the unknown volume:
This means that software A is most economical from 0 reports to 2,857 reports ( ).
Similarly, to determine the crossover point for we set the cost of software B equal to the cost of
software C:
This means that software B is most economical if the number of reports is between 2,857 ( ) and
6,666 ( ) and that software C is most economical if reports exceed 6,666 ( ).
INSIGHT � As you can see, the software and related process chosen is highly dependent on the
forecasted volume.
LEARNING EXERCISE � If the vendor of software A reduces the fixed cost to $150,000, what
is the new crossover point between A and B? [Answer: 4,286.]
RELATED PROBLEMS � 7.5, 7.6, 7.7, 7.8, 7.9, 7.10, 7.11, 7.12, 7.14
EXCEL OM Data File Ch07Ex1.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 7.1 This example is further illustrated in Active Model 7.1 at www.pearsonhighered.com/heizer.
V2V2
V1
V2 = 6,666
15V2 = 100,000
300,000 + 1252V2 = 400,000 + 1102V2
V2,
V1
V1 = 2,857
35V1 = 100,000
200,000 + 1602V1 = 300,000 + 1252V1
V1
V1,
V1,
To
ta
l p
ro
ce
ss
A
c
os
ts
To
tal
pr
oc
es
s B
co
sts
Tota
l pro
cess
C c
osts
Fixed costs
$
$
Low volume, high variety
Process A
Variable
costs
Fixed costs
$
Repetitive
Process B
Variable
costs
Fixed costs
$
High volume, low variety
Process C
Volume
400,000
300,000
200,000
(2,857) (6,666)
V2V1
Variable
costs
Fixed
cost
Process A
Fixed
cost
Process B
Fixed
cost
Process C
� FIGURE 7.4
Crossover Charts
AUTHOR COMMENT
Different processes can be
expected to have different
costs. However, at any given
volume, only one will have
the lowest cost.
LO2: Compute crossover
points for different
processes

www.pearsonhighered.com/heizer

www.pearsonhighered.com/heizer

Chapter 7 Process Strategy and Sustainability 211
Focused Processes In an ongoing quest for efficiency, industrialized societies continue to move
toward specialization. The focus that comes with specialization contributes to efficiency. Managers
who focus on a limited number of activities, products, and technologies do better. As the variety of
products in a facility increase, overhead costs increase even faster. Similarly, as the variety of prod-
ucts, customers, and technology increases, so does complexity. The resources necessary to cope
with the complexity expand disproportionately. A focus on depth of product line as opposed to
breadth is typical of outstanding firms, of which Intel, Motorola, L.M. Ericsson, Nokia, and Bosch
are world-class examples. Specialization, simplification, concentration, and focus yield efficiency.
They also contribute to building a core competence that yields market and financial success. The
focus can be:
• Customers (such as Winterhalter Gastronom, a German company that focuses on dishwashers
for hotels and restaurants, for whom spotless glasses and dishes are critical)
• Products with similar attributes (such as Nucor Steel’s Crawford, Ohio, plant, which processes
only high-quality sheet steels, and Gallagher, a New Zealand company, which has 45% of the
world market in electric fences)
• Service (such as Orlando’s Arnold Palmer Hospital, with a focus on children and women; or
Shouldice Hospital, in Canada, with a focus on hernia repair).
• Technology (such as Texas Instruments, with a focus on only certain specialized kinds of semi-
conductors; and SAP, which in spite of a world of opportunities, remains focused on software).
The key for the operations manager is to move continuously toward specialization, focusing on
the products, technology, customers, processes, and talents necessary to excel in that specialty.
Changing Processes Changing the production system from one process model to another is
difficult and expensive. In some cases, the change may mean starting over. Consider what would
be required of a rather simple change—McDonald’s adding the flexibility necessary to serve you
a charbroiled hamburger. What appears to be rather straightforward would require changes in
many of our 10 OM decisions. For instance, changes may be necessary in (1) purchasing (a dif-
ferent quality of meat, perhaps with more fat content, and supplies such as charcoal); (2) quality
standards (how long and at what temperature the patty will cook); (3) equipment (the char-
broiler); (4) layout (space for the new process and for new exhaust vents); and (5) training. So
choosing where to operate on the process strategy continuum may determine the transformation
strategy for an extended period. This critical decision must be done right the first time.
PROCESS ANALYSIS AND DESIGN
When analyzing and designing processes, we ask questions such as the following:
• Is the process designed to achieve competitive advantage in terms of differentiation, response,
or low cost?
• Does the process eliminate steps that do not add value?
• Does the process maximize customer value as perceived by the customer?
• Will the process win orders?
A number of tools help us understand the complexities of process design and redesign. They are sim-
ply ways of making sense of what happens or must happen in a process. Let’s look at five of them:
flowcharts, time-function mapping, value-stream mapping, process charts, and service blueprinting.
Flowchart
The first tool is the flowchart, which is a schematic or drawing of the movement of material, prod-
uct, or people. For instance, the flowchart in the Global Company Profile for this chapter shows the
assembly processes for Harley-Davidson. Such charts can help understanding, analysis, and com-
munication of a process.
Time-Function Mapping
A second tool for process analysis and design is a flowchart, but with time added on the horizon-
tal axis. Such charts are sometimes called time-function mapping, or process mapping. With
time-function mapping, nodes indicate the activities and the arrows indicate the flow direction,
with time on the horizontal axis. This type of analysis allows users to identify and eliminate
VIDEO 7.1
Process Strategy at Wheeled
Coach Ambulance
Flowchart
A drawing used to analyze
movement of people or
material.
AUTHOR COMMENT
Here we look at 5 tools
that help understand
processes.
LO3: Use the tools of
process analysis
Time-function mapping
(or process mapping)
A flowchart with time added on
the horizontal axis.

212 PART 2 Designing Operations
waste such as extra steps, duplication, and delay. Figure 7.5 shows the use of process mapping
before and after process improvement at American National Can Company. In this example, sub-
stantial reduction in waiting time and process improvement in order processing contributed to a
savings of 46 days.
Value-Stream Mapping
A variation of time-function mapping is value-stream mapping (VSM); however, value-stream
mapping takes an expanded look at where value is added (and not added) in the entire production
process, including the supply chain. As with time-function mapping, the idea is to start with the
customer and understand the production process, but value-stream mapping extends the analysis
back to suppliers.
Value-stream mapping
(VSM)
A process that helps managers
understand how to add value
in the flow of material and
information through the entire
production process.
EXAMPLE 2 �
Value-stream
mapping
Motorola has received an order for 11,000 cell phones per month and wants to understand how the
order will be processed through manufacturing.
APPROACH � To fully understand the process from customer to supplier, Motorola prepares a
value-stream map.
SOLUTION � Although value-stream maps appear complex, their construction is easy. Here are
the steps needed to complete the value-stream map shown in Figure 7.6.
1. Begin with symbols for customer, supplier, and production to ensure the big picture.
2. Enter customer order requirements.
3. Calculate the daily production requirements.
4. Enter the outbound shipping requirements and delivery frequency.
5. Determine inbound shipping method and delivery frequency.
6. Add the process steps (i.e., machine, assemble) in sequence, left to right.
7. Add communication methods, add their frequency, and show the direction with arrows.
8. Add inventory quantities (shown with ) between every step of the entire flow.
9. Determine total working time (value-added time) and delay (non-value-added time).
“Baseline” Time-Function Map “Target” Time-Function Map
12 days 13 days
52 days
1 day 4 days 1 day 10 days 1 day 9 days 1 day
Customer
Sales
Production
control
Plant A
Warehouse
Plant B
Transport
Order
product
Receive
product
Process
order
Print
Wait
Wait Wait Wait
Move Move
Extrude
O
rd
e
r
O
rd
e
r
W
IP
W
IP
W
IP
W
IP
P
ro
d
u
ct
P
ro
d
u
ct
P
ro
d
u
ct
1 day
6 days
2 days 1 day 1 day 1 day
Customer
Sales
Production
control
Plant
Warehouse
Transport
Order
product
Receive
product
Process
order
Print
Wait
Wait
Move
Extrude
O
rd
e
r
O
rd
e
r
WIP
P
ro
d
u
ct
P
ro
d
u
ct
P
ro
d
u
ct
(a) (b)
� FIGURE 7.5 Time-Function Mapping (Process Mapping) for a Product Requiring Printing and Extruding Operations at American National
Can Company
This technique clearly shows that waiting and order processing contributed substantially to the 46 days that can be eliminated in this operation.
Source: Excerpted from Elaine J. Labach, “Faster, Better, and Cheaper,” Target no. 5: 43 with permission of the Association for Manufacturing Excellence, 380 West Palatine Road, Wheeling,
IL 60090-5863, 847/520-3282. www.ame.org. Reprinted with permission of Target Magazine.

www.ame.org

Chapter 7 Process Strategy and Sustainability 213
Manufacturing
Management
Production
Supervisor
500 needed
each day
Weekly
Orders
2,500
Weekly
Orders
2,500
Monthly Forecast = 11,000
Monthly
Forecast
Weekly
Daily Communication
1 operator
Machine
1 operator
Ship 500
Package
1 operator
Test
2 operators
Assemble
1 operator
45 seconds 20 seconds
4 days4 days4 days6 days3 days5 days
20 seconds15 seconds
Non-value-added time = 26 days
Value-added time = 140 seconds
40 seconds
Component
Mounting
Supplier Customer
1,500 2,500 2,000 2,000
2,0002,500
Weekly Daily
� FIGURE 7.6
Value-Stream Mapping
(VSM)
INSIGHT � From Figure 7.6 we note that large inventories exist in incoming raw material and
between processing steps, and that the value-added time is low as a proportion of the entire process.
LEARNING EXERCISE � How might raw material inventory be reduced? [Answer: Have
deliveries twice per week rather than once per week.]
RELATED PROBLEM � 7.13
Value-stream mapping takes into account not only the process but, as shown in Example 2,
also the management decisions and information systems that support the process.
Process Charts
The fourth tool is the process chart. Process charts use symbols, time, and distance to provide
an objective and structured way to analyze and record the activities that make up a process.2 They
allow us to focus on value-added activities. For instance, the process chart shown in Figure 7.7,
which includes the present method of hamburger assembly at a fast-food restaurant, includes a
value-added line to help us distinguish between value-added activities and waste. Identifying all
value-added operations (as opposed to inspection, storage, delay, and transportation, which
add no value) allows us to determine the percent of value added to total activities.3 We can see
from the computation at the bottom of Figure 7.7 that the value added in this case is 85.7%. The
2An additional example of a process chart is shown in Chapter 10.
3Waste includes inspection (if the task is done properly, then inspection is unnecessary); transportation (movement of
material within a process may be a necessary evil, but it adds no value); delay (an asset sitting idle and taking up space
is waste); storage (unless part of a “curing” process, storage is waste).
Process charts
Charts that use symbols to
analyze the movement of people
or material.

214 PART 2 Designing Operations
operations manager’s job is to reduce waste and increase the percent of value added. The non-
value-added items are a waste; they are resources lost to the firm and to society forever.
Service Blueprinting
Products with a high service content may warrant use of yet a fifth process technique. Service
blueprinting is a process analysis technique that focuses on the customer and the provider’s inter-
action with the customer. For instance, the activities at level one of Figure 7.8 are under the con-
trol of the customer. In the second level are activities of the service provider interacting with the
customer. The third level includes those activities that are performed away from, and not immedi-
ately visible to, the customer. Each level suggests different management issues. For instance, the
top level may suggest educating the customer or modifying expectations, whereas the second level
may require a focus on personnel selection and training. Finally, the third level lends itself to more
typical process innovations. The service blueprint shown in Figure 7.8 also notes potential failure
points and shows how poka-yoke techniques can be added to improve quality. The consequences
of these failure points can be greatly reduced if identified at the design stage when modifications
or appropriate poka-yokes can be included. A time dimension is included in Figure 7.8 to aid
understanding, extend insight, and provide a focus on customer service.
Each of these five process analysis tools has its strengths and variations. Flowcharts are a
quick way to view the big picture and try to make sense of the entire system. Time-function map-
ping adds some rigor and a time element to the macro analysis. Value-stream mapping extends
beyond the immediate organization to customers and suppliers. Process charts are designed to
provide a much more detailed view of the process, adding items such as value-added time, delay,
distance, storage, and so forth. Service blueprinting, on the other hand, is designed to help us
focus on the customer interaction part of the process. Because customer interaction is often an
important variable in process design, we now examine some additional aspects of service process
design.
SPECIAL CONSIDERATIONS FOR SERVICE PROCESS DESIGN
Interaction with the customer often affects process performance adversely. But a service, by
its very nature, implies that some interaction and customization is needed. Recognizing that
the customer’s unique desires tend to play havoc with a process, the more the manager
designs the process to accommodate these special requirements, the more effective and effi-
cient the process will be. Notice how well Align Technology has managed the interface
Present Method Proposed Method
SUBJECT CHARTED
DEPARTMENT CHART BY
DIST.
IN
FEET
TIME
IN
MINS.
CHART
SYMBOLS
DATE
SHEET NO. OF
PROCESS CHART
PROCESS DESCRIPTION
TOTALS
Value-added time = Operation time/Total time = (2.50+.20)/3.15 = 85.7%
= operation; = transportation; = inspection; = delay; = storage.
X
Hamburger Assembly Process
1.5
1.0
.5
.5
3.5 3.15
.05
.20
.10
.15
.05
.05
2.50
.05
2 4 1 – 2
Meat Patty in Storage
Assemble Order
Obtain Buns, Lettuce, etc.
Place in Finish Rack
Transfer to Broiler
Broiler
Visual Inspection
Transfer to Rack
Temporary Storage
8 / 1 / 10
1KH 1
� FIGURE 7.7
Process Chart Showing a
Hamburger Assembly
Process at a Fast-Food
Restaurant
Service blueprinting
A process analysis technique
that lends itself to a focus on
the customer and the provider’s
interaction with the customer.
AUTHOR COMMENT
Customer interaction with
service processes increases
the design challenge.

Chapter 7 Process Strategy and Sustainability 215
Level #1
Customer
is in control.
Level #2
Customer may
interact with
service provider.
Level #3
Service is
removed from
customer’s control
and interaction.
Customer arrives
for service.
(3 min)
Customer departs.
Customer pays bill.
(4 min)
Warm greeting
and obtain
service request.
(10 sec)
Determine
specifics.
(5 min)
Direct customer
to waiting room.
Perform
required work.
(varies)
Prepare invoice.
(3 min)
Standard
request.
(3 min)
Can
service be
done and does
customer
approve?
(5 min)
No
No
F
FF
F
YesYes
Poka-yoke: Bell in
driveway in case customer
arrival was unnoticed.
Poka-yoke: If customer
remains in the work area,
offer coffee and reading
material in waiting room.
Personal Greeting
Poka-yoke: Conduct
dialog with customer
to identify customer
expectation and
assure customer
acceptance.
Service Diagnosis
Poka-yoke: Review
checklist for
compliance.
Poka-yoke: Service
personnel review
invoice for accuracy.
Perform Service
Poka-yoke: Customer
approves invoice.
Poka-yoke: Customer
inspects car.
Friendly Close
Notify customer
that car
is ready.
(3 min)
Notify
customer
and recommend
an alternative
provider.
(7 min)
F
F
F
F
F
Poka-yokes to address potential failure points
� FIGURE 7.8
Service Blueprint for Service at Speedy Lube, Inc.
AUTHOR COMMENT
Service blueprinting helps
us focus on the impact
of customer interaction
with the process.
between the customer and the process by using the Internet (see the OM in Action box “Mass
Customization for Straight Teeth”). The trick is to find the right combination of cost and cus-
tomer interaction.
Customer Interaction and Process Design
The four quadrants of Figure 7.9 provide additional insight on how operations managers design
service processes to find the best level of specialization and focus while maintaining the neces-
sary customer interaction and customization. The 10 operations decisions we introduced in
Chapters 1 and 2 are used with a different emphasis in each quadrant. For instance:
• In the upper sections (quadrants) of mass service and professional service, where labor con-
tent is high, we expect the manager to focus extensively on human resources. This is often
done with personalized services, requiring high labor involvement and therefore significant
selection and training issues in the human resources area. This is particularly true in the pro-
fessional service quadrant.
LO4: Describe customer
interaction in process
design

• The quadrants with low customization tend to (1) standardize or restrict some offerings, as do
fast-food restaurants, (2) automate, as have airlines with ticket-vending machines, or (3) remove
some services, such as seat assignments, as has Southwest Airlines. Offloading some aspect of
the service through automation may require innovations in process design as well as capital
investment. Such is the case with airline ticket vending and bank ATMs. This move to standard-
ization and automation may require added capital expenditure, as well as putting operations
managers under pressure to develop new skills for the purchase and maintenance of such equip-
ment. A reduction in a customization capability will require added strength in other areas.
• Because customer feedback is lower in the quadrants with low customization, tight control
may be required to maintain quality standards.
• Operations with low labor intensity may lend themselves particularly well to innovations in
process technology and scheduling.
Table 7.3 shows some additional techniques for innovative process design in services.
Managers focus on designing innovative processes that enhance the service. For instance, super-
market self-service reduces cost while it allows customers to check for the specific features they
216 PART 2 Designing Operations
Low High
Low
D
e
g
re
e
o
f
L
a
b
o
r
High
Professional ServiceMass Service
Service ShopService Factory
Commercial
banking
Limited-service
stockbroker
Private
banking
Specialized
hospitals
Digitized
orthodontics
Fast-food
restaurants
No-frills
airlines
Fine-dining
restaurants
Traditional
orthodontics
Airlines
Hospitals
General-
purpose law firms
Retailing
Full-service
stockbroker
Boutiques
Degree of Customization
Warehouse and
catalog stores
Law clinics
� FIGURE 7.9
Services Moving toward
Specialization and Focus
within the Service Process
Matrix
Source: Adapted from work by
Roger Schmenner, “Service
Business and Productivity,”
Decision Sciences 35, no. 3
(Summer 2004): 333–347.
AUTHOR COMMENT
Notice how services find a
competitive opportunity by
moving from the rectangles
to the ovals.
Align Technology of Santa Clara, California, wants to
straighten your teeth with a clear plastic removable aligner.
The company is a mass customizer for orthodontic
treatments. Each patient is very custom, requiring a truly
unique product; no two patients are alike. Based on dental
impressions, X-rays, and photos taken at the dentist’s
office and sent to Align headquarters, the firm builds a
precise 3-D computer model and file of the patient’s mouth.
This digitized file is then sent to Costa Rica, where
technicians develop a comprehensive treatment plan,
which is then returned to the dentist for approval. After
approval, data from the 3-D virtual models and treatment
plan are used to program stereolithography equipment (see
photo on page 137 in Chapter 5) to form molds. The molds
are then shipped to Juarez, Mexico, where a series of
customized teeth aligners—usually about 19 pairs—are
made. The time required
for this process: about 3
weeks from start to finish.
The clear aligners take the
place of the traditional
“wire and brackets.” Align
calls the product “complex
to make, easy to use.”
With good OM, mass
customization works, even
for a very complex, very individualized product, such as
teeth aligners.
Sources: Laura Rock Kopezak and M. Eric Johnson, “Aligning the Supply
Chain,” Case #6-0024, Dartmouth College, 2006; and www.invisalign.com
Annual Report, 2007.
OM in Action � Mass Customization for Straight Teeth

www.invisalign.com

Chapter 7 Process Strategy and Sustainability 217
want, such as freshness or color. Dell Computer provides another version of self-service by
allowing customers to design their own product on the Web. Customers seem to like this, and it
is cheaper and faster for Dell.
More Opportunities to Improve
Service Processes
Layout Layout design is an integral part of many service processes, particularly in retailing,
dining, and banking. In retailing, layout can provide not only product exposure but also customer
education and product enhancement. In restaurants, layout can enhance the dining experience as
well as provide an effective flow between bar, kitchen, and dining area. In banks, layout provides
security as well as work flow and personal comfort. Because layout is such an integral part of
many services, it provides continuing opportunity for winning orders.
Human Resources Because so many services involve direct interaction with the customer
(as the upper quadrants of Figure 7.9 suggest), the human resource issues of recruiting and train-
ing can be particularly important ingredients in service processes. Additionally, a committed
workforce that exhibits flexibility when schedules are made and is cross-trained to fill in when
the process requires less than a full-time person, can have a tremendous impact on overall
process performance.
SELECTION OF EQUIPMENT AND TECHNOLOGY
Ultimately, the decisions about a particular process require decisions about equipment and tech-
nology. Those decisions can be complex because alternative methods of production are present
in virtually all operations functions, be they hospitals, restaurants, or manufacturing facilities.
Picking the best equipment means understanding the specific industry and available processes
and technology. That choice of equipment, be it an X-ray machine for a hospital, a computer-
controlled lathe for a factory, or a new computer for an office, requires considering cost, qual-
ity, capacity, and flexibility. To make this decision, operations personnel develop
documentation that indicates the capacity, size, and tolerances of each option, as well as its
maintenance requirements. Any one of these attributes may be the deciding factor regarding
selection.
The selection of equipment for a particular type of process can also provide competitive
advantage. Many firms, for instance, develop unique machines or techniques within established
VIDEO 7.2
Process Analysis at Arnold
Palmer Hospital
� TABLE 7.3
Techniques for Improving
Service Productivity
AUTHOR COMMENT
A process that is going to
win orders often depends
on the selection of the
proper equipment.
Strategy Technique Example
Separation Structuring service so customers must Bank customers go to a manager to
go where the service is offered open a new account, to loan officers for
loans, and to tellers for deposits
Self-service Self-service so customers examine, Supermarkets and department stores
compare, and evaluate at their own pace Internet ordering
Postponement Customizing at delivery Customizing vans at delivery rather than
at production
Focus Restricting the offerings Limited-menu restaurant
Modules Modular selection of service Investment and insurance selection
Modular production Prepackaged food modules in restaurants
Automation Separating services that may lend Automatic teller machines
themselves to some type of automation
Scheduling Precise personnel scheduling Scheduling ticket counter personnel at
15-minute intervals at airlines
Training Clarifying the service options Investment counselor, funeral directors
Explaining how to avoid problems After-sale maintenance personnel

processes that provide an advantage. This advantage may result in added flexibility in meeting
customer requirements, lower cost, or higher quality. Innovations and equipment modification
might also allow for a more stable production process requiring less adjustment, maintenance,
and operator training. In any case, specialized equipment often provides a way to win orders.
Modern technology also allows operations managers to enlarge the scope of their processes.
As a result, an important attribute to look for in new equipment and process selection is flexible
equipment. Flexibility is the ability to respond with little penalty in time, cost, or customer
value. This may mean modular, movable, even cheap equipment. Flexibility may also mean the
development of sophisticated electronic equipment, which increasingly provides the rapid
changes that mass customization demands. The technological advances that influence
OM process strategy are substantial and are discussed next.
PRODUCTION TECHNOLOGY
Advances in technology that enhance production and productivity have a wide range of applica-
tions in both manufacturing and services. In this section, we introduce nine areas of technology:
(1) machine technology, (2) automatic identification systems (AIS), (3) process control,
(4) vision systems, (5) robots, (6) automated storage and retrieval systems (ASRSs), (7) auto-
mated guided vehicles (AGVs), (8) flexible manufacturing systems (FMSs), and (9) computer-
integrated manufacturing (CIM).
Machine Technology
Most of the world’s machinery that performs operations such as cutting, drilling, boring, and
milling is undergoing tremendous progress in both precision and control. New machinery turns
out metal components that vary less than a micron—1/76 the width of a human hair. They can
accelerate water to three times the speed of sound to cut titanium for surgical tools. Machinery of
the 21st century is often five times more productive than that of previous generations while being
smaller and using less power. And continuing advances in lubricants now allow the use of water-
based lubricants rather than oil-based. Using water-based lubricants eliminates hazardous waste
and allows shavings to be easily recovered and recycled.
The intelligence now available for the control of new machinery via computer chips allows
more complex and precise items to be made faster. Electronic controls increase speed by reduc-
ing changeover time, reducing waste (because of fewer mistakes), and enhancing flexibility.
Machinery with its own computer and memory is called computer numerical control (CNC)
machinery.
Advanced versions of such technology are used on Pratt and Whitney’s turbine blade plant in
Connecticut. The machinery has improved the loading and alignment task so much that Pratt has
cut the total time for the grinding process of a turbine blade from 10 days to 2 hours. The new
machinery has also contributed to process improvements that mean the blades now travel just
1,800 feet in the plant, down from 8,100 feet. The total throughput time for a turbine blade has
been cut from 22 days to 7 days.
Automatic Identification Systems (AISs) and RFID
New equipment, from numerically controlled manufacturing machinery to ATM machines, is
controlled by digital electronic signals. Electrons are a great vehicle for transmitting informa-
tion, but they have a major limitation—most OM data does not start out in bits and bytes.
Therefore, operations managers must get the data into an electronic form. Making data digital is
done via computer keyboards, bar codes, radio frequencies, optical characters, and so forth.
These automatic identification systems (AISs) help us move data into electronic form, where it
is easily manipulated.
Because of its decreasing cost and increasing pervasiveness, radio frequency identification
(RFID) warrants special note. RFID is integrated circuitry with its own tiny antennas that use
radio waves to send signals a limited range—usually a matter of yards. These RFID tags (some-
times called RFID circuits) provide unique identification that enables the tracking and monitor-
ing of parts, pallets, people, and pets—virtually everything that moves. RFID requires no line of
sight between tag and reader.
218 PART 2 Designing Operations
Flexibility
The ability to respond with
little penalty in time, cost,
or customer value.
Computer numerical
control (CNC)
Machinery with its own
computer and memory.
Automatic identification
system (AIS)
A system for transforming data
into electronic form, for
example, bar codes.
AUTHOR COMMENT
Here are 9 technologies
that can improve employee
safety, product quality,
and productivity.
Radio frequency
identification (RFID)
A wireless system in which
integrated circuits with
antennas send radio waves.
LO5: Identify recent
advances in production
technology

Chapter 7 Process Strategy and Sustainability 219
Innovative OM examples of AISs and RFID include:
• Nurses reduce errors in hospitals by matching bar codes on medication to ID bracelets on
patients.
• RFID tags in agriculture monitor the temperature at which fruit is kept. They can also track
what chemicals and fertilizers have been used on the fruit.
• Transponders attached to cars allow McDonald’s to identify and bill customers who can now
zip through the drive-through line without having to stop and pay. The transponders use the
same technology that permits motorists to skip stops on some toll roads. McDonald’s esti-
mates that the change speeds up throughput time by 15 seconds.
• Stanford University School of Medicine doctors are using sponges embedded with RFID tags.
Waving a detector over an incision can tell if a surgeon accidentally left a sponge in the patient.
• FedEx tags major airplane parts, which allows them to be scanned so maintenance data (e.g.,
part number, installation date, country of origin) can be tracked.
Process Control
Process control is the use of information technology to monitor and control a physical process.
For instance, process control is used to measure the moisture content and thickness of paper as it
travels over a paper machine at thousands of feet per minute. Process control is also used to deter-
mine and control temperatures, pressures, and quantities in petroleum refineries, petrochemical
processes, cement plants, steel mills, nuclear reactors, and other product-focused facilities.
Process control systems operate in a number of ways, but the following is typical:
• Sensors collect data.
• Devices read data on some periodic basis, perhaps once a minute or once every second.
• Measurements are translated into digital signals, which are transmitted to a computer.
• Computer programs read the file (the digital data) and analyze the data.
• The resulting output may take numerous forms. These include messages on computer con-
soles or printers, signals to motors to change valve settings, warning lights or horns, or statis-
tical process control charts.
Vision Systems
Vision systems combine video cameras and computer technology and are often used in
inspection roles. Visual inspection is an important task in most food-processing and manufac-
turing organizations. Moreover, in many applications, visual inspection performed by humans
is tedious, mind-numbing, and error prone. Thus vision systems are widely used when the
items being inspected are very similar. For instance, vision systems are used to inspect Frito-
Lay’s potato chips so that imperfections can be identified as the chips proceed down the
With RFID, a cashier could scan
the entire contents of a
shopping cart in seconds.
Process control
The use of information
technology to control a physical
process.
Vision systems
Systems that use video cameras
and computer technology in
inspection roles.
In Anheuser-Busch’s brewhouse
control room, process control software
monitors the process where wort is
being fermented into beer.

production line. Vision systems are used to ensure that sealant is present and in the proper
amount on Whirlpool’s washing-machine transmissions, and to inspect switch assemblies at
the Foster Plant in Des Plaines, Illinois. Vision systems are consistently accurate, do not
become bored, and are of modest cost. These systems are vastly superior to individuals trying
to perform these tasks.
Robots
When a machine is flexible and has the ability to hold, move, and perhaps “grab” items, we tend
to use the word robot. Robots are mechanical devices that use electronic impulses to activate
motors and switches. Robots may be used effectively to perform tasks that are especially monot-
onous or dangerous or those that can be improved by the substitution of mechanical for human
effort. Such is the case when consistency, accuracy, speed, strength, or power can be enhanced by
the substitution of machines for people. Ford, for example, uses robots to do 98% of the welding
and most of the painting on some automobiles.
Automated Storage and Retrieval
Systems (ASRSs)
Because of the tremendous labor involved in error-prone warehousing, computer-controlled
warehouses have been developed. These systems, known as automated storage and retrieval
systems (ASRSs), provide for the automatic placement and withdrawal of parts and products
into and from designated places in a warehouse. Such systems are commonly used in distribution
facilities of retailers such as Wal-Mart, Tupperware, and Benetton. These systems are also found
in inventory and test areas of manufacturing firms.
Automated Guided Vehicles (AGVs)
Automated material handling can take the form of monorails, conveyors, robots, or automated
guided vehicles. Automated guided vehicles (AGVs) are electronically guided and controlled
carts used in manufacturing to move parts and equipment. They are also used in offices to move
mail and in hospitals and in jails to deliver meals.
Flexible Manufacturing Systems (FMSs)
When a central computer provides instructions to each workstation and to the material-handling
equipment (which moves material to that station), the system is known as an automated work cell
or, more commonly, a flexible manufacturing system (FMS). An FMS is flexible because both
the material-handling devices and the machines themselves are controlled by easily changed
electronic signals (computer programs). Operators simply load new programs, as necessary, to
produce different products. The result is a system that can economically produce low volume but
high variety. For example, the Lockheed Martin facility, near Dallas, efficiently builds one-of-a-
kind spare parts for military aircraft. The costs associated with changeover and low utilization
have been reduced substantially. FMSs bridge the gap between product-focused and process-
focused facilities.
Computer-Integrated Manufacturing (CIM)
Flexible manufacturing systems can be extended backward electronically into the engineering
and inventory control departments and forward to the warehousing and shipping departments. In
this way, computer-aided design (CAD) generates the necessary electronic instructions to run a
numerically controlled machine. In a computer-integrated manufacturing environment, a design
change initiated at a CAD terminal can result in that change being made in the part produced on
the shop floor in a matter of minutes. When this capability is integrated with inventory control,
warehousing, and shipping as a part of a flexible manufacturing system, the entire system is
called computer-integrated manufacturing (CIM) (Figure 7.10).
Flexible manufacturing systems and computer-integrated manufacturing are reducing the dis-
tinction between low-volume/high-variety and high-volume/low-variety production. Information
technology is allowing FMS and CIM to handle increasing variety while expanding to include a
growing range of volumes.
220 PART 2 Designing Operations
Robot
A flexible machine with the
ability to hold, move, or grab
items. It functions through
electronic impulses that activate
motors and switches.
Automated storage
and retrieval system
(ASRS)
Computer-controlled
warehouses that provide for the
automatic placement of parts
into and from designated places
within a warehouse.
Automated guided
vehicle (AGV)
Electronically guided and
controlled cart used to move
materials.
Flexible manufacturing
system (FMS)
A system that uses an
automated work cell controlled
by electronic signals from a
common centralized computer
facility.
Computer-integrated
manufacturing (CIM)
A manufacturing system in
which CAD, FMS, inventory
control, warehousing, and
shipping are integrated.

Chapter 7 Process Strategy and Sustainability 221
TECHNOLOGY IN SERVICES
Just as we have seen rapid advances in technology in the manufacturing sector, so we also
find dramatic changes in the service sector. These range from electronic diagnostic equip-
ment at auto repair shops, to blood- and urine-testing equipment in hospitals, to retinal secu-
rity scanners at airports and high-security facilities. The hospitality industry provides other
examples, as discussed in the OM in Action box “Technology Changes the Hotel Industry.”
The McDonald’s approach is to use self-serve kiosks. The labor savings when ordering and
speedier checkout service provide valuable productivity increases for both the restaurant and
the customer.
Similarly, Andersen Windows, of Minnesota, has developed user-friendly computer software
that enables customers to design their own window specifications. The customer calls up a product
AUTHOR COMMENT
Although less dramatic than
manufacturing, technology
also improves quality and
productivity in services.
� FIGURE 7.10 Computer-Integrated Manufacturing (CIM)
CIM includes computer-aided design (CAD), computer-aided manufacturing (CAM), flexible manufacturing systems (FMSs),
automated storage and retrieval systems (ASRSs), automated guided vehicles (AGVs), and robots to provide an integrated and
flexible manufacturing process.
Management decides to make a product
OM runs production process,
purchasing components,
coordinating suppliers,
planning and scheduling
operations, overseeing
quality and the workforce,
and shipping to customers.
Computer-aided Manufacturing
(CAM) converts raw materials into
components or products
Robots and specialized
equipment weld, insert,
and assemble components.
Robots test it and box the finished
product.
Information Flows
Material Flows
ASRS (above) and AGVs
move incoming materials
and parts, work-in-process,
and complete product.
Computer-aided design (CAD)
designs the product and programs
the automated production equipment.
C
o
m
p
u
te
r
In
te
g
ra
te
d
M
a
n
u
fa
c
tu
ri
n
g
(
C
IM
)
F
le
x
ib
le
M
a
n
u
fa
c
tu
ri
n
g
S
y
s
te
m
(
F
M
S
)

information guide, promotion material, a gallery of designs, and a sketch pad to create the designs
desired. The software also allows the customer to determine likely energy savings and see a graphic
view of their home fitted with the new window.
In retail stores, POS terminals download prices quickly to reflect changing costs or market
conditions, and sales are tracked in 15-minute segments to aid scheduling. Drug companies, such
as Purdue Pharma LP, have begun tracking critical medications with radio frequency identifica-
tion (RFID) tags to reduce counterfeiting and theft.
Table 7.4 provides a glimpse of the impact of technology on services. Operations managers in
services, as in manufacturing, must be able to evaluate the impact of technology on their firm.
This ability requires particular skill when evaluating reliability, investment analysis, human
resource requirements, and maintenance/service.
222 PART 2 Designing Operations
Technology is introducing “intelligent rooms” to the hotel
industry. Hotel management can now precisely track a
maid’s time through the use of a security system. When a
maid enters a room, a card is inserted that notifies the
front-desk computer of the maid’s location. “We can show
her a printout of how long she takes to do a room,” says
one manager.
Security systems also enable guests to use their own
credit cards as keys to unlock their doors. There are also
other uses for the system. The computer can bar a guest’s
access to the room after checkout time and automatically
control the air conditioning or heat, turning it on at check-
in and off at checkout.
Minibars are now equipped with sensors that alert the
central computer system at the hotel when an item is
removed. Such items are immediately billed to the room.
And now, with a handheld infrared unit, housekeeping staff
can check, from the hallway, to see if a room is physically
occupied. This both eliminates the embarrassment of having
a hotel staffer walk in on a guest and improves security for
housekeepers.
At Loew’s Portofino Bay Hotel at Universal Studios,
Orlando, guest smart cards act as credit cards in both
the theme park and the hotel, and staff smart cards
(programmed for different levels of security access) create
an audit trail of employee movement. Starwood Hotels,
which runs such properties as Sheraton and Westins, use
Casio Pocket PCs to communicate with a hotel wireless
network. Now guests can check in and out from any place
on the property, such as at their restaurant table after
breakfast or lunch.
Sources: Hotel and Motel Management (November 5, 2007): 16; Hotels
(April 2004): 51–54; and Newsweek (international ed.) (September 27,
2004): 73.
�TABLE 7.4
Examples of Technology’s
Impact on Services
Service Industry Example
Financial Services Debit cards, electronic funds transfer, automatic teller machines,
Internet stock trading, online banking via cell phone.
Education Online newspapers, online journals, interactive assignments
via Web CT, Blackboard, and smart phones.
Utilities and government Automated one-man garbage trucks, optical mail scanners,
flood-warning systems, meters allowing homeowners to control
energy usage and costs.
Restaurants and foods Wireless orders from waiters to the kitchen, robot butchering,
transponders on cars that track sales at drive-throughs.
Communications Interactive TV, ebooks via Kindle 2.
Hotels Electronic check-in/checkout, electronic key/lock systems,
mobile Web bookings.
Wholesale/retail trade Point-of-sale (POS) terminals, e-commerce, electronic communication
between store and supplier, bar-coded data, RFID
Transportation Automatic toll booths, satellite-directed navigation systems,
Wi-Fi in automobiles
Health care Online patient-monitoring systems, online medical information
systems, robotic surgery
Airlines Ticketless travel, scheduling, Internet purchases, boarding passes
downloaded as two-dimensional bar codes on smart phones
OM in Action � Technology Changes the Hotel Industry

Chapter 7 Process Strategy and Sustainability 223
Process redesign
The fundamental rethinking of
business processes to bring
about dramatic improvements
in performance.
PROCESS REDESIGN
Often a firm finds that the initial assumptions of its process are no longer valid. The world is a
dynamic place, and customer desires, product technology, and product mix change.
Consequently, processes are redesigned. Process redesign is the fundamental rethinking
of business processes to bring about dramatic improvements in performance. Effective process
redesign relies on reevaluating the purpose of the process and questioning both purpose and
underlying assumptions. It works only if the basic process and its objectives are reexamined.
Process redesign also focuses on those activities that cross functional lines. Because man-
agers are often in charge of specific “functions” or specialized areas of responsibility, those
activities (processes) that cross from one function or specialty to another may be neglected.
Redesign casts aside all notions of how the process is currently being done and focuses on dra-
matic improvements in cost, time, and customer value. Any process is a candidate for radical
redesign. The process can be a factory layout, a purchasing procedure, a new way of processing
credit applications, or a new order-fulfillment process.
Shell Lubricants, for example, reinvented its order-fulfillment process by replacing a group of
people who handled different parts of an order with one individual who does it all. As a result,
Shell has cut the cycle time of turning an order into cash by 75%, reduced operating expenses by
45%, and boosted customer satisfaction 105%—all by introducing a new way of handling orders.
Time, cost, and customer satisfaction—the dimension of performance shaped by operations—get
major boosts from operational innovation.
SUSTAINABILITY
In Chapter 5 we discussed goods and services design and its potential impact on ethics, the envi-
ronment, and sustainability. We now introduce the issue of sustainability in production processes.4
Managers may find it helpful to think in terms of four Rs as they address sustainability. These are
(1) the resources used by the production process, (2) the recycling of production materials and
product components, (3) the regulations that apply, and (4) the firm’s reputation. All four areas
provide impetus for managers to perform well as they develop and refine production processes.
Resources
Operations is often the primary user of the firm’s resources. This puts special pressure on using human,
financial, and material resources in a sustainable way. Most firms are good at reducing resource use as
it is a win–win situation: reducing resources lowers cost as well as being a positive force toward sus-
tainability. Examples of sustainable use in production processes are actions taken by:
• Wal-Mart and Frito-Lay have both driven down their water and energy use. (These firms’ efforts
in sustainability are discussed in the video cases in the Lecture Guide & Activities Manual.)
• Subaru’s Indiana plant has driven down energy use by 14% per car.
• Pepsi has reduced the weight of its plastic bottles for Aquafina by 20%. This reduces resource
use and saves weight with the added advantage of cutting delivery cost.
• The Ritz-Carlton is doing laundry at night to reduce electricity costs.
Recycle
As managers seek sustainability, they should realize that there are only three things that can be
done with waste: burn it, bury it, or reuse it. The first two have undesirable consequences.
Burned waste pumps unwanted emissions into the atmosphere, and burying has the potential of
releasing methane and ammonia, as well as creating fires, explosions, and water table issues.
While recycling begins at design by specifying products and components that have recycle
potential, managers must build processes that facilitate disassembly and reuse of those materials.
Whether it is plastic, glass, or lead in an automobile or plastic bags and Styrofoam from the gro-
cery store, recycling has a significant role in sustainability. Examples are:
• Anheuser-Busch saves over $30 million per year in energy and waste-treatment costs by using
treated plant wastewater to generate the gas that powers its St. Louis brewery.
• Standard Register, a major manufacturer of multipart paper forms, produces considerable
paper scrap—almost 20 tons of punch holes alone per month—which creates a significant
AUTHOR COMMENT
Most processes we design
are existing processes, so
the ability to redesign them
is important.
AUTHOR COMMENT
Process selection and
management can support
conservation and renewal
of resources.
4We define sustainable in an OM context as a production system that supports conservation and renewal of resources.
VIDEO 7.3
Green Manufacturing and
sustainability at Frito-Lay
LO6: Discuss the four R’s
of sustainability

224 PART 2 Designing Operations
waste issue. But the company developed ways to recycle the paper scrap, as well as aluminum
and silver from the plate-making process.
Regulations
Laws and regulations affecting transportation, waste, and noise are proliferating and can be as
much of a challenge as reducing resource use. While the challenge can be difficult, firms must
abide by the legal requirements of the host nation; society expects no less. The resources avail-
able from Planet Earth are finite and many by-products are undesirable. So organizations are
increasingly under pressure from regulatory agencies to reduce by-products that yield green-
house gasses and pollute the air and water. Greenhouse gasses (GHG) include carbon dioxide,
methane, nitrous oxide, and fluorinated gasses that are believed to contribute to global warning.
To meet regulatory requirements, firms design, redesign, and invest substantial human and finan-
cial resources. Some examples are:
• Home builders are required not just to manage water runoff but to have a pollution prevention
plan for each site.
• Public drinking water systems must comply with the federal Safe Drinking Water Act’s
arsenic standard, even for existing facilities.
• Hospitals are required to meet the terms of the Resource Conservation and Recovery Act,
which governs the storage and handling of hazardous material.
• Manufacturers, miners, dairies, refineries, and other firms that emit 25,000 metric tons or
more per year of GHG emissions are required to submit annual reports to the EPA.
Carbon Footprint. Another sustainability issue is evaluating and reducing the carbon foot-
print. This is a measurement of greenhouse gasses for which international regulation is pending.
A substantial portion of greenhouse gasses are released naturally by farming, cattle, and decaying
forests, but also by manufacturing and services. Operations personnel are being asked to
contribute to their reduction.
Industry leaders such as Frito-Lay have been able to break down the carbon emissions from var-
ious stages in the production process. For instance in potato chip production, a 34.5 gram (1.2
ounce) bag of chips is responsible for about twice its weight in emissions—75 grams per bag—with
contributions coming from: (1) raw materials (potatoes, oil, seasonings), 44%; (2) manufacture
(producing the chips in the factory), 30%; (3) packaging, 15%; (4) shipping, 9%; and (5) disposal by
the customer of an empty bag, 2%. Frito-Lay has targeted its raw material suppliers and distributors
to reduce the carbon footprint, which has already gone down by 7% in the past two years.
Reputation
The marketplace may reward leadership in sustainability. The free enterprise system operates on
a voluntary basis: if employees, suppliers, distributors, providers of capital, and, of course, cus-
tomers, do not want to do business with a firm, they are not required to do so. Those organiza-
tions that do not meet society’s expectations can expect these voluntary relationships to be
difficult to build and maintain. A bad reputation does have negative consequences. Our society is
Hospitals use
RFID sensors to
track patients,
staff, and
equipment.
Pharmaceutical
companies are
counting on RFID
to aid the tracking
and tracing of
drugs in the
distribution system
to reduce losses
that total over $30
billion a year.

Chapter 7 Process Strategy and Sustainability 225
increasingly transparent, and both good news and bad news travel rapidly. But green processes
can yield good news, a good reputation, and good results. Here are three examples:
• British cosmetic firm The Body Shop has successfully differentiated its products by stressing
environmental sensitivity. It pursues a product design, development, and testing strategy that
it believes to be ethical and socially responsible. This includes environment-friendly ingredi-
ents and elimination of animal testing.
• Ben & Jerry’s pursues its socially responsible image (and saves $250,000 annually) just by
using energy-efficient lighting.
• Frito-Lay has built a plant powered by solar energy in Modesto, California, and advertises the
product as Sun Chips.
Imaginative, well-led firms are finding opportunities to build sustainable production processes
that conserve resources, recycle, meet regulatory requirements, and foster a positive reputation.
Effective operations managers understand how to use process
strategy as a competitive weapon. They select a production
process with the necessary quality, flexibility, and cost structure
to meet product and volume requirements. They also seek cre-
ative ways to combine the low unit cost of high-volume, low-
variety manufacturing with the customization available through
low-volume, high-variety facilities. Managers use the tech-
niques of lean production and employee participation to
encourage the development of efficient
equipment and processes. They design
their equipment and processes to have
capabilities beyond the tolerance required
by their customers, while ensuring the
flexibility needed for adjustments in technol-
ogy, features, and volumes.
CHAPTER SUMMARY
Key Terms
Process strategy (p. 204)
Process focus (p. 204)
Repetitive process (p. 205)
Modules (p. 205)
Product focus (p. 206)
Mass customization (p. 206)
Build-to-order (BTO) (p. 207)
Postponement (p. 207)
Crossover chart (p. 209)
Flowchart (p. 211)
Time-function mapping (or process
mapping) (p. 211)
Value-stream mapping
(VSM) (p. 212)
Process charts (p. 213)
Service blueprinting (p. 214)
Flexibility (p. 218)
Computer numerical control (CNC) (p. 218)
Automatic identification system
(AIS) (p. 218)
Radio frequency identification
(RFID) (p. 218)
Process control (p. 219)
Vision systems (p. 219)
Robot (p. 220)
Automated storage and retrieval
system (ASRS) (p. 220)
Automated guided vehicle
(AGV) (p. 220)
Flexible manufacturing system
(FMS) (p. 220)
Computer-integrated manufacturing
(CIM) (p. 220)
Process redesign (p. 223)
� SOLVED PROBLEM 7.1
Bagot Copy Shop has a volume of 125,000 black-and-white copies
per month. Two salesmen have made presentations to Gordon
Bagot for machines of equal quality and reliability. The Print Shop
5 has a cost of $2,000 per month and a variable cost of $.03. The
other machine (a Speed Copy 100) will cost only $1,500 per month
but the toner is more expensive, driving the cost per copy up to
$.035. If cost and volume are the only considerations, which
machine should Bagot purchase?
Solved Problem Virtual Office Hours help is available at www.myomlab.com
� SOLUTION
Because Bagot expects his volume to exceed 100,000 units, he
should choose the Print Shop 5.
500 = .005 X
2,000 – 1,500 = .035 X – .03 X
2,000 + .03 X = 1,500 + .035 X

www.myomlab.com

226 PART 2 Designing Operations
�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Matthew Yachts, Inc.: Examines a possible process change as the market for yachts changes.
Bibliography
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Disassembly-to-order Problems under Stochastic Yields.” In
Logistik Management. Heidelberg: Physica, 2004: 309–331.
Moeeni, F. “From Light Frequency Identification to Radio Frequency
Identification in the Supply Chain,” Decision Line 37, no. 3
(May 2006): 8–13.
Rugtusanatham, M. Johnny, and Fabrizio Salvador. “From
Mass Production to Mass Customization.” Production and
Operations Management 17, no. 3 (May–June 2008): 385–396.
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Postponement Structures to Accommodate Mass
Customization.” Journal of Operations Management 23,
no. 3–4 (April 2005): 305–318.
Swamidass, Paul M. Innovations in Competitive Manufacturing.
Dordrecht, NL: Kluwer, 2000.
Welborn, Cliff. “Mass Customization.” OR/MS Today (December
2007): 38–42.
Zipkin, Paul. “The Limits of Mass Customization.” MIT Sloan
Management Review 40, no. 1 (Spring 2001): 81–88.
Davenport, T. H. “The Coming Commoditization of Processes.”
Harvard Business Review 83, no. 6 (June 2005): 101–108.
Debo, L. G., L. B. Toktay, and L. N. Van Wassenhove. “Market
Segmentation and Product Technology Selection for
Remanufacturable Products.” Management Science 51, no. 8
(August 2005): 1193–1205.
Duray, R., P. T. Ward, G. W. Milligan, and W. L. Berry.
“Approaches to Mass Customization: Configurations and
Empirical Validation.” Journal of Operations Management 18,
no. 6 (November 2000): 605–625.
Duray, R. “Mass Customization Origins: Mass or Custom
Manufacturing.” International Journal of Operations and
Production Management 22, no. 3 (2002): 314–328.
Gilmore, James H., and Joseph Pine II (eds.). Markets of
One: Creating Customer-Unique Value through Mass
Customization. Boston: Harvard Business Review Book, 2000.
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Art, Not Science?” Harvard Business Review 87, no. 3
(March 2009): 58–65.
Hegde, V. G., et al. “Customization: Impact on Product and
Process Performance.” Production and Operations
Management 14, no. 4 (Winter 2005): 388–399.

www.myomlab.com

www.pearsonhighered.com/heizer

Capacity and
Constraint Management
Supplement Outline
Capacity 228
Bottleneck Analysis and the
Theory of Constraints 234
Break-Even Analysis 238
Reducing Risk with Incremental
Changes 242
Applying Expected Monetary Value
(EMV) to Capacity Decisions 243
Applying Investment Analysis
to Strategy-Driven Investments 244
227
SUPPLEMENTSUPPLEMENT

228 PART 2 Designing Operations
LO1: Define capacity 228
LO2: Determine design capacity, effective
capacity, and utilization 230
LO3: Perform bottleneck analysis 234
Supplement 7 Learning Objectives
When designing a concert
hall, management hopes
that the forecasted
capacity (the product
mix—opera, symphony,
and special events—and
the technology needed
for these events) is
accurate and adequate
for operation above the
break-even point.
However, in many
concert halls, even when
operating at full capacity,
break-even is not
achieved, and
supplemental funding
must be obtained.
CAPACITY
What should be the seating capacity of a concert hall? How many customers per day should an
Olive Garden or a Hard Rock Cafe be able to serve? How large should a Frito-Lay plant be to
produce 75,000 bags of Ruffles in an 8-hour shift? In this supplement we look at tools that help
a manager make these decisions.
After selection of a production process (Chapter 7), managers need to determine capacity.
Capacity is the “throughput,” or the number of units a facility can hold, receive, store, or pro-
duce in a given time. Capacity decisions often determine capital requirements and therefore a
large portion of fixed cost. Capacity also determines whether demand will be satisfied or whether
facilities will be idle. If a facility is too large, portions of it will sit unused and add cost to exist-
ing production. If a facility is too small, customers—and perhaps entire markets—will be lost.
Determining facility size, with an objective of achieving high levels of utilization and a high
return on investment, is critical.
Capacity planning can be viewed in three time horizons. In Figure S7.1 we note that long-
range capacity (greater than 1 year) is a function of adding facilities and equipment that have a
long lead time. In the intermediate range (3 to 18 months), we can add equipment, personnel, and
shifts; we can subcontract; and we can build or use inventory. This is the “aggregate planning”
task. In the short run (usually up to 3 months), we are primarily concerned with scheduling jobs
and people, as well as allocating machinery. Modifying capacity in the short run is difficult, as
we are usually constrained by existing capacity.
Design and Effective Capacity
Design capacity is the maximum theoretical output of a system in a given period under ideal
conditions. It is normally expressed as a rate, such as the number of tons of steel that can be pro-
duced per week, per month, or per year. For many companies, measuring capacity can be
Capacity
The “throughput,” or number of
units a facility can hold, receive,
store, or produce in a period of
time.
LO1: Define capacity
LO4: Compute break-even 239
LO5: Determine expected monetary value
of a capacity decision 243
LO6: Compute net present value 245
AUTHOR COMMENT
Too little capacity loses
customers and too much
capacity is expensive. Like
Goldilocks’s porridge,
capacity needs to be
just right.
Design capacity
The theoretical maximum
output of a system in a given
period under ideal conditions.

Supplement 7 Capacity and Constraint Management 229
Long-range planning
Options for Adjusting Capacity
Time Horizon
Intermediate-range planning
Short-range planning
(aggregate planning)
(scheduling)
Modify capacity Use capacity
Add facilities.
Add long lead time equipment.
Subcontract.
Add equipment.
Add shifts.
Add personnel.
Build or use inventory.
Schedule jobs.
Schedule personnel.
Allocate machinery.
*
*
* Difficult to adjust capacity as limited options exist
� FIGURE S7.1
Time Horizons and
Capacity Options
straightforward: It is the maximum number of units the company is capable of producing in a spe-
cific time. However, for some organizations, determining capacity can be more difficult. Capacity
can be measured in terms of beds (a hospital), active members (a church), or classroom size (a
school). Other organizations use total work time available as a measure of overall capacity.
Most organizations operate their facilities at a rate less than the design capacity. They do so
because they have found that they can operate more efficiently when their resources are not
stretched to the limit. For example, lan’s Bistro has tables set with 2 or 4 chairs seating a total of
270 guests. But the tables are never filled that way. Some tables will have 1 or 3 guests; tables
can be pulled together for parties of 6 or 8. There are always unused chairs. Design capacity is
270, but effective capacity is often closer to 220, which is 81% of design capacity.
Effective capacity is the capacity a firm expects to achieve given the current operating con-
straints. Effective capacity is often lower than design capacity because the facility may have been
designed for an earlier version of the product or a different product mix than is currently being
produced.
Two measures of system performance are particularly useful: utilization and efficiency.
Utilization is simply the percent of design capacity actually achieved. Efficiency is the percent
of effective capacity actually achieved. Depending on how facilities are used and managed, it
may be difficult or impossible to reach 100% efficiency. Operations managers tend to be evalu-
ated on efficiency. The key to improving efficiency is often found in correcting quality problems
and in effective scheduling, training, and maintenance. Utilization and efficiency are computed
below:
(S7-1)
(S7-2)
In Example S1 we determine these values.
Efficiency = Actual output/Effective capacity
Utilization = Actual output/Design capacity
Effective capacity
The capacity a firm can expect
to achieve, given its product
mix, methods of scheduling,
maintenance, and standards of
quality.
Utilization
Actual output as a percent of
design capacity.
Efficiency
Actual output as a percent of
effective capacity.
Sara James Bakery has a plant for processing Deluxe breakfast rolls and wants to better understand its
capability. Determine the design capacity, utilization, and efficiency for this plant when producing this
Deluxe roll.
APPROACH � Last week the facility produced 148,000 rolls. The effective capacity is 175,000
rolls. The production line operates 7 days per week, with three 8-hour shifts per day. The line was
designed to process the nut-filled, cinnamon-flavored Deluxe roll at a rate of 1,200 per hour. The firm
first computes the design capacity and then uses Equation (S7-1) to determine utilization and Equation
(S7-2) to determine efficiency.
SOLUTION �
Efficiency = Actual output/Effective capacity = 148,000/175,000 = 84.6%
Utilization = Actual output/Design capacity = 148,000/201,600 = 73.4%
Design capacity = 17 days * 3 shifts * 8 hours2 * 11,200 rolls per hour2 = 201,600 rolls
� EXAMPLE S1
Determining
capacity
utilization and
efficiency

230 PART 2 Designing Operations
Design capacity, utilization, and efficiency are all important measures for an operations man-
ager. But managers often need to know the expected output of a facility or process. To do this,
we solve for actual (or in this case, future or expected) output as shown in Equation (S7-3):
(S7-3)
Expected output is sometimes referred to as rated capacity. With a knowledge of effective
capacity and efficiency, a manager can find the expected output of a facility. We do so in
Example S2.
Actual 1or Expected2 output = 1Effective capacity21Efficiency2
EXAMPLE S2 �
Determining
expected output
The manager of Sara James Bakery (see Example S1) now needs to increase production of the increas-
ingly popular Deluxe roll. To meet this demand, she will be adding a second production line.
APPROACH � The manager must determine the expected output of this second line for the sales
department. Effective capacity on the second line is the same as on the first line, which is 175,000
Deluxe rolls. The first line is operating at an efficiency of 84.6%, as computed in Example S1. But out-
put on the second line will be less than the first line because the crew will be primarily new hires; so the
efficiency can be expected to be no more than 75%. What is the expected output?
SOLUTION � Use Equation (S7-3) to determine the expected output:
INSIGHT � The sales department can now be told the expected output is 131,250 Deluxe rolls.
LEARNING EXERCISE � After 1 month of training, the crew on the second production line is
expected to perform at 80% efficiency. What is the revised expected output of Deluxe rolls? [Answer:
140,000.]
RELATED PROBLEMS � S7.3, S7.6, S7.8
Expected output = 1Effective capacity21Efficiency2 = 1175,00021.752 = 131,250 rolls
INSIGHT � The bakery now has the information necessary to evaluate efficiency.
LEARNING EXERCISE � If the actual output is 150,000, what is the efficiency? [Answer:
85.7%.]
RELATED PROBLEMS � S7.1, S7.2, S7.4, S7.5, S7.7
ACTIVE MODEL S7.1 This example is further illustrated in Active Model S7.1 at www.pearsonhighered.com/heizer.
LO2: Determine design
capacity, effective capacity,
and utilization
If the expected output is inadequate, additional capacity may be needed. Much of the remainder
of this supplement addresses how to effectively and efficiently add that capacity.
Capacity and Strategy
Sustained profits come from building competitive advantage, not just from a good financial
return on a specific process. Capacity decisions must be integrated into the organization’s mis-
sion and strategy. Investments are not to be made as isolated expenditures, but as part of a
coordinated plan that will place the firm in an advantageous position. The questions to be
asked are, “Will these investments eventually win profitable customers?” and “What competi-
tive advantage (such as process flexibility, speed of delivery, improved quality, and so on) do
we obtain?”
All 10 decisions of operations management we discuss in this text, as well as other organiza-
tional elements such as marketing and finance, are affected by changes in capacity. Change in
capacity will have sales and cash flow implications, just as capacity changes have quality, supply
chain, human resource, and maintenance implications. All must be considered.

www.pearsonhighered.com/heizer

Supplement 7 Capacity and Constraint Management 231
Capacity Considerations
In addition to tight integration of strategy and investments, there are four special considerations
for a good capacity decision:
1. Forecast demand accurately: An accurate forecast is paramount to the capacity decision.
The new product may be Olive Garden’s veal scampi, a dish that places added demands on
the restaurant’s food service, or the product may be a new maternity capability at Arnold
Palmer Hospital, or the new hybrid Lexus. Whatever the new product, its prospects and the
life cycle of existing products, must be determined. Management must know which products
are being added and which are being dropped, as well as their expected volumes.
2. Understand the technology and capacity increments: The number of initial alternatives may
be large, but once the volume is determined, technology decisions may be aided by analysis
of cost, human resources required, quality, and reliability. Such a review often reduces the
number of alternatives to a few. The technology may dictate the capacity increment. Meeting
added demand with a few extra tables in an Olive Garden may not be difficult, but meeting
increased demand for a new automobile by adding a new assembly line at BMW may be
very difficult—and expensive. The operations manager is held responsible for the technol-
ogy and the correct capacity increment.
3. Find the optimum operating size (volume): Technology and capacity increments often dic-
tate an optimal size for a facility. A roadside motel may require 50 rooms to be viable. If
smaller, the fixed cost is too burdensome; if larger, the facility becomes more than one man-
ager can supervise. A hypothetical optimum for the motel is shown in Figure S7.2. This
issue is known as economies and diseconomies of scale. As the Krispy Kreme photo sug-
gests, most businesses have an optimal size—at least until someone comes along with a new
business model. For decades, very large integrated steel mills were considered optimal.
Then along came Nucor, CMC, and other minimills with a new process and a new business
model that changed the optimum size of a steel mill.
4. Build for change: In our fast-paced world, change is inevitable. So operations managers build
flexibility into the facility and equipment. They evaluate the sensitivity of the decision by test-
ing several revenue projections on both the upside and downside for potential risks. Buildings
can often be built in phases; and buildings and equipment can be designed with modifications
in mind to accommodate future changes in product, product mix, and processes.
Rather than strategically manage capacity, managers may tactically manage demand.
Managing Demand
Even with good forecasting and facilities built to that forecast, there may be a poor match
between the actual demand that occurs and available capacity. A poor match may mean demand
exceeds capacity or capacity exceeds demand. However, in both cases, firms have options.
Demand Exceeds Capacity When demand exceeds capacity, the firm may be able to curtail
demand simply by raising prices, scheduling long lead times (which may be inevitable), and dis-
couraging marginally profitable business. However, because inadequate facilities reduce revenue
25-room
roadside motel 50-room
roadside motel
Economies
of scale
75-room
roadside motel
Diseconomies
of scale
Number of Rooms
A
v
e
ra
g
e
u
n
it
c
o
s
t
(d
o
ll
a
rs
p
e
r
ro
o
m
p
e
r
n
ig
h
t)
Cost Structure for Roadside Motel (no pool and no dining room)
25 50 75
� FIGURE S7.2
Economies and
Diseconomies of Scale
AUTHOR COMMENT
Each industry and technology
has an optimum size.

232 PART 2 Designing Operations
Krispy Kreme originally had
8,000-square-foot stores but
found them too large and too
expensive for many markets.
Then they tried tiny 1,300-
square-foot stores, which
required less investment, but
such stores were too small to
provide the mystique of seeing
and smelling Krispy Kreme
donuts being made. Krispy
Kreme finally got it right with a
2,600-foot-store. This one
includes a huge glass window
to view doughnut production.
below what is possible, the long-term solution is usually to increase capacity (as we see in the OM
in Action box “Too Little Capacity at Dalrymple Bay”).
Capacity Exceeds Demand When capacity exceeds demand, the firm may want to stimu-
late demand through price reductions or aggressive marketing, or it may accommodate the mar-
ket through product changes. When decreasing customer demand is combined with old and
inflexible processes, layoffs and plant closings may be necessary to bring capacity in line with
demand.
Adjusting to Seasonal Demands A seasonal or cyclical pattern of demand is another
capacity challenge. In such cases, management may find it helpful to offer products with com-
plementary demand patterns—that is, products for which the demand is high for one when low
for the other. For example, in Figure S7.3 the firm is adding a line of snowmobile motors to its
line of jet skis to smooth demand. With appropriate complementing of products, perhaps the uti-
lization of facility, equipment, and personnel can be smoothed.
Nearly 20 ships were anchored in the Coral Sea on a
recent morning. They were waiting to be loaded with coal
to fuel Asia’s voracious steel mills. Australia has some of
the most prolific coal mines in the world, but its key port of
Dalrymple Bay, just outside Queensland, isn’t big enough
to meet demand. So the ships sit idle for days. Capacity
at the port is far below what is needed for the current
worldwide demand. This makes Dalrymple Bay one of
the key choke points.
The process is rather simple but expensive. Trains
are loaded with coal at the mines, travel several hours to
the port, and dump their coal into piles that are sprayed
with water to prevent black coal dust from blowing onto
homes and beaches. Eventually, the coal is loaded onto a
conveyor belt that moves 2.5 miles out into the Coral Sea,
to be loaded onto ships.
The current plan is to invest $610 million to expand
port capacity to 85 million metric tons of coal in the next
3 years. But this is still less than the estimated demand
requirement of 107 million metric tons needed. As a result,
coal companies, even after the expansion is completed,
may still find access to shipping rationed.
The demand must exist, the port must expand, and
the mines must enlarge. Without that assurance, the risk
remains high and the necessary ROI (return on
investment) is not there. Managers are not going to put
significant money into expanding port capacity until they
are comfortable that both the demand and coal supply
support a larger port. To justify investment in capacity,
each phase of the chain must support that investment.
Sources: Railway Gazette International (December 2008): 947–951 and
The Wall Street Journal (July 7, 2005): C1, C4.
OM in Action � Too Little Capacity at Dalrymple Bay

Supplement 7 Capacity and Constraint Management 233
Combining the two
demand patterns
reduces the
variation.
Snowmobile
motor sales
Jet ski engine
sales
1,000
2,000
3,000
4,000
S
a
le
s
in
u
n
its
Time (months)
J F M A M J J A S O N D J F M A M J J A S O N D J
� FIGURE S7.3
By Combining Products
That Have Complementary
Seasonal Patterns, Capacity
Can Be Better Utilized
Tactics for Matching Capacity to Demand Various tactics for matching capacity to
demand exist. Options for adjusting capacity include:
1. Making staffing changes (increasing or decreasing the number of employees or shifts)
2. Adjusting equipment (purchasing additional machinery or selling or leasing out existing
equipment)
3. Improving processes to increase throughput
4. Redesigning products to facilitate more throughput
5. Adding process flexibility to better meet changing product preferences
6. Closing facilities
The foregoing tactics can be used to adjust demand to existing facilities. The strategic issue is, of
course, how to have a facility of the correct size.
Demand and Capacity Management
in the Service Sector
In the service sector, scheduling customers is demand management, and scheduling the work-
force is capacity management.
Demand Management When demand and capacity are fairly well matched, demand manage-
ment can often be handled with appointments, reservations, or a first-come, first-served rule. In
some businesses, such as doctors’ and lawyers’ offices, an appointment system is the schedule and
is adequate. Reservations systems work well in rental car agencies, hotels, and some restaurants as
a means of minimizing customer waiting time and avoiding disappointment over unfilled service.
AUTHOR COMMENT
A smoother sales demand
contributes to improved
scheduling and better human
resource strategies.
Matching capacity and demand can be a challenge. When market share is declining the mismatch between demand and capacity
means empty plants and laying off employees (left photo). On the other hand, when demand exceeds capacity, as at this opening
of the Apple store on the outskirts of Rome, Italy, the mismatch may mean frustrated customers and lost revenue (right photo).

234 PART 2 Designing Operations
In retail shops, a post office, or a fast-food restaurant, a first-come, first-served rule for serving
customers may suffice. Each industry develops its own approaches to matching demand and capac-
ity. Other more aggressive approaches to demand management include many variations of dis-
counts: “early bird” specials in restaurants, discounts for matinee performances or for seats at odd
hours on an airline, and cheap weekend phone calls.
Capacity Management When managing demand is not feasible, then managing capacity
through changes in full-time, temporary, or part-time staff may be an option. This is the approach
in many services. For instance, hospitals may find capacity limited by a shortage of board-certified
radiologists willing to cover the graveyard shifts. Getting fast and reliable radiology readings can
be the difference between life and death for an emergency room patient. As the photo above
illustrates, when an overnight reading is required (and 40% of CT scans are done between 8 P.M.
and 8 A.M.), the image can be sent by e-mail to a doctor in Europe or Australia for immediate
analysis.
BOTTLENECK ANALYSIS AND THE THEORY
OF CONSTRAINTS
As managers seek to match capacity to demand, decisions must be made about the size of spe-
cific operations or work areas in the larger system. Each of the interdependent work areas can be
expected to have its own unique capacity. Capacity analysis involves determining the through-
put capacity of workstations in a system and ultimately the capacity of the entire system.
A key concept in capacity analysis is the role of a constraint or bottleneck. A bottleneck is an
operation that is the limiting factor or constraint. The term bottleneck refers to the literal neck of
a bottle that constrains flow or, in the case of a production system, constrains throughput. A bot-
tleneck has the lowest effective capacity of any operation in the system and thus limits the sys-
tem’s output. Bottlenecks occur in all facets of life—from job shops where a machine is
constraining the work flow to highway traffic where two lanes converge into one inadequate
lane, resulting in traffic congestion.
Arnold Palmer Hospital provides an example of managing a bottleneck. Its constraint
for delivering more babies was hospital bed availability. The long-term solution to this bottle-
neck was to add capacity via a 4-year construction project. But the hospital staff sought
an immediate way to increase capacity of the bottleneck. The solution: If a woman is ready
for discharge and cannot be picked up prior to 5 P.M., staffers drive home the woman and
her baby themselves. Not only does this free up a bed for the next patient, it also creates
good will.
LO3: Perform bottleneck
analysis
Capacity analysis
A means of determining
throughput capacity of
workstations or an entire
production system.
AUTHOR COMMENT
There are always bottlenecks;
a manager must identify
and manage them.
Bottleneck
The limiting factor or constraint
in a system.
Many U.S. hospitals use services abroad to
manage capacity for radiologists during night
shifts. Night Hawk, an Idaho-based service
with 50 radiologists in Zurich and Sydney,
contracts with 900 facilities (20% of all U.S.
hospitals). These trained experts, wide awake
and alert in their daylight hours, usually return
a diagnosis in 10 to 20 minutes, with a
guarantee of 30 minutes.

Supplement 7 Capacity and Constraint Management 235
Process Times for Stations, Systems, and Cycles
Three metrics are important to help us analyze production system capacity. First, we define
process time of a station as the time to produce a given number of units (or a batch of units) at
that workstation. For example, if 60 windshields on a Ford assembly line can be installed in 30
minutes, then the process time is 0.5 minutes per windshield. (Process time is simply the inverse
of capacity, which in this case is 60 minutes per hour/0.5 minutes per windshield 120 wind-
shields installed per hour.) Process time of a system is the time of the longest process (the slow-
est workstation) in the system, which is defined as the process time of the bottleneck. Process
cycle time, on the other hand, is the time it takes for a unit of product, such as a car, to go through
the entire empty system, from start to finish.1
Process time of a system and process cycle time may be quite different. For example, a Ford
assembly line may roll out a new car every minute (process time of the system, because this is the
longest workstation), but it may take 30 hours to actually make a car from start to finish (process
cycle time). This is because the assembly line has many workstations, with each station contribut-
ing to the completed car. Thus, the system’s process time determines its capacity (one car per
minute), while its process cycle time determines potential ability to build a product (30 hours).
Figure S7.4 displays a simple assembly line using a flowchart, with the individual process
station times shown as 2, 4, and 3 minutes. The process time for the system is 4 minutes because
station B is the slowest station, the bottleneck, with a 4-minute process time. Station A could
work faster than that, but the result would be a pile of inventory continuously building in front of
station B. Station C could also potentially work faster than 4 minutes per unit, but there is no way
to tap into its excess capacity because station B will not be able to feed products to station C to
work on any faster than one every 4 minutes. Thus, we see that the excess capacity at non-bottle-
neck stations cannot be used to somehow “make up for the bottleneck.” Finally, the time to pro-
duce a new unit, the process cycle time, is minutes.
The following two examples illustrate capacity analysis for slightly more complex systems.
Example S3 introduces the concept of parallel processes, and Example S4 introduces the concept
of simultaneous processing.
2 + 4 + 3 = 9
=
1The more general term is manufacturing cycle time, but we use process cycle time here to note that we are defining the
time in an empty system. Cycle time varies, depending on the status of the system, from empty to substantial work-in-
process.
Process time of a station
The time to produce units at a
single workstation.
Process time of a system
The time of the longest (slowest)
process; the bottleneck.
Process cycle time
The time it takes for a product
to go through the production
process with no waiting.
3 min/unit
C
4 min/unit
B
2 min/unit
A
� FIGURE S7.4 Three-Station Assembly Line
A box represents an operation, a triangle represents inventory, and arrows represent precedence relationships
� EXAMPLE S3
Capacity analysis
with parallel
processes
Howard Kraye’s sandwich shop provides healthy sandwiches for customers. Howard has two identi-
cal sandwich assembly lines. A customer first places and pays for an order, which takes approxi-
mately 30 seconds. The order is then sent to one of the two lines. Each assembly line has two
workers and three major operations: (1) worker 1 retrieves and cuts the bread (15 seconds/
sandwich), (2) worker 2 adds ingredients and places the sandwich onto the toaster conveyor belt
(20 seconds/sandwich), and (3) the toaster heats the sandwich (40 seconds/sandwich). A wrapper
then wraps heated sandwiches coming from both lines and provides final packaging for the customer
(37.5 seconds/sandwich). A flowchart of the customer order is shown below.
15 sec/sandwich
Bread
20 sec/sandwich
Fill
40 sec/sandwich
Toast
15 sec/sandwich
Bread
20 sec/sandwich
Fill
40 sec/sandwich
Toast30 sec/sandwich
Order
37.5 sec/sandwich
Wrap

236 PART 2 Designing Operations
In Example S3, how could we claim that the process time of the toaster was 20 seconds per sand-
wich when it takes 40 seconds to toast a sandwich? Because we had two toasters, two sand-
wiches could be toasted every 40 seconds, for an average of 1 sandwich every 20 seconds. And
that time for a toaster can actually be achieved if the start times for the two are staggered (i.e., a
new sandwich is placed in a toaster every 20 seconds). In that case, even though each sandwich
will sit in the toaster for 40 seconds, a sandwich could emerge from one of the two toasters every
20 seconds. As we see, doubling the number of resources effectively cuts the process time in
half, resulting in a doubling of the capacity of those resources.
EXAMPLE S4 �
Capacity analysis
with simultaneous
processes
Dr. Cynthia Knott’s dentistry practice has been cleaning customers’ teeth for decades. The process for
a basic dental cleaning is relatively straightforward: (1) the customer checks in (2 minutes); (2) a lab
technician takes and develops four X-rays (2 and 4 minutes, respectively); (3) the dentist processes and
examines the X-rays (5 minutes) while the hygienist cleans the teeth (24 minutes); (4) the dentist meets
with the patient to poke at a few teeth, explain the X-ray results, and tell the patient to floss more often
(8 minutes); and (5) the customer pays and books her next appointment (6 minutes). A flowchart of the
customer visit is shown below.
24 min/unit
Cleaning
5 min/unit
X-ray exam4 min/unit
Develops
X-ray
2 min/unit2 min/unit
Check in
6 min/unit
Check out
8 min/unit
Dentist
Takes
X-ray
APPROACH � With simultaneous processes, an order or a product is essentially split into differ-
ent paths to be rejoined later on. To find the process time, each operation is treated separately, just as
though all operations were on a sequential path. To find the process cycle time, the time over all paths
must be computed, and it is the longest path.
SOLUTION � The bottleneck in this system is the hygienist, at 24 minutes per patient, resulting in
an hourly system capacity of 60 minutes/24 minutes per patient = 2.5 patients. The process cycle time is
the maximum of the two paths through the system. The path through the X-ray exam is 2 + 2 + 4 + 5 +
8 + 6 = 27 minutes, while the path through the hygienist is 2 + 2 + 4 + 24 + 8 + 6 = 46 minutes. Thus a
patient should be out the door after 46 minutes (i.e., the maximum of 27 and 46).
INSIGHT � With simultaneous processing, all process times in the entire system are not simply
added together to compute process cycle time, because some operations are occurring at the same time.
Instead, the longest path through the system is deemed the process cycle time.
APPROACH � Clearly the toaster is the single slowest resource in the five-step process, but is it
the bottleneck? Howard should first determine the process time of each assembly line, then the process
time of the combined assembly lines, and finally the process time of the entire operation.
SOLUTION � Because each of the three assembly-line operations uses a separate resource (worker
or machine), separate partially completed sandwiches can be worked on simultaneously at each station.
Thus, the process time of each assembly line is the longest process time of each of the three operations.
In this case, the 40-second toasting time represents the process time of each assembly line. Next, the
process time of the combined assembly-line operations is 40 seconds per two sandwiches, or 20 seconds
per sandwich. Therefore, the wrapping and delivering operation becomes the bottleneck for the entire
customer order operation, and the system process time is 37.5 seconds—the maximum of 30, 20, and
37.5. The capacity per hour equals 3,600 seconds per hour/37.5 seconds per sandwich 96 sandwiches
per hour. Finally, the process cycle time equals seconds (or
2 minutes and 22.5 seconds), assuming no waiting in line to begin with.
INSIGHT � If n parallel (redundant) operations are added, the process time of the combined oper-
ation will equal times the process time of the original.
LEARNING EXERCISE � If Howard hires an additional wrapper, what will be the new hourly
capacity? [Answer: The new bottleneck is now the order-taking station: Capacity 3,600 seconds per
hour/30 seconds per sandwich 120 sandwiches per hour]
RELATED PROBLEMS � S7.9, S7.10, S7.11, S7.12, S7.13
=
=
1>n
30 + 15 + 20 + 40 + 37.5 = 142.5
=

Supplement 7 Capacity and Constraint Management 237
Theory of constraints
(TOC)
A body of knowledge that deals
with anything that limits an
organization’s ability to achieve
its goals.
2See E. M. Goldratt and J. Cox, The Goal: A Process of Ongoing Improvement, 3rd rev. ed., Great Barrington, MA:
North River Press, 2004).
To summarize: (1) the system process time is the process time of the bottleneck, which is the oper-
ation with the longest (slowest) process time, after dividing by the number of parallel (redundant)
operations, (2) the system capacity is the inverse of the system process time, and (3) the process
cycle time is the total time through the longest path in the system, assuming no waiting.
Theory of Constraints
The theory of constraints (TOC) has been popularized by the book The Goal: A Process of
Ongoing Improvement, by Goldratt and Cox.2 TOC is a body of knowledge that deals with any-
thing that limits or constrains an organization’s ability to achieve its goals. Constraints can be
physical (e.g., process or personnel availability, raw materials, or supplies) or non-physical (e.g.,
procedures, morale, and training). Recognizing and managing these limitations through a five-
step process is the basis of TOC.
Step 1: Identify the constraints.
Step 2: Develop a plan for overcoming the identified constraints.
Step 3: Focus resources on accomplishing Step 2.
Step 4: Reduce the effects of the constraints by offloading work or by expanding capability. Make
sure that the constraints are recognized by all those who can have an impact on them.
Step 5: When one set of constraints is overcome, go back to Step 1 and identify new constraints.
The OM in Action box “Banking and the Theory of Constraints (TOC)” illustrates these five
steps and shows that TOC is used in services as well as manufacturing.
Bottleneck Management
A crucial constraint in any system is the bottleneck, and managers must focus significant atten-
tion on it. We present four principles of bottleneck management:
1. Release work orders to the system at the pace set by the bottleneck’s capacity: The theory
of constraints utilizes the concept of drum, buffer, rope to aid in the implementation of bot-
tleneck and non-bottleneck scheduling. In brief, the drum is the beat of the system. It pro-
vides the schedule—the pace of production. The buffer is the resource, usually inventory,
which may be helpful to keep the bottleneck operating at the pace of the drum. Finally, the
rope provides the synchronization or communication necessary to pull units through the sys-
tem. The rope can be thought of as signals between workstations.
2. Lost time at the bottleneck represents lost capacity for the whole system: This principle implies
that the bottleneck should always be kept busy with work. Well-trained and cross-trained
employees and inspections prior to the bottleneck can reduce lost capacity at a bottleneck.
3. Increasing the capacity of a non-bottleneck station is a mirage: Increasing the capacity of non-
bottleneck stations has no impact on the system’s overall capacity. Working faster on a non-
bottleneck station may just create extra inventory, with all of its adverse effects. This implies
that non-bottlenecks should have planned idle time. Extra work or setups at non-bottleneck sta-
tions will not cause delay, which allows for smaller batch sizes and more frequent product
changeovers at non-bottleneck stations.
4. Increasing the capacity of the bottleneck increases capacity for the whole system: Managers
should focus improvement efforts on the bottleneck. Bottleneck capacity may be improved by
LEARNING EXERCISE � Suppose that the same technician now has the hygienist start imme-
diately after the X-rays are taken (allowing the hygienist to start 4 minutes sooner). The technician then
processes the X-rays while the hygienist is cleaning teeth. The dentist still analyzes the X-rays while
the teeth cleaning is occurring. What would be the new system capacity and process cycle time?
[Answer: The X-ray development/processing operation is no longer on the initial path, reducing the
total patient visit duration by 4 minutes, for a process cycle time of 42 minutes (the maximum of 27 and
42). However, the hygienist is still the bottleneck, so the capacity remains at 2.5 patients per hour.]
RELATED PROBLEMS � S7.14, S7.15

238 PART 2 Designing Operations
various means, including offloading some of the bottleneck operations to another workstation
(e.g., let the beer foam settle next to the tap at the bar, not under it, so the next beer can be
poured), increasing capacity of the bottleneck (adding resources, working longer or working
faster), subcontracting, developing alternative routings, and reducing setup times.
Even when managers have process and quality variability under control, changing technology,
personnel, products, product mixes, and volumes can create multiple and shifting bottlenecks.
Identifying and managing bottlenecks is a required operations task, but by definition, bottlenecks
cannot be “eliminated.” A system will always have at least one.
BREAK-EVEN ANALYSIS
Break-even analysis is the critical tool for determining the capacity a facility must have to
achieve profitability. The objective of break-even analysis is to find the point, in dollars and
units, at which costs equal revenue. This point is the break-even point. Firms must operate above
this level to achieve profitability. As shown in Figure S7.5, break-even analysis requires an
estimation of fixed costs, variable costs, and revenue.
Break-even analysis
A means of finding the point, in
dollars and units, at which costs
equal revenues.
0
Volume (units per period)
C
o
st
(
d
o
lla
rs
) Break-even point:
Total cost = Total revenue
100
200
300
400
500
600
700
800
900
100 200 300 400 500 600 700 800 900 1000 1100
Total revenue line
Total cost line
Fixed cost
Variable cost
Pr
ofi
t C
or
rid
or
Lo
ss
Co
rri
do
r
� FIGURE S7.5
Basic Break-Even Point
AUTHOR COMMENT
Failure to operate above
break-even can be
devastating.
When a Midwestern U.S. bank identified its weakest link as
the mortgage department, with a home-loan processing
time of over a month, it turned to the principles of TOC to
reduce the average loan time. A cross-functional mortgage
improvement team of eight people employed the five
steps outlined in the text. Using flowcharting, the team
discovered that it was taking too long to (1) conduct
property appraisals and surveys and (2) verify applicant
employment. So the first step of TOC was to identify these
two constraints.
The second step in TOC was to develop a plan to
reduce the time taken for employment verification and for
conducting appraisals and surveys. The team learned that
it could reduce employment verification to 2 weeks by
having the loan officer request the last 2 years of W-2
forms and the last month’s pay stub. It found similar
solutions to reducing survey/appraisal time.
As a third step, it had personnel refocus their resources
so the two constraints could be performed at a higher level
of efficiency. The result was decreased operating expense
and inventory (money, in this banking example) and
increased throughput.
The fourth TOC step required that employees support
the earlier steps by focusing on the two time constraints.
The bank also placed a higher priority on verification so
that constraint could be overcome.
Finally, the bank began to look for new constraints
once the first ones were overcome. Like all continuing
improvement efforts, the process starts over before
complacency sets in.
Sources: Decision Support Systems (March 2001): 451–468; The Banker’s
Magazine (January–February 1997): 53–59; and Bank Systems and
Technology (September 1999): S10.
OM in Action � Banking and the Theory of Constraints (TOC)

Supplement 7 Capacity and Constraint Management 239
Fixed costs are costs that continue even if no units are produced. Examples include deprecia-
tion, taxes, debt, and mortgage payments. Variable costs are those that vary with the volume of
units produced. The major components of variable costs are labor and materials. However, other
costs, such as the portion of the utilities that varies with volume, are also variable costs. The dif-
ference between selling price and variable cost is contribution. Only when total contribution
exceeds total fixed cost will there be profit.
Another element in break-even analysis is the revenue function. In Figure S7.5, revenue
begins at the origin and proceeds upward to the right, increasing by the selling price of
each unit. Where the revenue function crosses the total cost line (the sum of fixed and vari-
able costs), is the break-even point, with a profit corridor to the right and a loss corridor to
the left.
Assumptions A number of assumptions underlie the basic break-even model. Notably, costs
and revenue are shown as straight lines. They are shown to increase linearly—that is, in direct
proportion to the volume of units being produced. However, neither fixed costs nor variable costs
(nor, for that matter, the revenue function) need be a straight line. For example, fixed costs
change as more capital equipment or warehouse space is used; labor costs change with overtime
or as marginally skilled workers are employed; the revenue function may change with such fac-
tors as volume discounts.
Graphic Approach The first step in the graphic approach to break-even analysis is to define
those costs that are fixed and sum them. The fixed costs are drawn as a horizontal line beginning
at that dollar amount on the vertical axis. The variable costs are then estimated by an analysis of
labor, materials, and other costs connected with the production of each unit. The variable costs
are shown as an incrementally increasing cost, originating at the intersection of the fixed cost on
the vertical axis and increasing with each change in volume as we move to the right on the vol-
ume (or horizontal) axis.
Algebraic Approach The formulas for the break-even point in units and dollars are shown
below. Let:
The break-even point occurs where total revenue equals total costs. Therefore:
Solving for x, we get
and:
Using these equations, we can solve directly for break-even point and profitability. The two
break-even formulas of particular interest are:
(S7-4)
(S7-5)Break-even in dollars =
Total fixed cost
1 –
Variable cost
Selling price
Break-even in units =
Total fixed cost
Price – Variable cost
Profit = TR – TC = Px – 1F + Vx2 = Px – F – Vx = 1P – V2x – F
Break-even point in dollars 1BEP$2 = BEPxP =
F
P – V
P =
F
1P – V2>P
=
F
1 – V>P
Break-even point in units (BEPx) =
F
P – V
TR = TC or Px = F + Vx
x = number of units produced TC = total costs = F + Vx
P = price per unit (after all discounts) V = variable costs per unit
BEP$ = break-even point in dollars F = fixed costs
BEPx = break-even point in units TR = total revenue = Px
LO4: Compute break-even

240 PART 2 Designing Operations
EXAMPLE S5 �
Single product
break-even analysis
Stephens, Inc., wants to determine the minimum dollar volume and unit volume needed at its new facil-
ity to break even.
APPROACH � The firm first determines that it has fixed costs of $10,000 this period. Direct labor
is $1.50 per unit, and material is $.75 per unit. The selling price is $4.00 per unit.
SOLUTION � The break-even point in dollars is computed as follows:
The break-even point in units is:
Note that we use total variable costs (that is, both labor and material).
INSIGHT � The management of Stevens, Inc., now has an estimate in both units and dollars of the
volume necessary for the new facility.
LEARNING EXERCISE � If Stevens finds that fixed cost will increase to $12,000, what hap-
pens to the break-even in units and dollars? [Answer: The break-even in units increases to 6,857, and
break-even in dollars increases to $27,428.57.]
RELATED PROBLEMS � S7.16, S7.17, S7.18, S7.19, S7.20, S7.21, S7.22, S7.23, S7.24,
S7.25
EXCEL OM Data File Ch07SExS3.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL S7.2 This example is further illustrated in Active Model S7.2 at www.pearsonhighered.com/heizer.
BEPx =
F
P – V
=
$10,000
4.00 – 11.50 + .752
= 5,714
BEP$ =
F
1 – 1V>P2
=
$10,000
1 – 311.50 + .752>14.0024
=
$10,000
.4375
= $22,857.14
Multiproduct Case
Most firms, from manufacturers to restaurants (even fast-food restaurants), have a variety of
offerings. Each offering may have a different selling price and variable cost. Utilizing break-
even analysis, we modify Equation (S7-5) to reflect the proportion of sales for each product.
Single-Product Case
In Example S5, we determine the break-even point in dollars and units for one product.
Recessions (e.g., 2008—
2010) and terrorist attacks
(e.g., September 11, 2001)
can make even the best
capacity decision for an
airline look bad. And
excess capacity for an
airline can be very
expensive, with storage
costs running as high
as $60,000 per month
per aircraft. Here, as
a testimonial to excess
capacity, aircraft sit idle in
the Mojave Desert.

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www.pearsonhighered.com/heizer

Supplement 7 Capacity and Constraint Management 241
Paper machines such as
the one shown here require
a high capital investment.
This investment results in a
high fixed cost but allows
production of paper at a
very low variable cost. The
production manager’s job
is to maintain utilization
above the break-even
point to achieve
profitability.
Le Bistro, like most other resturants, makes more than one product and would like to know its break-
even point in dollars.
APPROACH � Information for Le Bistro follows. Fixed costs are $3,000 per month.
With a variety of offerings, we proceed with break-even analysis just as in a single-product case, except
that we weight each of the products by its proportion of total sales using Equation (S7.6).
SOLUTION � Multiproduct Break-even: Determining Contribution
Item Price Cost
Annual Forecasted
Sales Units
Sandwich $5.00 $3.00 9,000
Drinks 1.50 .50 9,000
Baked potato 2.00 1.00 7,000
� EXAMPLE S6
Multiproduct
break-even
analysis
We do this by “weighting” each product’s contribution by its proportion of sales. The formula
is then:
(S7-6)
where V variable cost per unit
P price per unit
F fixed cost=
=
=
Break-even point in dollars (BEP$) =
F
aBa1 – ViPib * 1Wi2R
1 2 3 4 5 6 7 8
Item (i)
Selling
Price (P)
Variable
Cost (V) (V/P) 1 – (V/P)
Annual
Forecasted
Sales $
% of
Sales
Weighted
Contribution
(col. 5 col. 7):
Sandwich $5.00 $3.00 .60 .40 $45,000 .621 .248
Drinks 1.50 0.50 .33 .67 13,500 .186 .125
Baked potato 2.00 1.00 .50 .50 14,000 .193 .096
$72,500 1.000 .469
W percent each product is of total dollar sales
i each product=
=
Example S6 shows how to determine the break-even point for the multiproduct case at the Le
Bistro restaurant.

242 PART 2 Designing Operations
Note: Revenue for sandwiches is $45,000 ( ), which is 62.1% of the total revenue of
$72,500. Therefore, the contribution for sandwiches is “weighted” by .621. The weighted contribution
is In this manner, its relative contribution is properly reflected.
Using this approach for each product, we find that the total weighted contribution is .469 for each
dollar of sales, and the break-even point in dollars is $76,759.
The information given in this example implies total daily sales (52 weeks at 6 days each) of:
INSIGHT � The management of Le Bistro now knows that it must generate average sales of
$246.02 each day to break even. Management also knows that if the forecasted sales of $72,500 are
correct, Le Bistro will lose money, as break-even is $76,759.
LEARNING EXERCISE � If the manager of Le Bistro wants to make an additional $1,000 per
month in salary, and considers this a fixed cost, what is the new break-even point in average sales per
day? [Answer: $328.03.]
RELATED PROBLEMS � S7.26, S7.27
$76,759
312 days
= $246.02
BEP$ =
F
aBa1 – ViPib * 1Wi2R = $3,000 * 12.469 = $36,000.469 = $76,759.
.621 * .40 = .248.
5.00 * 9,000
Break-even figures by product provide the manager with added insight as to the realism of his
or her sales forecast. They indicate exactly what must be sold each day, as we illustrate in
Example S7.
EXAMPLE S7 �
Unit sales at
break-even
Le Bistro also wants to know the break-even for the number of sandwiches that must be sold every day.
APPROACH � Using the data in Example S6, we take the forecast sandwich sales of 62.1% times
the daily break-even of $246.02 divided by the selling price of each sandwich ($5.00).
SOLUTION � At break-even, sandwich sales must then be:
INSIGHT � With knowledge of individual product sales, the manager has a basis for determining
material and labor requirements.
LEARNING EXERCISE � At a dollar break-even of $328.03 per day, how many sandwiches
must Le Bistro sell each day? [Answer: 40.]
RELATED PROBLEMS � S7.26b, S7.27b
.621 * $246.02
5.00
= Number of sandwiches = 30.6 L 31 sandwiches each day
VIDEO S7.1
Capacity Planning at Arnold
Palmer Hospital
AUTHOR COMMENT
Capacity decisions require
matching capacity
to forecasts, which is
always difficult.
Once break-even analysis has been prepared, analyzed, and judged to be reasonable, decisions
can be made about the type and capacity of equipment needed. Indeed, a better judgment of the
likelihood of success of the enterprise can now be made.
REDUCING RISK WITH INCREMENTAL CHANGES
When demand for goods and services can be forecast with a reasonable degree of precision,
determining a break-even point and capacity requirements can be rather straightforward. But,
more likely, determining the capacity and how to achieve it will be complicated, as many factors
are difficult to measure and quantify. Factors such as technology, competitors, building restric-
tions, cost of capital, human resource options, and regulations make the decision interesting. To
complicate matters further, demand growth is usually in small units, while capacity additions are

Supplement 7 Capacity and Constraint Management 243
D
e
m
a
n
d
1 2 3
Time (periods)
New
capacity
Expected
demand
(a) Leading Strategy
Management leads capacity in
periodic increments. Management
could also add enough capacity in
one period to handle expected
demand for multiple periods.
(b) Lag Strategy
Here management lags (chases)
demand.
(c) Straddle Strategy
Here management uses average
capacity increments to straddle
demand.
D
e
m
a
n
d
1 2 3
Time (periods)
New
capacity
Expected
demand
New
capacity
D
e
m
a
n
d
1 2 3
Time (periods)
Expected
demand
� FIGURE S7.6 Approaches to Capacity Expansion
LO5: Determine expected
monetary value of a capacity
decision
AUTHOR COMMENT
Uncertainty in capacity
decisions makes EMV a
helpful tool.
likely to be both instantaneous and in large units. This contradiction adds to the capacity decision
risk. To reduce risk, incremental changes that hedge demand forecasts may be a good option.
Figure S7.6 illustrates three approaches to new capacity.
Alternative Figure S7.6(a) leads capacity—that is, acquires capacity to stay ahead of demand,
with new capacity being acquired at the beginning of period 1. This capacity handles increased
demand, until the beginning of period 2. At the beginning of period 2, new capacity is again
acquired, which will allow the organization to stay ahead of demand until the beginning of
period 3. This process can be continued indefinitely into the future. Here capacity is acquired
incrementally—at the beginning of period 1 and at the beginning of period 2. But managers can
also elect to make a larger increase at the beginning of period 1—an increase that may satisfy
expected demand until the beginning of period 3.
Excess capacity gives operations managers flexibility. For instance, in the hotel industry,
added (extra) capacity in the form of rooms can allow a wider variety of room options and per-
haps flexibility in room cleanup schedules. In manufacturing, excess capacity can be used to do
more setups, shorten production runs, and drive down inventory costs.
But Figure S7.6(b) shows an option that lags capacity, perhaps using overtime or subcontract-
ing to accommodate excess demand. Figure S7.6(c) straddles demand by building capacity that
is “average,” sometimes lagging demand and sometimes leading it. Both the lag and straddle
option have the advantage of delaying capital expenditure.
In cases where the business climate is stable, deciding between alternatives can be relatively
easy. The total cost of each alternative can be computed, and the alternative with the least total
cost can be selected. However, when capacity requirements are subject to significant unknowns,
“probabilistic” models may be appropriate. One technique for making successful capacity plan-
ning decisions with an uncertain demand is decision theory, including the use of expected mone-
tary value.
APPLYING EXPECTED MONETARY VALUE (EMV)
TO CAPACITY DECISIONS
Determining expected monetary value (EMV) requires specifying alternatives and various states
of nature. For capacity planning situations, the state of nature usually is future demand or market
favorability. By assigning probability values to the various states of nature, we can make
decisions that maximize the expected value of the alternatives. Example S8 shows how to apply
EMV to a capacity decision.

AUTHOR COMMENT
An operations manager may
be held responsible for return
on investment (ROI).
244 PART 2 Designing Operations
APPLYING INVESTMENT ANALYSIS
TO STRATEGY-DRIVEN INVESTMENTS
Once the strategy implications of potential investments have been considered, tradi-
tional investment analysis is appropriate. We introduce the investment aspects of capacity
next.
Investment, Variable Cost, and Cash Flow
Because capacity and process alternatives exist, so do options regarding capital investment and
variable cost. Managers must choose from among different financial options as well as capacity
and process alternatives. Analysis should show the capital investment, variable cost, and cash
flows as well as net present value for each alternative.
Net Present Value
Determining the discount value of a series of future cash receipts is known as the net present value
technique. By way of introduction, let us consider the time value of money. Say you invest $100.00
in a bank at 5% for 1 year. Your investment will be worth $100.00 + ($100.00)(.05) = $105.00. If
you invest the $105.00 for a second year, it will be worth $105.00 + ($105.00)(.05) = $110.25 at the
end of the second year. Of course, we could calculate the future value of $100.00 at 5% for as many
years as we wanted by simply extending this analysis. However, there is an easier way to express
this relationship mathematically. For the first year:
$105 = $10011 + .052
Net present value
A means of determining the
discounted value of a series of
future cash receipts.
Southern Hospital Supplies, a company that makes hospital gowns, is considering capacity
expansion.
APPROACH � Southern’s major alternatives are to do nothing, build a small plant, build a
medium plant, or build a large plant. The new facility would produce a new type of gown, and currently
the potential or marketability for this product is unknown. If a large plant is built and a favorable
market exists, a profit of $100,000 could be realized. An unfavorable market would yield a $90,000
loss. However, a medium plant would earn a $60,000 profit with a favorable market. A $10,000 loss
would result from an unfavorable market. A small plant, on the other hand, would return $40,000 with
favorable market conditions and lose only $5,000 in an unfavorable market. Of course, there is always
the option of doing nothing.
Recent market research indicates that there is a .4 probability of a favorable market, which means
that there is also a .6 probability of an unfavorable market. With this information, the alternative that
will result in the highest expected monetary value (EMV) can be selected.
SOLUTION � Compute the EMV for each alternative:
Based on EMV criteria, Southern should build a medium plant.
INSIGHT � If Southern makes many decisions like this, then determining the EMV for each alter-
native and selecting the highest EMV is a good decision criterion.
LEARNING EXERCISE � If a new estimate of the loss from a medium plant in an unfavorable
market increases to –$20,000 what is the new EMV for this alternative? [Answer: $12,000, which
changes the decision because the small plant EMV is now higher.]
RELATED PROBLEMS � S7.28, S7.29.
EMV 1do nothing2 = $0
EMV (small plant) = 1.421$40,0002 + 1.621- $5,0002 = + $13,000
EMV (medium plant) = 1.421$60,0002 + 1.621- $10,0002 = + $18,000
EMV (large plant) = 1.421$100,0002 + 1.621- $90,0002 = – $14,000
EXAMPLE S8 �
EMV applied to
capacity decision

Supplement 7 Capacity and Constraint Management 245
For the second year:
In general:
(S7-7)
where
In most investment decisions, however, we are interested in calculating the present value of a
series of future cash receipts. Solving for P, we get:
(S7-8)
When the number of years is not too large, the preceding equation is effective. However, when the
number of years, N, is large, the formula is cumbersome. For 20 years, you would have to compute
Without a sophisticated calculator, this computation would be difficult. Interest-rate
tables, such as Table S7.1, alleviate this situation. First, let us restate the present value equation:
(S7-9)
where factor from Table S7.1 defined as and
Thus, all we have to do is find the factor X and multiply it by F to calculate the present value, P.
The factors, of course, are a function of the interest rate, i, and the number of years, N. Table S7.1
lists some of these factors.
Equations (S7-8) and (S7-9) are used to determine the present value of one future
cash amount, but there are situations in which an investment generates a series of uniform and
equal cash amounts. This type of investment is called an annuity. For example, an investment
might yield $300 per year for 3 years. Easy-to-use factors have been developed for the present
value of annuities. These factors are shown in Table S7.2. The basic relationship is
where X = factor from Table S7.2
S = present value of a series of uniform annual receipts
R = receipts that are received every year for the life of the investment (the annuity)
S = RX
F = future value= 1>11 + i2NX = a
P =
F
11 + i2N
= FX
11 + i220.
P =
F
11 + i2N
N = number of years 1such as 1 year or 2 years2
i = interest rate 1such as .052
P = present value 1such as $100.002
F = future value 1such as $110.25 or $1052
F = P11 + i2N
$110.25 = $10511 + .052 = $10011 + .0522
LO6: Compute net present
value
Year 6% 8% 10% 12% 14% � TABLE S7.1
Present Value of $1
1 .943 .926 .909 .893 .877
2 .890 .857 .826 .797 .769
3 .840 .794 .751 .712 .675
4 .792 .735 .683 .636 .592
5 .747 .681 .621 .567 .519
6 .705 .630 .564 .507 .456
7 .665 .583 .513 .452 .400
8 .627 .540 .467 .404 .351
9 .592 .500 .424 .361 .308
10 .558 .463 .386 .322 .270
15 .417 .315 .239 .183 .140
20 .312 .215 .149 .104 .073

246 PART 2 Designing Operations
The present value of a uniform annual series of amounts is an extension of the present value of a
single amount, and thus Table S7.2 can be directly developed from Table S7.1. The factors for
any given interest rate in Table S7.2 are the cumulative sum of the values in Table S7.1. In Table
S7.1, for example, .943, .890, and .840 are the factors for years 1, 2, and 3 when the interest rate
is 6%. The cumulative sum of these factors is 2.673. Now look at the point in Table S7.2 where
the interest rate is 6% and the number of years is 3. The factor for the present value of an annu-
ity is 2.673, as you would expect.
Example S9 shows how to determine the present value of an annuity.
EXAMPLE S9 �
Determining net
present value of
future receipts of
equal value
River Road Medical Clinic is thinking of investing in a sophisticated new piece of medical equipment.
It will generate $7,000 per year in receipts for 5 years.
APPROACH � Determine the present value of this cash flow; assume an interest rate of 6%.
SOLUTION � The factor from Table S7.2 (4.212) is obtained by finding that value when the inter-
est rate is 6% and the number of years is 5:
INSIGHT � There is another way of looking at this example. If you went to a bank and took a loan
for $29,484 today, your payments would be $7,000 per year for 5 years if the bank used an interest rate
of 6% compounded yearly. Thus, $29,484 is the present value.
LEARNING EXERCISE � If the interest rate is 8%, what is the present value? [Answer:
$27,951.]
RELATED PROBLEMS � S7.30, S7.31, S7.32, S7.33, S7.34, S7.35
EXCEL OM Data File Ch07SExS9.xls can be found at www.pearsonhighered.com/heizer.
S = RX = $7,00014.2122 = $29,484
The net present value method is straightforward: You simply compute the present value of all
cash flows for each investment alternative. When deciding among investment alternatives, you
pick the investment with the highest net present value. Similarly, when making several invest-
ments, those with higher net present values are preferable to investments with lower net present
values.
Solved Problem S7.4 shows how to use the net present value to choose between investment
alternatives.
Although net present value is one of the best approaches to evaluating investment alter-
natives, it does have its faults. Limitations of the net present value approach include the
following:
1. Investments with the same net present value may have significantly different projected lives
and different salvage values.
2. Investments with the same net present value may have different cash flows. Different cash
flows may make substantial differences in the company’s ability to pay its bills.
�TABLE S7.2
Present Value of an
Annuity of $1
Year 6% 8% 10% 12% 14%
1 .943 .926 .909 .893 .877
2 1.833 1.783 1.736 1.690 1.647
3 2.673 2.577 2.487 2.402 2.322
4 3.465 3.312 3.170 3.037 2.914
5 4.212 3.993 3.791 3.605 3.433
6 4.917 4.623 4.355 4.111 3.889
7 5.582 5.206 4.868 4.564 4.288
8 6.210 5.747 5.335 4.968 4.639
9 6.802 6.247 5.759 5.328 4.946
10 7.360 6.710 6.145 5.650 5.216
15 9.712 8.559 7.606 6.811 6.142
20 11.470 9.818 8.514 7.469 6.623

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Supplement 7 Capacity and Constraint Management 247
SUPPLEMENT SUMMARY
Managers tie equipment selection and capacity decisions
to the organization’s missions and strategy. Four addi-
tional considerations are critical: (1) accurately forecast-
ing demand; (2) understanding the equipment, processes,
and capacity increments; (3) finding the optimum operat-
ing size; and (4) ensuring the flexibility needed for adjust-
ments in technology, product features and mix, and
volumes.
Techniques that are particularly useful to operations man-
agers when making capacity decisions include good fore-
casting, bottleneck analysis, break-even analysis, expected
monetary value, cash flow, and net present value (NPV).
The single most important criterion for investment deci-
sions is the contribution to the overall strategic plan and the
winning of profitable orders. Successful firms select the cor-
rect process and capacity.
Key Terms
Capacity (p. 228)
Design capacity (p. 228)
Effective capacity (p. 229)
Utilization (p. 229)
Efficiency (p. 229)
Capacity analysis (p. 234)
Bottleneck (p. 234)
Process time of a station (p. 235)
Process time of a system (p. 235)
Process cycle time (p. 235)
Theory of constraints (TOC) (p. 237)
Break-even analysis (p. 238)
Net present value (p. 244)
Using Software for Break-Even Analysis
Excel, Excel OM, and POM for Windows all handle break-even and cost–volume analysis problems.
Using Excel
It is a straightforward task to develop the formulas to do a break-even analysis in Excel. Although we do
not demonstrate the basics here, Active Model S7.2 provides a working example. You can see similar
spreadsheet analysis in the Excel OM preprogrammed software that accompanies this text.
X Using Excel OM
Excel OM’s Break-Even Analysis module provides the Excel formulas needed to compute the break-
even points, and the solution and graphical output.
P Using POM for Windows
Similar to Excel OM, POM for Windows also contains a break-even/cost–volume analysis module.
3. The assumption is that we know future interest rates, which we do not.
4. Payments are always made at the end of the period (week, month, or year), which is not
always the case.
� SOLVED PROBLEM S7.1
Sara James Bakery, described in Examples S1 and S2, has decided
to increase its facilities by adding one additional process line. The
firm will have two process lines, each working 7 days a week, 3
shifts per day, 8 hours per shift, with effective capacity of 300,000
rolls. This addition, however, will reduce overall system efficiency
to 85%. Compute the expected production with this new effective
capacity.
� SOLUTION
= 255,000 rolls per week
= 300,0001.852
Expected production = 1Effective capacity21Efficiency2
Solved Problems Virtual Office Hours help is available at www.myomlab.com

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248 PART 2 Designing Operations
1 2 3 4 5 6 7 8 9
Selling
Price (P)
Variable
Cost (V)
Percent
Variable
Cost (V/P)
Contribution
1 – (V/P)
Estimated
Quantity of
Sales Units
(sales)
Dollar Sales
( )sales : P
Percent
of
Sales
Contribution
Weighted by
Percent Sales
(col. 5 col. 8):
Tickets with Dinner $22.50 $10.50 0.467 0.533 175 $3,938 0.741 0.395
Drinks $ 5.00 $ 1.75 0.350 0.650 175 $ 875 0.165 0.107
Parking $ 5.00 $ 2.00 0.400 0.600 100 $ 500 0.094 0.056
450 $5,313 1.000 0.558
Machine A Machine B
Original cost $13,000 $20,000
Labor cost per year 2,000 3,000
Floor space per year 500 600
Energy (electricity) per year 1,000 900
Maintenance per year 2,500 500
Total annual cost $ 6,000 $ 5,000
Salvage value $ 2,000 $ 7,000
� SOLVED PROBLEM S7.2
Marty McDonald has a business packaging software in Wisconsin.
His annual fixed cost is $10,000, direct labor is $3.50 per package,
and material is $4.50 per package. The selling price will be $12.50
per package. What is the break-even point in dollars? What is
break-even in units?
� SOLUTION
BEPx =
F
P – V
=
$10,000
$12.50 – $8.00
=
$10,000
$4.50
= 2,222 units
BEP$ =
F
1 – 1V>P2
=
$10,000
1 – 1$8.00>$12.502
=
$10,000
.36
= $27,777
� SOLVED PROBLEM S7.3
John has been asked to determine whether the $22.50 cost of tickets for the community dinner theater will allow the group to achieve
break-even and whether the 175 seating capacity is adequate. The cost for each performance of a 10-performance run is $2,500. The
facility rental cost for the entire 10 performances is $10,000. Drinks and parking are extra charges and have their own price and variable
costs, as shown below:
� SOLUTION
Revenue for each performance (from column 7)
Total forecasted revenue for the 10 performances
Forecasted revenue with this mix of sales shows a break-even of $62,724
Thus, given this mix of costs, sales, and capacity John determines that the theater will not break even.
= 110 * $5,3132 = $53,130
= $5,313
BEP$ =
F
aC£1 – ViPi≥ * 1Wi2S = $110 * 2,5002 + $10,0000.558 = $35,0000.558 = $62,724
� SOLVED PROBLEM S7.4
Your boss has told you to evaluate the cost of two machines. After
some questioning, you are assured that they have the costs shown
at the right. Assume:
a) The life of each machine is 3 years, and
b) The company thinks it knows how to make 14% on investments
no riskier than this one.
Determine via the present value method which machine to purchase.
� SOLUTION
Machine A Machine B
Column 1 Column 2 Column 3 Column 4 Column 5 Column 6
Now Expense 1.000 $13,000 $13,000 1.000 $20,000 $20,000
1 yr. Expense .877 6,000 5,262 .877 5,000 4,385
2 yr. Expense .769 6,000 4,614 .769 5,000 3,845
3 yr. Expense .675 6,000 4,050 .675 5,000 3,375
$26,926 $31,605
3 yr. Salvage Revenue .675 $ 2,000 – 1,350 .675 $ 7,000 –4,725
$25,576 $26,880

Supplement 7 Capacity and Constraint Management 249
We use 1.0 for payments with no discount applied against them
(that is, when payments are made now, there is no need for a dis-
count). The other values in columns 1 and 4 are from the 14%
column and the respective year in Table S7.1 (for example, the
intersection of 14% and 1 year is .877, etc.). Columns 3 and 6
are the products of the present value figures times the combined
costs. This computation is made for each year and for the sal-
vage value.
The calculation for machine A for the first year is:
The salvage value of the product is subtracted from the summed
costs, because it is a receipt of cash. Since the sum of the net costs
for machine B is larger than the sum of the net costs for machine
A, machine A is the low-cost purchase, and your boss should be so
informed.
.877 * 1$2,000 + $500 + $1,000 + $2,5002 = $5,262
� SOLVED PROBLEM S7.5
T. Smunt Manufacturing Corp. has the process displayed below.
The drilling operation occurs separately from and simultaneously
with the sawing and sanding operations. The product only needs
to go through one of the three assembly operations (the assembly
operations are “parallel”).
a. Which operation is the bottleneck?
b. What is the system’s process time?
c. What is the process cycle time for the overall system?
d. If the firm operates 8 hours per day, 22 days per month, what is
the monthly capacity of the manufacturing process?
e. Suppose that a second drilling machine is added, and it has the
same process time as the original drilling machine. What is the
new process time of the system?
f. Suppose that a second drilling machine is added, and it has the
same process time as the original drilling machine. What is the
new process cycle time?
15 min/unit
Sanding
27 min/unit
Drilling
15 min/unit
Sawing
78 min/unit
Assembly
78 min/unit
Assembly
78 min/unit
Assembly
25 min/unit
Welding
� SOLUTION
a. The process time of Assembly is 78 minutes/3 operators = 26 minutes per unit, so the station with the longest process time, hence the
bottleneck, is Drilling, at 27 minutes.
b. The system’s process time is 27 minutes per unit (the longest process, Drilling).
c. System process cycle time is the maximum of (15 + 15 + 25 + 78), (27 + 25 + 78) = maximum of (133, 130) = 133 minutes
d.
e. The bottleneck shifts to assembly, with a process time of 26 minutes per unit.
f. Redundancy does not affect process cycle time. It is still 133 minutes.
391.11 units>month.
Monthly capacity = 160 minutes218 hours2122 days2>27 minutes per unit = 10,560 minutes per month >27 minutes per unit =
�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Southwestern University D: Requires the development of a multiproduct break-even solution.

www.myomlab.com

www.pearsonhighered.com/heizer

250 PART 2 Designing Operations
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Constraints.” Journal of Operations Management 25, no. 2
(March 2007): 387–402.

Location Strategies
Chapter Outline
GLOBAL COMPANY PROFILE: FEDEX
The Strategic Importance of Location 254
Factors That Affect Location Decisions 255
Methods of Evaluating Location
Alternatives 259
Service Location Strategy 264
� Design of Goods and Services
� Managing Quality
� Process Strategy
� Location Strategies
� Layout Strategies
� Human Resources
� Supply-Chain Management
� Inventory Management
� Scheduling
� Maintenance
251

GLOBAL COMPANY PROFILE: FEDEX
LOCATION PROVIDES COMPETITIVE ADVANTAGE FOR FEDEX
O
vernight-delivery powerhouse FedEx has
believed in the hub concept for its 40-year
existence. Even though Fred Smith, founder
and CEO, got a C on his college paper
proposing a hub for small-package delivery, the idea
has proven extremely successful. Starting with a hub
in Memphis, Tennessee (now called its superhub), the
$38 billion firm has added a European hub in Paris, an
Asian hub in Guangzhou, China, a Latin American hub
in Miami, and a Canadian hub in Toronto. FedEx’s fleet
of 672 planes flies into 375 airports worldwide, then
delivers to the door with more than 80,000 vans and
trucks.
Why was Memphis picked as FedEx’s central
location? (1) It is located in the middle of the U.S.
(2) It has very few hours of bad weather closures,
perhaps contributing to the firm’s excellent flight-safety
record.
Each night, except Sunday, FedEx brings to
Memphis packages from throughout the world that are
going to cities for which FedEx does not have direct
flights. The central hub permits service to a far greater
At the FedEx hub in Memphis, Tennessee, approximately
100 FedEx aircraft converge each night around midnight
with more than 5 million documents and packages.
252
At the preliminary sorting area, packages
and documents are sorted and sent to a
secondary sorting area. The Memphis facility
covers 1.5 million square feet; it is big
enough to hold 33 football fields. Packages
are sorted and exchanged until 4 A.M.

Packages and documents that have
already gone through the primary and
secondary sorts are checked by city,
state, and zip code. They are then placed
in containers that are loaded onto aircraft
for delivery to their final destinations in
215 countries.
FedEx’s fleet of 672 planes makes it the
largest airline in the world. Over 80,000
trucks complete the delivery process.
The $150 million hub opened in Guangzhou
in 2009 lies in the heart of one of China’s
fastest-growing manufacturing districts.
FedEx controls 39% of the China-to-U.S.
air express market.
number of points with fewer aircraft than the traditional
City A–to–City B system. It also allows FedEx to match
aircraft flights with package loads each night and to
reroute flights when load volume requires it, a major
cost savings. Moreover, FedEx also believes that the
central hub system helps reduce mishandling and
delay in transit because there is total control over the
packages from pickup point through delivery.
FEDEX �
253

254 PART 2 Designing Operations
THE STRATEGIC IMPORTANCE OF LOCATION
World markets continue to expand, and the global nature of business is accelerating. Indeed, one
of the most important strategic decisions made by many companies, including FedEx, Mercedes-
Benz, and Hard Rock, is where to locate their operations. When FedEx opened its Asian hub in
Guangzhou, China, in 2009, it set the stage for “round-the-world” flights linking its Paris and
Memphis package hubs to Asia. When Mercedes-Benz announced its plans to build its first
major overseas plant in Vance, Alabama, it completed a year of competition among 170 sites in
30 states and two countries. When Hard Rock Cafe opened in Moscow, it ended 3 years of
advance preparation of a Russian food-supply chain. The strategic impact, cost, and international
aspect of these decisions indicate how significant location decisions are.
Firms throughout the world are using the concepts and techniques of this chapter to address
the location decision because location greatly affects both fixed and variable costs. Location has
a major impact on the overall risk and profit of the company. For instance, depending on the
product and type of production or service taking place, transportation costs alone can total as
much as 25% of the product’s selling price. That is, one-fourth of a firm’s total revenue may be
needed just to cover freight expenses of the raw materials coming in and finished products going
out. Other costs that may be influenced by location include taxes, wages, raw material costs, and
rents. When all costs are considered, location may alter total operating expenses as much as 50%.
Companies make location decisions relatively infrequently, usually because demand has out-
grown the current plant’s capacity or because of changes in labor productivity, exchange rates,
costs, or local attitudes. Companies may also relocate their manufacturing or service facilities
because of shifts in demographics and customer demand.
Location options include (1) expanding an existing facility instead of moving, (2) maintaining
current sites while adding another facility elsewhere, or (3) closing the existing facility and mov-
ing to another location.
The location decision often depends on the type of business. For industrial location decisions,
the strategy is usually minimizing costs, although innovation and creativity may also be critical.
For retail and professional service organizations, the strategy focuses on maximizing revenue.
Warehouse location strategy, however, may be driven by a combination of cost and speed of
delivery. The objective of location strategy is to maximize the benefit of location to the firm.
Location and Costs Because location is such a significant cost and revenue driver, location
often has the power to make (or break) a company’s business strategy. Key multinationals in
every major industry, from automobiles to cellular phones, now have or are planning a presence
in each of their major markets. Location decisions to support a low-cost strategy require particu-
larly careful consideration.
Once management is committed to a specific location, many costs are firmly in place and dif-
ficult to reduce. For instance, if a new factory location is in a region with high energy costs, even
good management with an outstanding energy strategy is starting at a disadvantage. Management
is in a similar bind with its human resource strategy if labor in the selected location is expensive,
ill-trained, or has a poor work ethic. Consequently, hard work to determine an optimal facility
location is a good investment.
Location and Innovation When creativity, innovation, and research and development
investments are critical to the operations strategy, the location criteria may change from a focus
on costs. When innovation is the focus, four attributes seem to affect overall competitiveness as
well as innovation:1
VIDEO 8.1
Hard Rock’s Location Selection
1See Michael E. Porter and Scott Stern, “Innovation: Location Matters,” MIT Sloan Management Review 42, no. 4
(Summer 2001): 28–36.
Chapter 8 Learning Objectives
LO1: Identify and explain seven major factors
that affect location decisions 256
LO2: Compute labor productivity 256
LO3: Apply the factor-rating method 260
LO4: Complete a locational break-even analysis
graphically and mathematically 261
LO5: Use the center-of-gravity method 262
LO6: Understand the differences between service-
and industrial-sector location analysis 266
AUTHOR COMMENT
This chapter illustrates
techniques organizations use
to locate plants, warehouses,
stores, or offices.

Chapter 8 Location Strategies 255
• The presence of high-quality and specialized inputs such as scientific and technical talent
• An environment that encourages investment and intense local rivalry
• Pressure and insight gained from a sophisticated local market
• Local presence of related and supporting industries
Motorola and Intel are among those firms that have rejected low-cost locations when those loca-
tions could not support other important aspects of the strategy. In the case of Motorola, when
analysis indicated that the infrastructure and education levels could not support specific produc-
tion technologies, the locations were removed from consideration, even if they were low cost.
And Intel opened its newest plant not in Asia but in the U.S. The $3 billion semiconductor facil-
ity, with 1,000 workers, ended up in Arizona in 2007 for four reasons: (1) the skilled labor
requirements (for employees who understand statistics and scientific principles), (2) protection
of intellectual property in the U.S., (3) tax breaks to help cover the cost of equipment, and
(4) easy oversight from Intel’s California headquarters.
FACTORS THAT AFFECT LOCATION DECISIONS
Selecting a facility location is becoming much more complex with the globalization of the work-
place. As we saw in Chapter 2, globalization has taken place because of the development of
(1) market economics; (2) better international communications; (3) more rapid, reliable travel
and shipping; (4) ease of capital flow between countries; and (5) high differences in labor costs.
Many firms now consider opening new offices, factories, retail stores, or banks outside their
home country. Location decisions transcend national borders. In fact, as Figure 8.1 shows, the
sequence of location decisions often begins with choosing a country in which to operate.
One approach to selecting a country is to identify what the parent organization believes are key
success factors (KSFs) needed to achieve competitive advantage. Six possible country KSFs are
listed at the top of Figure 8.1. Using such factors (including some negative ones, such as crime) the
World Economic Forum biannually ranks the global competitiveness of 133 countries (see
Table 8.1). Switzerland landed first, with the U.S. a close second, in 2009–2010 because of their high
rates of saving and investment, openness to trade, quality education, and efficient governments.
Once a firm decides which country is best for its location, it focuses on a region of the chosen
country and a community. The final step in the location decision process is choosing a specific
Political risks, government rules, attitudes, incentives
Cultural and economic issues
Location of markets
Labor talent, attitudes, productivity, costs
Availability of supplies, communications, energy
Exchange rates and currency risk
1.
2.
3.
4.
5.
6.
Country Decision Key Success Factors
Region/Community Decision
Site Decision
12
4
3
Corporate desires
Attractiveness of region (culture, taxes, climate, etc.)
Labor availability, costs, attitudes toward unions
Cost and availability of utilities
Environmental regulations of state and town
Government incentives and fiscal policies
Proximity to raw materials and customers
Land/construction costs
1.
2.
3.
4.
5.
6.
7.
8.
Site size and cost
Air, rail, highway, and waterway systems
Zoning restrictions
Proximity of services/supplies needed
Environmental impact issues
1.
2.
3.
4.
5.
MN
WI
IL IN OH
MI
465
465
465
465
70
65
65
69
70
Indianapolis
� FIGURE 8.1
Some Considerations and
Factors That Affect Location
Decisions
AUTHOR COMMENT
We now look at major
location issues.
� TABLE 8.1
Competitiveness of 133
Selected Countries, Based on
Annual Surveys of 13,000
Business Executives
Country
2009–2010
Ranking
Switzerland 1
U.S. 2
o
Japan 8
Canada 9
o
UK 13
o
Israel 27
o
China 29
o
Italy 48
India 49
o
Mexico 60
o
Russia 63
o
o
Vietnam 75
o
Zimbabwe 132
Burundi 133
Source: www.weforum.org, 2010.
Used with permission of World
Economic Forum.

www.weforum.org

256 PART 2 Designing Operations
site within a community. The company must pick the one location that is best suited for shipping
and receiving, zoning, utilities, size, and cost. Again, Figure 8.1 summarizes this series of decisions
and the factors that affect them.
Besides globalization, a number of other factors affect the location decision. Among these are
labor productivity, foreign exchange, culture, changing attitudes toward the industry, and proximity
to markets, suppliers, and competitors.
Labor Productivity
When deciding on a location, management may be tempted by an area’s low wage rates.
However, wage rates cannot be considered by themselves, as Quality Coils, Inc., discovered
when it opened its plant in Mexico (see the OM in Action box “Quality Coils Pulls the Plug on
Mexico”). Management must also consider productivity.
As discussed in Chapter 1, differences exist in productivity in various countries. What man-
agement is really interested in is the combination of production and the wage rate. For example,
if Quality Coils pays $70 per day with 60 units produced per day in Connecticut, it will spend
less on labor than at a Mexican plant that pays $25 per day with production of 20 units per day:
Case 1: Connecticut plant:
Case 2: Juarez, Mexico, plant:
Employees with poor training, poor education, or poor work habits may not be a good buy even
at low wages. By the same token, employees who cannot or will not always reach their places of
work are not much good to the organization, even at low wages. (Labor cost per unit is some-
times called the labor content of the product.)
Exchange Rates and Currency Risk
Although wage rates and productivity may make a country seem economical, unfavorable
exchange rates may negate any savings. Sometimes, though, firms can take advantage of a partic-
ularly favorable exchange rate by relocating or exporting to a foreign country. However, the values
of foreign currencies continually rise and fall in most countries. Such changes could well make
what was a good location in 2010 a disastrous one in 2015.
$25 Wages per day
20 Units produced per day
=
$25
20
= $1.25 per unit
$70 Wages per day
60 Units produced per day
=
$70
60
= $1.17 per unit
Labor cost per day
Production 1that is, units per day2
= Labor cost per unit
LO1: Identify and explain
seven major factors that
affect location decisions
LO2: Compute labor
productivity
Keith Gibson, president of Quality Coils, Inc., saw the
savings of low Mexican wages and headed south. He shut
down a factory in Connecticut and opened one in Juarez,
where he could pay Mexicans one-third the wage rates he
was paying Americans. “All the figures pointed out we
should make a killing,” says Gibson.
Instead, his company was nearly destroyed. The
electromagnetic coil maker regularly lost money during
4 years in Mexico. High absenteeism, low productivity,
and problems of long-distance management wore down
Gibson until he finally pulled the plug on Juarez.
Moving back to the U.S. and rehiring some of his
original workers, Gibson learned, “I can hire one person
in Connecticut for what three were doing in Juarez.”
When U.S. unions complain that they cannot compete
against the low wages in other countries and when the
teamster rallies chant “$4 a day/No way!” they overlook
several factors. First, productivity in low-wage countries
often erases a wage advantage that is not nearly as great
as people believe. Second, a host of problems, from poor
roads to corrupt governments, run up operating costs.
Third, although labor costs in many underdeveloped
countries are only one-third of those in the U.S., they may
represent less than 10% of total manufacturing costs. Thus,
the difference may not overcome other disadvantages. And
most importantly, the cost of labor for most U.S.
manufacturers is less important than such factors as the
skill of the workforce, the quality of transportation, and
access to technology.
Sources: Global Information Network (January 8, 2004): 1; and The Wall
Street Journal (January 13, 2004): A12 and (September 15, 1993): A1.
OM in Action OMinActio� ..Quality Coils Pulls the Plug on Mexico
AUTHOR COMMENT
Final cost is the critical factor
and low productivity can
negate low cost.

Chapter 8 Location Strategies 257
Costs
We can divide location costs into two categories, tangible and intangible. Tangible costs are
those costs that are readily identifiable and precisely measured. They include utilities, labor,
material, taxes, depreciation, and other costs that the accounting department and management
can identify. In addition, such costs as transportation of raw materials, transportation of finished
goods, and site construction are all factored into the overall cost of a location. Government
incentives, as we see in the OM in Action box “How Alabama Won the Auto Industry,” certainly
affect a location’s cost.
Intangible costs are less easily quantified. They include quality of education, public trans-
portation facilities, community attitudes toward the industry and the company, and quality and
attitude of prospective employees. They also include quality-of-life variables, such as climate
and sports teams, that may influence personnel recruiting.
Ethical Issues Location decisions based on costs alone may create ethical situations such as
the United Airlines case in Indianapolis (see the Ethical Dilemma in the Lecture Guide &
Activities Manual ). United accepted $320 million in incentives to open a facility in that location,
only to renege a decade later, leaving residents and government holding the bag.2
Assembly plants operating along the Mexican
side of the border, from Texas to California, are
called maquiladoras. Some 3,000 firms and
industrial giants such as Toyota, Panasonic,
Zenith, Hitachi, and GE operate these plants,
which employ over 1 million workers. Mexican
wages are low, but at current exchange rates,
companies also look to Asia.
2So what’s a city, county, or state to do? According to Forbes (June 19, 2006): 42, “Keep taxes low. Don’t grant favors.
Pursue non-discriminatory reforms like reining in debt and public spending. Remove barriers rather than trying to steer
economic growth to this favored corporation or that one.” While many inner cities have languished, Chicago has pros-
pered by focusing on infrastructure and quality-of-life issues. Also see “Is There a Better Way to Court a Company?”
Business Week (July 23, 2007): 55.
Fifteen years ago, Alabama persuaded Mercedes-Benz to
build its first U.S. auto plant in the town of Vance by offering
the luxury carmaker $253 million worth of incentives—
$169,000 for every job Mercedes promised the state.
Taxpayers considered the deal such a boondoggle that
they voted Governor Jim Folsom out of office long before
the first Mercedes SUV rolled off the new assembly line in
1997. Today, with 50,000 car-related jobs in Alabama, the
deal looks a little more like a bargain—suggesting that the
practice of paying millions of taxpayer dollars to lure big
employers can sometimes have a big payoff.
Mercedes surpassed its pledge to create 1,500 jobs at the
Vance plant and currently has a workforce of about 4,000.
In 2001, Honda opened a factory 70 miles east of the
Mercedes plant, to build its Odyssey minivan. Toyota Motor
Corp.’s plant near Huntsville started producing engines in
2002. Those two automakers also received incentives.
To cement Alabama’s reputation as the South’s busiest
auto-making center, Hyundai Motor Co. of South Korea
picked a site near Montgomery for its first U.S. assembly
plant. The factory began production in 2005, employing
2,000 workers to make 300,000 sedans and SUVs
a year.
Is the state giving away more than it gets in return?
That’s what many economists argue. Other former foes of
incentives now argue that manufacturers’ arrivals herald
“Alabama’s new day.”
Sources: Automotive News (June 2, 2008): 30D and (March 10, 2008):16;
and The Wall Street Journal (August 14, 2007): A6.
OM in Action � How Alabama Won the Auto Industry
Tangible costs
Readily identifiable costs that
can be measured with some
precision.
Intangible costs
A category of location costs that
cannot be easily quantified,
such as quality of life and
government.

258 PART 2 Designing Operations
To what extent do companies owe long-term allegiance to a particular country or state or town
if they are losing money—or if the firm can make greater profits elsewhere? Is it ethical for
developed countries to locate plants in undeveloped countries where sweatshops and child labor
are commonly used? Where low wages and poor working conditions are the norm? It has been
said that the factory of the future will be a large ship, capable of moving from port to port as costs
in one port become noncompetitive.
Political Risk, Values, and Culture
The political risk associated with national, state, and local governments’ attitudes toward private
and intellectual property, zoning, pollution, and employment stability may be in flux.
Governmental positions at the time a location decision is made may not be lasting ones.
However, management may find that these attitudes can be influenced by their own leadership.
Worker values may also differ from country to country, region to region, and small town to
city. Worker views regarding turnover, unions, and absenteeism are all relevant factors. In turn,
these values can affect a company’s decision whether to make offers to current workers if the
firm relocates to a new location. The case study in the Lecture Guide & Activities Manual,
“Southern Recreational Vehicle Company,” describes a St. Louis firm that actively chose not to
relocate any of its workers when it moved to Mississippi.
One of the greatest challenges in a global operations decision is dealing with another coun-
try’s culture. Cultural variations in punctuality by employees and suppliers make a marked dif-
ference in production and delivery schedules. Bribery likewise creates substantial economic
inefficiency, as well as ethical and legal problems in the global arena. As a result, operations
managers face significant challenges when building effective supply chains across cultures.
Table 8.2 provides one ranking of corruption in countries around the world.
Proximity to Markets
For many firms, locating near customers is extremely important. Particularly, service organiza-
tions, like drugstores, restaurants, post offices, or barbers, find that proximity to market is the pri-
mary location factor. Manufacturing firms find it useful to be close to customers when
transporting finished goods is expensive or difficult (perhaps because they are bulky, heavy, or
fragile). Foreign-owned auto giants such as Mercedes, Honda, Toyota, and Hyundai are building
millions of cars each year in the U.S.
In addition, with just-in-time production, suppliers want to locate near users. For a firm like
Coca-Cola, whose product’s primary ingredient is water, it makes sense to have bottling plants in
many cities rather than shipping heavy (and sometimes fragile glass) containers cross country.
Proximity to Suppliers
Firms locate near their raw materials and suppliers because of (1) perishability, (2) transportation
costs, or (3) bulk. Bakeries, dairy plants, and frozen seafood processors deal with perishable raw
materials, so they often locate close to suppliers. Companies dependent on inputs of heavy or
bulky raw materials (such as steel producers using coal and iron ore) face expensive inbound
transportation costs, so transportation costs become a major factor. And goods for which there is
a reduction in bulk during production (such as lumber mills locating in the Northwest near tim-
ber resources) typically need to be near the raw material.
Proximity to Competitors (Clustering)
Both manufacturing and service organizations also like to locate, somewhat surprisingly, near com-
petitors. This tendency, called clustering, often occurs when a major resource is found in that region.
Such resources include natural resources, information resources, venture capital resources, and talent
resources. Table 8.3 presents nine examples of industries that exhibit clustering, and the reasons why.
Italy may be the true leader when it comes to clustering, however, with northern zones of that
country holding world leadership in such specialties as ceramic tile (Modena), gold jewelry
(Vicenza), machine tools (Busto Arsizio), cashmere and wool (Biella), designer eyeglasses
(Belluma), and pasta machines (Parma).
Clustering
The location of competing
companies near each other,
often because of a critical mass
of information, talent, venture
capital, or natural resources.
� TABLE 8.2
Ranking Corruption in
Selected Countries (score of
10 represents a corruption-free
country)
Rank Score
1 Denmark, 9.3
New Zealand, (tie)
Sweden
o
9 Canada,
Australia
8.7
(tie)
o
18 Japan, U.S.,
Belgium
7.3
(tie)
o
33 Israel,
Dominica
6.0
(tie)
o
80 Brazil,
Thailand,
Saudi
Arabia
3.5
(tie)
o
143 Iran, Yemen
o
2.3
(tie)
177 Haiti 1.4
o
180 Somalia 1.0
Source: Transparency
International’s 2008 survey,
at www.transparency.org. Used
with permission of Transparency
International.

www.transparency.org

Chapter 8 Location Strategies 259
METHODS OF EVALUATING LOCATION ALTERNATIVES
Four major methods are used for solving location problems: the factor-rating method, locational
break-even analysis, the center-of-gravity method, and the transportation model. This section
describes these approaches.
The Factor-Rating Method
There are many factors, both qualitative and quantitative, to consider in choosing a location.
Some of these factors are more important than others, so managers can use weightings to make
the decision process more objective. The factor-rating method is popular because a wide vari-
ety of factors, from education to recreation to labor skills, can be objectively included. Figure 8.1
listed a few of the many factors that affect location decisions.
The factor-rating method has six steps:
1. Develop a list of relevant factors called key success factors (such as those in Figure 8.1).
2. Assign a weight to each factor to reflect its relative importance in the company’s objectives.
3. Develop a scale for each factor (for example, 1 to 10 or 1 to 100 points).
4. Have management score each location for each factor, using the scale in step 3.
5. Multiply the score by the weights for each factor and total the score for each location.
6. Make a recommendation based on the maximum point score, considering the results of other
quantitative approaches as well.
� TABLE 8.3 Clustering of Companies
Industry Locations Reason for Clustering
Wine making Napa Valley (U.S.), Bordeaux region
(France)
Natural resources of land and climate
Software firms Silicon Valley, Boston, Bangalore (India) Talent resources of bright graduates in
scientific/technical areas, venture
capitalists nearby
Race car building Huntington/North Hampton region
(England)
Critical mass of talent and information
Theme parks (including Disney World,
Universal Studios, and Sea World)
Orlando, Florida A hot spot for entertainment, warm
weather, tourists, and inexpensive labor
Electronics firms (such as Sony, IBM, HP,
Motorola, and Panasonic)
Northern Mexico NAFTA, duty-free export to U.S. (24% of
all TVs are built here)
Computer hardware manufacturing Singapore, Taiwan High technological penetration rates and
per capita GDP, skilled/educated
workforce with large pool of engineers
Fast-food chains (such as Wendy’s,
McDonald’s, Burger King, and
Pizza Hut)
Sites within 1 mile of one another Stimulate food sales, high traffic flows
General aviation aircraft (including
Cessna, Learjet, Boeing, and Raytheon)
Wichita, Kansas Mass of aviation skills (60–70% of world’s
small planes/jets built here)
Orthopedic device manufacturing Warsaw, Indiana Ready supply of skilled workers, strong
U.S. market
AUTHOR COMMENT
Here are four techniques that
help in making good location
decisions.
Factor-rating method
A location method that instills
objectivity into the process of
identifying hard-to-evaluate
costs.
Five Flags over Florida, a U.S. chain of 10 family-oriented theme parks, has decided to expand over-
seas by opening its first park in Europe. It wishes to select between France and Denmark.
APPROACH � The ratings sheet in Table 8.4 lists key success factors that management has
decided are important; their weightings and their rating for two possible sites—Dijon, France, and
Copenhagen, Denmark—are shown.
� EXAMPLE 1
Factor-rating
method for an
expanding theme
park

260 PART 2 Designing Operations
When a decision is sensitive to minor changes, further analysis of the weighting and the points
assigned may be appropriate. Alternatively, management may conclude that these intangible fac-
tors are not the proper criteria on which to base a location decision. Managers therefore place
primary weight on the more quantitative aspects of the decision.
Locational Break-Even Analysis
Locational break-even analysis is the use of cost–volume analysis to make an economic com-
parison of location alternatives. By identifying fixed and variable costs and graphing them for
each location, we can determine which one provides the lowest cost. Locational break-even
analysis can be done mathematically or graphically. The graphic approach has the advantage of
providing the range of volume over which each location is preferable.
The three steps to locational break-even analysis are as follows:
1. Determine the fixed and variable cost for each location.
2. Plot the costs for each location, with costs on the vertical axis of the graph and annual vol-
ume on the horizontal axis.
3. Select the location that has the lowest total cost for the expected production volume.
LO3: Apply the factor-
rating method
Key
Scores
(out of 100) Weighted Scores
Success Factor Weight France Denmark France Denmark
Labor availability
and attitude .25 70 60 1.2521702 = 17.5 1.2521602 = 15.0
People-to-car ratio .05 50 60 1.0521502 = 2.5 1.0521602 = 3.0
Per capita income .10 85 80 1.1021852 = 8.5 1.1021802 = 8.0
Tax structure .39 75 70 1.3921752 = 29.3 1.3921702 = 27.3
Education and
health
Totals
.21
1.00
60 70
70.4
1.2121602 = 12.6
68.0
1.2121702 = 14.7
SOLUTION � Table 8.4 uses weights and scores to evaluate alternative site locations. Given the
option of 100 points assigned to each factor, the French location is preferable.
INSIGHT � By changing the points or weights slightly for those factors about which there is some
doubt, we can analyze the sensitivity of the decision. For instance, we can see that changing the scores
for “labor availability and attitude” by 10 points can change the decision. The numbers used in factor
weighting can be subjective and the model’s results are not “exact” even though this is a quantitative
approach.
LEARNING EXERCISE � If the weight for “tax structure” drops to .20 and the weight for
“education and health” increases to .40, what is the new result? [Answer: Denmark is now chosen, with
a 68.0 vs. a 67.5 score for France.]
RELATED PROBLEMS � 8.5, 8.6, 8.7, 8.8, 8.9, 8.10, 8.11, 8.12, 8.13, 8.14, 8.15, 8.24, 8.25
EXCEL OM Data File Ch08Ex1.xls can be found at www.pearsonhighered.com/heizer.
�TABLE 8.4
Weights, Scores, and Solution
AUTHOR COMMENT
These weights do not need to
be on a 0–1 scale or total to
1. We can use a 1–10 scale,
1–100 scale, or any other
scale we prefer.
EXAMPLE 2 �
Locational break-
even for a parts
manufacturer
John Kros, owner of Carolina Ignitions Manufacturing, needs to expand his capacity. He is considering
three locations—Akron, Bowling Green, and Chicago—for a new plant. The company wishes to find
the most economical location for an expected volume of 2,000 units per year.
APPROACH � Kros conducts locational break-even analysis. To do so, he determines that fixed
costs per year at the sites are $30,000, $60,000, and $110,000, respectively; and variable costs are $75
per unit, $45 per unit, and $25 per unit, respectively. The expected selling price of each ignition system
produced is $120.
Locational break-even
analysis
A cost–volume analysis to make
an economic comparison of
location alternatives.

www.pearsonhighered.com/heizer

Chapter 8 Location Strategies 261
SOLUTION � For each of the three locations, Kros can plot the fixed costs (those at a volume of
zero units) and the total cost ( ) at the expected volume of output. These
lines have been plotted in Figure 8.2.
fixed costs + variable costs
For Akron:
For Bowling Green:
For Chicago:
With an expected volume of 2,000 units per year, Bowling Green provides the lowest cost location. The
expected profit is:
The crossover point for Akron and Bowling Green is:
and the crossover point for Bowling Green and Chicago is:
INSIGHT � As with every other OM model, locational break-even results can be sensitive to input
data. For example, for a volume of less than 1,000, Akron would be preferred. For a volume greater
than 2,500, Chicago would yield the greatest profit.
LEARNING EXERCISE � The variable cost for Chicago is now expected to be $22 per unit.
What is the new crossover point between Bowling Green and Chicago? [Answer: 2,174 units.]
RELATED PROBLEMS � 8.16, 8.17, 8.18, 8.19
EXCEL OM Data File Ch08Ex2.xls can be found at www.pearsonhighered.com/heizer.
x = 2,500
201×2 = 50,000
60,000 + 451×2 = 110,000 + 251×2
x = 1,000
301×2 = 30,000
30,000 + 751×2 = 60,000 + 451×2
Total revenue – Total cost = $12012,0002 – $150,000 = $90,000 per year
Total cost = $110,000 + $2512,0002 = $160,000
Total cost = $60,000 + $4512,0002 = $150,000
Total cost = $30,000 + $7512,0002 = $180,000
$10,000
$30,000
$60,000
$80,000
$110,000
$130,000
$150,000
$160,000
$180,000
0 500 1,000 1,500 2,000 2,500 3,000
Chic
ago c
ost c
urve
Bo
wli
ng
Gr
ee
n
cos
t cu
rve
Ak
ro
n
co
st
c
ur
veA
n
n
u
a
l c
o
st
Volume
Akron
lowest cost
Bowling Green
lowest cost
Chicago
lowest cost
� FIGURE 8.2
Crossover Chart for
Locational Break-Even
Analysis
LO4: Complete a locational
break-even analysis
graphically and
mathematically

www.pearsonhighered.com/heizer

262 PART 2 Designing Operations
Center-of-Gravity Method
The center-of-gravity method is a mathematical technique used for finding the location of a dis-
tribution center that will minimize distribution costs. The method takes into account the location
of markets, the volume of goods shipped to those markets, and shipping costs in finding the best
location for a distribution center.
The first step in the center-of-gravity method is to place the locations on a coordinate system.
This will be illustrated in Example 3. The origin of the coordinate system and the scale used are
arbitrary, just as long as the relative distances are correctly represented. This can be done easily
by placing a grid over an ordinary map. The center of gravity is determined using Equations (8-1)
and (8-2):
(8-1)
(8-2)
where dix � x-coordinate of location i
diy � y-coordinate of location i
Qi � Quantity of goods moved to or from location i
Note that Equations (8-1) and (8-2) include the term , the quantity of supplies transferred to or
from location i.
Since the number of containers shipped each month affects cost, distance alone should not be
the principal criterion. The center-of-gravity method assumes that cost is directly proportional to
both distance and volume shipped. The ideal location is that which minimizes the weighted dis-
tance between the warehouse and its retail outlets, where the distance is weighted by the number
of containers shipped.3
Qi
y – coordinate of the center of gravity =
©
i
diyQi
©
i
Qi
x- coordinate of the center of gravity =
©
i
dixQi
©
i
Qi
Center-of-gravity
method
A mathematical technique used
for finding the best location for
a single distribution point that
services several stores or areas.
3Equations (8–1) and (8–2) compute a center of gravity (COG) under “squared Euclidean” distances and may actually
result in transportation costs slightly (less than 2%) higher than an optimal COG computed using “Euclidean” (straight-
line) distances. The latter, however, is a more complex and involved procedure mathematically, so the formulas we pre-
sent are generally used as an attractive substitute. See C. Kuo and R. E. White, “A Note on the Treatment of the
Center-of-Gravity Method in Operations Management Textbooks,” Decision Sciences Journal of Innovative Education 2
(Fall 2004): 219–227.
EXAMPLE 3 �
Center of gravity
Quain’s Discount Department Stores, a chain of four large Target-type outlets, has store locations in
Chicago, Pittsburgh, New York, and Atlanta; they are currently being supplied out of an old and inade-
quate warehouse in Pittsburgh, the site of the chain’s first store. The firm wants to find some “central”
location in which to build a new warehouse.
APPROACH � Quain will apply the center-of-gravity method. It gathers data on demand rates at
each outlet (see Table 8.5).
Store Location
Number of Containers
Shipped per Month
Chicago 2,000
Pittsburgh 1,000
New York 1,000
Atlanta 2,000
�TABLE 8.5
Demand for Quain’s Discount
Department Stores
LO5: Use the center-of-
gravity method

Its current store locations are shown in Figure 8.3. For example, location 1 is Chicago, and from Table 8.5
and Figure 8.3, we have:
SOLUTION � Using the data in Table 8.5 and Figure 8.3 for each of the other cities, and Equations
(8–1) and (8–2) we find:
x-coordinate of the center of gravity:
y-coordinate of the center of gravity:
This location (66.7, 93.3) is shown by the crosshairs in Figure 8.3.
INSIGHT � By overlaying a U.S. map on this exhibit, we find this location is near central Ohio.
The firm may well wish to consider Columbus, Ohio, or a nearby city as an appropriate location. But it
is important to have both North–South and East–West interstate highways near the city selected to
make delivery times quicker.
LEARNING EXERCISE � The number of containers shipped per month to Atlanta is expected
to grow quickly to 3,000. How does this change the center of gravity, and where should the new ware-
house be located? [Answer: (65.7, 85.7), which is closer to Cincinnati, Ohio.]
RELATED PROBLEMS � 8.20, 8.21, 8.22, 8.23
EXCEL OM Data File Ch08Ex3.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 8.1 This example is further illustrated in Active Model 8.1 at www.pearsonhighered.com/heizer.
= 93.3
=
11202120002 + 11102110002 + 11302110002 + 1402120002
2000 + 1000 + 1000 + 2000
=
560,000
6,000
= 66.7
=
1302120002 + 1902110002 + 11302110002 + 1602120002
2000 + 1000 + 1000 + 2000
=
400,000
6,000
Q1 = 2,000
d1y = 120
d1x = 30
Chapter 8 Location Strategies 263
30
60
90
120
30 60 90 120 150
Arbitrary
origin
North–South
East–West
Chicago (30, 120)
Pittsburgh (90, 110)
New York (130, 130)
Atlanta (60, 40)
Center of gravity (66.7, 93.3)
� FIGURE 8.3
Coordinate Locations of Four
Quain’s Department Stores
and Center of Gravity
Transportation Model
The objective of the transportation model is to determine the best pattern of shipments from
several points of supply (sources) to several points of demand (destinations) so as to minimize
total production and transportation costs. Every firm with a network of supply-and-demand
points faces such a problem. The complex Volkswagen supply network (shown in Figure 8.4)
Transportation model
A technique for solving a class
of linear programming
problems.

www.pearsonhighered.com/heizer

www.pearsonhighered.com/heizer

264 PART 2 Designing Operations
provides one such illustration. We note in Figure 8.4, for example, that VW de Mexico ships
vehicles for assembly and parts to VW of Nigeria, sends assemblies to VW do Brasil, and
receives parts and assemblies from headquarters in Germany.
Although the linear programming (LP) technique can be used to solve this type of problem,
more efficient, special-purpose algorithms have been developed for the transportation applica-
tion. The transportation model finds an initial feasible solution and then makes step-by-step
improvement until an optimal solution is reached.
SERVICE LOCATION STRATEGY
While the focus in industrial-sector location analysis is on minimizing cost, the focus in the ser-
vice sector is on maximizing revenue. This is because manufacturing firms find that costs tend to
vary substantially among locations, while service firms find that location often has more impact
on revenue than cost. Therefore, for the service firm, a specific location often influences revenue
more than it does cost. This means that the location focus for service firms should be on deter-
mining the volume of business and revenue. See the OM in Action box “Location Analysis Tools
Help Starbucks Brew Up New Cafes.”
Volkswagen
VW
Asia
Shanghai-
Volkswagen
VW of
South Africa
VW of
Nigeria
VW do Brasil
VW Argentina
VW de
Mexico
VW of
America
Volkswagen
VW
Bruxelles Audi
3
3
2,34
4
4
4
4
1
VW of
Canada
2,3
3,4
1,3,
4
1,3
3,4
1,3
4
4
2
3 2
,3
4
3
3
2,3
2
1 1,3
3
2
1,4
VW’s supply network
TAS
Tvornica
1 Finished vehicles
2 Vehicles for assembly
3 Parts
4 Assemblies (engines,
suspension units, etc.)
� FIGURE 8.4
Worldwide Distribution of
Volkswagens and Parts
Source: The Economist, Ltd. Distributed
by The New York Times/Special Edition.
AUTHOR COMMENT
Retail stores often attract
more shoppers when
competitors are close.
The secret to Starbucks Coffee’s plan to open three new
cafes around the world every day isn’t in the coffee beans–
it is in the location. The company’s phenomenal growth has
been fueled by site-selection software that strengthens the
strategic decision-making process. The analysis is as
follows: If a site’s potential is not within a certain ROI
parameter, the company doesn’t waste its time.
Every site-acquisition decision evaluates geocoded
demographic and consumer data. In the U.S., this is
simple. Data from geographic information systems provides
population, age, purchasing power, traffic counts, and
competition on virtually every block in the country. Planners
instantly see all the surrounding shops, proposed locations,
and competing sites. When Starbucks entered Japan and
China, the unavailability of these data was the biggest
challenge.
“In the U.S., if you see a mall, it will probably still be
there in two years,” says Ernest Luk, VP for Starbucks Asia-
Pacific. “A year passes by in a Chinese location, and you
almost won’t know your way around there anymore.” So a
team of “hot-spot” seekers traces the paths of where
potential customers live, work, and play. Although
Starbucks is a barely affordable luxury (at $2.65 for a
medium latte where the average income is $143 per month
in Shanghai), people don’t go for just the coffee. “They go
there to present themselves as modern Chinese in a public
setting. Chinese are proudly conspicuous,” says the North
Asia director of the ad firm J. Walter Thompson.
With more than 500 stores in Japan and reaching
saturation in key cities like Tokyo, Starbucks and its
competition are finding more innovative locations. New
cafes in a Nissan auto showroom, in office building lobbies,
and in supermarkets remind us that it all boils down to
location, location, location . . . determined by the latest
site-selection technology.
Sources: The Wall Street Journal (April 3, 2007): B1, (September 1, 2006):
A11–A12, and (July 29, 2005): C2; and SinoCast China Business Daily
News (September 21, 2005): 1.
OM in Action OMinActio� Location Analysis Tools Help Starbucks Brew Up New Cafes

Chapter 8 Location Strategies 265
There are eight major determinants of volume and revenue for the service firm:
1. Purchasing power of the customer-drawing area
2. Service and image compatibility with demographics of the customer-drawing area
3. Competition in the area
4. Quality of the competition
5. Uniqueness of the firm’s and competitors’ locations
6. Physical qualities of facilities and neighboring businesses
7. Operating policies of the firm
8. Quality of management
Realistic analysis of these factors can provide a reasonable picture of the revenue expected. The
techniques used in the service sector include correlation analysis, traffic counts, demographic
analysis, purchasing power analysis, the factor-rating method, the center-of-gravity method, and
geographic information systems. Table 8.6 provides a summary of location strategies for both ser-
vice and goods-producing organizations.
How Hotel Chains Select Sites
One of the most important decisions in the hospitality industry is location. Hotel chains that pick
good sites more accurately and quickly than competitors have a distinct strategic advantage.
La Quinta Corporation is a moderately priced chain of 590 motels oriented toward frequent busi-
ness travelers. To model motel-selection behavior and predict success of a site, La Quinta turned
to statistical regression analysis.4
The hotel started by testing 35 independent variables, trying to find which of them would have
the highest correlation with predicted profitability, the dependent variable. “Competitive” inde-
pendent variables included the number of hotel rooms in the vicinity and average room rates.
“Demand generator” variables were such local attractions as office buildings and hospitals that
drew potential customers to a 4-mile-radius trade area. “Demographic” variables, such as local
population and unemployment rate, can also affect the success of a hotel. “Market awareness”
factors, such as the number of inns in a region, were a fourth category. Finally, “physical charac-
teristics” of the site, such as ease of access or sign visibility, provided the last group of the
35 independent variables.
In the end, the regression model chosen, with a coefficient of determination ( ) of 51%,
included just four predictive variables. They are the price of the inn, median income levels, the
r2
Picking good sites for service operations such as fast-food restaurants and hotels is increasingly difficult
because of saturated markets. But opportunities still exist. Subway (on the left), with over 20,000 U.S.
outlets (vs. 13,700 for McDonald’s) has found success with “nontraditional” locations. True Bethel Baptist
Church in Buffalo, New York, now houses a Subway. Similarly, a kosher Subway just opened in the Jewish
Community Center of Cleveland. Good sites for hotels include those near hospitals and medical centers
(right photo). Outpatient care, shorter hospital stays, and more diagnostic tests increase this need to house
patients and their families.
4Sheryl Kimes and James Fitzsimmons, “Selecting Profitable Hotel Sites at La Quinta Motor Inns,” Interfaces
(March–April 1990): 12–20. Also see The Wall Street Journal (July 19, 1995): B1, B5, for a discussion of how
Amerihost Inns makes its location decisions.

266 PART 2 Designing Operations
state population per inn, and the location of nearby colleges (which serves as a proxy for other
demand generators). La Quinta then used the regression model to predict profitability and
developed a cutoff that gave the best results for predicting success or failure of a site. A spread-
sheet is now used to implement the model, which applies the decision rule and suggests “build”
or “don’t build.”
The Call Center Industry
Industries and office activities that require neither face-to-face contact with the customer nor
movement of material broaden location options substantially. A case in point is the call center
industry, in which the traditional variables are no longer relevant. Where inexpensive fiber-optic
phone lines are available, the cost and availability of labor may drive the location decision.
A decade or so ago, big U.S. companies started hiring call center staff in low-wage countries
like India to deal with customer contact jobs, such as product support, hotel reservations, and bill
collection. India’s highly educated, English-speaking workforce still attracts a large call center
business. But the Philippines, Mexico, Canada, Ireland, and small-town U.S. are increasingly
destinations of choice for matching employees and in-depth knowledge of American popular
culture. The VP of Client-Logic, Inc., a firm that sets up call centers for companies such as
DIRECTV, Sony, and TiVo, says “I’m looking for people who already know that Barbie’s
boyfriend is Ken.” He increasingly likes Monterrey, Mexico, because the town’s mall has an
American-style 13-screen Cineplex, which shows almost all Hollywood films—meaning locals
pick up U.S. slang, fashion trends, brands, and geography.5
How to use quantitative techniques to locate call centers is discussed in detail in Supplement 11.
LO6: Understand the
differences between
service- and industrial-
sector location analysis
5“Siting a Call Center? Check Out the Mall First.” The Wall Street Journal (July 3, 2006): B1, B3.
AUTHOR COMMENT
This table helps differentiate
between service- and
manufacturing-sector
decisions. Almost every
aspect of the decision is
different.
SERVICE/RETAIL/PROFESSIONAL GOODS-PRODUCING
Revenue Focus Cost Focus
Volume/revenue Tangible costs
Drawing area; purchasing power Transportation cost of raw material
Competition; advertising/pricing Shipment cost of finished goods
Physical quality
Parking/access; security/lighting;
Energy and utility cost; labor; raw material;
taxes, and so on
appearance/image Intangible and future costs
Cost determinants
Rent
Management caliber
Operation policies (hours, wage rates)
Attitude toward union
Quality of life
Education expenditures by state
Quality of state and local government
Techniques Techniques
Regression models to determine
importance of various factors
Transportation method
Factor-rating method
Factor-rating method Locational break-even analysis
Traffic counts Crossover charts
Demographic analysis of drawing area
Purchasing power analysis of area
Center-of-gravity method
Geographic information systems
Assumptions Assumptions
Location is a major determinant of revenue Location is a major determinant of cost
High customer-contact issues are critical
Costs are relatively constant for a given area;
Most major costs can be identified explicitly
for each site
therefore, the revenue function is critical Low customer contact allows focus on the
identifiable costs
Intangible costs can be evaluated
�TABLE 8.6
Location Strategies—Service
vs. Goods-Producing
Organizations

Chapter 8 Location Strategies 267
Geographic Information Systems
Geographic information systems are an important tool to help firms make successful, analytical
decisions with regard to location. A geographic information system (GIS) stores and displays
information that can be linked to a geographical location. For instance, retailers, banks, food
chains, gas stations, and print shop franchises can all use geographically coded files from a GIS
to conduct demographic analyses. By combining population, age, income, traffic flow, and den-
sity figures with geography, a retailer can pinpoint the best location for a new store or restaurant.
Here are some of the geographic databases available in many GISs:
• Census data by block, tract, city, county, congressional district, metropolitan area, state, zip code
• Maps of every street, highway, bridge, and tunnel in the U.S.
• Utilities such as electrical, water, and gas lines
• All rivers, mountains, lakes, forests
• All major airports, colleges, hospitals
For example, airlines use GISs to identify airports where ground services are the most effective.
This information is then used to help schedule and to decide where to purchase fuel, meals, and
other services.
Commercial office building developers use GISs in the selection of cities for future construc-
tion. Building new office space takes several years so developers value the database approach
that a GIS can offer. GIS is used to analyze factors that influence the location decisions by
addressing five elements for each city: (1) residential areas, (2) retail shops, (3) cultural and
entertainment centers, (4) crime incidence, and (5) transportation options. For example, one
study of Tampa, Florida, showed that the city’s central business district lacks the characteristics
to sustain a viable high-demand office market, suggesting that builders should look elsewhere.
Here are five more examples of how location-scouting GIS software is turning commercial
real estate into a science6:
• Carvel Ice Cream: This 73-year-old chain of ice cream shops uses GIS to create a demo-
graphic profile of what a typically successful neighborhood for a Carvel looks like—mostly
in terms of income and ages.
Geographic information
system (GIS)
A system that stores and
displays information that can be
linked to a geographic location.
6The Wall Street Journal (July 3, 2007): B1 and (July 18, 2005): R-7; and Business 2.0 (May 2004): 76–77.
Geographic information
systems (GISs) are used
by a variety of firms,
including Darden
Restaurants, to identify
target markets by income,
ethnicity, product use,
age, etc. Here, data from
MapInfo helps with
competitive analysis.
Three concentric blue
rings, each representing
various mile radii, were
drawn around the
competitor’s store. The
heavy red line indicates
the “drive” time to the
firm’s own central store
(the red dot).

268 PART 2 Designing Operations
• Saber Roofing: Rather than send workers out to estimate the costs for reroofing jobs, this
Redwood City, California, firm pulls up aerial shots of the building via Google Earth. The
owner can measure roofs, eyeball the conditions, and e-mail the client an estimate, saving
hundreds of miles of driving daily. In one case, while on the phone, a potential client was
told her roof was too steep for the company to tackle after the Saber employee quickly looked
up the home on Google Earth.
• Arby’s: As this fast-food chain learned, specific products can affect behavior. Using MapInfo,
Arby’s discovered that diners drove up to 20% farther for their roast beef sandwich (which
they consider a “destination” product) than for its chicken sandwich.
• Home Depot: Wanting a store in New York City, even though Home Depot demographics are
usually for customers who own big homes, the company opened in Queens when GIS soft-
ware predicted it would do well. Although most people there live in apartments and very
small homes, the store has become one of the chain’s highest-volume outlets. Similarly, Home
Depot thought it had saturated Atlanta two decades ago, but GIS analysis suggested expansion.
There are now over 40 Home Depots in that area.
• Jo-Ann Stores: This fabric and craft retailer’s 70 superstores were doing well a few years ago,
but managers were afraid more big-box stores could not justify building expenses. So Jo-Ann
used its GIS to create an ideal customer profile—female homeowners with families—and
mapped it against demographics. The firm found it could build 700 superstores, which in turn
increased the sales from $105 to $150 per square foot.
Other packages similar to MapInfo are Hemisphere Solutions (by Unisys Corp.), Atlas GIS
(from Strategic Mapping, Inc.), Arc/Info (by ESRI), SAS/GIS (by SAS Institute, Inc.), Market
Base (by National Decision Systems, Inc.), and MapPoint 2009 (by Microsoft).
To illustrate how extensive some of these GISs can be, consider Microsoft’s MapPoint 2009,
which includes a comprehensive set of map and demographic data. Its North American maps
have more than 6.4 million miles of streets and 1.9 million points of interest to allow users to
locate restaurants, airports, hotels, gas stations, ATMs, museums, campgrounds, and freeway
exits. Demographic data includes statistics for population, age, income, education, and housing
for 1980, 1990, 2000, and 2005. These data can be mapped by state, county, city, zip code, or
census tract. MapPoint 2009 produces maps that identify business trends; pinpoint market graph-
ics; locate clients, customers, and competitors; and visualize sales performance and product dis-
tribution. The European version of MapPoint includes 7.8 million kilometers of roads as well as
400,000 points of interest (see www.mapapps.net).
The Video Case Study “Locating the Next Red Lobster Restaurant” that appears in the Lecture
Guide & Activities Manual describes how that chain uses its GIS to define trade areas based on
market size and population density.
VIDEO 8.2
Locating the Next Red Lobster
Restaurant
Location may determine up to 50% of operating expense.
Location is also a critical element in determining revenue
for the service, retail, or professional firm. Industrial firms
need to consider both tangible and intangible costs.
Industrial location problems are typically addressed via a
factor-rating method, locational break-even analysis, the
center-of-gravity method, and the transportation method of
linear programming.
For service, retail, and professional
organizations, analysis is typically made
of a variety of variables including pur-
chasing power of a drawing area, compe-
tition, advertising and promotion, physical
qualities of the location, and operating
policies of the organization.
CHAPTER SUMMARY
Key Terms
Tangible costs (p. 257)
Intangible costs (p. 257)
Clustering (p. 258)
Factor-rating method (p. 259)
Locational break-even analysis (p. 260)
Center-of-gravity method (p. 262)
Transportation model (p. 263)
Graphical information
system (GIS) (p. 267)

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Chapter 8 Location Strategies 269
Using Software to Solve Location Problems
This section presents three ways to solve location problems with computer software. First, you can create
your own spreadsheets to compute factor ratings, the center of gravity, and break-even analysis. Second,
Excel OM (free with your text and found at our website) is programmed to solve all three models. Third,
POM for Windows is also found at www.pearsonhighered.com/heizer and can solve all problems
labelled with a P.
Creating Your Own Excel Spreadsheets
Excel (and other spreadsheets) are easily developed to solve most of the problems in this chapter. We do
not provide an example here, but you can see from Program 8.1 how the formulas are created.
X Using Excel OM
Excel OM may be used to solve Example 1 (with the Factor Rating module), Example 2 (with the
Break-Even Analysis module), and Example 3 (with the Center-of-Gravity module), as well as other
location problems. To illustrate the factor-rating method, consider the case of Five Flags over Florida
(Example 1), which wishes to expand its corporate presence to Europe. Program 8.1 provides the data
inputs for five important factors, including their weights, and ratings on a 1–100 scale (where 100 is the
highest rating) for each country. As we see, France is more highly rated, with a 70.4 score versus 68.0
for Denmark.
P Using POM for Windows
POM for Windows also includes three different facility location models: the factor-rating method, the
center-of-gravity model, and locational break-even analysis. For details, refer to Appendix IV.
Enter factor names and weights
in columns A and B.
Enter scores (that come from
manager ratings) for France and
Denmark on each factor in
columns C and D.
Although not a requirement of the procedure, choosing
weights that sum to 1 makes it easier to communicate the
decision process to others involved. = SUM(B8:B12) In this
case, since the weights sum to 1, the weighted sum and
weighted average are identical.
Compute the weighted scores as the product of the weights
and the scores for each city using the SUMPRODUCT
function. = SUMPRODUCT ($B$8:$B$12, D8)
� PROGRAM 8.1
Excel OM’s Factor Rating
Module, Including Inputs,
Selected Formulas, and
Outputs Using Five Flags
over Florida Data in
Example 1

www.pearsonhighered.com/heizer

270 PART 2 Designing Operations
Solved Problems Virtual Office Hours help is available at www.myomlab.com
� SOLVED PROBLEM 8.1
Just as cities and communities can be compared for location selec-
tion by the weighted approach model, as we saw earlier in this chap-
ter, so can actual site decisions within those cities. Table 8.7
illustrates four factors of importance to Washington, DC, and the
health officials charged with opening that city’s first public drug
treatment clinic. Of primary concern (and given a weight of 5) was
location of the clinic so it would be as accessible as possible to the
largest number of patients. Due to a tight budget, the annual lease
cost was also of some concern. A suite in the city hall, at 14th and U
Streets, was highly rated because its rent would be free. An old
office building near the downtown bus station received a much
lower rating because of its cost. Equally important as lease cost was
the need for confidentiality of patients and, therefore, for a relatively
inconspicuous clinic. Finally, because so many of the staff at the
clinic would be donating their time, the safety, parking, and accessi-
bility of each site were of concern as well.
Using the factor-rating method, which site is preferred?
� SOLUTION
From the three rightmost columns in Table 8.7, the weighted
scores are summed. The bus terminal area has a low score and can
be excluded from further consideration. The other two sites are
virtually identical in total score. The city may now want to con-
sider other factors, including political ones, in selecting between
the two remaining sites.
� TABLE 8.7 Potential Clinic Sites in Washington, DC
Potential Locationsa
Factor
Importance
Weight
Homeless
Shelter
(2nd and
D, SE)
City
Hall
(14th
and U,
NW)
Bus
Terminal
Area (7th
and H,
NW)
Weighted Scores
Accessibility for addicts 5 9 7 7 45 35 35
Annual lease cost 3 6 10 3 18 30 9
Inconspicuous 3 5 2 7 15 6 21
Accessibility for health staff 2 3 6 2 6 12 4
Total scores: 84 83 69
Homeless
Shelter
City
Hall
Bus
Terminal
Area
a All sites are rated on a 1 to 10 basis, with 10 as the highest score and 1 as the lowest.
Source: From Service Management and Operations, 2/e, by Haksever/Render/Russell/Murdick, p. 266. Copyright © 2000. Reprinted by permission of Prentice Hall, Inc.,
Upper Saddle River, NJ.
� SOLVED PROBLEM 8.2
Ching-Chang Kau is considering opening a new foundry in
Denton, Texas; Edwardsville, Illinois; or Fayetteville, Arkansas,
to produce high-quality rifle sights. He has assembled the follow-
ing fixed-cost and variable-cost data:
Per-Unit Costs
Fixed Cost Variable
Location per Year Material Labor Overhead
Denton $200,000 $ .20 $ .40 $ .40
Edwardsville $180,000 $ .25 $ .75 $ .75
Fayetteville $170,000 $1.00 $1.00 $1.00
a) Graph the total cost lines.
b) Over what range of annual volume is each facility going to
have a competitive advantage?
c) What is the volume at the intersection of the Edwardsville
and Fayetteville cost lines?
� SOLUTION
(a) A graph of the total cost lines is shown in Figure 8.5.
(b) Below 8,000 units, the Fayetteville facility will have a
competitive advantage (lowest cost); between 8,000 units
and 26,666 units, Edwardsville has an advantage; and above
26,666, Denton has the advantage. (We have made the assump-
tion in this problem that other costs—that is, delivery and
intangible factors—are constant regardless of the decision.)
(c) From Figure 8.5, we see that the cost line for Fayetteville and
the cost line for Edwardsville cross at about 8,000. We can
also determine this point with a little algebra:
8,000 = Q
$10,000 = 1.25Q
$180,000 + 1.75Q = $170,000 + 3.00Q

www.myomlab.com

Chapter 8 Location Strategies 271
$250,000
0 5,000
Fayetteville
lowest
cost
Edwardsville
lowest cost
Units (or rifle sights)
T
o
ta
l c
o
st
Denton
lowest cost
10,000 15,000 20,000 25,000 30,000 35,000
$225,000
$200,000
$175,000
$150,000
0
8,000 26,666
Denton
Edwa
rdsvil
le
Fa
yet
tev
ille
� FIGURE 8.5
Graph of Total Cost Lines for Ching-Chang Kau
Bibliography
Ballou, Ronald H. Business Logistics Management, 5th ed. Upper
Saddle River, NJ: Prentice Hall, 2004.
Bartness, A. D. “The Plant Location Puzzle.” Harvard Business
Review 72, no. 2 (March–April 1994).
Denton, B. “Decision Analysis, Location Models, and Scheduling
Problems.” Interfaces 30, no. 3 (May–June 2005): 262–263.
Drezner, Z. Facility Location: Applications and Theory. Berlin:
Springer-Verlag, 2002.
Florida, R. The Flight of the Creative Class: The New Global
Competition for Talent. New York: HarperCollins, 2005.
Klamroth, K. Single Facility Location Problems. Berlin: Springer-
Verlag, 2002.
Kennedy, M. Introducing Geographic Information Systems with
ArcGIS. New York: Wiley, 2006.
Mentzer, John T. “Seven Keys to Facility Location.” Supply Chain
Management Review 12, no. 5 (May 2008): 25.
Partovi, F. Y. “An Analytic Model for Locating Facilities
Strategically.” Omega 34, no. 1 (January 2006): 41.
Porter, Michael E., and Scott Stern. “Innovation: Location Matters.”
MIT Sloan Management Review (Summer 2001): 28–36.
Render, B., R. M. Stair, and M. Hanna. Quantitative Analysis for
Management, 10th ed. Upper Saddle River, NJ: Prentice Hall,
2009.
Snyder, L. V. “Facility Location Under Uncertainty.” IIE
Transactions 38, no. 7 (July 2006): 547.
Tallman, Stephen, et al. “Knowledge, Clusters, and Competitive
Advantage.” The Academy of Management Review 29, no. 2
(April 2004): 258–271.
White, G. “Location, Location, Location.” Nation’s Restaurant
News 42, no. 27 (July 14, 2008): S10–S11.
�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Southwestern University (E): The university faces three choices where to locate its football stadium.

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Chapter Outline
GLOBAL COMPANY PROFILE: MCDONALD’S
The Strategic Importance of Layout
Decisions 276
Types of Layout 276
Office Layout 278
Retail Layout 279
Warehousing and Storage Layouts 281
Fixed-Position Layout 282
Process-Oriented Layout 283
Work Cells 288
Repetitive and Product-Oriented
Layout 292
Layout Strategies
273
� Design of Goods and Services
� Managing Quality
� Process Strategy
� Location Strategies
� Layout Strategies
� Human Resources
� Supply-Chain Management
� Inventory Management
� Scheduling
� Maintenance

274
GLOBAL COMPANY PROFILE: McDONALD’S
I
n its half-century of existence, McDonald’s
revolutionized the restaurant industry by inventing
the limited-menu fast-food restaurant. It has also
made seven major innovations. The first, the
introduction of indoor seating (1950s), was a layout
issue, as was the second, drive-through windows
(1970s). The third, adding breakfasts to the menu
(1980s), was a product strategy. The fourth, adding
play areas (late 1980s), was again a layout decision.
In the 1990s, McDonald’s completed its fifth
innovation, a radically new redesign of the kitchens in
its 14,000 North America outlets to facilitate a mass
customization process. Dubbed the “Made by You”
kitchen system, sandwiches were assembled to order
with the revamped layout.
In 2004, the chain began the rollout of its sixth
innovation, a new food ordering layout: the self-service
kiosk. Self-service kiosks have been infiltrating the
service sector since the introduction of ATMs in 1985
(there are over 1.5 million ATMs in banking). Alaska
McDONALD’S LOOKS FOR COMPETITIVE ADVANTAGE THROUGH LAYOUT
Airlines was the first airline to provide self-service
airport check-in, in 1996. Most passengers of the
major airlines now check themselves in for flights.
Kiosks take up less space than an employee and
reduce waiting line time.
Now, McDonald’s is working on its seventh
innovation, and not surprisingly, it also deals with
restaurant layout. The company, on an unprecedented
scale, is redesigning all 30,000 eateries around the
globe to take on a 21st century look. The dining area
will be separated into three sections with distinct
personalities: (1) the “linger” zone focuses on young
adults and offers comfortable furniture and Wi-Fi
connections; (2) the “grab and go” zone features tall
counters, bar stools, and plasma TVs; and (3) the
“flexible” zone has colorful family booths, flexible
seating, and kid-oriented music. The cost per outlet:
a whopping $300,000–$400,000 renovation fee.
As McDonald’s has discovered, facility layout is
indeed a source of competitive advantage.
McDonald’s finds that kiosks reduce both space requirements and waiting; order taking is faster. An added benefit is that
customers like them. Also, kiosks are reliable—they don’t call in sick. And, most importantly, sales are up 10%–15% (an
average of $1) when a customer orders from a kiosk, which consistently recommends the larger size and other extras.

Linger Zone �
Cozy armchairs and sofas,
plus Wi-Fi connections,
make these areas attractive
to those who want to hang
out and socialize.
Flexible Zone �
Booths with colorful
fabric cushions make
up the area geared to
family and larger
groups. Tables and
chairs are movable.
Grab & Go Zone �
This section has tall counters
with bar stools for customers
who eat alone. Plasma TVs
keep them company.
MCDONALD’S �
275
� The redesigned kitchen of a
McDonald’s in Manhattan. The more
efficient layout requires less labor,
reduces waste, and provides faster
service. A graphic of this “assembly
line” is shown in Figure 9.12

276 PART 2 Designing Operations
THE STRATEGIC IMPORTANCE OF LAYOUT DECISIONS
Layout is one of the key decisions that determines the long-run efficiency of operations. Layout has
numerous strategic implications because it establishes an organization’s competitive priorities in
regard to capacity, processes, flexibility, and cost, as well as quality of work life, customer contact,
and image. An effective layout can help an organization achieve a strategy that supports differenti-
ation, low cost, or response. Benetton, for example, supports a differentiation strategy by heavy
investment in warehouse layouts that contribute to fast, accurate sorting and shipping to its 5,000
outlets. Wal-Mart store layouts support a strategy of low cost, as do its warehouse layouts.
Hallmark’s office layouts, where many professionals operate with open communication in work
cells, support rapid development of greeting cards. The objective of layout strategy is to develop an
effective and efficient layout that will meet the firm’s competitive requirements.These firms have
done so.
In all cases, layout design must consider how to achieve the following:
• Higher utilization of space, equipment, and people
• Improved flow of information, materials, or people
• Improved employee morale and safer working conditions
• Improved customer/client interaction
• Flexibility (whatever the layout is now, it will need to change).
In our increasingly short-life-cycle, mass-customized world, layout designs need to be viewed as
dynamic.This means considering small, movable, and flexible equipment. Store displays need to
be movable, office desks and partitions modular, and warehouse racks prefabricated. To make
quick and easy changes in product models and in production rates, operations managers must
design flexibility into layouts. To obtain flexibility in layout, managers cross train their workers,
maintain equipment, keep investments low, place workstations close together, and use small,
movable equipment. In some cases, equipment on wheels is appropriate, in anticipation of the
next change in product, process, or volume.
TYPES OF LAYOUT
Layout decisions include the best placement of machines (in production settings), offices and
desks (in office settings), or service centers (in settings such as hospitals or department stores).
An effective layout facilitates the flow of materials, people, and information within and between
areas. To achieve these objectives, a variety of approaches has been developed. We will discuss
seven of them in this chapter:
1. Office layout: Positions workers, their equipment, and spaces/offices to provide for move-
ment of information.
2. Retail layout: Allocates shelf space and responds to customer behavior.
3. Warehouse layout: Addresses trade-offs between space and material handling.
4. Fixed-position layout: Addresses the layout requirements of large, bulky projects such as
ships and buildings.
5. Process-oriented layout: Deals with low-volume, high-variety production (also called “job
shop,” or intermittent production).
LO1: Discuss important issues in
office layout 278
LO2: Define the objectives of retail layout 279
LO3: Discuss modern warehouse
management and terms such as ASRS,
cross-docking, and random stocking 281
LO4: Identify when fixed-position
layouts are appropriate 283
Chapter 9 Learning Objectives
LO5: Explain how to achieve a good
process-oriented facility layout 284
LO6: Define work cell and the
requirements of a work cell 288
LO7: Define product-oriented layout 292
LO8: Explain how to balance
production flow in a repetitive or
product-oriented facility 293

Chapter 9 Layout Strategies 277
6. Work-cell layout: Arranges machinery and equipment to focus on production of a single
product or group of related products.
7. Product-oriented layout: Seeks the best personnel and machine utilization in repetitive or
continuous production.
Examples for each of these classes of layouts are noted in Table 9.1.
Because only a few of these seven classes can be modeled mathematically, layout and design
of physical facilities are still something of an art. However, we do know that a good layout
requires determining the following:
• Material handling equipment: Managers must decide about equipment to be used, including
conveyors, cranes, automated storage and retrieval systems, and automatic carts to deliver and
store material.
• Capacity and space requirements: Only when personnel, machines, and equipment require-
ments are known can managers proceed with layout and provide space for each component. In
the case of office work, operations managers must make judgments about the space require-
� TABLE 9.1 Layout Strategies
Objectives Examples
Office Locate workers requiring frequent
contact close to one another
Allstate Insurance
Microsoft Corp.
Retail Expose customer to high-margin
items
Kroger’s Supermarket
Walgreen’s
Bloomingdale’s
Warehouse (storage) Balance low-cost storage with
low cost material handling
Federal-Mogul’s warehouse
The Gap’s distribution center
Project (fixed position) Move material to the limited storage
areas around the site
Ingall Ship Building Corp.
Trump Plaza
Pittsburgh Airport
Job Shop (process
oriented)
Manage varied material flow for
each product
Arnold Palmer Hospital
Hard Rock Cafe
Olive Garden
Work Cell (product
families)
Identify a product family, build
teams, cross train team members
Hallmark Cards
Wheeled Coach
Standard Aero
Repetitive/Continuous
(product oriented)
Equalize the task time at each
workstation
Sony’s TV assembly line
Toyota Scion
This open office offers a large shared
space that encourages employees to
interact. Before Steelcase, the office
furniture maker, went to an open
office system, 80% of its office space
was private; now it is just 20%
private. The CEO even went from a
private 700-square-foot office to a
48-square-foot enclosure in an open
area. This dramatically increases
unplanned and spontaneous
communication between employees.

278 PART 2 Designing Operations
ments for each employee. It may be a -foot cubicle plus allowance for hallways, aisles,
rest rooms, cafeterias, stairwells, elevators, and so forth, or it may be spacious executive offices
and conference rooms. Management must also consider allowances for requirements that
address safety, noise, dust, fumes, temperature, and space around equipment and machines.
• Environment and aesthetics: Layout concerns often require decisions about windows, planters,
and height of partitions to facilitate air flow, reduce noise, provide privacy, and so forth.
• Flows of information: Communication is important to any organization and must be facili-
tated by the layout. This issue may require decisions about proximity as well as decisions
about open spaces versus half-height dividers versus private offices.
• Cost of moving between various work areas: There may be unique considerations related to
moving materials or to the importance of having certain areas next to each other. For example,
moving molten steel is more difficult than moving cold steel.
OFFICE LAYOUT
Office layouts require the grouping of workers, their equipment, and spaces to provide for com-
fort, safety, and movement of information. The main distinction of office layouts is the impor-
tance placed on the flow of information. Office layouts are in constant flux as the technological
change sweeping society alters the way offices function.
Even though the movement of information is increasingly electronic, analysis of office layouts
still requires a task-based approach. Paper correspondence, contracts, legal documents, confidential
patient records, and hard-copy scripts, artwork, and designs still play a major role in many offices.
Managers therefore examine both electronic and conventional communication patterns, separation
needs, and other conditions affecting employee effectiveness. A useful tool for such an analysis is
the relationship chart shown in Figure 9.1. This chart, prepared for an office of product designers,
indicates that the chief marketing officer must be (1) near the designers’ area, (2) less near the sec-
retary and central files, and (3) not at all near the copy center or accounting department.
General office-area guidelines allot an average of about 100 square feet per person (including
corridors). A major executive is allotted about 400 square feet, and a conference room area is
based on 25 square feet per person.
On the other hand, some layout considerations are universal (many of which apply to factories
as well as to offices). They have to do with working conditions, teamwork, authority, and status.
Should offices be private or open cubicles, have low file cabinets to foster informal communica-
tion or high cabinets to reduce noise and contribute to privacy? (See the Steelcase photo on the
previous page). Should all employees use the same entrance, rest rooms, lockers, and cafeteria?
As mentioned earlier, layout decisions are part art and part science.
As a final comment on office layout, we note two major trends. First, technology, such as cell
phones, iPods, faxes, the Internet, laptop computers, and PDAs, allows increasing layout flexibil-
ity by moving information electronically and allowing employees to work offsite. Second, mod-
ern firms create dynamic needs for space and services.
6 * 6
CLOSENESSValue
A
E
I
O
U
X
Absolutely
necessary
Especially
important
Important
Ordinary
OK
Unimportant
Not desirable
1 CEO
2 Chief marketing officer
3 Designers’ area
4 Secretary
5 Sales area
6 Central files
7 Computer services
8 Copy center
9 Accounting
1
2
3
4
5
6
7
8
9
A
A
A
A
A
A
E
E
E
E
E
I
I
I
I
I
I
I
I
O
OO
O
O
O
O
O
U
U
U
U
U
U
U
X
X
� FIGURE 9.1
Office Relationship Chart
Source: Adapted from Richard Muther,
Simplified Systematic Layout Planning,
3rd ed. (Kansas City, Mgt. & Ind’l
Research Publications). Used by
permission of the publisher.
LO1: Discuss important
issues in office layout
Office layout
The grouping of workers, their
equipment, and spaces/offices
to provide for comfort, safety,
and movement of information.

Chapter 9 Layout Strategies 279
1“Square Feet. Oh, How Square!” Business Week (July 3, 2006): 100–101.
Here are two examples:1
• When Deloitte & Touche found that 30% to 40% of desks were empty at any given time, the firm
developed its “hoteling programs.” Consultants lost their permanent offices; anyone who plans to
be in the building (rather than out with clients) books an office through a “concierge,” who hangs
that consultant’s name on the door for the day and stocks the space with requested supplies.
• Cisco Systems cut rent and workplace service costs by 37% and saw productivity benefits of
$2.4 billion per year by reducing square footage, reconfiguring space, creating movable,
everything-on-wheels offices, and designing “get away from it all” innovation areas.
RETAIL LAYOUT
Retail layouts are based on the idea that sales and profitability vary directly with customer expo-
sure to products. Thus, most retail operations managers try to expose customers to as many prod-
ucts as possible. Studies do show that the greater the rate of exposure, the greater the sales and
the higher the return on investment. The operations manager can change exposure with store
arrangement and the allocation of space to various products within that arrangement.
Five ideas are helpful for determining the overall arrangement of many stores:
1. Locate the high-draw items around the periphery of the store. Thus, we tend to find dairy
products on one side of a supermarket and bread and bakery products on another. An exam-
ple of this tactic is shown in Figure 9.2.
2. Use prominent locations for high-impulse and high-margin items. Best Buy puts fast-growing,
high-margin digital goods—such as cameras and DVDs—in the front and center of its stores.
3. Distribute what are known in the trade as “power items”—items that may dominate a purchas-
ing trip—to both sides of an aisle, and disperse them to increase the viewing of other items.
4. Use end-aisle locations because they have a very high exposure rate.
5. Convey the mission of the store by carefully selecting the position of the lead-off depart-
ment. For instance, if prepared foods are part of a supermarket’s mission, position the bak-
ery and deli up front to appeal to convenience-oriented customers. Wal-Mart’s push to
increase sales of clothes means those departments are in broad view upon entering a store.
Once the overall layout of a retail store has been decided, products need to be arranged for
sale. Many considerations go into this arrangement. However, the main objective of retail layout
is to maximize profitability per square foot of floor space (or, in some stores, on linear foot of
shelf space). Big-ticket, or expensive, items may yield greater dollar sales, but the profit per
square foot may be lower. Computerized programs are available to assist managers in evaluating
the profitability of various merchandising plans for hundreds of categories: this technique is
known as category management.
An additional, and somewhat controversial, issue in retail layout is called slotting. Slotting
fees are fees manufacturers pay to get their goods on the shelf in a retail store or supermarket
Retail layout
An approach that addresses
flow, allocates space, and
responds to customer behavior
LO2: Define the objectives
of retail layout
WINE
BEER
DELI
FAST
FOOD
VIDEO
BAKERY
W
A
L
L
O
F
V
A
L
U
E
S
PHOTO
LAB
DAIRY
PRODUCE
MEAT/FISH
CHEESE
SEA
FOOD
ETHNIC
FOOD
CHECKSTANDS
� FIGURE 9.2
Store Layout with Dairy and
Bakery, High-Draw Items, in
Different Areas of the Store
AUTHOR COMMENT
The goal in a retail layout is to
maximize profit per square
foot of store space.
Slotting fees
Fees manufacturers pay to get
shelf space for their products.

280 PART 2 Designing Operations
Trying to penetrate urban areas that
have lofty land prices and strong
antidevelopment movements, Wal-
Mart is changing its layout to up, not
out. A new generation of multi-level
stores take only one-third the space
of the traditional 25-acre swaths.
Here, in the El Cajon, California,
store, Wal-Mart trained workers to
help shoppers confused by the
device next to the escalator that
carries shopping carts from one floor
to another.
chain. The result of massive new-product introductions, retailers can now demand up to $25,000
to place an item in their chain. During the last decade, marketplace economics, consolidations,
and technology have provided retailers with this leverage. The competition for shelf space is
advanced by POS systems and scanner technology, which improve supply-chain management and
inventory control. Many small firms question the legality and ethics of slotting fees, claiming the
fees stifle new products, limit their ability to expand, and cost consumers money. Wal-Mart is one of
the few major retailers that does not demand slotting fees. This removes the barrier to entry that
small companies usually face. (See the Ethical Dilemma in the Lecture Guide & Activities Manual.)
Servicescapes
Although the main objective of retail layout is to maximize profit through product exposure, there
are other aspects of the service that managers consider. The term servicescape describes the physi-
cal surroundings in which the service is delivered and how the surroundings have a humanistic effect
on customers and employees. To provide a good service layout, a firm considers three elements:
1. Ambient conditions, which are background characteristics such as lighting, sound, smell,
and temperature. All these affect workers and customers and can affect how much is spent
and how long a person stays in the building.
2. Spatial layout and functionality, which involve customer circulation path planning, aisle
characteristics (such as width, direction, angle, and shelf spacing), and product grouping.
3. Signs, symbols, and artifacts, which are characteristics of building design that carry social
significance (such as carpeted areas of a department store that encourage shoppers to slow
down and browse).
Servicescape
The physical surroundings in
which a service takes place, and
how they affect customers and
employees.
A critical element contributing to the
bottom line at Hard Rock Cafe is
the layout of each cafe’s retail shop
space. The retail space, from 600 to
1,300 square feet in size, is laid out
in conjunction with the restaurant
area to create the maximum traffic
flow before and after eating. The
payoffs for cafes like this one in
London are huge. Almost half of a
cafe’s annual sales are generated
from these small shops, which have
very high retail sales per square foot.

Chapter 9 Layout Strategies 281
Examples of each of these three elements of servicescape are:
• Ambient conditions: Fine-dining restaurants with linen tablecloths and candlelit atmosphere;
Mrs. Field’s Cookie bakery smells permeating the shopping mall; leather chairs at Starbucks.
• Layout/functionality: Kroger’s long aisles and high shelves; Best Buy’s wide center aisle.
• Signs, symbols, and artifacts: Wal-Mart’s greeter at the door; Hard Rock Cafe’s wall of guitars;
Disneyland’s entrance looking like hometown heaven.
WAREHOUSING AND STORAGE LAYOUTS
The objective of warehouse layout is to find the optimum trade-off between handling cost and costs
associated with warehouse space. Consequently, management’s task is to maximize the utilization of
the total “cube” of the warehouse—that is, utilize its full volume while maintaining low material han-
dling costs. We define material handling costs as all the costs related to the transaction. This consists
of incoming transport, storage, and outgoing transport of the materials to be warehoused. These costs
include equipment, people, material, supervision, insurance, and depreciation. Effective warehouse
layouts do, of course, also minimize the damage and spoilage of material within the warehouse.
Management minimizes the sum of the resources spent on finding and moving material plus
the deterioration and damage to the material itself. The variety of items stored and the number of
items “picked” has direct bearing on the optimum layout. A warehouse storing a few unique
items lends itself to higher density than a warehouse storing a variety of items. Modern ware-
house management is, in many instances, an automated procedure using automated storage and
retrieval systems (ASRSs).
The Stop & Shop grocery chain, with 350 supermarkets in New England, has recently com-
pleted the largest ASRS in the world. The 1.3-million-square-foot distribution center in
Freetown, Massachusetts, employs 77 rotating-fork automated storage and retrieval machines.
These 77 cranes each access 11,500 pick slots on 90 aisles—a total of 64,000 pallets of food. The
Wolfsburg, Germany parking garage photo (below) indicates that an ASRS can take many forms.
An important component of warehouse layout is the relationship between the receiving/
unloading area and the shipping/loading area. Facility design depends on the type of supplies
unloaded, what they are unloaded from (trucks, rail cars, barges, and so on), and where they are
unloaded. In some companies, the receiving and shipping facilities, or docks, as they are called,
are even in the same area; sometimes they are receiving docks in the morning and shipping docks
in the afternoon.
Warehouse layout
A design that attempts to
minimize total cost by
addressing trade-offs between
space and material handing.
Automated storage and retrieval systems
are not found only in traditional warehouses.
This parking garage in Wolfsburg, Germany,
occupies only 20% of the space of a
traditionally designed garage. The ASRS
“retrieves” autos in less time, without the
potential of the cars being damaged by an
attendant.
AUTHOR COMMENT
In warehouse layout, we want
to maximize use of the whole
building—from floor to
ceiling.
LO3: Discuss modern
warehouse management and
terms such as ASRS, cross-
docking, and random
stocking

282 PART 2 Designing Operations
Cross-Docking
Cross-docking means to avoid placing materials or supplies in storage by processing them as
they are received. In a manufacturing facility, product is received directly to the assembly line. In
a distribution center, labeled and presorted loads arrive at the shipping dock for immediate
rerouting, thereby avoiding formal receiving, stocking/storing, and order-selection activities.
Because these activities add no value to the product, their elimination is 100% cost savings. Wal-
Mart, an early advocate of cross-docking, uses the technique as a major component of its contin-
uing low-cost strategy. With cross-docking, Wal-Mart reduces distribution costs and speeds
restocking of stores, thereby improving customer service. Although cross-docking reduces
product handling, inventory, and facility costs, it requires both (1) tight scheduling and (2)
accurate inbound product identification.
Random Stocking
Automatic identification systems (AISs), usually in the form of bar codes, allow accurate
and rapid item identification. When automatic identification systems are combined with
effective management information systems, operations managers know the quantity and
location of every unit. This information can be used with human operators or with auto-
matic storage and retrieval systems to load units anywhere in the warehouse—randomly.
Accurate inventory quantities and locations mean the potential utilization of the whole facil-
ity because space does not need to be reserved for certain stock-keeping units (SKUs) or part fam-
ilies. Computerized random stocking systems often include the following tasks:
1. Maintaining a list of “open” locations
2. Maintaining accurate records of existing inventory and its locations
3. Sequencing items to minimize the travel time required to “pick” orders
4. Combining orders to reduce picking time
5. Assigning certain items or classes of items, such as high-usage items, to particular ware-
house areas so that the total distance traveled within the warehouse is minimized
Random stocking systems can increase facility utilization and decrease labor cost, but they
require accurate records.
Customizing
Although we expect warehouses to store as little product as possible and hold it for as short a
time as possible, we are now asking warehouses to customize products. Warehouses can be
places where value is added through customizing. Warehouse customization is a particularly
useful way to generate competitive advantage in markets where products have multiple configu-
rations. For instance, a warehouse can be a place where computer components are put together,
software loaded, and repairs made. Warehouses may also provide customized labeling and pack-
aging for retailers so items arrive ready for display.
Increasingly, this type of work goes on adjacent to major airports, in facilities such as the
FedEx terminal in Memphis. Adding value at warehouses adjacent to major airports also facili-
tates overnight delivery. For example, if your computer has failed, the replacement may be sent to
you from such a warehouse for delivery the next morning. When your old machine arrives back at
the warehouse, it is repaired and sent to someone else. These value-added activities at “quasi-
warehouses” contribute to strategies of differentiation, low cost, and rapid response.
FIXED-POSITION LAYOUT
In a fixed-position layout, the project remains in one place and workers and equipment come to
that one work area. Examples of this type of project are a ship, a highway, a bridge, a house, and
an operating table in a hospital operating room.
The techniques for addressing the fixed-position layout are complicated by three factors.
First, there is limited space at virtually all sites. Second, at different stages of a project, different
materials are needed; therefore, different items become critical as the project develops. Third, the
volume of materials needed is dynamic. For example, the rate of use of steel panels for the hull
of a ship changes as the project progresses.
Random stocking
Used in warehousing to locate
stock wherever there is an open
location.
INBOUND
OUTBOUND
No
delay
No
storage
System in
place for information
exchange and product
movement
Customizing
Using warehousing to add value
to a product through component
modification, repair, labeling,
and packaging.
Fixed-position layout
A system that addresses the
layout requirements of
stationary projects.
AUTHOR COMMENT
Fixed-position layout brings
all the workers and materials
to the project’s site.
Cross-docking
Avoiding the placement of
materials or supplies in storage
by processing them as they are
received for shipment.

Chapter 9 Layout Strategies 283
Because problems with fixed-position layouts are so difficult to solve well onsite, an alter-
native strategy is to complete as much of the project as possible offsite. This approach is used
in the shipbuilding industry when standard units—say, pipe-holding brackets—are assembled
on a nearby assembly line (a product-oriented facility). In an attempt to add efficiency to ship-
building, Ingall Ship Building Corporation has moved toward product-oriented production
when sections of a ship (modules) are similar or when it has a contract to build the same sec-
tion of several similar ships. Also, as the top photo on the page shows, many home builders are
moving from a fixed-position layout strategy to one that is more product oriented. About one-
third of all new homes in the U.S. are built this way. In addition, many houses that are built
onsite (fixed position) have the majority of components such as doors, windows, fixtures,
trusses, stairs, and wallboard built as modules with more efficient offsite processes.
PROCESS-ORIENTED LAYOUT
A process-oriented layout can simultaneously handle a wide variety of products or services. This
is the traditional way to support a product differentiation strategy. It is most efficient when making
products with different requirements or when handling customers, patients, or clients with different
Here are three versions of the fixed-position
layout.
A house built via traditional fixed-position layout would be
constructed onsite, with equipment, materials, and workers brought
to the site. Then a “meeting of the trades” would assign space for
various time periods. However, the home pictured here can be built
at a much lower cost. The house is built in two movable modules in
a factory. Scaffolding and hoists make the job easier, quicker, and
cheaper, and the indoor work environment aids labor productivity.
A service example of a fixed-position layout is an operating room;
the patient remains stationary on the table, and medical personnel
and equipment are brought to the site.
In shipbuilding, there is limited space next to the fixed-position
layout. Shipyards call these loading areas platens, and they are
assigned for various time periods to each contractor.
LO4: Identify when fixed-
position layouts are
appropriate
Process-oriented layout
A layout that deals with low-
volume, high-variety production
in which like machines and
equipment are grouped together.

284 PART 2 Designing Operations
needs. A process-oriented layout is typically the low-volume, high-variety strategy discussed in
Chapter 7. In this job-shop environment, each product or each small group of products undergoes a
different sequence of operations. A product or small order is produced by moving it from one
department to another in the sequence required for that product. A good example of the process-
oriented layout is a hospital or clinic. Figure 9.3 illustrates the process for two patients, A and B, at
an emergency clinic in Chicago. An inflow of patients, each with his or her own needs, requires
routing through admissions, laboratories, operating rooms, radiology, pharmacies, nursing beds,
and so on. Equipment, skills, and supervision are organized around these processes.
A big advantage of process-oriented layout is its flexibility in equipment and labor assign-
ments. The breakdown of one machine, for example, need not halt an entire process; work can be
transferred to other machines in the department. Process-oriented layout is also especially good
for handling the manufacture of parts in small batches, or job lots, and for the production of a
wide variety of parts in different sizes or forms.
The disadvantages of process-oriented layout come from the general-purpose use of
the equipment. Orders take more time to move through the system because of difficult sched-
uling, changing setups, and unique material handling. In addition, general-purpose equipment
requires high labor skills, and work-in-process inventories are higher because of imbalances in
the production process. High labor-skill needs also increase the required level of training and
experience, and high work-in-process levels increase capital investment.
When designing a process layout, the most common tactic is to arrange departments or work
centers so as to minimize the costs of material handling. In other words, departments with large
flows of parts or people between them should be placed next to one another. Material handling
costs in this approach depend on (1) the number of loads (or people) to be moved between two
departments during some period of time and (2) the distance-related costs of moving loads (or
people) between departments. Cost is assumed to be a function of distance between departments.
The objective can be expressed as follows:
(9-1)
Process-oriented facilities (and fixed-position layouts as well) try to minimize loads, or trips, times
distance-related costs. The term combines distance and other costs into one factor. We thereby
assume not only that the difficulty of movement is equal but also that the pickup and setdown costs
are constant. Although they are not always constant, for simplicity’s sake we summarize these data
(that is, distance, difficulty, and pickup and setdown costs) in this one variable, cost. The best way
to understand the steps involved in designing a process layout is to look at an example.
Cij
Cij = cost to move a load between department i and department j
Xij = number of loads moved from department i to department j
i, j = individual departments
where n = total number of work centers or departments
Minimize cost = a
n
i=1
a
n
j=1
XijCij
Job lots
Groups or batches of parts
processed together.
LO5: Explain how to
achieve a good process-
oriented facility layout
Surgery
Radiology
ER triage room
Patient A–broken leg
Patient B–erratic heart
pacemaker
Emergency room admissions
Laboratories
ER beds Pharmacy Billing/exit
� FIGURE 9.3
An Emergency Room Process
Layout Showing the Routing
of Two Patients
AUTHOR COMMENT
Patient A (broken leg)
proceeds (blue arrow) to ER
triage, to radiology, to
surgery, to a bed, to
pharmacy, to billing. Patient B
(pacemaker problem) moves
(red arrow) to ER triage, to
surgery, to pharmacy, to lab,
to a bed, to billing.
VIDEO 9.1
Layout at Arnold Palmer Hospital

Chapter 9 Layout Strategies 285
� EXAMPLE 1
Designing a
process layout
Walters Company management wants to arrange the six departments of its factory in a way that will
minimize interdepartmental material handling costs. They make an initial assumption (to simplify the
problem) that each department is feet and that the building is 60 feet long and 40 feet wide.
APPROACH AND SOLUTION � The process layout procedure that they follow involves six steps:
STEP 1: Construct a “from–to matrix” showing the flow of parts or materials from department to
department (see Figure 9.4).
20 * 20
� FIGURE 9.4
Interdepartmental Flow
of Parts
Assembly
(1)
Painting
(2)
Machine
Shop (3)
Receiving
(4)
Shipping
(5)
Testing
(6)
50 100 0 0 20
30 50 10 0
0
0
20 0 100
Number of loads per week
Department
50
Assembly (1)
Painting (2)
Machine Shop (3)
Receiving (4)
Shipping (5)
Testing (6)
Area A
Assembly
Department
(1)
Receiving
Department
(4)
Area B
Painting
Department
(2)
Shipping
Department
(5)
Area C
Machine Shop
Department
(3)
Testing
Department
(6)
Area D Area E Area F
60′
40′
� FIGURE 9.5
Building Dimensions and
One Possible Department
Layout
Receiving
(4)
100
50 30
50
10
100
20
50
20
Assembly
(1)
Painting
(2)
Machine
Shop (3)
Shipping
(5)
Testing
(6)
� FIGURE 9.6
Interdepartmental Flow
Graph Showing Number of
Weekly Loads
STEP 2: Determine the space requirements for each department. (Figure 9.5 shows available plant
space.)
STEP 3: Develop an initial schematic diagram showing the sequence of departments through
which parts must move. Try to place departments with a heavy flow of materials or parts
next to one another. (See Figure 9.6.)
AUTHOR COMMENT
The high flows between
1 and 3 and between 3 and
6 are immediately apparent.
Departments 1, 3, and 6,
therefore, should be
close together.
AUTHOR COMMENT
Think of this as a starting,
initial, layout. Our goal is to
improve it, if possible.
AUTHOR COMMENT
This shows that 100 loads
also move weekly between
Assembly and the Machine
Shop. We will probably want
to move these two
departments closer to one
another to minimize the flow
of parts through the factory.

286 PART 2 Designing Operations
� FIGURE 9.7
Second Interdepartmental
Flow Graph
4 5 6
30
50 100
50
100
10
50
20 20
Receiving
(4)
Assembly
(1)
Painting
(2)
Machine
Shop (3)
Shipping
(5)
Testing
(6)
STEP 4: Determine the cost of this layout by using the material-handling cost equation:
For this problem, Walters Company assumes that a forklift carries all interdepartmental loads. The cost
of moving one load between adjacent departments is estimated to be $1. Moving a load
between nonadjacent departments costs $2. Looking at Figures 9.4 and 9.5, we thus see that the han-
dling cost between departments 1 and 2 is $50 ($ loads), $200 between departments 1 and 3
($ loads), $40 between departments 1 and 6 ($ loads), and so on. Work areas that are
diagonal to one another, such as 2 and 4, are treated as adjacent. The total cost for the layout shown
in Figure 9.6 is:
Cost � $50 � $200 � $40 � $30 � $50
(1 and 2) (1 and 3) (1 and 6) (2 and 3) (2 and 4)
� $10 � $40 � $100 � $50
(2 and 5) (3 and 4) (3 and 6) (4 and 5)
� $570
STEP 5. By trial and error (or by a more sophisticated computer program approach that we dis-
cuss shortly), try to improve the layout pictured in Figure 9.5 to establish a better
arrangement of departments.
By looking at both the flow graph (Figure 9.6) and the cost calculations, we see that placing depart-
ments 1 and 3 closer together appears desirable. They currently are nonadjacent, and the high volume
of flow between them causes a large handling expense. Looking the situation over, we need to check
the effect of shifting departments and possibly raising, instead of lowering, overall costs.
One possibility is to switch departments 1 and 2. This exchange produces a second depart-
mental flow graph (Figure 9.7), which shows a reduction in cost to $480, a savings in material
handling of $90:
Cost � $50 � $100 � $20 � $60 � $50
(1 and 2) (1 and 3) (1 and 6) (2 and 3) (2 and 4)
� $10 � $40 � $100 � $50
(2 and 5) (3 and 4) (3 and 6) (4 and 5)
� $480
2 * 202 * 100
1 * 50
Cost = a
n
i=1
a
n
j=1
XijCij
Suppose Walters Company is satisfied with the cost figure of $480 and the flow graph of Figure 9.7.
The problem may not be solved yet. Often, a sixth step is necessary:
STEP 6: Prepare a detailed plan arranging the departments to fit the shape of the building and its
nonmovable areas (such as the loading dock, washrooms, and stairways). Often this step
involves ensuring that the final plan can be accommodated by the electrical system, floor
loads, aesthetics, and other factors.
In the case of Walters Company, space requirements are a simple matter (see Figure 9.8).
AUTHOR COMMENT
Notice how Assembly and
Machine Shop are now
adjacent. Testing stayed close
to the Machine Shop also.

Chapter 9 Layout Strategies 287
INSIGHT � This switch of departments is only one of a large number of possible changes. For a
six-department problem, there are actually 720 (or 6! ) potential arrange-
ments! In layout problems, we may not find the optimal solution and may have to be satisfied with a
“reasonable” one.
LEARNING EXERCISE � Can you improve on the layout in Figures 9.7 and 9.8? [Answer:
Yes, it can be lowered to $430 by placing Shipping in area A, Painting in area B, Assembly in area C,
Receiving in area D (no change), Machine Shop in area E, and Testing in area F (no change).]
RELATED PROBLEMS � 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9
EXCEL OM Data File Ch09Ex1.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 9.1 Example 1 is further illustrated in Active Model 9.1 at www.pearsonhighered.com/heizer.
= 6 * 5 * 4 * 3 * 2 * 1
Area A
Painting
Department
(2)
Receiving
Department
(4)
Area B
Assembly
Department
(1)
Shipping
Department
(5)
Area C
Machine Shop
Department
(3)
Testing
Department
(6)
Area D Area E Area F
� FIGURE 9.8
A Feasible Layout for
Walters Company
Computer Software for Process-Oriented Layouts
The graphic approach in Example 1 is fine for small problems. It does not, however, suffice
for larger problems. When 20 departments are involved in a layout problem, more than
600 trillion different department configurations are possible. Fortunately, computer programs
have been written to handle large layouts. These programs often add sophistication with flow-
charts, multiple-story capability, storage and container placement, material volumes, time
analysis, and cost comparisons. Such programs include CRAFT (Computerized Relative
Allocation of Facilities Technique) (see Figure 9.9), Automated Layout Design program
(ALDEP), Computerized Relationship Layout Planning (CORELAP), and Factory Flow.
These programs tend to be interactive—that is, require participation by the user. And most
only claim to provide “good,” not “optimal,” solutions.
� FIGURE 9.9
In This Six-Department
Outpatient Hospital Example,
(a) CRAFT Has Rearranged
the Initial Layout, with a Cost
of $20,100, into (b) the New
Layout with a Lower Cost of
$14,390.
A
A
D
C
F
E
A
A
D
C
F
E
A
A
D
D
F
E
A
A
D
D
F
E
B
B
D
D
F
E
B
B
D
D
D
D
1
2
3
4
5
6
TOTAL COST $20,100
EST. COST REDUCTION .00
ITERATION 0
TOTAL COST $14,390
EST. COST REDUCTION 5,710
ITERATION 3
(a) (b)
D
D
D
C
A
A
D
D
D
C
A
A
D
D
D
D
A
A
D
D
E
E
A
F
B
B
E
E
A
F
B
B
E
F
F
F
Legend:
A = X-ray/MRI rooms
B = laboratories
C = admissions
D = exam rooms
E = operating rooms
F = recovery rooms
AUTHOR COMMENT
Here we see the departments
moved to areas A–F to try to
improve the flow.
AUTHOR COMMENT
CRAFT does this by
systematically testing pairs of
departments to see if moving
them closer to each other
lowers total cost.

www.pearsonhighered.com/heizer

www.pearsonhighered.com/heizer

288 PART 2 Designing Operations
WORK CELLS
A work cell reorganizes people and machines that would ordinarily be dispersed in various depart-
ments into a group so that they can focus on making a single product or a group of related products
(Figure 9.10). Cellular work arrangements are used when volume warrants a special arrangement
of machinery and equipment. In a manufacturing environment, group technology (Chapter 5) iden-
tifies products that have similar characteristics and lend themselves to being processed in a particu-
lar work cell. These work cells are reconfigured as product designs change or volume fluctuates.
Although the idea of work cells was first presented by R. E. Flanders in 1925, only with the increas-
ing use of group technology has the technique reasserted itself. The advantages of work cells are:
1. Reduced work-in-process inventory because the work cell is set up to provide one-piece flow
from machine to machine.
2. Less floor space required because less space is needed between machines to accommodate
work-in-process inventory.
3. Reduced raw material and finished goods inventories because less work-in-process allows
more rapid movement of materials through the work cell.
4. Reduced direct labor cost because of improved communication among employees, better
material flow, and improved scheduling.
5. Heightened sense of employee participation in the organization and the product: employees
accept the added responsibility of product quality because it is directly associated with them
and their work cell.
6. Increased equipment and machinery utilization because of better scheduling and faster
material flow.
7. Reduced investment in machinery and equipment because good utilization reduces the num-
ber of machines and the amount of equipment and tooling.
Requirements of Work Cells
The requirements of cellular production include:
• Identification of families of products, often through the use of group technology codes or
equivalents
• A high level of training, flexibility, and empowerment of employees
• Being self-contained, with its own equipment and resources.
• Test (poka-yoke) at each station in the cell
Work cells have at least five advantages over assembly lines and process facilities:
(1) because tasks are grouped, inspection is often immediate; (2) fewer workers are needed;
(3) workers can reach more of the work area; (4) the work area can be more efficiently
Work cell
An arrangement of machines
and personnel that focuses on
making a single product or
family of related products.
LO6: Define work cell
and the requirements of a
work cell
Contemporary software
such as this from e-factory
(UGS Corp.) allows
operations managers to
quickly place and connect
symbols for factory
equipment for a full three-
dimensional view of the
layout. Such presentations
provide added insight into
the issues of facility layout
in terms of process,
material handling,
efficiency, and safety.
AUTHOR COMMENT
Using work cells is a big step
toward manufacturing
efficiency. They can make
jobs more interesting, save
space, and cut inventory.

Chapter 9 Layout Strategies 289
Current layout–workers in
small closed areas.
(a)
(b)
Improved layout—cross-trained
workers can assist each other.
May be able to add a third worker
as added output is needed.
Improved layout—in U shape,
workers have better access.
Four cross-trained workers were
reduced to three.
Material
Note in both (a) and (b) that U-shaped work cells can reduce material and employee
movement. The U shape may also reduce space requirements, enhance communication,
cut the number of workers, and make inspection easier.
Current layout—straight lines make
it hard to balance tasks because
work may not be divided evenly.
� FIGURE 9.10
Improving Layouts by Moving
to the Work Cell Concept
balanced; and (5) communication is enhanced. Work cells are sometimes organized in a U
shape, as shown on the right side of Figure 9.10.
About half of U.S. plants with fewer than 100 employees use some sort of cellular system,
whereas 75% of larger plants have adopted cellular production methods. Bayside Controls in
Queens, New York, for example, has in the past decade increased sales from $300,000 per year
to $11 million. Much of the gain was attributed to its move to cellular manufacturing. As noted
in the OM in Action box, Canon has had similar success with work cells.
Staffing and Balancing Work Cells
Once the work cell has the appropriate equipment located in the proper sequence, the next task is
to staff and balance the cell. Efficient production in a work cell requires appropriate staffing.
Look quickly at Canon’s factory near Tokyo, and you might
think you stepped back a few decades. Instead of the swiftly
moving assembly lines you might expect to see in a high-
cost, sophisticated digital camera and photo copier giant,
you see workers gathered in small groups called work cells.
Each cell is responsible for one product or a small family
of products. The product focus encourages employees to
exchange ideas about how to improve the assembly process.
They also accept more responsibility for their work.
Canon’s work cells have increased productivity by 30%.
But how?
First, conveyor belts and their spare parts take up
space, an expensive commodity in Japan. The shift to
the cell system has freed 12 miles of conveyor-belt
space at 54 plants and allowed Canon to close 29 parts
warehouses, saving $280 million in real estate costs.
Employees are encouraged to work in ever-tighter cells,
with prizes given to those who free up the most space.
Second, the cells enable Canon to change the product
mix more quickly to meet market demands for innovative
products—a big advantage as product life cycles become
shorter and shorter.
Third, staff morale has increased because instead of
performing a single task over and over, employees are
trained to put together whole machines. Some of Canon’s
fastest workers are so admired that they have become TV
celebrities.
A layout change that improves morale while increasing
productivity is a win–win for Canon.
Sources: The Wall Street Journal (September 27, 2004): R11; and Financial
Times (September 23, 2003): 14.
OM in Action OMinAction� Work Cells Increase Productivity at Canon

290 PART 2 Designing Operations
This involves two steps. First, determine the takt time,2 which is the pace (frequency) of produc-
tion units necessary to meet customer orders:
(9-2)
Second, determine the number of operators required. This requires dividing the total operation
time in the work cell by the takt time:
(9-3)
Example 2 considers these two steps when staffing work cells.
Workers required = Total operation time required>Takt time
Takt time = Total work time available>Units required
Takt time
Pace of production to meet
customer demands.
EXAMPLE 2 �
Staffing work cells
Stephen Hall’s company in Dayton makes auto mirrors. The major customer is the Honda plant nearby.
Honda expects 600 mirrors delivered daily, and the work cell producing the mirrors is scheduled for 8
hours. Hall wants to determine the takt time and the number of workers required.
APPROACH � Hall uses Equations (9-2) and (9-3) and develops a work balance chart to help
determine the time for each operation in the work cell, as well as total time.
SOLUTION �
Therefore, the customer requirement is one mirror every 48 seconds.
The work balance chart in Figure 9.11 shows that 5 operations are necessary, for a total operation
time of 140 seconds:
= 140>48 = 2.92
= 150 + 45 + 10 + 20 + 152>48
Workers required = Total operation time required>Takt time
Takt time = 18 hours * 60 minutes2>600 units = 480>600 = .8 minute = 48 seconds
Operations
50
40
30
20
10
Assemble Paint Test Label
S
ta
n
d
a
rd
t
im
e
re
q
u
ir
e
d
(
s
e
c
o
n
d
s
)
60
Pack for
shipping
� FIGURE 9.11
Work Balance Chart for
Mirror Production
INSIGHT � To produce one unit every 48 seconds will require 2.92 people. With three operators
this work cell will be producing one unit each 46.67 seconds ( ) and
617 units per day ( seconds for each ).
LEARNING EXERCISE � If testing time is expanded to 20 seconds, what is the staffing
requirement? [Answer: 3.125 employees.]
RELATED PROBLEM � 9.10
unit = 617480 minutes available * 60 seconds2>46.67
140 seconds>3 employees = 46.67
A work balance chart (like the one in Example 2) is also valuable for evaluating the operation
times in work cells. Some consideration must be given to determining the bottleneck operation.
Bottleneck operations can constrain the flow through the cell. Imbalance in a work cell is seldom
an issue if the operation is manual, as cell members by definition are part of a cross-trained team.
2Takt is German for “time,” “measure,” or “beat” and is used in this context as the rate at which completed units must
be produced to satisfy customer demand.

Chapter 9 Layout Strategies 291
Consequently, the inherent flexibility of work cells typically overcome modest imbalance issues
within a cell. However, if the imbalance is a machine constraint, then an adjustment in machin-
ery, process, or operations may be necessary. In such situations the use of traditional assembly-
line-balancing analysis, the topic of our next section, may be helpful.
In many arrangements, without cells and without cross training, if one operation is halted for
whatever reason (reading a drawing, getting a tool, machine maintenance, etc.), the entire flow
stops. Multiple-operator cells are therefore preferred. However, we should note that the increas-
ing capability of multitasking machines can complicate work cell design and staffing.
The success of work cells is not limited to manufacturing. Kansas City’s Hallmark, which has
over half the U.S. greeting card market and produces some 40,000 different cards, has modified
the offices into a cellular design. In the past, its 700 creative professionals would take up to 2
years to develop a new card. Hallmark’s decision to create work cells consisting of artists, writ-
ers, lithographers, merchandisers, and accountants, all located in the same area, has resulted in
card preparation in a fraction of the time that the old layout required. Work cells have also
yielded higher performance and better service for the American Red Cross blood donation
process.3
Commercial software, such as ProPlanner and Factory Flow, is available to aid managers in
their move to work cells. These programs typically require information that includes AutoCAD
layout drawings; part routing data; and cost, times, and speeds of material handling systems.
The Focused Work Center and the Focused Factory
When a firm has identified a family of similar products that have a large and stable demand,
it may organize a focused work center. A focused work center moves production from a
general-purpose, process-oriented facility to a large work cell that remains part of the present
plant. If the focused work center is in a separate facility, it is often called a focused factory.
A fast-food restaurant is a focused factory—most are easily reconfigured for adjustments to
product mix and volume. Burger King, for example, changes the number of personnel and task
assignments rather than moving machines and equipment. In this manner, Burger King bal-
ances the assembly line to meet changing production demands. In effect, the “layout” changes
numerous times each day.
The term focused factories may also refer to facilities that are focused in ways other than by
product line or layout. For instance, facilities may be focused in regard to meeting quality, new
product introduction, or flexibility requirements.
Focused facilities in both manufacturing and services appear to be better able to stay in tune
with their customers, to produce quality products, and to operate at higher margins. This is true
whether they are steel mills like CMC, Nucor, or Chaparral; restaurants like McDonald’s and
Burger King; or a hospital like Arnold Palmer.
Table 9.2 summarizes our discussion of work cells, focused work centers, and focused factories.
Work Cell Focused Work Center Focused Factory
Description A work cell is a temporary
product-oriented
arrangement of machines
and personnel in what is
ordinarily a process-
oriented facility
A focused work center is a
permanent product-
oriented arrangement of
machines and personnel
in what is ordinarily a
process-oriented facility
A focused factory is a
permanent facility to
produce a product or
component in a product-
oriented facility. Many
of the focused factories
currently being built
were originally part of a
process-oriented facility
Example A job shop with machinery
and personnel rearranged
to produce 300 unique
control panels
Pipe bracket manufacturing
at a shipyard
A plant to produce window
mechanisms or seat belts
for automobiles
3Mark Pagell and Steven A. Melnyk, “Assessing the Impact of Alternative Manufacturing Layouts in a Service Setting,”
Journal of Operations Management 22 (2004): 413–429.
Focused work center
A permanent or semi-
permanent product-oriented
arrangement of machines and
personnel.
Focused factory
A facility designed to produce
similar products or
components.
� TABLE 9.2
Work Cells, Focused Work
Centers, and the Focused
Factory

292 PART 2 Designing Operations
REPETITIVE AND PRODUCT-ORIENTED LAYOUT
Product-oriented layouts are organized around products or families of similar high-volume, low-
variety products. Repetitive production and continuous production, which are discussed in
Chapter 7, use product layouts. The assumptions are that:
1. Volume is adequate for high equipment utilization
2. Product demand is stable enough to justify high investment in specialized equipment
3. Product is standardized or approaching a phase of its life cycle that justifies investment in
specialized equipment
4. Supplies of raw materials and components are adequate and of uniform quality (adequately
standardized) to ensure that they will work with the specialized equipment
Two types of a product-oriented layout are fabrication and assembly lines. The fabrication
line builds components, such as automobile tires or metal parts for a refrigerator, on a series of
machines, while an assembly line puts the fabricated parts together at a series of workstations.
However, both are repetitive processes, and in both cases, the line must be “balanced”: That is,
the time spent to perform work on one machine must equal or “balance” the time spent to per-
form work on the next machine in the fabrication line, just as the time spent at one workstation
by one assembly-line employee must “balance” the time spent at the next workstation by the
next employee. The same issues arise when designing the “disassembly lines” of slaughter-
houses and automobile makers (see the OM in Action box “From Assembly Lines to Green
Disassembly Lines”).
Fabrication lines tend to be machine-paced and require mechanical and engineering changes
to facilitate balancing. Assembly lines, on the other hand, tend to be paced by work tasks
assigned to individuals or to workstations. Assembly lines, therefore, can be balanced by moving
tasks from one individual to another. The central problem, then, in product-oriented layout plan-
ning is to balance the tasks at each workstation on the production line so that it is nearly the same
while obtaining the desired amount of output.
Management’s goal is to create a smooth, continuing flow along the assembly line with a min-
imum of idle time at each workstation. A well-balanced assembly line has the advantage of high
LO7: Define product-
oriented layout
Fabrication line
A machine-paced, product-
oriented facility for building
components.
Assembly line
An approach that puts
fabricated parts together at a
series of workstations; used in
repetitive processes.
Almost 100 years have passed since assembly lines were
developed to make automobiles—and now we’re developing
disassembly lines to take them apart. Sprawling graveyards
of rusting cars and trucks bear testimony to the need for
automotive disassembly lines. But those graveyards are
slowly beginning to shrink as we learn the art of automobile
disassembly. New disassembly lines now take apart so
many automobiles that recycling is the 16th-largest industry
in the U.S. The motivation for this disassembly comes from
many sources, including mandated industry recycling
standards and a growing consumer interest in purchasing
cars based on how “green” they are.
New car designs have traditionally been unfriendly to
recyclers, with little thought given to disassembly. However,
manufacturers now design in such a way that materials can
be easily reused in the next generation of cars. The 2009
Mercedes S-class is 95% recyclable and already meets the
2015 EU standard. BMW has disassembly plants in Europe
and Japan as well as U.S. salvage centers in New York,
Los Angeles, and Orlando. A giant 200,000-square-foot
facility in Baltimore (called CARS) can disassemble up to
30,000 vehicles per year. At CARS’s initial “greening
station,” special tools puncture tanks and drain fluids, and
the battery and gas tank are removed. Then on a semi-
automated track, which includes a giant steel vise that can
flip a 7,500-pound car upside-down, wheels, doors, hood,
and trunk are removed; next come the interior items; then
plastic parts are removed and sorted for recycling; then
glass and interior and trunk materials. Eventually the
chassis is in a bale and sold as a commodity to minimills
that use scrap steel.
Disassembly lines are not easy. Some components, like
air bags, are hard to handle, dangerous, and take time to
disassemble. Reusable parts are bar coded and entered
into a database. Various color-coded plastics must be
recycled differently to support being remelted and turned
into new parts, such as intake manifolds. After the engines,
transmissions, radios, and exhausts have been removed,
the remaining metal parts of the disassembly line are
easier: with shredders and magnets, baseball-sized chunks
of metal are sorted. Assembly lines put cars together, and
disassembly lines take them apart.
Sources: The Wall Street Journal (April 29, 2008): A1, A9; The New York Times
(September 19, 2005): D5; and Automotive Industry Trends (March 2004).
OM in Action � From Assembly Lines to Green Disassembly Lines
AUTHOR COMMENT
The traditional assembly line
handles repetitive production.

Chapter 9 Layout Strategies 293
Assembly-line
balancing
Obtaining output at each
workstation on a production line
so delay is minimized.
personnel and facility utilization and equity among employees’ work loads. Some union con-
tracts require that work loads be nearly equal among those on the same assembly line. The term
most often used to describe this process is assembly-line balancing. Indeed, the objective of the
product-oriented layout is to minimize imbalance in the fabrication or assembly line.
The main advantages of product-oriented layout are:
1. The low variable cost per unit usually associated with high-volume, standardized products
2. Low material handling costs
3. Reduced work-in-process inventories
4. Easier training and supervision
5. Rapid throughput
The disadvantages of product layout are:
1. The high volume required because of the large investment needed to establish the process
2. Work stoppage at any one point ties up the whole operation
3. A lack of flexibility when handling a variety of products or production rates
Because the problems of fabrication lines and assembly lines are similar, we focus our dis-
cussion on assembly lines. On an assembly line, the product typically moves via automated
means, such as a conveyor, through a series of workstations until completed. This is the
way fast-food hamburgers are made (see Figure 9.12), automobiles and some planes (see
the photo of the Boeing 737 on the next page) are assembled, television sets and ovens are
produced. Product-oriented layouts use more automated and specially designed equipment
than do process layouts.
Assembly-Line Balancing
Line balancing is usually undertaken to minimize imbalance between machines or personnel
while meeting a required output from the line. To produce at a specified rate, management must
know the tools, equipment, and work methods used. Then the time requirements for each assem-
bly task (e.g., drilling a hole, tightening a nut, or spray-painting a part) must be determined.
Management also needs to know the precedence relationship among the activities—that is, the
sequence in which various tasks must be performed. Example 3 shows how to turn these task
data into a precedence diagram.
VIDEO 9.2
Facility Layout at Wheeled Coach
Ambulances
LO8: Explain how to
balance production flow
in a repetitive or product-
oriented facility
2
1
5
43
2. Bun toasting1. Order
11 20 14 0 45
0:11
Task
Elapsed
time
Task time
(seconds)
0:00 0:31 0:45 1:30
Order read on
a video screen
Toaster Condiments
More personnel
added during
busy periods
5. Order picked
up immediately
to keep it fresh
Heated
cabinet
for the
grilled
patties
Buns
3. Assembly with
condiments
4. Wrapping of patty
with bun
6. Customer service
(order and payment)
Heated
landing
pad
6
� FIGURE 9.12 McDonald’s Hamburger Assembly Line

294 PART 2 Designing Operations
EXAMPLE 3 �
Developing a
precedence diagram
for an assembly line
Boeing wants to develop a precedence diagram for an electrostatic wing component that requires a total
assembly time of 66 minutes.
APPROACH � Staff gather tasks, assembly times, and sequence requirements for the component
in Table 9.3.
Task
Assembly Time
(minutes)
Task Must Follow
Task Listed
Below
A 10 — This means that
B 11 A tasks B and E
C 5 B cannot be done
D 4 B until task A has
E 12 A been completed.
F 3 C, D
G 7 F
H 11 E
I 3 G, H
Total time 66
� TABLE 9.3
Precedence Data
for Wing Component
11
12
4
5
3
11
7
F
10 minutes
I
3
A B
E
D
C
H
G
� FIGURE 9.13
Precedence Diagram
SOLUTION � Figure 9.13 shows the precedence diagram.
The Boeing 737, the world’s most popular
commercial airplane, is produced on a moving
production line, traveling at 2 inches a minute
through the final assembly process. The
moving line, one of several lean manufacturing
innovations at the Renton, Washington, facility,
has enhanced quality, reduced flow time,
slashed inventory levels, and cut space
requirements. Final assembly is only 11 days—
a time savings of 50%—and inventory is down
more than 55%. Boeing has expanded the
moving line concept to its 747 jumbo jet.

Chapter 9 Layout Strategies 295
INSIGHT � The diagram helps structure an assembly line and workstations, and it makes it easier
to visualize the sequence of tasks.
LEARNING EXERCISE � If task D had a second preceding task (C), how would Figure 9.13
change? [Answer: There would also be an arrow pointing from C to D.]
RELATED PROBLEMS � 9.12a, 9.14a, 9.15a, 9.16a, 9.19a
Once we have constructed a precedence chart summarizing the sequences and performance
times, we turn to the job of grouping tasks into job stations so that we can meet the specified pro-
duction rate. This process involves three steps:
1. Take the units required (demand or production rate) per day and divide it into the productive
time available per day (in minutes or seconds). This operation gives us what is called the
cycle time4—namely, the maximum time allowed at each workstation if the production rate
is to be achieved:
(9-4)
2. Calculate the theoretical minimum number of workstations. This is the total task-duration
time (the time it takes to make the product) divided by the cycle time. Fractions are rounded
to the next higher whole number:
(9-5)
where n is the number of assembly tasks.
3. Balance the line by assigning specific assembly tasks to each workstation. An efficient bal-
ance is one that will complete the required assembly, follow the specified sequence, and
keep the idle time at each workstation to a minimum. A formal procedure for doing this is
the following:
a. Identify a master list of tasks.
b. Eliminate those tasks that have been assigned.
c. Eliminate those tasks whose precedence relationship has not been satisfied.
d. Eliminate those tasks for which inadequate time is available at the workstation.
e. Use one of the line-balancing “heuristics” described in Table 9.4. The five choices are
(1) longest task time, (2) most following tasks, (3) ranked positional weight, (4) shortest
Minimum number of workstations =
a
n
i=1
Time for task i
Cycle time
Cycle time =
Production time available per day
Units required per day
Cycle time
The maximum time that a
product is allowed at each
workstation.
4Cycle time is the actual time to accomplish a task or process step. Several process steps may be necessary to complete
the product. Takt time, discussed earlier, is determined by the customer and is the speed at which completed units must
be produced to satisfy customer demand.
1. Longest task (operation) time From the available tasks, choose the task with the largest (longest) time.
2. Most following tasks From the available tasks, choose the task with the largest number of
following tasks.
3. Ranked positional weight From the available tasks, choose the task for which the sum of the
times for each following task is longest. (In Example 4 we see that the
ranked positional weight of task C = 5(C) + 3(F) + 7(G) + 3(I) = 18,
whereas the ranked positional weight of task D = 4(D) + 3(F) + 7(G) +
3(I) =17; therefore, C would be chosen first, using this heuristic.)
4. Shortest task (operations) time From the available tasks, choose the task with the shortest task time.
5. Least number of following tasks From the available tasks, choose the task with the least number of
subsequent tasks.
� TABLE 9.4
Layout Heuristics That May
Be Used to Assign Tasks to
Workstations in Assembly-
Line Balancing

296 PART 2 Designing Operations
task time, and (5) least number of following tasks. You may wish to test several of these
heuristics to see which generates the “best” solution—that is, the smallest number of
workstations and highest efficiency. Remember, however, that although heuristics pro-
vide solutions, they do not guarantee an optimal solution.
Example 4 illustrates a simple line-balancing procedure.
EXAMPLE 4 �
Balancing the
assembly line
Heuristic
Problem solving using
procedures and rules rather
than mathematical optimization.
On the basis of the precedence diagram and activity times given in Example 3, Boeing determines that
there are 480 productive minutes of work available per day. Furthermore, the production schedule
requires that 40 units of the wing component be completed as output from the assembly line each day.
It now wants to group the tasks into workstations.
APPROACH � Following the three steps above, we compute the cycle time using Equation (9-4)
and minimum number of workstations using Equation (9-5), and we assign tasks to workstations—in
this case using the most following tasks heuristic.
SOLUTION �
Figure 9.14 shows one solution that does not violate the sequence requirements and that groups
tasks into six one-person stations. To obtain this solution, activities with the most following tasks were
moved into workstations to use as much of the available cycle time of 12 minutes as possible. The first
workstation consumes 10 minutes and has an idle time of 2 minutes.
= 5.5 or 6 stations
Minimum number of workstations =
Total task time
Cycle time
=
66
12
= 12 minutes>unit
Cycle time 1in minutes2 =
480 minutes
40 units
Station 2
Station 1
Station 3 Station 5
Station 6
Station 4
10 min
A
11 min
B
3 min
F
7 min
5 min
C
4 min
D
12 min
E
11 min
H
3 min
I
G
� FIGURE 9.14
A Six-Station Solution
to the Line-Balancing
Problem
INSIGHT � This is a reasonably well-balanced assembly line. The second workstation uses 11
minutes, and the third consumes the full 12 minutes. The fourth workstation groups three small
tasks and balances perfectly at 12 minutes. The fifth has 1 minute of idle time, and the sixth (con-
sisting of tasks G and I) has 2 minutes of idle time per cycle. Total idle time for this solution is
6 minutes per cycle.
LEARNING EXERCISE � If task I required 6 minutes (instead of 3 minutes), how would this
change the solution? [Answer: The cycle time would not change, and the theoretical minimum number
of workstations would still be 6 (rounded up from 5.75), but it would take 7 stations to balance the line.]
RELATED PROBLEMS � 9.11, 9.12, 9.13, 9.14, 9.15, 9.16, 9.17, 9.18, 9.19, 9.20, 9.21, 9.22, 9.23
EXCEL OM Data File Ch09Ex4.xls can be found at www.pearsonhighered.com/heizer.
AUTHOR COMMENT
Tasks C, D, and F can be
grouped together in one
workstation, provided that
the physical facilities and skill
levels meet the work
requirements.

www.pearsonhighered.com/heizer

Chapter 9 Layout Strategies 297
We can compute the efficiency of a line balance by dividing the total task time by the product
of the number of workstations required times the assigned (actual) cycle time of the longest
workstation:
(9-6)
Operations managers compare different levels of efficiency for various numbers of workstations.
In this way, a firm can determine the sensitivity of the line to changes in the production rate and
workstation assignments.
Efficiency =
© Task times
1Actual number of workstations2 * 1Largest assigned cycle time2
� EXAMPLE 5
Determining line
efficiency
Boeing needs to calculate the balance efficiency for Example 4.
APPROACH � Equation (9-6) is applied.
SOLUTION �
Note that opening a seventh workstation, for whatever reason, would decrease the efficiency of the balance
to 78.6% (assuming that at least one of the workstations still required 12 minutes):
INSIGHT � Increasing efficiency may require that some tasks be divided into smaller elements
and reassigned to other tasks. This facilitates a better balance between workstations and means higher
efficiency.
LEARNING EXERCISE � What is the efficiency if an eighth workstation is opened? [Answer:
]
RELATED PROBLEMS � 9.12f, 9.13c, 9.14f, 9.16c, 9.17b, 9.18b, 9.19e,g
Efficiency = 68.75%.
Efficiency =
66 minutes
17 stations2 * 112 minutes2
= 78.6%
Efficiency =
66 minutes
16 stations2 * 112 minutes2
=
66
72
= 91.7%
Large-scale line-balancing problems, like large process-layout problems, are often solved by
computers. Several computer programs are available to handle the assignment of workstations on
assembly lines with 100 (or more) individual work activities. Two computer routines, COM-
SOAL (Computer Method for Sequencing Operations for Assembly Lines) and ASYBL
(General Electric’s Assembly Line Configuration program), are widely used in larger problems
to evaluate the thousands, or even millions, of possible workstation combinations much more
efficiently than could ever be done by hand.
In the case of slaughtering
operations, the assembly line is
actually a disassembly line. The line-
balancing procedures described in
this chapter are the same as for
an assembly line. The chicken-
processing plant shown here must
balance the work of several hundred
employees. The total labor content
in each of the chickens processed is
a few minutes.

298 PART 2 Designing Operations
CHAPTER SUMMARY
Layouts make a substantial difference in operating efficiency.
The seven layout situations discussed in this chapter are
(1) office, (2) retail, (3) warehouse, (4) fixed position,
(5) process oriented, (6) work cells, and (7) product oriented.
A variety of techniques have been developed to solve these
layout problems. Office layouts often seek to maximize infor-
mation flows, retail firms focus on product exposure, and
warehouses attempt to optimize the trade-off between storage
space and material handling cost.
The fixed-position layout problem attempts to minimize
material handling costs within the constraint of limited
space at the site. Process layouts minimize travel distances
times the number of trips. Product lay-
outs focus on reducing waste and the
imbalance in an assembly line. Work
cells are the result of identifying a fam-
ily of products that justify a special con-
figuration of machinery and equipment
that reduces material travel and adjusts imbalances with
cross-trained personnel.
Often, the issues in a layout problem are so wide-ranging
that finding an optimal solution is not possible. For this rea-
son, layout decisions, although the subject of substantial
research effort, remain something of an art.
Key Terms
Office layout (p. 278)
Retail layout (p. 279)
Slotting fees (p. 279)
Servicescape (p. 280)
Warehouse layout (p. 281)
Cross-docking (p. 282)
Random stocking (p. 282)
Customizing (p. 282)
Fixed-position layout (p. 282)
Process-oriented layout (p. 283)
Job lots (p. 284)
Work cell (p. 288)
Takt time (p. 290)
Focused work center (p. 291)
Focused factory (p. 291)
Fabrication line (p. 292)
Assembly line (p. 292)
Assembly-line balancing (p. 293)
Cycle time (p. 295)
Heuristic (p. 296)
Using Software to Solve Layout Problems
In addition to the many commercial software packages available for addressing layout problems, Excel
OM and POM for Windows, both of which accompany this text, contain modules for the process prob-
lem and the assembly-line-balancing problem.
X Using Excel OM
Excel OM can assist in evaluating a series of department work assignments like the one we saw for the
Walters Company in Example 1. The layout module can generate an optimal solution by enumeration or
by computing the “total movement” cost for each layout you wish to examine. As such, it provides a
speedy calculator for each flow–distance pairing.
Program 9.1 illustrates our inputs in the top two tables. We first enter department flows, then provide
distances between work areas. Entering area assignments on a trial-and-error basis in the upper left of
the top table generates movement computations at the bottom of the screen. Total movement is recalcu-
lated each time we try a new area assignment. It turns out that the assignment shown is optimal at 430
feet of movement.

Chapter 9 Layout Strategies 299
= C28*F28
Lookup the cost as
= INDEX ($D$16: $I$21, D28, E28).
Get the loads from the load table above using
= INDEX ($D$8: $I$13, A28, B28).
Calculations continue
below row 30.
Columns A and B together
contain all possible 6 by 6 = 36
combinations of pairs of areas.
� PROGRAM 9.1 Using Excel OM’s Process Layout Module to Solve the Walters Company Problem in Example 1
Solved Problems Virtual Office Hours help is available at www.myomlab.com
� SOLVED PROBLEM 9.1
Aero Maintenance is a small aircraft engine maintenance facility
located in Wichita, Kansas. Its new administrator, Ann Daniel,
decides to improve material flow in the facility, using the process-
layout method she studied at Wichita State University. The current
layout of Aero Maintenance’s eight departments is shown in
Figure 9.15.
The only physical restriction perceived by Daniel is the need
to keep the entrance in its current location. All other departments
can be moved to a different work area (each 10 feet square) if lay-
out analysis indicates a move would be beneficial.
Receiving
(2)
Entrance
(1)
Parts
(3)
Metallurgy
(4)
Breakdown
(5)
Assembly
(6)
Inspection
(7)
Test
(8)
40′
10′
10′
Current Aero Maintenance Layout
Area A
Area E
Area B
Area F
Area C
Area G
Area D
Area H
� FIGURE 9.15 Aero Maintenance Layout
P Using POM for Windows
The POM for Windows facility layout module can be used to place up to 10 departments in 10 rooms to
minimize the total distance traveled as a function of the distances between the rooms and the flow
between departments. The program exchanges departments until no exchange will reduce the total
amount of movement, meaning an optimal solution has been reached.
The POM for Windows and Excel OM modules for line balancing can handle a line with up to
99 tasks, each with up to 6 immediate predecessors. In this program, cycle time can be entered either
(1) given, if known, or (2) the demand rate can be entered with time available as shown. All five “heuristic
rules” are used: (1) longest operation (task) time, (2) most following tasks, (3) ranked positional weight,
(4) shortest operation (task) time, and (5) least number of following tasks. No one rule can guarantee an
optimal solution, but POM for Windows displays the number of stations needed for each rule.
Appendix IV discusses further details regarding POM for Windows.

www.myomlab.com

300 PART 2 Designing Operations
Department
Entrance (1)
Receiving (2)
Parts (3)
Entrance
(1)
Receiving
(2)
Parts
(3)
Metallurgy
(4)
Breakdown
(5)
Assembly
(6)
Inspection
(7)
Test
(8)
Metallurgy (4)
Breakdown (5)
Assembly (6)
Inspection (7)
Test (8)
100
0
30
100 0 0 0 0 0
50 20 0 0 0
30 0 0 0
20 0 0 20
20 0 10
30 0
0
� FIGURE 9.16
Number of Material
Movements (Loads)
between Departments in
One Month
� SOLUTION
First, establish Aero Maintenance’s current layout, as shown in Figure 9.17. Then, by analyzing the current layout, compute material movement:
Total movement � (100 � 10�) � (100 � 20�) � (50 � 20�) � (20 � 10�)
1 to 2 1 to 3 2 to 4 2 to 5
� (30 � 10�) � (30 � 20�) � (20 � 30�) � (20 � 10�)
3 to 4 3 to 5 4 to 5 4 to 8
� (20 � 10�) � (10 � 30�) � (30 � 10�)
5 to 6 5 to 8 6 to 7
�1,000 � 2,000 � 1,000 � 200 � 300 � 600 � 600
� 200 � 200 � 300 � 300
�6,700 feet
First, Daniel analyzes records to determine the number of
material movements among departments in an average month.
These data are shown in Figure 9.16. Her objective, Daniel
decides, is to lay out the departments so as to minimize the total
movement (distance traveled) of material in the facility. She writes
her objective as:
where Xij � number of material movements per month
(loads or trips) moving from department i
to department j
Cij � distance in feet between departments i and j
(which, in this case, is the equivalent of cost
per load to move between departments)
Minimize material movement = a
8
i= 1
a
8
j= 1
XijCij
Note that this is only a slight modification of the cost-objective
equation shown earlier in the chapter.
Daniel assumes that adjacent departments, such as entrance
(now in work area A) and receiving (now in work area B), have a
walking distance of 10 feet. Diagonal departments are also con-
sidered adjacent and assigned a distance of 10 feet. Nonadjacent
departments, such as the entrance and parts (now in area C) or
the entrance and inspection (area G) are 20 feet apart, and non-
adjacent rooms, such as entrance and metallurgy (area D), are
30 feet apart. (Hence, 10 feet is considered 10 units of cost,
20 feet is 20 units of cost, and 30 feet is 30 units of cost.)
Given the above information, redesign Aero Maintenance’s
layout to improve its material flow efficiency.

Chapter 9 Layout Strategies 301
100 trips
100
20
30
20 30
20
50
10
20
30
Entrance
(1)
Receiving
(2)
Parts
(3)
Metallurgy
(4)
Breakdown
(5)
Assembly
(6)
Inspection
(7)
Test
(8)
� FIGURE 9.17
Current Material Flow
20
30
20
100 20
30
20
100 50
10 30
Entrance
(1)
Receiving
(2)
Parts
(3)
Metallurgy
(4)
Breakdown
(5)
Assembly
(6)
Inspection
(7)
Test
(8)
� FIGURE 9.18
Improved Layout
Propose a new layout that will reduce the current figure of 6,700 feet. Two useful changes, for example, are to switch departments 3 and
5 and to interchange departments 4 and 6. This change would result in the schematic shown in Figure 9.18:
Total movement � (100 � 10�) � (100 � 10�) � (50 � 10�) � (20 � 10�)
1 to 2 1 to 3 2 to 4 2 to 5
� (30 � 10�) � (30 � 20�) � (20 � 10�) � (20 � 20�)
3 to 4 3 to 5 4 to 5 4 to 8
� (20 � 10�) � (10 � 10�) � (30 � 10�)
5 to 6 5 to 8 6 to 7
�1,000 � 1,000 � 500 � 200 � 300 � 600 � 200
� 400 � 200 � 100 � 300
�4,800 feet
Do you see any room for further improvement?
� SOLVED PROBLEM 9.2
The assembly line whose activities are shown in Figure 9.19 has
an 8-minute cycle time. Draw the precedence graph and find the
minimum possible number of one-person workstations. Then
arrange the work activities into workstations so as to balance the
line. What is the efficiency of your line balance?
Task
Performance
Time (minutes)
Task Must
Follow This Task
A 5 —
B 3 A
C 4 B
D 3 B
E 6 C
F 1 C
G 4 D, E, F
H 2 G
28

302 PART 2 Designing Operations
Bibliography
Birchfield, J. C., and J. Birchfield. Design and Layout of Foodservice
Facilities, 3rd ed. New York, Wiley & Sons, 2007.
Francis, R. L., L. F. McGinnis, and J. A. White. Facility Layout and
Location, 3rd ed. Upper Saddle River, NJ: Prentice Hall, 1998.
Gultekin, H., O. Y. Karasan, and M. S. Akturk. “Pure Cycles in
Flexible Robotic Cells.” Computers & Operations Research
36, no. 2 (February 2009): 329.
Heragu, S. S. Facilities Design, 3rd ed. New York: CRC Press, 2008.
Heyer, N., and U. Wemmerlöv. Reorganizing the Factory:
Competing through Cellular Manufacturing. Portland, OR:
Productivity Press, 2002.
Johnson, Alan. “Getting the Right Factory Layout.” Manufacturer’s
Monthly (July 2008): 16.
Kator, C. “Crossdocking on the Rise.” Modern Materials Handling
63, no. 6 (June 2008): 15.
Kee, Micah R. “The Well-Ordered Warehouse.” APICS: The
Performance Advantage (March 2003): 20–24.
Keeps, David A. “Out-of-the-Box Offices.” Fortune 159, no.1
(January 19, 2009): 45.
Larson, S. “Extreme Makover—OR Edition.” Nursing Management
(November 2005): 26.
�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Microfix, Inc.: This company needs to balance its PC manufacturing assembly line and deal with sensitivity analysis of time estimates.
Panchalavarapu, P. R., and V. Chankong. “Design of Cellular
Manufacturing System with Assembly Considerations.”
Computers & Industrial Engineering 48, no. 3 (May 2005):
448.
Roodbergen, K. J., and I. F. A. Vis. “A Model for Warehouse
Layout.” IIE Transactions 38, no. 10 (October 2006):
799–811.
Stanowy, A. “Evolutionary Strategy for Manufacturing Cell
Design.” Omega 34, no. 1 (January 2006): 1.
Tompkins, James A. Facility Planning, 4th ed. New York: Wiley,
2009.
Upton, David. “What Really Makes Factories Flexible?”
Harvard Business Review 73, no. 4 (July–August 1995):
74–84.
Zeng, A. Z., M. Mahan, and N. Fleut. “Designing an Efficient
Warehouse Layout to Facilitate the Order-Filling Process.”
Production and Inventory Management Journal 43, no. 3–4
(3rd/4th Quarter 2002): 83–88.
Zhao, T., and C. L. Tseng. “Flexible Facility Interior Layout.” The
Journal of the Operational Research Society 58, no. 6 (June
2007): 729–740.
A
35
B
4
C
3
D
1
F
6
E
G
2
H
4
Workstation 1
Workstation 2
Workstation 3
Workstation 4
� FIGURE 9.19
Four-Station Solution to the
Line-Balancing Problem
� SOLUTION
The theoretical minimum number of workstations is:
The precedence graph and one good layout are shown in Figure 9.19.
Efficiency =
Total task time
1Number of workstations2 * 1Largest cycle time2
=
28
142182
= 87.5%
©ti
Cycle time
=
28 minutes
8 minutes
= 3.5, or 4 stations

www.myomlab.com

www.pearsonhighered.com/heizer

Human Resources,
Job Design, and
Work Measurement
Chapter Outline
GLOBAL COMPANY PROFILE: RUSTY WALLACE’S
NASCAR RACING TEAM
Human Resource Strategy
for Competitive Advantage 306
Labor Planning 307
Job Design 308
Ergonomics and the Work
Environment 311
Methods Analysis 314
The Visual Workplace 315
Labor Standards 317
Ethics 328
� Ten OM Strategy Decisions
� Design of Goods and Services
� Managing Quality
� Process Strategy
� Location Strategies
� Layout Strategies
� Human Resources
� Supply-Chain Management
� Inventory Management
� Scheduling
� Maintenance
303

GLOBAL COMPANY PROFILE: RUSTY WALLACE’S NASCAR RACING TEAM
HIGH-PERFORMANCE TEAMWORK FROM THE PIT CREW MAKES
THE DIFFERENCE BETWEEN WINNING AND LOSING
I
n the 1990s, the popularity of NASCAR (National
Association for Stock Car Auto Racing) exploded,
bringing hundreds of millions of TV and
sponsorship dollars into the sport. With more
money, competition increased, as did the rewards for
winning on Sunday. The teams, headed by such names
as Rusty Wallace, Jeff Gordon, Dale Earnhardt, Jr.,
and Tony Stewart, are as famous as the New York
Yankees, Atlanta Hawks, or Chicago Bears.
The race car drivers may be famous, but it’s the pit
crews who often determine the outcome of a race.
Twenty years ago, crews were auto mechanics during
the week who simply did double duty on Sundays in
the pits. They did pretty well to change four tires in
less than 30 seconds. Today, because NASCAR teams
find competitive advantage wherever they can, taking
more than 16 seconds can be disastrous. A botched
pit stop is the equivalent of ramming your car against
the wall—crushing all hopes for the day.
On Rusty Wallace’s team, as on all the top NASCAR
squads, the crewmen who go “over the wall” are now
athletes, usually ex-college football or basketball
players with proven agility and strength. The Evernham
team, for example, includes a former defensive back
from Fairleigh Dickinson (who is now a professional tire
carrier) and a 300-pound lineman from East Carolina
University (who handles the jack). The Chip Ganassi
racing team includes baseball players from Wake
Forest, football players from University of Kentucky and
North Carolina, and a hockey player from Dartmouth.
Tire changers—the guys who wrench lug nuts off
and on—are a scarce human resource and average
$100,000 a year in salary. Jeff Gordon was reminded
of the importance of coordinated teamwork when five
of his “over-the-wall” guys jumped to Dale Jarrett’s
organization a few years ago; it was believed to be a
$500,000 per year deal.
A pit crew consists of seven men: a front-tire changer; a
rear-tire changer; front- and rear-tire carriers; a man who
jacks the car up; and two gas men with an 11-gallon can.
Every sport has its core competencies and key
metrics—for example, the speed of a pitcher’s fastball,� This Goodyear tire comes off Rusty Wallace’s car and is
no longer needed after going around the track for more
than 40 laps in a June 19 Michigan International
Speedway race.
� Lap 91—Tire is removed from Rusty Wallace’s car.
� Jamie Rolewicz takes tires from a pile of used
tires and puts them onto a cart.
304

a running back’s time on the 40-yard dash. In
NASCAR, a tire changer should get 5 lug nuts off in
1.2 seconds. The jackman should haul his 25-pound
aluminum jack from the car’s right side to left in 3.8
seconds. For tire carriers, it should take .7 seconds to
get a tire from the ground to mounted on the car.
The seven men who go over the wall are coached
and orchestrated. Coaches use the tools of OM and
watch “game tape” of pit stops and make intricate
adjustments to the choreography.
“There’s a lot of pressure,” says D. J.
Richardson, a Rusty Wallace team tire changer—
and one of the best in the business. Richardson
trains daily with the rest of the crew in the shop of
the team owner. They focus on cardiovascular work
and two muscle groups daily. Twice a week, they
simulate pit stops—there can be from 12 to 14
variations—to work on their timing.
In a recent race in Michigan, Richardson and the rest
of the Rusty Wallace team, with ergonomically designed
gas cans, tools, and special safety gear, were ready. On
lap 43, the split-second frenzy began, with Richardson—
air gun in hand—jumping over a 2-foot white wall and
sprinting to the right side of the team’s Dodge. A teammate
grabbed the tire and set it in place while Richardson
secured it to the car. The process was repeated on the left
side while the front crew followed the same procedure.
Coupled with refueling, the pit stop took 12.734 seconds.
After catching their breath for a minute, Richardson
and the other pit crew guys reviewed a videotape,
looking for split-second flaws.
The same process was repeated on lap 91. The Wallace
driver made a late charge on Jeff Burton and Kurt Busch on
the last lap and went from 14th place to a 10th place finish.
Sources: The Wall Street Journal (June 15, 2005): A1; and Orlando Sentinel
(June 26, 2005): C10–C12 and (February 11, 2001): M10–M11.
RUSTY WALLACE’S NASCAR RACING TEAM �
1 Wallace’s car pulls into the pit; the crew rushes tothe right side of the car to begin service.
FTC
CFT JM
CRT
RTC
GM#2
GM#1
Wall
2 Right side is jacked up, tire starts to come off; gasman is emptying his first can.
Wall
FTC
CFT
JM CRT
RTC
GM#2
GM#1
3 Action shifts to driver’s side of the car; gas mancarries second can of gas in.
Wall
FTC CFT CRTJM
RTC
GM#2
GM#1
sec. sec. sec.
4 The second can of gas is being emptied; driver’sside tires are being changed.
Wall
5 Service is complete. The jackman drops the car,which is the signal to the Wallace driver to exit
the pit.
Wall
Movement of the pit crew members who go over
the wall…
FTC CFT CRTJM
RTC
GM#2
GM#1
FTC CFT CRTJM RTC
GM#2
GM#1
JM = Jackman
FTC = Front tire carrier
CFT = Changer front tire
RTC = Rear tire carrier
CRT = Changer rear tire
GM#1 = Gas man #1
GM#2 = Gas man #2
sec. sec.
A good pit stop will take about 16 seconds.
� JM (Jackman) The jackman carries the hydraulic jack from the pit wall to raise the car’s right side. After new tires are bolted
on, he drops the car to the ground and repeats the process on the left side. His timing is crucial during this left side change,
because when he drops the car again, it’s the signal for the driver to go. The jackman has the most dangerous job of all the crew
members; during the right-side change, he is exposed to oncoming traffic down pit row. FTC (Front tire carrier) Each tire carrier
hauls a new 75-pound tire to the car’s right side, places it on the wheel studs and removes the old tire after the tire change. They
repeat this process on the left side of the car with a new tire rolled to them by crew members behind the pit wall. CFT (Changer
front tire) Tire changers run to the car’s right side and using an air impact wrench, they remove five lug nuts off the old tire and
bolt on a new tire. They repeat the process on the left side. RTC (Rear tire carrier) Same as front tire carrier, except RTC may
also adjust the rear jack bolt to alter the car’s handling. CRT (Changer rear tire) Same as FT but on two rear tires. Gas man #1
This gas man is usually the biggest and strongest person on the team. He goes over the wall carrying a 75-pound, 11-gallon
“dump can” whose nozzle he jams into the car’s fuel cell receptacle. He is then handed (or tossed) another can, and the process
is repeated. Gas man #2 Gets second gas can to Gas man #1 and catches excess fuel that spills out.
305

306 PART 2 Designing Operations
HUMAN RESOURCE STRATEGY FOR COMPETITIVE ADVANTAGE
Good human resource strategies are expensive, difficult to achieve, and hard to sustain. But, like
a NASCAR team, many organizations, from Hard Rock Cafe to Frito-Lay to Southwest Airlines,
have demonstrated that sustainable competitive advantage can be built through a human resource
strategy. The payoff can be significant and difficult for others to duplicate. In this chapter, we
will examine some of the tools available to operations managers for achieving competitive
advantage via human resource management.
The objective of a human resource strategy is to manage labor and design jobs so people are
effectively and efficiently utilized. As we focus on a human resource strategy, we want to ensure
that people:
1. Are efficiently utilized within the constraints of other operations management decisions.
2. Have a reasonable quality of work life in an atmosphere of mutual commitment and trust.
By reasonable quality of work life we mean a job that is not only reasonably safe and for which the
pay is equitable but that also achieves an appropriate level of both physical and psychological
requirements. Mutual commitment means that both management and employee strive to meet
common objectives. Mutual trust is reflected in reasonable, documented employment policies that
are honestly and equitably implemented to the satisfaction of both management and employee.1
When management has a genuine respect for its employees and their contributions to the firm,
establishing a reasonable quality of work life and mutual trust is not particularly difficult.
This chapter is devoted to showing how operations managers can achieve an effective human
resource strategy, which, as we have suggested in our opening profile of NASCAR racing teams,
may provide a competitive advantage.
Constraints on Human Resource Strategy
As Figure 10.1 suggests, many decisions made about people are constrained by other decisions.
First, the product mix may determine seasonality and stability of employment. Second, technol-
ogy, equipment, and processes may have implications for safety and job content. Third, the loca-
tion decision may have an impact on the ambient environment in which the employees work.
Finally, layout decisions, such as assembly line versus work cell, influence job content.
Technology decisions impose substantial constraints. For instance, some of the jobs in steel
mills are dirty, noisy, and dangerous; slaughterhouse jobs may be stressful and subject workers to
stomach-crunching stench; assembly-line jobs are often boring and mind numbing; and high
capital investments such as those required for manufacturing semiconductor chips may require
24-hour, 7-day-a-week operation in restrictive clothing.
We are not going to change these jobs without making changes in our other strategic deci-
sions. So, the trade-offs necessary to reach a tolerable quality of work life are difficult. Effective
managers consider such decisions simultaneously. The result: an effective, efficient system in
which both individual and team performance are enhanced through optimum job design.
Acknowledging the constraints imposed on human resource strategy, we now look at three
distinct decision areas of human resource strategy: labor planning, job design, and labor
standards.
VIDEO 10.1
Human Resources at Hard Rock
Cafe
1We find many companies calling their employees associates, individual contributors, or members of a particular team.
LO1: Describe labor planning policies 307
LO2: Identify the major issues in job
design 308
LO3: Identify major ergonomic and work
environment issues 312
LO4: Use the tools of methods analysis 314
Chapter 10 Learning Objectives
LO5: Identify four ways of establishing
labor standards 317
LO6: Compute the normal and standard
times in a time study 319
LO7: Find the proper sample size for
a time study 322
AUTHOR COMMENT
Mutual trust and
commitment are key
to a successful human
resource strategy.

Chapter 10 Human Resources, Job Design, and Work Measurement 307
LABOR PLANNING
Labor planning is determining staffing policies that deal with (1) employment stability,
(2) work schedules, and (3) work rules.
Employment-Stability Policies
Employment stability deals with the number of employees maintained by an organization at any
given time. There are two very basic policies for dealing with stability:
1. Follow demand exactly: Following demand exactly keeps direct labor costs tied to produc-
tion but incurs other costs. These other costs include (a) hiring and layoff costs, (b) unem-
ployment insurance, and (c) premium wages to entice personnel to accept unstable
employment. This policy tends to treat labor as a variable cost.
2. Hold employment constant: Holding employment levels constant maintains a trained work-
force and keeps hiring, layoff, and unemployment costs to a minimum. However, with
employment held constant, employees may not be utilized fully when demand is low, and
the firm may not have the human resources it needs when demand is high. This policy tends
to treat labor as a fixed cost.
The above policies are only two of many that can be efficient and provide a reasonable qual-
ity of work life. Firms must determine policies about employment stability.
Work Schedules
Although the standard work schedule in the U.S. is still five 8-hour days, many variations exist.
A currently popular variation is a work schedule called flextime. Flextime allows employees,
within limits, to determine their own schedules. A flextime policy might allow an employee
(with proper notification) to be at work at 8 A.M. plus or minus 2 hours. This policy allows more
autonomy and independence on the part of the employee. Some firms have found flextime a low-
cost fringe benefit that enhances job satisfaction. The problem from the OM perspective is that
much production work requires full staffing for efficient operations. A machine that requires
three people cannot run at all if only two show up. Having a waiter show up to serve lunch at 1:30
P.M. rather than 11:30 A.M. is not much help either.
Similarly, some industries find that their process strategies severely constrain their human
resource scheduling options. For instance, paper manufacturing, petroleum refining, and power
stations require around-the-clock staffing except for maintenance and repair shutdown.
Product strategy
Skills needed
Talents needed
Materials used
Safety
Process strategy
Technology
Machinery and
equipment used
Safety
HUMAN
RESOURCE
STRATEGY
Schedules
Time of day
Time of year
(seasonal)
Stability of
schedules
Individual differences
Strength and fatigue
Information processing
and response
Location strategy
Climate
Temperature
Noise
Light
Air quality
Layout strategy
Fixed position
Process
Assembly line
Work cell
Product
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
W
hat
W
he
re
H
ow
Pr
oc
ed
ur
e
When Who
� FIGURE 10.1
Constraints on Human
Resource Strategy
Labor planning
A means of determining
staffing policies dealing with
employment stability, work
schedules, and work rules.
LO1: Describe labor
planning policies
AUTHOR COMMENT
An operations manager
knows how to build
an effective human
resource strategy.
AUTHOR COMMENT
Achieving employment
stability, favorable work
schedules, and acceptable
work rules can be
challenging.

308 PART 2 Designing Operations
Another option is the flexible workweek. This plan often calls for fewer but longer days, such
as four 10-hour days or, as in the case of light-assembly plants, 12-hour shifts. Working 12-hour
shifts usually means working 3 days one week and 4 the next. Such shifts are sometimes called
compressed workweeks. These schedules are viable for many operations functions—as long as
suppliers and customers can be accommodated.
Another option is shorter days rather than longer days. This plan often moves employees to
part-time status. Such an option is particularly attractive in service industries, where staffing for
peak loads is necessary. Banks and restaurants often hire part-time workers. Also, many firms
reduce labor costs by reducing fringe benefits for part-time employees.
Job Classifications and Work Rules
Many organizations have strict job classifications and work rules that specify who can do what,
when they can do it, and under what conditions they can do it, often as a result of union pressure.
These job classifications and work rules restrict employee flexibility on the job, which in turn
reduces the flexibility of the operations function. Yet part of an operations manager’s task is to
manage the unexpected. Therefore, the more flexibility a firm has when staffing and establishing
work schedules, the more efficient and responsive it can be. This is particularly true in service orga-
nizations, where extra capacity often resides in extra or flexible staff. Building morale and meeting
staffing requirements that result in an efficient, responsive operation are easier if managers have
fewer job classifications and work-rule constraints. If the strategy is to achieve a competitive
advantage by responding rapidly to the customer, a flexible workforce may be a prerequisite.
JOB DESIGN
Job design specifies the tasks that constitute a job for an individual or a group. We examine five
components of job design: (1) job specialization, (2) job expansion, (3) psychological compo-
nents, (4) self-directed teams, and (5) motivation and incentive systems.
Labor Specialization
The importance of job design as a management variable is credited to the 18th-century econo-
mist Adam Smith. Smith suggested that a division of labor, also known as labor specialization
(or job specialization), would assist in reducing labor costs of multiskilled artisans. This is
accomplished in several ways:
1. Development of dexterity and faster learning by the employee because of repetition
2. Less loss of time because the employee would not be changing jobs or tools
3. Development of specialized tools and the reduction of investment because each employee
has only a few tools needed for a particular task
The 19th-century British mathematician Charles Babbage determined that a fourth consideration
was also important for labor efficiency. Because pay tends to follow skill with a rather high cor-
relation, Babbage suggested paying exactly the wage needed for the particular skill required. If
the entire job consists of only one skill, then we would pay for only that skill. Otherwise, we
would tend to pay for the highest skill contributed by the employee. These four advantages of
labor specialization are still valid today.
A classic example of labor specialization is the assembly line. Such a system is often very effi-
cient, although it may require employees to do short, repetitive, mind-numbing jobs. The wage rate
for many of these jobs, however, is very good. Given the relatively high wage rate for the modest
skills required in many of these jobs, there is often a large pool of employees from which to choose.
From the manager’s point of view, a major limitation of specialized jobs is their failure to
bring the whole person to the job. Job specialization tends to bring only the employee’s manual
skills to work. In an increasingly sophisticated knowledge-based society, managers may want
employees to bring their mind to work as well.
Job Expansion
Moving from labor specialization toward more varied job design may improve the quality of work
life. The theory is that variety makes the job “better” and that the employee therefore enjoys a
higher quality of work life. This flexibility thus benefits the employee and the organization.
Job design
An approach that specifies the
tasks that constitute a job for an
individual or a group.
Labor specialization
(or job specialization)
The division of labor into unique
(“special”) tasks.
LO2: Identify the major
issues in job design
AUTHOR COMMENT
Job design is a key
ingredient of a motivated
workforce.

Chapter 10 Human Resources, Job Design, and Work Measurement 309
We modify jobs in a variety of ways. The first approach is job enlargement, which occurs
when we add tasks requiring similar skill to an existing job. Job rotation is a version of job
enlargement that occurs when the employee is allowed to move from one specialized job to
another. Variety has been added to the employee’s perspective of the job. Another approach is
job enrichment, which adds planning and control to the job. An example is to have department
store salespeople responsible for ordering, as well as selling, their goods. Job enrichment can be
thought of as vertical expansion, as opposed to job enlargement, which is horizontal. These
ideas are shown in Figure 10.2.
A popular extension of job enrichment, employee empowerment is the practice of enriching
jobs so employees accept responsibility for a variety of decisions normally associated with staff
specialists. Empowering employees helps them take “ownership” of their jobs so they have a per-
sonal interest in improving performance.
Psychological Components of Job Design
An effective human resources strategy also requires consideration of the psychological compo-
nents of job design. These components focus on how to design jobs that meet some minimum
psychological requirements.
Hawthorne Studies The Hawthorne studies introduced psychology to the workplace. They
were conducted in the late 1920s at Western Electric’s Hawthorne plant near Chicago. These studies
were initiated to determine the impact of lighting on productivity. Instead, they found the dynamic
social system and distinct roles played by employees to be more important than the intensity of the
lighting. They also found that individual differences may be dominant in what an employee expects
from the job and what the employee thinks her or his contribution to the job should be.
Core Job Characteristics In the decades since the Hawthorne studies, substantial research
regarding the psychological components of job design has taken place. Hackman and Oldham
have incorporated much of that work into five desirable characteristics of job design.2 They sug-
gest that jobs should include the following characteristics:
1. Skill variety, requiring the worker to use a variety of skills and talents
2. Job identity, allowing the worker to perceive the job as a whole and recognize a start and a
finish
3. Job significance, providing a sense that the job has an impact on the organization and society
4. Autonomy, offering freedom, independence, and discretion
5. Feedback, providing clear, timely information about performance
Including these five ingredients in job design is consistent with job enlargement, job enrichment,
and employee empowerment. We now want to look at some of the ways in which teams can be
used to expand jobs and achieve these five job characteristics.
Enriched job
Planning
(Participate in a cross-function
quality improvement team.)
Task #2
(Adhere labels
to printed
circuit board.)
Enlarged job
Task #3
(Lock printed circuit
board into fixture for
next operation.)
Control
(Test circuits after assembly.)
Present job
(Manually insert and
solder six resistors.)
� FIGURE 10.2
An Example of Job
Enlargement (horizontal
job expansion) and Job
Enrichment (vertical job
expansion)
AUTHOR COMMENT
The job can be enlarged
horizontally by job rotation
to tasks 2 and 3, or these
tasks can be made a part
of the present job.
AUTHOR COMMENT
Job enrichment, expanding
the job vertically, can occur
by adding other types of
tasks, such as participation in
a quality team (planning) and
testing tasks (control).
Job enlargement
The grouping of a variety of
tasks about the same skill level;
horizontal enlargement.
Job rotation
A system in which an employee
is moved from one specialized
job to another.
Job enrichment
A method of giving an employee
more responsibility that
includes some of the planning
and control necessary for job
accomplishment; vertical
expansion.
Employee
empowerment
Enlarging employee jobs so that
the added responsibility and
authority is moved to the lowest
level possible.
2See “Motivation Through the Design of Work,” in Jay Richard Hackman and Greg R. Oldham, eds., Work Redesign
(Reading, MA: Addison-Wesley, 1980), and A. Thomas, W. C. Buboltz, and C. Winkelspecht, “Job Characteristics and
Personality as Predictors of Job Satisfaction,” Organizational Analysis, 12, no. 2 (2004): 205–219.

310 PART 2 Designing Operations
Self-Directed Teams
Many world-class organizations have adopted teams to foster mutual trust and commitment, and
provide the core job characteristics. One team concept of particular note is the self-directed team:
a group of empowered individuals working together to reach a common goal. These teams may be
organized for long- or short-term objectives. Teams are effective primarily because they can eas-
ily provide employee empowerment, ensure core job characteristics, and satisfy many of the psy-
chological needs of individual team members. A job design continuum is shown in Figure 10.3.
Limitations of Job Expansion If job designs that enlarge, enrich, empower, and use teams
are so good, why are they not universally used? Mostly it is because of costs. Here are a few lim-
itations of expanded job designs:
• Higher capital cost: Job expansion may require additional equipment and facilities.
• Individual differences: Some employees opt for the less complex jobs.
• Higher wage rates: Expanded jobs may well require a higher average wage.
• Smaller labor pool: Because expanded jobs require more skill and acceptance of more
responsibility, job requirements have increased.
• Higher training costs: Job expansion requires training and cross-training. Therefore, training
budgets need to increase.
Despite these limitations, firms are finding a substantial payoff in job expansion.
Specialization
Job expansion
S
e
lf-
d
ir
e
ct
io
n
Enlargement
Enrichment
Empowerment
Self-directed
teams
� FIGURE 10.3
Job Design Continuum
Southwest Airlines—consistently at the top of the airline pack in travel surveys, fewest lost bags and complaints, and highest
profits—hires people with enthusiasm and empowers them to excel. A barefoot chairman of the board, Herb Kelleher,
clings to the tail of a jet (left photo). Says Kelleher, “I’ve tried to create a culture of caring for people in the totality of their
lives, not just at work. Someone can go out and buy airplanes and ticket counters, but they can’t buy our culture, our
esprit de corps.”
AUTHOR COMMENT
Increasing reliance on an
employee’s contribution and
increasing responsibility
accepted by the employee.
Self-directed team
A group of empowered
individuals working together to
reach a common goal.

Chapter 10 Human Resources, Job Design, and Work Measurement 311
Motivation and Incentive Systems
Our discussion of the psychological components of job design provides insight into the factors
that contribute to job satisfaction and motivation. In addition to these psychological factors, there
are monetary factors. Money often serves as a psychological as well as financial motivator.
Monetary rewards take the form of bonuses, profit and gain sharing, and incentive systems.
Bonuses, typically in cash or stock options, are often used at executive levels to reward man-
agement. Profit-sharing systems provide some part of the profit for distribution to employees. A
variation of profit sharing is gain sharing, which rewards employees for improvements made in
an organization’s performance. The most popular of these is the Scanlon plan, in which any
reduction in the cost of labor is shared between management and labor.
Incentive systems based on individual or group productivity are used throughout the world in a
wide variety of applications, including nearly half of the manufacturing firms in America.
Production incentives often require employees or crews to produce at or above a predetermined
standard. The standard can be based on a “standard time” per task or number of pieces made. Both
systems typically guarantee the employee at least a base rate. Incentives, of course, need not be
monetary. Awards, recognition, and other kinds of preferences such as a preferred work schedule
can be effective. (See the OM in Action box “Using Incentives to Unsnarl Traffic Jams in the
OR.”) Hard Rock Cafe has successfully reduced its turnover by giving every employee—from the
CEO to the busboys—a $10,000 gold Rolex watch on their 10th anniversary with the firm.
With the increasing use of teams, various forms of team-based pay are also being developed.
Many are based on traditional pay systems supplemented with some form of bonus or incentive
system. However, because many team environments require cross training of enlarged jobs,
knowledge-based pay systems have also been developed. Under knowledge-based (or skill-
based) pay systems, a portion of the employee’s pay depends on demonstrated knowledge or
skills possessed. At Wisconsin’s Johnsonville Sausage Co., employees receive pay raises only by
mastering new skills such as scheduling, budgeting, and quality control.
ERGONOMICS AND THE WORK ENVIRONMENT
With the foundation provided by Frederick W. Taylor, the father of the era of scientific manage-
ment, we have developed a body of knowledge about people’s capabilities and limitations. This
knowledge is necessary because humans are hand/eye animals possessing exceptional capabili-
ties and some limitations. Because managers must design jobs that can be done, we now intro-
duce a few of the issues related to people’s capabilities and limitations.
Ergonomics The operations manager is interested in building a good interface between
humans, the environment, and machines. Studies of this interface are known as ergonomics.
Ergonomics means “the study of work.” (Ergon is the Greek word for “work.”) The term human
Hospitals have long offered surgeons a precious perk:
scheduling the bulk of their elective surgeries in the middle
of the week so they can attend conferences, teach, or relax
during long weekends. But at Boston Medical Center,
St. John’s Health Center (in Missouri), and Elliot Health
System (in New Hampshire), this practice, one of the
biggest impediments to a smooth-running hospital, is
changing. “Block scheduling” jams up operating rooms,
overloads nurses at peak times, and bumps scheduled
patients for hours and even days.
Boston Medical Center’s delays and cancellations of
elective surgeries were nearly eliminated after surgeons
agreed to stop block scheduling and to dedicate one OR
for emergency cases. Cancellations dropped to 3, from
334, in just one 6-month period. In general, hospitals
changing to the new system of spreading out elective
surgeries during the week increase their surgery capacity
by 10%, move patients through the operating room faster,
and reduce nursing overtime.
To get doctors on board at St. John’s, the hospital
offered a carrot and two sticks: Doctors who were more
than 10 minutes late 10% of the time lost their coveted
7:30 A.M. start times and were fined a portion of their fee—
with proceeds going to a kitty that rewarded the best on-
time performers. Surgeons’ late start times quickly dropped
from 16% to 5% and then to less than 1% within a year.
Sources: International Journal of Production Economics (January–February
2006): 52; The Wall Street Journal (August 10, 2005): D1, D3; and Hospitals
& Health Networks (September 2005): 24–25.
OM in Action � Using Incentives to Unsnarl Traffic Jams in the OR
Ergonomics
The study of the human
interface with the environment
and machines.
AUTHOR COMMENT
Ergonomics becomes
more critical as
technologies become
more complex.

312 PART 2 Designing Operations
factors is often substituted for the word ergonomics. Understanding ergonomic issues helps to
improve human performance.
Male and female adults come in limited configurations. Therefore, design of tools and the
workplace depends on the study of people to determine what they can and cannot do. Substantial
data have been collected that provide basic strength and measurement data needed to design
tools and the workplace. The design of the workplace can make the job easier or impossible.
Additionally, we now have the ability, through the use of computer modeling, to analyze human
motions and efforts.
Operator Input to Machines Operator response to machines, be they hand tools, pedals,
levers, or buttons, needs to be evaluated. Operations managers need to be sure that operators have
the strength, reflexes, perception, and mental capacity to provide necessary control. Such prob-
lems as carpal tunnel syndrome may result when a tool as simple as a keyboard is poorly
designed. The photo of the Champ race car steering wheel below shows one innovative approach
to critical operator input.
Feedback to Operators Feedback to operators is provided by sight, sound, and feel; it
should not be left to chance. The mishap at the Three Mile Island nuclear facility, America’s
worst nuclear experience, was in large part the result of poor feedback to the operators about
reactor performance. Nonfunctional groups of large, unclear instruments and inaccessible con-
trols, combined with hundreds of confusing warning lights, contributed to that failure. Such
relatively simple issues make a difference in operator response and, therefore, performance.
The photos showing changes in aircraft cockpits indicate recent efforts to improve feedback to
operators.
The Work Environment The physical environment in which employees work affects their
performance, safety, and quality of work life. Illumination, noise and vibration, temperature,
humidity, and air quality are work-environment factors under the control of the organization and
the operations manager. The manager must approach them as controllable.
Ergonomic issues occur in the
office as well as in the factory.
Here an ergonomics consultant is
measuring the angle of a computer
operator’s neck. Posture, which
is related to desk height, chair
height and position, keyboard
placement, and computer screen,
is an important factor in reducing
back and neck pain that can be
caused by extended hours at a
computer.
Drivers of race cars have no
time to grasp for controls or
to look for small hidden
gauges. Controls and
instrumentation for modern
race cars have migrated to
the steering wheel itself—the
critical interface between
man and machine.
LO3: Identify major
ergonomic and work
environment issues

Chapter 10 Human Resources, Job Design, and Work Measurement 313
Illumination is necessary, but the proper level depends on the work being performed. Figure
10.4a provides some guidelines. However, other lighting factors are important. These include
reflective ability, contrast of the work surface with surroundings, glare, and shadows.
Noise of some form is usually present in the work area, and most employees seem to adjust
well. However, high levels of sound will damage hearing. Figure 10.4b provides indications of
500 and up
Exacting Tasks
(electronic and
watch assembly,
dentistry)
Normal Visual
(office, classroom,
machining)
General Interiors
(conference,
rest rooms,
restaurants)
Assembly Tasks
(parts assembly)
Large Objects
(warehouses,
hallways)
Small Details
(engraving,
detail drafting)
75–100 25–50
50–75
10–25100–200
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
Fragile
� FIGURE 10.4a Recommended Levels of Illumination (using foot-candles (ft-c) as the measure of illumination)
30
Soft
whisper
Very
quiet
Quiet Intrusive Ear protection
needed if exposed
8 hours or more
Very
annoying
Painful
Subway
train
Printing
press
Pneumatic
hammer
Prop
airplane
Jet
take-off
Residential
area of
Chicago at
night
Near
freeway
auto
traffic
Light
traffic
(100 feet)
Vacuum
cleaner
(10 feet)
40 50 60 70 80 90 100 110 120
� FIGURE 10.4b Decibel (dB) Levels for Various Sounds
Adapted from A. P. G. Peterson and E. E. Gross, Jr., Handbook of Noise Measurement, 7th ed. (New Concord, MA: General Radio Co.).
AUTHOR COMMENT
Noise in the work
environment can increase
the risk of a heart attack
by 50% or more.
An important human
factor/ergonomic issue in the
aircraft industry is cockpit
design. Newer “glass
cockpits” (on the right)
display information in more
concise form than the
traditional rows of round
analog dials and gauges (on
the left). New displays reduce
the chance of human error,
which is a factor in about
two-thirds of commercial air
accidents. Fractions of a
second in the cockpit can
literally mean the difference
between life and death.

the sound generated by various activities. Extended periods of exposure to decibel levels above
85 dB are permanently damaging. The Occupational Safety and Health Administration (OSHA)
requires ear protection above this level if exposure equals or exceeds 8 hours. Even at low levels,
noise and vibration can be distracting and can raise a person’s blood pressure, so managers make
substantial effort to reduce noise and vibration through good machine design, enclosures, or
insulation.
Temperature and humidity parameters have also been well established. Managers with activi-
ties operating outside the established comfort zone should expect adverse effect on performance.
METHODS ANALYSIS
Methods analysis focuses on how a task is accomplished. Whether controlling a machine or
making or assembling components, how a task is done makes a difference in performance,
safety, and quality. Using knowledge from ergonomics and methods analysis, methods engineers
are charged with ensuring that quality and quantity standards are achieved efficiently and safely.
Methods analysis and related techniques are useful in office environments as well as in the fac-
tory. Methods techniques are used to analyze:
1. Movement of individuals or material. The analysis is performed using flow diagrams and
process charts with varying amounts of detail.
2. Activity of human and machine and crew activity. This analysis is performed using
activity charts (also known as man–machine charts and crew charts).
3. Body movement (primarily arms and hands). This analysis is performed using operations
charts.
Flow diagrams are schematics (drawings) used to investigate movement of people or mate-
rial. Britain’s Paddy Hopkirk Factory in Figure 10.5 shows one version of a flow diagram,
and the OM in Action box “Saving Steps on the B-2 Bomber” provides another way to ana-
lyze long-cycle repetitive tasks. Hopkirk’s old method is shown in Figure 10.5(a), and a new
314 PART 2 Designing Operations
Flow diagram
A drawing used to analyze
movement of people or
material.
gather chemicals, hose, gauges, and other material
needed just to get ready for the job. By making
prepackaged kits for the job, Northrop Grumman cut
preparation time to zero and the time to complete the job
dropped from 8.4 hours to 1.6 hours (as seen above).
Sources: BusinessWeek (May 28, 2001): 14; Aviation Week & Space
Technology (January 17, 2000): 44; and New York Times (March 9, 1999):
C1, C9.
The 26 trips to various workstations to gather the tools and equipment to
apply tape to the B-2 bomber are shown as blue lines above.
The mechanic’s work path is reduced to the small area of blue lines
shown here.
OM in Action � Saving Steps on the B-2 Bomber
The aerospace industry is noted for making exotic
products, but it is also known for doing so in a very
expensive way. The historical batch-based processes used
in the industry have left a lot of room for improvement. In
leading the way, Northrop Grumman analyzed the work
flow of a mechanic whose job in the Palmsdale, California,
plant was to apply about 70 feet of tape to the B-2 stealth
bomber. The mechanic (see the graphic below) walked
away from the plane 26 times and took 3 hours just to
AUTHOR COMMENT
Methods analysis
provides the tools for
understanding systems.
Methods analysis
A system that involves
developing work procedures
that are safe and produce
quality products efficiently.
LO4: Use the tools of
methods analysis

Chapter 10 Human Resources, Job Design, and Work Measurement 315
� FIGURE 10.5 Flow Diagrams and Process Chart of Axle-Stand Production at Paddy Hopkirk Factory
(a) Old method; (b) new method; (c) process chart of axle-stand production using Paddy Hopkirk’s new method
(shown in (b)).
From
press
machine
Mach. 2
Mach. 3
Storage bins
Storage
bins
Paint
shop
(a) (c)
(b)
Welding
Paint
shop
Machine 2
Machine 3
Machine 4
Machine 1
Mach. 4
Present Method
Proposed Method
SUBJECT CHARTED
DEPARTMENT
DIST.
IN
FEET
TIME
IN
MINS.
CHART
SYMBOLS
DATE
CHART BY
CHART NO.
SHEET NO. OF
PROCESS CHART
PROCESS DESCRIPTION
TOTAL
= operation; = transportation; = inspection; = delay; = storage
X
Axle-stand Production
Work cell for axle stand
50
5
4
4
4
20
10
97
3
4
2.5
3.5
4
Poka-
yoke
4
4
25
From press machine to storage bins at work cell
Move to machine 4
Move to welding
Poka-yoke inspection at welding
Weld
Move to painting
Paint
Operation at machine 4
Storage bins
Move to machine 1
Operation at machine 1
Move to machine 2
Move to machine 3
Operation at machine 3
Operation at machine 2
8 / 1 / 10
JH
1
1 1
From
press
mach.
Machine 1
Welding
AUTHOR COMMENT
Flow diagrams provide
an excellent way of
understanding layout issues.
Operations chart
A chart depicting right- and left-
hand motions.
Process chart
Graphic representations that
depict a sequence of steps for
a process.
Activity chart
A way of improving utilization
of an operator and a machine or
some combination of operators
(a crew) and machines.
method, with improved work flow and requiring less storage and space, is shown in Figure
10.5(b). Process charts use symbols, as in Figure 10.5(c), to help us understand the move-
ment of people or material. In this way non-value-added activities can be recognized and
operations made more efficient. Figure 10.5(c) is a process chart used to supplement the flow
diagrams shown in Figure 10.5(b).
Activity charts are used to study and improve the utilization of an operator and a machine or
some combination of operators (a “crew”) and machines. The typical approach is for the analyst
to record the present method through direct observation and then propose the improvement on a
second chart. Figure 10.6 is an activity chart to show a proposed improvement for a two-person
crew at Quick Car Lube.
Body movement is analyzed by an operations chart. It is designed to show economy of
motion by pointing out wasted motion and idle time (delay). The operations chart (also known as
a right-hand/left-hand chart) is shown in Figure 10.7.
THE VISUAL WORKPLACE
A visual workplace uses low-cost visual devices to share information quickly and accurately.
Well-designed displays and graphs root out confusion and replace difficult-to-understand print-
outs and paperwork. Because workplace data change quickly and often, operations managers
Visual workplace
Uses a variety of visual
communication techniques
to rapidly communicate
information to stakeholders.

316 PART 2 Designing Operations
Quantities in bins indicate ongoing
daily requirements and clipboards
provide information on schedule
changes.
A “3-minute
service” clock
reminds
employees
of the goal.
Process specifications and operating
procedures are posted in each work area.
Andon
Visual signals at the machine
notify support personnel.
Line/machine
stoppage
Parts/
maintenance
needed
All systems go
Reorder
point
Visual kanbans reduce inventory
and foster JIT.
Part A Part B Part C
Visual utensil holder
encourages housekeeping.
GOAL
ACTUAL
3:00
2:10
� FIGURE 10.8 The Visual Workplace
OPERATOR #1 OPERATOR #2
TIME % TIME %
WORK
IDLE
OPERATION:
EQUIPMENT:
OPERATOR:
STUDY NO.: ANALYST:
SUBJECT
PRESENT
PROPOSED DEPT.
SHEET
OF
CHART
BY
DATE
TIME TIME TIME
ACTIVITY CHART
Repeat
cycle
12 100 12 100
Oil change & fluid check
Quick Car Lube 8-1-10
LSA
Move car to pitTake order
Drain oilVacuum car
Check
transmission
Check
transmission
Clean windows
Change oil filter
Check under
hood
Replace oil plugFill with oil
Move car to front
for customerComplete bill
Move next car to pitGreet nextcustomer
Drain oilVacuum car
Clean windows
Operator #1 Operator #2
1
1
One bay/pit
Two-person crew
NG0 0 0 0
� FIGURE 10.6 Activity Chart for Two-Person Crew Doing
an Oil Change in 12 Minutes at Quick Car Lube
1
2
3
4
5
6
7
Reach for bolt
Grasp bolt
Move bolt
Hold bolt
Hold bolt
Hold bolt
Hold bolt
OPERATION
TRANSPORT.
INSPECTION
DELAY
STORAGE
SYMBOLS PRESENT PROPOSED
LH RH LH RH
OPERATIONS CHART
Idle
Idle
Idle
Reach for washer
Grasp washer
Move washer to bolt
Place washer on bolt
LEFT-HAND ACTIVITY
METHODPresent
RIGHT-HAND ACTIVITY
METHODPresentSYMBOLS SYMBOLS DIST.DIST.
PROCESS:
EQUIPMENT:
OPERATOR:
STUDY NO:
DATE:
METHOD ( PRESENT / PROPOSED )
REMARKS:
/ / SHEET NO. of
Bolt–washer assembly
KJH
8 1 10 1 1
2 3
4 3
1 1 ANALYST:
6″
8″
� FIGURE 10.7 Operations Chart (right-hand/left-hand chart) for
Bolt-Washer Assembly
AUTHOR COMMENT
Activity charts are helpful for
understanding crew or
man–machine interaction. need to share accurate and up-to-date information. Changing customer requirements, specifica-
tions, schedules, and other details must be rapidly communicated to those who can make things
happen.
The visual workplace can eliminate non-value-added activities by making standards, prob-
lems, and abnormalities visual (see Figure 10.8). The visual workplace needs less supervision
because employees understand the standard, see the results, and know what to do.

Chapter 10 Human Resources, Job Design, and Work Measurement 317
LABOR STANDARDS
So far in this chapter, we have discussed labor planning and job design. The third requirement of
an effective human resource strategy is the establishment of labor standards. Labor standards
are the amount of time required to perform a job or part of a job. Effective manpower planning is
dependent on a knowledge of the labor required.
Modern labor standards originated with the works of Frederick W. Taylor and Frank and
Lillian Gilbreth at the beginning of the 20th century. At that time, a large proportion of work was
manual, and the resulting labor content of products was high. Little was known about what con-
stituted a fair day’s work, so managers initiated studies to improve work methods and understand
human effort. These efforts continue to this day. Although labor costs are often less than 10% of
sales, labor standards remain important and continue to play a major role in both service and
manufacturing organizations. They are often a beginning point for determining staffing require-
ments. With over half of the manufacturing plants in America using some form of labor incentive
system, good labor standards are a requirement.
Effective operations management requires meaningful standards that help a firm determine:
1. Labor content of items produced (the labor cost)
2. Staffing needs (how many people it will take to meet required production)
3. Cost and time estimates prior to production (to assist in a variety of decisions, from cost esti-
mates to make-or-buy decisions)
4. Crew size and work balance (who does what in a group activity or on an assembly line)
5. Expected production (so that both manager and worker know what constitutes a fair day’s
work)
6. Basis of wage-incentive plans (what provides a reasonable incentive)
7. Efficiency of employees and supervision (a standard is necessary against which to determine
efficiency)
Properly set labor standards represent the amount of time that it should take an average employee
to perform specific job activities under normal working conditions. Labor standards are set in
four ways:
1. Historical experience
2. Time studies
3. Predetermined time standards
4. Work sampling
Historical Experience
Labor standards can be estimated based on historical experience—that is, how many labor-
hours were required to do a task the last time it was performed. Historical standards have the
advantage of being relatively easy and inexpensive to obtain. They are usually available from
employee time cards or production records. However, they are not objective, and we do not
know their accuracy, whether they represent a reasonable or a poor work pace, and whether
unusual occurrences are included. Because these variables are unknown, their use is not
recommended. Instead, time studies, predetermined time standards, and work sampling are
preferred.
Time Studies
The classical stopwatch study, or time study, originally proposed by Frederick W. Taylor in
1881, involves timing a sample of a worker’s performance and using it to set a standard. (See the
OM In Action box, “Saving Seconds at Retail Boosts Productivity.”) A trained and experienced
person can establish a standard by following these eight steps:
1. Define the task to be studied (after methods analysis has been conducted).
2. Divide the task into precise elements (parts of a task that often take no more than a few seconds).
3. Decide how many times to measure the task (the number of job cycles or samples needed).
4. Time and record elemental times and ratings of performance.
AUTHOR COMMENT
Labor standards exist for
check-out clerks, mechanics,
UPS drivers, and many
factory workers.
Labor standards
The amount of time required to
perform a job or part of a job.
LO5: Identify four ways of
establishing labor standards
AUTHOR COMMENT
Stopwatch studies are
the most widely used
labor standard method.
Time study
Timing a sample of a worker’s
performance and using it as a
basis for setting a standard
time.

318 PART 2 Designing Operations
5. Compute the average observed (actual) time. The average observed time is the arithmetic
mean of the times for each element measured, adjusted for unusual influence for each element:
(10-1)
6. Determine performance rating (work pace) and then compute the normal time for each
element.
(10-2)
The performance rating adjusts the average observed time to what a trained worker could
expect to accomplish working at a normal pace. For example, a worker should be able to
walk 3 miles per hour. He or she should also be able to deal a deck of 52 cards into 4 equal
piles in 30 seconds. A performance rating of 1.05 would indicate that the observed worker
performs the task slightly faster than average. Numerous videos specify work pace on
Normal time = 1Average observed time2 * 1Performance rating factor2
Average observed time =
(Sum of the times recorded to perform each element)
Number of observations
Each day—in fact, 130 times each day—
Tim Nelson leans back into a La-Z-Boy
recliner, sofa section, or love seat at the
company’s Dayton factory. He inspects
for overall comfort; he must sink slightly
into the chair, but not too far. As in the
fable “Goldilocks and the Three Bears,”
the chair must not be too firm or too soft;
it must be just right—or it is sent back for
restuffing. If it passes the “firm” test, he
then rocks back and forth, making certain
the chair is properly balanced and moves
smoothly. Then Tim checks the footrest,
arches his back, and holds the position.
Hopping to his feet, he does a walk-
around visual check; then it is on to the
next chair. One down, and 129 to go.
Retail services, like factory assembly lines, need labor
standards. And the Gap, Office Depot, Toys “R” Us, and
Meijer are among the many firms that use them. Labor is
usually the largest single expense after purchases in
retailing, meaning it gets special attention. Labor standards
are set for everything from greeting customers, to number
of cases loaded onto shelves, to scanning merchandise at
the cash register.
Meijer, a Midwestern chain of 190 “big box” stores,
includes cashiers in its labor standards. Since Meijer sells
everything from groceries to clothes to automotive goods,
cashier labor standards include adjustments of allowances
for the vast variety of merchandise being purchased. This
includes clothes with hard-to-find bar codes and bulky
items that are not usually removed from the shopping cart.
Allowances are also made for how customers pay, the
number of customers returning to an aisle for a forgotten
item, and elderly and handicapped customers.
Employees are
expected to meet 95% of
the standard. Failure to do
so moves an employee to
counseling, training, and
other alternatives. Meijer
has added fingerprint
readers to cash registers,
allowing cashiers to sign
in directly at their register. This saves time and boosts
productivity by avoiding a stop at the time clock.
The bottom line: as retail firms seek competitive
advantage via lower prices, they are finding that good
labor standards are not only shaving personnel costs by
5% to 15% but also contributing to more accurate data
for improved scheduling.
Sources: The Wall Street Journal, (November 17, 2008): A1, A15; www.
Meijer.com; and Labor Talk, (Summer 2007).
OM in Action � Saving Seconds at Retail Boosts Productivity
Average observed time
The arithmetic mean of the
times for each element
measured, adjusted for unusual
influence for each element.
Normal time
The average observed time,
adjusted for pace.

www.Meijer.com

www.Meijer.com

Chapter 10 Human Resources, Job Design, and Work Measurement 319
which professionals agree, and benchmarks have been established by the Society for the
Advancement of Management. Performance rating, however, is still something of an art.
7. Add the normal times for each element to develop a total normal time for the task.
8. Compute the standard time. This adjustment to the total normal time provides for
allowances such as personal needs, unavoidable work delays, and worker fatigue:
(10-3)
Personal time allowances are often established in the range of 4% to 7% of total time, depending
on nearness to rest rooms, water fountains, and other facilities. Delay allowances are often set as
a result of the actual studies of the delay that occurs. Fatigue allowances are based on our grow-
ing knowledge of human energy expenditure under various physical and environmental condi-
tions. A sample set of personal and fatigue allowances is shown in Table 10.1. Example 1
illustrates the computation of standard time.
Standard time =
Total normal time
1 – Allowance factor
LO6: Compute the normal
and standard times in a time
study
1. Constant allowances:
(A) Personal allowance . . . . . . . . . . . . . . . . . . . . . . . . . . 5
(B) Basic fatigue allowance . . . . . . . . . . . . . . . . . . . . . . 4
2. Variable allowances:
(A) Standing allowance . . . . . . . . . . . . . . . . . . . . . . . . . . 2
(B) Abnormal position allowance:
(i) Awkward (bending) . . . . . . . . . . . . . . . . . . . . . . . 2
(ii) Very awkward (lying, stretching) . . . . . . . . . . . . . . 7
(C) Use of force or muscular energy in
lifting, pulling, pushing
Weight lifted (pounds):
20 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
40 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
60 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
(D) Bad light:
(i) Well below recommended . . . . . . . . . . . . . . . . . . 2
(ii) Quite inadequate . . . . . . . . . . . . . . . . . . . . . . . . . 5
(E) Atmospheric conditions (heat and humidity):
Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0–10
(F) Close attention:
(i) Fine or exacting . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
(ii) Very fine or very exacting . . . . . . . . . . . . . . . . . . .5
(G) Noise level:
(i) Intermittent—loud . . . . . . . . . . . . . . . . . . . . . . . . . 2
(ii) Intermittent—very loud or high pitched . . . . . . . . 5
(H) Mental strain:
(i) Complex or wide span of attention . . . . . . . . . . . . 4
(ii) Very complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
(I) Tediousness:
(i) Tedious . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
(ii) Very tedious . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
� TABLE 10.1 Allowance Factors (in percentage) for Various Classes of Work
Standard time
An adjustment to the total
normal time; the adjustment
provides allowances for
personal needs, unavoidable
work delays, and fatigue.
� EXAMPLE 1
Determining
normal and
standard time
The time study of a work operation at a Red Lobster restaurant yielded an average observed time of 4.0
minutes. The analyst rated the observed worker at 85%. This means the worker performed at 85% of
normal when the study was made. The firm uses a 13% allowance factor. Red Lobster wants to com-
pute the normal time and the standard time for this operation.
APPROACH � The firm needs to apply Equations (10–2) and (10–3).
SOLUTION �
INSIGHT � Because the observed worker was rated at 85% (slower than average), the normal time
is less than the worker’s 4.0-minute average time.
LEARNING EXERCISE � If the observed worker is rated at 115% (faster than average), what
are the new normal and standard times? [Answer: 4.6 min, 5.287 min.]
RELATED PROBLEMS � 10.13, 10.14, 10.15, 10.16, 10.17, 10.18, 10.19, 10.20, 10.21, 10.33
EXCEL OM Data File Ch10Ex1.xls can be found at www.pearsonhighered.com/heizer.
= 3.9 min
Standard time =
Normal time
1 – Allowance factor
=
3.4
1 – .13
=
3.4
.87
= 3.4 min
= 14.021.852
Normal time = 1Average observed time2 * 1Performance rating factor2
Average observed time = 4.0 min

www.pearsonhighered.com/heizer

320 PART 2 Designing Operations
Example 2 uses a series of actual stopwatch times for each element.
Observations (minutes)
Job Element 1 2 3 4 5 Performance Rating
(A) Compose and type letter 8 10 9 21* 11 120%
(B) Type envelope address 2 3 2 1 3 105%
(C) Stuff, stamp, seal, and sort envelopes 2 1 5* 2 1 110%
EXAMPLE 2 �
Using time studies
to compute
standard time
Management Science Associates promotes its management development seminars by mailing thou-
sands of individually composed and typed letters to various firms. A time study has been conducted on
the task of preparing letters for mailing. On the basis of the following observations, Management
Science Associates wants to develop a time standard for this task. The firm’s personal, delay, and
fatigue allowance factor is 15%.
APPROACH � Once the data have been collected, the procedure is to:
1. Delete unusual or nonrecurring observations.
2. Compute the average time for each element, using Equation (10-1).
3. Compute the normal time for each element, using Equation (10-2).
4. Find the total normal time.
5. Compute the standard time, using Equation (10-3).
SOLUTION �
1. Delete observations such as those marked with an asterisk (*). (These may be due to business
interruptions, conferences with the boss, or mistakes of an unusual nature; they are not part of the
job element, but may be personal or delay time.)
2. Average time for each job element:
3. Normal time for each job element:
Note: Normal times are computed for each element because the performance rating factor (work pace)
may vary for each element, as it did in this case.
4. Add the normal times for each element to find the total normal time (the normal time for the
whole job):
= 15.36 min
Total normal time = 11.40 + 2.31 + 1.65
= 1.65 min
Normal time for C = 11.5211.102
= 2.31 min
Normal time for B = 12.2211.052
= 11.4 min
= 19.5211.22
Normal time for A = 1Average observed time2 * (Performance rating)
= 1.5 min
Average time for C =
2 + 1 + 2 + 1
4
= 2.2 min
Average time for B =
2 + 3 + 2 + 1 + 3
5
= 9.5 min
Average time for A =
8 + 10 + 9 + 11
4

Chapter 10 Human Resources, Job Design, and Work Measurement 321
5. Standard time for the job:
Thus, 18.07 minutes is the time standard for this job.
INSIGHT � When observed times are not consistent they need to be reviewed. Abnormally short
times may be the result of an observational error and are usually discarded. Abnormally long times
need to be analyzed to determine if they, too, are an error. However, they may include a seldom occur-
ring but legitimate activity for the element (such as a machine adjustment) or may be personal, delay,
or fatigue time.
LEARNING EXERCISE � If the two observations marked with an asterisk were not deleted,
what would be the total normal time and the standard time? [Answer: 18.89 min, 22.22 min.]
RELATED PROBLEMS � 10.22, 10.23, 10.24, 10.25, 10.28a,b, 10.29a, 10.30a
= 18.07 min
Standard time =
Total normal time
1 – Allowance factor
=
15.36
1 – .15
Time study requires a sampling process; so the question of sampling error in the average
observed time naturally arises. In statistics, error varies inversely with sample size. Thus, to
determine just how many cycles we should time, we must consider the variability of each ele-
ment in the study.
To determine an adequate sample size, three items must be considered:
1. How accurate we want to be (e.g., is 5% of observed time close enough?).
2. The desired level of confidence (e.g., the z-value; is 95% adequate or is 99% required?).
3. How much variation exists within the job elements (e.g., if the variation is large, a larger
sample will be required).
The formula for finding the appropriate sample size, given these three variables, is:
(10-4)Required sample size = n = a
zs
hx
b
2
;
Since the days of F. W. Taylor,
time studies have been
performed by using a
stopwatch. However, with
the development of PDA
software, such as the program
shown here, study elements,
time, performance rate, and
statistical confidence intervals
can be created, edited,
managed, and logged with
a PDA. Handheld technology
eliminates the need for data
entry and sends the data
directly to a program to be
analyzed. The software
shown here is available from
Laubrass Inc. (www.
laubrass.com).
where h accuracy level (acceptable error) desired in percent of the job element, expressed
as a decimal (5% .05)
z number of standard deviations required for desired level of confidence
see Table 10.2 or Appendix I for more z-values)
s standard deviation of the initial sample
mean of the initial sample
n required sample size=
x =
=
190% confidence = 1.65;
=
=
=
� TABLE 10.2
Common z-Values
z-Value
(standard
deviation
required
Desired for desired
Confidence level of
(%) confidence)
90.0 1.65
95.0 1.96
95.45 2.00
99.0 2.58
99.73 3.00

www.laubrass.com

www.laubrass.com

322 PART 2 Designing Operations
We demonstrate with Example 3.
Thomas W. Jones Manufacturing Co. has asked you to check a labor standard prepared by a recently
terminated analyst. Your first task is to determine the correct sample size. Your accuracy is to be within
5% and your confidence level at 95%. The standard deviation of the sample is 1.0 and the mean 3.00.
APPROACH � You apply Equation (10-4).
SOLUTION �
(from Table 10.2 or Appendix I)
Therefore, you recommend a sample size of 171.
INSIGHT � Notice that as the confidence level required increases, the sample size also increases.
Similarly, as the desired accuracy level increases (say, from 5% to 1%), the sample size increases.
LEARNING EXERCISE � The confidence level for Jones Manufacturing Co. can be set lower,
at 90%, while retaining the same 5% accuracy levels. What sample size is needed now? [Answer:
n 121.]
RELATED PROBLEMS � 10.26, 10.27, 10.28c, 10.29b, 10.30b
EXCEL OM Data File Ch10Ex3.xls can be found at www.pearsonhighered.com/heizer.
=
;
n = a
1.96 * 1.0
.05 * 3
b
2
= 170.74 L 171
n = a
zs
hx
b
2
z = 1.96
h = .05 x = 3.00 s = 1.0
EXAMPLE 3 �
Computing
sample size
AUTHOR COMMENT
Families of predetermined
time standards have been
developed for many
occupations.
Predetermined time
standards
A division of manual work into
small basic elements that have
established and widely accepted
times.
Now let’s look at two variations of Example 3.
First, if h, the desired accuracy, is expressed as an absolute amount of error (say, 1 minute of
error is acceptable), then substitute e for and the appropriate formula is:
(10-5)
where e is the absolute time amount of acceptable error.
Second, for those cases when s, the standard deviation of the sample, is not provided (which
is typically the case outside the classroom), it must be computed. The formula for doing so is
given in Equation (10-6):
(10-6)
where value of each observation
mean of the observations
n number of observations in the sample
An example of this computation is provided in Solved Problem 10.4 on page 330.
Although time studies provide accuracy in setting labor standards (see the OM in Action box
“UPS: The Tightest Ship in the Shipping Business”), they have two disadvantages. First, they
require a trained staff of analysts. Second, these standards cannot be set before tasks are actually
performed. This leads us to two alternative work-measurement techniques that we discuss next.
Predetermined Time Standards
In addition to historical experience and time studies, we can set production standards by using
predetermined time standards. Predetermined time standards divide manual work into small
basic elements that already have established times (based on very large samples of workers). To
estimate the time for a particular task, the time factors for each basic element of that task are
added together. Developing a comprehensive system of predetermined time standards would be
prohibitively expensive for any given firm. Consequently, a number of systems are commercially
=
x =
xi =
s =
Q
a(xi – x)
2
n – 1
=
Q
a(Each sample observation – x)
2
Number in sample – 1
n = a
zs
e
b
2
hx,
LO7: Find the proper
sample size for a time study

www.pearsonhighered.com/heizer

Chapter 10 Human Resources, Job Design, and Work Measurement 323
United Parcel Service (UPS) employs 425,000 people
and delivers an average of 16 million packages a day to
locations throughout the U.S. and 200 other countries.
To achieve its claim of “running the tightest ship in the
shipping business,” UPS methodically trains its delivery
drivers in how to do their jobs as efficiently as possible.
Industrial engineers at UPS have time-studied each
driver’s route and set standards for each delivery, stop,
and pickup. These engineers have recorded every second
taken up by stoplights, traffic volume, detours, doorbells,
walkways, stairways, and coffee breaks. Even bathroom
stops are factored into the standards. All this information
is then fed into company computers to provide detailed
time standards for every driver, every day.
To meet their objective of 200 deliveries and pickups
each day (versus only 80 at FedEx), UPS drivers must
follow procedures exactly. As they approach a delivery
stop, drivers unbuckle their seat belts, honk their horns,
and cut their engines. In one seamless motion, they are
required to yank up their emergency brakes and push their
gearshifts into first. Then they slide to the ground with
their electronic clipboards under their right arm and their
packages in their left hand. Ignition keys, teeth up, are in
their right hand. They walk to the customer’s door at the
prescribed 3 feet per second and knock first to avoid lost
seconds searching for the doorbell. After making the
delivery, they do the paperwork on the way back to the
truck.
Productivity experts describe UPS as one of the most
efficient companies anywhere in applying effective labor
standards.
Sources: G.Niemann Big Brown: 1; The Untold Story of UPS NewYork:
Wiley, 2007; and IIE Solutions (March 2002): 16.
OM in Action �UPS: The Tightest Ship in the Shipping Business
available. The most common predetermined time standard is methods time measurement
(MTM), which is a product of the MTM Association.3
Predetermined time standards are an outgrowth of basic motions called therbligs. The term
therblig was coined by Frank Gilbreth (Gilbreth spelled backwards, with the t and h
reversed). Therbligs include such activities as select, grasp, position, assemble, reach, hold,
rest, and inspect. These activities are stated in terms of time measurement units (TMUs),
which are equal to only .00001 hour, or .0006 minute each. MTM values for various therbligs
are specified in very detailed tables. Figure 10.9, for example, provides the set of time stan-
dards for the motion GET and PLACE. To use GET and PLACE, one must know what is
“gotten,” its approximate weight, and where and how far it is supposed to be placed.
Example 4 shows a use of predetermined time standards in setting service labor standards.
Before an assembly line, like
this one in China, is set up,
the company establishes labor
standards to assist in layout and
staff planning.
Therbligs
Basic physical elements of
motion.
Time measurement
units (TMUs)
Units for very basic
micromotions in which 1
TMU = .0006 min or 100,000
TMUs = 1 hr.
3MTM is really a family of products available from the Methods Time Measurement Association. For example, MTM-
HC deals with the health care industry, MTM-C handles clerical activities, MTM-M involves microscope activities,
MTM-V deals with machine shop tasks, and so on.

324 PART 2 Designing Operations
GET and PLACE
DISTANCE
RANGE IN
IN.
<8 WEIGHT CONDITIONS OF GET PLACE ACCURACY APPROXIMATE LOOSE TIGHT APPROXIMATE LOOSE TIGHT APPROXIMATE APPROXIMATE LOOSE TIGHT APPROXIMATE LOOSE TIGHT MTM CODE AA AB AC AD AE AF AG AH AJ AK AL AM AN 20 30 40 20 30 40 40 25 40 50 90 95 120 35 45 55 45 55 65 65 45 65 75 106 120 145 50 60 70 60 70 80 80 55 75 85 115 130 160 1 >8
<20 <2 LB >2 LB <18 LB >18 LB <45 LB EASY DIFFICULT HANDFUL 2 >20
<32 3 � FIGURE 10.9 Sample MTM Table for GET and PLACE Motion Time values are in TMUs. Source: Copyrighted by the MTM Association for Standards and Research. No reprint permission without consent from the MTM Association, 16–01 Broadway, Fair Lawn, NJ 07410 Used with permission of MTM Association for Standards & Research. EXAMPLE 4 � Using predetermined time (MTM analysis) to determine standard time General Hospital wants to set the standard time for lab technicians to pour a tube specimen using MTM.4 APPROACH � This is a repetitive task for which the MTM data in Table 10.3 may be used to develop standard times. The sample tube is in a rack and the centrifuge tubes in a nearby box. A technician removes the sample tube from the rack, uncaps it, gets the centrifuge tube, pours, and places both tubes in the rack. SOLUTION � The first work element involves getting the tube from the rack. The conditions for GETTING the tube and PLACING it in front of the technician are: • Weight: (less than 2 pounds) • Conditions of GET: (easy) • Place accuracy: (approximate) • Distance range: (8 to 20 inches) Then the MTM element for this activity is AA2 (as seen in Figure 10.9). The rest of Table 10.3 is developed from similar MTM tables. INSIGHT � Most MTM calculations are computerized, so the user need only key in the appropri- ate MTM codes, such as AA2 in this example. LEARNING EXERCISE � General Hospital decides that the first step in this process really involves a distance range of 4 inches (getting the tube from the rack). The other work elements are unchanged. What is the new standard time? [Answer: .134 min. or just over 8 seconds] RELATED PROBLEM � 10.36 4A. S. Helms, B. W. Shaw, and C. A. Lindner, “The Development of Laboratory Workload Standards through Computer-Based Work Measurement Technique, Part I,” Journal of Methods-Time Measurement 12: 43. Used with per- mission of MTM Association for Standards and Research. Element Description Element Time Get tube from rack AA2 35 Uncap, place on counter AA2 35 Get centrifuge tube, place at sample tube AD2 45 Pour (3 sec) PT 83 Place tubes in rack (simo) PC2 40 Total TMU 238 .0006 * 238 = Total standard minutes = .143 or about 8.6 seconds TABLE 10.3 � MTM-HC Analysis: Pouring Tube Specimen Chapter 10 Human Resources, Job Design, and Work Measurement 325 Predetermined time standards have several advantages over direct time studies. First, they may be established in a laboratory environment, where the procedure will not upset actual production activities (which time studies tend to do). Second, because the standard can be set before a task is actually performed, it can be used for planning. Third, no performance ratings are necessary. Fourth, unions tend to accept this method as a fair means of setting standards. Finally, predeter- mined time standards are particularly effective in firms that do substantial numbers of studies of similar tasks. To ensure accurate labor standards, some firms use both time studies and predeter- mined time standards. Work Sampling The fourth method of developing labor or production standards, work sampling, was developed in England by L. Tippet in the 1930s. Work sampling estimates the percent of the time that a worker spends on various tasks. Random observations are used to record the activity that a worker is performing. The results are primarily used to determine how employees allocate their time among various activities. Knowledge of this allocation may lead to staffing changes, reas- signment of duties, estimates of activity cost, and the setting of delay allowances for labor stan- dards. When work sampling is done to establish delay allowances, it is sometimes called a ratio delay study. The work-sampling procedure can be summarized in five steps: 1. Take a preliminary sample to obtain an estimate of the parameter value (e.g., percent of time a worker is busy). 2. Compute the sample size required. 3. Prepare a schedule for observing the worker at appropriate times. The concept of random numbers is used to provide for random observation. For example, let’s say we draw the fol- lowing five random numbers from a table: 07, 12, 22, 25, and 49. These can then be used to create an observation schedule of 9:07 A.M., 9:12, 9:22, 9:25, 9:49. 4. Observe and record worker activities. 5. Determine how workers spend their time (usually as a percentage). Work sampling An estimate, via sampling, of the percent of the time that a worker spends on various tasks. Using the techniques of this chapter to develop labor standards, operations managers at Orlando’s Arnold Palmer Hospital determined that nurses walked an average of 2.7 miles per day. This constitutes up to 30% of the nurse’s time, a terrible waste of critical talent. Analysis resulted in a new layout design that has reduced walking distances by 20%. 326 PART 2 Designing Operations To determine the number of observations required, management must decide on the desired confidence level and accuracy. First, however, the analyst must select a preliminary value for the para- meter under study (Step 1 above). The choice is usually based on a small sample of perhaps 50 obser- vations. The following formula then gives the sample size for a desired confidence and accuracy: (10-7) where n � required sample size z � number of standard deviations for the desired confidence level (z � 1 for 68% confidence, for 95.45% confidence, and for 99.73% confidence—these values are obtained from Table 10.2 or the normal table in Appendix I) p � estimated value of sample proportion (of time worker is observed busy or idle) h � acceptable error level, in percent Example 5 shows how to apply this formula. z = 3z = 2 n = z2p11 - p2 h2 EXAMPLE 5 � Determining the number of work sample observations needed The manager of Michigan County’s welfare office, Dana Johnson, estimates that her employees are idle 25% of the time. She would like to take a work sample that is accurate within 3% and wants to have 95.45% confidence in the results. APPROACH � Dana applies Equation (10-7) to determine how many observations should be taken. SOLUTION � Dana computes n: where required sample size 2 for 95.45% confidence level estimate of idle proportion 25% acceptable error of She finds that INSIGHT � Thus, 833 observations should be taken. If the percent of idle time observed is not close to 25% as the study progresses, then the number of observations may have to be recalculated and increased or decreased as appropriate. LEARNING EXERCISE � If the confidence level increases to 99.73%, how does the sample size change? [Answer: ] RELATED PROBLEMS � 10.31, 10.32, 10.35, 10.37 ACTIVE MODEL 10.1 This example is further illustrated in Active Model 10.1 at www.pearsonhighered.com/heizer. n = 1,875. n = 12221.2521.752 1.0322 = 833 observations 3% = .03h = = .25=p = p = n = n = z2p11 - p2 h2 The focus of work sampling is to determine how workers allocate their time among various activities. This is accomplished by establishing the percent of time individuals spend on these activities rather than the exact amount of time spent on specific tasks. The analyst simply records in a random, nonbiased way the occurrence of each activity. Example 6 shows the procedure for evaluating employees at the state welfare office introduced in Example 5. EXAMPLE 6 � Determining employee time allocation with work sampling Dana Johnson, the manager of Michigan County’s welfare office, wants to be sure her employees have adequate time to provide prompt, helpful service. She believes that service to welfare clients who phone or walk in without an appointment deteriorates rapidly when employees are busy more than 75% of the time. Consequently, she does not want her employees to be occupied with client service activi- ties more than 75% of the time. APPROACH � The study requires several things: First, based on the calculations in Example 5, 833 observations are needed. Second, observations are to be made in a random, nonbiased way www.pearsonhighered.com/heizer Chapter 10 Human Resources, Job Design, and Work Measurement 327 over a period of 2 weeks to ensure a true sample. Third, the analyst must define the activities that are “work.” In this case, work is defined as all the activities necessary to take care of the client (fil- ing, meetings, data entry, discussions with the supervisor, etc.). Fourth, personal time is to be included in the 25% of nonwork time. Fifth, the observations are made in a nonintrusive way so as not to distort the normal work patterns. At the end of the 2 weeks, the 833 observations yield the following results: SOLUTION � The analyst concludes that all but 188 observations (126 idle and 62 personal) are work related. Since 22.6% ( 188/833) is less idle time than Dana believes necessary to ensure a high client service level, she needs to find a way to reduce current workloads. This could be done through a reassignment of duties or the hiring of additional personnel. INSIGHT � Work sampling is particularly helpful when determining staffing needs or the reallo- cation of duties (see Figure 10.10). LEARNING EXERCISE � The analyst working for Dana recategorizes several observations. There are now 450 “on the phone/meeting with client” observations, 156 “idle,” and 67 “personal time” observations. The last two categories saw no changes. Do the conclusions change? [Answer: Yes; now about 27% of employee time is not work related—over the 25% Dana desires.] RELATED PROBLEM � 10.34 = No. of Observations Activity 485 On the phone or meeting with a welfare client 126 Idle 62 Personal time 23 Discussions with supervisor 137 Filing, meeting, and computer data entry 833 The results of similar studies of salespeople and assembly-line employees are shown in Figure 10.10. Work sampling offers several advantages over time-study methods. First, because a single observer can observe several workers simultaneously, it is less expensive. Second, observers usu- ally do not require much training, and no timing devices are needed. Third, the study can be tem- porarily delayed at any time with little impact on the results. Fourth, because work sampling uses instantaneous observations over a long period, the worker has little chance of affecting the study’s outcome. Fifth, the procedure is less intrusive and therefore less likely to generate objections. � FIGURE 10.10 Work-Sampling Time Studies These two work-sampling time studies were done to determine what salespeople do at a wholesale electronics distributor (left) and a composite of several auto assembly-line employees (right). Startup/pep talk 3% Unscheduled tasks and downtime 4% Breaks and lunch 10% Dead time between tasks 13% Productive work 67% Cleanup 3% Sales in person 20% Lunch and personal 10% Travel 20% Paperwork 17% Telephone sales 12% Telephone within firm 13% Meetings and other 8% Salespeople Assembly-Line Employees 328 PART 2 Designing Operations 5The Occupational Safety and Health Administration (OSHA) is a federal government agency whose task is to ensure the safety and health of U.S. workers. 6Material safety data sheets (MSDS) contain details of hazards associated with chemicals and give information on their safe use. The disadvantages of work sampling are (1) it does not divide work elements as completely as time studies, (2) it can yield biased or incorrect results if the observer does not follow ran- dom routes of travel and observation, and (3) because it is less intrusive, it tends to be less accurate; this is particularly true when job content times are short. ETHICS Ethics in the workplace presents some interesting challenges. As we have suggested in this chapter, many constraints influence job design. The issues of fairness, equity, and ethics are pervasive. Whether the issue is equal opportunity or safe working conditions, an operations manager is often the one responsible. Managers do have some guidelines. By knowing the law, working with OSHA,5 MSDS,6 state agencies, unions, trade associations, insurers, and employees, managers can often determine the parameters of their decisions. Human resource and legal departments are also available for help and guidance through the labyrinth of laws and regulations. Management’s role is to educate employees; specify the necessary equipment, work rules, and work environment; and then enforce those requirements, even when employees think it is not necessary to wear safety equipment. We began this chapter with a discussion of mutual trust and commitment, and that is the environment that managers should foster. Ethical man- agement requires no less. CHAPTER SUMMARY Outstanding firms know that their human resource strategy can yield a competitive advantage. Often a large percentage of employees and a large part of labor costs are under the direction of OM. Consequently, an operations manager usually has a major role to play in achieving human resource objectives. A requirement is to build an environment with mutual respect and commitment and a reasonable quality of work life. Successful organizations have designed jobs that use both the mental and physical capabilities of their employees. Regardless of the strategy chosen, the skill with which a firm manages its human resources ultimately determines its success. Labor standards are required for an efficient operations system. They are needed for production planning, labor planning, costing, and evaluating per- formance. They are used throughout industry—from the factory to finance, sales, and the office. They can also be used as a basis for incentive systems. Standards may be established via historical data, time studies, predetermined time standards, and work sampling. Key Terms Labor planning (p. 307) Job design (p. 308) Labor specialization (or job specialization) (p. 308) Job enlargement (p. 309) Job rotation (p. 309) Job enrichment (p. 309) Employee empowerment (p. 309) Self-directed team (p. 310) Ergonomics (p. 311) Methods analysis (p. 314) Flow diagram (p. 314) Process chart (p. 315) Activity chart (p. 315) Operations chart (p. 315) Visual workplace (p. 315) Labor standards (p. 317) Time study (p. 317) Average observed time (p. 318) Normal time (p. 318) Standard time (p. 319) Predetermined time standards (p. 322) Therbligs (p. 323) Time measurement units (TMUs) (p. 323) Work sampling (p. 325) AUTHOR COMMENT Mutual trust and commitment cannot be acheived without ethical behavior. Chapter 10 Human Resources, Job Design, and Work Measurement 329 � SOLVED PROBLEM 10.1 As pit crew manager for Rusty Wallace’s NASCAR team (see the Global Company Profile that opens this chapter), you would like to evaluate how your “Jackman” (JM) and “Gas Man #1” (GM #1) are utilized. Recent stopwatch studies have verified the following times: Pit crew Activity Time (seconds) JM Move to right side of car and raise car 4.0 GM #1 Move to rear gas filler 2.5 JM Move to left side of car and raise car 3.8 JM Wait for tire 1.0 GM #1 Load fuel (per gallon) 0.5 JM Wait for tire 1.2 JM Move back over wall from left side 2.5 GM #1 Move back over the wall from gas filler 2.5 Use an activity chart similar to the one in Figure 10.6 as an aid. � SOLUTION Solved Problems Virtual Office Hours help is available at www.myomlab.com 4.0 Jackman (Seconds) Move to right side of car and raise car Move to rear gas filler Move back over the wall from gas filler Load 11 gallons of fuel (one can of fuel) Wait for tire exchange to finish Wait for tire exchange to finish Move to left side of car and raise car Move back over wall from left side Gas Man #1 (Seconds) 1.0 3.8 1.2 2.5 2.5 2.5 5.5 � SOLVED PROBLEM 10.2 A work operation consisting of three elements has been subjected to a stopwatch time study. The recorded observations are shown in the following table. By union contract, the allowance time for the operation is personal time 5%, delay 5%, and fatigue 10%. Determine the standard time for the work operation. � SOLUTION First, delete the two observations that appear to be very unusual (.9 minute for job element A and 3.2 minutes for job element B). Then: Then: Standard time = 1.31 1 - .20 = 1.64 min Note, the total allowance factor = .05 + .05 + .10 = .20 Normal time for job = .16 + .75 + .40 = 1.31 min C’s normal time = 1.5021.802 = .40 min B’s normal time = 1.68211.102 = .75 min A’s normal time = 1.1821.902 = .16 min C’s average observed time = .5 + .5 + .4 + .5 + .6 + .5 6 = .50 min B’s average observed time = .8 + .6 + .8 + .5 + .7 5 = .68 min A’s average observed time = .1 + .3 + .2 + .2 + .1 5 = .18 min Job Observations (minutes) Performance Element 1 2 3 4 5 6 Rating (%) A .1 .3 .2 .9 .2 .1 90 B .8 .6 .8 .5 3.2 .7 110 C .5 .5 .4 .5 .6 .5 80 www.myomlab.com 330 PART 2 Designing Operations � SOLVED PROBLEM 10.3 The preliminary work sample of an operation indicate the following: Number of times operator working 60 Number of times operator idle 40 Total number of preliminary observations 100 What is the required sample size for a 99.73% confidence level with precision?; 4% � SOLUTION for 99.73% confidence; So: n = z2p11 - p2 h2 = 13221.621.42 1.0422 = 1,350 sample size p = 60 100 = .6; h = .04z = 3 � SOLVED PROBLEM 10.4 Amor Manufacturing Co. of Geneva, Switzerland, has just observed a job in its laboratory in anticipation of releasing the job to the factory for production. The firm wants rather good accuracy for costing and labor forecasting. Specifically, it wants to provide a 99% confidence level and a cycle time that is within 3% of the true value. How many observations should it make? The data col- lected so far are as follows: � SOLUTION First, solve for the mean, , and the sample standard deviation, s: s = Q a(Each sample observation - x) 2 Number in sample - 1 x Observation Time 1 1.7 2 1.6 3 1.4 4 1.4 5 1.4 Observation xi x xi - x 1xi - x22 1 1.7 1.5 .2 0.04 2 1.6 1.5 .1 0.01 3 1.4 1.5 - .1 0.01 4 1.4 1.5 - .1 0.01 5 1.4 1.5 - .1 0.01 x = 1.5 0.08 = a(xi-x) 2 where (from Table 10.2) Therefore, you round up to 66 observations. h = .03 z = 2.58 s = .141 x = 1.5 Then, solve for n = ¢ zs hx ≤2 = B12.5821.1412 1.03211.52 R2 = 65.3s = A .08n - 1 = A .084 = .141 � SOLVED PROBLEM 10.5 At Maggard Micro Manufacturing, Inc., workers press semicon- ductors into predrilled slots on printed circuit boards. The ele- mental motions for normal time used by the company are as follows: � SOLUTION Add the time measurement units: Time in seconds = 1.122160 sec2 = 7.2 sec Time in minutes = 120021.0006 min.2 = .12 min 40 + 10 + 30 + 35 + 65 + 20 = 200 Reach 6 inches for semiconductors 40 TMU Grasp the semiconductor 10 TMU Move semiconductor to printed circuit board 30 TMU Position semiconductor 35 TMU Press semiconductor into slots 65 TMU Move board aside 20 TMU (Each time measurement unit is equal to .0006 min.) Determine the normal time for this operation in minutes and in seconds. Chapter 10 Human Resources, Job Design, and Work Measurement 331 � SOLVED PROBLEM 10.6 To obtain the estimate of time a worker is busy for a work sam- pling study, a manager divides a typical workday into 480 min- utes. Using a random-number table to decide what time to go to an area to sample work occurrences, the manager records observa- tions on a tally sheet like the following: � SOLUTION In this case, the supervisor made 20 observations and found that employees were working 80% of the time. So, out of 480 minutes in an office workday, 20%, or 96 minutes, was idle time, and 384 minutes was productive. Note that this procedure describes that a worker is busy, not necessarily what he or she should be doing. Status Tally Productively working |||| |||| |||| | Idle |||| �Additional Case Studies: Visit www.myomlab.com or www.pearsonhighered.com/heizer for these free case studies: Chicago Southern Hospital: Examines the requirements for a work-sampling plan for nurses. Karstadt versus JCPenney: Compares the work culture in retailing in the U.S. to Germany. The Fleet That Wanders: Requires a look at ergonomic issues for truck drivers. Bibliography Aft, Larry, and Neil Schmeidler. “Work Measurement Practices.” Industrial Engineer 35, no. 11 (November 2003): 44. Barber, Felix, and Rainer Strack. “The Surprising Economics of a People Business.” Harvard Business Review 83, no. 6 (June 2005): 81–90. Barnes, R. M. Motion and Time Study, Design and Measurement of Work, 7th ed. New York: Wiley, 1980. Bridger, R. S. Introduction to Ergonomics, 3rd ed. New York: CRC Press, 2008. De Jong, A., K. De Ruyter, and J. Lemmink. “Service Climate in Self-Managing Teams.” The Journal of Management Studies 42, no. 8 (December 2005): 1593. Elnekave, M., and I. Gilad. “Rapid Video-Based Analysis System for Advanced Work Measurement.” International Journal of Production Research 44, no. 2 (January 2006): 271. Freivalds, Andris, and B. W. Niebel. Methods, Standards, and Work Design, 12th ed. New York: Irwin/McGraw-Hill, 2009. Huselid, Mark A., Richard W. Beatty, and Brian E. Becker. “ ‘A Players’ or ‘A Positions’? The Strategic Logic of Workforce Management.” Harvard Business Review (December 2005): 110–117. Konz, S., and Steven Johnson. Work Design: Industrial Ergonomics, 6th ed. Scottsdale, AZ: Holcomb Hathaway, 2004. Muthusamy, S. K., J. V. Wheeler, and B. L. Simmons. “Self- Managing Work Teams.” Organization Development Journal 23, no. 3 (Fall 2005): 53–66. Pfeffer, Jeffrey. “Producing Sustainable Competitive Advantage Through the Effective Management of People.” Academy of Management Executive 19, no. 4 (2005): 95. Sadikoglu, E. “Integration of Work Measurement and Total Quality Management.” Total Quality Management and Business Excellence 16, no. 5 (July 2005): 597. Salvendy, G., ed. Handbook of Human Factors and Ergonomics, 3rd ed. New York: Wiley, 2006. Tolo, B. “21st-Century Stopwatch.” Industrial Engineer 37, no. 7 (July 2005): 34–37. Walsh, Ellen. “Get Results with Workload Management.” Nursing Management (October 2003): 16. www.myomlab.com www.pearsonhighered.com/heizer This page intentionally left blank Chapter Outline GLOBAL COMPANY PROFILE: DARDEN RESTAURANTS The Supply Chain’s Strategic Importance 336 Ethics and Sustainability 339 Supply-Chain Economics 340 Supply-Chain Strategies 341 Managing the Supply Chain 343 E-Procurement 347 Vendor Selection 349 Logistics Management 350 Measuring Supply-Chain Performance 354 � Design of Goods and Services � Managing Quality � Process Strategy � Location Strategies � Layout Strategies � Human Resources � Supply-Chain Management � Inventory Management � Scheduling � Maintenance PART THREE Managing Operations (Chapters 11–17) 333 Supply-Chain Management D arden Restaurants, Inc., is the largest publicly traded casual dining restaurant company in the world. It serves over 400 million meals annually from more than 1,700 restaurants in the U.S. and Canada. Each of its well-known flagship brands—Olive Garden and Red Lobster—generates sales of $2 billion annually. Darden’s other brands include Bahama Breeze, Seasons 52, Capital Grille, and LongHorn Steakhouse. The firm employs more than 150,000 people and is the 29th largest employer in the U.S. “Operations is typically thought of as an execution of strategy. For us it is the strategy,” Darden’s former chairman, Joe R. Lee, stated. In the restaurant business, a winning strategy requires a winning supply chain. Nothing is more important than sourcing and delivering healthy, high- quality food; and there are very few other industries where supplier performance is so closely tied to the customer. Darden sources its food from five continents and thousands of suppliers. To meet Darden’s needs for fresh ingredients, the company has developed four distinct supply chains: one for seafood; one for dairy/produce/other refrigerated foods; a third for other food items, like baked goods; and a fourth for restaurant supplies (everything from dishes to ovens to uniforms). Over $1.5 billion is spent in these supply chains annually. (See the Video Case Study in the Lecture Guide & Activities Manual for details.) Darden’s four supply channels have some common characteristics. They all require supplier qualification, have product tracking, are subject to independent audits, and employ just-in-time delivery. With best- in-class techniques and processes, Darden creates worldwide supply-chain partnerships and alliances that are rapid, transparent, and efficient. Darden achieves competitive advantage through its superior supply chain. � Aquaculture Certification: Shrimp in this Asian plant are certified to ensure traceability. The focus is on quality control certified by the Aquaculture Certification Council, of which Darden is a member. Farming and inspection practices yield safe and wholesome shrimp. � Qualifying Worldwide Sources: Part of Darden’s supply chain begins with a crab harvest in the frigid waters off the coast of Alaska. But long before a supplier is qualified to sell to Darden, a total quality team is appointed. The team provides guidance, assistance, support, and training to the suppliers to ensure that overall objectives are understood and desired results accomplished. GLOBAL COMPANY PROFILE: DARDEN RESTAURANTS DARDEN’S SUPPLY CHAIN YIELDS A COMPETITIVE EDGE 334 � Product tracking: Darden’s seafood inspection team developed an integral system that uses a lot ID to track seafood from its origin through shipping and receipt. Darden uses a modified atmosphere packaging (MAP) process to extend the shelf life and preserve the quality of its fresh fish. The tracking includes time temperature monitoring. � Independent audits of suppliers: To provide fair and accurate assessment, Darden’s Total Quality Supplier Program includes an independent verification program. Each supplier is evaluated regularly by independent auditors on a risk-based schedule to determine the supplier’s effectiveness. � JIT Delivery: For many products, temperature monitoring begins immediately and is tracked through the entire supply chain, to the kitchen at each of Darden’s 1,700 restaurants and ultimately to the guest. DARDEN RESTAURANTS � 335 336 PART 3 Managing Operations THE SUPPLY CHAIN’S STRATEGIC IMPORTANCE Most firms, like Darden, spend a huge portion of their sales dollars on purchases. Because an increasing percentage of an organization’s costs are determined by purchasing, relationships with suppliers are increasingly integrated and long term. Joint efforts that improve innovation, speed design, and reduce costs are common. Such efforts, when part of a corporate-wide strat- egy, can dramatically improve both partners’ competitiveness. This integrated focus places added emphasis on managing supplier relationships. Supply-chain management is the integration of the activities that procure materials and ser- vices, transform them into intermediate goods and final products, and deliver them to customers. These activities include purchasing and outsourcing activities, plus many other functions that are important to the relationship with suppliers and distributors. As Figure 11.1 suggests, supply- chain management includes determining (1) transportation vendors, (2) credit and cash transfers, (3) suppliers, (4) distributors, (5) accounts payable and receivable, (6) warehousing and LO1: Explain the strategic importance of the supply chain 337 LO2: Identify six supply-chain strategies 341 LO3: Explain issues and opportunities in the supply chain 344 LO4: Describe the steps in vendor selection 349 Chapter 11 Learning Objectives Supply-chain management Management of activities that procure materials and services, transform them into intermediate goods and final products, and deliver them through a distribution system. LO5: Explain major issues in logistics management 351 LO6: Compute the percentage of assets committed to inventory and inventory turnover 354 AUTHOR COMMENT Competition today is not between companies; it is between supply chains. Distributor Sam’s GroceryFarm Bottle manufacturing Tier 3 suppliers Tier 2 suppliers Tier 1 suppliers Can manufacturing $1.18 Customer $3.36 $4.62 6 12-oz beers S3 S2 S3 S3 S2 S2 S1 S1 Brewer Sam’s Grocery Hops/grains Hops, grain $0.34 • Scheduling information • Order and cash flow • Market research data • Design data $6.99 • Credit flow • Ideas and design to satisfy the end customers • Material flow � FIGURE 11.1 A Supply Chain for Beer The supply chain includes all the interactions among suppliers, manufacturers, distributors, and customers. The chain includes transportation, scheduling information, cash and credit transfers, as well as ideas, designs, and material transfers. Even can and bottle manufacturers have their own tiers of suppliers providing components such as lids, labels, packing containers, etc. (Costs are approximate and include substantial taxes.) Chapter 11 Supply-Chain Management 337 inventory, (7) order fulfillment, and (8) sharing customer, forecasting, and production informa- tion. The objective is to build a chain of suppliers that focuses on maximizing value to the ulti- mate customer. As firms strive to increase their competitiveness via product customization, high quality, cost reductions, and speed to market, added emphasis is placed on the supply chain. Effective supply chain management makes suppliers “partners” in the firm’s strategy to satisfy an ever-changing marketplace. A competitive advantage may depend on a close long-term strategic relationship with a few suppliers. To ensure that the supply chain supports the firm’s strategy, managers need to consider the supply chain issues shown in Table 11.1. Activities of supply chain managers cut across account- ing, finance, marketing, and the operations discipline. Just as the OM function supports the firm’s overall strategy, the supply chain must support the OM strategy. Strategies of low cost or rapid response demand different things from a supply chain than a strategy of differentiation. For instance, a low-cost strategy, as Table 11.1 indicates, requires suppliers be selected based primar- ily on cost. Such suppliers should have the ability to design low-cost products that meet the func- tional requirements, minimize inventory, and drive down lead times. However, if you want roses that are fresh, build a supply chain that focuses on response (see the OM in Action box “A Rose Is a Rose, but Only if It Is Fresh”). Firms must achieve integration of strategy up and down the supply chain, and must expect that strategy to be different for different products and to change as products move through their life cycle. Darden Restaurants, as noted in the opening Global Company Profile, has mastered worldwide product and service complexity by segmenting its supply chain and at the same time integrating four unique supply chains into its overall strategy. Supply-Chain Risk In this age of increasing specialization, low communication cost, and fast transportation, compa- nies are making less and buying more. This means more reliance on supply chains and more risk. Managing the new integrated supply chain is a strategic challenge. Having fewer suppliers makes the supplier and customer more dependent on each other, increasing risk for both. This risk is compounded by globalization and logistical complexity. In any supply chain, vendor reliability and quality may be challenging, but the new paradigm of a tight, fast, low-inventory supply chain, operating across political and cultural boundaries, adds a new dimension to risk. As organizations go global, shipping time may increase, logistics may be less reliable, and tariffs LO1: Explain the strategic importance of the supply chain � TABLE 11.1 How Supply-Chain Decisions Affect Strategy* Low-Cost Strategy Response Strategy Differentiation Strategy Supplier’s goal Supply demand at lowest possible cost (e.g., Emerson Electric, Taco Bell) Respond quickly to changing requirements and demand to minimize stockouts (e.g., Dell Computer) Share market research; jointly develop products and options (e.g., Benetton) Primary selection criteria Select primarily for cost Select primarily for capacity, speed, and flexibility Select primarily for product development skills Process characteristics Maintain high average utilization Invest in excess capacity and flexible processes Use modular processes that lend themselves to mass customization Inventory characteristics Minimize inventory throughout the chain to hold down costs Develop responsive system, with buffer stocks positioned to ensure supply Minimize inventory in the chain to avoid obsolescence Lead-time characteristics Shorten lead time as long as it does not increase costs Invest aggressively to reduce production lead time Invest aggressively to reduce development lead time Product-design characteristics Maximize performance and minimize cost Use product designs that lead to low setup time and rapid production ramp-up Use modular design to postpone product differentiation for as long as possible *See related table and discussion in Marshall L. Fisher, “What Is the Right Supply Chain for Your Product?” Harvard Business Review (March–April 1997): 105. AUTHOR COMMENT The environment, controls, and process performance all affect supply-chain risk. VIDEO 11.1 Darden’s Global Supply Chain 338 PART 3 Managing Operations and quotas may block companies from doing business. In addition, international supply chains complicate information flows and increase political and currency risks. Thus, the development of a successful strategic plan for supply-chain management requires careful research, an understanding of the risk involved, and innovative planning. Reducing risk in this increasingly global environment suggests that management must be able to mitigate and react to disruptions in: 1. Processes (raw material and component availability, quality, and logistics) 2. Controls (management metrics and reliable secure communication for financial trans- actions, product designs, and logistics scheduling) 3. Environment (customs duties, tariffs, security screening, natural disaster, currency fluctua- tions, terrorist attacks, and political issues) Let’s look at how several organizations address these risks in their supply chains: • To reduce process risk, McDonald’s planned its supply chain 6 years in advance of its open- ing in Russia. Creating a $60 million “food town,” it developed independently owned supply plants in Moscow to keep its transportation costs and handling times low and its quality and customer-service levels high. Every component in this food chain—meat plant, chicken plant, bakery, fish plant, and lettuce plant—is closely monitored to make sure that all the system’s links are strong. • Ford’s process risk reduction strategy is to develop a global network of few but exceptional suppliers who will provide the lowest cost and highest quality. This has driven one division’s supplier base down to only 227 suppliers worldwide, compared with 700 previously. • Darden Restaurants has placed extensive controls, including third-party audits, on supplier processes and logistics to ensure constant monitoring and reduction of risk. • Boeing is reducing control risk through its state-of-the-art international communication system that transmits engineering, scheduling, and logistics data not only to Boeing facilities but to the suppliers of the 75% to 80% of the 787 Dreamliner that is built by non-Boeing companies. • Hard Rock Cafe is reducing environmental (political) risk by franchising and licensing, rather than owning, when the political and cultural barriers seem significant. • Toyota, after its experience with both fire and earthquakes, has moved to reduce environmental (natural disaster) risk with a policy of having at least two suppliers for each component. Tight integration of the supply chain can have significant benefits, but the risks can and must be managed. Supply chains for food and flowers must be fast, and they must be good. When the food supply chain has a problem, the best that can happen is the customer does not get fed on time; the worst that happens is the customer gets food poisoning and dies. In the floral industry, the timing and temperature are also critical. Indeed, flowers are the most perishable agricultural item—even more so than fish. Flowers not only need to move fast, but they must also be kept cool, at a constant temperature of 33 to 37 degrees. And they must be provided preservative-treated water while in transit. Roses are especially delicate, fragile, and perishable. Seventy percent of the roses sold in the U.S. market arrive by air from rural Colombia and Ecuador. Roses move through this supply chain via an intricate but fast transportation network. This network stretches from growers who cut, grade, bundle, pack and ship, to importers who make the deal, to the U.S. Department of Agriculture personnel who quarantine and inspect for insects, diseases, and parasites, to U.S. Customs agents who inspect and approve, to facilitators who provide clearance and labeling, to wholesalers who distribute, to retailers who arrange and sell, and finally to the customer. Each and every minute the product is deteriorating. The time and temperature sensitivity of perishables like roses requires sophistication and refined standards in the supply chain. Success yields quality and low losses. After all, when it’s Valentine’s Day, what good is a shipment of roses that arrives wilted or late? This is a difficult supply chain; only an excellent one will get the job done. Sources: IIE Solutions (February 2002): 26–32; and World Trade (June 2004): 22–25. OM in Action OMinAction� A Rose Is a Rose, but Only if It Is Fresh Chapter 11 Supply-Chain Management 339 ETHICS AND SUSTAINABILITY Let’s look at three aspects of ethics in the supply chain: personal ethics, ethics within the supply chain, and ethical behavior regarding the environment. Personal Ethics Ethical decisions are critical to the long-term success of any organization. However, the supply chain is particularly susceptible to ethical lapses, as the opportunities for uneth- ical behavior are enormous. With sales personnel anxious to sell and purchasing agents spending huge sums, temptations abound. Many salespeople become friends with customers, do favors for them, take them to lunch, or present small (or large) gifts. Determining when tokens of friendship become bribes can be challenging. Many companies have strict rules and codes of conduct that limit what is acceptable. Recognizing these issues, the Institute for Supply Management has developed principles and standards to be used as guidelines for ethical behavior (as shown in Table 11.2). As the supply chain becomes international, operations managers need to expect an additional set of ethical issues to manifest themselves as they deal with new cultural values. Ethics within the Supply Chain In this age of hyper-specialization, much of any organiza- tion’s resources are purchased, putting great stress on ethics in the supply chain. Managers may be tempted to ignore ethical lapses by suppliers or offload pollution to suppliers. But firms must establish standards for their suppliers, just as they have established standards for themselves. Society expects ethical performance throughout the supply chain. For instance, Gap Inc. reported that of its 3,000-plus factories worldwide, about 90% failed their initial evaluation.1 The report indicated that 10% to 25% of its Chinese factories engaged in psychological or verbal abuse, and more than 50% of the factories visited in sub-Saharan Africa operate without proper safety devices. The challenge of enforcing ethical standards is significant, but responsible firms such as Gap are finding ways to deal with this difficult issue. Ethical Behavior Regarding the Environment While ethics on both a personal basis and in the supply chain are important, so is ethical behavior in regard to the environment. Good ethics extends to doing business in a way that supports conservation and renewal of resources. This requires evaluation of the entire environmental impact, from raw material, to manufacture, through use, and final disposal. For instance, Darden and Walmart require their shrimp and fish suppliers in Southeast Asia to abide by the standards of the Global Aquaculture Alliance. These standards must be met if suppliers want to maintain the business relationship. Operations � TABLE 11.2 Principles and Standards of Ethical Supply Management Conduct INTEGRITY IN YOUR DECISIONS AND ACTIONS; VALUE FOR YOUR EMPLOYER; LOYALTY TO YOUR PROFESSION 1. PERCEIVED IMPROPRIETY Prevent the intent and appearance of unethical or compromising conduct in relationships, actions and communications. 2. CONFLICTS OF INTEREST Ensure that any personal, business or other activity does not conflict with the lawful interests of your employer. 3. ISSUES OF INFLUENCE Avoid behaviors or actions that may negatively influence, or appear to influence, supply management decisions. 4. RESPONSIBILITIES TO YOUR EMPLOYER Uphold fiduciary and other responsibilities using reasonable care and granted authority to deliver value to your employer. 5. SUPPLIER AND CUSTOMER RELATIONSHIPS Promote positive supplier and customer relationships. 6. SUSTAINABILITY AND SOCIAL RESPONSIBILITY Champion social responsibility and sustainability practices in supply management. 7. CONFIDENTIAL AND PROPRIETARY INFORMATION Protect confidential and proprietary information. 8. RECIPROCITY Avoid improper reciprocal agreements. 9. APPLICABLE LAWS, REGULATIONS AND TRADE AGREEMENTS Know and obey the letter and spirit of laws, regulations and trade agreements applicable to supply management. 10. PROFESSIONAL COMPETENCE Develop skills, expand knowledge and conduct business that demonstrates competence and promotes the supply management profession. Source: www.ism.ws 1Amy Merrick, “Gap Offers Unusual Look at Factory Conditions,” The Wall Street Journal (May 12, 2004): A1, A12. AUTHOR COMMENT Because so much money passes through the supply chain, the opportunity for ethical lapses is significant. www.ism.ws 340 PART 3 Managing Operations EXAMPLE 1 � Profit potential in the supply chain Hau Lee Furniture Inc. spends 50% of its sales dollar in the supply chain and has a net profit of 4%. Hau wants to know how many dollars of sales is equivalent to supply-chain savings of $1. APPROACH � Table 11.4 (given Hau’s assumptions) can be used to make the analysis. SOLUTION � Table 11.4 indicates that every $1 Hau can save in the supply chain results in the same profit that would be generated by $3.70 in sales. INSIGHT � Effective management of the supply chain can generate substantial benefits. LEARNING EXERCISE � If Hau increases his profit to 6%, how much of an increase in sales is necessary to equal $1 savings? [Answer: $3.57.] RELATED PROBLEMS � 11.6, 11.7 Percentage of Sales Spent in the Supply Chain Percentage Net Profit of Firm 30% 40% 50% 60% 70% 80% 90% 2 $2.78 $3.23 $3.85 $4.76 $6.25 $9.09 $16.67 4 $2.70 $3.13 $3.70 $4.55 $5.88 $8.33 $14.29 6 $2.63 $3.03 $3.57 $4.35 $5.56 $7.69 $12.50 8 $2.56 $2.94 $3.45 $4.17 $5.26 $7.14 $11.11 10 $2.50 $2.86 $3.33 $4.00 $5.00 $6.67 $10.00 aThe required increase in sales assumes that 50% of the costs other than purchases are variable and that half the remaining costs (less profit) are fixed. Therefore, at sales of $100 (50% purchases and 2% mar- gin), $50 are purchases, $24 are other variable costs, $24 are fixed costs, and $2 profit. Increasing sales by $3.85 yields the following: Purchases at 50% $ 51.93 (50% of $103.85) Other Variable Costs 24.92 (24% of $103.85) Fixed Cost 24.00 (fixed) Profit 3.00 (from $2 to $3 profit) $103.85 Through $3.85 of additional sales, we have increased profit by $1, from $2 to $3. The same increase in margin could have been obtained by reducing supply-chain costs by $1. � TABLE 11.4 Dollars of Additional Sales Needed to Equal $1 Saved through the Supply Chaina managers also ensure that sustainability is reflected in the performance of second- and third-tier suppliers. Enforcement can be done by in-house inspectors, third-party auditors, governmental agencies, or nongovernmental watchdog organizations. All four approaches are used. The incoming supply chain garners most of the attention, but it is only part of the ethical chal- lenge of sustainability. The “return” supply chain is also significant. Returned products can only be burned, buried, or reused. And the first two options have adverse consequences. Once viewed in this manner, the need for operations managers to evaluate the entire product life cycle is apparent. While 84% of an automobile and 90% of an airplane are recycled, these levels are not easily achieved. Recycling efforts began at product and process design. Then special end-of-product-life processes were developed. Oil, lead, gasoline, explosives in air bags, acid in batteries, and the many components (axles, differentials, jet engines, hydraulic valves) that still have many years of service all demand their own unique recovery, remanufacturing, or recycling process. This complexity places significant demands on the producer as well as return and reuse supply chains in the quest for sustainability. But pursuing this quest is the ethical thing to do. Saving the earth is a challenging task. SUPPLY-CHAIN ECONOMICS The supply chain receives such attention because it is an integral part of a firm’s strategy and the most costly activity in most firms. For both goods and services, supply chain costs as a percent of sales are often substantial (see Table 11.3). Because such a huge portion of revenue is devoted to the supply chain, an effective strategy is vital. The supply chain provides a major opportunity to reduce costs and increase contribution margins. Table 11.4 and Example 1 illustrate the amount of leverage available to the operations man- ager through the supply chain. These numbers indicate the strong role that supply chains play in profitability. � TABLE 11.3 Supply-Chain Costs as a Percentage of Sales % Industry Purchased Automobile 67 Beverages 52 Chemical 62 Food 60 Lumber 61 Metals 65 Paper 55 Petroleum 79 Transportation 62 AUTHOR COMMENT A huge part of a firm’s revenue is typically spent on purchases, so this is a good place to look for savings. Chapter 11 Supply-Chain Management 341 Make-or-Buy Decisions A wholesaler or retailer buys everything that it sells; a manufacturing operation hardly ever does. Manufacturers, restaurants, and assemblers of products buy components and subassemblies that go into final products. As we saw in Chapter 5, choosing products and services that can be advant- ageously obtained externally as opposed to produced internally is known as the make-or-buy decision. Supply-chain personnel evaluate alternative suppliers and provide current, accurate, and complete data relevant to the buy alternative. Increasingly, firms focus not on an analytical make-or-buy decision but on identifying their core competencies. Outsourcing Outsourcing transfers some of what are traditional internal activities and resources of a firm to outside vendors, making it slightly different from the traditional make-or-buy decision. Outsourcing is part of the continuing trend toward utilizing the efficiency that comes with specialization. The vendor performing the outsourced service is an expert in that particular specialty. This leaves the outsourcing firm to focus on its critical success factors, that is, its core competencies that yield a competitive advantage. Outsourcing is the focus of the supplement to this chapter. SUPPLY-CHAIN STRATEGIES For goods and services to be obtained from outside sources, the firm must decide on a supply chain strategy. One such strategy is the approach of negotiating with many suppliers and play- ing one supplier against another. A second strategy is to develop long-term “partnering” rela- tionships with a few suppliers to satisfy the end customer. A third strategy is vertical integration, in which a firm decides to use vertical backward integration by actually buying the supplier. A fourth approach is some type of collaboration that allows two or more firms to com- bine resources—typically in what is called a joint venture—to produce a component. A fifth variation is a combination of few suppliers and vertical integration, known as a keiretsu. In a keiretsu, suppliers become part of a company coalition. Finally, a sixth strategy is to develop virtual companies that use suppliers on an as-needed basis. We will now discuss each of these strategies. Many Suppliers With the many-suppliers strategy, a supplier responds to the demands and specifications of a “request for quotation,” with the order usually going to the low bidder. This is a common strategy when products are commodities. This strategy plays one supplier against another and places the burden of meeting the buyer’s demands on the supplier. Suppliers aggressively compete with one another. Although many approaches to negotiations can be used with this strategy, long-term “partnering” relationships are not the goal. This approach holds the supplier responsible for maintaining the necessary technology, expertise, and forecasting abilities, as well as cost, qual- ity, and delivery competencies. Few Suppliers A strategy of few suppliers implies that rather than looking for short-term attributes, such as low cost, a buyer is better off forming a long-term relationship with a few dedicated suppliers. Long- term suppliers are more likely to understand the broad objectives of the procuring firm and the end customer. Using few suppliers can create value by allowing suppliers to have economies of scale and a learning curve that yields both lower transaction costs and lower production costs. Few suppliers, each with a large commitment to the buyer, may also be more willing to partic- ipate in JIT systems as well as provide design innovations and technological expertise. Many firms have moved aggressively to incorporate suppliers into their supply systems. Ford, for one, now seeks to choose suppliers even before parts are designed. Motorola also evaluates sup- pliers on rigorous criteria, but in many instances has eliminated traditional supplier bidding, placing added emphasis on quality and reliability. On occasion these relationships yield contracts that extend through the product’s life cycle. The expectation is that both the purchaser and Make-or-buy decision A choice between producing a component or service in-house or purchasing it from an outside source. Outsourcing Transferring a firm’s activities that have traditionally been internal to external suppliers. LO2: Identify six supply- chain strategies AUTHOR COMMENT Supply-chain strategies come in many varieties; choosing the correct one is the trick. VIDEO 11.2 Supply-Chain Management at Regal Marine 342 PART 3 Managing Operations supplier collaborate, becoming more efficient and reducing prices over time. The natural out- come of such relationships is fewer suppliers, but those that remain have long-term relationships. Service companies like Marks & Spencer, a British retailer, have also demonstrated that coop- eration with suppliers can yield cost savings for customers and suppliers alike. This strategy has resulted in suppliers that develop new products, winning customers for Marks & Spencer and the supplier. The move toward tight integration of the suppliers and purchasers is occurring in both manufacturing and services. Like all strategies, a downside exists. With few suppliers, the cost of changing partners is huge, so both buyer and supplier run the risk of becoming captives of the other. Poor supplier perfor- mance is only one risk the purchaser faces. The purchaser must also be concerned about trade secrets and suppliers that make other alliances or venture out on their own. This happened when the U.S. Schwinn Bicycle Co., needing additional capacity, taught Taiwan’s Giant Manufacturing Company to make and sell bicycles. Giant Manufacturing is now the largest bicycle manufacturer in the world, and Schwinn was acquired out of bankruptcy by Pacific Cycle LLC. Vertical Integration Purchasing can be extended to take the form of vertical integration. By vertical integration, we mean developing the ability to produce goods or services previously purchased or to actually buy a supplier or a distributor. As shown in Figure 11.2, vertical integration can take the form of forward or backward integration. Backward integration suggests a firm purchase its suppliers, as in the case of Ford Motor Company deciding to manufacture its own car radios. Forward integration, on the other hand, suggests that a manufacturer of components make the finished product. An example is Texas Instruments, a manufacturer of integrated circuits that also makes calculators and flat-screens containing integrated circuits for TVs. Vertical integration can offer a strategic opportunity for the operations manager. For firms with the capital, managerial talent, and required demand, vertical integration may provide sub- stantial opportunities for cost reduction, quality adherence, and timely delivery. Other advan- tages, such as inventory reduction and scheduling, can accrue to the company that effectively manages vertical integration or close, mutually beneficial relationships with suppliers. Because purchased items represent such a large part of the costs of sales, it is obvious why so many organizations find interest in vertical integration. Vertical integration appears to work best when the organization has large market share and the management talent to operate an acquired vendor successfully. The relentless march of specialization continues, meaning that a model of “doing everything” or “vertical integration” is increasingly difficult. Backward integration may be particularly dan- gerous for firms in industries undergoing technological change if management cannot keep abreast of those changes or invest the financial resources necessary for the next wave of technol- ogy. The alternative, particularly in high-tech industries, is to establish close-relationship suppli- ers. This allows partners to focus on their specific contribution. Research and development costs are too high and technology changes too rapid for one company to sustain leadership in every component. Most organizations are better served concentrating on their specialty and leveraging Vertical integration Developing the ability to produce goods or services previously purchased or actually buying a supplier or a distributor. Raw material (suppliers) Iron ore Silicon Vertical Integration Examples of Vertical Integration Farming Flour milling Baked goods Integrated circuits Circuit boards Computers Watches Calculators Steel Automobiles Distribution system Dealers Backward integration Current transformation Forward integration Finished goods (customers) � FIGURE 11.2 Vertical Integration Can Be Forward or Backward Chapter 11 Supply-Chain Management 343 the partners’ contributions. Exceptions do exist. Where capital, management talent, and technol- ogy are available and the components are also highly integrated, vertical integration may make sense. On the other hand, it made no sense for Jaguar to make commodity components for its autos as it did until recently. Joint Ventures Because vertical integration is so dangerous, firms may opt for some form of formal collabora- tion. As we noted in Chapter 5, firms may engage in collaboration to enhance their new product prowess or technological skills. But firms also engage in collaboration to secure supply or reduce costs. One version of a joint venture is the current Daimler–BMW effort to develop and produce standard automobile components. Given the global consolidation of the auto industry, these two rivals in the luxury segment of the automobile market are at a disadvantage in volume. Their rel- atively low volume means fewer units over which to spread fixed costs, hence the interest in con- solidating to cut development and production costs. As in all other such collaborations, the trick is to cooperate without diluting the brand or conceding a competitive advantage. Keiretsu Networks Many large Japanese manufacturers have found another strategy; it is part collaboration, part purchasing from few suppliers, and part vertical integration. These manufacturers are often financial supporters of suppliers through ownership or loans. The supplier becomes part of a company coalition known as a keiretsu. Members of the keiretsu are assured long-term relation- ships and are therefore expected to collaborate as partners, providing technical expertise and sta- ble quality production to the manufacturer. Members of the keiretsu can also have suppliers farther down the chain, making second- and even third-tier suppliers part of the coalition. Virtual Companies The limitations to vertical integration are severe. Our technological society continually demands more specialization, which complicates vertical integration. Moreover, a firm that has a depart- ment or division of its own for everything may be too bureaucratic to be world class. So rather than letting vertical integration lock an organization into businesses that it may not understand or be able to manage, another approach is to find good flexible suppliers. Virtual companies rely on a variety of supplier relationships to provide services on demand. Virtual companies have fluid, moving organizational boundaries that allow them to create a unique enterprise to meet changing market demands. Suppliers may provide a variety of services that include doing the payroll, hiring personnel, designing products, providing consulting services, manufacturing components, conducting tests, or distributing products. The relationships may be short or long term and may include true partners, collaborators, or simply able suppliers and subcontractors. Whatever the formal relationship, the result can be exceptionally lean performance. The advan- tages of virtual companies include specialized management expertise, low capital investment, flexibility, and speed. The result is efficiency. The apparel business provides a traditional example of virtual organizations. The designers of clothes seldom manufacture their designs; rather, they license the manufacture. The manufac- turer may then rent space, lease sewing machines, and contract for labor. The result is an organi- zation that has low overhead, remains flexible, and can respond rapidly to the market. A contemporary example is exemplified by Vizio, Inc., a California-based producer of LCD TVs that has only 85 employees but huge sales. Vizio uses modules to assemble its own brand of TVs. Because the key components of TVs are now readily available and sold almost as com- modities, innovative firms such as Vizio can specify the components, hire a contract manufac- turer, and market the TVs with very little startup cost. In a virtual company, the supply chain is the company. Managing it is dynamic and demanding. MANAGING THE SUPPLY CHAIN As managers move toward integration of the supply chain, substantial efficiencies are possible. The cycle of materials—as they flow from suppliers, to production, to warehousing, to distribu- tion, to the customer—takes place among separate and often very independent organizations. Therefore, there are significant management issues that may result in serious inefficiencies. Keiretsu A Japanese term that describes suppliers who become part of a company coalition. Virtual companies Companies that rely on a variety of supplier relationships to provide services on demand. Also known as hollow corporations or network companies. AUTHOR COMMENT Trust, agreed-upon goals, and compatible cultures make supply-chain management easier. 344 PART 3 Managing Operations Success begins with mutual agreement on goals, followed by mutual trust, and continues with compatible organizational cultures. Mutual Agreement on Goals An integrated supply chain requires more than just agree- ment on the contractual terms of a buy/sell relationship. Partners in the chain must appreciate that the only entity that puts money into a supply chain is the end customer. Therefore, establish- ing a mutual understanding of the mission, strategy, and goals of participating organizations is essential. The integrated supply chain is about adding economic value and maximizing the total content of the product. Trust Trust is critical to an effective and efficient supply chain. Members of the chain must enter into a relationship that shares information. Visibility throughout the supply chain—what Darden Restaurants calls a transparent supply chain—is a requirement. Supplier relationships are more likely to be successful if risk and cost savings are shared—and activities such as end- customer research, sales analysis, forecasting, and production planning are joint activities. Such relationships are built on mutual trust. Compatible Organizational Cultures A positive relationship between the purchasing and supplying organizations that comes with compatible organizational cultures can be a real advan- tage when making a supply chain hum. A champion within one of the two firms promotes both formal and informal contacts, and those contacts contribute to the alignment of the organiza- tional cultures, further strengthening the relationship. The operations manager is dealing with a supply chain that is made up of independent special- ists, each trying to satisfy its own customers at a profit. This leads to actions that may not opti- mize the entire chain. On the other hand, the supply chain is replete with opportunities to reduce waste and enhance value. We now look at some of the significant issues and opportunities. Issues in an Integrated Supply Chain Three issues complicate development of an efficient, integrated supply chain: local optimization, incentives, and large lots. Local Optimization Members of the chain are inclined to focus on maximizing local profit or minimizing immediate cost based on their limited knowledge. Slight upturns in demand are overcompensated for because no one wants to be caught short. Similarly, slight downturns are over- compensated for because no one wants to be caught holding excess inventory. So fluctuations are magnified. For instance, a pasta distributor does not want to run out of pasta for its retail cus- tomers; the natural response to an extra large order from the retailer is to compensate with an even larger order to the manufacturer on the assumption that retail sales are picking up. Neither the distributor nor the manufacturer knows that the retailer had a major one-time promotion that moved a lot of pasta. This is exactly the issue that complicated the implementation of efficient distribution at the Italian pasta maker Barilla. Incentives (Sales Incentives, Quantity Discounts, Quotas, and Promotions) Incentives push merchandise into the chain for sales that have not occurred. This generates fluc- tuations that are ultimately expensive to all members of the chain. Large Lots There is often a bias toward large lots because large lots tend to reduce unit costs. A logistics manager wants to ship large lots, preferably in full trucks, and a production manager wants long production runs. Both actions drive down unit shipping and production costs, but fail to reflect actual sales and increased holding costs. These three common occurrences—local optimization, incentives, and large lots—contribute to distortions of information about what is really occurring in the supply chain. A well-running supply system needs to be based on accurate information about how many products are truly being pulled through the chain. The inaccurate information is unintentional, but it results in dis- tortions and fluctuations in the supply chain and causes what is known as the bullwhip effect. The bullwhip effect occurs as orders are relayed from retailers, to distributors, to whole- salers, to manufacturers, with fluctuations increasing at each step in the sequence. The “bull- whip” fluctuations in the supply chain increase the costs associated with inventory, transportation, shipping, and receiving, while decreasing customer service and profitability. Procter & Gamble found that although the use of Pampers diapers was steady and the retail-store orders had little fluctuation, as orders moved through the supply chain, fluctuations increased. By the time orders VIDEO 11.3 Arnold Palmer Hospital’s Supply Chain LO3: Explain issues and opportunities in the supply chain Bullwhip effect The increasing fluctuation in orders that often occurs as orders move through the supply chain. Chapter 11 Supply-Chain Management 345 were initiated for raw material, the variability was substantial. Similar behavior has been observed and documented at many companies, including Campbell Soup, Hewlett-Packard, and Applied Materials. The bullwhip effect can occur when orders decrease as well as when they increase. A number of opportunities exist for reducing the bullwhip effect and improving opportunities in the supply chain. These are discussed in the following section. Opportunities in an Integrated Supply Chain Opportunities for effective management in the supply chain include the following 11 items. Accurate “Pull” Data Accurate pull data are generated by sharing (1) point-of-sales (POS) information so that each member of the chain can schedule effectively and (2) computer-assisted ordering (CAO). This implies using POS systems that collect sales data and then adjusting that data for market factors, inventory on hand, and outstanding orders. Then a net order is sent directly to the supplier who is responsible for maintaining the finished-goods inventory. Lot Size Reduction Lot sizes are reduced through aggressive management. This may include (1) developing economical shipments of less than truckload lots; (2) providing discounts based on total annual volume rather than size of individual shipments; and (3) reducing the cost of ordering through techniques such as standing orders and various forms of electronic purchasing. Single-Stage Control of Replenishment Single-stage control of replenishment means designating a member in the chain as responsible for monitoring and managing inventory in the supply chain based on the “pull” from the end user. This approach removes distorted information and multiple forecasts that create the bullwhip effect. Control may be in the hands of: • A sophisticated retailer who understands demand patterns. Walmart does this for some of its inventory with radio frequency ID (RFID) tags as shown in the OM in Action box “Radio Fre- quency Tags: Keeping the Shelves Stocked.” Pull data Accurate sales data that initiate transactions to “pull” product through the supply chain. Single-stage control of replenishment Fixing responsibility for monitoring and managing inventory for the retailer. Supply chains work smoothly when sales are steady, but often break down when confronted by a sudden surge or rapid drop in demand. Radio frequency ID (or RFID) tags can change that by providing real-time information about what’s happening on store shelves. Here’s how the system works for Procter & Gamble’s (P&G’s) Pampers. OM in Action �Radio Frequency Tags: Keeping the Shelves Stocked W al m ar t in ven tory management system Pr oc te r & Gam ble supply-chain software P&G suppliers P&G regional supply manager Walmart distribution center Walmart SHELF ALERT! NEED PAMPERS! STORE ALERT! NEED PAMPERS! WAREHOUSE ALERT! NEED PAMPERS! RESUPPLY RESUPPLY RESUPPLY ! ! ! ! 1. A special promotion causes Walmart shoppers to snap up boxes of Pampers Baby-Dry. 5. P&G’s logistics software tracks its trucks with GPS locators, and tracks their contents with RFID tag readers. Regional managers can reroute trucks to fill urgent needs. 6. P&G suppliers also use RFID tags and readers on their raw materials, giving P&G visibility several tiers down the supply chain, and giving suppliers the ability to accurately forecast demand and production. 2. Each box of Pampers has an RFID tag. Shelf-mounted scanners alert the stockroom of urgent need for restock. 3. Walmart’s inventory management system tracks and links its in-store stock and its warehouse stock, prompting quicker replenish- ment and providing accurate real-time data. 4. Walmart’s systems are linked to the P&G supply- chain management system. Demand spikes reported by RFID tags are immediately visible throughout the supply chain. WAL*MART RE-ROUTE # 237 237 Sources: Financial Times (August 22, 2008): 12; Business 2.0 (May 2002): 86; and Knight Ridder Tribune Business News (August 6, 2006): 1. 346 PART 3 Managing Operations • A distributor who manages the inventory for a particular distribution area. Distributors who handle grocery items, beer, and soft drinks may do this. Anheuser-Busch manages beer inven- tory and delivery for many of its customers. • A manufacturer who has a well-managed forecasting, manufacturing, and distribution sys- tem. TAL Apparel Ltd., discussed in the OM in Action box, “The JCPenney Supply Chain for Dress Shirts,” does this for JCPenney. Vendor-Managed Inventory Vendor-managed inventory (VMI) means the use of a local supplier (usually a distributor) to maintain inventory for the manufacturer or retailer. The sup- plier delivers directly to the purchaser’s using department rather than to a receiving dock or stockroom. If the supplier can maintain the stock of inventory for a variety of customers who use the same product or whose differences are very minor (say, at the packaging stage), then there should be a net savings. These systems work without the immediate direction of the purchaser. Collaborative Planning, Forecasting, and Replenishment Like single-stage control and vendor-managed inventory, Collaborative planning, forecasting, and replenishment (CPFR) is another effort to manage inventory in the supply chain. With CPFR, members of the supply chain share planning, forecasting, and inventory information. Partners in a CPFR effort begin with collaboration on product definition and a joint marketing plan. Promotion, advertising, forecasts, and timing of shipments are all included in the plan in a concerted effort to drive down inventory and related costs. Blanket Orders Blanket orders are unfilled orders with a vendor.2 A blanket order is a contract to purchase certain items from a vendor. It is not an authorization to ship anything. Shipment is made only on receipt of an agreed-on document, perhaps a shipping requisition or shipment release. Standardization The purchasing department should make special efforts to increase levels of standardization. That is, rather than obtaining a variety of similar components with labeling, coloring, packaging, or perhaps even slightly different engineering specifications, the purchasing agent should try to have those components standardized. Postponement Postponement withholds any modification or customization to the product (keeping it generic) as long as possible. The concept is to minimize internal variety while maximizing external variety. For instance, after analyzing the supply chain for its printers, Hewlett-Packard (HP) determined that if the printer’s power supply was moved out of the printer itself and into a power cord, HP could ship the basic printer anywhere in the world. HP modified the printer, its power cord, its packaging, and its documentation so that only the power cord and documentation needed to be added at the final distribution point. This modification allowed the Postponement Delaying any modifications or customization to a product as long as possible in the production process. Purchase a white Stafford wrinkle-free dress shirt, size 17 neck, 34/35 sleeve at JCPenney at Atlanta’s Northlake Mall on a Tuesday, and the supply chain responds. Within a day, TAL Apparel Ltd. in Hong Kong downloads a record of the sale. After a run through its forecasting model, TAL decides how many shirts to make and in what styles, colors, and sizes. By Wednesday afternoon, the replacement shirt is packed to be shipped directly to the JCPenney Northlake Mall store. The system bypasses the JCPenney warehouse—indeed all warehouses—as well as the JCPenney corporate decision makers. In a second instance, two shirts are sold, leaving none in stock. TAL, after downloading the data, runs its forecasting model but comes to the decision that this store needs to have two in stock. Without consulting JCPenney, a TAL factory in Taiwan makes two new shirts. It sends one by ship, but because of the outage, the other goes by air. As retailers deal with mass customization, fads, and seasonal swings they also strive to cut costs—making a responsive supply chain critical. Before globalization of the supply chain, JCPenney would have had thousands of shirts warehoused across the country. Now JCPenney stores, like those of many retailers, hold a very limited inventory of shirts. JCPenney’s supplier, TAL, is providing both sales forecasting and inventory management, a situation not acceptable to many retailers. But what is most startling is that TAL also places its own orders! A supply chain like this works only when there is trust between partners. The rapid changes in supply-chain management not only place increasing technical demands on suppliers but also increase demands for trust between the parties. Sources: Apparel (April 2006): 14–18; The Wall Street Journal (September 11, 2003): A1, A9; and International Trade Forum (Issue 3, 2005): 12–13. OM in Action � The JCPenney Supply Chain for Dress Shirts Vendor-managed inventory (VMI) A system in which a supplier maintains material for the buyer, often delivering directly to the buyer’s using department. Collaborative planning, forecasting, and replen- ishment (CPFR) A joint effort of members of a supply chain to share information in order to reduce supply-chain costs. Blanket order A long-term purchase commitment to a supplier for items that are to be delivered against short-term releases to ship. 2Unfilled orders are also referred to as “open” orders, or “incomplete” orders. Chapter 11 Supply-Chain Management 347 firm to manufacture and hold centralized inventories of the generic printer for shipment as demand changed. Only the unique power system and documentation had to be held in each coun- try. This understanding of the entire supply chain reduced both risk and investment in inventory. Drop Shipping and Special Packaging Drop shipping means the supplier will ship directly to the end consumer, rather than to the seller, saving both time and reshipping costs. Other cost-saving measures include the use of special packaging, labels, and optimal placement of labels and bar codes on containers. The final location down to the department and number of units in each shipping container can also be indicated. Substantial savings can be obtained through management techniques such as these. Some of these techniques can be of particular benefit to wholesalers and retailers by reducing shrinkage (lost, damaged, or stolen merchandise) and handling cost. For instance, Dell Computer has decided that its core competence is not in stocking peripher- als, but in assembling PCs. So if you order a PC from Dell, with a printer and perhaps other com- ponents, the computer comes from Dell, but the printer and many of the other components will be drop shipped from the manufacturer. Pass-through Facility A pass-through facility is a distribution center where merchandise is held, but it functions less as a holding area and more as a shipping hub. These facilities, often run by logistics vendors, use the latest technology and automated systems to expedite orders. For instance, UPS works with Nike at such a facility in Louisville, Kentucky, to immediately handle orders. Similarly, FedEx’s warehouse next to the airport in Memphis can receive an order after a store closes for the evening and can locate, package, and ship the merchandise that night. Delivery is guaranteed by 10 A.M. the next day. Channel Assembly Channel assembly is an extension of the pass-through facility. Channel assembly sends individual components and modules, rather than finished products, to the distributor. The distributor then assembles, tests, and ships. Channel assembly treats distributors more as manufacturing partners than as distributors. This technique has proven suc- cessful in industries where products are undergoing rapid change, such as personal computers. With this strategy, finished-goods inventory is reduced because units are built to a shorter, more accurate forecast. Consequently, market response is better, with lower investment—a nice combination. E-PROCUREMENT E-procurement uses the Internet to facilitate purchasing. E-procurement speeds purchasing, reduces costs, and integrates the supply chain, enhancing an organization’s competitive advan- tage. The traditional supply chain is full of paper transactions, such as requisitions, requests for bids, bid evaluations, purchase orders, order releases, receiving documents, invoices, and the issuance of checks. E-procurement reduces this barrage of paperwork and at the same time pro- vides purchasing personnel with an extensive database of vendor, delivery, and quality data. With this history, vendor selection has improved. In this section, we discuss traditional techniques of electronic ordering and funds transfer and then move on to online catalogs, auctions, RFQs, and real-time inventory tracking. Electronic Ordering and Funds Transfer Electronic ordering and bank transfers are traditional approaches to speeding transactions and reducing paperwork. Transactions between firms often use electronic data interchange (EDI), which is a standardized data-transmittal format for computerized communications between organizations. EDI provides data transfer for virtually any business application, including purchasing. Under EDI, data for a purchase order, such as order date, due date, quantity, part number, purchase order number, address, and so forth, are fitted into the standard EDI format. EDI also provides for the use of advanced shipping notice (ASN), which notifies the purchaser that the vendor is ready to ship. Although some firms are still moving to EDI and ASN, the Internet’s ease of use and lower cost is proving more popular. Online Catalogs Purchase of standard items is often accomplished via online catalogs. Such catalogs provide cur- rent information about products in electronic form. Online catalogs support cost comparisons Drop shipping Shipping directly from the supplier to the end consumer rather than from the seller, saving both time and reshipping costs. Pass-through facility Expedites shipment by holding merchandise and delivering from shipping hubs. Channel assembly Postpones final assembly of a product so the distribution channel can assemble it. E-procurement Purchasing facilitated through the Internet. Electronic data interchange (EDI) A standardized data-transmittal format for computerized communications between organizations. Advanced shipping notice (ASN) A shipping notice delivered directly from vendor to purchaser. AUTHOR COMMENT The Internet has revolutionized procurement. 348 PART 3 Managing Operations and incorporate voice and video clips, making the process efficient for both buyers and sellers. Online catalogs are available in three versions: 1. Typical of catalogs provided by vendors are those of W. W. Grainger and Office Depot. W. W. Grainger is probably the world’s largest seller of MRO items (items for maintenance, repair, and operations), while Office Depot provides the same service for office supplies. 2. Catalogs provided by intermediaries are Internet sites where business buyers and sellers can meet. These intermediaries typically create industry specific catalogs with content from many suppliers. 3. One of the first online exchanges provided by buyers was Avendra (www.avendra.com). Avendra was created by Marriott and Hyatt (and subsequently joined by other large hotel firms) to economically purchase the huge range of goods needed by the 2,800 hotels now in the exchange. Such exchanges—and there are many—move companies from a multitude of individual phone calls, faxes, and e-mails to a centralized online system, and drive billions of dollars of waste out of the supply chain. Auctions Online auction sites can be maintained by sellers, buyers, or intermediaries. Operations man- agers find online auctions a fertile area for disposing of excess raw material and discontinued or excess inventory. Online auctions lower entry barriers, encouraging sellers to join and simultane- ously increase the potential number of buyers. The key for auction firms, such as Ariba of Sunnyvale, California (see the photo), is to find and build a huge base of potential bidders, improve client buying procedures, and qualify new suppliers. RFQs When purchasing requirements are nonstandard, time spent preparing requests for quotes (RFQs) and the related bid package can be substantial. Consequently, e-procurement has now moved these often expensive parts of the purchasing process online, allowing purchasing agents to inexpensively attach electronic copies of the necessary drawings to RFQs. Real-Time Inventory Tracking FedEx’s pioneering efforts at tracking packages from pickup to delivery has shown the way for operations managers to do the same for their shipments and inventory. Because tracking cars and trucks has been a chronic and embarrassingly inexact science, Ford has hired UPS to track millions of vehicles as they move from factory to dealers. Using bar codes and the Internet, Ford dealers are now able to log onto a Web site and find out exactly where the ordered vehicles are in the dis- tribution system. As operations managers move to an era of mass customization, with customers ordering exactly the cars they want, customers will expect to know where their cars are and Here an Ariba team monitors an online market from the firm’s Global Market Operations Center. Ariba provides support for the entire global sourcing process, including software, supplier development, competitive negotiations, and savings implementation. Online bidding leads to greater cost savings than more traditional procurement. www.avendra.com Chapter 11 Supply-Chain Management 349 exactly when they can be picked up. E-procurement, supported by bar codes and RFID, can pro- vide economical inventory tracking on the shop floor, in warehouses, and in logistics. VENDOR SELECTION For those goods and services a firm buys, vendors must be selected. Vendor selection considers numerous factors, such as strategic fit, vendor competence, delivery, and quality performance. Because a firm may have some competence in all areas and may have exceptional competence in only a few, selection can be challenging. Procurement policies also need to be established. Those might address issues such as percent of business done with any one supplier or with minority businesses. We now examine vendor selection as a three-stage process: (1) vendor evaluation, (2) vendor development, and (3) negotiations. Vendor Evaluation The first stage of vendor selection, vendor evaluation, involves finding potential vendors and determining the likelihood of their becoming good suppliers. This phase requires the develop- ment of evaluation criteria such as criteria shown in Example 2. However, both the criteria and the weights selected vary depending on the supply-chain strategy being implemented. (Refer to Table 11.1, on page 337.) � EXAMPLE 2 Weighted approach to vendor evaluation Erin Davis, president of Creative Toys in Palo Alto, is interested in evaluating suppliers who will work with him to make nontoxic, environmentally friendly paints and dyes for his line of children’s toys. This is a critical strategic element of his supply chain, and he desires a firm that will contribute to his product. APPROACH � Erin begins his analysis of one potential supplier, Faber Paint and Dye, by using the weighted approach to vendor evaluation. SOLUTION � Erin first reviews the supplier differentiation attributes in Table 11.1 and develops the following list of selection criteria. He then assigns the weights shown to help him perform an objec- tive review of potential vendors. His staff assigns the scores shown and computes the total weighted score. Scores (1–5) Weight � Criteria Weights (5 highest) Score Engineering/research/innovation skills .20 5 1.0 Production process capability (flexibility/technical assistance) .15 4 .6 Distribution/delivery capability .05 4 .2 Quality systems and performance .10 2 .2 Facilities/location .05 2 .1 Financial and managerial strength (stability and cost structure) .15 4 .6 Information systems capability (e-procurement, ERP) .10 2 .2 Integrity (environmental compliance/ethics) .20 5 1.0 1.00 3.9 Total Faber Paint and Dye receives an overall score of 3.9. INSIGHT � Erin now has a basis for comparison with other potential vendors, selecting the one with the highest overall rating. LEARNING EXERCISE � If Erin believes that the weight for “engineering/research/innovation skills” should be increased to .25 and the weight for “financial and managerial strength” reduced to .10, what is the new score? [Answer: Faber Paint and Dye now goes to 3.95.] RELATED PROBLEMS � 11.2, 11.3, 11.4 LO4: Describe the steps in vendor selection The selection of competent suppliers is critical. If good suppliers are not selected, then all other supply-chain efforts are wasted. As firms move toward using fewer longer-term suppliers, the issues of financial strength, quality, management, research, technical ability, and potential for a close long-term relationship play an increasingly important role. These attributes should be noted in the evaluation process. EXCEL OM Data File Ch11Ex2.xls can be found at www.pearsonhighered.com/heizer. www.pearsonhighered.com/heizer 350 PART 3 Managing Operations Vendor Development The second stage of vendor selection is vendor development. Assuming that a firm wants to pro- ceed with a particular vendor, how does it integrate this supplier into its system? The buyer makes sure the vendor has an appreciation of quality requirements, product specifications, schedules and delivery, the purchaser’s payment system, and procurement policies. Vendor development may include everything from training, to engineering and production help, to proce- dures for information transfer. Negotiations Regardless of the supply chain strategy adopted, negotiations regarding the critical elements of the contractual relationship must take place. These negotiations often focus on quality, delivery, payment, and cost. We will look at three classic types of negotiation strategies: the cost-based model, the market-based price model, and competitive bidding. Cost-Based Price Model The cost-based price model requires that the supplier open its books to the purchaser. The contract price is then based on time and materials or on a fixed cost with an escalation clause to accommodate changes in the vendor’s labor and materials cost. Market-Based Price Model In the market-based price model, price is based on a pub- lished, auction, or index price. Many commodities (agriculture products, paper, metal, etc.) are priced this way. Paperboard prices, for instance, are available via the Official Board Markets weekly publication (www.advanstar.com). Nonferrous metal prices are quoted in Platt’s Metals Week (www.platts.com/plattsmetals/), and prices of other metals are quoted at www. metalworld.com. Competitive Bidding When suppliers are not willing to discuss costs or where near-perfect markets do not exist, competitive bidding is often appropriate. Infrequent work (such as con- struction, tooling, and dies) is usually purchased based on a bid. Bidding may take place via mail, fax, or an Internet auction. Competitive bidding is the typical policy in many firms for the majority of their purchases. Bidding policies usually require that the purchasing agent have sev- eral potential suppliers of the product (or its equivalent) and quotations from each. The major disadvantage of this method, as mentioned earlier, is that the development of long-term relations between buyer and seller is hindered. Competitive bidding may effectively determine initial cost. However, it may also make difficult the communication and performance that are vital for engi- neering changes, quality, and delivery. Yet a fourth approach is to combine one or more of the preceding negotiation techniques. The supplier and purchaser may agree on review of certain cost data, accept some form of market data for raw material costs, or agree that the supplier will “remain competitive.” In any case, a good supplier relationship is one in which both partners have established a degree of mutual trust and a belief in each other’s competence, honesty, and fair dealing. LOGISTICS MANAGEMENT Procurement activities may be combined with various shipping, warehousing, and inventory activities to form a logistics system. The purpose of logistics management is to obtain effi- ciency of operations through the integration of all material acquisition, movement, and storage activities. When transportation and inventory costs are substantial on both the input and output sides of the production process, an emphasis on logistics may be appropriate. When logistics issues are significant or expensive, many firms opt for outsourcing the logistics function. Logistics specialists can often bring expertise not available in-house. For instance, logistics com- panies often have tracking technology that reduces transportation losses and supports delivery schedules that adhere to precise delivery windows. The potential for competitive advantage is found via both reduced costs and improved customer service. Firms recognize that the distribution of goods to and from their facilities can represent as much as 25% of the cost of products. In addition, the total distribution cost in the U.S. is over 10% of the gross national product (GNP). Because of this high cost, firms constantly evaluate their means of distribution. Five major means of distribution are trucking, railroads, airfreight, waterways, and pipelines. Negotiation strategies Approaches taken by supply chain personnel to develop contractual relationships with suppliers. AUTHOR COMMENT Time, cost, and reliability variables make logistic decisions demanding. Logistics management An approach that seeks efficiency of operations through the integration of all material acquisition, movement, and storage activities. www.advanstar.com www.platts.com/plattsmetals/ www.metalworld.com www.metalworld.com Chapter 11 Supply-Chain Management 351 Distribution Systems Trucking The vast majority of manufactured goods moves by truck. The flexibility of shipping by truck is only one of its many advantages. Companies that have adopted JIT programs in recent years have put increased pressure on truckers to pick up and deliver on time, with no damage, with paperwork in order, and at low cost. Trucking firms are using computers to monitor weather, find the most effective route, reduce fuel cost, and analyze the most efficient way to unload. In spite of these advances, the motor carrier industry averages a capacity utilization of only 50%. That under- utilized space costs the U.S. economy over $31 billion per year. To improve logistics efficiency, the industry is establishing Web sites such as Schneider National’s connection (www.schneider.com), which lets shippers and truckers find each other to use some of this idle capacity. Shippers may pick from thousands of approved North American carriers that have registered with Schneider logistics. Railroads Railroads in the U.S. employ 187,000 people and ship 90% of all coal, 67% of autos, 68% of paper products, and about half of all food, lumber, and chemicals. Containerization has made intermodal shipping of truck trailers on railroad flat cars, often piggybacked as double-deckers, a popular means of distribution. More than 36 million trailer loads are moved in the U.S. each year by rail. With the growth of JIT, however, rail transport has been the biggest loser because small-batch manufacture requires frequent, smaller shipments that are likely to move via truck or air. Airfreight Airfreight represents only about 1% of tonnage shipped in the U.S. However, the recent proliferation of airfreight carriers such as FedEx, UPS, and DHL makes it the fastest- growing mode of shipping. Clearly, for national and international movement of lightweight items, such as medical and emergency supplies, flowers, fruits, and electronic components, air- freight offers speed and reliability. Waterways Waterways are one of the nation’s oldest means of freight transportation, dating back to construction of the Erie Canal in 1817. Included in U.S. waterways are the nation’s rivers, canals, the Great Lakes, coastlines, and oceans connecting to other countries. The usual cargo on waterways is bulky, low-value cargo such as iron ore, grains, cement, coal, chemicals, limestone, and petroleum products. Internationally, millions of containers are shipped at very low cost via huge oceangoing ships each year. Water transportation is important when shipping cost is more important than speed. Pipelines Pipelines are an important form of transporting crude oil, natural gas, and other petroleum and chemical products. An amazing 90% of the state of Alaska’s budget is derived from the 1.5 million barrels of oil pumped daily through the pipeline at Prudhoe Bay. Third-Party Logistics Supply-chain managers may find that outsourcing logistics is advantageous in driving down inventory investment and costs while improving delivery reliability and speed. Specialized logis- tics firms support this goal by coordinating the supplier’s inventory system with the service LO5: Explain major issues in logistics management As this photo of the port of Charleston suggests, with 16 million containers entering the U.S. annually, tracking location, content, and condition of trucks and containers is a challenge. But new technology may improve both security and JIT shipments. www.schneider.com 352 PART 3 Managing Operations capabilities of the delivery firm. FedEx, for example, has a successful history of using the Internet for online tracking. At FedEx.com, a customer can compute shipping costs, print labels, adjust invoices, and track package status all on the same Web site. FedEx, UPS, and DHL play a core role in other firms’ logistics processes. In some cases, they even run the server for retailer Web sites. In other cases, such as for Dell Computer, FedEx operates warehouses that pick, pack, test, and assemble products, then it handles delivery and customs clearance when necessary. The OM in Action box “DHL’s Role in the Supply Chain” provides another example of how outsourc- ing logistics can reduce costs while shrinking inventory and delivery times. Cost of Shipping Alternatives The longer a product is in transit, the longer the firm has its money invested. But faster shipping is usually more expensive than slow shipping. A simple way to obtain some insight into this trade-off is to evaluate holding cost against shipping options. We do this in Example 3. Seven farms within a 2-hour drive of Kenya’s Nairobi Airport supply 300 tons of fresh beans, bok choy, okra, and other produce that is packaged at the airport and shipped overnight to Europe. The time between harvest and arrival in Europe is 2 days. When a good supply chain and good logistics work together, the results can be startling—and fresh food. It’s the dead of night at DHL International’s air express hub in Brussels, yet the massive building is alive with busy forklifts and sorting workers. The boxes going on and off the DHL plane range from Dell computers and Cisco routers to Caterpillar mufflers and Komatsu hydraulic pumps. Sun Microsystems computers from California are earmarked for Finland; DVDs from Teac’s plant in Malaysia are destined for Bulgaria. The door-to-door movement of time-sensitive packages is key to the global supply chain. JIT, short product life cycles, mass customization, and reduced inventories depend on logistics firms such as DHL, FedEx, and UPS. These powerhouses are in continuous motion. With a decentralized network covering 225 countries and territories (more than are in the UN), DHL is a true multinational. The Brussels headquarters has only 450 of the company’s 124,000 employees but includes 26 nationalities. DHL has assembled an extensive global network of express logistics centers for strategic goods. In its Brussels logistics center, for instance, DHL upgrades, repairs, and configures Fijitsu computers, InFocus projectors, and Johnson & Johnson medical equipment. It stores and provides parts for EMC and Hewlett-Packard and replaces Nokia and Philips phones. “If something breaks down on a Thursday at 4 o’clock, the relevant warehouse knows at 4:05, and the part is on a DHL plane at 7 or 8 that evening,” says Robert Kuijpers, DHL International’s CEO. Sources: Journal of Commerce (August 15, 2005): 1; Hoover’s Company Records (May 1, 2009): 40126; and Forbes (October 18, 1999): 120–124. OM in Action � DHL’s Role in the Supply Chain Chapter 11 Supply-Chain Management 353 � EXAMPLE 3 Determining daily cost of holding A shipment of new connectors for semiconductors needs to go from San Jose to Singapore for assem- bly. The value of the connectors is $1,750 and holding cost is 40% per year. One airfreight carrier can ship the connectors 1 day faster than its competitor, at an extra cost of $20.00. Which carrier should be selected? APPROACH � First we determine the daily holding cost and then compare the daily holding cost with the cost of faster shipment. SOLUTION � Since the cost of saving one day is $20.00, which is much more than the daily holding cost of $1.92, we decide on the less costly of the carriers and take the extra day to make the shipment. This saves $18.08 ($20.00 – $1.92). INSIGHT � The solution becomes radically different if the 1-day delay in getting the connectors to Singapore delays delivery (making a customer angry) or delays payment of a $150,000 final product. (Even 1 day’s interest on $150,000 or an angry customer makes a savings of $18.08 insignificant.) LEARNING EXERCISE � If the holding cost is 100% per year, what is the decision? [Answer: Even with a holding cost of $4.79 per day, the less costly carrier is selected.] RELATED PROBLEMS � 11.8, 11.9, 11.10 = $1.92 = 1.40 * $1,7502>365
Daily cost of holding the product = 1Annual holding cost * Product value2>365
Example 3 looks only at holding costs versus shipping cost. For the operations or logistics
manager there are many other considerations, including coordinating shipments to maintain
a schedule, getting a new product to market, and keeping a customer happy. Estimates of these
other costs can be added to the estimate of daily holding cost. Determining the impact and cost
of these many other considerations makes the evaluation of shipping alternatives interesting.
Security and JIT
There is probably no society more open than the U.S. This includes its borders and ports—but
they are swamped. About 7 million containers enter U.S. ports each year, along with thousands
of planes, cars, and trucks each day. Even under the best of conditions, some 5% of the container
movements are misrouted, stolen, damaged, or excessively delayed.
Since the September 11, 2001, terrorist attacks, supply chains have become more complex.
Technological innovations, though, in the supply chain are improving security and JIT, making
logistics more reliable. Technology is now capable of knowing truck and container location, con-
tent, and condition. New devices can detect whether someone has broken into a sealed container
Speed and accuracy in the supply chain are supported by bar-code tracking of shipments. At each step of a journey,
from initial pickup to final destination, bar codes (left) are read and stored. Within seconds, this tracking information is
available online to customers worldwide (right).

354 PART 3 Managing Operations
and can communicate that information to the shipper or receiver via satellite or radio. Motion
detectors can also be installed inside containers. Other sensors can record interior data including
temperature, shock, radioactivity, and whether a container is moving. Tracking lost containers,
identifying delays, or just reminding individuals in the supply chain that a shipment is on its way
will help expedite shipments. Improvements in security may aid JIT, and improvements in JIT
may aid security—both of which can improve supply-chain logistics.
MEASURING SUPPLY-CHAIN PERFORMANCE
Like all other managers, supply-chain managers require standards (or metrics, as they are often
called) to evaluate performance. Evaluation of the supply chain is particularly critical for these
managers because they spend most of the organization’s money. In addition, they make schedul-
ing and quantity decisions that determine the assets committed to inventory. Only with effective
metrics can managers determine: (1) how well the supply chain is performing and (2) the assets
committed to inventory. We will now discuss these two metrics.
Supply-Chain Performance The benchmark metrics shown in Table 11.5 focus on procure-
ment and vendor performance issues. World-class benchmarks are the result of well-managed sup-
ply chains that drive down costs, lead times, late deliveries, and shortages while improving quality.
Assets Committed to Inventory Three specific measures can be helpful here. The first is
the amount of money invested in inventory, usually expressed as a percentage of assets, as shown
in Equation (11-1) and Example 4:
(11-1)Percentage invested in inventory = 1Total inventory investment>Total assets2 * 100
LO6: Compute the
percentage of assets
committed to inventory
and inventory turnover
AUTHOR COMMENT
If you can’t measure it,
you can’t control it.
�TABLE 11.5
Metrics for Supply-Chain
Performance
Typical Firms Benchmark Firms
Lead time (weeks) 15 8
Time spent placing an order 42 minutes 15 minutes
Percent of late deliveries 33% 2%
Percent of rejected material 1.5% .0001%
Number of shortages per year 400 4
Source: Adapted from a McKinsey & Company report.
EXAMPLE 4 �
Tracking Home
Depot’s inventory
investment
Home Depot’s management wishes to track its investment in inventory as one of its performance mea-
sures. Home Depot had $11.4 billion invested in inventory and total assets of $44.4 billion in 2006.
APPROACH � Determine the investment in inventory and total assets and then use Equation (11-1).
SOLUTION �
INSIGHT � Over one-fourth of Home Depot assets are committed to inventory.
LEARNING EXERCISE � If Home Depot can drive its investment down to 20% of assets, how
much money will it free up for other uses? [Answer: ]
RELATED PROBLEMS � 11.11b, 11.12b
11.4 – 144.4 * .22 = $2.52 billion.
Percent invested in inventory = 111.4>44.42 * 100 = 25.7%
Inventory turnover
Cost of goods sold divided by
average inventory.
Specific comparisons with competitors may assist evaluation. Total assets committed to inven-
tory in manufacturing approach 15%, in wholesale 34%, and retail 27%—with wide variations,
depending on the specific business model, the business cycle, and management (see Table 11.6).
The second common measure of supply chain performance is inventory turnover (see Table
11.7). Its reciprocal, weeks of supply, is the third. Inventory turnover is computed on an annual
basis, using Equation (11-2):
(11-2)
Cost of goods sold is the cost to produce the goods or services sold for a given period. Inventory
investment is the average inventory value for the same period. This may be the average of
several periods of inventory or beginning and ending inventory added together and divided by 2.
Inventory turnover = Cost of goods sold>Inventory investment

Chapter 11 Supply-Chain Management 355
Often, average inventory investment is based on nothing more than the inventory investment at
the end of the period—typically at year-end.3
In Example 5, we look at inventory turnover applied to PepsiCo.
� TABLE 11.6
Inventory as Percentage of Total Assets (with
examples of exceptional performance)
Manufacturer 15%
(Toyota 5%)
Wholesale 34%
(Coca-Cola 2.9%)
Restaurants 2.9%
(McDonald’s .05%)
Retail 27%
(Home Depot 25.7%)
� TABLE 11.7
Examples of Annual Inventory Turnover
Food, Beverage, Retail
Anheuser Busch 15
Coca-Cola 14
Home Depot 5
McDonald’s 112
Manufacturing
Dell Computer 90
Johnson Controls 22
Toyota (overall) 13
Nissan (assembly) 150
� EXAMPLE 5
Inventory
turnover at
PepsiCo, Inc.
PepsiCo, Inc., manufacturer and distributor of drinks, Frito-Lay, and Quaker Foods, provides the fol-
lowing in its 2005 annual report (shown here in $ billions). Determine PepsiCo’s turnover.
APPROACH � Use the inventory turnover computation in Equation (11-2) to measure inventory
performance. Cost of goods sold is $14.2 billion. Total inventory is the sum of raw material at $.74 bil-
lion, work-in-process at $.11 billion, and finished goods at $.84 billion, for total inventory investment
of $1.69 billion.
SOLUTION �
INSIGHT � We now have a standard, popular measure by which to evaluate performance.
LEARNING EXERCISE � If Coca-Cola’s cost of goods sold is $10.8 billion and inventory
investment is $.76 billion, what is its inventory turnover? [Answer: 14.2.]
RELATED PROBLEMS � 11.11a, 11.12c, 11.13
= 8.4
= 14.2>1.69
Inventory Turnover = Cost of goods sold>Inventory investment
Net revenue $32.5
Cost of goods sold $14.2
Inventory:
Raw material inventory $.74
Work-in-process inventory $.11
Finished goods inventory $.84
Total inventory investment $1.69
3Inventory quantities often fluctuate wildly, and various types of inventory exist (e.g., raw material, work-in-process,
finished goods, and maintenance, repair, and operating supplies [MRO]). Therefore, care must be taken when using
inventory values; they may reflect more than just supply-chain performance.
Weeks of supply, as shown in Example 6, may have more meaning in the wholesale and retail
portions of the service sector than in manufacturing. It is computed below as the reciprocal of
inventory turnover:
(11-3)Weeks of supply = Inventory investment>1Annual cost of goods sold>52 weeks2

356 PART 3 Managing Operations
EXAMPLE 6 �
Determining weeks
of supply at PepsiCo
Using the PepsiCo data in Example 5, management wants to know the weeks of supply.
APPROACH � We know that inventory investment is $1.69 billion and that weekly sales equal
annual cost of goods sold ($14.2 billion) divided by
SOLUTION � Using Equation (11-3), we compute weeks of supply as:
INSIGHT � We now have a standard measurement by which to evaluate a company’s continuing
performance or by which to compare companies.
LEARNING EXERCISE � If Coca-Cola’s average inventory investment is $.76 billion and its
average weekly cost of goods sold is $.207 billion, what is the firm’s weeks of supply? [Answer: 3.67
weeks.]
RELATED PROBLEMS � 11.12a, 11.14
= 1.69>.273 = 6.19 weeks
Weeks of supply = 1Inventory investment>Average weekly cost of goods sold2
52 = $14.2>52 = $.273 billion.
Supply-chain management is critical in driving down inventory investment. The rapid move-
ment of goods is key. Walmart, for example, has set the pace in the retailing sector with its world-
renowned supply-chain management. By doing so, it has established a competitive advantage.
With its own truck fleet, distribution centers, and a state-of-the-art communication system,
Walmart (with the help of its suppliers) replenishes store shelves an average of twice per week.
Competitors resupply every other week. Economical and speedy resupply means both rapid
response to product changes and customer preferences, as well as lower inventory investment.
Similarly, while many manufacturers struggle to move inventory turnover up to 10 times per year,
Dell Computer has inventory turns exceeding 90 and supply measured in days—not weeks.
Supply-chain management provides a competitive advantage when firms effectively respond to
the demands of global markets and global sources.
The SCOR Model
In addition to the metrics presented above, the Supply-Chain Council (SCC) has developed 200
process elements, 550 metrics, and 500 best practices. The SCC (www.supply-chain.org) is a
900-member not-for-profit association for the improvement of supply-chain effectiveness. The
council has developed the five-part Supply-Chain Operations Reference (SCOR) model. The
five parts are Plan, Source, Make, Deliver, and Return, as shown in Figure 11.3.
The council believes the model provides a structure for its processes, metrics, and best prac-
tices to be (1) implemented for competitive advantage; (2) defined and communicated pre-
cisely; (3) measured, managed, and controlled; and (4) fine-tuned as necessary to a specific
application.
Supply-Chain
Operations Reference
(SCOR) model
A set of processes, metrics, and
best practices developed by the
Supply-Chain Council.
Plan: Demand/Supply Planning and Management
Deliver: Invoice,
warehouse, transport,
and install
Make: Manage
production execution,
testing, and packaging
Source: Identify,
select, manage, and
assess sources
Return: Finished goodsReturn: Raw material
� FIGURE 11.3
The Supply-Chain Operations
Reference (SCOR) Model

www.supply-chain.org

Chapter 11 Supply-Chain Management 357
Competition is no longer between companies but between
supply chains. For many firms, the supply chain determines
a substantial portion of product cost and quality, as well as
opportunities for responsiveness and differentiation. Six
supply-chain strategies have been identified: (1) many sup-
pliers, (2) few suppliers, (3) vertical integration, (4) joint
ventures, (5) keiretsu networks, and
(6) virtual companies. Skillful supply-
chain management provides a great
strategic opportunity for competitive
advantage.
CHAPTER SUMMARY
Key Terms
Supply-chain management (p. 336)
Make-or-buy decision (p. 341)
Outsourcing (p. 341)
Vertical integration (p. 342)
Keiretsu (p. 343)
Virtual companies (p. 343)
Bullwhip effect (p. 344)
Pull data (p. 345)
Single-stage control of replenishment (p. 345)
Vendor-managed inventory (VMI) (p. 346)
Collaborative planning, forecasting, and
replenishment (CPFR) (p. 346)
Blanket order (p. 346)
Postponement (p. 346)
Drop shipping (p. 347)
Pass-through facility (p. 347)
Channel assembly (p. 347)
E-procurement (p. 347)
Electronic data interchange (EDI) (p. 347)
Advanced shipping notice (ASN) (p. 347)
Negotiation strategies (p. 350)
Logistics management (p. 350)
Inventory turnover (p. 354)
Supply-Chain Operations Reference
(SCOR) model (p. 356)
Solved Problem Virtual Office Hours help is available at www.myomlab.com
chain performance by measuring his percent of assets in inven-
tory, his inventory turnover, and his weeks of supply. We use
Equations (11-1), (11-2), and (11-3) to provide these measures.
� SOLVED PROBLEM 11.1
Jack’s Pottery Outlet has total end-of-year assets of $5 million.
The first-of-the-year inventory was $375,000, with a year-end
inventory of $325,000. The annual cost of goods sold was
$7 million. The owner, Eric Jack, wants to evaluate his supply
� SOLUTION
First, determine average inventory:
Then, use Equation (11-1) to determine percent invested in inventory:
Third, determine inventory turnover, using Equation (11-2):
Finally, to determine weeks of inventory, use Equation (11-3), adjusted to weeks:
We conclude that Jack’s Pottery Outlet has 7% of its assets invested in inventory, that the inventory turnover
is 20, and that weeks of supply is 2.6.
= 2.6
= 350,000>134,615
= 350,000>17,000,000>522
Weeks of inventory = Inventory investment>Weekly cost of goods sold
= 20
= 7,000,000>350,000
Inventory turnover = Cost of goods sold>Inventory investment
= 7%
= 1350,000>5,000,0002 * 100
Percent invested in inventory = 1Total inventory investment>Total assets2 * 100
1$375,000 + $325,0002>2 = $350,000

www.myomlab.com

358 PART 3 Managing Operations
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�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Amazon.com: Discusses opportunities and issues in an innovative business model for the Internet.

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Outsourcing as a
Supply-Chain Strategy
Supplement Outline
What Is Outsourcing? 360
Strategic Planning and Core
Competencies 361
Risks of Outsourcing 363
Evaluating Outsourcing Risk with
Factor Rating 365
Advantages and Disadvantages
of Outsourcing 367
Audits and Metrics to Evaluate
Performance 368
Ethical Issues in Outsourcing 368
359
SUPPLEMENTSUPPLEMENT

360 PART 3 Managing Operations
LO1: Explain how core competencies relate
to outsourcing 362
LO2: Describe the risks of
outsourcing 363
AUTHOR COMMENT
Outsourcing is a supply-chain
strategy that can deliver
tremendous value to an
organization.
WHAT IS OUTSOURCING?
Outsourcing is a creative management strategy. Indeed, some organizations use outsourcing to
replace entire purchasing, information systems, marketing, finance, and operations departments.
Outsourcing is applicable to firms throughout the world. And because outsourcing decisions are
risky and many are not successful, making the right decision may mean the difference between
success and failure.1
Because outsourcing grows by double digits every year, students and managers need to under-
stand the issues, concepts, models, philosophies, procedures, and practices of outsourcing. This
supplement describes current concepts, methodologies, and outsourcing strategies.
Outsourcing means procuring from external suppliers services or products that are normally
a part of an organization. In other words, a firm takes functions it was performing in-house (such
as accounting, janitorial, or call center functions) and has another company do the same job. If a
company owns two plants and reallocates production from the first to the second, this is not con-
sidered outsourcing. If a company moves some of its business processes to a foreign country but
retains control, we define this move as offshoring, not outsourcing. For example, China’s Haier
Group recently offshored a $40 million refrigerator factory to South Carolina (with huge savings
in transportation costs). Or, as Thomas Friedman wrote in his book The World is Flat,
“Offshoring is when a company takes one of its factories that it is operating in Canton, Ohio and
moves the whole factory to Canton, China.”
1The authors wish to thank Professor Marc J. Schneiderjans, of the University of Nebraska–Lincoln, for help with the
development of this supplement. His book Outsourcing and Insourcing in an International Context, with Ashlyn
Schniederjans and Dara Schniederjans (Armonk, NY: M.E. Sharpe, 2005), provided insight, content, and references
that shaped our approach to the topic.
Outsourcing
Procuring from external sources
services or products that
are normally part of an
organization.
Offshoring
Moving a business process to
a foreign country but retaining
control of it.
Contract manufacturers such as Flextronics
provide outsourcing service to IBM, Cisco
Systems, HP, Microsoft, Motorola, Sony, Nortel,
Ericsson, and Sun, among many others.
Flextronics is a high-quality producer that has
won over 450 awards, including the Malcolm
Baldrige Award. One of the side benefits of
outsourcing is that client firms such as IBM can
actually improve their performance by using the
competencies of an outstanding firm like
Flextronics. But there are risks involved in
outsourcing. Outsourcing decisions, as part of
the supply-chain strategy, are explored in this
supplement.
Supplement 11 Learning Objectives
LO3: Use factor rating to evaluate both
country and provider outsourcers 365
LO4: List the advantages and disadvantages
of outsourcing 367

Supplement 11 Outsourcing as a Supply-Chain Strategy 361
AUTHOR COMMENT
Ford Motor used to mine its
own ore, make and ship its
own steel, and sell cars
directly, but those days are
long gone.
Early in their lives, many businesses handle their activities internally. As businesses mature
and grow, however, they often find competitive advantage in the specialization provided by out-
side firms. They may also find limitations on locally available labor, services, materials, or other
resources. So organizations balance the potential benefits of outsourcing with its potential risks.
Outsourcing the wrong activities can cause major problems.
Outsourcing is not a new concept; it is simply an extension of the long-standing practice of
subcontracting production activities. Indeed, the classic make-or-buy decision concerning prod-
ucts (which we discussed in Chapter 11) is an example of outsourcing.
So why has outsourcing expanded to become a major strategy in business the world over?
From an economic perspective, it is due to the continuing move toward specialization in an
increasingly technological society. More specifically, outsourcing’s continuing growth is due to
(1) increasing expertise, (2) reduced costs of more reliable transportation, and (3) the rapid
development and deployment of advancements in telecommunications and computers. Low-cost
communication, including the Internet, permits firms anywhere in the world to provide previ-
ously limited information services.
Examples of outsourcing include:
• Call centers for Brazil in Angola (a former Portuguese colony in Africa) and for the U.S. and
England in India
• DuPont’s legal services routed to the Philippines
• IBM handling travel services and payroll, and Hewlett-Packard providing IT services to P&G
• ADP providing payroll services for thousands of firms
• Production of the Audi A4 convertible and Mercedes CLK convertible by Wilheim Karmann
in Osnabruck, Germany
• Blue Cross sending hip resurfacing surgery patients to India
Outsourced manufacturing, also known as contract manufacturing, is becoming standard
practice in many industries, from computers to automobiles.
Paralleling the growth of outsourcing is the growth of international trade. With the passage of
landmark trade agreements like the North American Free Trade Agreement (NAFTA), the work
of the World Trade Organization and the European Union, and other international trade zones
established throughout the world, we are witnessing the greatest expansion of international com-
merce in history.
Table S11.1 provides a ranking of the top five and bottom five outsourcing locations (out of
50 countries) in the annual A.T. Kearney Global Options survey. Scores are based on a Global
Services Location Index tallying financial attractiveness, workforce availability, employee skill
set, and business environment.
Types of Outsourcing Nearly any business activity can be outsourced. A general contractor
in the building industry, who subcontracts various construction activities needed to build a home,
is a perfect example of an outsourcer. Every component of the building process, including the
architect’s design, a consultant’s site location analysis, a lawyer’s work to obtain the building
permits, plumbing, electrical work, dry walling, painting, furnace installation, landscaping, and
sales, is usually outsourced. Outsourcing implies an agreement (typically a legally binding con-
tract) with an external organization.
Among the business processes outsourced are (1) purchasing, (2) logistics, (3) R&D, (4) oper-
ation of facilities, (5) management of services, (6) human resources, (7) finance/accounting,
(8) customer relations, (9) sales/marketing, (10) training, and (11) legal processes. Note that the
first six of these are OM functions that we discuss in this text.
STRATEGIC PLANNING AND CORE COMPETENCIES
As we saw in Chapter 2, organizations develop missions, long-term goals, and strategies as gen-
eral guides for operating their businesses. The strategic planning process begins with a basic mis-
sion statement and establishing goals. Given the mission and goals, strategic planners next
undertake an internal analysis of the organization to identify how much or little each business
activity contributes to the achievement of the mission.
During such an analysis, firms identify their strengths—what they do well or better than their
competitors. These unique skills, talents, and capabilities are called core competencies. Core
� TABLE S11.1
Desirable Outsourcing
Destinations
Rank Country Score
1 India 6.9
2 China 6.6
3 Malaysia 6.1
4 Thailand 6.0
5 Brazil 5.9
o
46 Ukraine 4.9
47 France 4.9
48 Turkey 4.8
49 Portugal 4.8
50 Ireland 4.2
Core competencies
An organization’s unique skills,
talents, and capabilities.
VIDEO S11.1
Outsourcing Offshore at Darden
Source: Based on A. T. Kearney,
2009.

362 PART 3 Managing Operations
competencies may include specialized knowledge, proprietary technology or information, and
unique production methods. The trick is to identify what the organization does better than any-
one else. Common sense dictates that core competencies are the activities that a firm should per-
form. By contrast, non-core activities, which can be a sizable portion of an organization’s total
business, are good candidates for outsourcing.
Sony’s core competency, for example, is electromechanical design of chips. This is its core, and
Sony is one of the best in the world when it comes to rapid response and specialized production of
these chips. But, as Figure S11.1 suggests, outsourcing could offer Sony continuous innovation and
flexibility. Leading specialized outsource providers are likely to come up with major innovations in
such areas as software, human resources, and distribution. That is their business, not Sony’s.
Managers evaluate their strategies and core competencies and ask themselves how to use the
assets entrusted to them. Do they want to be the offshore company that does low-margin work at
3%–4% or the innovative firm that makes a 30%–40% margin? PC or iPod assemblers in China and
Taiwan earn 3%–4%, but Apple, which innovates, designs, and sells, has a margin 10 times as large.
To summarize, management must be cautious in outsourcing those elements of the product or
service that provide a competitive advantage.
The Theory of Comparative Advantage
The motivation for international outsourcing comes from the theory of comparative advantage.
This theory focuses on the basic economics of outsourcing internationally. According to the the-
ory, if an external provider, regardless of its geographic location, can perform activities more
productively than the purchasing firm, then the external provider should do the work. This allows
the purchasing firm to focus on what it does best, its core competencies.
However, comparative advantage is not static. Companies, and indeed countries, strive to find
comparative advantage. Countries such as India, China, and Russia have made it a government
priority and set up agencies to support the easy transition of foreign firms into their outsourcing
markets. Work and jobs go to countries that reduce risk through the necessary legal structures,
effective infrastructure, and an educated workforce.
The dynamics of comparative advantage are evident from a recent study of five manufactured
products. In an effort to meet “optimal” prices on auto parts in 2005, companies were moving work
from Mexico to China. At that time China had a 22% price advantage on these parts over the U.S.
But by 2009 that gap had dropped to 5.5%—and in some instances manufacturing in China was
LO1: Explain how core
competencies relate to
outsourcing
Outsourcers could provide Sony with:
Core
Competency
Best in world at
electromechanical
miniaturization
design
Post-sales
service
Financial
functions
Logistics
Maintenance
Real estate
management
Parts
manufacture
Marketing
Distribution
Accounting
Employee
benefit
management
� FIGURE S11.1
Sony, an Outsourcing
Company
Based on J. B. Quinn.
“Outsourcing Innovation.”
Sloan Management Review
(Summer 2000): 20.
Theory of comparative
advantage
A theory which states that
countries benefit from
specializing in (and exporting)
products and services in which
they have relative advantage,
and importing goods in which
they have a relative
disadvantage.
AUTHOR COMMENT
Author James Champy writes,
“Although you may be good
at something tactically,
someone else may do it better
and at lower cost.”

Supplement 11 Outsourcing as a Supply-Chain Strategy 363
20% more expensive than Mexico. As a result, some manufacturing began migrating back to
Mexico and the U.S.; the price gap wasn’t large enough to merit the hassle of manufacturing
halfway around the world.2
Nonetheless, consistent with the theory of comparative advantage, the trend toward outsourc-
ing continues to grow. This does not mean all existing outsourcing decisions are perfect. The
term backsourcing has been used to describe the return of business activity to the original firm.
We will now discuss the risks associated with outsourcing.
RISKS OF OUTSOURCING
Risk management starts with a realistic analysis of risks and results in a strategy that minimizes
the impact of these uncertainties. Indeed, outsourcing can look very risky. And it is. Perhaps half
of all outsourcing agreements fail because of inappropriate planning and analysis. For one thing,
few promoters of international outsourcing mention the erratic power grids in some foreign
countries or the difficulties with local government officials, inexperienced managers, and unmo-
tivated employees. On the other hand, when managers set an outsourcing goal of 75% cost reduc-
tion and receive only a 30%–40% cost reduction, they view the outsourcing as a failure, when, in
fact, it may be a success.
Quality can also be at risk. A recent survey of 150 North American companies found that, as
a group, those that outsourced customer service saw a drop in their score on the American
Consumer Satisfaction Index. We should point out that the declines were roughly the same
whether companies outsourced domestically or overseas.3
Another risk is the political backlash that results from outsourcing to foreign countries. The
perceived loss of U.S. jobs (as well as the loss of jobs in European countries) has fueled anti-
outsourcing rhetoric and action from government officials. (See the OM in Action box
“Backsourcing to Small-Town U.S.A.”).
Despite the negative impression created by government actions, the press, and public opinion,
data suggest that foreigners outsource far more services to the U.S. than U.S. companies send
abroad. And while U.S. jobs are outsourced, a minuscule few are outsourced offshore. A recent
Organization for Economic Cooperation and Development (OECD) report on the subject shows
that outsourcing is not as big a cause in job losses as, say, improved technology, and has an over-
all positive effect.4 It is also a two-way street. India’s cartoon producer Jadoo Works, for exam-
ple, outsources projects to U.S. animators.
Backsourcing
The return of business activity
to the original firm.
2“China’s Eroding Advantage” Business Week (June 15, 2009): 54:55. The report dealt with five categories of machined
products, ranging from large engine parts requiring significant labor to small plastic components that need little.
3J. Whitaker, M. S. Krishnan, and C. Fornell. “How Offshore Outsourcing Affects Customer Satisfaction.” The Wall
Street Journal (July 7, 2008): R4.
4“Outsourcing: Old Assumptions Are Being Challenged as the Outsourcing Industry Matures,” The Economist (July 28,
2007): 65–66.
In the ultimate risk in outsourcing, NASA has
awarded contracts of $3.5 billion to a team, led
by Orbital Sciences Corp., to ship cargo to the
International Space Station starting in 2011. The
company will be solely responsible for designing,
building, and launching rockets on a regular basis.
NASA hopes to save time and money by
outsourcing.
AUTHOR COMMENT
The substantial risk in
outsourcing requires
managers to invest the effort
to make sure they do it right.
LO2: Describe the risks of
outsourcing

364 PART 3 Managing Operations
Table S11.2 lists some of the risks inherent in outsourcing.
In addition to the external risks, operations managers must deal with other issues that out-
sourcing brings. These include (1) changes in employment levels, (2) changes in facilities and
processes needed to receive components in a different state of assembly, and (3) vastly expanded
logistics issues, including insurance, customs, and timing.
What can be done to mitigate the risks of outsourcing? Research indicates that of all the rea-
sons given for outsourcing failure, the most common is that the decision was made without suf-
ficient understanding and analysis. The next section provides a methodology that helps analyze
the outsourcing decision process.
Outsourcing Process Examples of Possible Risks
Identify non-core competencies Can be incorrectly identified as a non-core
competency.
Identify non-core activities that should be
outsourced
Just because the activity is not a core competency
for your firm does not mean an outsource provider
is more competent and efficient.
Identify impact on existing facilities, capacity, and
logistics
Failing to understand the change in resources and
talents needed internally.
Establish goals and draft outsourcing agreement
specifications
Setting goals so high that failure is certain.
Identify and select outsource provider Selecting the wrong outsource provider.
Negotiate goals and measures of outsourcing
performance
Misinterpreting measures and goals, how they are
measured, and what they mean.
Monitor and control current outsourcing
program
Being unable to control product development,
schedules, and quality.
Evaluate and give feedback to outsource
provider
Having a non-responsive provider (i.e., one that
ignores feedback).
Evaluate international political and currency
risks
Evaluate coordination needed for shipping and
distribution
Country’s currency may be unstable, a country may
be politically unstable, or cultural and language
differences may inhibit successful operations.
Understanding of the timing necessary to manage
flows to different facilities and markets.
U.S. companies continue their global search for efficiency
by outsourcing call centers and back-office operations, but
many find they need to look no farther than a place like
Dubuque, Iowa.
To U.S. firms facing quality problems with their outsourcing
operations overseas and bad publicity at home, small-town
America is emerging as a pleasant alternative. Dubuque
(pop’n 57,313), Nacogdoches, Texas (pop’n 29,914), or Twin
Falls, Idaho (pop’n 34,469), may be the perfect call center
location. Even though the pay is only $8 an hour, the jobs are
some of the best available to small-town residents.
By moving out of big cities to the cheaper labor and real
estate of small towns, companies can save millions and still
increase productivity. A call center in a town that just lost its
major manufacturing plant finds the jobs easy to fill.
IBM, which has been criticized in the past for moving
jobs to India and other offshore locations, picked Dubuque
for its new remote computer-services center that opened in
2010 with 1,300 jobs.
Taking advantage of even cheaper wages in other
countries will not stop soon, though. Is India the
unstoppable overseas
call center capital that
people think it is? Not
at all. Despite its
population of 1.2 billion,
only a small percent of
its workers have the
language skills and
technical education to
work in Western-style
industries. Already, India
has been warned that if
call centers can’t recruit
at reasonable wages, its jobs will move to the Philippines,
South Africa, and Ghana. And indeed, Dell, Apple, and
Britain’s Powergen have backsourced from Indian call
centers, claiming their costs had become too high.
Sources: The Wall Street Journal (January 15, 2009): B2; (April 18–19,
2009): B1, B5; and (May 30/31, 2009): A14
OM in Action OMinAction� Backsourcing to Small-Town U.S.A.
� TABLE S11.2
The Outsourcing Process and
Related Risks
AUTHOR COMMENT
Cultural differences may
indeed be why companies are
less frequently outsourcing
their call centers.

Supplement 11 Outsourcing as a Supply-Chain Strategy 365
EVALUATING OUTSOURCING RISK WITH FACTOR RATING
The factor-rating method, first introduced in Chapter 8, is an excellent tool for dealing with both
country risk assessment and provider selection problems.
Rating International Risk Factors
Suppose a company has identified for outsourcing an area of production that is a non-core com-
petency. Example S1 shows how to rate several international risk factors using an unweighted
factor-rating approach.
AUTHOR COMMENT
The factor-rating model adds
objectivity to decision
making.
� EXAMPLE S1
Establishing risk
factors for four
countries
Toronto Airbags produces auto and truck airbags for Nissan, Chrysler, Mercedes, and BMW. It wants
to conduct a risk assessment of outsourcing manufacturing. Four countries—England, Mexico, Spain,
and Canada (the current home nation)—are being considered. Only English- or Spanish-speaking
countries are included because they “fit” with organizational capabilities.
APPROACH � Toronto’s management identifies nine factors, listed in Table S11.3, and rates each
country on a 0–3 scale, where 0 is no risk and 3 is high risk. Risk ratings are added to find the lowest-
risk location.
Risk Factor England Mexico Spain Canada (home country)
Economic: Labor cost/laws 1 0 2 1
Economic: Capital availability 0 2 1 0
Economic: Infrastructure 0 2 2 0
Culture: Language 0 0 0 0
Culture: Social norms 2 0 1 2
Migration: Uncontrolled 0 2 0 0
Politics: Ideology 2 0 1 2
Politics: Instability 0 1 2 2
Politics: Legalities 3 0 2 3
Total risk rating scores 8 7 11 10
*Risk rating scale: 0 = no risk, 1 = minor risk, 2 = average risk, 3 = high risk
� TABLE S11.3
Toronto Airbag’s International
Risk Factors, by Country (an
unweighted approach)*
SOLUTION � Based on these ratings, Mexico is the least risky of the four locations being considered.
INSIGHT � As with many other quantitative methods, assessing risk factors is not easy and may
require considerable research, but the technique adds objectivity to a decision.
LEARNING EXERCISE � Social norms in England have just been rescored by an economist,
and the new rating is “no risk.” How does this affect Toronto’s decision? [Answer: England now has the
lowest rating, at 6, for risk.]
RELATED PROBLEMS � S11.1, S11.3
EXCEL OM Data File Ch11SExS1.xls can be found at www.pearsonhighered.com/heizer.
In Example S1, Toronto Airbags considered only English- and Spanish-speaking countries.
But it is worth mentioning that countries like China, India, and Russia have millions of English-
speaking personnel. This may have an impact on the final decision.
Example S1 considered the home country of the outsourcing firm. This inclusion helps docu-
ment the risks that a domestic outsourcing provider poses compared to the risks posed by inter-
national providers. Including the home country in the analysis also helps justify final strategy
selection to stakeholders who might question it.
Indeed, nearshoring (i.e., choosing an outsource provider located in the home country or in a
nearby country) can be a good strategy for businesses and governments seeking both control and
cost advantages. U.S. firms are interested in nearshoring to Canada because of Canada’s cultural
similarity and geographic nearness to the U.S. This allows the company wanting to outsource to
exert more control than would be possible when outsourcing to most other countries.
Nearshoring represents a compromise in which some cost savings are sacrificed for greater con-
trol because Canada’s smaller wage differential limits the labor cost reduction advantage.
LO3: Use factor rating to
evaluate both country and
provider outsourcers
Nearshoring
Choosing an outsource provider
in the home country or in a
nearby country.

www.pearsonhighered.com/heizer

366 PART 3 Managing Operations
Rating Outsource Providers
In Chapter 8 (see Example 1) we illustrated the factor-rating method’s computations when each
factor has its own importance weight. We now apply that concept in Example S2 to compare out-
sourcing providers being considered by a firm.
EXAMPLE S2 �
Rating provider
selection criteria
National Architects, Inc., a San Francisco–based designer of high-rise buildings, has decided to out-
source its information technology (IT) function. Three outsourcing providers are being actively consid-
ered: one in the U.S., one in India, and one in Israel.
APPROACH � National’s VP–Operations, Susan Cholette, has made a list of seven criteria she
considers critical. After putting together a committee of four other VPs, she has rated each firm (on a
1–5 scale, with 5 being highest) and has also placed an importance weight on each of the factors, as
shown in Table S11.4.
Outsource Providers
Factor
(criterion)*
Importance
Weight
BIM
(U.S.)
S.P.C.
(India)
Telco
(Israel)
1. Can reduce operating costs .2 3 3 5
2. Can reduce capital investment .2 4 3 3
3. Skilled personnel .2 5 4 3
4. Can improve quality .1 4 5 2
5. Can gain access
to technology not in company .1 5 3 5
6. Can create additional capacity .1 4 2 4
7. Aligns with policy/
philosophy/culture .1 2 3 5
Totals 1.0 3.9 3.3 3.8
* These seven major criteria are based on a survey of 165 procurement executives, as reported in J. Schildhouse, “Outsourcing
Ins and Outs,” Inside Supply Management (December 2005): 22–29.
�TABLE S11.4
Factor Ratings Applied to
National Architects’s Potential
IT Outsourcing Providers
SOLUTION � Susan multiplies each rating by the weight and sums the products in each column to
generate a total score for each outsourcing provider. She selects BIM, which has the highest overall rating.
INSIGHT � When the total scores are as close (3.9 vs. 3.8) as they are in this case, it is important
to examine the sensitivity of the results to inputs. For example, if one of the importance weights or fac-
tor scores changes even marginally, the final selection may change. Management preference may also
play a role here.
LEARNING EXERCISE � Susan decides that “Skilled personnel” should instead get a weight
of 0.1 and “Aligns with policy/philosophy/culture” should increase to 0.2. How do the total scores
change? [Answer: and , so Telco is selected.]
RELATED PROBLEMS � S11.2, S11.4, S11.5, S11.6, S11.7
EXCEL OM Data File Ch11SExS2.xls can be found at www.pearsonhighered.com/heizer.
Telco = 4.0BIM = 3.6, S.P.C. = 3.2,
Most U.S. toy companies now outsource their
production to Chinese manufacturers. Cost savings
are significant, but there are several downsides,
including loss of control over such issues as
quality. In 2007 alone, Mattel had to recall 10.5
million Elmos, Big Birds, and SpongeBobs. These
made-in-China toys contained excessive levels of
lead in their paint. In 2008 the quality headlines
dealt with poisonous pet food from China, and in
2009 it was tainted milk products.

www.pearsonhighered.com/heizer

Supplement 11 Outsourcing as a Supply-Chain Strategy 367
ADVANTAGES AND DISADVANTAGES OF OUTSOURCING
Advantages of Outsourcing
As mentioned earlier, companies outsource for five main reasons. They are, in order of impor-
tance: (1) cost savings, (2) gaining outside expertise, (3) improving operations and service,
(4) focusing on core competencies, and (5) gaining outside technology.
Cost Savings The number-one reason driving outsourcing for many firms is the possibility
of significant cost savings, particularly for labor. (See the OM in Action box “Walmart’s Link
to China.”)
Gaining Outside Expertise In addition to gaining access to a broad base of skills that are
unavailable in-house, an outsourcing provider may be a source of innovation for improving prod-
ucts, processes, and services.
Improving Operations and Service An outsourcing provider may have production flexi-
bility. This may allow the firm outsourcing its work to win orders by more quickly introducing
new products and services.
Focusing on Core Competencies An outsourcing provider brings its core competencies to
the supply chain. This frees up a firm’s human, physical, and financial resources to reallocate to
core competencies.
Gaining Outside Technology Firms can outsource to state-of-the-art providers instead of
retaining old (legacy) systems. This means they do not have to invest in new technology, thereby
cutting risks.
Other Advantages There are additional advantages in outsourcing. For example, a firm may
improve its performance and image by associating with an outstanding supplier. Outsourcing can
also be used as a strategy for downsizing, or “reengineering,” a firm.
Disadvantages of Outsourcing
There are a number of potential disadvantages in outsourcing. Here are just a few.
Increased Transportation Costs Delivery costs may rise substantially if distance
increases from an outsourcing provider to a firm using that provider.
Loss of Control This disadvantage can permeate and link to all other problems with outsourc-
ing. When managers lose control of some operations, costs may increase because it’s harder to
assess and control them. For example, production of most of the world’s laptops is now outsourced.
This means that companies like Dell and HP find themselves using the same contractor (Quanta) to
make their machines in China. This can leave them struggling to maintain control over the supplier.
LO4: List the advantages
and disadvantages of
outsourcing
No other company has a more efficient supply chain, and
no other company has embraced outsourcing to China
more vigorously than Walmart. Perhaps as much as 85%
of Walmart’s merchandise is made abroad, and Chinese
factories are by far the most important and fastest growing
of these sources.
A whopping 10%–13% of everything China sends to the
U.S. ends up on Walmart’s shelves—over $15 billion worth
of goods a year. Walmart has almost 600 people on the
ground in China just to negotiate and make purchases.
As much as Walmart has been demonized for its part
in offshoring jobs, its critical mass allows Chinese firms to
build assembly lines that are so huge that they drive prices
down through economies of scale.
Walmart’s Chinese suppliers achieve startling, market-
shaking price cuts. For example, the price of portable
DVDs with 7 ′′ LCD screens dropped in half when Walmart
found a Chinese factory to build in giant quantities.
Walmart’s success in going abroad and pressing suppliers
for price breaks has forced both retailers and
manufacturers to reevaluate their supply chains.
The company has also led the way to sustainability and
product safety through its “Responsible Sourcing” program,
announced in 2008. Because Chinese products have been
riddled with safety issues, Walmart in 2009 required “an
identifiable trail” from raw materials to suppliers.
It also told its top 200 Chinese suppliers that they have
until 2012 to become energy and resource efficient, cutting
energy use by 20%.
Sources: The Wall Street Journal (October 22, 2008): B1; About.com:
Logistics/Supply Chain (November 26, 2008); and Financial Times (December
12, 2008): 9.
OM in Action � Walmart’s Link to China

368 PART 3 Managing Operations
Creating Future Competition Intel, for example, outsourced a core competency, chip pro-
duction, to AMD when it could not keep up with early demands. Within a few years, AMD
became a leading competitor, manufacturing its own chips.
Negative Impact on Employees Employee morale may drop when functions are out-
sourced, particularly when friends lose their jobs. Employees believe they may be next, and
indeed they may be. Productivity, loyalty, and trust—all of which are needed for a healthy, grow-
ing business—may suffer.
Longer-Term Impact Some disadvantages of outsourcing tend to be longer term than the
advantages of outsourcing. In other words, many of the risks firms run by outsourcing may not
show up on the bottom line until some time in the future. This permits CEOs who prefer short-
term planning and are interested only in bottom-line improvements to use the outsourcing strat-
egy to make quick gains at the expense of longer-term objectives.
The advantages and disadvantages of outsourcing may or may not occur but should be
thought of as possibilities to be managed effectively.
AUDITS AND METRICS TO EVALUATE PERFORMANCE
Regardless of the techniques and success in selection of outsourcing providers, agreements must
specify results and outcomes. Whatever the outsourced component or service, management needs
an evaluation process to ensure satisfactory continuing performance. At a minimum, the product
or service must be defined in terms of quality, customer satisfaction, delivery, cost, and improve-
ment. The mix and detail of the performance measures will depend on the nature of the product.
In situations where the outsourced product or service plays a major role in strategy and win-
ning orders, the relationship needs to be more than after-the-fact audits and reports. It needs to be
based on continuing communication, understanding, trust, and performance. The relationship
should manifest itself in the mutual belief that “we are in this together” and go well beyond the
written agreement.
However, when outsourcing is for less critical components, agreements that include the tradi-
tional mix of audits and metrics (such as cost, logistics, quality, and delivery) may be reported
weekly or monthly. When a service has been outsourced, more imaginative metrics may be nec-
essary. For instance, in an outsourced call center, these metrics may deal with personnel evalua-
tion and training, call volume, call type, and response time, as well as tracking complaints. In this
dynamic environment, reporting of such metrics may be required daily.
ETHICAL ISSUES IN OUTSOURCING
Laws, trade agreements, and business practices are contributing to a growing set of international,
ethical practices for the outsourcing industry. Table S11.5 presents several tenets of conduct that
have fairly universal acceptance.
In the electronics industry, HP, Dell, IBM, Intel and twelve other companies have created the
Electronics Industry Code of Conduct (EICC). The EICC sets environmental standards, bans
child labor and excessive overtime, and audits outsourcing producers to ensure compliance.
Ethics Principle Outsourcing Linkage
Do no harm to indigenous cultures Avoid outsourcing in a way that violates religious holidays
(e.g., making employees work during religious holidays).
Do no harm to the ecological
systems
Don’t use outsourcing to move pollution from one country to
another.
Uphold universal labor standards Don’t use outsourcing to take advantage of cheap labor that leads
to employee abuse.
Uphold basic human rights Don’t accept outsourcing that violates basic human rights.
Pursue long-term involvement Don’t use outsourcing as a short-term arrangement to reduce
costs; view it as a long-term partnership.
Share knowledge and technology Don’t think outsourcing agreements will prevent loss of
technology, but use the inevitable sharing to build good
relationships.
�TABLE S11.5
Ethical Principles and Related
Outsourcing Linkages
AUTHOR COMMENT
Because outsourcing is rife
with potential abuse,
companies have to be careful
not to harm individuals,
societies, or nature.

Supplement 11 Outsourcing as a Supply-Chain Strategy 369
SUPPLEMENT SUMMARY
Companies can give many different reasons why they out-
source, but the reality is that outsourcing’s most attractive
feature is that it helps firms cut costs. Workers in low-cost
countries simply work much more cheaply, with fewer
fringe benefits, work rules, and legal restrictions, than
their U.S. and European counterparts. For example, a
comparable hourly wage of $20 in the U.S. and $30 in
Europe is well above the $1.26 per hour in China. Yet
China often achieves quality levels
equivalent to (or even higher than)
plants in the West.
There is a growing economic pres-
sure to outsource. But there is also a need
for planning outsourcing to make it accept-
able to all participants. When outsourcing is done in the right
way, it creates a win–win situation.
Key Terms
Outsourcing (p. 360)
Offshoring (p. 360)
Core competencies (p. 361)
Theory of comparative advantage (p. 362)
Backsourcing (p. 363)
Nearshoring (p. 365)
Using Software to Solve Outsourcing Problems
Excel, Excel OM, and POM for Windows may be used to solve most of the problems in this supplement.
Excel OM and POM for Windows both contain Factor Rating modules that can address issues such as the ones we saw in
Examples S1 and S2. The Factor-Rating module was illustrated earlier in Program 8.1 in Chapter 8.
Bibliography
Aron, R., and J. V. Singh. “Getting Offshoring Right.” Harvard
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Bravard, J., and R. Morgan. Smarter Outsourcing. Upper Saddle
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Champy, James. Avoiding the Seven Deadly Sins of Outsourcing
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Friedman, Thomas. The World Is Flat: A Brief History of the 21st
Century. New York: Farrar, Straus, and Giroux (2005).
Greenwald, Bruce C., and Judd Kahn. Globalization: The
Irrational Fear That Someone in China Will Take Your Job.
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Inventory Management
Chapter Outline
GLOBAL COMPANY PROFILE: AMAZON.COM
The Importance of Inventory 374
Managing Inventory 375
Inventory Models 380
Inventory Models for Independent
Demand 380
Probabilistic Models and Safety Stock 393
Single-Period Model 398
Fixed-Period (P ) Systems 399
� Design of Goods and Services
� Managing Quality
� Process Strategy
� Location Strategies
� Layout Strategies
� Human Resources
� Supply-Chain Management
� Inventory Management
� Independent Demand
� Dependent Demand
� JIT & Lean Operations
� Scheduling
� Maintenance
371

GLOBAL COMPANY PROFILE: AMAZON.COM
INVENTORY MANAGEMENT PROVIDES COMPETITIVE
ADVANTAGE AT AMAZON.COM
W
hen Jeff Bezos opened his
revolutionary business in 1995,
Amazon.com was intended to
be a “virtual” retailer—no
inventory, no warehouses, no overhead—
just a bunch of computers taking orders and
authorizing others to fill them. Things clearly
didn’t work out that way. Now, Amazon
stocks millions of items of inventory, amid
hundreds of thousands of bins on metal
shelves, in warehouses (seven around the
U.S. and three in Europe) that have twice
the floor space of the Empire State Building.
Precisely managing this massive
inventory has forced Amazon into
becoming a world-class leader in
warehouse management and automation,
with annual sales of over $20 billion to
88 million customers. This profile shows
what goes on to accurately fill orders.
When you place an order at
Amazon.com, not only are you doing
business with an Internet company, you
are doing business with a company that
obtains competitive advantage through
inventory management.
1. You order three items,
and a computer in Seattle
takes charge. A computer
assigns your order—a book,
a game, and a digital
camera—to one of Amazon’s
massive U.S. distribution
centers, such as the
750,000-square-foot facility
in Coffeyville, Kansas.
2. The “flow meister” in
Coffeyville receives your
order. She determines which
workers go where to fill your
order.
3. Rows of red lights show
which products are
ordered. Workers move from
bulb to bulb, retrieving an
item from the shelf above
and pressing a button that
resets the light. This is
known as a “pick-to-light”
system. This system doubles
the picking speed of manual
operators and drops the
error rate to nearly zero.
4. Your items are put into
crates on moving belts. Each
item goes into a large green
crate that contains many
customers’ orders. When full,
the crates ride a series of
conveyor belts that wind more
than 10 miles through the plant
at a constant speed of 2.9 feet
per second. The bar code on
each item is scanned 15 times,
by machines and by many of the
600 workers. The goal is to
reduce errors to zero—returns
are very expensive.
372

5. All three items converge in a chute and then
inside a box. All the crates arrive at a central point
where bar codes are matched with order numbers
to determine who gets what. Your three items end
up in a 3-foot-wide chute—one of several
thousand—and are placed into a cardboard box
with a new bar code that identifies your order.
Picking is sequenced to reduce operator travel.
6. Any gifts you’ve chosen are wrapped by
hand. Amazon trains an elite group of gift
wrappers, each of whom processes 30 packages
an hour.
7. The box is packed, taped, weighed, and
labeled before leaving the warehouse in a
truck. The Coffeyville plant was designed to
ship as many as 200,000 pieces a day. About
60% of orders are shipped via the U.S.
Postal Service; nearly everything else goes
through United Parcel Service.
8. Your order arrives at your doorstep. In
one or two days, your order is delivered.
AMAZON.COM �
373

374 PART 3 Managing Operations
VIDEO 12.1
Frito-Lay’s Inventory
LO1: Conduct an ABC analysis 376
LO2: Explain and use cycle counting 378
LO3: Explain and use the EOQ model for
independent inventory demand 381
LO4: Compute a reorder point and
explain safety stock 387
Chapter 12 Learning Objectives
LO5: Apply the production order
quantity model 388
LO6: Explain and use the quantity
discount model 391
LO7: Understand service levels and
probabilistic inventory models 396
THE IMPORTANCE OF INVENTORY
As Amazon.com well knows, inventory is one of the most expensive assets of many companies,
representing as much as 50% of total invested capital. Operations managers around the globe
have long recognized that good inventory management is crucial. On the one hand, a firm can
reduce costs by reducing inventory. On the other hand, production may stop and customers
become dissatisfied when an item is out of stock. The objective of inventory management is to
strike a balance between inventory investment and customer service. You can never achieve a
low-cost strategy without good inventory management.
All organizations have some type of inventory planning and control system. A bank has meth-
ods to control its inventory of cash. A hospital has methods to control blood supplies and phar-
maceuticals. Government agencies, schools, and, of course, virtually every manufacturing and
production organization are concerned with inventory planning and control.
In cases of physical products, the organization must determine whether to produce goods or to
purchase them. Once this decision has been made, the next step is to forecast demand, as dis-
cussed in Chapter 4. Then operations managers determine the inventory necessary to service that
demand. In this chapter, we discuss the functions, types, and management of inventory. We then
address two basic inventory issues: how much to order and when to order.
Functions of Inventory
Inventory can serve several functions that add flexibility to a firm’s operations. The four func-
tions of inventory are:
1. To “decouple” or separate various parts of the production process. For example, if a firm’s
supplies fluctuate, extra inventory may be necessary to decouple the production process
from suppliers.
Hedging inventories of oil is
complicated when onshore storage
units are full. This supertanker is the
world’s newest kind of inventory
warehouse. When oil traders are betting
that prices will rise and want to store oil,
a supertanker may be used as a
warehouse. These huge floating
warehouses can stay at sea for months,
waiting for a price that makes the hedge
successful.

Chapter 12 Inventory Management 375
2. To decouple the firm from fluctuations in demand and provide a stock of goods that will pro-
vide a selection for customers. Such inventories are typical in retail establishments.
3. To take advantage of quantity discounts, because purchases in larger quantities may reduce
the cost of goods or their delivery.
4. To hedge against inflation and upward price changes (as shown in the supertanker photo).
Types of Inventory
To accommodate the functions of inventory, firms maintain four types of inventories: (1) raw
material inventory, (2) work-in-process inventory, (3) maintenance/repair/operating supply
(MRO) inventory, and (4) finished-goods inventory.
Raw material inventory has been purchased but not processed. This inventory can be used
to decouple (i.e., separate) suppliers from the production process. However, the preferred
approach is to eliminate supplier variability in quality, quantity, or delivery time so that separa-
tion is not needed. Work-in-process (WIP) inventory is components or raw material that have
undergone some change but are not completed. WIP exists because of the time it takes for a
product to be made (called cycle time). Reducing cycle time reduces inventory. Often this task
is not difficult: During most of the time a product is “being made,” it is in fact sitting idle. As
Figure 12.1. shows, actual work time, or “run” time, is a small portion of the material flow time,
perhaps as low as 5%.
MROs are inventories devoted to maintenance/repair/operating supplies necessary to keep
machinery and processes productive. They exist because the need and timing for maintenance
and repair of some equipment are unknown. Although the demand for MRO inventory is often a
function of maintenance schedules, other unscheduled MRO demands must be anticipated.
Finished-goods inventory is completed product awaiting shipment. Finished goods may be
inventoried because future customer demands are unknown.
MANAGING INVENTORY
Operations managers establish systems for managing inventory. In this section, we briefly exam-
ine two ingredients of such systems: (1) how inventory items can be classified (called ABC
analysis) and (2) how accurate inventory records can be maintained. We will then look at inven-
tory control in the service sector.
ABC Analysis
ABC analysis divides on-hand inventory into three classifications on the basis of annual dollar
volume. ABC analysis is an inventory application of what is known as the Pareto principle
(named after Vilfredo Pareto, a 19th century Italian economist). The Pareto principle states that
there are a “critical few and trivial many.” The idea is to establish inventory policies that focus
resources on the few critical inventory parts and not the many trivial ones. It is not realistic to
monitor inexpensive items with the same intensity as very expensive items.
To determine annual dollar volume for ABC analysis, we measure the annual demand of each
inventory item times the cost per unit. Class A items are those on which the annual dollar volume
95% 5%
Input Wait for
inspection
Wait to
be moved
Move
time
Wait in queue
for operator
Setup
time
Run
time
Output
Cycle time
� FIGURE 12.1 The Material Flow Cycle
Most of the time that work is in-process (95% of the cycle time) is not productive time.
Raw material inventory
Materials that are usually
purchased but have yet to enter
the manufacturing process.
Work-in-process (WIP)
inventory
Products or components that
are no longer raw materials but
have yet to become finished
products.
MRO
Maintenance, repair, and
operating materials.
Finished-goods inventory
An end item ready to be sold,
but still an asset on the
company’s books.
AUTHOR COMMENT
Firms must carefully control
critical items, keep accurate
records, count inventory
regularly, and avoid theft
and damage.
ABC analysis
A method for dividing on-hand
inventory into three classifications
based on annual dollar volume.

376 PART 3 Managing Operations
is high. Although such items may represent only about 15% of the total inventory items, they
represent 70% to 80% of the total dollar usage. Class B items are those inventory items of
medium annual dollar volume. These items may represent about 30% of inventory items and
15% to 25% of the total value. Those with low annual dollar volume are Class C, which may rep-
resent only 5% of the annual dollar volume but about 55% of the total inventory items.
Graphically, the inventory of many organizations would appear as presented in Figure 12.2.
An example of the use of ABC analysis is shown in Example 1.
EXAMPLE 1 �
ABC analysis for a
chip manufacturer
A items
80
70
60
50
40
30
20
10
0
P
e
rc
e
n
ta
g
e
o
f
a
n
n
u
a
l d
o
lla
r
u
sa
g
e
10 20 30 40 50 60 70 80 90 100
B items
C items
Percentage of inventory items
� FIGURE 12.2
Graphic Representation
of ABC Analysis
Silicon Chips, Inc., maker of superfast DRAM chips, wants to categorize its 10 major inventory items
using ABC analysis.
APPROACH � ABC analysis organizes the items on an annual dollar-volume basis. Shown
below (in columns 1–4) are the 10 items (identified by stock numbers), their annual demands, and
unit costs.
SOLUTION � Annual dollar volume is computed in column 5, along with the percentage of the
total represented by each item in column 6. Column 7 groups the 10 items into A, B, and C categories.
ABC Calculation
(1)
Item
Stock
Number
(2)
Percentage
of Number
of Items
Stocked
(3)
Annual
Volume
(units)
:
(4)
Unit
Cost
�
(5)
Annual
Dollar
Volume
(6)
Percentage of
Annual
Dollar
Volume
(7)
Class
#10286
20%
1,000 $ 90.00 $ 90,000 38.8% A
#11526 500 154.00 77,000 33.2% A
#12760 1,550 17.00 26,350 11.3% B
#10867 30% 350 42.86 15,001 6.4% B
#10500 1,000 12.50 12,500 5.4% B
#12572 600 14.17 8,502 3.7% C
#14075 2,000 .60 1,200 .5% C
#01036 50% 100 8.50 850 .4% C
#01307 1,200 .42 504 .2% C
#10572 250 .60 150 .1% C
8,550 $232,057 100.0%
72%
23%
5%
INSIGHT � The breakdown into A, B, and C categories is not hard and fast. The objective is to try
to separate the “important” from the “unimportant.”
LEARNING EXERCISE � The unit cost for Item #10286 has increased from $90.00 to
$120.00. How does this impact the ABC analysis? [Answer: The total annual dollar volume increases
by $30,000, to $262,057, and the two A items now comprise 75% of that amount.]
RELATED PROBLEMS � 12.1, 12.2, 12.3
EXCEL OM Data File Ch12Ex1.xls can be found at www.pearsonhighered.com/heizer.
AUTHOR COMMENT
A, B, and C categories need
not be exact. The idea is to
recognize that levels of control
should match the risk.
LO1: Conduct an ABC
analysis

www.pearsonhighered.com/heizer

Chapter 12 Inventory Management 377
Criteria other than annual dollar volume can determine item classification. For instance, antici-
pated engineering changes, delivery problems, quality problems, or high unit cost may dictate
upgrading items to a higher classification. The advantage of dividing inventory items into classes
allows policies and controls to be established for each class.
Policies that may be based on ABC analysis include the following:
1. Purchasing resources expended on supplier development should be much higher for individ-
ual A items than for C items.
2. A items, as opposed to B and C items, should have tighter physical inventory control; per-
haps they belong in a more secure area, and perhaps the accuracy of inventory records for A
items should be verified more frequently.
3. Forecasting A items may warrant more care than forecasting other items.
Better forecasting, physical control, supplier reliability, and an ultimate reduction in safety stock
can all result from appropriate inventory management policies. ABC analysis guides the devel-
opment of those policies.
Record Accuracy
Good inventory policies are meaningless if management does not know what inventory is on hand.
Accuracy of records is a critical ingredient in production and inventory systems. Record accuracy
allows organizations to focus on those items that are needed, rather than settling for being sure
that “some of everything” is in inventory. Only when an organization can determine accurately
what it has on hand can it make precise decisions about ordering, scheduling, and shipping.
To ensure accuracy, incoming and outgoing record keeping must be good, as must be stock-
room security. A well-organized stockroom will have limited access, good housekeeping, and
storage areas that hold fixed amounts of inventory. Bins, shelf space, and parts will be labeled
accurately. The U.S. Marines’ approach to improved inventory record accuracy is discussed in
the OM in Action box “What the Marines Learned about Inventory from Walmart.”
Cycle Counting
Even though an organization may have made substantial efforts to record inventory accurately,
these records must be verified through a continuing audit. Such audits are known as cycle
counting. Historically, many firms performed annual physical inventories. This practice often
meant shutting down the facility and having inexperienced people count parts and material.
Inventory records should instead be verified via cycle counting. Cycle counting uses inventory clas-
sifications developed through ABC analysis. With cycle counting procedures, items are counted,
records are verified, and inaccuracies are periodically documented. The cause of inaccuracies is
then traced and appropriate remedial action taken to ensure integrity of the inventory system.
The U.S. Marine Corps knew it had inventory problems. A few
years ago, when a soldier at Camp Pendleton, near San
Diego, put in an order for a spare part, it took him a week to
get it—from the other side of the base. Worse, the Corps had
207 computer systems worldwide. Called the “Rats’ Nest” by
Marine techies, most systems didn’t even talk to each other.
To execute a victory over uncontrolled supplies, the
Corps studied Walmart, Caterpillar, Inc., and UPS.
“We’re in the middle of a revolution,” says General Gary
McKissock. McKissock aims to reduce inventory for the
Corps by half, saving $200 million, and to shift 2,000
Marines from inventory detail to the battlefield.
By replacing inventory with information, the Corps won’t
have to stockpile tons of supplies near the battlefield, as it
did during the Gulf War, only to find it couldn’t keep track of
what was in containers. Then there was the Marine policy
requiring a 60-day supply of everything. McKissock figured
out there was no need to overstock commodity items, like
office supplies, that can be obtained anywhere. And with
advice from the private sector, the Marines have been
upgrading warehouses, adding wireless scanners for real-
time inventory placement and tracking. Now, if containers
need to be sent into a war zone, they will have radio
frequency transponders that, when scanned, will link to a
database detailing what’s inside.
Sources: Modern Materials Handling (August 2005): 24–25; and Business-
Week (December 24, 2001): 24.
OM in Action � What the Marines Learned about Inventory from Walmart
Cycle counting
A continuing reconciliation of
inventory with inventory
records.

378 PART 3 Managing Operations
EXAMPLE 2 �
Cycle counting at a
truck manufacturer
A items will be counted frequently, perhaps once a month; B items will be counted less frequently,
perhaps once a quarter; and C items will be counted perhaps once every 6 months. Example 2 illus-
trates how to compute the number of items of each classification to be counted each day.
At John Deere, two workers fill orders for
3,000 parts from a six-stand carousel
system, using a sophisticated computer
system. The computer saves time
searching for parts and speeds orders in
the miles of warehouse shelving. While a
worker pulls a part from one carousel, the
computer sends the next request to the
adjacent carousel.
Cole’s Trucks, Inc., a builder of high-quality refuse trucks, has about 5,000 items in its inventory. It
wants to determine how many items to cycle count each day.
APPROACH � After hiring Matt Clark, a bright young OM student, for the summer, the firm
determined that it has 500 A items, 1,750 B items, and 2,750 C items. Company policy is to count all
A items every month (every 20 working days), all B items every quarter (every 60 working days), and
all C items every 6 months (every 120 working days). The firm then allocates some items to be
counted each day.
SOLUTION �
Item
Class Quantity Cycle Counting Policy
Number of Items
Counted per Day
A 500 Each month (20 working days) 500/20 = 25/day
B 1,750 Each quarter (60 working days) 1,750/60 = 29/day
C 2,750 Every 6 months (120 working days) 2,750/120 = 23/day
77/day
Seventy-seven items are counted each day.
INSIGHT � This daily audit of 77 items is much more efficient and accurate than conducting a
massive inventory count once a year.
LEARNING EXERCISE � Cole’s reclassifies some B and C items so there are now 1,500 B
items and 3,000 C items. How does this change the cycle count? [Answer: B and C both change to 25
items each per day, for a total of 75 items per day.]
RELATED PROBLEM � 12.4
In Example 2, the particular items to be cycle counted can be sequentially or randomly selected
each day. Another option is to cycle count items when they are reordered.
Cycle counting also has the following advantages:
1. Eliminates the shutdown and interruption of production necessary for annual physical
inventories.
2. Eliminates annual inventory adjustments.
3. Trained personnel audit the accuracy of inventory.
LO2: Explain and use cycle
counting

Chapter 12 Inventory Management 379
Shrinkage
Retail inventory that is
unaccounted for between
receipt and sale.
Pilferage
A small amount of theft.
Pharmaceutical distributor
McKesson Corp., which is one of
Arnold Palmer Hospital’s main
suppliers of surgical materials,
makes heavy use of bar-code
readers to automate inventory
control. The device on the
warehouse worker’s arm combines
a scanner, a computer, and a two-
way radio to check orders. With
rapid and accurate data, items are
easily verified, improving inventory
and shipment accuracy.
A handheld reader can scan
RFID tags, aiding control of both
incoming and outgoing
shipments.
4. Allows the cause of the errors to be identified and remedial action to be taken.
5. Maintains accurate inventory records.
Control of Service Inventories
Management of service inventories deserves special consideration. Although we may think of the
service sector of our economy as not having inventory, that is not always the case. For instance,
extensive inventory is held in wholesale and retail businesses, making inventory management cru-
cial and often a factor in a manager’s advancement. In the food-service business, for example,
control of inventory can make the difference between success and failure. Moreover, inventory
that is in transit or idle in a warehouse is lost value. Similarly, inventory damaged or stolen prior
to sale is a loss. In retailing, inventory that is unaccounted for between receipt and time of sale is
known as shrinkage. Shrinkage occurs from damage and theft as well as from sloppy paperwork.
Inventory theft is also known as pilferage. Retail inventory loss of 1% of sales is considered good,
with losses in many stores exceeding 3%. Because the impact on profitability is substantial, inven-
tory accuracy and control are critical. Applicable techniques include the following:
1. Good personnel selection, training, and discipline: These are never easy but very necessary
in food-service, wholesale, and retail operations, where employees have access to directly
consumable merchandise.
2. Tight control of incoming shipments: This task is being addressed by many firms through
the use of bar-code and radio frequency ID (RFID) systems that read every incoming ship-
ment and automatically check tallies against purchase orders. When properly designed,
these systems are very hard to defeat. Each item has its own unique stock keeping unit
(SKU; pronounced “skew”).
3. Effective control of all goods leaving the facility: This job is accomplished with bar codes
on items being shipped, magnetic strips on merchandise, or via direct observation. Direct
observation can be personnel stationed at exits (as at Costco and Sam’s Club wholesale
stores) and in potentially high-loss areas or can take the form of one-way mirrors and video
surveillance.
Successful retail operations require very good store-level control with accurate inventory in its
proper location. One recent study found that consumers and clerks could not find 16% of the
items at one of the U.S.’s largest retailers—not because the items were out of stock but because
they were misplaced (in a backroom, a storage area, or on the wrong aisle). By the researcher’s
estimates, major retailers lose 10% to 25% of overall profits due to poor or inaccurate inven-
tory records.1
1See E. Malykhina, “Retailers Take Stock,” Information Week (February 7, 2005): 20–22 and A. Raman, N. DeHoratius,
and Z. Ton, “Execution: The Missing Link in Retail Operations,” California Management Review 43, no. 3 (Spring
2001): 136–141.

380 PART 3 Managing Operations
INVENTORY MODELS
We now examine a variety of inventory models and the costs associated with them.
Independent vs. Dependent Demand
Inventory control models assume that demand for an item is either independent of or dependent
on the demand for other items. For example, the demand for refrigerators is independent of the
demand for toaster ovens. However, the demand for toaster oven components is dependent on the
requirements of toaster ovens.
This chapter focuses on managing inventory where demand is independent. Chapter 14
presents dependent demand management.
Holding, Ordering, and Setup Costs
Holding costs are the costs associated with holding or “carrying” inventory over time.
Therefore, holding costs also include obsolescence and costs related to storage, such as insur-
ance, extra staffing, and interest payments. Table 12.1 shows the kinds of costs that need to be
evaluated to determine holding costs. Many firms fail to include all the inventory holding costs.
Consequently, inventory holding costs are often understated.
Ordering cost includes costs of supplies, forms, order processing, purchasing, clerical sup-
port, and so forth. When orders are being manufactured, ordering costs also exist, but they are a
part of what is called setup costs. Setup cost is the cost to prepare a machine or process for man-
ufacturing an order. This includes time and labor to clean and change tools or holders.
Operations managers can lower ordering costs by reducing setup costs and by using such effi-
cient procedures as electronic ordering and payment.
In manufacturing environments, setup cost is highly correlated with setup time. Setups
usually require a substantial amount of work even before a setup is actually performed at the
work center. With proper planning much of the preparation required by a setup can be done
prior to shutting down the machine or process. Setup times can thus be reduced substantially.
Machines and processes that traditionally have taken hours to set up are now being set up in
less than a minute by the more imaginative world-class manufacturers. As we shall see later in
this chapter, reducing setup times is an excellent way to reduce inventory investment and to
improve productivity.
INVENTORY MODELS FOR INDEPENDENT DEMAND
In this section, we introduce three inventory models that address two important questions: when
to order and how much to order. These independent demand models are:
1. Basic economic order quantity (EOQ) model
2. Production order quantity model
3. Quantity discount model
Holding cost
The cost to keep or carry
inventory in stock.
Ordering cost
The cost of the ordering
process.
Setup cost
The cost to prepare a machine
or process for production.
Category
Cost (and range)
as a Percentage of
Inventory Value
Housing costs (building rent or depreciation, operating cost, taxes, insurance) 6% (3–10%)
Material handling costs (equipment lease or depreciation, power,
operating cost) 3% (1–3.5%)
Labor cost (receiving, warehousing, security) 3% (3–5%)
Investment costs (borrowing costs, taxes, and insurance on inventory) 11% (6–24%)
Pilferage, scrap, and obsolescence (much higher in industries undergoing rapid
change like PCs and cell phones) 3% (2–5%)
Overall carrying cost 26%
Note: All numbers are approximate, as they vary substantially depending on the nature of the business, location, and current interest
rates.
�TABLE 12.1
Determining Inventory
Holding Costs
Setup time
The time required to prepare
a machine or process for
production.
AUTHOR COMMENT
An overall inventory carrying
cost of less than 15% is
very unlikely, but this cost
can exceed 40%, especially
in high-tech and fashion
industries.
VIDEO 12.2
Inventory Control at Wheeled
Coach Ambulance

Chapter 12 Inventory Management 381
The Basic Economic Order Quantity
(EOQ) Model
The economic order quantity (EOQ) model is one of the most commonly used inven-
tory-control techniques. This technique is relatively easy to use but is based on several
assumptions:
1. Demand for an item is known, reasonably constant, and independent of decisions for other
items.
2. Lead time—that is, the time between placement and receipt of the order—is known and
consistent.
3. Receipt of inventory is instantaneous and complete. In other words, the inventory from an
order arrives in one batch at one time.
4. Quantity discounts are not possible.
5. The only variable costs are the cost of setting up or placing an order (setup or ordering cost)
and the cost of holding or storing inventory over time (holding or carrying cost). These costs
were discussed in the previous section.
6. Stockouts (shortages) can be completely avoided if orders are placed at the right time.
With these assumptions, the graph of inventory usage over time has a sawtooth shape, as in
Figure 12.3. In Figure 12.3, Q represents the amount that is ordered. If this amount is 500
dresses, all 500 dresses arrive at one time (when an order is received). Thus, the inventory level
jumps from 0 to 500 dresses. In general, an inventory level increases from 0 to Q units when an
order arrives.
Because demand is constant over time, inventory drops at a uniform rate over time. (Refer to
the sloped lines in Figure 12.3.) Each time the inventory level reaches 0, the new order is placed
and received, and the inventory level again jumps to Q units (represented by the vertical lines).
This process continues indefinitely over time.
Minimizing Costs
The objective of most inventory models is to minimize total costs. With the assumptions just
given, significant costs are setup (or ordering) cost and holding (or carrying) cost. All other
costs, such as the cost of the inventory itself, are constant. Thus, if we minimize the sum of
setup and holding costs, we will also be minimizing total costs. To help you visualize this, in
Figure 12.4 we graph total costs as a function of the order quantity, Q. The optimal order size,
Q*, will be the quantity that minimizes the total costs. As the quantity ordered increases, the
total number of orders placed per year will decrease. Thus, as the quantity ordered increases,
the annual setup or ordering cost will decrease (Figure 12.4[a]). But as the order quantity
increases, the holding cost will increase due to the larger average inventories that are main-
tained (Figure 12.4[b]).
As we can see in Figure 12.4(c), a reduction in either holding or setup cost will reduce the
total cost curve. A reduction in the setup cost curve also reduces the optimal order quantity (lot
size). In addition, smaller lot sizes have a positive impact on quality and production flexibility.
In
ve
n
to
ry
le
ve
l
Order quantity = Q
(maximum inventory
level)
Minimum
inventory 0
Time
Average inventory
on hand
Q—
2( )
Usage rate
Total order received � FIGURE 12.3
Inventory Usage over Time
AUTHOR COMMENT
If the maximum we can ever
have is Q (say, 500 units) and
the minimum is zero, then if
inventory is used (or sold) on
a fairly steady rate, the
average = (Q + 0)/2 = Q/2.
LO3: Explain and use the
EOQ model for independent
inventory demand
Economic order
quantity (EOQ) model
An inventory-control technique
that minimizes the total of
ordering and holding costs.

382 PART 3 Managing Operations
At Toshiba, the $40 billion Japanese conglomerate, workers can make as few as 10 laptop com-
puters before changing models. This lot-size flexibility has allowed Toshiba to move toward a
“build-to-order” mass customization system, an important ability in an industry that has product
life cycles measured in months, not years.
You should note that in Figure 12.4(c), the optimal order quantity occurs at the point where the
ordering-cost curve and the carrying-cost curve intersect. This was not by chance. With the EOQ
model, the optimal order quantity will occur at a point where the total setup cost is equal to the
total holding cost.2 We use this fact to develop equations that solve directly for Q*. The necessary
steps are:
1. Develop an expression for setup or ordering cost.
2. Develop an expression for holding cost.
3. Set setup (order) cost equal to holding cost.
4. Solve the equation for the optimal order quantity.
Using the following variables, we can determine setup and holding costs and solve for Q*:
1.
=
D
Q
S
= ¢D
Q
≤(S)= ¢ Annual demandNumber of units in each order≤1Setup or order cost per order2
Annual setup cost = 1Number of orders placed per year2 * 1Setup or order cost per order2
H = Holding or carrying cost per unit per year
S = Setup or ordering cost for each order
D = Annual demand in units for the inventory item
Q* = Optimum number of units per order (EOQ)
Q = Number of units per order
Setup (order) cost
Order quantity
(a) Annual setup (order) cost (b) Annual holding cost
A
n
n
u
a
l c
o
st
(c) Total costs
Order quantity
A
n
n
u
a
l c
o
st
Holding cost
Holding cost
Setup (order) cost
Total cost for holding
and setup (order)
Order quantityOptimal order
quantity (Q *)
Minimum
total cost
A
n
n
u
a
l c
o
st
� FIGURE 12.4 Costs as a Function of Order Quantity AUTHOR COMMENT
This graph is the heart of EOQ
inventory modeling. We want
to find the smallest total cost
(top curve), which is the sum
of the two curves below it.
2This is the case when holding costs are linear and begin at the origin—that is, when inventory costs do not decline (or
they increase) as inventory volume increases and all holding costs are in small increments. In addition, there is proba-
bly some learning each time a setup (or order) is executed—a fact that lowers subsequent setup costs. Consequently,
the EOQ model is probably a special case. However, we abide by the conventional wisdom that this model is a reason-
able approximation.

Chapter 12 Inventory Management 383
2.
3. Optimal order quantity is found when annual setup (order) cost equals annual holding cost,
namely:
4. To solve for Q*, simply cross-multiply terms and isolate Q on the left of the equal sign:
(12-1)
Now that we have derived the equation for the optimal order quantity, Q*, it is possible to solve
inventory problems directly, as in Example 3.
Q* =
A
2DS
H
Q2 =
2DS
H
2DS = Q2H
D
Q
S =
Q
2
H
= ¢Q
2
≤(H) = Q
2
H
= ¢Order quantity
2
≤1Holding cost per unit per year2Annual holding cost = 1Average inventory level2 * 1Holding cost per unit per year2
� EXAMPLE 3
Finding the
optimal order
size at Sharp, Inc.
Sharp, Inc., a company that markets painless hypodermic needles to hospitals, would like to reduce its
inventory cost by determining the optimal number of hypodermic needles to obtain per order.
APPROACH � The annual demand is 1,000 units; the setup or ordering cost is $10 per order; and
the holding cost per unit per year is $.50.
SOLUTION � Using these figures, we can calculate the optimal number of units per order:
INSIGHT � Sharp, Inc., now knows how many needles to order per order. The firm also has a basis
for determining ordering and holding costs for this item, as well as the number of orders to be
processed by the receiving and inventory departments.
LEARNING EXERCISE � If D increases to 1,200 units, what is the new Q*? [Answer:
]
RELATED PROBLEMS � 12.5, 12.6, 12.7, 12.8, 12.9, 12.12, 12.13, 12.15, 12.35, 12.37
EXCEL OM Data File Ch12Ex3.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 12.1 This example is further illustrated in Active Model 12.1 at www.pearsonhighered.com/heizer.
Q* = 219 units.
Q* = A
211,00021102
0.50
= 240,000 = 200 units
Q* =
A
2DS
H
We can also determine the expected number of orders placed during the year (N) and the
expected time between orders (T), as follows:
(12-2)
(12-3)
Example 4 illustrates this concept.
Expected time between orders = T =
Number of working days per year
N
Expected number of orders = N =
Demand
Order quantity
=
D
Q*

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384 PART 3 Managing Operations
EXAMPLE 4 �
Computing number
of orders and time
between orders at
Sharp, Inc.
Sharp, Inc. (in Example 3), has a 250-day working year and wants to find the number of orders (N) and
the expected time between orders (T).
APPROACH � Using Equations (12-2) and (12-3), Sharp enters the data given in Example 3.
SOLUTION �
INSIGHT � The company now knows not only how many needles to order per order but that the
time between orders is 50 days and that there are five orders per year.
LEARNING EXERCISE � If D = 1,200 units instead of 1,000, find N and T. [Answer:
]
RELATED PROBLEMS � 12.12, 12.13, 12.15
N � 5.48, T = 45.62.
=
250 working days per year
5 orders
= 50 days between orders
T =
Number of working days per year
Expected number of orders
=
1,000
200
= 5 orders per year
N =
Demand
Order quantity
As mentioned earlier in this section, the total annual variable inventory cost is the sum of setup
and holding costs:
(12-4)
In terms of the variables in the model, we can express the total cost TC as:
(12-5)
Example 5 shows how to use this formula.
TC =
D
Q
S +
Q
2
H
Total annual cost = Setup 1order2 cost + Holding cost
EXAMPLE 5 �
Computing
combined cost of
ordering and holding
Sharp, Inc. (from Examples 3 and 4), wants to determine the combined annual ordering and holding costs.
APPROACH � Apply Equation (12-5), using the data in Example 3.
SOLUTION �
INSIGHT � These are the annual setup and holding costs. The $100 total does not include the
actual cost of goods. Notice that in the EOQ model, holding costs always equal setup (order) costs.
LEARNING EXERCISE � Find the total annual cost if D = 1,200 units in Example 3. [Answer:
$109.54.]
RELATED PROBLEMS � 12.9, 12.12, 12.13, 12.14, 12.37b,c
= $50 + $50 = $100
= 1521$102 + 110021$.502
=
1,000
200
1$102 +
200
2
1$.502
TC =
D
Q
S +
Q
2
H
Inventory costs may also be expressed to include the actual cost of the material purchased. If we
assume that the annual demand and the price per hypodermic needle are known values (e.g.,
1,000 hypodermics per year at P = $10) and total annual cost should include purchase cost, then
Equation (12-5) becomes:
TC =
D
Q
S +
Q
2
H + PD

Chapter 12 Inventory Management 385
This store takes 4 weeks to get an order for Levis 501 jeans
filled by the manufacturer. If the store sells 10 pairs of size
30–32 Levis a week, the store manager could set up two
containers, keep 40 pairs of jeans in the second container, and
place an order whenever the first container is empty. This
would be a fixed-quantity reordering system. It is also called a
“two-bin” system and is an example of a very elementary, but
effective, approach to inventory management
3The formula for the economic order quantity (Q*) can also be determined by finding where the total cost curve is at a
minimum (i.e., where the slope of the total cost curve is zero). Using calculus, we set the derivative of the total cost
with respect to Q* equal to 0.
The calculations for finding the minimum of
are
. Thus, Q* =
A
2DS
H
d1TC2
dQ
= ¢ –DS
Q2
≤ + H
2
+ 0 = 0
TC =
D
Q
S +
Q
2
H + PD
Robust
Giving satisfactory answers
even with substantial variation
in the parameters.
� EXAMPLE 6
EOQ is a robust
model
Management in the Sharp, Inc., examples underestimates total annual demand by 50% (say demand is
actually 1,500 needles rather than 1,000 needles) while using the same Q. How will the annual inven-
tory cost be impacted?
APPROACH � We will solve for annual costs twice. First, we will apply the wrong EOQ; then we
will recompute costs with the correct EOQ.
SOLUTION � If demand in Example 5 is actually 1,500 needles rather than 1,000, but manage-
ment uses an order quantity of Q = 200 (when it should be Q = 244.9 based on D = 1,500), the sum of
holding and ordering cost increases to $125:
= $75 + $50 = $125
=
1,500
200
1$102 +
200
2
1$.502
Annual cost =
D
Q
S +
Q
2
H
Because material cost does not depend on the particular order policy, we still incur an annual
material cost of (Later in this chapter we will discuss the
case in which this may not be true—namely, when a quantity discount is available.)3
Robust Model A benefit of the EOQ model is that it is robust. By robust we mean that it
gives satisfactory answers even with substantial variation in its parameters. As we have
observed, determining accurate ordering costs and holding costs for inventory is often difficult.
Consequently, a robust model is advantageous. Total cost of the EOQ changes little in the neigh-
borhood of the minimum. The curve is very shallow. This means that variations in setup costs,
holding costs, demand, or even EOQ make relatively modest differences in total cost. Example 6
shows the robustness of EOQ.
D * P = 11,00021$102 = $10,000.

386 PART 3 Managing Operations
We may conclude that the EOQ is indeed robust and that significant errors do not cost us very
much. This attribute of the EOQ model is most convenient because our ability to accurately
determine demand, holding cost, and ordering cost is limited.
Reorder Points
Now that we have decided how much to order, we will look at the second inventory question, when
to order. Simple inventory models assume that receipt of an order is instantaneous. In other words,
they assume (1) that a firm will place an order when the inventory level for that particular item
reaches zero and (2) that it will receive the ordered items immediately. However, the time between
placement and receipt of an order, called lead time, or delivery time, can be as short as a few
hours or as long as months. Thus, the when-to-order decision is usually expressed in terms of a
reorder point (ROP)—the inventory level at which an order should be placed (see Figure 12.5).
The reorder point (ROP) is given as:
(12-6)= d * L
ROP = 1Demand per day2 * 1Lead time for a new order in days2
However, had we known that the demand was for 1,500 with an EOQ of 244.9 units, we would have
spent $122.47, as shown:
INSIGHT � Note that the expenditure of $125.00, made with an estimate of demand that was sub-
stantially wrong, is only 2% ($2.52/$122.47) higher than we would have paid had we known the actual
demand and ordered accordingly. Note also that were it not due to rounding, the annual holding costs
and ordering costs would be exactly equal.
LEARNING EXERCISE � Demand at Sharp remains at 1,000, H is still $.50, and we order 200
needles at a time (as in Example 5). But if the true order cost = S = $15 (rather than $10), what is the
annual cost? [Answer: Annual order cost increases to $75, and annual holding cost stays at $50. So the
total cost = $125.]
RELATED PROBLEMS � 12.8b, 12.14
= $61.25 + $61.22 = $122.47
= 6.1251$102 + 122.451$.502
Annual cost =
1,500
244.9
1$102 +
244.9
2
1$.502
Slope = units/day = d
Resupply takes place as
order arrives
Lead time = L
Q *
In
ve
n
to
ry
le
ve
l (
u
n
its
)
Time (days)
ROP
(units)
0
� FIGURE 12.5
The Reorder Point (ROP)
Q* is the optimum order
quantity, and lead time
represents the time between
placing and receiving an
order.
Lead time
In purchasing systems, the time
between placing an order and
receiving it; in production
systems, the wait, move, queue,
setup, and run times for each
component produced.
Reorder point (ROP)
The inventory level (point) at
which action is taken to
replenish the stocked item.

Chapter 12 Inventory Management 387
This equation for ROP assumes that demand during lead time and lead time itself are constant.
When this is not the case, extra stock, often called safety stock, should be added.
The demand per day, d, is found by dividing the annual demand, D, by the number of working
days in a year:
Computing the reorder point is demonstrated in Example 7.
d =
D
Number of working days in a year
Safety stock
Extra stock to allow for uneven
demand; a buffer.
� EXAMPLE 7
Computing
reorder points
(ROP) for iPods
An Apple distributor has a demand for 8,000 iPods per year. The firm operates a 250-day working year.
On average, delivery of an order takes 3 working days. It wants to calculate the reorder point.
APPROACH � Compute the daily demand and then apply Equation (12-6).
SOLUTION �
INSIGHT � Thus, when iPod inventory stock drops to 96 units, an order should be placed. The
order will arrive 3 days later, just as the distributor’s stock is depleted.
LEARNING EXERCISE � If there are only 200 working days per year, what is the correct
ROP? [Answer: 120 iPods.]
RELATED PROBLEMS � 12.9d, 12.10, 12.11, 12.13f
= 96 units
ROP = Reorder point = d * L = 32 units per day * 3 days
= 32 units
d =
D
Number of working days in a year
=
8,000
250
Safety stock is especially important in firms whose raw material deliveries may be uniquely
unreliable. For example, San Miguel Corp. in the Philippines uses cheese curd imported from
Europe. Because the normal mode of delivery is highly variable, safety stock may be substantial.
Production Order Quantity Model
In the previous inventory model, we assumed that the entire inventory order was received at one
time. There are times, however, when the firm may receive its inventory over a period of time.
Such cases require a different model, one that does not require the instantaneous-receipt assump-
tion. This model is applicable under two situations: (1) when inventory continuously flows or
builds up over a period of time after an order has been placed or (2) when units are produced and
sold simultaneously. Under these circumstances, we take into account daily production (or
inventory-flow) rate and daily demand rate. Figure 12.6 shows inventory levels as a function of
time (and inventory dropping to zero between orders).
LO4: Compute a reorder
point and explain safety
stock
t
Demand part of cycle with no production
(only usage takes place)
Part of inventory cycle
during which production (and usage)
takes place
Maximum
inventory
In
ve
n
to
ry
le
ve
l
Time
� FIGURE 12.6
Change in Inventory Levels
over Time for the Production
Model
AUTHOR COMMENT
Note that inventory buildup is
not instantaneous but
gradual. So the formula
reduces the average inventory
and thus the holding cost by
the ratio of that buildup.

388 PART 3 Managing Operations
Because this model is especially suitable for the production environment, it is commonly
called the production order quantity model. It is useful when inventory continuously builds up
over time, and traditional economic order quantity assumptions are valid. We derive this model
by setting ordering or setup costs equal to holding costs and solving for optimal order size, Q*.
Using the following symbols, we can determine the expression for annual inventory holding cost
for the production order quantity model:
1.
2.
3.
However, Therefore:
4. Annual inventory holding cost (or simply holding cost) =
Maximum inventory level
2
1H2 =
Q
2
C1 – ¢d
p
≤ SH
= Q¢1 – d
p
≤ Maximum inventory level = p¢Qp≤ – d¢Qp≤ = Q – dpQ
Q = total produced = pt, and thus t = Q/P.
= pt – dt
¢ Maximum
inventory level
≤ = ¢Total production during
the production run
≤ – ¢ Total used during
the production run
≤1Average inventory level2 = 1Maximum inventory level2>2
¢Annual inventory
holding cost
≤ = (Average inventory level) * ¢ Holding cost
per unit per year
≤t = Length of the production run in daysd = Daily demand rate, or usage rate
p = Daily production rate
H = Holding cost per unit per year
Q = Number of units per order
Production order
quantity model
An economic order quantity
technique applied to production
orders.
Each order may require
a change in the way a
machine or process is
set up. Reducing setup
time usually means a
reduction in setup
cost; and reductions in
setup costs make
smaller batches (lots)
economical to
produce. Increasingly,
set up (and operation)
is performed by
computer-controlled
machines, such as this
one, operating from
previously written
programs.
LO5: Apply the production
order quantity model

Chapter 12 Inventory Management 389
Using this expression for holding cost and the expression for setup cost developed in the basic
EOQ model, we solve for the optimal number of pieces per order by equating setup cost and
holding cost:
Set ordering cost equal to holding cost to obtain
(12-7)
In Example 8, we use the above equation, , to solve for the optimum order or production
quantity when inventory is consumed as it is produced.
Q*p
Q*p =
A
2DS
H[1 – 1d>p2]
Q2 =
2DS
H31 – 1d>p24
D
Q
S = 12 HQ31 – 1d>p24
Q*p:
Holding cost = 12HQ31 – 1d>p24
Setup cost = 1D>Q2S
� EXAMPLE 8
A production
order quantity
model
Nathan Manufacturing, Inc., makes and sells specialty hubcaps for the retail automobile aftermarket.
Nathan’s forecast for its wire-wheel hubcap is 1,000 units next year, with an average daily demand of 4
units. However, the production process is most efficient at 8 units per day. So the company produces 8
per day but uses only 4 per day. The company wants to solve for the optimum number of units per
order. (Note: This plant schedules production of this hubcap only as needed, during the 250 days per
year the shop operates.)
APPROACH � Gather the cost data and apply Equation (12-7):
SOLUTION �
INSIGHT � The difference between the production order quantity model and the basic EOQ
model is the annual holding cost, which is reduced in the production order quantity model.
LEARNING EXERCISE � If Nathan can increase its daily production rate from 8 to 10, how
does change? [Answer:
RELATED PROBLEMS � 12.16, 12.17, 12.18, 12.39
EXCEL OM Data File Ch12Ex8.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 12.2 This example is further illustrated in Active Model 12.2 at www.pearsonhighered.com/heizer.
Q*p = 258.]Q*p
= 282.8 hubcaps, or 283 hubcaps
=
A
20,000
0.5011>22
= 280,000
Q*p = A
211,00021102
0.5031 – 14>824
Q*p =
A
2DS
H31 – 1d>p24
Daily demand rate = d = 4 units daily
Daily production rate = p = 8 units daily
Holding cost = H = $0.50 per unit per year
Setup costs = S = $10
Annual demand = D = 1,000 units

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390 PART 3 Managing Operations
You may want to compare this solution with the answer in Example 3, which had identical D, S,
and H values. Eliminating the instantaneous-receipt assumption, where p = 8 and d = 4, resulted
in an increase in Q* from 200 in Example 3 to 283 in Example 8. This increase in
Q* occurred because holding cost dropped from $.50 to [ ], making a larger
order quantity optimal. Also note that:
We can also calculate when annual data are available. When annual data are used, we can
express as:
(12-8)
Quantity Discount Models
To increase sales, many companies offer quantity discounts to their customers. A quantity dis-
count is simply a reduced price (P) for an item when it is purchased in larger quantities.
Discount schedules with several discounts for large orders are common. A typical quantity dis-
count schedule appears in Table 12.2. As can be seen in the table, the normal price of the item is
$5. When 1,000 to 1,999 units are ordered at one time, the price per unit drops to $4.80; when the
quantity ordered at one time is 2,000 units or more, the price is $4.75 per unit. As always, man-
agement must decide when and how much to order. However, with an opportunity to save money
on quantity discounts, how does the operations manager make these decisions?
Q*p =
Q
2DS
H¢1 – Annual demand rate
Annual production rate
≤Q*p
Q*p
d = 4 =
D
Number of days the plant is in operation
=
1,000
250
$.50 * (1 – d/p)
Milton Bradley, a division of Hasbro, Inc., has been
manufacturing toys for 150 years. Founded by Milton
Bradley in 1860, the company started by making a
lithograph of Abraham Lincoln. Using his printing skills,
Bradley developed games, including the Game of Life,
Chutes and Ladders, Candy Land, Scrabble, and Lite Brite.
Today, the company produces hundreds of games,
requiring billions of plastic parts.
Once Milton Bradley has determined the optimal
quantities for each production run, it must make them
and assemble them as a part of the proper game. Some
games require literally hundreds of plastic parts, including
spinners, hotels, people, animals, cars, and so on.
According to Gary Brennan, director of manufacturing,
getting the right number of pieces to the right toys and
production lines is the most important issue for the
credibility of the company. Some orders can require
20,000 or more perfectly assembled games delivered
to their warehouses in a matter of days.
Games with the incorrect number of parts and pieces
can result in some very unhappy customers. It is also
time-consuming and
expensive for Milton
Bradley to supply the extra
parts or to have toys or
games returned. When
shortages are found during
the assembly stage, the
entire production run is
stopped until the problem is corrected. Counting parts
by hand or machine is not always accurate. As a result,
Milton Bradley now weighs pieces and completed games
to determine if the correct number of parts have been
included. If the weight is not exact, there is a problem that
is resolved before shipment. Using highly accurate digital
scales, Milton Bradley is now able to get the right parts
in the right game at the right time. Without this simple
innovation, the most sophisticated production schedule
is meaningless.
Sources: The Wall Street Journal (April 15, 1999): B1; Plastics World (March
1997): 22–26; and Modern Materials Handling (September 1997): 55–57.
OM in Action � Inventory Accuracy at Milton Bradley
Discount
Number Discount Quantity Discount (%)
Discount
Price (P)
1 0 to 999 no discount $5.00
2 1,000 to 1,999 4 $4.80
3 2,000 and over 5 $4.75
Quantity discount
A reduced price for items
purchased in large quantities.
AUTHOR COMMENT
Think of the discount model
as the EOQ model run once
for each of the discount price
levels offered.
� TABLE 12.2
A Quantity Discount Schedule

Chapter 12 Inventory Management 391
As with other inventory models discussed so far, the overall objective is to minimize total
cost. Because the unit cost for the third discount in Table 12.2 is the lowest, you may be tempted
to order 2,000 units or more merely to take advantage of the lower product cost. Placing an
order for that quantity, however, even with the greatest discount price, may not minimize total
inventory cost. Granted, as discount quantity goes up, the product cost goes down. However,
holding cost increases because orders are larger. Thus the major trade-off when considering
quantity discounts is between reduced product cost and increased holding cost. When we
include the cost of the product, the equation for the total annual inventory cost can be calculated
as follows:
or
(12-9)
where
Now, we have to determine the quantity that will minimize the total annual inventory cost.
Because there are several discounts, this process involves four steps:
STEP 1: For each discount, calculate a value for optimal order size Q*, using the following
equation:
(12-10)
Note that the holding cost is IP instead of H. Because the price of the item is a factor in
annual holding cost, we cannot assume that the holding cost is a constant when the price
per unit changes for each quantity discount. Thus, it is common to express the holding
cost as a percent (I) of unit price (P) instead of as a constant cost per unit per year, H.
STEP 2: For any discount, if the order quantity is too low to qualify for the discount, adjust the
order quantity upward to the lowest quantity that will qualify for the discount. For
example, if Q* for discount 2 in Table 12.2 were 500 units, you would adjust this
value up to 1,000 units. Look at the second discount in Table 12.2. Order quantities
between 1,000 and 1,999 will qualify for the 4% discount. Thus, if Q* is below 1,000
units, we will adjust the order quantity up to 1,000 units.
The reasoning for Step 2 may not be obvious. If the order quantity, Q*, is below
the range that will qualify for a discount, a quantity within this range may still result
in the lowest total cost.
As shown in Figure 12.7, the total cost curve is broken into three different total
cost curves. There is a total cost curve for the first ( ), second
( ), and third ( ) discount. Look at the total cost (TC)
curve for discount 2. Q* for discount 2 is less than the allowable discount range,
which is from 1,000 to 1,999 units. As the figure shows, the lowest allowable quan-
tity in this range, which is 1,000 units, is the quantity that minimizes total cost.
Thus, the second step is needed to ensure that we do not discard an order quantity
that may indeed produce the minimum cost. Note that an order quantity computed
in step 1 that is greater than the range that would qualify it for a discount may be
discarded.
STEP 3: Using the preceding total cost equation, compute a total cost for every Q* determined
in Steps 1 and 2. If you had to adjust Q* upward because it was below the allowable
quantity range, be sure to use the adjusted value for Q*.
STEP 4: Select the Q* that has the lowest total cost, as computed in Step 3. It will be the quan-
tity that will minimize the total inventory cost.
Q Ú 2,0001,000 … Q … 1,999
0 … Q … 999
Q* =
A
2DS
IP
H = Holding cost per unit per year
P = Price per unit
S = Ordering or setup cost per order
D = Annual demand in units
Q = Quantity ordered
TC =
D
Q
S +
Q
2
H + PD
Total cost = Ordering (setup) cost + Holding cost + Product cost
LO6: Explain and use the
quantity discount model

392 PART 3 Managing Operations
T
o
ta
l c
o
st
(
d
o
lla
rs
)
Total cost curve for discount 3
Total cost
curve for
discount 1
Total cost curve for discount 2
Q* for discount 2 is below the allowable range at point a
and must be adjusted upward to 1,000 units at point b.
1st price
break
2nd price
break
a
b
0 1,000 2,000
Order quantity
� FIGURE 12.7
Total Cost Curve for the
Quantity Discount Model
EXAMPLE 9 �
Quantity discount
model
Wohl’s Discount Store stocks toy race cars. Recently, the store has been given a quantity discount
schedule for these cars. This quantity schedule was shown in Table 12.2. Thus, the normal cost for the
toy race cars is $5.00. For orders between 1,000 and 1,999 units, the unit cost drops to $4.80; for orders
of 2,000 or more units, the unit cost is only $4.75. Furthermore, ordering cost is $49.00 per order,
annual demand is 5,000 race cars, and inventory carrying charge, as a percent of cost, I, is 20%, or .2.
What order quantity will minimize the total inventory cost?
APPROACH � We will follow the four steps just outlined for a quantity discount model.
SOLUTION � The first step is to compute Q* for every discount in Table 12.2. This is done as follows:
The second step is to adjust upward those values of Q* that are below the allowable discount range.
Since is between 0 and 999, it need not be adjusted. Because is below the allowable range of
1,000 to 1,999, it must be adjusted to 1,000 units. The same is true for : It must be adjusted to 2,000
units. After this step, the following order quantities must be tested in the total cost equation:
The third step is to use Equation (12-9) and compute a total cost for each order quantity. This step is
taken with the aid of Table 12.3, which presents the computations for each level of discount introduced
in Table 12.2.
Q*3 = 2,000 — adjusted
Q*2 = 1,000 — adjusted
Q*1 = 700
Q*3
Q*2Q*1
Q*3 = A
215,00021492
1.2214.752
= 718 cars per order
Q*2 = A
215,00021492
1.2214.802
= 714 cars per order
Q*1 = A
215,00021492
1.2215.002
= 700 cars per order
Discount
Number
Unit
Price
Order
Quantity
Annual
Product
Cost
Annual
Ordering
Cost
Annual
Holding
Cost Total
1 $5.00 700 $25,000 $350 $350 $25,700
2 $4.80 1,000 $24,000 $245 $480 $24,725
3 $4.75 2,000 $23,750 $122.50 $950 $24,822.50
AUTHOR COMMENT
Don’t forget to adjust order
quantity upward if the
quantity is too low to qualify
for the discount.
�TABLE 12.3
Total Cost Computations for
Wohl’s Discount Store
The fourth step is to select that order quantity with the lowest total cost. Looking at Table 12.3, you can
see that an order quantity of 1,000 toy race cars will minimize the total cost. You should see, however,
that the total cost for ordering 2,000 cars is only slightly greater than the total cost for ordering 1,000
Let us see how this procedure can be applied with an example.

Chapter 12 Inventory Management 393
cars. Thus, if the third discount cost is lowered to $4.65, for example, then this quantity might be the
one that minimizes total inventory cost.
INSIGHT � The quantity discount model’s third cost factor, annual product cost, is now a major
variable with impact on the final cost and decision. It takes substantial increases in order and holding
costs to compensate for a large quantity price break.
LEARNING EXERCISE � Wohl’s has just been offered a third price break. If it orders 2,500 or
more cars at a time, the unit cost drops to $4.60. What is the optimal order quantity now? [Answer:
, for a total cost of $24,248.]
RELATED PROBLEMS � 12.19, 12.20, 12.21, 12.22, 12.23, 12.24, 12.25
EXCEL OM Data File Ch12Ex9.xls can be found at www.pearsonhighered.com/heizer.
Q*4 = 2,500
PROBABILISTIC MODELS AND SAFETY STOCK
All the inventory models we have discussed so far make the assumption that demand for a prod-
uct is constant and certain. We now relax this assumption. The following inventory models apply
when product demand is not known but can be specified by means of a probability distribution.
These types of models are called probabilistic models.
An important concern of management is maintaining an adequate service level in the face of
uncertain demand. The service level is the complement of the probability of a stockout. For
instance, if the probability of a stockout is 0.05, then the service level is .95. Uncertain demand
raises the possibility of a stockout. One method of reducing stockouts is to hold extra units in
inventory. As we noted, such inventory is usually referred to as safety stock. It involves adding a
number of units as a buffer to the reorder point. As you recall from our previous discussion:
where
Order lead time, or number of working days it takes to deliver an order
The inclusion of safety stock (ss) changes the expression to:
(12-11)
The amount of safety stock maintained depends on the cost of incurring a stockout and the cost
of holding the extra inventory. Annual stockout cost is computed as follows:
(12-12)
Example 10 illustrates this concept.
Annual stockout costs = The sum of the units short for each demand level
* The probability of that demand level * The stockout cost>unit
* The number of orders per year
ROP = d * L + ss
L =
d = Daily demand
Reorder point = ROP = d * L
Probabilistic model
A statistical model applicable
when product demand or any
other variable is not known but
can be specified by means of a
probability distribution.
Service level
The complement of the
probability of a stockout.
AUTHOR COMMENT
Probabilistic models are a
real-world adjustment
because demand and lead
time won’t always be known
and constant.
David Rivera Optical has determined that its reorder point for eyeglass frames is 50 ( ) units. Its
carrying cost per frame per year is $5, and stockout (or lost sale) cost is $40 per frame. The store has
experienced the following probability distribution for inventory demand during the lead time (reorder
period). The optimum number of orders per year is six.
d * L
Number of Units Probability
30 .2
40 .2
ROP : 50 .3
60 .2
70 .1
1.0
� EXAMPLE 10
Determining
safety stock with
probabilistic
demand and
constant lead
time

www.pearsonhighered.com/heizer

394 PART 3 Managing Operations
How much safety stock should David Rivera keep on hand?
APPROACH � The objective is to find the amount of safety stock that minimizes the sum of the
additional inventory holding costs and stockout costs. The annual holding cost is simply the holding
cost per unit multiplied by the units added to the ROP. For example, a safety stock of 20 frames, which
implies that the new ROP, with safety stock, is , raises the annual carrying cost by
$5(20) = $100.
However, computing annual stockout cost is more interesting. For any level of safety stock,
stockout cost is the expected cost of stocking out. We can compute it, as in Equation (12-12), by
multiplying the number of frames short (Demand ROP) by the probability of demand at that
level, by the stockout cost, by the number of times per year the stockout can occur (which in our
case is the number of orders per year). Then we add stockout costs for each possible stockout level
for a given ROP.
SOLUTION � We begin by looking at zero safety stock. For this safety stock, a shortage of 10
frames will occur if demand is 60, and a shortage of 20 frames will occur if the demand is 70. Thus the
stockout costs for zero safety stock are:
The following table summarizes the total costs for each of the three alternatives:
110 frames short21.221$40 per stockout216 possible stockouts per year2
+ 120 frames short21.121$402162 = $960
–
701= 50 + 202
Safety
Stock
Additional
Holding Cost Stockout Cost
Total
Cost
20 (20) ($5) = $100 $ 0 $100
10 (10) ($5) = $ 50 (10) (.1) ($40) (6) = $240 $290
0 $ 0 (10) (.2) ($40) (6) + (20) (.1) ($40) (6) = $960 $960
The safety stock with the lowest total cost is 20 frames. Therefore, this safety stock changes the reorder
point to frames.
INSIGHT � The optical company now knows that a safety stock of 20 frames will be the most eco-
nomical decision.
LEARNING EXERCISE � David Rivera’s holding cost per frame is now estimated to be $20,
while the stockout cost is $30 per frame. Does the reorder point change? [Answer: Safety stock = 10
now, with a total cost of $380, which is the lowest of the three. ROP = 60 frames.]
RELATED PROBLEMS � 12.29, 12.30, 12.31
50 + 20 = 70
When it is difficult or impossible to determine the cost of being out of stock, a manager may
decide to follow a policy of keeping enough safety stock on hand to meet a prescribed customer
service level. For instance, Figure 12.8 shows the use of safety stock when demand (for hospital
resuscitation kits) is probabilistic. We see that the safety stock in Figure 12.8 is 16.5 units, and
the reorder point is also increased by 16.5.
The manager may want to define the service level as meeting 95% of the demand (or,
conversely, having stockouts only 5% of the time). Assuming that demand during lead time
(the reorder period) follows a normal curve, only the mean and standard deviation are
needed to define the inventory requirements for any given service level. Sales data are usu-
ally adequate for computing the mean and standard deviation. In the following example
we use a normal curve with a known mean ( ) and standard deviation ( ) to determine
the reorder point and safety stock necessary for a 95% service level. We use the following
formula:
(12-13)
where
sdLT = Standard deviation of demand during lead time
Z = Number of standard deviations
ROP = Expected demand during lead time + ZsdLT
sm

Chapter 12 Inventory Management 395
ROP = 350 + safety stock of 16.5 = 366.5
Expected demand during lead time (350 kits)
Safety stock
Normal distribution probability of
demand during lead time
Mean demand during lead time
Maximum demand during lead time
Minimum demand during lead time
ROP
(reorder point)
In
ve
n
to
ry

le
ve
l
Lead
time
Time
0
Receive
order
Place
order
16.5 units
Risk of stockout
� FIGURE 12.8
Probabilistic Demand
for a Hospital Item
Expected number of kits
needed during lead time is
350, but for a 95% service
level, the reorder point should
be raised to 366.5.
Memphis Regional Hospital stocks a “code blue” resuscitation kit that has a normally distributed
demand during the reorder period. The mean (average) demand during the reorder period is 350 kits,
and the standard deviation is 10 kits. The hospital administrator wants to follow a policy that results in
stockouts only 5% of the time.
(a) What is the appropriate value of Z? (b) How much safety stock should the hospital maintain? (c)
What reorder point should be used?
APPROACH � The hospital determines how much inventory is needed to meet the demand 95%
of the time. The figure in this example may help you visualize the approach. The data are as follows:
Z = Number of standard normal deviations
sdLT = Standard deviation of demand during lead time = 10 kits
m = Mean demand = 350 kits
Mean
demand
350
ROP = ? kits Quantity
0 z
Safety
stock
Number of
standard deviations
Risk of a stockout
(5% of area of
normal curve)
Probability of
no stockout
95% of the time
SOLUTION �
a. We use the properties of a standardized normal curve to get a Z-value for an area under the normal
curve of .95 (or ). Using a normal table (see Appendix I), we find a Z-value of 1.65 standard
deviations from the mean.
b. Because:
and:
then: (12-14)Safety stock = ZsdLT
Z =
x – m
sdLT
Safety stock = x – m
1 – .05
AUTHOR COMMENT
Recall that the service
level is 1 minus the risk
of a stockout.
� EXAMPLE 11
Safety stock with
probabilistic
demand

396 PART 3 Managing Operations
Solving for safety stock, as in Equation (12-14), gives:
This is the situation illustrated in Figure 12.8.
c. The reorder point is:
INSIGHT � The cost of the inventory policy increases dramatically (exponentially) with an
increase in service levels.
LEARNING EXERCISE � What policy results in stockouts 10% of the time? [Answer:
safety stock ROP kits.]
RELATED PROBLEMS � 12.27, 12.28, 12.40
= 363= 12.8;Z = 1.28;
= 350 kits + 16.5 kits of safety stock = 366.5, or 367 kits
ROP = Expected demand during lead time + Safety stock
Safety stock = 1.651102 = 16.5 kits
Other Probabilistic Models
Equations (12-13) and (12-14) assume that both an estimate of expected demand during lead
times and its standard deviation are available. When data on lead time demand are not at hand,
these formulas cannot be applied. However, three other models are available. We need to deter-
mine which model to use for three situations:
1. Demand is variable and lead time is constant
2. Lead time is variable, and demand is constant
3. Both demand and lead time are variable
All three models assume that demand and lead time are independent variables. Note that our
examples use days, but weeks can also be used. Let us examine these three situations separately,
because a different formula for the ROP is needed for each.
Demand Is Variable and Lead Time Is Constant When only the demand is variable, then:
(12-15)
where
and sd = Standard deviation of demand per day
sdLT = Standard deviation of demand during lead time = sd2Lead time
ROP = 1Average daily demand * Lead time in days2 + ZsdLT
LO7: Understand service
levels and probabilistic
inventory models
The average daily demand for Apple iPods at a Circuit Town store is 15, with a standard deviation of 5
units. The lead time is constant at 2 days. Find the reorder point if management wants a 90% service
level (i.e., risk stockouts only 10% of the time). How much of this is safety stock?
APPROACH � Apply Equation (12-15) to the following data:
Average daily demand (normally distributed)
Lead time in days (constant)
Standard deviation of daily demand
Service level
SOLUTION � From the normal table (Appendix I), we derive a Z-value for 90% of 1.28. Then:
Thus, safety stock is about 9 iPods.
INSIGHT � The value of Z depends on the manager’s stockout risk level. The smaller the risk, the
higher the Z.
LEARNING EXERCISE � If the Circuit Town manager wants a 95% service level, what is the
new ROP? [Answer: , or 42.]
RELATED PROBLEM � 12.32
ROP = 41.63
= 30 + 1.2815211.412 = 30 + 9.02 = 39.02 � 39
= 30 + 1.281521222
ROP = 115 units * 2 days2 + Zsd2Lead time
= 90%
= sd = 5
= 2
= 15
EXAMPLE 12 �
ROP for variable
demand and
constant lead time

Chapter 12 Inventory Management 397
Lead Time Is Variable and Demand Is Constant When the demand is constant and only
the lead time is variable, then:
(12-16)
where sLT = Standard deviation of lead time in days
ROP = 1Daily demand * Average lead time in days2 + Z1Daily demand2 * sLT
� EXAMPLE 13
ROP for constant
demand and
variable lead
time
The Circuit Town store in Example 12 sells about 10 digital cameras a day (almost a constant quantity).
Lead time for camera delivery is normally distributed with a mean time of 6 days and a standard devi-
ation of 3 days. A 98% service level is set. Find the ROP.
APPROACH � Apply Equation (12-16) to the following data:
Daily demand
Average lead time days
Standard deviation of lead time days
Service level so Z (from Appendix I)
SOLUTION � From the equation we get:
The reorder point is about 122 cameras.
INSIGHT � Note how the very high service level of 98% drives the ROP up.
LEARNING EXERCISE � If a 90% service level is applied, what does the ROP drop to?
[Answer: since the Z-value is only 1.28.]
RELATED PROBLEM � 12.33
ROP = 60 + 11.2821102132 = 60 + 38.4 = 98.4,
= 60 + 61.65 = 121.65
ROP = 110 units * 6 days2 + 2.055110 units2132
= 2.055= 98%,
= sLT = 3
= 6
= 10
Both Demand and Lead Time Are Variable When both the demand and lead time are
variable, the formula for reorder point becomes more complex4:
(12-17)
where
and sdLT = 21Average lead time * sd 22 + 1Average daily demand22sLT 2
sLT = Standard deviation of lead time in days
sd = Standard deviation of demand per day
ROP = 1Average daily demand * Average lead time2 + ZsdLT
4Refer to S. Narasimhan, D. W. McLeavey, and P. Billington, Production Planning and Inventory Control, 2nd ed.
(Upper Saddle River, NJ: Prentice Hall, 1995), Chap. 6, for details. Note that Equation (12-17) can also be expressed as
ROP = Average daily demand * Average lead time + Z21Average lead time * sd 22 + d
2
sLT
2 .
The Circuit Town store’s most popular item is six-packs of 9-volt batteries. About 150 packs are sold
per day, following a normal distribution with a standard deviation of 16 packs. Batteries are ordered
from an out-of-state distributor; lead time is normally distributed with an average of 5 days and a stan-
dard deviation of 1 day. To maintain a 95% service level, what ROP is appropriate?
APPROACH � Determine a quantity at which to reorder by applying Equation (12-17) to the fol-
lowing data:
Average daily demand packs
Standard deviation of demand packs
Average lead time days
Standard deviation of lead time day
Service level so (from Appendix I)
SOLUTION � From the equation we compute:
ROP = 1150 packs * 5 days2 + 1.65 sdLT
Z = 1.65= 95%,
= sLT = 1
= 5
= sd = 16
= 150
� EXAMPLE 14
ROP for variable
demand and
variable lead
time

398 PART 3 Managing Operations
where
So
INSIGHT � When both demand and lead time are variable, the formula looks quite complex. But
it is just the result of squaring the standard deviations in Equations (12-15) and (12-16) to get their vari-
ances, then summing them, and finally taking the square root.
LEARNING EXERCISE � For an 80% service level, what is the ROP? [Answer: and
packs.]
RELATED PROBLEM � 12.34
ROP = 879
Z = .84
ROP = 1150 * 52 + 1.6511542 � 750 + 254 = 1,004 packs
= 21,280 + 22,500 = 223,780 � 154
= 215 * 2562 + 122,500 * 12
sdLT = 215 days * 1622 + 11502 * 12)
SINGLE-PERIOD MODEL
A single-period inventory model describes a situation in which one order is placed for a
product. At the end of the sales period, any remaining product has little or no value. This is a
typical problem for Christmas trees, seasonal goods, bakery goods, newspapers, and maga-
zines. (Indeed, this inventory issue is often called the “newsstand problem.”) In other words,
even though items at a newsstand are ordered weekly or daily, they cannot be held over and
used as inventory in the next sales period. So our decision is how much to order at the begin-
ning of the period.
Because the exact demand for such seasonal products is never known, we consider a probabil-
ity distribution related to demand. If the normal distribution is assumed, and we stocked and sold
an average (mean) of 100 Christmas trees each season, then there is a 50% chance we would
stock out and a 50% chance we would have trees left over. To determine the optimal stocking
policy for trees before the season begins, we also need to know the standard deviation and con-
sider these two marginal costs:
The service level, that is, the probability of not stocking out, is set at:
(12-18)
Therefore, we should consider increasing our order quantity until the service level is less than or
equal to the ratio of
This model, illustrated in Example 15, is used in many service industries, from hotels to air-
lines to bakeries to clothing retailers.
3Cs>(Cs + Co).4
Service level =
Cs
Cs + Co
Co = Cost of overage 1we overestimated2 = Cost>unit – Salvage value>unit 1if there is any2
Cs = Cost of shortage 1we underestimated2 = Sales price>unit – Cost>unit
Single-period inventory
model
A system for ordering items that
have little or no value at the end
of a sales period.
Chris Ellis’s newsstand, just outside the Smithsonian subway station in Washington, DC, usually sells
120 copies of the Washington Post each day. Chris believes the sale of the Post is normally distributed,
with a standard deviation of 15 papers. He pays 70 cents for each paper, which sells for $1.25. The Post
gives him a 30-cent credit for each unsold paper. He wants to determine how many papers he should
order each day and the stockout risk for that quantity.
APPROACH � Chris’s data are as follows:
Chris will apply Equation (12-18) and the normal table, using and s = 15.m = 120
Co = cost of overage = $.70 – $.30 1salvage value2 = $.40
Cs = cost of shortage = $1.25 – $.70 = $.55
EXAMPLE 15 �
Single-period
inventory decision

Chapter 12 Inventory Management 399
SOLUTION �
a) Service level =
b) Chris needs to find the Z score for his normal distribution that yields a probability of .578.
Cs
Cs + Co
=
.55
.55 + .40
=
.55
.95
= .578
� = 120
� = 15 copies
Service
level
57.8%
Optimal stocking level
So 57.8% of the area under the normal curve must be to the left of the optimal stocking level.
c) Using Appendix I5, for an area of .578, the Z value
The stockout risk if Chris orders 123 copies of the Post each day is 1 – service level = 1 – .578 =
.422 = 42.2%.
INSIGHT � If the service level is ever under .50, Chris should order fewer than 120 copies per day.
LEARNING EXERCISE � How does Chris’s decision change if the Post changes its policy and
offers no credit for unsold papers, a policy many publishers are adopting?
[Answer: Service level . Therefore, stock or 118 papers].
RELATED PROBLEMS � 12.36, 12.37 12.38
120 + 1- .1521152 = 117.75= .44, Z = – .15
= 120 + 1.2021152 = 120 + 3 = 123 papers
Then, the optimal stocking level = 120 copies + 1.2021s2
� .20.
5Alternatively, Microsoft Excel’s NORMSINV (probability) function can be applied.
6Some in OM call these continuous review systems.
FIXED-PERIOD (P ) SYSTEMS
The inventory models that we have considered so far are fixed-quantity, or Q, systems. That is,
the same fixed amount is added to inventory every time an order for an item is placed. We saw
that orders are event triggered. When inventory decreases to the reorder point (ROP), a new order
for Q units is placed.
To use the fixed-quantity model, inventory must be continuously monitored.6 This requires a
perpetual inventory system. Every time an item is added to or withdrawn from inventory,
records must be updated to determine whether the ROP has been reached.
In a fixed-period system (also called a periodic review, or P system), on the other hand,
inventory is ordered at the end of a given period. Then, and only then, is on-hand inventory
counted. Only the amount necessary to bring total inventory up to a prespecified target level (T )
is ordered. Figure 12.9 illustrates this concept.
AUTHOR COMMENT
A fixed-period model orders
a different quantity each time.
Fixed-quantity (Q)
system
An ordering system with the
same order amount each time.
Perpetual inventory
system
A system that keeps track of
each withdrawal or addition to
inventory continuously, so
records are always current.
Fixed-period (P) system
A system in which inventory
orders are made at regular time
intervals.
P
Q1
Q2
Q 3
Q4
P
P
O
n
-h
a
n
d
in
ve
n
to
ry
Time
Target quantity (T )
� FIGURE 12.9
Inventory Level in a
Fixed-Period (P) System
Various amounts (Q1, Q2, Q3,
etc.) are ordered at regular
time intervals (P ) based on the
quantity necessary to bring
inventory up to the target
quantity (T ).

400 PART 3 Managing Operations
Fixed-period systems have several of the same assumptions as the basic EOQ fixed-quantity
system:
• The only relevant costs are the ordering and holding costs.
• Lead times are known and constant.
• Items are independent of one another.
The downward-sloped lines in Figure 12.9 again represent on-hand inventory levels. But now,
when the time between orders (P) passes, we place an order to raise inventory up to the target quan-
tity (T). The amount ordered during the first period may be the second period and so on.
The value is the difference between current on-hand inventory and the target inventory level.
The advantage of the fixed-period system is that there is no physical count of inventory items
after an item is withdrawn—this occurs only when the time for the next review comes up. This
procedure is also convenient administratively.
A fixed-period system is appropriate when vendors make routine (i.e., at fixed-time interval)
visits to customers to take fresh orders or when purchasers want to combine orders to save order-
ing and transportation costs (therefore, they will have the same review period for similar inven-
tory items). For example, a vending machine company may come to refill its machines every
Tuesday. This is also the case at Anheuser-Busch, whose sales reps may visit a store every 5 days
(see the OM in Action box “66,207,896 Bottles of Beer on the Wall”).
The disadvantage of the P system is that because there is no tally of inventory during the
review period, there is the possibility of a stockout during this time. This scenario is possible if a
large order draws the inventory level down to zero right after an order is placed. Therefore, a
higher level of safety stock (as compared to a fixed-quantity system) needs to be maintained to
provide protection against stockout during both the time between reviews and the lead time.
Qi
Q2,Q1,
When Dereck Gurden pulls up at one of his customers’
stores—7-Eleven, Buy N Save, or one of dozens of liquor
marts and restaurants in the 800-square-mile territory he
covers in California’s Central Valley—managers usually stop
what they’re doing and grab a note pad. This is because, as
Gurden claims, “I know more about these guys’ businesses
than they do . . . at least in the beer section.”
What makes Gurden and other sales reps for Anheuser-
Busch distributors so smart? It’s BudNet, the King of Beer’s
top-secret crown jewel—a nationwide data network through
which drivers and reps report, in excruciating detail, on
sales, shelf space, inventory, and displays at thousands of
stores. How does it work? As Gurden walks a store, he
inputs what he sees to his handheld PC, then plugs into a
cell phone and fires off new orders, along with the data he
has gathered. Anheuser has made a deadly accurate
science of finding out what beer lovers are buying, as
well as when, where, and why.
Matching these data with
U.S. census figures of
neighborhoods, Anheuser
mines data down to the sales at
individual stores. The company
can pinpoint age, ethnicity,
education, political, and sexual
orientation of customers at your
local 7-Eleven. BudNet is the primary reason Anheuser’s
share of the $75 billion U.S. beer market continues to
increase, and the company has posted double-digit profit
gains for 20 straight quarters while its competitors have
flat-lined.
Sources: Business 2.0 (January/February 2004): 47–49; Beverage
Industry (May 2004): 20–23; and The Wall Street Journal (March 23,
2004): C3.
OM in Action � 66,207,896 Bottles of Beer on the Wall
CHAPTER SUMMARY
Inventory represents a major investment for many firms. This
investment is often larger than it should be because firms find
it easier to have “just-in-case” inventory rather than “just-in-
time” inventory. Inventories are of four types:
1. Raw material and purchased components
2. Work-in-process
3. Maintenance, repair, and operating (MRO)
4. Finished goods
In this chapter, we discussed
independent inventory, ABC analy-
sis, record accuracy, cycle counting,
and inventory models used to con-
trol independent demands. The EOQ
model, production order quantity
model, and quantity discount model can all be solved using
Excel, Excel OM, or POM for Windows software.

Chapter 12 Inventory Management 401
Key Terms
Raw material inventory (p. 375)
Work-in-process (WIP) inventory (p. 375)
MRO (p. 375)
Finished-goods inventory (p. 375)
ABC analysis (p. 375)
Cycle counting (p. 377)
Shrinkage (p. 379)
Pilferage (p. 379)
Holding cost (p. 380)
Ordering cost (p. 380)
Setup cost (p. 380)
Setup time (p. 380)
Economic order quantity (EOQ) model
(p. 381)
Robust (p. 385)
Lead time (p. 386)
Reorder point (ROP) (p. 386)
Safety stock (p. 387)
Production order quantity model (p. 388)
Quantity discount (p. 390)
Probabilistic model (p. 393)
Service level (p. 393)
Single-period inventory model (p. 398)
Fixed-quantity (Q) system (p. 399)
Perpetual inventory system (p. 399)
Fixed-period (P) system (p. 399)
Using Software to Solve Inventory Problems
This section presents three ways to solve inventory problems with computer software. First, you can create your own Excel
spreadsheets. Second, you can use the Excel OM software that comes with this text and is found on our website. Third, POM for
Windows, also on our website at www.pearsonhighered.com/heizer, can solve all problems marked with a P.
� PROGRAM 12.1 Using Excel for a Production Model, with Data from Example 8
Creating Your Own Excel Spreadsheets
Program 12.1 illustrates how you can make an Excel model to solve Example 8 (p. 389). This is
a production order quantity model. A listing of the formulas needed to create the spreadsheet is
shown.
=SQRT(2*B3*B4/B5)*SQRT
(B6/(B6-B7))
=B12*(B6-B7)/B6
=B18+B19+B21
=B13/2
=B3/B12
=B8/B15
=B14*B5
=B15*B4
=B9*B3
X Using Excel OM
Excel OM allows us to easily model inventory problems ranging from ABC analysis, to the basic EOQ
model, to the production model, to quantity discount situations.
Program 12.2 shows the input data, selected formulas, and results for an ABC analysis, using data
from Example 1 (on p. 376). After the data are entered, we use the Data and Sort Excel commands to
rank the items from largest to smallest dollar volumes.

www.pearsonhighered.com/heizer

402 PART 3 Managing Operations
The cumulative dollar volumes in column
G make sense only after the items have
been sorted by dollar volume. Either use
the copy and sort button, or, to sort by
hand, highlight cells A7 through E17 and
then use Data, Sort from Excel 2007
Ribbon or Excel 2003 menu.
Calculate the total dollar
volume for each item. = B8*C8
Calculate the percentage of
the grand total dollar volume
for each item. = E8/E18
= SUM(E8:E17)
= SUM($F$8:F8)
Enter the item
name or number,
its sales volume,
and the unit cost
in columns A, B,
and C.
� PROGRAM 12.2 Using Excel OM for an ABC Analysis, with Data from Example 1
Solved Problems Virtual Office help is available at www.myomlab.com
� SOLVED PROBLEM 12.1
David Alexander has compiled the following table of six items in
inventory at Angelo Products, along with the unit cost and the
annual demand in units:
� SOLUTION
The item that needs strict control is 33CP, so it is an A item. Items
that do not need to be strictly controlled are 3CPO, R2D2, and
RMS; these are C items. The B items will be XX1 and B66.
Identification
Code Unit Cost ($)
Annual
Demand (units)
XX1 5.84 1,200
B66 5.40 1,110
3CPO 1.12 896
33CP 74.54 1,104
R2D2 2.00 1,110
RMS 2.08 961
P Using POM for Windows
The POM for Windows Inventory module can also solve the entire EOQ family of problems. Please refer
to Appendix IV for further details.
Code
Annual dollar volume
= Unit Cost � Demand
XX1 $ 7,008.00
B66 $ 5,994.00
3CPO $ 1,003.52
33CP $ 82,292.16
R2D2 $ 2,220.00
RMS $ 1,998.88
Total cost = $100,516.56
70% of total cost = $70,347.92
� SOLVED PROBLEM 12.2
The Warren W. Fisher Computer Corporation purchases 8,000
transistors each year as components in minicomputers. The unit
cost of each transistor is $10, and the cost of carrying one transis-
tor in inventory for a year is $3. Ordering cost is $30 per order.
What are (a) the optimal order quantity, (b) the expected
number of orders placed each year, and (c) the expected time
between orders? Assume that Fisher operates on a 200-day work-
ing year.
� SOLUTION
a)
b)
c)
With 20 orders placed each year, an order for 400 transistors is placed every 10 working days.
Time between orders = T =
Number of working days
N
=
200
20
= 10 working days
N =
D
Q*
=
8,000
400
= 20 orders
Q* =
A
2DS
H
= A
218,00021302
3
= 400 units
Use ABC analysis to determine which item(s) should be carefully
controlled using a quantitative inventory technique and which
item(s) should not be closely controlled.

www.myomlab.com

Chapter 12 Inventory Management 403
� SOLVED PROBLEM 12.3
Annual demand for notebook binders at Meyer’s Stationery Shop
is 10,000 units. Brad Meyer operates his business 300 days per
year and finds that deliveries from his supplier generally take 5
working days. Calculate the reorder point for the notebook binders.
� SOLUTION
Thus, Brad should reorder when his stock reaches 167 units.
= 166.7 units
ROP = d * L = 133.3 units per day215 days2
d =
10,000
300
= 33.3 units per day
L = 5 days
� SOLVED PROBLEM 12.4
Leonard Presby, Inc., has an annual demand rate of 1,000 units but
can produce at an average production rate of 2,000 units. Setup
cost is $10; carrying cost is $1. What is the optimal number of
units to be produced each time?
� SOLUTION
=
A
20,000
1>2
= 240,000 = 200 units
Q*p =
Q
2DS
H¢1 – Annual demand rate
Annual production rate
≤ = A 211,00021102131 – 11,000>2,00024
� SOLVED PROBLEM 12.5
Whole Nature Foods sells a gluten-free product for which the
annual demand is 5,000 boxes. At the moment, it is paying $6.40
for each box; carrying cost is 25% of the unit cost; ordering costs
are $25. A new supplier has offered to sell the same item for $6.00
if Whole Nature Foods buys at least 3,000 boxes per order. Should
the firm stick with the old supplier, or take advantage of the new
quantity discount?
� SOLUTION
Under present price of $6.40 per box:
Economic order quantity, using Equation (12-10):
where D = period demand
S = ordering cost
P = price per box
I = holding cost as percent
H = holding cost = IP
= 395.3, or 395 boxes
Q* = A
215,00021252
10.25216.402
Q* =
A
2DS
IP
Note: Order and carrying costs are rounded.
Under the quantity discount price of $6.00 per box:
We compute which is below the required
order level of 3,000 boxes. So Q* is adjusted to 3,000.
Therefore, the new supplier with which Whole Nature Foods would
incur a total cost of $32,292 is preferable, but not by a large amount. If
buying 3,000 boxes at a time raises problems of storage or freshness,
the company may very well wish to stay with the current supplier.
= $32,292
= 42 + 2,250 + 30,000
=
15,00021252
3,000
+
13,000210.25216.002
2
+ 16.00215,0002
=
DS
Q
+
Q
2
H + PD
Total cost = Ordering cost + Holding cost + Purchase cost
Q* = 408.25,
= $32,632
= 316 + 316 + 32,000
=
15,00021252
395
+
13952(0.25)16.402
2
+ 16.40215,0002
=
DS
Q
+
Q
2
H + PD
Total cost = Order cost + Holding cost + Purchase cost
� SOLVED PROBLEM 12.6
Children’s art sets are ordered once each year by Ashok Kumar,
Inc., and the reorder point, without safety stock (dL) is 100 art
sets. Inventory carrying cost is $10 per set per year, and the cost of
a stockout is $50 per set per year. Given the following demand
probabilities during the lead time, how much safety stock should
be carried?

404 PART 3 Managing Operations
� SOLVED PROBLEM 12.8
The daily demand for 52′′ plasma TVs at Sarah’s Discount
Emporium is normally distributed, with an average of 5 and a stan-
dard deviation of 2 units. The lead time for receiving a shipment of
new TVs is 10 days and is fairly constant. Determine the reorder
point and safety stock for a 95% service level.
Demand during Lead Time Probability
0 .1
50 .2
ROP : 100 .4
150 .2
200 .1
1.0
Incremental Costs
Safety Stock Carrying Cost Stockout Cost Total Cost
0 0 50 * 150 * 0.2 + 100 * 0.12 = 1,000 $1,000
50 50 * 10 = 500 50 * 10.1 * 502 = 250 750
100 100 * 10 = 1,000 0 1,000
� SOLUTION
The safety stock that minimizes total incremental cost is 50 sets. The reorder point then becomes
or 150 sets.100 sets + 50 sets,
� SOLVED PROBLEM 12.7
What safety stock should Ron Satterfield Corporation maintain if mean sales are 80 during the reorder period, the standard deviation is
7, and Ron can tolerate stockouts 10% of the time?
� = 80
�dLT = 7
10% area under the normal curve
Safety
stock
From Appendix I, Z at an area of .9 (or 1 – .10) = 1.28, and Equation (12-14):
= 1.28172 = 8.96 units, or 9 units
Safety stock = ZsdLT
� SOLUTION
The ROP for this variable demand and constant lead time model uses Equation (12-15):
where
So, with
The safety stock is 10.4, or about 10 TVs.
= 50 + 10.4 = 60.4 � 60 TVs
ROP = 15 * 102 + 1.65122210
Z = 1.65,
sdLT = sd2Lead time
ROP = 1Average daily demand * Lead time in days2 + ZsdLT
� SOLUTION

Chapter 12 Inventory Management 405
� SOLVED PROBLEM 12.9
The demand at Arnold Palmer Hospital for a specialized surgery
pack is 60 per week, virtually every week. The lead time from
McKesson, its main supplier, is normally distributed, with a mean
of 6 weeks for this product and a standard deviation of 2 weeks. A
90% weekly service level is desired. Find the ROP.
� SOLUTION
Here the demand is constant and lead time is variable, with data given in weeks, not days. We apply
Equation (12-16):
where
So, with for a 90% service level:
= 360 + 153.6 = 513.6 � 514 surgery packs
ROP = 160 * 62 + 1.281602122
Z = 1.28,
sLT = standard deviation of lead time in weeks = 2
ROP = 1Weekly demand * Average lead time in weeks2 + Z 1Weekly demand2sLT
�Additional Case Studies: Visit www.myomlab.com or www.pearsonhighered.com/heizer for these free case studies:
Southwestern University (F): The university must decide how many football day programs to order, and from whom.
LaPlace Power and Light: This utility company is evaluating its current inventory policies.
Bibliography
Abernathy, Frederick H., et al. “Control Your Inventory in a World
of Lean Retailing.” Harvard Business Review 78, no. 6
(November–December 2000): 169–176.
Arnold, J. R., S. N. Chapman, and L. M. Clive. Introduction to
Materials Management, 6th ed. Upper Saddle River, NJ:
Prentice Hall (2008).
Bradley, James R., and Richard W. Conway. “Managing Cyclic
Inventories.” Production and Operations Management 12,
no. 4 (Winter 2003): 464–479.
Burt, D. N., S. Petcavage, and R. Pinkerton. Supply Management,
8th ed. Burr Ridge, IL: Irwin/McGraw (2010).
Chapman, Stephen. Fundamentals of Production Planning and
Control. Upper Saddle River, NJ: Prentice Hall (2006).
Chopra, Sunil, Gilles Reinhardt, and Maqbool Dada. “The Effect
of Lead Time Uncertainty on Safety Stocks.” Decision
Sciences 35, no. 1 (Winter 2004): 1–24.
Keren, Baruch. “The Single Period Inventory Model.” Omega 37,
no. 4 (August 2009): 801.
Liu, X., and Z. Lian. “Cost-effective Inventory Control in a Value-
added Manufacturing System.” European Journal of
Operational Research 196, no. 2 (July 2009): 534.
McDonald, Stan C. Materials Management. New York: Wiley (2009).
Noblitt, James M. “The Economic Order Quantity Model: Panacea
or Plague?” APICS—The Performance Advantage (February
2001): 53–57.
Render, B., R. M. Stair, and M. Hanna. Quantitative Analysis for
Management, 11th ed. Upper Saddle River, NJ: Prentice Hall
(2011).
Rubin, Paul A., and W. C. Benton. “A Generalized Framework for
Quantity Discount Pricing Schedules.” Decision Sciences 34,
no. 1 (Winter 2003): 173–188.
Vollmann, T. E., W. L. Berry, D. C. Whybark, and F. R. Jacobs.
Manufacturing Planning and Control for Supply Chain
Management, 5th ed. Burr Ridge, IL: Irwin/McGraw (2005).
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Material Handling Management 60, no. 8 (August 2005): 24–25.

www.myomlab.com

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Aggregate Planning
Chapter Outline
GLOBAL COMPANY PROFILE: FRITO-LAY
The Planning Process 410
The Nature of Aggregate Planning 411
Aggregate Planning Strategies 412
Methods for Aggregate Planning 415
Aggregate Planning in Services 422
Yield Management 425
� Design of Goods and Services
� Managing Quality
� Process Strategy
� Location Strategies
� Layout Strategies
� Human Resources
� Supply-Chain Management
� Inventory Management
� Scheduling
� Aggregate
� Short-Term
� Maintenance
407

GLOBAL COMPANY PROFILE: FRITO-LAY
AGGREGATE PLANNING PROVIDES A COMPETITIVE ADVANTAGE AT FRITO-LAY
L
ike other organizations throughout the world,
Frito-Lay relies on effective aggregate
planning to match fluctuating multi-billion-
dollar demand to capacity in its 36 North
American plants. Planning for the intermediate term (3
to 18 months) is the heart of aggregate planning.
Effective aggregate planning combined with tight
scheduling, effective maintenance, and efficient
employee and facility scheduling are the keys to high
plant utilization. High utilization is a critical factor in
facilities such as Frito-Lay where capital investment
is substantial.
Frito-Lay has more than three dozen brands of
snacks and chips, 15 of which sell more than $100
million annually and 7 of which sell over $1 billion. Its
brands include such well-known names as Fritos,
Lay’s, Doritos, Sun Chips, Cheetos, Tostitos, Flat
Earth, and Ruffles. Unique processes using specially
designed equipment are required to produce each of
these products. Because these specialized processes
generate high fixed cost, they must operate at very
high volume. But such product-focused facilities
benefit by having low variable costs. High utilization
and performance above the break-even point require a
good match between demand and capacity. Idle
equipment is disastrous.
At Frito-Lay’s headquarters near Dallas, planners
create a total demand profile. They use historical
product sales, forecasts of new products, product
innovations, product promotions, and dynamic local
demand data from account managers to forecast
demand. Planners then match the total demand profile
to existing capacity, capacity expansion plans, and
cost. This becomes the aggregate plan. The aggregate
plan is communicated to each of the firm’s 17 regions
and to the 36 plants. Every quarter, headquarters and
each plant modify the respective plans to incorporate
changing market conditions and plant performance.
Each plant uses its quarterly plan to develop a
4-week plan, which in turn assigns specific products to
specific product lines for production runs. Finally, each
week raw materials and labor are assigned to each
process. Effective aggregate planning is a major factor
in high utilization and low cost. As the company’s 60%
market share indicates, excellent aggregate planning
yields a competitive advantage at Frito-Lay.
The aggregate plan adjusts for farm
location, yield, and quantities for
timely delivery of Frito-Lay’s unique
varieties of potatoes. During harvest
times, potatoes go directly to the
plant. During non-harvest months,
potatoes are stored in climate-
controlled environments to maintain
quality, texture, and taste.
408

As potatoes arrive at the plant, they are promptly washed and peeled to ensure freshness and taste.
After peeling, potatoes are cut into thin slices,
rinsed of excess starch, and cooked in
sunflower and/or corn oil.
After cooking is complete, inspection,
bagging, weighing, and packing
operations prepare Lay’s potato chips
for shipment to customers—all in a matter
of hours.
FRITO-LAY �
409

LO1: Define aggregate planning 410
LO2: Identify optional strategies for
developing an aggregate plan 412
LO3: Prepare a graphical aggregate plan 416
THE PLANNING PROCESS
Manufacturers such as Frito-Lay, Anheuser-Busch, GE, and Yamaha face tough decisions when
trying to schedule products such as snack foods, beer, air conditioners, and jet skis, the demand
for which is heavily dependent on seasonal variation. Developing plans that minimize costs con-
nected with such forecasts is aggregate planning, one of the main functions of an operations
manager. Aggregate planning (also known as aggregate scheduling) is concerned with deter-
mining the quantity and timing of production for the intermediate future, often from 3 to 18
months ahead. Operations managers try to determine the best way to meet forecasted demand by
adjusting production rates, labor levels, inventory levels, overtime work, subcontracting rates,
and other controllable variables. Usually, the objective of aggregate planning is to meet fore-
casted demand while minimizing cost over the planning period. However, other strategic issues
may be more important than low cost. These strategies may be to smooth employment levels, to
drive down inventory levels, or to meet a high level of service.
For manufacturers, the aggregate schedule ties the firm’s strategic goals to production plans,
but for service organizations, the aggregate schedule ties strategic goals to workforce schedules.
Four things are needed for aggregate planning:
• A logical overall unit for measuring sales and output, such as pounds of Doritos at Frito-Lay,
air-conditioning units at GE, or cases of beer at Anheuser-Busch
• A forecast of demand for a reasonable intermediate planning period in these aggregate terms
• A method for determining the relevant costs
• A model that combines forecasts and costs so that scheduling decisions can be made for the
planning period
In this chapter we describe the aggregate planning decision, show how the aggregate plan fits
into the overall planning process, and describe several techniques that managers use when devel-
oping an aggregate plan. We stress both manufacturing and service-sector firms.
Planning Horizons
In Chapter 4, we saw that demand forecasting can address short-, medium-, and long-range prob-
lems. Long-range forecasts help managers deal with capacity and strategic issues and are the
responsibility of top management (see Figure 13.1). Top management formulates policy-related
questions, such as facility location and expansion, new product development, research funding,
and investment over a period of several years.
Medium-range planning begins once long-term capacity decisions are made. This is the job of
the operations manager. Scheduling decisions address the problem of matching productivity to fluc-
tuating demands. These plans need to be consistent with top management’s long-range strategy and
work within the resources allocated by earlier strategic decisions. Medium- (or “intermediate-”)
range planning is accomplished by building an aggregate production plan.
Short-range planning may extend up to a year but is usually less than 3 months. This plan is
also the responsibility of operations personnel, who work with supervisors and foremen to “dis-
aggregate” the intermediate plan into weekly, daily, and hourly schedules. Tactics for dealing
with short-term planning involve loading, sequencing, expediting, and dispatching, which are
discussed in Chapter 15.
Figure 13.1 illustrates the time horizons and features for short-, intermediate-, and long-range
planning.
Aggregate planning (or
aggregate scheduling)
An approach to determine the
quantity and timing of
production for the intermediate
future (usually 3 to 18 months
ahead).
LO1: Define aggregate
planning
LO4: Solve an aggregate plan via the
transportation method of linear
programming 421
LO5: Understand and solve a yield
management problem 426
AUTHOR COMMENT
Idle capacity is expensive,
and inadequate capacity
loses customers.
Scheduling decisions
Plans that match production
to changes in demand.
410 PART 3 Managing Operations
Chapter 13 Learning Objectives

Chapter 13 Aggregate Planning 411
Long-range plans (over one year)
Capacity decisions (Supplement 7) are critical to
long-range plans.
Research and Development
New product plans
Capital investments
Facility location/expansion
Top
executives
Operations
managers
Operations
managers,
supervisors,
foremen
Responsibility Planning tasks and time horizons
Short-range plans (up to 3 months)
The scheduling techniques (Chapter
15) help managers prepare short-
range plans.
Job assignments
Ordering
Job scheduling
Dispatching
Overtime
Part-time help
Intermediate-range plans (3 to 18 months)
The aggregate planning techniques of this
chapter aid managers in building intermediate-
range plans.
Sales planning
Production planning and budgeting
Setting employment, inventory,
subcontracting levels
Analyzing operating plans
� FIGURE 13.1
Planning Tasks and
Responsibilities
THE NATURE OF AGGREGATE PLANNING
As the term aggregate implies, an aggregate plan means combining appropriate resources into
general, or overall, terms. Given demand forecast, facility capacity, inventory levels, workforce
size, and related inputs, the planner has to select the rate of output for a facility over the next 3 to
18 months. The plan can be for firms such as Frito-Lay and Whirlpool, hospitals, colleges, or
Prentice Hall, the company that published this textbook.
Take, for a manufacturing example, Snapper, which produces many different models of lawn
mowers. It makes walk-behind mowers, rear-engine riding mowers, garden tractors, and many
more, for a total of 145 models. For each month in the upcoming 3 quarters, the aggregate plan
for Snapper might have the following output (in units of production) for Snapper’s “family” of
mowers:
Quarter 1 Quarter 2 Quarter 3
Jan. Feb. March April May June July Aug. Sept.
150,000 120,000 110,000 100,000 130,000 150,000 180,000 150,000 140,000
AUTHOR COMMENT
Aggregate plans are
formulated in a variety of
units, such as pounds of
Fritos, tons of steel, or
number of students.
AUTHOR COMMENT
If long-term planning is done
poorly, problems will develop
that make the aggregate
planner’s job very tough.
Operations personnel build an aggregate plan using the total expected demand for all of the family products, such as 145 models
at Snapper (a few of which are shown above). Only when the forecasts are assembled in the aggregate plan does the company
decide how to meet the total requirement with the available resources. These resource constraints include facility capacity,
workforce size, supply-chain limitations, inventory issues, and financial resources.

412 PART 3 Managing Operations
Every bright red Snapper lawn mower sold anywhere in
the world comes from a factory in McDonough, Georgia.
Ten years ago, the Snapper line had about 40 models of
mowers, leaf blowers, and snow blowers. Today, reflecting
the demands of mass customization, the product line is
much more complex. Snapper designs, manufactures, and
sells 145 models. This means that aggregate planning and
the related short-term scheduling have become more
complex, too.
In the past, Snapper met demand by carrying a huge
inventory for 52 regional distributors and thousands of
independent dealerships. It manufactured and shipped tens
of thousands of lawn mowers, worth tens of millions of
dollars, without quite knowing when they would be sold—
a very expensive approach to meeting demand. Some
changes were necessary. The new plan’s goal is for each
distribution center to receive only the minimum inventory
necessary to meet demand. Today, operations managers
at Snapper evaluate production capacity and use frequent
data from the field as inputs to sophisticated software
to forecast sales. The new system tracks customer
demand and aggregates forecasts for every model in
every region of the country. It even adjusts for holidays
and weather. And the number of distribution centers has
been cut from 52 to 4.
Once evaluation of the aggregate plan against capacity
determines the plan to be feasible, Snapper’s planners
break down the plan into production needs for each model.
Production by model is accomplished by building rolling
monthly and weekly plans. These plans track the pace at
which various units are selling. Then, the final step requires
juggling work assignments to various work centers for each
shift, such as 265 lawn mowers in an 8-hour shift. That’s a
new Snapper every 109 seconds.
Sources: Fair Disclosure Wire (January 17, 2008); The Wall Street Journal
(July 14, 2006): B1, B6; Fast Company (January/February 2006): 67–71;
and www.snapper.com.
OM in Action � Building the Plan at Snapper
Note that the plan looks at production in the aggregate (the family of mowers), not as a product-
by-product breakdown. Likewise, an aggregate plan for BMW tells the auto manufacturer how
many cars to make but not how many should be two-door vs. four-door or red vs. green. It tells
Nucor Steel how many tons of steel to produce but does not differentiate grades of steel. (We
extend the discussion of planning at Snapper in the OM in Action box “Building the Plan at
Snapper.”)
Aggregate planning is part of a larger production planning system. Therefore, understanding
the interfaces between the plan and several internal and external factors is useful. Figure 13.2
shows that the operations manager not only receives input from the marketing department’s
demand forecast, but must also deal with financial data, personnel, capacity, and availability of
raw materials. In a manufacturing environment, the process of breaking the aggregate plan
down into greater detail is called disaggregation. Disaggregation results in a master produc-
tion schedule, which provides input to material requirements planning (MRP) systems. The
master production schedule addresses the purchasing or production of parts or components
needed to make final products (see Chapter 14). Detailed work schedules for people and prior-
ity scheduling for products result as the final step of the production planning system (and are
discussed in Chapter 15).
AGGREGATE PLANNING STRATEGIES
When generating an aggregate plan, the operations manager must answer several questions:
1. Should inventories be used to absorb changes in demand during the planning period?
2. Should changes be accommodated by varying the size of the workforce?
3. Should part-timers be used, or should overtime and idle time absorb fluctuations?
4. Should subcontractors be used on fluctuating orders so a stable workforce can be maintained?
5. Should prices or other factors be changed to influence demand?
All of these are legitimate planning strategies. They involve the manipulation of inventory,
production rates, labor levels, capacity, and other controllable variables. We will now exam-
ine eight options in more detail. The first five are called capacity options because they do not
try to change demand but attempt to absorb demand fluctuations. The last three are demand
options through which firms try to smooth out changes in the demand pattern over the plan-
ning period.
AUTHOR COMMENT
Managers can meet aggregate
plans by adjusting either
capacity or demand.
LO2: Identify optional
strategies for developing an
aggregate plan
Disaggregation
The process of breaking an
aggregate plan into greater
detail.
Master production
schedule
A timetable that specifies what
is to be made and when.

www.snapper.com

Chapter 13 Aggregate Planning 413
Capacity Options
A firm can choose from the following basic capacity (production) options:
1. Changing inventory levels: Managers can increase inventory during periods of low demand
to meet high demand in future periods. If this strategy is selected, costs associated with stor-
age, insurance, handling, obsolescence, pilferage, and capital invested will increase. On the
other hand, with low inventory on hand and increasing demand, shortages can occur, result-
ing in longer lead times and poor customer service.
2. Varying workforce size by hiring or layoffs: One way to meet demand is to hire or lay off
production workers to match production rates. However, new employees need to be
trained, and productivity drops temporarily as they are absorbed into the workforce.
Layoffs or terminations, of course, lower the morale of all workers and also lead to lower
productivity.
3. Varying production rates through overtime or idle time: Keeping a constant workforce while
varying working hours may be possible. Yet when demand is on a large upswing, there is a
limit on how much overtime is realistic. Overtime pay increases costs and too much over-
time can result in worker fatigue and a drop in productivity. Overtime also implies added
overhead costs to keep a facility open. On the other hand, when there is a period of
decreased demand, the company must somehow absorb workers’ idle time—often a difficult
and expensive process.
4. Subcontracting: A firm can acquire temporary capacity by subcontracting work during peak
demand periods. Subcontracting, however, has several pitfalls. First, it may be costly; sec-
ond, it risks opening the door to a competitor. Third, developing the perfect subcontract sup-
plier can be a challenge.
5. Using part-time workers: Especially in the service sector, part-time workers can fill labor
needs. This practice is common in restaurants, retail stores, and supermarkets.
Product
decisions
(Ch. 5)
1st
Qtr
D
e
m
a
n
d
2nd
Qtr
3rd
Qtr
4th
Qtr
Demand forecasts, orders
(Ch.4)
Process planning
and
capacity
decisions
(Ch. 7 and S7)
Marketplace and demand
Master
production
schedule and
MRP systems
(Ch.14)
Detailed
work
schedules
(Ch.15)
Aggregate plan
for production
Research and technology
Workforce (Ch.10)
Inventory on hand (Ch.12)
Supply-chain support (Ch.11)
External capacity (subcontractors)
� FIGURE 13.2 Relationships of an Aggregate Plan

414 PART 3 Managing Operations
Demand Options
The basic demand options are:
1. Influencing demand: When demand is low, a company can try to increase demand through
advertising, promotion, personal selling, and price cuts. Airlines and hotels have long
offered weekend discounts and off-season rates; telephone companies charge less at night;
some colleges give discounts to senior citizens; and air conditioners are least expensive in
winter. However, even special advertising, promotions, selling, and pricing are not always
able to balance demand with production capacity.
2. Back ordering during high-demand periods: Back orders are orders for goods or services
that a firm accepts but is unable (either on purpose or by chance) to fill at the moment. If
customers are willing to wait without loss of their goodwill or order, back ordering is a pos-
sible strategy. Many firms back order, but the approach often results in lost sales.
3. Counterseasonal product and service mixing: A widely used active smoothing technique
among manufacturers is to develop a product mix of counterseasonal items. Examples
include companies that make both furnaces and air conditioners or lawn mowers and
snowblowers. However, companies that follow this approach may find themselves
involved in products or services beyond their area of expertise or beyond their target
market.
These eight options, along with their advantages and disadvantages, are summarized in Table 13.1.
Mixing Options to Develop a Plan
Although each of the five capacity options and three demand options discussed above may pro-
duce an effective aggregate schedule, some combination of capacity options and demand options
may be better.
Many manufacturers assume that the use of the demand options has been fully explored by the
marketing department and those reasonable options incorporated into the demand forecast.
The operations manager then builds the aggregate plan based on that forecast. However, using
the five capacity options at his command, the operations manager still has a multitude of possi-
ble plans. These plans can embody, at one extreme, a chase strategy and, at the other, a level-
scheduling strategy. They may, of course, fall somewhere in between.
Chase Strategy A chase strategy typically attempts to achieve output rates for each period
that match the demand forecast for that period. This strategy can be accomplished in a variety of
ways. For example, the operations manager can vary workforce levels by hiring or laying off or
John Deere and Company, the “granddaddy” of
farm equipment manufacturers, uses sales
incentives to smooth demand. During the fall and
winter off-seasons, sales are boosted with price
cuts and other incentives. About 70% of Deere’s
big machines are ordered in advance of seasonal
use—about double the industry rate. Incentives
hurt margins, but Deere keeps its market share
and controls costs by producing more steadily
all year long. Similarly, in service businesses
like L.L. Bean, some customers are offered
free shipping on orders placed before the
Christmas rush.
Chase strategy
A planning strategy that sets
production equal to forecasted
demand.

Chapter 13 Aggregate Planning 415
can vary production by means of overtime, idle time, part-time employees, or subcontracting.
Many service organizations favor the chase strategy because the changing inventory levels option
is difficult or impossible to adopt. Industries that have moved toward a chase strategy include
education, hospitality, and construction.
Level Strategy A level strategy (or level scheduling) is an aggregate plan in which produc-
tion is uniform from period to period. Firms like Toyota and Nissan attempt to keep production
at uniform levels and may (1) let the finished-goods inventory vary to buffer the difference
between demand and production or (2) find alternative work for employees. Their philosophy is
that a stable workforce leads to a better-quality product, less turnover and absenteeism, and more
employee commitment to corporate goals. Other hidden savings include employees who are
more experienced, easier scheduling and supervision, and fewer dramatic startups and shut-
downs. Level scheduling works well when demand is reasonably stable.
For most firms, neither a chase strategy nor a level strategy is likely to prove ideal, so a com-
bination of the eight options (called a mixed strategy) must be investigated to achieve minimum
cost. However, because there are a huge number of possible mixed strategies, managers find that
aggregate planning can be a challenging task. Finding the one “optimal” plan is not always pos-
sible, but as we will see in the next section, a number of techniques have been developed to aid
the aggregate planning process.
METHODS FOR AGGREGATE PLANNING
In this section, we introduce several techniques that operations managers use to develop aggre-
gate plans. They range from the widely used graphical method to a series of more formal mathe-
matical approaches, including the transportation method of linear programming.
Graphical Methods
Graphical techniques are popular because they are easy to understand and use. These plans
work with a few variables at a time to allow planners to compare projected demand with existing
capacity. They are trial-and-error approaches that do not guarantee an optimal production plan,
� TABLE 13.1 Aggregate Planning Options: Advantages and Disadvantages
Option Advantages Disadvantages Comments
Changing inventory
levels
Changes in human resources
are gradual or none; no abrupt
production changes.
Inventory holding costs may
increase. Shortages may
result in lost sales.
Applies mainly to production,
not service, operations.
Varying workforce size
by hiring or layoffs
Avoids the costs of other
alternatives.
Hiring, layoff, and training
costs may be significant.
Used where size of labor pool
is large.
Varying production rates
through overtime or
idle time
Matches seasonal fluctuations
without hiring/training costs.
Overtime premiums; tired
workers; may not meet
demand.
Allows flexibility within the
aggregate plan.
Subcontracting Permits flexibility and smoothing
of the firm’s output.
Loss of quality control; reduced
profits; loss of future business.
Applies mainly in production
settings.
Using part-time workers Is less costly and more flexible
than full-time workers.
High turnover/training costs;
quality suffers; scheduling
difficult.
Good for unskilled jobs in
areas with large temporary
labor pools.
Influencing demand Tries to use excess capacity.
Discounts draw new
customers.
Uncertainty in demand. Hard
to match demand to supply
exactly.
Creates marketing ideas.
Overbooking used in some
businesses.
Back ordering during
high-demand periods
May avoid overtime. Keeps
capacity constant.
Customer must be willing to
wait, but goodwill is lost.
Many companies back order.
Counterseasonal product
and service mixing
Fully utilizes resources;
allows stable workforce.
May require skills or equipment
outside firm’s areas of
expertise.
Risky finding products or
services with opposite
demand patterns.
Level scheduling
Maintaining a constant output
rate, production rate, or
workforce level over the
planning horizon.
AUTHOR COMMENT
Managers must commit to
employment levels, material
purchases, and inventory
levels; aggregate plans help
managers do that.
Mixed strategy
A planning strategy that uses
two or more controllable
variables to set a feasible
production plan.
Graphical techniques
Aggregate planning techniques
that work with a few variables at
a time to allow planners to
compare projected demand with
existing capacity.

EXAMPLE 1 �
Graphical approach
to aggregate
planning for a
roofing supplier
A Juarez, Mexico, manufacturer of roofing supplies has developed monthly forecasts for a family of
products. Data for the 6-month period January to June are presented in Table 13.2. The firm would like
to begin development of an aggregate plan.
Month Expected Demand Production Days
Demand per Day
(computed)
Jan. 900 22 41
Feb. 700 18 39
Mar. 800 21 38
Apr. 1,200 21 57
May 1,500 22 68
June 1,100
6,200
20
124
55
416 PART 3 Managing Operations
but they require only limited computations and can be performed by clerical staff. Following are
the five steps in the graphical method:
1. Determine the demand in each period.
2. Determine capacity for regular time, overtime, and subcontracting each period.
3. Find labor costs, hiring and layoff costs, and inventory holding costs.
4. Consider company policy that may apply to the workers or to stock levels.
5. Develop alternative plans and examine their total costs.
These steps are illustrated in Examples 1 through 4.
LO3: Prepare a graphical
aggregate plan
�TABLE 13.2
Monthly Forecasts
APPROACH � Plot daily and average demand to illustrate the nature of the aggregate planning
problem.
SOLUTION � First, compute demand per day by dividing the expected monthly demand by the
number of production days (working days) each month and drawing a graph of those forecasted
demands (Figure 13.3). Second, draw a dotted line across the chart that represents the production rate
required to meet average demand over the 6-month period. The chart is computed as follows:
Average requirement =
Total expected demand
Number of production days
=
6,200
124
= 50 units per day
Level production, using average
monthly forecast demand
Jan.
22
Forecast demand
70
60
50
40
30
0
P
ro
d
u
ct
io
n
r
a
te
p
e
r
w
o
rk
in
g
d
a
y
Feb.
18
Mar.
21
Apr.
21
May
22
June
20
Month
Number of
working days
=
=
� FIGURE 13.3
Graph of Forecast and
Average Forecast Demand
INSIGHT � Changes in the production rate become obvious when the data are graphed. Note that
in the first 3 months, expected demand is lower than average, while expected demand in April, May,
and June is above average.
LEARNING EXERCISE � If demand for June increases to 1,200 (from 1,100), what is the
impact on Figure 13.3? [Answer: The daily rate for June will go up to 60, and average production will
increase to 50.8 ( ).]
RELATED PROBLEM � 13.1
6,300>124

Chapter 13 Aggregate Planning 417
The graph in Figure 13.3 illustrates how the forecast differs from the average demand. Some
strategies for meeting the forecast were listed earlier. The firm, for example, might staff in order
to yield a production rate that meets average demand (as indicated by the dashed line). Or it
might produce a steady rate of, say, 30 units and then subcontract excess demand to other roof-
ing suppliers. Other plans might combine overtime work with subcontracting to absorb demand.
Examples 2 to 4 illustrate three possible strategies.
One possible strategy (call it plan 1) for the manufacturer described in Example 1 is to maintain a con-
stant workforce throughout the 6-month period. A second (plan 2) is to maintain a constant workforce
at a level necessary to meet the lowest demand month (March) and to meet all demand above this level
by subcontracting. Both plan 1 and plan 2 have level production and are, therefore, called level strate-
gies. Plan 3 is to hire and lay off workers as needed to produce exact monthly requirements—a chase
strategy. Table 13.3 provides cost information necessary for analyzing these three alternatives:
Inventory carrying cost $ 5 per unit per month
Subcontracting cost per unit $ 20 per unit
Average pay rate $ 10 per hour ($80 per day)
Overtime pay rate $ 17 per hour (above 8 hours per day)
Labor-hours to produce a unit 1.6 hours per unit
Cost of increasing daily production rate
(hiring and training)
$300 per unit
Cost of decreasing daily production rate (layoffs) $600 per unit
� TABLE 13.3
Cost Information
ANALYSIS OF PLAN 1. APPROACH � Here we assume that 50 units are produced per
day and that we have a constant workforce, no overtime or idle time, no safety stock, and no subcon-
tractors. The firm accumulates inventory during the slack period of demand, January through March,
and depletes it during the higher-demand warm season, April through June. We assume beginning
inventory and planned ending inventory
SOLUTION � We construct the table below and accumulate the costs:
= 0.= 0
Month
Production
Days
Production at
50 Units per Day
Demand
Forecast
Monthly
Inventory
Change
Ending
Inventory
Jan. 22 1,100 900 + 200 200
Feb. 18 900 700 + 200 400
Mar. 21 1,050 800 + 250 650
Apr. 21 1,050 1,200 – 150 500
May 22 1,100 1,500 – 400 100
June 20 1,000 1,100 –100 0
1,850
Because each unit requires 1.6 labor-hours to produce, each worker can make 5 units in an 8-hour day.
Therefore, to produce 50 units, 10 workers are needed.
Finally, the costs of plan 1 are computed as follows:
Workforce required to produce 50 units per day = 10 workers
Total units of inventory carried over from one month to the next month = 1,850 units
Cost Calculations
Inventory carrying $ 9,250 per unit)(= 1,850 units carried * $5
Regular-time labor 99,200 days)(= 10 workers * $80 per day * 124
Other costs (overtime, hiring,
layoffs, subcontracting)
Total cost
0
$108,450
� EXAMPLE 2
Plan 1 for the
roofing supplier—a
constant workforce

418 PART 3 Managing Operations
INSIGHT � Note the significant cost of carrying the inventory.
LEARNING EXERCISE � If demand for June decreases to 1,000 (from 1,100), what is the
change in cost? [Answer: Total inventory carried will increase to 1,950 at $5, for an inventory cost of
$9,750 and total cost of $108,950]
RELATED PROBLEMS � 13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 13.10, 13.11, 13.12, 13.19
EXCEL OM Data File Ch13Ex2.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 13.1 This example is further illustrated in Active Model 13.1 at www.pearsonhighered.com/heizer.
The graph for Example 2 was shown in Figure 13.3. Some planners prefer a cumulative graph to
display visually how the forecast deviates from the average requirements. Such a graph is pro-
vided in Figure 13.4. Note that both the level production line and the forecast line produce the
same total production.
7,000
C
u
m
u
la
tiv
e
d
e
m
a
n
d
u
n
its
6,000
5,000
4,000
3,000
2,000
1,000
Jan. Feb. Mar. Apr. May June
Month
Cumulative forecast
requirements
Cumulative level of production,
using average monthly
forecast requirements
Excess inventory
Reduction
of inventory
6,200 units
� FIGURE 13.4
Cumulative Graph for Plan 1
AUTHOR COMMENT
We saw another way to graph
this data in Figure 13.3.
ANALYSIS OF PLAN 2. APPROACH � Although a constant workforce is also maintained
in plan 2, it is set low enough to meet demand only in March, the lowest demand-per-day month. To
produce 38 units per day (800/21) in-house, 7.6 workers are needed. (You can think of this as 7 full-
time workers and 1 part-timer.) All other demand is met by subcontracting. Subcontracting is thus
required in every other month. No inventory holding costs are incurred in plan 2.
SOLUTION � Because 6,200 units are required during the aggregate plan period, we must com-
pute how many can be made by the firm and how many must be subcontracted:
The costs of plan 2 are computed as follows:
Subcontract units = 6,200 – 4,712 = 1,488 units
= 4,712 units
In-house production = 38 units per day * 124 production days
Cost Calculations
Regular-time labor $ 75,392 days)(= 7.6 workers * $80 per day * 124
Subcontracting
Total cost
29,760
$105,152
per unit)(= 1,488 units * $20
EXAMPLE 3 �
Plan 2 for the roofing
supplier—use of
subcontractors within
a constant workforce

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www.pearsonhighered.com/heizer

Chapter 13 Aggregate Planning 419
INSIGHT � Note the lower cost of regular labor but the added subcontracting cost.
LEARNING EXERCISE � If demand for June increases to 1,200 (from 1,100), what is the
change in cost? [Answer: Subcontracting requirements increase to 1,588 at $20 per unit, for a subcon-
tracting cost of $31,760 and a total cost of $107,152.]
RELATED PROBLEMS � 13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 13.10, 13.11, 13.12, 13.19
� EXAMPLE 4
Plan 3 for the
roofing supplier—
hiring and layoffs
ANALYSIS OF PLAN 3. APPROACH � The final strategy, plan 3, involves varying the
workforce size by hiring and layoffs as necessary. The production rate will equal the demand, and there
is no change in production from the previous month, December.
SOLUTION � Table 13.4 shows the calculations and the total cost of plan 3. Recall that it costs
$600 per unit produced to reduce production from the previous month’s daily level and $300 per unit
change to increase the daily rate of production through hirings.
Thus, the total cost, including production, hiring, and layoff, for plan 3 is $117,800.
INSIGHT � Note the substantial cost associated with changing (both increasing and decreasing)
the production levels.
LEARNING EXERCISE � If demand for June increases to 1,200 (from 1,100), what is the
change in cost? [Answer: Daily production for June is 60 units, which is a decrease of 8 units in the
daily production rate from May’s 68 units, so the new June layoff cost is with a
total plan 3 cost of $114,800.]
RELATED PROBLEMS � 13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 13.10, 13.11, 13.12, 13.19
$4,800 1= 8 * $6002,
The final step in the graphical method is to compare the costs of each proposed plan and to select
the approach with the least total cost. A summary analysis is provided in Table 13.5. We see that
because plan 2 has the lowest cost, it is the best of the three options.
Cost
Plan 1
(constant
workforce of
10 workers)
Plan 2
(workforce of
7.6 workers plus
subcontract)
Plan 3
(hiring and
layoffs to
meet demand)
Inventory carrying $ 9,250 $ 0 $ 0
Regular labor 99,200 75,392 99,200
Overtime labor 0 0 0
Hiring 0 0 9,000
Layoffs 0 0 9,600
Subcontracting 0 29,760 0
Total cost $108,450 $105,152 $117,800
� TABLE 13.5
Comparison of the
Three Plans
� TABLE 13.4
Cost Computations for Plan 3
Month
Forecast
(units)
Daily
Production
Rate
Basic
Production
Cost
(demand 1.6
hr per unit
$10 per hr)
:
:
Extra
Cost of
Increasing
Production
(hiring cost)
Extra
Cost of
Decreasing
Production
(layoff cost)
Total
Cost
Jan. 900 41 $14,400 — — $ 14,400
Feb. 700 39 11,200 — $1,200 )(= 2 * $600 12,400
Mar. 800 38 12,800 — $ 600 )(= 1 * $600 13,400
Apr. 1,200 57 19,200 $5,700 (= 19 * $300) — 24,900
May 1,500 68 24,000 $3,300 (= 11 * $300) — 27,300
June 1,100 55 17,600 — $7,800 ( = 13 * $600) $ 25,400
$99,200 $9,000 $9,600 $117,800

420 PART 3 Managing Operations
Of course, many other feasible strategies can be considered in a problem like this, including
combinations that use some overtime. Although graphing is a popular management tool, its help
is in evaluating strategies, not generating them. To generate strategies, a systematic approach that
considers all costs and produces an effective solution is needed.
Mathematical Approaches
This section briefly describes some of the mathematical approaches to aggregate planning.
The Transportation Method of Linear Programming When an aggregate planning
problem is viewed as one of allocating operating capacity to meet forecasted demand, it can be
formulated in a linear programming format. The transportation method of linear program-
ming is not a trial-and-error approach like graphing but rather produces an optimal plan for min-
imizing costs. It is also flexible in that it can specify regular and overtime production in each
time period, the number of units to be subcontracted, extra shifts, and the inventory carryover
from period to period.
In Example 5, the supply consists of on-hand inventory and units produced by regular time,
overtime, and subcontracting. Costs per unit, in the upper-right corner of each cell of the matrix
in Table 13.7, relate to units produced in a given period or units carried in inventory from an ear-
lier period.
EXAMPLE 5 �
Aggregate planning
with the transportation
method
Sales Period
Mar. Apr. May
Demand 800 1,000 750
Capacity:
Regular 700 700 700
Overtime 50 50 50
Subcontracting 150 150 130
Beginning inventory 100 tires
Costs
Regular time $40 per tire
Overtime $50 per tire
Subcontract $70 per tire
Carrying cost $ 2 per tire per month
Farnsworth Tire Company would like to develop an aggregate plan via the transportation method.
Data that relate to production, demand, capacity, and cost at its West Virginia plant are shown in
Table 13.6.
� TABLE 13.6
Farnsworth’s Production,
Demand, Capacity, and
Cost Data
APPROACH � Solve the aggregate planning problem by minimizing the costs of matching pro-
duction in various periods to future demands.
SOLUTION � Table 13.7 illustrates the structure of the transportation table and an initial feasible
solution.
Transportation method
of linear programming
A way of solving for the optimal
solution to an aggregate
planning problem.

Chapter 13 Aggregate Planning 421
� TABLE 13.7
Farnsworth’s Transportation
Tablea
aCells with an x indicate that back orders are not used at Farnsworth. When using Excel OM or POM for Windows to solve,
you must insert a very high cost (e.g., 9999) in each cell that is not used for production.
When setting up and analyzing this table, you should note the following:
1. Carrying costs are $2/tire per month. Tires produced in 1 period and held for 1 month will have a
$2 higher cost. Because holding cost is linear, 2 months’ holdover costs $4. So when you move
across a row from left to right, regular time, overtime, and subcontracting costs are lowest when
output is used the same period it is produced. If goods are made in one period and carried over to
the next, holding costs are incurred. Beginning inventory, however, is generally given a unit cost of
0 if it is used to satisfy demand in period 1.
2. Transportation problems require that supply equals demand; so, a dummy column called “unused
capacity” has been added. Costs of not using capacity are zero.
3. Because back ordering is not a viable alternative for this particular company, no production is pos-
sible in those cells that represent production in a period to satisfy demand in a past period (i.e.,
those periods with an “X”). If back ordering is allowed, costs of expediting, loss of goodwill, and
loss of sales revenues are summed to estimate backorder cost.
4. Quantities in red in each column of Table 13.7 designate the levels of inventory needed to meet
demand requirements (shown in the bottom row of the table). Demand of 800 tires in March is met
by using 100 tires from beginning inventory and 700 tires from regular time.
5. In general, to complete the table, allocate as much production as you can to a cell with the small-
est cost without exceeding the unused capacity in that row or demand in that column. If there is
still some demand left in that row, allocate as much as you can to the next-lowest-cost cell. You
then repeat this process for periods 2 and 3 (and beyond, if necessary). When you are finished, the
sum of all your entries in a row must equal the total row capacity, and the sum of all entries in a
column must equal the demand for that period. (This step can be accomplished by the transporta-
tion method or by using POM for Windows or Excel OM software.)
Try to confirm that the cost of this initial solution is $105,900. The initial solution is not optimal,
however. See if you can find the production schedule that yields the least cost (which turns out to be
$105,700) using software or by hand.
LO4: Solve an aggregate
plan via the transportation
method of linear
programming
DEMAND FOR
TOTAL
Unused CAPACITY
Period 1 Period 2 Period 3 Capacity AVAILABLE
SUPPLY FROM (Mar.) (Apr.) (May) (dummy) (supply)
0 2 4 0
Beginning inventory 100 100
40 42 44 0
Regular time 700 700
50 52 54 0
Overtime 50 50
70 72 74 0
Subcontract 150 150
40 42 0
Regular time � 700 700
50 52 0
Overtime � 50 50
70 72 0
Subcontract � 50 100 150
40 0
Regular time � � 700 700
50 0
Overtime � � 50 50
70 0
Subcontract � � 130 130
TOTAL DEMAND 800 1,000 750 230 2,780
P
e
r
i
o
d
1
P
e
r
i
o
d
2
P
e
r
i
o
d
3

422 PART 3 Managing Operations
The transportation method of linear programming described in the above example was originally
formulated by E. H. Bowman in 1956. Although it works well in analyzing the effects of holding
inventories, using overtime, and subcontracting, it does not work when nonlinear or negative fac-
tors are introduced. Thus, when other factors such as hiring and layoffs are introduced, the more
general method of linear programming must be used.
Management Coefficients Model Bowman’s management coefficients model1 builds a
formal decision model around a manager’s experience and performance. The assumption is that
the manager’s past performance is pretty good; therefore, it can be used as a basis for future deci-
sions. The technique uses a regression analysis of past production decisions made by managers.
The regression line provides the relationship between variables (such as demand and labor) for
future decisions. According to Bowman, managers’ deficiencies are mostly inconsistencies in
decision making.
Other Models Two additional aggregate planning models are the linear decision rule and
simulation. The linear decision rule (LDR) attempts to specify an optimum production rate and
workforce level over a specific period. It minimizes the total costs of payroll, hiring, layoffs,
overtime, and inventory through a series of quadratic cost curves.2
A computer model called scheduling by simulation uses a search procedure to look for the
minimum-cost combination of values for workforce size and production rate.
Comparison of Aggregate Planning Methods
Although these mathematical models have been found by researchers to work well under cer-
tain conditions, and linear programming has found some acceptance in industry, the fact is
that most sophisticated planning models are not widely used. Why? Perhaps it reflects the
average manager’s attitude about what he or she views as overly complex models. Like all of
us, planners like to understand how and why the models on which they are basing important
decisions work. Additionally, operations managers need to make decisions quickly based on
the changing dynamics of the competitive environment—and building good models is time-
consuming. This may explain why the simpler graphical approach is more generally
accepted.
Table 13.8 highlights some of the main features of graphing, transportation, management
coefficients, and simulation planning models.
AGGREGATE PLANNING IN SERVICES
Some service organizations conduct aggregate planning in exactly the same way as we did in
Examples 1 through 5 in this chapter, but with demand management taking a more active role.
Because most services pursue combinations of the eight capacity and demand options discussed
Management
coefficients model
A formal planning model built
around a manager’s experience
and performance.
1E. H. Bowman, “Consistency and Optimality in Managerial Decision Making,” Management Science 9, no. 2 (January
1963): 310–321.
2Because LDR was developed by Charles C. Holt, Franco Modigliani, John F. Muth, and Herbert Simon, it is popularly
known as the HMMS rule. For details, see Martin K. Starr, Production and Operations Management (Cincinnati, OH:
Atomic Dog Publishing, 2004): 490–493.
INSIGHT � The transportation method is flexible when costs are linear but does not work when
costs are nonlinear.
LEARNING EXAMPLE � What is the impact on this problem if there is no beginning inven-
tory? [Answer: Total capacity (units) available is reduced by 100 units and the need to subcontract
increases by 100 units.]
RELATED PROBLEMS � 13.13, 13.14, 13.15, 13.16, 13.17, 13.18
EXCEL OM Data File Ch13Ex5.xls can be found at www.pearsonhighered.com/heizer.
AUTHOR COMMENT
The major variable in capacity
management for services
is labor.

www.pearsonhighered.com/heizer

Chapter 13 Aggregate Planning 423
Technique
Solution
Approaches Important Aspects
Graphical methods Trial and error Simple to understand and easy to use. Many solutions;
one chosen may not be optimal.
Transportation method
of linear programming
Optimization LP software available; permits sensitivity analysis
and new constraints; linear functions may not be
realistic.
Management coefficients
model
Heuristic Simple, easy to implement; tries to mimic
manager’s decision process; uses regression.
Simulation Change
parameters
Complex; model may be difficult to build and
for managers to understand.
earlier, they usually formulate mixed aggregate planning strategies. In industries such as bank-
ing, trucking, and fast foods, aggregate planning may be easier than in manufacturing.
Controlling the cost of labor in service firms is critical. Successful techniques include:
1. Accurate scheduling of labor-hours to assure quick response to customer demand
2. An on-call labor resource that can be added or deleted to meet unexpected demand
3. Flexibility of individual worker skills that permits reallocation of available labor
4. Flexibility in rate of output or hours of work to meet changing demand
These options may seem demanding, but they are not unusual in service industries, in which
labor is the primary aggregate planning vehicle. For instance:
• Excess capacity is used to provide study and planning time by real estate and auto salespersons.
• Police and fire departments have provisions for calling in off-duty personnel for major emer-
gencies. Where the emergency is extended, police or fire personnel may work longer hours
and extra shifts.
• When business is unexpectedly light, restaurants and retail stores send personnel home early.
• Supermarket stock clerks work cash registers when checkout lines become too lengthy.
• Experienced waitresses increase their pace and efficiency of service as crowds of customers
arrive.
Approaches to aggregate planning differ by the type of service provided. Here we discuss five
service scenarios.
� TABLE 13.8
Summary of Four Major
Aggregate Planning Methods
The heavy demands of the
December holiday season
place a special burden on
aggregate planning at
UPS. UPS maximizes
truck and plane resource
availability for the season,
as well as overtime and
temporary workers to
match capacity to
demand.

424 PART 3 Managing Operations
Restaurants
In a business with a highly variable demand, such as a restaurant, aggregate scheduling is
directed toward (1) smoothing the production rate and (2) finding the optimal size of the work-
force. The general approach usually requires building very modest levels of inventory during
slack periods and depleting inventory during peak periods, but using labor to accommodate most
of the changes in demand. Because this situation is very similar to those found in manufacturing,
traditional aggregate planning methods may be applied to services as well. One difference that
should be noted is that even modest amounts of inventory may be perishable. In addition, the rel-
evant units of time may be much smaller than in manufacturing. For example, in fast-food restau-
rants, peak and slack periods may be measured in fractions of an hour and the “product” may be
inventoried for as little as 10 minutes.
Hospitals
Hospitals face aggregate planning problems in allocating money, staff, and supplies to meet the
demands of patients. Michigan’s Henry Ford Hospital, for example, plans for bed capacity and
personnel needs in light of a patient-load forecast developed by moving averages. The necessary
labor focus of its aggregate plan has led to the creation of a new floating staff pool serving each
nursing pod.
National Chains of Small Service Firms
With the advent of national chains of small service businesses such as funeral homes, oil change
outlets, and photocopy/printing centers, the question of aggregate planning versus independent
planning at each business establishment becomes an issue. Both purchases and production
capacity may be centrally planned when demand can be influenced through special promotions.
This approach to aggregate scheduling is often advantageous because it reduces costs and helps
manage cash flow at independent sites.
Miscellaneous Services
Most “miscellaneous” services—financial, transportation, and many communication and recre-
ation services—provide intangible output. Aggregate planning for these services deals mainly
with planning for human resource requirements and managing demand. The twofold goal is to
level demand peaks and to design methods for fully utilizing labor resources during low-demand
periods. Example 6 illustrates such a plan for a legal firm.
EXAMPLE 6 �
Aggregate planning
in a law firm
Klasson and Avalon, a medium-size Tampa law firm of 32 legal professionals, wants to develop an
aggregate plan for the next quarter. The firm has developed 3 forecasts of billable hours for the next
quarter for each of 5 categories of legal business it performs (column 1, Table 13.9). The 3 forecasts
(best, likely, and worst) are shown in columns 2, 3, and 4 of Table 13.9.
Labor-Hours Required Capacity Constraints
(1) (2) (3) (4) (5)
Maximum
Demand in
People
(6)
Number of
Qualified
Personnel
Forecasts
Category of
Legal Business
Best
(hours)
Likely
(hours)
Worst
(hours)
Trial work 1,800 1,500 1,200 3.6 4
Legal research 4,500 4,000 3,500 9.0 32
Corporate law 8,000 7,000 6,500 16.0 15
Real estate law 1,700 1,500 1,300 3.4 6
Criminal law 3,500 3,000 2,500 7.0 12
Total hours 19,500 17,000 15,000
Lawyers needed 39 34 30
� TABLE 13.9
Labor Allocation at Klasson
and Avalon, Forecasts for
Coming Quarter (1 lawyer =
500 hours of labor)

Chapter 13 Aggregate Planning 425
APPROACH � If we make some assumptions about the workweek and skills, we can provide an
aggregate plan for the firm. Assuming a 40-hour workweek and that 100% of each lawyer’s hours are
billed, about 500 billable hours are available from each lawyer this fiscal quarter.
SOLUTION � We divide hours of billable time (which is the demand) by 500 to provide a count
of lawyers needed (lawyers represent the capacity) to cover the estimated demand. Capacity then is
shown to be 39, 34, and 30 for the three forecasts, best, likely, and worst, respectively. For example, the
best-case scenario of 19,500 total hours, divided by 500 hours per lawyer, equals 39 lawyers needed.
Because all 32 lawyers at Klasson and Avalon are qualified to perform basic legal research, this skill
has maximum scheduling flexibility (column 6). The most highly skilled (and capacity-constrained)
categories are trial work and corporate law. The firm’s best-case forecast just barely covers trial work,
with 3.6 lawyers needed (see column 5) and 4 qualified (column 6). And corporate law is short 1 full
person.
Overtime may be used to cover the excess this quarter, but as business expands, it may be necessary
to hire or develop talent in both of these areas. Available staff adequately covers real estate and crimi-
nal practice, as long as other needs do not use their excess capacity. With its current legal staff of 32,
Klasson and Avalon’s best-case forecast will increase the workload by [( 21.8%
(assuming no new hires). This represents 1 extra day of work per lawyer per week. The worst-case sce-
nario will result in about a 6% underutilization of talent. For both of these scenarios, the firm has deter-
mined that available staff will provide adequate service.
INSIGHT � While our definitions of demand and capacity are different than for a manufactur-
ing firm, aggregate planning is as appropriate, useful, and necessary in a service environment as in
manufacturing.
LEARNING EXERCISE � If the criminal law best-case forecast increases to 4,500 hours, what
happens to the number of lawyers needed? [Answer: The demand for lawyers increases to 41.]
RELATED PROBLEMS � 13.20, 13.21
Source: Based on Glenn Bassett, Operations Management for Service Industries (Westport, CT: Quorum Books,
1992): 110.
39 – 322>32 = ]
Airline Industry
Airlines and auto-rental firms also have unique aggregate scheduling problems. Consider an air-
line that has its headquarters in New York, two hub sites in cities such as Atlanta and Dallas, and
150 offices in airports throughout the country. This planning is considerably more complex than
aggregate planning for a single site or even for a number of independent sites.
Aggregate planning consists of tables or schedules for (1) number of flights in and out of each
hub; (2) number of flights on all routes; (3) number of passengers to be serviced on all flights;
(4) number of air personnel and ground personnel required at each hub and airport; and (5) deter-
mining the seats to be allocated to various fare classes. Techniques for determining seat alloca-
tion are called yield, or revenue, management, our next topic.
YIELD MANAGEMENT
Most operations models, like most business models, assume that firms charge all customers the
same price for a product. In fact, many firms work hard at charging different prices. The idea is
to match the demand curve by charging based on differences in the customer’s willingness to
pay. The management challenge is to identify those differences and price accordingly. The tech-
nique for multiple price points is called yield management.
Yield (or revenue) management is the aggregate planning process of allocating the com-
pany’s scarce resources to customers at prices that will maximize yield or revenue. Popular use
of the technique dates to the 1980s, when American Airlines’s reservation system (called
SABRE) allowed the airline to alter ticket prices, in real time and on any route, based on
demand information. If it looked like demand for expensive seats was low, more discounted
seats were offered. If demand for full-fare seats was high, the number of discounted seats was
reduced.
AUTHOR COMMENT
Yield management changes
the focus of aggregate
planning from capacity
management to demand
management.
Yield (or revenue)
management
Capacity decisions that
determine the allocation of
resources to maximize profit
or yield.

426 PART 3 Managing Operations
American Airlines’s success in yield management spawned many other companies and
industries to adopt the concept. Yield management in the hotel industry began in the late
1980s at Marriott International, which now claims an additional $400 million a year in profit
from its management of revenue. The competing Omni hotel chain uses software that per-
forms more than 100,000 calculations every night at each facility. The Dallas Omni, for
example, charges its highest rates on weekdays but heavily discounts on weekends. Its sister
hotel in San Antonio, which is in a more tourist-oriented destination, reverses this rating
scheme, with better deals for its consumers on weekdays. Similarly, Walt Disney World has
multiple prices: an annual admission pass for an adult was recently quoted at $421; but for a
Florida resident, $318; for a member of the AAA, $307; and for active-duty military, $385.
The OM in Action box “Yield Management at Hertz” describes this practice in the rental car
industry.
Organizations that have perishable inventory, such as airlines, hotels, car rental agencies,
cruise lines, and even electrical utilities, have the following shared characteristics that make
yield management of interest3:
1. Service or product can be sold in advance of consumption.
2. Demand fluctuates.
3. The resource (capacity) is relatively fixed.
4. Demand can be segmented.
5. Variable costs are low and fixed costs are high.
Example 7 illustrates how yield management works in a hotel.
LO5: Understand and
solve a yield management
problem
EXAMPLE 7 �
Yield management
The Cleveland Downtown Inn is a 100-room hotel that has historically charged one set price for its
rooms, $150 per night. The variable cost of a room being occupied is low. Management believes the
cleaning, air-conditioning, and incidental costs of soap, shampoo, and so forth, are $15 per room per
night. Sales average 50 rooms per night. Figure 13.5 illustrates the current pricing scheme. Net sales
are $6,750 per night with a single price point.
APPROACH � Analyze pricing from the perspective of yield management. We note in Figure 13.5 that
some guests would have been willing to spend more than $150 per room—“money left on the table.” Others
would be willing to pay more than the variable cost of $15 but less than $150—“passed-up contribution.”
3R. Oberwetter, “Revenue Management,” OR/MS Today (June 2001): 41–44.
For over 90 years, Hertz has been renting standard cars
for a fixed amount per day. During the past two decades,
however, a significant increase in demand has derived
from airline travelers flying for business purposes. As the
auto-rental market has changed and matured, Hertz has
offered more options, including allowing customers to pick
up and drop off in different locations. This option has
resulted in excess capacity in some cities and shortages
in others.
These shortages and overages alerted Hertz to the need
for a yield management system similar to those used in the
airline industry. The system is used to set prices, regulate
the movement, and ultimately determine the availability of
cars at each location. Through research, Hertz found that
different city locations peak on different days of the week.
So cars are moved to peak-demand locations from
locations where the demand is low. By altering both the
price and quantity
of cars at various
locations, Hertz has
been able to increase
“yield” and boost
revenue.
The yield
management system
is primarily used by
regional and local managers to better deal with changes
in demand in the U.S. market. Hertz’s plan to go global
with the system, however, faces major challenges in
foreign countries, where restrictions against moving
empty cars across national borders are common.
Sources: The Wall Street Journal (December 30, 2003): D1 and (March 3,
2000): W-4; and Cornell Hotel and Restaurant Quarterly (December 2001):
33–46.
OM in Action � Yield Management at Hertz

Chapter 13 Aggregate Planning 427
Passed-up
contribution
Money left
on the table
Demand curve
100
50
Potential customers exist who
are willing to pay more than the
$15 variable cost of the room,
but not $150.
Some customers who paid
$150 were actually willing
to pay more for the room.
Total $ contribution =
(Price) × (50 rooms) =
($150 � $15)(50) = $6,750
$15
Variable cost
of room
(e.g., cleaning, A/C)
$150
Price charged
for room
Price
Room Sales � FIGURE 13.5
Hotel Sets Only One Price
Level
INSIGHT � Yield management has increased total contribution to $8,100 ($2,550 from $100
rooms and $5,550 from $200 rooms). It may be that even more price levels are called for at Cleveland
Downtown Inn.
LEARNING EXERCISE � If the hotel develops a third price of $150 and can sell half of the $100
rooms at the increased rate, what is the contribution? [Answer: $8,850 = (15 � $85) + (15 � $135) +
30 � $185).]
RELATED PROBLEM � 13.22
SOLUTION � In Figure 13.6, the inn decides to set two price levels. It estimates that 30 rooms per
night can be sold at $100 and another 30 rooms at $200, using yield management software that is
widely available.
Demand curve
Total $ contribution =
(1st price) � 30 rooms + (2nd price) � 30 rooms =
($100 � $15) � 30 + ($200 � $15) � 30 =
$2,550 + $5,550 = $8,100
$15
Variable
cost
of room
100
60
30
$100
Price 1
for room
$200
Price 2
for room
Price
Room Sales � FIGURE 13.6
Hotel with Two Price Levels
Industries traditionally associated with revenue management operate in quadrant 2 of Figure 13.7.
They are able to apply variable pricing for their product and control product use or availability
(number of airline seats or hotel rooms sold at economy rate). On the other hand, movie theaters,
arenas, or performing arts centers (quadrant 1) have less pricing flexibility but still use time
(evening or matinee) and location (orchestra, side, or balcony) to manage revenue. In both cases,
management has control over the amount of the resource used—both the quantity and the duration
of the resource.

investment. Output from the aggre-
gate schedule leads to a more
detailed master production schedule,
which is the basis for disaggregation,
job scheduling, and MRP systems.
Aggregate plans for manufactur-
ing firms and service systems are similar. Restaurants, air-
lines, and hotels are all service systems that employ aggregate
plans, and have an opportunity to implement yield manage-
ment. But regardless of the industry or planning method, the
most important issue is the implementation of the plan. In this
respect, managers appear to be more comfortable with faster,
less complex, and less mathematical approaches to planning.
428 PART 3 Managing Operations
� FIGURE 13.7
Yield Management Matrix
Industries in quadrant 2 are
traditionally associated with
revenue management.
Source: Adapted from S. Kimes and
K. McGuire, “Function Space Revenue
Management,” Cornell Hotel and
Restaurant Administration Quarterly 42,
no. 6 (December 2001): 33–46.
Quadrant 1:
Movies
Stadiums/arenas
Convention centers
Hotel meeting space
Quadrant 2:
Hotels
Airlines
Rental cars
Cruise lines
Quadrant 3:
Restaurants
Golf courses
Internet service
providers
Quadrant 4:
Hospitals
Continuing care
T
e
n
d
t
o
b
e
u
n
c
e
rt
a
inU
s
e
Tend to be fixed Tend to be variable
Price
T
e
n
d
t
o
b
e
p
re
d
ic
ta
b
le
In the lower half of Figure 13.7, the manager’s job is more difficult because the duration of the
use of the resource is less controllable. However, with imagination, managers are using excess
capacity even for these industries. For instance, the golf course may sell less desirable tee times
at a reduced rate, and the restaurant may have an “early bird” special to generate business before
the usual dinner hour.
To make yield management work, the company needs to manage three issues:
1. Multiple pricing structures: These structures must be feasible and appear logical (and
preferably fair) to the customer. Such justification may take various forms, for example,
first-class seats on an airline or the preferred starting time at a golf course. (See the Ethical
Dilemma in the Lecture Guide & Activities Manual).
2. Forecasts of the use and duration of the use: How many economy seats should be available?
How much will customers pay for a room with an ocean view?
3. Changes in demand: This means managing the increased use as more capacity is sold. It also
means dealing with issues that occur because the pricing structure may not seem logical and
fair to all customers. Finally, it means managing new issues, such as overbooking because
the forecast was not perfect.
Precise pricing through yield management has substantial potential. Therefore, several firms
now have software available to address the issue. These include NCR’s Teradata, SPS,
DemandTec, and Oracle with Profit Logic.
CHAPTER SUMMARY
Aggregate planning provides companies with a necessary
weapon to help capture market shares in the global economy.
The aggregate plan provides both manufacturing and service
firms the ability to respond to changing customer demands
while still producing at low-cost and high-quality levels.
Aggregate schedules set levels of inventory, production,
subcontracting, and employment over an intermediate time
range, usually 3 to 18 months. This chapter describes several
aggregate planning techniques, ranging from the popular
graphical approach to a variety of mathematical models such
as linear programming.
The aggregate plan is an important responsibility of an oper-
ations manager and a key to efficient use of existing capital

Chapter 13 Aggregate Planning 429
Key Terms
Aggregate planning (or aggregate
scheduling (p. 410)
Scheduling decisions (p. 410)
Disaggregation (p. 412)
Master production schedule (p. 412)
Chase strategy (p. 414)
Level scheduling (p. 415)
Mixed strategy (p. 415)
Graphical techniques (p. 415)
Transportation method of linear
programming (p. 420)
Management coefficients model (p. 422)
Yield (or revenue) management (p. 425)
Using Software for Aggregate Planning
This section illustrates the use of Excel OM and POM for Windows in aggregate planning.
X Using Excel OM
Excel OM’s Aggregate Planning module is demonstrated in Program 13.1. Again using data from
Example 2, Program 13.1 provides input and some of the formulas used to compute the costs of regular
time, overtime, subcontracting, holding, shortage, and increase or decrease in production. The user must
provide the production plan for Excel OM to analyze.
Enter the demands
in column B and
the number of
units produced in
each period in
column C.
Enter the costs. Regular time and overtime costs
must be computed based on production hours
and labor rates, i.e., 10*1.6 and 17*1.6.
= SUM(B17:B22)
= SUM(B25:L25) Although the first period inventory relies on the initial inventory
(B12), the others rely on the previous inventory in column G. Thus
inventory in the first period is computed somewhat differently than
the inventory in the other periods. The formula for G22 is = G21 +
SUM(C22:E22) – B22.
The IF function is used
[with the command =
IF(G17> 0, –G17, 0)] to
determine whether the
inventory is positive
(and therefore held) or
negative (and therefore
short).
$99,200
$108,450
16
27.2
20
� PROGRAM 13.1 Using Excel OM for Aggregate Planning, with Example 2 Data
P Using POM for Windows
The POM for Windows Aggregate Planning module performs aggregate or production planning for up to
90 time periods. Given a set of demands for future periods, you can try various plans to determine the low-
est-cost plan based on holding, shortage, production, and changeover costs. Four methods are available for
planning. More help is available on each after you choose the method. See Appendix IV for further details.
Solved Problems Virtual Office Hours help is available at www.myomlab.com
� SOLVED PROBLEM 13.1
The roofing manufacturer described in Examples 1 to 4 of this chapter wishes to consider yet a fourth planning strategy (plan 4). This
one maintains a constant workforce of eight people and uses overtime whenever necessary to meet demand. Use the information found
in Table 13.3 on page 417. Again, assume beginning and ending inventories are equal to zero.

www.myomlab.com

430 PART 3 Managing Operations
Plan 2 is still preferable at $105,152.
Month
Production
Days
Production
at 40
Units per Day
Beginning-
of-Month
Inventory
Forecast
Demand This
Month
Overtime
Production
Needed
Ending
Inventory
Jan. 22 880 — 900 20 units 0 units
Feb. 18 720 0 700 0 units 20 units
Mar. 21 840 20 800 0 units 60 units
Apr. 21 840 60 1,200 300 units 0 units
May 22 880 0 1,500 620 units 0 units
June 20 800 0 1,100 300 units 0 units
1,240 units 80 units
Plan 4
Costs (workforce of 8 plus overtime)
Carrying cost $ 400 180 units carried * $5>unit2
Regular labor 79,360 18 workers * $80>day * 124 days2
Overtime 33,728 11,984 hours * $17>hour2
Hiring or firing 0
Subcontracting 0
Total costs $113,488
Regular pay:
Overtime pay:
To produce 1,240 units at overtime rate requires
Overtime cost = $17>hour * 1,984 hours = $33,728
1,240 * 1.6 hours>unit = 1,984 hours.
8 workers * $80>day * 124 days = $79,360
Carrying cost totals = 80 units * $5>unit>month = $400
� SOLVED PROBLEM 13.2
A Dover, Delaware, plant has developed the accompanying sup-
ply, demand, cost, and inventory data. The firm has a constant
workforce and meets all its demand. Allocate production capac-
ity to satisfy demand at a minimum cost. What is the cost of this
plan?
Supply Capacity Available (units)
Period Regular Time Overtime Subcontract
1 300 50 200
2 400 50 200
3 450 50 200
Other Data
Initial inventory 50 units
Regular-time cost per unit $50
Overtime cost per unit $65
Subcontract cost per unit $80
Carrying cost per unit per period $ 1
Back order cost per unit per period $ 4
Demand Forecast
Period Demand (units)
1 450
2 550
3 750
� SOLUTION
Employ eight workers and use overtime when necessary. Note that carrying costs will be encountered in this plan.

Chapter 13 Aggregate Planning 431
Cost of plan:
$99,300
*Includes 50 units of subcontract and carrying cost.
Total cost
Period 3: 50($81) + 450($50) + 50($65) + 200($80) = $45,800*
Period 2: 400($50) + 50($65) + 100($80) = $31,250
Period 1: 50($0) + 300($50) + 50($65) + 50($80) = $22,250
� SOLUTION
DEMAND FOR
TOTAL
Unused CAPACITY
Capacity AVAILABLE
SUPPLY FROM Period 1 Period 2 Period 3 (dummy) (supply)
0 1 2 0
Beginning inventory 50 50
50 51 52 0
Regular time 300 300
65 66 67 0
Period Overtime 50 50
1 80 81 82 0
Subcontract 50 150 200
54 50 51 0
Regular time 400 400
69 65 66 0
Period Overtime 50 50
2 84 80 81 0
Subcontract 100 50 50 200
58 54 50 0
Regular time 450 450
73 69 65 0
Period Overtime 50 50
3 88 84 80 0
Subcontract 200 200
TOTAL DEMAND 450 550 750 200 1,950
Bibliography
Chen, Fangruo. “Salesforce Initiative, Market Information, and
Production/Inventory Planning.” Management Science 51,
no. 1 (January 2005): 60–75.
Hopp, Wallace J., and Mark L. Spearman. Factory Physics, 3rd ed.
New York: Irwin/McGraw-Hill (2008).
Kimes, S. E., and G. M. Thompson. “Restaurant Revenue
Management at Chevy’s.” Decision Sciences 35, no. 3
(Summer 2004): 371–393.
Metters, R., K. King-Metters, M. Pullman, and S. Walton.
Successful Service Operations Management. 2nd ed. Mason,
OH: Thompson-South-Western (2006).
Metters, Richard, et al. “The ‘Killer Application’ of Revenue
Management: Harrah’s Cherokee Casino and Hotel.”
Interfaces 38, no. 3 (May–June 2008): 161–178.
Mukhopadhyay, S., S. Samaddar, and G. Colville. “Improving
Revenue Management Decision Making for Airlines.”
Decision Science 38, no. 2 (May 2007): 309–327.
Plambeck, Erica L., and Terry A. Taylor. “Sell the Plant? The
Impact of Contract Manufacturing on Innovation, Capacity,
and Profitability.” Management Science 51, no. 1 (January
2005): 133–150.
Silver, E. A., D. F. Pyke, and R. Peterson. Inventory Management
and Production Planning and Scheduling. New York: Wiley
(1998).
Vollmann, T. E., W. L. Berry, D. C. Whybark, and F. R. Jacobs.
Manufacturing Planning and Control for Supply Chain
Management, 5th ed. Burr Ridge, IL: Irwin (2005).
�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Cornwell Glass: Involves setting a production schedule for an auto glass producer.

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Material Requirements
Planning (MRP) and ERP
Chapter Outline
GLOBAL COMPANY PROFILE: WHEELED COACH
Dependent Demand 436
Dependent Inventory Model
Requirements 436
MRP Structure 441
MRP Management 446
Lot-Sizing Techniques 447
Extensions of MRP 451
MRP in Services 454
Enterprise Resource Planning (ERP) 455
� Design of Goods and Services
� Managing Quality
� Process Strategy
� Location Strategies
� Layout Strategies
� Human Resources
� Supply-Chain Management
� Inventory Management
� Independent Demand
� Dependent Demand
� JIT and Lean Operations
� Scheduling
� Maintenance
433

GLOBAL COMPANY PROFILE: WHEELED COACH
MRP PROVIDES A COMPETITIVE ADVANTAGE FOR WHEELED COACH
W
heeled Coach, headquartered in Winter
Park, Florida, is the largest
manufacturer of ambulances in the
world. The $200 million firm is an
international competitor that sells more than 25% of
its vehicles to markets outside the U.S. Twelve major
ambulance designs are produced on assembly lines
(i.e., a repetitive process) at the Florida plant, using
18,000 different inventory items, of which 6,000 are
manufactured and 12,000 purchased. Most of the
product line is custom designed and assembled to
meet the specific and often unique requirements
This cutaway of one
ambulance interior
indicates the complexity
of the product, which for
some rural locations may
be the equivalent of a
hospital emergency
room in miniature. To
complicate production,
virtually every ambulance
is custom ordered.
This customization
necessitates precise
orders, excellent bills of
materials, exceptional
inventory control from
supplier to assembly,
and an MRP system
that works.
Wheeled Coach uses work cells to
feed the assembly line. It maintains a
complete carpentry shop (to provide
interior cabinetry), a paint shop (to
prepare, paint, and detail each
vehicle), an electrical shop (to
provide for the complex electronics
in a modern ambulance), an
upholstery shop (to make interior
seats and benches), and as shown
here, a metal fabrication shop (to
construct the shell of the
ambulance).
434

On six parallel lines,
ambulances
move forward each day
to the next workstation.
The MRP system makes
certain that just the
materials needed at each
station arrive overnight for
assembly the next day.
demanded by the ambulance’s application and
customer preferences.
This variety of products and the nature of the
process demand good material requirements planning.
Effective use of an MRP system requires accurate bills
of material and inventory records. The Wheeled Coach
system, which uses MAPICS DB software, provides
daily updates and has reduced inventory by more than
30% in just 2 years.
Wheeled Coach insists that four key tasks be
performed properly. First, the material plan must meet
both the requirements of the master schedule and the
capabilities of the production facility. Second, the plan
must be executed as designed. Third, inventory
investment must be minimized through effective “time-
phased” material deliveries, consignment inventories,
and a constant review of purchase methods. Finally,
excellent record integrity must be maintained. Record
accuracy is recognized as a fundamental ingredient of
Wheeled Coach’s successful MRP program. Its cycle
counters are charged with material audits that not only
correct errors but also investigate and correct problems.
Wheeled Coach Industries uses MRP as the
catalyst for low inventory, high quality, tight schedules,
and accurate records. Wheeled Coach has found
competitive advantage via MRP.
Here an employee is installing the wiring for an ambulance. There are an average of
15 miles of wire in a Wheeled Coach vehicle. This compares to 17 miles of wire in a
sophisticated F-16 fighter jet.
VIDEO 14.1
MRP at Wheeled Coach
Ambulances
WHEELED COACH �
435

LO1: Develop a product structure 439
LO2: Build a gross requirements plan 442
LO3: Build a net requirements plan 445
LO4: Determine lot sizes for lot-for-lot,
EOQ, and PPB 448
436 PART 3 Managing Operations
Chapter 14 Learning Objectives
DEPENDENT DEMAND
Wheeled Coach, the subject of the Global Company Profile, and many other firms have found
important benefits in MRP. These benefits include (1) better response to customer orders
as the result of improved adherence to schedules, (2) faster response to market changes,
(3) improved utilization of facilities and labor, and (4) reduced inventory levels. Better
response to customer orders and to the market wins orders and market share. Better utiliza-
tion of facilities and labor yields higher productivity and return on investment. Less inventory
frees up capital and floor space for other uses. These benefits are the result of a strategic deci-
sion to use a dependent inventory scheduling system. Demand for every component of an
ambulance is dependent.
Demand for items is dependent when the relationship between the items can be determined.
Therefore, once management receives an order or makes a forecast for the final product, quanti-
ties for all components can be computed. All components are dependent items. The Boeing
Aircraft operations manager who schedules production of one plane per week, for example,
knows the requirements down to the last rivet. For any product, all components of that product
are dependent demand items. More generally, for any product for which a schedule can be estab-
lished, dependent techniques should be used.
When the requirements of MRP are met, dependent models are preferable to the EOQ models
described in Chapter 12.1 Dependent models are better not only for manufacturers and distribu-
tors but also for a wide variety of firms from restaurants to hospitals. The dependent technique
used in a production environment is called material requirements planning (MRP).
Because MRP provides such a clean structure for dependent demand, it has evolved as the
basis for Enterprise Resource Planning (ERP). ERP is an information system for identifying and
planning the enterprise-wide resources needed to take, make, ship, and account for customer
orders. We will discuss ERP in the latter part of this chapter.
DEPENDENT INVENTORY MODEL REQUIREMENTS
Effective use of dependent inventory models requires that the operations manager know the
following:
1. Master production schedule (what is to be made and when)
2. Specifications or bill of material (materials and parts required to make the product)
3. Inventory availability (what is in stock)
4. Purchase orders outstanding (what is on order, also called expected receipts)
5. Lead times (how long it takes to get various components)
We now discuss each of these requirements in the context of material requirements planning.
Master Production Schedule
A master production schedule (MPS) specifies what is to be made (i.e., the number of finished
products or items) and when. The schedule must be in accordance with a production plan. The
production plan sets the overall level of output in broad terms (e.g., product families, standard
LO5: Describe MRP II 452
LO6: Describe closed-loop MRP 452
LO7: Describe ERP 455
AUTHOR COMMENT
“Dependent demand” means
the demand for one item is
related to the demand for
another item.
Material requirements
planning (MRP)
A dependent demand technique
that uses a bill-of-material,
inventory, expected receipts,
and a master production
schedule to determine material
requirements.
1The inventory models (EOQ) discussed in Chapter 12 assumed that the demand for one item was independent of the
demand for another item. For example, EOQ assumes the demand for refrigerator parts is independent of the demand
for refrigerators and that demand for parts is constant.
Master production
schedule (MPS)
A timetable that specifies what
is to be made and when.

Chapter 14 Material Requirements Planning (MRP) and ERP 437
Aggregate
production
plan
Management
Return on investment
Capital
Engineering
Design completion
Human resources
Staff planning
Finance
Cash flow
Marketing
Customer demand
Production
Capacity
Inventory
Procurement
Supplier performance
Master production
schedule
Material
requirements plan
Capacity
requirements plan
Realistic?
Execute
capacity plans
Execute
material plans
Yes
Change
capacity?
No
Change
requirements?
Change master
production
schedule?
Is execution
meeting the plan?
Is
capacity plan
being met?
Change production
plan?
� FIGURE 14.1
The Planning Process
hours, or dollar volume). The plan also includes a variety of inputs, including financial plans,
customer demand, engineering capabilities, labor availability, inventory fluctuations, supplier
performance, and other considerations. Each of these inputs contributes in its own way to the
production plan, as shown in Figure 14.1
As the planning process moves from the production plan to execution, each of the lower-level
plans must be feasible. When one is not, feedback to the next higher level is used to make the
necessary adjustment. One of the major strengths of MRP is its ability to determine precisely the
feasibility of a schedule within aggregate capacity constraints. This planning process can yield
excellent results. The production plan sets the upper and lower bounds on the master production
schedule. The result of this production planning process is the master production schedule.
The master production schedule tells us what is required to satisfy demand and meet the produc-
tion plan. This schedule establishes what items to make and when: It disaggregates the aggregate
production plan. While the aggregate production plan (as discussed in Chapter 13) is established in
gross terms such as families of products or tons of steel, the master production schedule is estab-
lished in terms of specific products. Figure 14.2 shows the master production schedules for three
stereo models that flow from the aggregate production plan for a family of stereo amplifiers.
Managers must adhere to the schedule for a reasonable length of time (usually a major portion
of the production cycle—the time it takes to produce a product). Many organizations establish a
master production schedule and establish a policy of not changing (“fixing”) the near-term portion
of the plan. This near-term portion of the plan is then referred to as the “fixed,” “firm,” or “frozen”
AUTHOR COMMENT
The master production
schedule is derived from the
aggregate schedule.

438 PART 3 Managing Operations
schedule. Wheeled Coach, the subject of the Global Company Profile for this chapter, fixes the
last 14 days of its schedule. Only changes farther out, beyond the fixed schedule, are permitted.
The master production schedule is a “rolling” production schedule. For example, a fixed 7-week
plan has an additional week added to it as each week is completed, so a 7-week fixed schedule is
maintained. Note that the master production schedule is a statement of what is to be produced, not
a forecast of demand. The master schedule can be expressed in any of the following terms:
1. A customer order in a job shop (make-to-order) company
2. Modules in a repetitive (assemble-to-order or forecast) company
3. An end item in a continuous (stock-to-forecast) company
This relationship of the master production schedule to the processes is shown in Figure 14.3.
A master production schedule for two of Nancy’s Specialty Foods’ products, crabmeat quiche
and spinach quiche, might look like Table 14.1.
Bills of Material
Defining what goes into a product may seem simple, but it can be difficult in practice. As we
noted in Chapter 5, to aid this process, manufactured items are defined via a bill of material. A
bill of material (BOM) is a list of quantities of components, ingredients, and materials required
to make a product. Individual drawings describe not only physical dimensions but also any spe-
cial processing as well as the raw material from which each part is made. Nancy’s Specialty
Months
Aggregate Production Plan
(Shows the total
quantity of amplifiers)
1,500 1,200
Master Production Schedule
(Shows the specific type and
quantity of amplifier to be
produced)
240-watt amplifier
150-watt amplifier
75-watt amplifier
100
500
100
500
100
450
100
450
300 100
Weeks 1
January February
2 3 4 5 6 7 8
� FIGURE 14.2
The Aggregate Production
Plan Is the Basis for
Development of the Detailed
Master Production Schedule
Make to Order Assemble to Order
or Forecast
Stock to Forecast
(Process Focus) (Repetitive) (Product Focus)
Schedule finished
product
Schedule orders
Schedule modules
Examples: Print shop
Machine shop
Fine-dining restaurant
Motorcycles
Autos, TVs
Fast-food restaurant
Steel, Beer, Bread
Lightbulbs
Paper
Number of end items
Number of inputs
Typical focus of
the master production
schedule
� FIGURE 14.3 Typical Focus of the Master Production Schedule in Three Process Strategies
Bill of material (BOM)
A listing of the components,
their description, and the
quantity of each required to
make one unit of a product.
AUTHOR COMMENT
The type of process
determines the units in the
master production schedule.

Chapter 14 Material Requirements Planning (MRP) and ERP 439
Foods has a recipe for quiche, specifying ingredients and quantities, just as Wheeled Coach has
a full set of drawings for an ambulance. Both are bills of material (although we call one a recipe,
and they do vary somewhat in scope).
Because there is often a rush to get a new product to market, however, drawings and bills of
material may be incomplete or even nonexistent. Moreover, complete drawings and BOMs (as
well as other forms of specifications) often contain errors in dimensions, quantities, or countless
other areas. When errors are identified, engineering change notices (ECNs) are created, further
complicating the process. An engineering change notice is a change or correction to an engineer-
ing drawing or bill of material.
One way a bill of material defines a product is by providing a product structure. Example 1
shows how to develop the product structure and “explode” it to reveal the requirements for each
component. A bill of material for item A in Example 1 consists of items B and C. Items above
any level are called parents; items below any level are called components or children. By conven-
tion, the top level in a BOM is the 0 level.
Gross Requirements for Crabmeat Quiche
Day 6 7 8 9 10 11 12 13 14 and so on
Amount 50 100 47 60 110 75
Gross Requirements for Spinach Quiche
Day 7 8 9 10 11 12 13 14 15 16 and so on
Amount 100 200 150 60 75 100
� EXAMPLE 1
Developing a
product structure
and gross
requirements
Speaker Kits, Inc., packages high-fidelity components for mail order. Components for the top-of-the-
line speaker kit, “Awesome” (A), include 2 standard 12-inch speaker kits (Bs) and 3 speaker kits with
amp-boosters (Cs).
Each B consists of 2 speakers (Ds) and 2 shipping boxes each with an installation kit (E). Each of
the three 300-watt speaker kits (Cs) has 2 speaker boosters (Fs) and 2 installation kits (Es). Each
speaker booster (F) includes 2 speakers (Ds) and 1 amp-booster (G). The total for each Awesome is
4 standard 12-inch speakers and twelve 12-inch speakers with the amp-booster. (Most purchasers
require hearing aids within 3 years, and at least one court case is pending because of structural damage
to a men’s dormitory.) As we can see, the demand for B, C, D, E, F, and G is completely dependent on
the master production schedule for A—the Awesome speaker kits.
APPROACH � Given the above information, we construct a product structure and “explode” the
requirements.
SOLUTION � This structure has four levels: 0, 1, 2, and 3. There are four parents: A, B, C, and F.
Each parent item has at least one level below it. Items B, C, D, E, F, and G are components because
each item has at least one level above it. In this structure, B, C, and F are both parents and components.
The number in parentheses indicates how many units of that particular item are needed to make the
item immediately above it. Thus, B(2) means that it takes two units of B for every unit of A, and F(2)
means that it takes two units of F for every unit of C.
A
Packing box and
installation kit of wire,
bolts, and screws
Amp-booster
12″ Speaker
0
1
2
3
Product structure for “Awesome” (A)Level
B(2) Std. 12″ Speaker kit C(3)
E(2) F(2)
G(1) D(2)D(2)
E(2)
Std. 12″ Speaker kit
w/ amp-booster
Std. 12″ Speaker
booster assembly
12″ Speaker
LO1: Develop a product
structure
� TABLE 14.1
Master Production Schedule
for Crabmeat Quiche and
Spinach Quiche at Nancy’s
Specialty Foods

440 PART 3 Managing Operations
Once we have developed the product structure, we can determine the number of units of each item
required to satisfy demand for a new order of 50 Awesome speaker kits. We “explode” the require-
ments as shown:
INSIGHT � We now have a visual picture of the Awesome speaker kit requirements and knowl-
edge of the quantities required. Thus, for 50 units of A, we will need 100 units of B, 150 units of C,
800 units of D, 500 units of E, 300 units of F, and 300 units of G.
LEARNING EXERCISE � If there are 100 Fs in stock, how many Ds do you need? [Answer: 600.]
RELATED PROBLEMS � 14.1, 14.3a, 14.13a, 14.25a
Part B: 2 * number of As = 1221502 = 100
Part C: 3 * number of As = 1321502 = 150
Part D: 2 * number of Bs + 2 * number of Fs = 12211002 + 12213002 = 800
Part E: 2 * number of Bs + 2 * number of Cs = 12211002 + 12211502 = 500
Part F: 2 * number of Cs = 12211502 = 300
Part G: 1 * number of Fs = 11213002 = 300
Bills of material not only specify requirements but also are useful for costing, and they can serve
as a list of items to be issued to production or assembly personnel. When bills of material are
used in this way, they are usually called pick lists.
Modular Bills Bills of material may be organized around product modules (see Chapter 5).
Modules are not final products to be sold but are components that can be produced and assem-
bled into units. They are often major components of the final product or product options. Bills of
material for modules are called modular bills. Bills of material are sometimes organized as
modules (rather than as part of a final product) because production scheduling and production
are often facilitated by organizing around relatively few modules rather than a multitude of final
assemblies. For instance, a firm may make 138,000 different final products but may have only
40 modules that are mixed and matched to produce those 138,000 final products. The firm builds
an aggregate production plan and prepares its master production schedule for the 40 modules,
not the 138,000 configurations of the final product. This approach allows the MPS to be prepared
for a reasonable number of items (the narrow portion of the middle graphic in Figure 14.3) and to
postpone assembly. The 40 modules can then be configured for specific orders at final assembly.
Planning Bills and Phantom Bills Two other special kinds of bills of material are planning
bills and phantom bills. Planning bills (sometimes called “pseudo” bills or super bills) are cre-
ated in order to assign an artificial parent to the bill of material. Such bills are used (1) when we
want to group subassemblies so the number of items to be scheduled is reduced and (2) when we
want to issue “kits” to the production department. For instance, it may not be efficient to issue
inexpensive items such as washers and cotter pins with each of numerous subassemblies, so we
call this a kit and generate a planning bill. The planning bill specifies the kit to be issued.
Consequently, a planning bill may also be known as kitted material, or kit. Phantom bills of
material are bills of material for components, usually subassemblies, that exist only temporarily.
These components go directly into another assembly and are never inventoried. Therefore, com-
ponents of phantom bills of material are coded to receive special treatment; lead times are zero,
and they are handled as an integral part of their parent item. An example is a transmission shaft
with gears and bearings assembly that is placed directly into a transmission.
Low-Level Coding Low-level coding of an item in a BOM is necessary when identical items
exist at various levels in the BOM. Low-level coding means that the item is coded at the lowest
level at which it occurs. For example, item D in Example 1 is coded at the lowest level at which
it is used. Item D could be coded as part of B and occur at level 2. However, because D is also
part of F, and F is level 2, item D becomes a level-3 item. Low-level coding is a convention to
allow easy computing of the requirements of an item. When the BOM has thousands of items or
when requirements are frequently recomputed, the ease and speed of computation become a
major concern.
Modular bills
Bills of material organized by
major subassemblies or by
product options.
Planning bills (or kits)
A material grouping created in
order to assign an artificial
parent to a bill of material; also
called “pseudo” bills.
Phantom bills of
material
Bills of material for
components, usually
assemblies, that exist only
temporarily; they are never
inventoried.
Low-level coding
A number that identifies items
at the lowest level at which they
occur.

Chapter 14 Material Requirements Planning (MRP) and ERP 441
Accurate Inventory Records
As we saw in Chapter 12, knowledge of what is in stock is the result of good inventory manage-
ment. Good inventory management is an absolute necessity for an MRP system to work. If the
firm does not exceed 99% record accuracy, then material requirements planning will not work.2
Purchase Orders Outstanding
Knowledge of outstanding orders exists as a by-product of well-managed purchasing and inven-
tory-control departments. When purchase orders are executed, records of those orders and their
scheduled delivery dates must be available to production personnel. Only with good purchasing
data can managers prepare meaningful production plans and effectively execute an MRP system.
Lead Times for Components
Once managers determine when products are needed, they determine when to acquire them. The
time required to acquire (that is, purchase, produce, or assemble) an item is known as lead time.
Lead time for a manufactured item consists of move, setup, and assembly or run times for each
component. For a purchased item, the lead time includes the time between recognition of need
for an order and when it is available for production.
When the bill of material for Awesome speaker kits (As), in Example 1, is turned on its side and
modified by adding lead times for each component (see Table 14.2), we then have a time-phased
product structure. Time in this structure is shown on the horizontal axis of Figure 14.4 with item A
due for completion in week 8. Each component is then offset to accommodate lead times.
MRP STRUCTURE
Although most MRP systems are computerized, the MRP procedure is straightforward and we
can illustrate a small one by hand. A master production schedule, a bill of material, inventory and
purchase records, and lead times for each item are the ingredients of a material requirements
planning system (see Figure 14.5).
Lead time
In purchasing systems, the time
between recognition of the need
for an order and receiving it;
in production systems, it is the
order, wait, move, queue,
setup, and run times for each
component.
2Record accuracy of 99% may sound good, but note that even when each component has an availability of 99% and a
product has only seven components, the likelihood of a product being completed is only .932 (because )..997 = .932
� TABLE 14.2
Lead Times for Awesome
Speaker Kits (As)
Lead
Component Time
A 1 week
B 2 weeks
C 1 week
D 1 week
E 2 weeks
F 3 weeks
G 2 weeks
Time in weeks
1 2 3 4 5 6 7 8
D
G
F
E
C
B
A
E
D
2 weeks
2 weeks
2 weeks
2 weeks to
produce
1 week
1 week
1 week
1 week
3 weeks
Must have D and E
completed here so
production can
begin on B
Start production of D
� FIGURE 14.4
Time-Phased Product
Structure
AUTHOR COMMENT
This is a product structure on
its side, with lead times.

442 PART 3 Managing Operations
Once these ingredients are available and accurate, the next step is to construct a gross mater-
ial requirements plan. The gross material requirements plan is a schedule, as shown in
Example 2. It combines a master production schedule (that requires one unit of A in week 8) and
the time-phased schedule (Figure 14.4). It shows when an item must be ordered from suppliers if
there is no inventory on hand or when the production of an item must be started to satisfy
demand for the finished product by a particular date.
Gross material
requirements plan
A schedule that shows the total
demand for an item (prior to
subtraction of on-hand
inventory and scheduled
receipts) and (1) when it must
be ordered from suppliers, or
(2) when production must be
started to meet its demand by a
particular date.
EXAMPLE 2 �
Building a gross
requirements plan
Each Awesome speaker kit (item A of Example 1) requires all the items in the product structure for A.
Lead times are shown in Table 14.2.
APPROACH � Using the information in Example 1 and Table 14.2, we construct the gross material
requirements plan with a production schedule that will satisfy the demand of 50 units of A by week 8.
SOLUTION � We prepare a schedule as shown in Table 14.3.
MRP by
period report
Planned order
report
Purchase advice
MRP by
date report
Order early or late
or not needed
Order quantity too
small or too large
Data Files
Material
requirements
planning
programs
(computer and
software)
Master
production schedule
Output Reports
Bill of material
Lead times
Inventory data
Purchasing data
(Item master file)
Exception reports
� FIGURE 14.5
Structure of the MRP System
AUTHOR COMMENT
MRP software programs
are popular because manual
approaches are slow
and error prone.
Week
1 2 3 4 5 6 7 8 Lead Time
A. Required date 50
Order release date 50 1 week
B. Required date 100
Order release date 100 2 weeks
C. Required date 150
Order release date 150 1 week
E. Required date 200 300
Order release date 200 300 2 weeks
F. Required date 300
Order release date 300 3 weeks
D. Required date 600 200
Order release date 600 200 1 week
G. Required date 300
Order release date 300 2 weeks
� TABLE 14.3
Gross Material Requirements
Plan for 50 Awesome Speaker
Kits (As)
LO2: Build a gross
requirements plan

Chapter 14 Material Requirements Planning (MRP) and ERP 443
You can interpret the gross material requirements shown in Table 14.3 as follows: If you want 50 units
of A at week 8, you must start assembling A in week 7. Thus, in week 7, you will need 100 units of B
and 150 units of C. These two items take 2 weeks and 1 week, respectively, to produce. Production of
B, therefore, should start in week 5, and production of C should start in week 6 (lead time subtracted
from the required date for these items). Working backward, we can perform the same computations for
all of the other items. Because D and E are used in two different places in Awesome speaker kits, there
are two entries in each data record.
INSIGHT � The gross material requirements plan shows when production of each item should
begin and end in order to have 50 units of A at week 8. Management now has an initial plan.
LEARNING EXERCISE � If the lead time for G decreases from 2 weeks to 1 week, what is the
new order release date for G? [Answer: 300 in week 2.]
RELATED PROBLEMS � 14.2, 14.4, 14.6, 14.8b, 14.9, 14.10a, 14.11a, 14.13b, 14.25b
EXCEL OM Data File Ch14Ex2.xls can be found at www.pearsonhighered.com/heizer.
So far, we have considered gross material requirements, which assumes that there is no inventory
on hand. When there is inventory on hand, we prepare a net requirements plan. When consid-
ering on-hand inventory, we must realize that many items in inventory contain subassemblies or
parts. If the gross requirement for Awesome speaker kits (As) is 100 and there are 20 of those
speakers on hand, the net requirement for Awesome speaker kits (As) is 80 (that is, 100 – 20).
However, each Awesome speaker kit on hand contains 2 Bs. As a result, the requirement for Bs
drops by 40 Bs Therefore, if inventory is on hand for a par-
ent item, the requirements for the parent item and all its components decrease because each
Awesome kit contains the components for lower-level items. Example 3 shows how to create a
net requirements plan.
120 A kits on hand * 2 Bs per A2.
� EXAMPLE 3
Determining net
requirements
Speaker Kits, Inc., developed a product structure from a bill of material in Example 1. Example 2
developed a gross requirements plan. Given the following on-hand inventory, Speaker Kits, Inc., now
wants to construct a net requirements plan.
APPROACH � A net material requirements plan includes gross requirements, on-hand inventory,
net requirements, planned order receipt, and planned order release for each item. We begin with A and
work backward through the components.
SOLUTION � Shown in the chart on the next page is the net material requirements plan for product A.
Constructing a net requirements plan is similar to constructing a gross requirements plan. Starting
with item A, we work backward to determine net requirements for all items. To do these computations,
we refer to the product structure, on-hand inventory, and lead times. The gross requirement for A is 50
units in week 8. Ten items are on hand; therefore, the net requirements and the scheduled planned
order receipt are both 40 items in week 8. Because of the 1-week lead time, the planned order
release is 40 items in week 7 (see the arrow connecting the order receipt and order release). Referring
to week 7 and the product structure in Example 1, we can see that items of B and 120
items of C are required in week 7 to have a total for 50 items of A in week 8. The letter
superscripted A to the right of the gross figure for items B and C was generated as a result of the
demand for the parent, A. Performing the same type of analysis for B and C yields the net requirements
for D, E, F, and G. Note the on-hand inventory in row E in week 6 is zero. It is zero because the on-
hand inventory (10 units) was used to make B in week 5. By the same token, the inventory for D was
used to make F in week 3.
INSIGHT � Once a net requirement plan is completed, management knows the quantities needed,
an ordering schedule, and a production schedule for each component.
13 * 402
80 12 * 402
Item On Hand Item On Hand
A 10 E 10
B 15 F 5
C 20 G 0
D 10
Net material
requirements
The result of adjusting gross
requirements for inventory on
hand and scheduled receipts.
Planned order receipt
The quantity planned to be
received at a future date.
Planned order release
The scheduled date for an order
to be released.

www.pearsonhighered.com/heizer

444 PART 3 Managing Operations
1
15 1515 15 15 15 15 15
80A
120A
65
65
65
Gross RequirementsA0——101Lot-
for-
Lot
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
2 3 4 5
Week
6 7 8
Lot
Size
Lead
Time
(weeks)
On
Hand
Safety
Stock
Allo-
cated
Low-
Level
Code
Item
Identi-
fication
200
120
120
120
195
195
195
20 2020 20 20 20 20 20
100
100
100
Gross RequirementsB1——152Lot-
for-
Lot
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
10 1010 10 10 10 10 10 10
50
40
40
40
Gross RequirementsC1——201Lot-
for-
Lot
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
10 1010 10 10 10
130B
130B390F
195F
200C
200C
200
200
Gross RequirementsE2——102Lot-
for-
Lot
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
5 55 5 5 5 5
195
195
195
Gross RequirementsF2——53Lot-
for-
Lot
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
10 1010 10
130
130
130
Gross RequirementsD3——101Lot-
for-
Lot
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
380
380
380
0
Gross RequirementsG3——02Lot-
for-
Lot
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
� Net Material Requirements Plan for Product A (the superscript is the source of the demand)
LEARNING EXERCISE � If the on-hand inventory quantity of component F is 95 rather than 5,
how many units of G will need to be ordered in week 1? [Answer: 105 units.]
RELATED PROBLEMS � 14.5, 14.7, 14.8c, 14.10b, 14.11b, 14.12, 14.13c, 14.14b, 14.15a,b,c,
14.16a, 14.25c, 14.27
EXCEL OM Data File Ch14Ex3.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 14.1 This example is further illustrated in Active Model 14.1 at www.pearsonhighered.com/heizer.
Examples 2 and 3 considered only product A, the Awesome speaker kit, and its completion only
in week 8. Fifty units of A were required in week 8. Normally, however, there is a demand for
many products over time. For each product, management must prepare a master production
schedule (as we saw earlier in Table 14.1). Scheduled production of each product is added to the

www.pearsonhighered.com/heizer

www.pearsonhighered.com/heizer

Chapter 14 Material Requirements Planning (MRP) and ERP 445
LO3: Build a net
requirements plan
master schedule and ultimately to the net material requirements plan. Figure 14.6 shows how
several product schedules, including requirements for components sold directly, can contribute
to one gross material requirements plan.
Most inventory systems also note the number of units in inventory that have been assigned to
specific future production but not yet used or issued from the stockroom. Such items are often
referred to as allocated items. Allocated items increase requirements and may then be included
in an MRP planning sheet, as shown in Figure 14.7.
The allocated quantity has the effect of increasing the requirements (or, alternatively, reducing
the quantity on hand). The logic, then, of a net requirements MRP is:
Safety Stock The continuing task of operations managers is to remove variability. This is the
case in MRP systems as in other operations systems. Realistically, however, managers need to
realize that bills of material and inventory records, like purchase and production quantities, as
well as lead times, may not be perfect. This means that some consideration of safety stock may
be prudent. Because of the significant domino effect of any change in requirements, safety stock
should be minimized, with a goal of ultimate elimination. When safety stock is deemed
absolutely necessary, the usual policy is to build it into the projected on-hand inventory of the
MRP logic. Distortion can be minimized when safety stock is held at the finished goods level and
at the purchased component or raw material level.
[(Gross requirements) + (Allocations)]
Total requirements
–
[(On hand) + (Scheduled receipts)]
Available inventory
= Net
requirements
Lot
Size
Lead
Time
On
Hand
Safety
Stock
Allocated
Low-
Level
Code
Item
ID
1 2 3 4 5 6 7 8
Period
Gross Requirements
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
� FIGURE 14.7 Sample MRP Planning Sheet for Item Z
A
B C B C
S
5 6 7 8 9 10 11
40 50 15
8 9 10 11
40
12 13
3020
1
10
2
10
3
Master schedule
for B
sold directly
Lead time = 6 for S
Master schedule for S
Lead time = 4 for A
Master schedule for A
7 8654321
15+30
=45
40+10
=50 20504010
Therefore, these
are the gross
requirements for B
Gross requirements: B
Periods
Periods
� FIGURE 14.6
Several Schedules
Contributing to a Gross
Requirements Schedule for B
One B is in each A, and one B
is in each S; in addition, 10 Bs
sold directly are scheduled in
week 1, and 10 more that are
sold directly are scheduled in
week 2.
AUTHOR COMMENT
MRP gross requirements
can combine multiple
products, spare parts, and
items sold directly.

446 PART 3 Managing Operations
MRP MANAGEMENT
The material requirements plan is not static. And since MRP systems increasingly are integrated
with just-in-time (JIT) techniques, we now discuss these two issues.
MRP Dynamics
Bills of material and material requirements plans are altered as changes in design, schedules, and
production processes occur. In addition, changes occur in material requirements whenever the mas-
ter production schedule is modified. Regardless of the cause of any changes, the MRP model can be
manipulated to reflect them. In this manner, an up-to-date requirements schedule is possible.
The inputs to MRP (the master schedule, BOM, lead times, purchasing, and inventory) fre-
quently change. Conveniently, a central strength of MRP systems is timely and accurate replan-
ning. This occurs in one of two ways: by recomputing (also known as “regenerating”) the
requirement and schedule periodically, often weekly, or via a “net change” calculation. Net
change in an MRP system means the MRP system creates new requirements in response to trans-
actions. However, many firms find they do not want to respond to minor scheduling or quantity
changes even if they are aware of them. These frequent changes generate what is called system
nervousness and can create havoc in purchasing and production departments if implemented.
Consequently, OM personnel reduce such nervousness by evaluating the need and impact of
changes prior to disseminating requests to other departments. Two tools are particularly helpful
when trying to reduce MRP system nervousness.
The first is time fences. Time fences allow a segment of the master schedule to be designated
as “not to be rescheduled.” This segment of the master schedule is therefore not changed during
the periodic regeneration of schedules. The second tool is pegging. Pegging means tracing
upward in the BOM from the component to the parent item. By pegging upward, the production
planner can determine the cause for the requirement and make a judgment about the necessity for
a change in the schedule.
With MRP, the operations manager can react to the dynamics of the real world. How fre-
quently the manager wishes to impose those changes on the firm requires professional judgment.
Moreover, if the nervousness is caused by legitimate changes, then the proper response may be to
investigate the production environment—not adjust via MRP.
MRP and JIT
MRP does not do detailed scheduling—it plans. MRP will tell you that a job needs to be com-
pleted on a certain week or day but does not tell you that Job X needs to run on Machine A at
10:30 A.M. and be completed by 11:30 A.M. so that Job X can then run on machine B. MRP is
also a planning technique with fixed lead times. Fixed lead times can be a limitation. For
instance, the lead time to produce 50 units may vary substantially from the lead time to produce
5 units. These limitations complicate the marriage of MRP and just-in-time (JIT). What is
needed is a way to make MRP more responsive to moving material rapidly in small batches. An
MRP system combined with JIT can provide the best of both worlds. MRP provides the plan and
an accurate picture of requirements; then JIT rapidly moves material in small batches, reducing
work-in-process inventory. Let’s look at four approaches for integrating MRP and JIT: finite
capacity scheduling, small buckets, balanced flow, and supermarkets.
Finite Capacity Scheduling (FCS) Most MRP software loads work into infinite size
“buckets.” The buckets are time units, usually one week. Traditionally, when work is to be done
in a given week, MRP puts the work there without regard to capacity. Consequently, MRP is con-
sidered an infinite scheduling technique. Frequently, as you might suspect, this is not realistic.
Finite capacity scheduling (FCS), which we discuss in Chapter 15, considers department and
machine capacity, which is finite, hence the name. FCS provides the precise scheduling needed
for rapid material movement. We are now witnessing a convergence of FCS and MRP.
Sophisticated FCS systems modify the output from MRP systems to provide a finite schedule.
Small Bucket Approach MRP is an excellent tool for resource and scheduling manage-
ment in process-focused facilities, that is, in job shops. Such facilities include machine shops,
hospitals, and restaurants, where lead times are relatively stable and poor balance between
System nervousness
Frequent changes in an MRP
system.
Time fences
A means for allowing a segment
of the master schedule to be
designated as “not to be
rescheduled.”
Pegging
In material requirements
planning systems, tracing
upward in the bill of material
from the component to the
parent item.
Buckets
Time units in a material
requirements planning system.
AUTHOR COMMENT
Using MRP to the utmost
is serious work.

Chapter 14 Material Requirements Planning (MRP) and ERP 447
work centers is expected. Schedules are often driven by work orders, and lot sizes are the
exploded bill-of-material size. In these enterprises, MRP can be integrated with JIT through
the following steps.
STEP 1: Reduce MRP “buckets” from weekly to daily to perhaps hourly. Buckets are time
units in an MRP system. Although the examples in this chapter have used weekly
time buckets, many firms now use daily or even fraction-of-a-day time buckets. Some
systems use a bucketless system in which all time-phased data have dates attached
rather than defined time periods or buckets.
STEP 2: The planned receipts that are part of a firm’s planned orders in an MRP system are com-
municated to the work areas for production purposes and used to sequence production.
STEP 3: Inventory is moved through the plant on a JIT basis.
STEP 4: As products are completed, they are moved into inventory (typically finished-goods
inventory) in the normal way. Receipt of these products into inventory reduces the
quantities required for subsequent planned orders in the MRP system.
STEP 5: A system known as back flush is used to reduce inventory balances. Back flushing
uses the bill of material to deduct component quantities from inventory as each unit is
completed.
The focus in these facilities becomes one of maintaining schedules. Nissan achieves success
with this approach by computer communication links to suppliers. These schedules are con-
firmed, updated, or changed every 15 to 20 minutes. Suppliers provide deliveries 4 to 16 times
per day. Master schedule performance is 99% on time, as measured every hour. On-time delivery
from suppliers is 99.9% and for manufactured piece parts, 99.5%.
Balanced Flow Approach MRP supports the planning and scheduling necessary for repeti-
tive operations, such as the assembly lines at Harley-Davidson, Whirlpool, and a thousand other
places. In these environments, the planning portion of MRP is combined with JIT execution. The
JIT portion uses kanbans, visual signals, and reliable suppliers to pull the material through the
facility. In these systems, execution is achieved by maintaining a carefully balanced flow of
material to assembly areas with small lot sizes.
Supermarket Another technique that joins MRP and JIT is the use of a “supermarket.” In
many firms, subassemblies, their components, and hardware items are common to a variety of
products. In such cases, releasing orders for these common items with traditional lead-time off-
set, as is done in an MRP system, is not necessary. The subassemblies, components, and hard-
ware items can be maintained in a common area, sometimes called a supermarket, adjacent to
the production areas where they are used. For instance, Ducati, Italy’s high-performance motor-
cycle manufacturer, pulls “kits” with the materials needed for one engine or vehicle from the
supermarket and delivers them to the assembly line on a JIT basis. Items in the supermarket are
replenished by a JIT/kanban system.
LOT-SIZING TECHNIQUES
An MRP system is an excellent way to determine production schedules and net requirements.
However, whenever we have a net requirement, a decision must be made about how much to
order. This decision is called a lot-sizing decision. There are a variety of ways to determine lot
sizes in an MRP system; commercial MRP software usually includes the choice of several lot-
sizing techniques. We now review a few of them.
Lot-for-Lot In Example 3, we used a lot-sizing technique known as lot-for-lot, which
produced exactly what was required. This decision is consistent with the objective of an MRP
system, which is to meet the requirements of dependent demand. Thus, an MRP system should
produce units only as needed, with no safety stock and no anticipation of further orders. When
frequent orders are economical and just-in-time inventory techniques implemented, lot-for-lot
can be very efficient. However, when setup costs are significant or management has been unable
to implement JIT, lot-for-lot can be expensive. Example 4 uses the lot-for-lot criteria and deter-
mines cost for 10 weeks of demand.
Bucketless system
Time-phased data are
referenced using dated records
rather than defined time
periods, or buckets.
Back flush
A system to reduce inventory
balances by deducting
everything in the bill of material
on completion of the unit.
Supermarket
An inventory area that holds
common items that are
replenished by a kanban
system.
Lot-sizing decision
The process of, or techniques
used in, determining lot size.
Lot-for-lot
A lot-sizing technique that
generates exactly what is
required to meet the plan.
AUTHOR COMMENT
Managers need to know how
to group/order the “planned
order releases.”

448 PART 3 Managing Operations
EXAMPLE 4 �
Lot sizing with
lot-for-lot
Speaker Kits, Inc., wants to compute its ordering and carrying cost of inventory on lot-for-lot criteria.
APPROACH � With lot-for-lot, we order material only as it is needed. Once we have the cost of
ordering (setting up), the cost of holding each unit for a given time period, and the production schedule,
we can assign orders to our net requirements plan.
SOLUTION � Speaker Kits has determined that, for the 12-inch speaker unit, setup cost is $100
and holding cost is $1 per period. The production schedule, as reflected in net requirements for assem-
blies, is as follows:
LO4: Determine lot sizes
for lot-for-lot, EOQ, and PPB
Economic Order Quantity As discussed in Chapter 12, EOQ can be used as a lot-sizing
technique. But as we indicated there, EOQ is preferable when relatively constant independent
demand exists, not when we know the demand. EOQ is a statistical technique using averages
(such as average demand for a year), whereas the MRP procedure assumes known (dependent)
MRP Lot Sizing: Lot-for-Lot Technique*
1 2 3 4 5 6 7 8 9 10
Gross requirements 35 30 40 0 10 40 30 0 30 55
Scheduled receipts
Projected on hand 35 35 0 0 0 0 0 0 0 0 0
Net requirements 0 30 40 0 10 40 30 0 30 55
Planned order receipts 30 40 10 40 30 30 55
Planned order releases 30 40 10 40 30 30 55
*Holding costs = $1/unit/week; setup cost = $100; gross requirements average per week = 27; lead time = 1 week.
The lot-sizing solution using the lot-for-lot technique is shown in the table. The holding cost is zero
as there is never any inventory. (Inventory in the first period is used immediately and therefore has no
holding cost.) But seven separate setups (one associated with each order) yield a total cost of $700.
(Holding cost )
INSIGHT � When supply is reliable and frequent orders are inexpensive, but holding cost or obso-
lescence is high, lot-for-lot ordering can be very efficient.
LEARNING EXERCISE � What is the impact on total cost if holding cost is $2 per period
rather than $1? [Answer: Total holding cost remains zero, as no units are held from one period to the
next with lot-for-lot.]
RELATED PROBLEMS � 14.17, 14.20, 14.21, 14.22
= 0 * 1 = 0; ordering cost = 7 * 100 = 700.
This Nissan line in Smyrna, Tennessee, has
little inventory because Nissan schedules
to a razor’s edge. At Nissan, MRP helps
reduce inventory to world-class standards.
World-class automobile assembly requires
that purchased parts have a turnover of
slightly more than once a day and that over-
all turnover approaches 150 times per year.

Chapter 14 Material Requirements Planning (MRP) and ERP 449
� EXAMPLE 5
Lot sizing with
EOQ
With a setup cost of $100 and a holding cost per week of $1, Speaker Kits, Inc., wants to examine its
cost with lot sizes based on an EOQ criteria.
APPROACH � Using the same cost and production schedule as in Example 4, we determine net
requirements and EOQ lot sizes.
SOLUTION � Ten-week usage equals a gross requirement of 270 units; therefore, weekly usage
equals 27, and 52 weeks (annual usage) equals 1,404 units. From Chapter 12, the EOQ model is:
where
(carrying) cost, on an annual basis per unit
Q* = 73 units
= $1 * 52 weeks = $52
H = holding
S = setup cost = $100
D = annual usage = 1,404
Q* =
A
2DS
H
demand reflected in a master production schedule. Operations managers should take advantage
of demand information when it is known, rather than assuming a constant demand. EOQ is
examined in Example 5.
MRP Lot Sizing: EOQ Technique*
1 2 3 4 5 6 7 8 9 10
Gross requirements 35 30 40 0 10 40 30 0 30 55
Scheduled receipts
Projected on hand 35 35 0 43 3 3 66 26 69 69 39
Net requirements 0 30 0 0 7 0 4 0 0 16
Planned order receipts 73 73 73 73
Planned order releases 73 73 73 73
*Holding costs = $1/unit/week; setup cost = $100; gross requirements average per week = 27; lead time = 1 week.
The EOQ solution yields a computed 10-week cost of $730 [ ].
INSIGHT � EOQ can be an effective lot-sizing technique when demand is relatively constant.
However, notice that actual holding cost will vary from the computed $730, depending on the rate of
actual usage. From the preceding table, we can see that in our 10-week example, costs really are $400
for four setups, plus a holding cost of 375 units (includes 57 remaining at the end of the period) at $1
per week for a total of $775. Because usage was not constant, the actual computed cost was in fact
more than the theoretical EOQ ($730) and the lot-for-lot rule ($700). If any stockouts had occurred,
these costs too would need to be added to our actual EOQ cost of $775.
LEARNING EXERCISE � What is the impact on total cost if holding cost is $2 per period
rather than $1? [Answer: The EOQ quantity becomes 52, the theoretical annual total cost becomes
$5,404, and the 10-week cost is $1,039 ( ]
RELATED PROBLEMS � 14.18, 14.20, 14.21, 14.22
$5,404 * 110>522.
$3,798 * 110 weeks>52 weeks2 = $730
Annual Setup cost + Holding cost = $1,900 + 1,898 = $3,798
Annual Holding cost = 732 * 1$1 * 52 weeks2 = $1,898
Annual Setup cost = 19 * $100 = $1,900
Setups = 1,404>73 = 19 per year
Part Period Balancing Part period balancing (PPB) is a more dynamic approach to bal-
ance setup and holding cost.3 PPB uses additional information by changing the lot size to reflect
requirements of the next lot size in the future. PPB attempts to balance setup and holding cost for
3J. J. DeMatteis, “An Economic Lot-Sizing Technique: The Part-Period Algorithms,” IBM Systems Journal 7 (1968):
30–38.
Part period balancing
(PPB)
An inventory ordering technique
that balances setup and holding
costs by changing the lot size to
reflect requirements of the next
lot size in the future.

450 PART 3 Managing Operations
Economic part period
(EPP)
A period of time when the ratio
of setup cost to holding cost is
equal.
EXAMPLE 6 �
Lot sizing with part
period balancing
Speaker Kits, Inc., wants to compute the costs associated with lot sizing using part period balancing. It
will use a setup cost of $100 and a $1 holding cost.
APPROACH � Using the same costs and production schedule as Examples 3 and 4, we develop a
format that helps us compute the PPB quantity and apply that to our net requirements plan.
SOLUTION � The procedure for computing the order releases of 80, 100, and 55 is shown in the
following PPB calculation. In the second table, we apply the PPB order quantities to the net require-
ments plan.
EPP is 100 (setup cost divided by holding cost $100/$1). The first lot is to cover periods 2, 3, 4, and
5 and is 80.
The total costs are $490, with setup costs totaling $300 and holding costs totaling $190.
INSIGHT � Both the EOQ and PPB approaches to lot sizing balance holding cost and ordering
cost. But PPB places an order each time holding cost equals ordering cost, while EOQ takes a longer
averaging approach.
LEARNING EXERCISE � What is the impact on total cost if holding cost is $2 per period
rather than $1? [Answer: With higher holding costs [PPB becomes 100/2 = 50], reorder points become
more frequent, with orders now being placed for 70 units in period 1, 50 in period 4, 60 in period 6, and
55 in period 9.]
RELATED PROBLEMS � 14.19, 14.20, 14.21, 14.22
=
known demands. Part period balancing develops an economic part period (EPP), which is the
ratio of setup cost to holding cost. For our Speaker Kits example, EPP $100/$1 100 units.
Therefore, holding 100 units for one period would cost $100, exactly the cost of one setup.
Similarly, holding 50 units for two periods also costs $100 (2 periods $1 50 units). PPB
merely adds requirements until the number of part periods approximates the EPP—in this case,
100. Example 6 shows the application of part period balancing.
**
==
PPB Calculations
Trial Lot Size
Periods (cumulative net Costs
Combined requirements) Part Periods Setup Holding Total
2 30 0 40 units held for 1 period = $40
2, 3 70 40 = 40 × 1 10 units held for 3 periods = $30
2, 3, 4 70 40
2, 3, 4, 5 80 70 = 40 × 1 + 10 × 3 100 + 70 = 170
2, 3, 4, 5, 6 120 230 = 40 × 1 + 10 × 3 + 40 × 4
(Therefore, combine periods 2 through 5; 70 is as close to our EPP of 100 as we are going to get.)
6 40 0
6, 7 70 30 = 30 ×1
6, 7, 8 70 30 = 30 × 1 + 0 × 2
6, 7, 8, 9 100 120 = 30 × 1 + 30 × 3 100 + 120 = 220
(Therefore, combine periods 6 through 9; 120 is as close to our EPP of 100 as we are going to get.)
10 55 0 100 + 0 = 100
300 + 190 = 490
MRP Lot Sizing: PPB Technique*
1 2 3 4 5 6 7 8 9 10
Gross requirements 35 30 40 0 10 40 30 0 30 55
Scheduled receipts
Projected on hand 35 35 0 50 10 10 0 60 30 30 0
Net requirements 0 30 0 0 0 40 0 0 0 55
Planned order receipts 80 100 55
Planned order releases 80 100 55
*Holding costs = $1/unit/week; setup cost = $100; gross requirements average per week = 27; lead time = 1 week.

Chapter 14 Material Requirements Planning (MRP) and ERP 451
Wagner-Whitin Algorithm The Wagner-Whitin procedure is a dynamic programming
model that adds some complexity to the lot-size computation. It assumes a finite time horizon
beyond which there are no additional net requirements. It does, however, provide good results.4
Lot-Sizing Summary In the three Speaker Kits lot-sizing examples, we found the following costs:
Lot-for-lot $700
EOQ $730
Part period balancing $490
These examples should not, however, lead operations personnel to hasty conclusions about the
preferred lot-sizing technique. In theory, new lot sizes should be computed whenever there is a
schedule or lot-size change anywhere in the MRP hierarchy. However, in practice, such changes
cause the instability and system nervousness referred to earlier in this chapter. Consequently,
such frequent changes are not made. This means that all lot sizes are wrong because the produc-
tion system cannot respond to frequent changes.
In general, the lot-for-lot approach should be used whenever low-cost deliveries can be
achieved. Lot-for-lot is the goal. Lots can be modified as necessary for scrap allowances, process
constraints (for example, a heat-treating process may require a lot of a given size), or raw mater-
ial purchase lots (for example, a truckload of chemicals may be available in only one lot size).
However, caution should be exercised prior to any modification of lot size because the modifica-
tion can cause substantial distortion of actual requirements at lower levels in the MRP hierarchy.
When setup costs are significant and demand is reasonably smooth, part period balancing (PPB),
Wagner-Whitin, or even EOQ should provide satisfactory results. Too much concern with lot siz-
ing yields false accuracy because of MRP dynamics. A correct lot size can be determined only
after the fact, based on what actually happened in terms of requirements.
EXTENSIONS OF MRP
In this section, we review three extensions of MRP.
Material Requirements Planning II (MRP II)
Material requirements planning II is an extremely powerful technique. Once a firm has MRP
in place, requirements data can be enriched by resources other than just components. When MRP
is used this way, resource is usually substituted for requirements, and MRP becomes MRP II. It
then stands for material resource planning.
Wagner-Whitin
procedure
A technique for lot-size
computation that assumes a
finite time horizon beyond
which there are no additional
net requirements to arrive at an
ordering strategy.
4We leave discussion of the algorithm to mathematical programming texts. The Wagner-Whitin algorithm yields a cost
of $455 for the data in Examples 4, 5, and 6.
Many MRP programs,
such as Resource
Manager for Excel and
DB, are commercially
available. Resource
Manager’s initial menu
screen is shown here.
A demo program is
available for student use at
www.usersolutions.com.
Material requirements
planning II (MRP II)
A system that allows, with MRP
in place, inventory data to be
augmented by other resource
variables; in this case, MRP
becomes material resource
planning.

www.usersolutions.com

452 PART 3 Managing Operations
Weeks
Lead Time 5 6 7 8
Computer 1 100
Labor-hours: .2 each 20
Machine-hours: .2 each 20
Scrap: 1 ounce fiberglass each 6.25 lbs
Payables: $0 $0
PC board (1 each) 2 100
Labor-hours: .15 each 15
Machine-hours: .1 each 10
Scrap: .5 ounces copper each 3.125 lb
Payables: raw material at $5 each $500
Processors (5 each) 4 500
Labor-hours: .2 each 100
Machine-hours: .2 each 100
Scrap: .01 ounces of acid waste each 0.3125 lb
Payables: processors at $10 each $5,000
So far in our discussion of MRP, we have scheduled products and their components. However,
products require many resources, such as energy and money, beyond the product’s tangible compo-
nents. In addition to these resource inputs, outputs can be generated as well. Outputs can include
such things as scrap, packaging waste, effluent, and carbon emissions. As OM becomes increas-
ingly sensitive to the environmental and sustainability issues, identifying and managing byproducts
becomes increasingly important. MRP II provides a vehicle for doing so. Table 14.4 provides an
example of labor-hours, machine-hours, pounds of scrap, and cash, in the format of a gross require-
ments plan. With MRP II, management can identify both the inputs and outputs as well as the rele-
vant schedule. MRP II provides another tool in OM’s battle for sustainable operations.
MRP II systems are seldom stand-alone programs. Most are tied into other computer software
that provide data to the MRP system or receive data from the MRP system. Purchasing, produc-
tion scheduling, capacity planning, inventory, and warehouse management are a few examples of
this data integration.
Closed-Loop MRP
Closed-loop material requirements planning implies an MRP system that provides feedback to
scheduling from the inventory control system. Specifically, a closed-loop MRP system provides
information to the capacity plan, master production schedule, and ultimately to the production
plan (as shown in Figure 14.8). Virtually all commercial MRP systems are closed-loop.
� TABLE 14.4
Material Resource Planning
(MRP II)
By utilizing the logic of MRP,
resources such as labor,
machine-hours, scrap, and cost
can be accurately determined
and scheduled. Weekly demand
for labor, machine-hours, scrap,
and payables for 100
computers are shown.
Closed-loop MRP
system
A system that provides
feedback to the capacity plan,
master production schedule,
and production plan so planning
can be kept valid at all times.
Priority Management
Develop Master Production Schedule
Prepare Materials Requirements Plan
Detailed Production Activity Control
(Shop Scheduling/Dispatching)
OK? YES
OK? YES
Capacity Management
Planning
(see this chapter)
(see Chapter 13)
Execution (see Chapter 15)
(in repetitive systems
JIT techniques are used)
Evaluate Resource Availability
(Rough Cut)
Determine Capacity Availability
Implement Input/Output Control
OK? NO
OK? NO
Aggregate Production Plan
� FIGURE 14.8 Closed-Loop Material Requirements Planning
LO6: Describe closed-loop
MRP
LO5: Describe MRP II

Chapter 14 Material Requirements Planning (MRP) and ERP 453
Capacity Planning
In keeping with the definition of closed-loop MRP, feedback about workload is obtained from each
work center. Load reports show the resource requirements in a work center for all work currently
assigned to the work center, all work planned, and expected orders. Figure 14.9(a) shows that the
initial load in the milling center exceeds capacity on days 2, 3, and 5. Closed-loop MRP systems
allow production planners to move the work between time periods to smooth the load or at least
bring it within capacity. (This is the “capacity planning” part of Figure 14.8.) The closed-loop MRP
system can then reschedule all items in the net requirements plan (see Figure 14.9[b]).
Load report
A report for showing the
resource requirements in a work
center for all work currently
assigned there as well as all
planned and expected orders.
� EXAMPLE 7
Order splitting
Kevin Watson, the production planner at Wiz Products, needs to develop a capacity plan for a work
center. He has the production orders shown below for the next 5 days. There are 12 hours available in
the work cell each day. The parts being produced require 1 hour each.
APPROACH � Compute the time available in the work center and the time necessary to complete
the production requirements.
SOLUTION �
Day 1 2 3 4 5
Orders 10 14 13 10 14
Utilization:
Capacity Capacity Over/ New
Units Required Available (Under) Production Production
Day Ordered (hours) (hours) (hours) Planner’s Action Schedule
1 10 10 12 (2) 12
2 14 14 12 2 Split order: move 2 units to day 1 12
3 13 13 12 1 Split order: move 1 unit to day 6 13
or request overtime
4 10 10 12 (2) 12
5 14 12 2 Split order: move 2 units to day 4 12
61
14
Capacity exceeded
on days 2, 3, and 5
41 2 3 5
Days
14
8
6
4
2
0
10
12
S
ta
n
d
a
rd
L
a
b
o
r-
H
o
u
rs
Available
capacity
Days
(a) (b)
2 orders moved to day 1 from day 2 (a day early)
1 order forced to overtime or to day 6
2 orders moved to day 4 (a day early)
41 2 3 5
14
8
6
4
2
0
10
12
S
ta
n
d
a
rd
L
a
b
o
r-
H
o
u
rs
� FIGURE 14.9
(a) Initial Resource
Requirements Profile for a
Work Center (b) Smoothed
Resource Requirements
Profile for a Work Center
Tactics for smoothing the load and minimizing the impact of changed lead time include the
following:
1. Overlapping, which reduces the lead time, sends pieces to the second operation before the
entire lot is completed on the first operation.
2. Operations splitting sends the lot to two different machines for the same operation. This
involves an additional setup, but results in shorter throughput times, because only part of the
lot is processed on each machine.
3. Order or, lot splitting, involves breaking up the order and running part of it earlier (or later) in
the schedule.
Example 7 shows a brief detailed capacity scheduling example using order splitting to improve
utilization.

454 PART 3 Managing Operations
When the workload consistently exceeds work-center capacity, the tactics just discussed are not
adequate. This may mean adding capacity. Options include adding capacity via personnel,
machinery, overtime, or subcontracting.
MRP IN SERVICES
The demand for many services or service items is classified as dependent demand when it is directly
related to or derived from the demand for other services. Such services often require product-struc-
ture trees, bills-of-material and labor, and scheduling. MRP can make a major contribution to opera-
tional performance in such services. Examples from restaurants, hospitals, and hotels follow.
Restaurants In restaurants, ingredients and side dishes (bread, vegetables, and condiments)
are typically meal components. These components are dependent on the demand for meals. The
meal is an end item in the master schedule. Figure 14.10 shows (a) a product-structure tree and
(b) a bill of material for veal picante, a top-selling entrée in a New Orleans restaurant. Note that
the various components of veal picante (that is, veal, sauce, spinach, and linguini) are prepared
by different kitchen personnel (see part [a] of Figure 14.10). These preparations also require dif-
ferent amounts of time to complete. Figure 14.10(c) shows a bill-of-labor for the veal dish. It lists
the operations to be performed, the order of operations, and the labor requirements for each oper-
ation (types of labor and labor-hours).
Hospitals MRP is also applied in hospitals, especially when dealing with surgeries that
require known equipment, materials, and supplies. Houston’s Park Plaza Hospital and many hos-
pital suppliers, for example, use the technique to improve the scheduling and management of
expensive surgical inventory.
Hotels Marriott develops a bill of material (BOM) and a bill of labor when it renovates each
of its hotel rooms. Marriott managers explode the BOM to compute requirements for materials,
furniture, and decorations. MRP then provides net requirements and a schedule for use by pur-
chasing and contractors.
Distribution Resource Planning (DRP)
When dependent techniques are used in the supply chain, they are called distribution resource
planning (DRP). Distribution resource planning (DRP) is a time-phased stock-replenishment
plan for all levels of the supply chain.
DRP procedures and logic are analogous to MRP. With DRP, expected demand becomes gross
requirements. Net requirements are determined by allocating available inventory to gross
requirements. The DRP procedure starts with the forecast at the retail level (or the most distant
point of the distribution network being supplied). All other levels are computed. As is the case
with MRP, inventory is then reviewed with an aim to satisfying demand. So that stock will arrive
when it is needed, net requirements are offset by the necessary lead time. A planned order release
quantity becomes the gross requirement at the next level down the distribution chain.
DRP pulls inventory through the system. Pulls are initiated when the retail level orders more
stock. Allocations are made to the retail level from available inventory and production after being
adjusted to obtain shipping economies. Effective use of DRP requires an integrated information sys-
tem to rapidly convey planned order releases from one level to the next. The goal of the DRP system
is small and frequent replenishment within the bounds of economical ordering and shipping.5
Distribution resource
planning (DRP)
A time-phased stock-
replenishment plan for all levels
of a distribution network.
5For an expanded discussion of time-phased stock-replenishment plans, see the section “Opportunities in an Integrated
Supply Chain” in Chapter 11 of this text.
INSIGHT � By moving orders, the production planner is able to utilize capacity more effectively
and still meet the order requirements, with only 1 order produced on overtime in day 3.
LEARNING EXERCISE � If the units ordered for day 5 increase to 16, what are the production
planner’s options? [Answer: In addition to moving 2 units to day 4, move 2 units of production to day
6, or request overtime.]
RELATED PROBLEMS � 14.23, 14.24

Chapter 14 Material Requirements Planning (MRP) and ERP 455
Cooked
linguini
#20002Helper one;
Work
Center #2
Part
Number Description
Uncooked
linguini
#30004
Veal
picante
#10001
Asst. Chef;
Work
Center #3
Prepared veal
and sauce
#20003
Spinach
#20004
Chef;
Work
Center #1
Sauce
#30006
Veal
#30005
10001
20002
20003
20004
30004
30005
30006
Veal picante
Cooked linguini
Prepared veal and sauce
Spinach
Uncooked linguini
Veal
Sauce
Quantity
1
1
1
0.1
0.5
1
1
—
—
—
0.94
—
2.15
0.80
Unit of
Measure
Unit
Cost
Serving
Serving
Serving
Bag
Pound
Serving
Serving
Work Center Operation
1
2
3
Assemble dish
Cook linguini
Cook veal
and sauce
Labor Type
Chef
Helper one
Assistant chef
.0041
.0022
.0500
Setup Time
Labor-Hours
Run Time
.0069
.0005
.0125
(a) PRODUCT STRUCTURE TREE
(b) BILL OF MATERIALS
(c) BILL OF LABOR FOR VEAL PICANTE
� FIGURE 14.10
Product Structure Tree, Bill-
of-Material, and Bill-of-
Labor for Veal Picante
Source: Adapted from John G. Wacker,
“Effective Planning and Cost Control for
Restaurants,” Production and Inventory
Management (Vol. 26, no. 1): 60.
Reprinted by permission of American
Production and Inventory Control Society.
ENTERPRISE RESOURCE PLANNING (ERP)
Advances in MRP II systems that tie customers and suppliers to MRP II have led to the develop-
ment of enterprise resource planning (ERP) systems. Enterprise resource planning (ERP) is
software that allows companies to (1) automate and integrate many of their business processes,
(2) share a common database and business practices throughout the enterprise, and (3) produce
information in real time. A schematic showing some of these relationships for a manufacturing
firm appears in Figure 14.11.
The objective of an ERP system is to coordinate a firm’s whole business, from supplier eval-
uation to customer invoicing. This objective is seldom achieved, but ERP systems are evolving as
umbrella systems that tie together a variety of specialized systems. This is accomplished by
using a centralized database to assist the flow of information among business functions. Exactly
what is tied together, and how, varies on a case-by-case basis. In addition to the traditional com-
ponents of MRP, ERP systems usually provide financial and human resource (HR) management
information. ERP systems also include:
• Supply chain management (SCM) software to support sophisticated vendor communication,
e-commerce, and those activities necessary for efficient warehousing and logistics. The idea
is to tie operations (MRP) to procurement, to materials management, and to suppliers, provid-
ing the tools necessary for effective management of all four areas.
• Customer relationship management (CRM) software for the incoming side of the business.
CRM is designed to aid analysis of sales, target the most profitable customers, and manage
the sales force.
In addition to data integration, ERP software promises reduced transaction costs and fast,
accurate information. A strategic emphasis on just-in-time systems and supply chain integration
AUTHOR COMMENT
ERP tries to integrate all of a
firm’s information.
LO7: Describe ERP
Enterprise resource
planning (ERP)
An information system for
identifying and planning the
enterprise-wide resources
needed to take, make, ship, and
account for customer orders.

456 PART 3 Managing Operations
Inventory
Management
Bills of
Material
Routings
and
Lead Times
Master
Production
Schedule
Sales Order
(order entry, product configuration,
sales management)
Shipping
Distributors,
retailers,
and end users
General
Ledger
Payroll
Accounts
Payable
Invoicing
Supply-Chain Management
Vendor Communication
(schedules, EDI, advanced shipping notice,
e-commerce, etc.)
Accounts
Receivable
MRP ERP
Purchasing
and
Lead Times
Work
Orders
Customer Relationship Management
Finance/
Accounting
� FIGURE 14.11
MRP and ERP Information
Flows, Showing Customer
Relationship Management
(CRM), Supply-Chain
Management (SCM), and
Finance/Accounting
Other functions such as
human resources are often
also included in ERP systems.
Thanks to ERP, the Italian sportswear company Benetton
can probably claim to have the world’s fastest factory and
the most efficient distribution in the garment industry.
Located in Ponzano, Italy, Benetton makes and ships 50
million pieces of clothing each year. That is 30,000 boxes
every day—boxes that must be filled with exactly the items
ordered going to the correct store of the 5,000 Benetton
outlets in 60 countries. This highly automated distribution
center uses only 19 people. Without ERP, hundreds of
people would be needed.
Here is how ERP software works:
1. Ordering: A salesperson in the south Boston store
finds that she is running out of a best-selling blue
sweater. Using a laptop PC, her local Benetton sales
agent taps into the ERP sales module.
2. Availability: ERP’s inventory software simultaneously
forwards the order to the mainframe in Italy and finds
that half the order can be filled immediately from the
Italian warehouse. The rest will be manufactured and
shipped in 4 weeks.
3. Production: Because the blue sweater was originally
created by computer-aided design (CAD), ERP manufac-
turing software passes the specifications to a knitting
machine. The knitting machine makes the sweaters.
4. Warehousing: The blue sweaters are boxed with a
radio frequency ID (RFID) tag addressed to the
Boston store and placed in one of the 300,000 slots in
the Italian warehouse. A robot flies by, reading RFID
tags, picks out any and all boxes ready for the Boston
store, and loads them for shipment.
5. Order tracking: The Boston salesperson logs onto the
ERP system through the Internet and sees that the
sweater (and other items) are completed and being
shipped.
6. Planning: Based on data from ERP’s forecasting and
financial modules, Benetton’s chief buyer decides that
blue sweaters are in high demand and quite profitable.
She decides to add three new hues.
Sources: The Wall Street Journal (April 10, 2007): B1; Frontline Solutions
(April 2003): 54; and MIT Sloan Management Review (Fall 2001): 46–53.
OM in Action � Managing Benetton with ERP Software
drives the desire for enterprise-wide software. The OM in Action box “Managing Benetton with
ERP Software” provides an example of how ERP software helps integrate company operations.

Chapter 14 Material Requirements Planning (MRP) and ERP 457
In an ERP system, data are entered only once into a common, complete, and consistent data-
base shared by all applications. For example, when a Nike salesperson enters an order into his
ERP system for 20,000 pairs of sneakers for Foot Locker, the data are instantly available on the
manufacturing floor. Production crews start filling the order if it is not in stock, accounting prints
Foot Locker’s invoice, and shipping notifies the Foot Locker of the future delivery date. The
salesperson, or even the customer, can check the progress of the order at any point. This is all
accomplished using the same data and common applications. To reach this consistency, however,
the data fields must be defined identically across the entire enterprise. In Nike’s case, this means
integrating operations at production sites from Vietnam to China to Mexico, at business units
across the globe, in many currencies, and with reports in a variety of languages.
Each ERP vendor produces unique products. The major vendors, SAP AG (a German firm),
BEA (Canada), SSAGlobal, American Software, PeopleSoft/Oracle, CMS Software (all of the
U.S.), sell software or modules designed for specific industries (a set of SAP’s modules is shown
in Figure 14.12). However, companies must determine if their way of doing business will fit the
standard ERP module. If they determine that the product will not fit the standard ERP product,
they can change the way they do business to accommodate the software. But such a change can
have an adverse impact on their business process, reducing a competitive advantage.
Alternatively, ERP software can be customized to meet their specific process requirements.
Although the vendors build the software to keep the customization process simple, many compa-
nies spend up to five times the cost of the software to customize it. In addition to the expense, the
major downside of customization is that when ERP vendors provide an upgrade or enhancement
to the software, the customized part of the code must be rewritten to fit into the new version. ERP
programs cost from a minimum of $300,000 for a small company to hundreds of millions of dol-
lars for global giants like Ford and Coca-Cola. It is easy to see, then, that ERP systems are
expensive, full of hidden issues, and time-consuming to install.
Covers all financial related activity:
Covers internal inventory management:
PROMOTE TO DELIVER
Covers front-end
customer-oriented activities:
DESIGN TO MANUFACTURE
Covers internal production activities:
PROCURE TO PAY
Covers sourcing activities:
RECRUIT TO RETIRE
Covers all HR- and payroll-oriented activity:
Accounts receivable
Accounts payable
General ledger
Treasury
Cash management
Asset management
Shop floor reporting
Warehousing
Distribution planning
Forecasting
Replenishment planning
Physical inventory
Material handling
Marketing
Quote and order processing
Transportation
Documentation and labeling
After sales service
Warranty and guarantees
Design engineering
Production engineering
Plant maintenance
Contract/project
management
Subcontractor
management
Vendor sourcing
Purchase requisitioning
Purchase ordering
Purchase contracts
Inbound logistics
Supplier invoicing/matching
Supplier payment/
settlement
Supplier performance Time and attendance Payroll
Travel and expenses
CASH TO CASH
DOCK TO DISPATCH
� FIGURE 14.12 SAP’s Modules for ERP
Source: www.sap.com. © Copyright 2009. SAP AG. All rights reserved.

www.sap.com

458 PART 3 Managing Operations
Advantages and Disadvantages of ERP Systems
We have alluded to some of the pluses and minuses of ERP. Here is a more complete list of both.
Advantages:
1. Provides integration of the supply chain, production, and administrative process.
2. Creates commonality of databases.
3. Can incorporate improved, reengineered, “best processes.”
4. Increases communication and collaboration among business units and sites.
5. Has a software database that is off-the-shelf coding.
6. May provide a strategic advantage over competitors.
Disadvantages:
1. Is very expensive to purchase, and even more costly to customize.
2. Implementation may require major changes in the company and its processes.
3. Is so complex that many companies cannot adjust to it.
4. Involves an ongoing process for implementation, which may never be completed.
5. Expertise in ERP is limited, with staffing an ongoing problem.
ERP in the Service Sector
ERP vendors have developed a series of service modules for such markets as health care, govern-
ment, retail stores, and financial services. Springer-Miller Systems, for example, has created an
ERP package for the hotel market with software that handles all front- and back-office functions.
This system integrates tasks such as maintaining guest histories, booking room and dinner
reservations, scheduling golf tee times, and managing multiple properties in a chain.
PeopleSoft/Oracle combines ERP with supply chain management to coordinate airline meal
preparation. In the grocery industry, these supply chain systems are known as efficient consumer
response (ECR) systems. As is the case in manufacturing, efficient consumer response systems
tie sales to buying, to inventory, to logistics, and to production.
Efficient consumer
response (ECR)
Supply chain management
systems in the grocery industry
that tie sales to buying, to
inventory, to logistics, and to
production.
Material requirements planning (MRP) schedules production
and inventory when demand is dependent. For MRP to work,
management must have a master schedule, precise require-
ments for all components, accurate inventory and purchasing
records, and accurate lead times.
Production should often be lot-for-lot in an MRP system.
When properly implemented, MRP can contribute in a major
way to reduction in inventory while improving customer ser-
vice levels. MRP techniques allow the operations manager to
schedule and replenish stock on a “need-to-order” basis rather
than simply a “time-to-order” basis.
The continuing development of
MRP systems has led to its use with
lean manufacturing techniques. In
addition, MRP can integrate produc-
tion data with a variety of other activi-
ties, including the supply chain and sales.
As a result, we now have integrated database-oriented
enterprise resource planning (ERP) systems. These expen-
sive and difficult-to-install ERP systems, when successful,
support strategies of differentiation, response, and cost
leadership.
CHAPTER SUMMARY
Key Terms
Material requirements planning (MRP)
(p. 436)
Master production schedule (MPS) (p. 436)
Bill of material (BOM) (p. 438)
Modular bills (p. 440)
Planning bills (or kits) (p. 440)
Phantom bills of material (p. 440)
Low-level coding (p. 440)
Lead time (p. 441)
Gross material requirements plan (p. 442)
Net material requirements (p. 443)
Planned order receipt (p. 443)
Planned order release (p. 443)
System nervousness (p. 446)
Time fences (p. 446)
Pegging (p. 446)
Buckets (p. 446)
Bucketless system (p. 447)
Back flush (p. 447)
Supermarket (p. 447)
Lot-sizing decision (p. 447)
Lot-for-lot (p. 447)
Part period balancing (PPB) (p. 449)
Economic part period (EPP) (p. 450)
Wagner-Whitin procedure (p. 451)
Material requirements planning II
(MRP II) (p. 451)
Closed-loop MRP system (p. 452)
Load report (p. 453)
Distribution resource planning
(DRP) (p. 454)
Enterprise resource planning (ERP) (p. 455)
Efficient consumer response (ECR) (p. 458)

Chapter 14 Material Requirements Planning (MRP) and ERP 459
Using Software to Solve MRP Problems
There are many commercial MRP software packages, for companies of all sizes. MRP software for
small and medium-size companies includes User Solutions, Inc., a demo of which is available at www.
usersolutions.com, and MAX, from Exact Software North America, Inc. Software for larger systems is
available from SAP, CMS, BEA, Oracle, i2 Technologies, and many others. The Excel OM software that
accompanies this text includes an MRP module, as does POM for Windows. The use of both is
explained in the following sections.
X Using Excel OM
Using Excel OM’s MRP module requires the careful entry of several pieces of data. The initial MRP
screen is where we enter (1) the total number of occurrences of items in the BOM (including the top
item), (2) what we want the BOM items to be called (i.e., Item no., Part), (3) total number of periods to
be scheduled, and (4) what we want the periods called (i.e., days, weeks).
Excel OM’s second MRP screen provides the data entry for an indented bill of material. Here we
enter (1) the name of each item in the BOM, (2) the quantity of that item in the assembly, and (3) the cor-
rect indent (i.e., parent/child relationship) for each item. The indentations are critical as they provide the
logic for the BOM explosion. The indentations should follow the logic of the product structure tree with
indents for each assembly item in that assembly.
Excel OM’s third MRP screen repeats the indented BOM and provides the standard MRP tableau for
entries. This is shown in Program 14.1 using the data from Examples 1, 2, and 3.
Enter the quantity on hand.
Enter the lead time.
The data in columns A, B, C, D (down to row 15) are entered
on the second screen and automatically transferred here.
Lot size must be ≥1.
� PROGRAM 14.1
Using Excel OM’s MRP
Module to Solve
Examples 1, 2, and 3
P Using POM for Windows
The POM for Windows MRP module can also solve Examples 1 to 3. Up to 18 periods can be analyzed.
Here are the inputs required:
1. Item names: The item names are entered in the left column. The same item name will appear in
more than one row if the item is used by two parent items. Each item must follow its parents.
2. Item level: The level in the indented BOM must be given here. The item cannot be placed at a
level more than one below the item immediately above.
3. Lead-time: The lead time for an item is entered here. The default is 1 week.
4. Number per parent: The number of units of this subassembly needed for its parent is entered
here. The default is 1.
5. On hand: List current inventory on hand once, even if the subassembly is listed twice.

www.usersolutions.com

www.usersolutions.com

460 PART 3 Managing Operations
Solved Problems Virtual Office Hours help is available at www.myomlab.com
B(1)
Alpha
C(1)B(1)
D(2) C(2)
Alpha
C(1)
E(1) F(1)
E(1) F(1)
� SOLUTION
Redraw the product structure with low-level coding.
Then multiply down the structure until the require-
ments of each branch are determined. Then add
across the structure until the total for each is
determined.
B(1)
D(2) C(2)
Alpha
C(1)
E(1) F(1)E(1) F(1) ⎧
⎪
⎨
⎪
⎩
⎧
⎪
⎨
⎪
⎩
Level 0
Level 1
Level 2
Level 3
Alpha = 1
B = 1
D = 2
F = 3
C = 3
E = 3
� SOLVED PROBLEM 14.1
Determine the low-level coding and the quantity of each component necessary to produce 10 units of
an assembly we will call Alpha. The product structure and quantities of each component needed for
each assembly are noted in parentheses.
6. Lot size: The lot size can be specified here. A 0 or 1 will perform lot-for-lot ordering. If another
number is placed here, then all orders for that item will be in integer multiples of that number.
7. Demands: The demands are entered in the end item row in the period in which the items are
demanded.
8. Scheduled receipts: If units are scheduled to be received in the future, they should be listed in
the appropriate time period (column) and item (row). (An entry here in level 1 is a demand; all
other levels are receipts.)
Further details regarding POM for Windows are seen in Appendix IV.

www.myomlab.com

Chapter 14 Material Requirements Planning (MRP) and ERP 461
Es required for left branch:
and Es required for right branch:
Then “explode” the requirement by multiplying each by 10, as
shown in the table to the right:
3 Es required in total
11alpha * 1C * 1E2 = 1 E
11alpha * 1B * 2 C * 1E2 = 2 Es
Quantity Total Requirements
Level Item per Unit for 10 Alpha
0 Alpha 1 10
1 B 1 10
2 C 3 30
2 D 2 20
3 E 3 30
3 F 3 30
� SOLVED PROBLEM 14.2
Using the product structure for Alpha in Solved Problem 14.1, and
the following lead times, quantity on hand, and master production
schedule, prepare a net MRP table for Alphas.
Lead Quantity
Item Time on Hand
Alpha 1 10
B 2 20
C 3 0
D 1 100
E 1 10
F 1 50
Master Production Schedule for Alpha
Period 6 7 8 9 10 11 12 13
Gross requirements 50 50 100
� SOLUTION
See the chart on the following page.

4
6
2
P
A
R
T
3
M
a
n
a
g
in
g
O
p
e
ra
tio
n
s
Gross Requirements 50 50 100
1010
40
40 50 100 100
40(A) 50(A) 100(A)
40(A)40(B) 200(B) + 50(A) 100(A)100(B)
40(B) 100(B) 200(B)
100100 60
0 40 200
0 40 200
200400
0
40(C) 40(C) 100(C) 100(C)250(C)
1010
30 40 100 100250
30 40 100 100250
30 40 100 100250
40(C) 40(C) 100(C) 100(C)250(C)
5050 10 —
0 30 100 100250
30 100 100250
30 100 100250
40 250 100100
40
40
40
1004040
250 100100
250 100
2020
20 50 100
20 50 100
5020 100
50
40 50
100
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
Gross Requirements
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
Gross Requirements
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
Gross Requirements
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
Gross Requirements
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
Gross Requirements
Scheduled Receipts
Projected On Hand
Net Requirements
Planned Order Receipts
Planned Order Releases
1 2 3 4 5
Period (week, day)
6 7 8 9 10 11 12 13
Lot
Size
Lead
Time (# of
Periods)
On
Hand
Safety
Stock
Allo-
cated
Low-
Level
Code
Item
ID
Alpha
(A)
0——101
Lot-
for-
Lot
B1——202
Lot-
for-
Lot
C2——03
Lot-
for-
Lot
D2——1001
Lot-
for-
Lot
E3——101
Lot-
for-
Lot
F3——501
Lot-
for-
Lot
Net Material Requirements Planning Sheet for Alpha
The letter in parentheses (A) is the source of the demand.

Chapter 14 Material Requirements Planning (MRP) and ERP 463
�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Ikon’s attempt at ERP: The giant office technology firm faces hurdles with ERP implementation.
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Wu, Jen-Hur, et al. “Using Multiple Variables Decision-Making
Analysis for ERP Selection.” International Journal of
Manufacturing Technology and Management 18, no. 2
(2009): 228.

www.myomlab.com

www.pearsonhighered.com/heizer

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Chapter Outline
GLOBAL COMPANY PROFILE: DELTA AIR LINES
The Importance of Short-Term
Scheduling 468
Scheduling Issues 468
Scheduling Process-Focused
Facilities 471
Loading Jobs 472
Sequencing Jobs 478
Finite Capacity Scheduling (FCS) 484
Scheduling Repetitive Facilities 485
Scheduling Services 486
� Design of Goods and Services
� Managing Quality
� Process Strategy
� Location Strategies
� Layout Strategies
� Human Resources
� Supply-Chain Management
� Inventory Management
� Scheduling
� Aggregate
� Short-Term
� Maintenance
Short-Term Scheduling
465

GLOBAL COMPANY PROFILE: DELTA AIR LINES
SCHEDULING AIRPLANES WHEN WEATHER IS THE ENEMY
O
perations managers at airlines learn to
expect the unexpected. Events that require
rapid rescheduling are a regular part of life.
Throughout the ordeals of tornadoes, ice
storms, and snowstorms, airlines across the globe
struggle to cope with delays, cancellations, and furious
passengers. The inevitable changes to the schedule
often create a ripple effect that impacts passengers
at dozens of airports in the network. Close to 10% of
12
6
3
1
2
11
10
5
4
7
8
12
3
1
210
5
4
7
8
9
6
3
111
10
5
4
7
8
9
4 A.M.
FORECAST:
Rain with a
chance of light
snow for Atlanta.
ACTION:
Discuss status of
planes and
possible need for
cancellations.
10 A.M.
FORECAST:
Freezing rain
after 5 P.M.
ACTION:
Ready deicing
trucks; develop
plans to cancel
50% to 80% of
flights after 6 P.M.
1:30 P.M.
FORECAST:
Rain changing to
snow.
ACTION:
Cancel half the
flights from
6 P.M. to 10 A.M.;
notify passengers
and reroute
planes.
5 P.M.
FORECAST:
Less snow than
expected.
ACTION:
Continue calling
passengers and
arrange
alternate flights.
10 P.M.
FORECAST:
Snow tapering
off.
ACTION:
Find hotels for
1,600
passengers
stranded by the
storm.
12
6
3
1
2
11
10
5
4
7
9
8
12
6
3
1
2
11
10
5
4
7
8
6
11
2
12
99
Here is what Delta officials had to do one
December day when a storm bore
down on Atlanta.
To improve flight rescheduling efforts, Delta employees monitor giant screens that display meteorological charts,
weather patterns, and maps of Delta flights at its Operations Control Center in Atlanta.
466
Delta Air Lines’s flights are disrupted in a typical year,
half because of weather; the cost is $440 million in lost
revenue, overtime pay, and food and lodging vouchers.
Now Delta is taking the sting out of the scheduling
nightmares that come from weather-related problems
with its $33-million high-tech nerve center adjacent to
the Hartsfield-Jackson Atlanta International Airport.
From computers to telecommunications systems to
deicers, Delta’s Operations Control Center more quickly

Weather-related disruptions can
create major scheduling and
expensive snow removal issues for
airlines (left), just as they create
major inconveniences for
passengers (right).
In an effort to maintain
schedules, Delta Air Lines
uses elaborate equipment as
shown here for ice removal.
notifies customers of schedule changes, reroutes
flights, and gets jets into the air. The Operations Control
Center’s job is to keep flights flowing as smoothly as
possible in spite of the disruptions.
With earlier access to information, the center’s
staff of 18 pores over streams of data transmitted
by computers and adjusts to changes quickly. Using
mathematical scheduling models described in this
chapter, Delta decides on schedule and route
changes. This means coordinating incoming and
outgoing aircraft, ensuring that the right crews are on
hand, rescheduling connections to coordinate arrival
times, and making sure information gets to passengers
as soon as possible.
Delta’s software, called the Inconvenienced
Passenger Rebooking System, notifies passengers of
cancellations or delays, and even books them onto
rival airlines if necessary. With 150,000 passengers
flying into and out of Atlanta every day, Delta estimates
its scheduling efforts save $35 million a year.
DELTA AIR LINES �
467

468 PART 3 Managing Operations
LO1: Explain the relationship between
short-term scheduling, capacity
planning, aggregate planning, and
a master schedule 468
LO2: Draw Gantt loading and
scheduling charts 474
LO3: Apply the assignment method for
loading jobs 475
Chapter 15 Learning Objectives
LO4: Name and describe each of the
priority sequencing rules 479
LO5: Use Johnson’s rule 483
LO6: Define finite capacity scheduling 484
LO7: Use the cyclical scheduling
technique 488
THE IMPORTANCE OF SHORT-TERM SCHEDULING
Delta Air Lines doesn’t schedule just its 753 aircraft every day. It also schedules over 10,000
pilots and flight attendants to accommodate passengers who wish to reach their destinations.
This schedule, based on huge computer programs, plays a major role in satisfying customers.
Delta finds competitive advantage with its flexibility for last-minute adjustments to demand and
weather disruptions.
Manufacturing firms also make schedules that match production to customer demands.
Lockheed Martin’s Dallas plant schedules machines, tools, and people to make aircraft parts.
Lockheed’s mainframe computer downloads schedules for parts production into a flexible
machining system (FMS) in which a manager makes the final scheduling decision. The FMS
allows parts of many sizes or shapes to be made, in any order. This scheduling versatility results
in parts produced on a just-in-time basis, with low setup times, little work-in-process, and high
machine utilization. Efficient scheduling is how companies like Lockheed Martin meet due dates
promised to customers and face time-based competition.
The strategic importance of scheduling is clear:
• Effective scheduling means faster movement of goods and services through a facility. This
means greater use of assets and hence greater capacity per dollar invested, which, in turn,
lowers cost.
• Added capacity, faster throughput, and the related flexibility mean better customer service
through faster delivery.
• Good scheduling also contributes to realistic commitments and hence dependable delivery.
SCHEDULING ISSUES
Scheduling deals with the timing of operations. The types of scheduling decisions made in five
organizations—a hospital, a college, a manufacturer, a restaurant, and an airline—are shown in
Table 15.1. As you can see from Figure 15.1, a sequence of decisions affects scheduling.
Schedule decisions begin with capacity planning, which involves total facility and equipment
resources available (discussed in Chapter 7 and Supplement 7). Capacity plans are usually
annual or quarterly as new equipment and facilities are purchased or discarded. Aggregate
planning (Chapter 13) makes decisions regarding the use of facilities, inventory, people, and
outside contractors. Aggregate plans are typically monthly, and resources are allocated in terms
of an aggregate measure such as total units, tons, or shop hours. However, the master schedule
breaks down the aggregate plan and develops a schedule for specific products or product lines
for each week. Short-term schedules then translate capacity decisions, aggregate (intermediate)
planning, and master schedules into job sequences and specific assignments of personnel, mate-
rials, and machinery. In this chapter, we describe the narrow issue of scheduling goods and
services in the short run (that is, matching daily or hourly requirements to specific personnel and
equipment).
The objective of scheduling is to allocate and prioritize demand (generated by either forecasts
or customer orders) to available facilities. Two significant factors in achieving this allocation and
prioritizing are (1) the type of scheduling, forward or backward, and (2) the criteria for priorities.
We discuss these two topics next.
LO1: Explain the
relationship between short-
term scheduling, capacity
planning, aggregate
planning, and a master
schedule
AUTHOR COMMENT
Good scheduling means
lower costs and faster and
more dependable delivery.
AUTHOR COMMENT
Scheduling decisions range
from years, for capacity
planning, to minutes/
hours/days, called short-
term scheduling. This chapter
focuses on the latter.

Chapter 15 Short-Term Scheduling 469
Organization Managers Schedule the Following:
Arnold Palmer Hospital Operating room use
Patient admissions
Nursing, security, maintenance staffs
Outpatient treatments
University of Missouri Classrooms and audiovisual equipment
Student and instructor schedules
Graduate and undergraduate courses
Lockheed Martin factory Production of goods
Purchases of materials
Workers
Hard Rock Cafe Chef, waiters, bartenders
Delivery of fresh foods
Entertainers
Opening of dining areas
Delta Air Lines Maintenance of aircraft
Departure timetables
Flight crews, catering, gate, and ticketing personnel
� TABLE 15.1
Scheduling Decisions
Capacity Plan for New Facilities
Adjust capacity to the demand suggested by strategic plan
Aggregate Production Plan for All Bikes
(Determine personnel or subcontracting necessary to
match aggregate demand to existing facilities/capacity)
Master Production Schedule for Bike Models
Work Assigned to Specific Personnel and Work Centers
(Determine weekly capacity schedule)
Make finite capacity schedule by matching specific
tasks to specific people and machines
Capacity Planning
(Long term; years)
Changes in Facilities
Changes in Equipment
See Chapter 7 and Supplement 7
Aggregate Planning
(Intermediate term; quarterly or monthly)
Facility utilization
Personnel changes
Subcontracting
See Chapter 13
Master Schedule
(Intermediate term; weekly)
Material requirements planning
Disaggregate the aggregate plan
See Chapters 13 and 14
Month 1 2
Bike Production 800 850
1
100
100
2
200
Month 1
3
100
100
Week
Model 22
Model 24
Model 26
4
200
5
150
100
6
200
7
100
100
8
200
Assemble
Model 22 in
work center 6
Month 2
Short-Term Scheduling
(Short term; days, hours, minutes)
Work center loading
Job sequencing/dispatching
See this chapter
� FIGURE 15.1 The Relationship between Capacity Planning, Aggregate Planning, Master Schedule, and
Short-Term Scheduling for a Bike Co.
VIDEO 15.1
Scheduling at Hard Rock

470 PART 3 Managing Operations
Forward scheduling
Scheduling that begins the
schedule as soon as the
requirements are known.
Backward scheduling
Scheduling that begins with the
due date and schedules the final
operation first and the other job
steps in reverse order.
U.S. Steel maintains its world-
class operation by automating the
scheduling of people, machines,
and tools through its cold-
reduction-mill control room.
Computerized scheduling software
helps managers monitor
production.
Forward and Backward Scheduling
Scheduling involves assigning due dates to specific jobs, but many jobs compete simultaneously
for the same resources. To help address the difficulties inherent in scheduling, we can categorize
scheduling techniques as (1) forward scheduling and (2) backward scheduling.
Forward scheduling starts the schedule as soon as the job requirements are known. Forward
scheduling is used in a variety of organizations such as hospitals, clinics, fine-dining restaurants,
and machine tool manufacturers. In these facilities, jobs are performed to customer order, and
delivery is often requested as soon as possible. Forward scheduling is usually designed to pro-
duce a schedule that can be accomplished even if it means not meeting the due date. In many
instances, forward scheduling causes a buildup of work-in-process inventory.
Backward scheduling begins with the due date, scheduling the final operation first. Steps in
the job are then scheduled, one at a time, in reverse order. By subtracting the lead time for each
item, the start time is obtained. However, the resources necessary to accomplish the schedule
may not exist. Backward scheduling is used in many manufacturing environments, as well as ser-
vice environments such as catering a banquet or scheduling surgery. In practice, a combination of
forward and backward scheduling is often used to find a reasonable trade-off between what can
be achieved and customer due dates.
Machine breakdowns, absenteeism, quality problems, shortages, and other factors further
complicate scheduling. (See the OM in Action box “Scheduling Workers Who Fall Asleep Is a
Killer—Literally.”) Consequently, assignment of a date does not ensure that the work will be per-
formed according to the schedule. Many specialized techniques have been developed to aid in
preparing reliable schedules.
Scheduling Criteria
The correct scheduling technique depends on the volume of orders, the nature of operations, and
the overall complexity of jobs, as well as the importance placed on each of four criteria. These
four criteria are:
1. Minimize completion time: This criterion is evaluated by determining the average comple-
tion time per job.
2. Maximize utilization: This is evaluated by determining the percent of the time the facility is utilized.
3. Minimize work-in-process (WIP) inventory: This is evaluated by determining the average num-
ber of jobs in the system. The relationship between the number of jobs in the system and WIP
inventory will be high. Therefore, the fewer the number of jobs that are in the system, the lower
the inventory.
4. Minimize customer waiting time: This is evaluated by determining the average number of
late days.

Chapter 15 Short-Term Scheduling 471
The accidents at the nuclear plants at Three Mile Island,
Pennsylvania, and Chernobyl, Russia, and the disaster at
Bhopal, India, all had one thing in common: they occurred
between midnight and 4:00 A.M. These facilities had other
problems, but the need for sleep simply results in unreliable
workplace performance. In some cases, unable to cope
with a constantly changing work schedule, workers just
plain fall asleep.
The same is true for pilots. Their inconsistent schedules
and long flights often force them to snooze in the cockpit to
get enough sleep. (Delta’s flight from Atlanta to Mumbai,
India, for example, takes about 18 hours.) The Bombardier
regional jet flying from Honolulu to Hilo, Hawaii, encoun-
tered a serious problem in 2008 as it flew over Maui: both
pilots were so fast asleep that they failed to respond to
frantic calls from air-traffic controllers for 18 minutes. (The
plane, with 40 passengers, overshot its destination as it flew
26 miles over the Pacific.) One FedEx pilot even
complained of falling asleep while taxiing to take off.
Millions of people work in industries that maintain
round-the-clock schedules. Employees from graveyard
shifts report tales of seeing sleeping assembly-line workers
fall off their stools, batches of defective parts sliding past
dozing inspectors, and exhausted forklift operators crashing
into walls. Virtually all shift workers are sleep deprived. And
the National Highway Traffic Safety Administration indicates
that drowsiness may be a factor in as many as 100,000
crashes annually.
Scheduling is a major problem in firms with 24/7 shifts,
but some managers are taking steps to deal with schedule-
related sleep problems among workers. Motorola, Dow
Chemical, Detroit Edison, Pennzoil, and Exxon, for
instance, all give workers several days off between shift
changes.
Operations managers can make shift work less
dangerous with shifts that do not exceed 12 hours, that
encourage 8 hours of sleep each day, and that have
extended time off between shift changes. As more is
learned about the economic toll of non-daytime schedules
and changing schedules, companies are learning to
improve scheduling.
Sources: The Wall Street Journal (September 12, 2008): A1, A14 and
(October 25, 2009): A:1; and Air Safety and Health (January 2004): 14.
These four criteria are used in this chapter, as they are in industry, to evaluate scheduling perfor-
mance. In addition, good scheduling approaches should be simple, clear, easily understood, easy
to carry out, flexible, and realistic.
Table 15.2 provides an overview of different processes and approaches to scheduling.
We now examine scheduling in process-focused facilities, in repetitive facilities, and in the
service sector.
SCHEDULING PROCESS-FOCUSED FACILITIES
Process-focused facilities (also known as intermittent or job-shop facilities),1 as we see in
Table 15.2, are high-variety, low-volume systems commonly found in manufacturing and service
organizations. These are production systems in which products are made to order. Items made
under this system usually differ considerably in terms of materials used, order of processing, pro-
cessing requirements, time of processing, and setup requirements. Because of these differences,
scheduling can be complex. To run a facility in a balanced and efficient manner, the manager
needs a production planning and control system. This system should:
• Schedule incoming orders without violating capacity constraints of individual work
centers.
• Check the availability of tools and materials before releasing an order to a department.
• Establish due dates for each job and check progress against need dates and order lead
times.
• Check work in progress as jobs move through the shop.
• Provide feedback on plant and production activities.
• Provide work efficiency statistics and monitor operator times for payroll and labor distribu-
tion analyses.
1Much of the literature on scheduling is about manufacturing; therefore, the traditional term job-shop scheduling is
often used.
AUTHOR COMMENT
The facilities discussed here
are built around processes.
OM in Action � Scheduling Workers Who Fall Asleep Is a Killer—Literally

472 PART 3 Managing Operations
Whether the scheduling system is manual or automated, it must be accurate and relevant. This
means it requires a production database with both planning and control files. Three types of
planning files are:
1. An item master file, which contains information about each component the firm produces or
purchases.
2. A routing file, which indicates each component’s flow through the shop.
3. A work-center master file, which contains information about the work center, such as capac-
ity and efficiency.
Control files track the actual progress made against the plan for each work order.
LOADING JOBS
Loading means the assignment of jobs to work or processing centers. Operations managers
assign jobs to work centers so that costs, idle time, or completion times are kept to a minimum.
Loading work centers takes two forms.2 One is oriented to capacity; the second is related to
assigning specific jobs to work centers.
First, we examine loading from the perspective of capacity via a technique known as
input–output control. Then, we present two approaches used for loading: Gantt charts and the
assignment method of linear programming.
Input–Output Control
Many firms have difficulty scheduling (that is, achieving effective throughput) because they
overload the production processes. This often occurs because they do not know actual performance
in the work centers. Effective scheduling depends on matching the schedule to performance.
Lack of knowledge about capacity and performance causes reduced throughput.
Input–output control is a technique that allows operations personnel to manage facility work
flows. If the work is arriving faster than it is being processed, the facility is overloaded, and a
backlog develops. Overloading causes crowding in the facility, leading to inefficiencies and qual-
ity problems. If the work is arriving at a slower rate than jobs are being performed, the facility is
Loading
The assigning of jobs to work
or processing centers.
2Note that this discussion can apply to facilities that might be called a “shop” in a manufacturing firm, a “unit” in a hospital,
or a “department” in an office or a large kitchen.
Input–output control
A system that allows operations
personnel to manage facility
work flows by tracking work
added to a work center and its
work completed.
Process-focused facilities (job shops)
• Focus is on generating a forward-looking schedule.
• MRP generates due dates that are refined with finite capacity scheduling techniques.
• Examples: foundries, machine shops, cabinet shops, print shops, many restaurants, and the
fashion industry.
Work cells (focused facilities that process families of similar components)
• Focus is on generating a forward-looking schedule.
• MRP generates due dates, and subsequent detail scheduling/dispatching is done at the work cell
with kanbans and priority rules.
• Examples: work cells at ambulance manufacturer Wheeled Coach, aircraft engine rebuilder Standard
Aero, greeting-card maker Hallmark.
Repetitive facilities (assembly lines)
• Focus is on generating a forward-looking schedule that is achieved by balancing the line with
traditional assembly-line techniques.
• Pull techniques, such as JIT and kanban, signal component scheduling to support the assembly line.
• Challenging scheduling problems typically occur only when the process is new or when products or
models change.
• Examples: assembly lines for a wide variety of products from autos to home appliances and computers.
Product-focused facilities (continuous)
• Focus is on generating a forward-looking schedule that can meet a reasonably stable demand with
the existing fixed capacity.
• Capacity in such facilities is usually limited by long-term capital investment.
• Capacity is usually known, as is the setup and run time for the limited range of products.
• Examples: facilities with very high volume production and limited-variety products such as paper on huge
machines at International Paper, beer in a brewery at Anheuser-Busch, or rolled steel in a Nucor plant.
� TABLE 15.2
Different Processes Suggest
Different Approaches to
Scheduling

Chapter 15 Short-Term Scheduling 473
� EXAMPLE 1
Input-output
control
DNC Machining, Inc., manufactures driveway security fences and gates. It wants to develop an
input–output control report for the aluminum machining work center for 5 weeks (weeks 6/6 through
7/4). The planned input is 280 standard hours per week. The actual input is close to this figure, varying
between 250 and 285. Output is scheduled at 320 standard hours, which is the assumed capacity. A
backlog exists in the work center.
APPROACH � DNC uses schedule information to create Figure 15.2, which monitors the
workload-capacity relationship at the work center.
SOLUTION � The deviations between scheduled input and actual output are shown in Figure 15.2.
Actual output (270 hours) is substantially less than planned. Therefore, neither the input plan nor the
output plan is being achieved.
Week
Ending
Planned
Input
Actual
Input
Planned
Output
Actual
Output
*Sum of actual inputs minus sum of actual outputs = cumulative change in backlog
6/6 6/13 6/20 6/27 7/4
280
270 250 280 285 280
280 280 280 280
320 320 320 320
270270270270
Explanation:
270 input,
270 output, implies
0 change.
7/11
Explanation: 250 input,
270 output, implies –20
change. (20 standard
hours less work in the
work center)
Work Center DNC Milling (In standard hours)
–10 – 40 – 40 – 35
Cumulative
Deviation
Cumulative
Deviation
– 50 –100 –150 –200
Cumulative
Change
in Backlog*
0 – 20 –10 +5
� FIGURE 15.2
Input–Output Control
INSIGHT � The backlog of work in this work center has actually increased by 5 hours by week
6/27. This increases work-in-process inventory, complicating the scheduling task and indicating the
need for manager action.
LEARNING EXERCISE � If actual output for the week of 6/27 was 275 (instead of 270), what
changes? [Answer: Output cumulative deviation now is –195, and cumulative change in backlog is 0.]
RELATED PROBLEM � 15.21
Input–output control can be maintained by a system of ConWIP cards, which control
the amount of work in a work center. ConWIP is an acronym for constant work-in-process. The
ConWIP card travels with a job (or batch) through the work center. When the job is finished,
the card is released and returned to the initial workstation, authorizing the entry of a new batch
into the work center. The ConWIP card effectively limits the amount of work in the work center,
controls lead time, and monitors the backlog.
The options available to operations personnel to manage facility work flow include the following:
1. Correcting performances
2. Increasing capacity
3. Increasing or reducing input to the work center by (a) routing work to or from other work
centers, (b) increasing or decreasing subcontracting, (c) producing less (or producing more)
Producing less is not a popular solution, but the advantages can be substantial. First, customer-
service levels may improve because units may be produced on time. Second, efficiency may actually
improve because there is less work in process cluttering the work center and adding to overhead
costs. Third, quality may improve because less work-in-process hides fewer problems.
ConWIP cards
Cards that control the amount
of work in a work center, aiding
input–output control.
underloaded, and the work center may run out of work. Underloading the facility results in idle
capacity and wasted resources. Example 1 shows the use of input–output controls.

474 PART 3 Managing Operations
Gantt charts
Planning charts used to
schedule resources and
allocate time.
EXAMPLE 2 �
Gantt load chart
A New Orleans washing machine manufacturer accepts special orders for machines to be used in such
unique facilities as submarines, hospitals, and large industrial laundries. The production of each
machine requires varying tasks and durations. The company wants to build a load chart for the week of
March 8.
APPROACH � The Gantt chart is selected as the appropriate graphical tool.
SOLUTION � Figure 15.3 shows the completed Gantt chart.
Processing
Work
Center
Day
Metalworks
Mechanical
Electronics
Painting
Job 408
Job 295 Job 408 Job 349
Job 349
Job 349 Job 350
Job 349 Job 408
Monday Tuesday Wednesday Thursday Friday
Center not available
(e.g., maintenance
time, repairs, shortages)
Unscheduled
� FIGURE 15.3
Gantt Load Chart for the
Week of March 8
The Gantt load chart has a major limitation: It does not account for production variability
such as unexpected breakdowns or human errors that require reworking a job. Consequently,
the chart must also be updated regularly to account for new jobs and revised time estimates.
A Gantt schedule chart is used to monitor jobs in progress (and is also used for project sched-
uling). It indicates which jobs are on schedule and which are ahead of or behind schedule. In
practice, many versions of the chart are found. The schedule chart in Example 3 places jobs in
progress on the vertical axis and time on the horizontal axis.
LO2: Draw Gantt loading
and scheduling charts
Gantt Charts
Gantt charts are visual aids that are useful in loading and scheduling. The name is derived from
Henry Gantt, who developed them in the late 1800s. The charts show the use of resources, such
as work centers and labor.
When used in loading, Gantt charts show the loading and idle times of several departments,
machines, or facilities. They display the relative workloads in the system so that the manager
knows what adjustments are appropriate. For example, when one work center becomes over-
loaded, employees from a low-load center can be transferred temporarily to increase the work-
force. Or if waiting jobs can be processed at different work centers, some jobs at high-load
centers can be transferred to low-load centers. Versatile equipment may also be transferred
among centers. Example 2 illustrates a simple Gantt load chart.
INSIGHT � The four work centers process several jobs during the week. This particular chart indi-
cates that the metalworks and painting centers are completely loaded for the entire week. The mechan-
ical and electronic centers have some idle time scattered during the week. We also note that the
metalworks center is unavailable on Tuesday, and the painting center is unavailable on Thursday, per-
haps for preventive maintenance.
LEARNING EXERCISE � What impact results from the electronics work center closing on
Tuesday for preventive maintenance? [Answer: none.]
RELATED PROBLEM � 15.1b

Chapter 15 Short-Term Scheduling 475
� EXAMPLE 3
Gantt scheduling
chart
First Printing in Winter Park, Florida, wants to use a Gantt chart to show the scheduling of three orders,
jobs A, B, and C.
APPROACH � In Figure 15.4, each pair of brackets on the time axis denotes the estimated start-
ing and finishing of a job enclosed within it. The solid bars reflect the actual status or progress of the
job. We are just finishing day 5.
SOLUTION �
Job Day
1
Day
2
Day
3
Day
4
Day
5
Day
6
Day
7
Day
8
A
B
C
Now
Maintenance
Start of an
activity
End of an
activity
Scheduled
activity time
allowed
Actual work
progress
Nonproduction
time
Point in time
when chart is
reviewed
Gantt scheduling
chart symbols:
� FIGURE 15.4
Gantt Scheduling Chart for
Jobs A, B, and C at a Printing
Firm
INSIGHT � Figure 15.4 illustrates that job A is about a half-day behind schedule at the end
of day 5. Job B was completed after equipment maintenance. We also see that job C is ahead of
schedule.
LEARNING EXERCISE � Redraw the Gantt chart to show that job A is a half-day ahead of
schedule. [Answer: The orangish bar now extends all the way to the end of the activity.]
RELATED PROBLEMS � 15.1a, 15.2
Assignment Method
The assignment method involves assigning tasks or jobs to resources. Examples include assign-
ing jobs to machines, contracts to bidders, people to projects, and salespeople to territories. The
objective is most often to minimize total costs or time required to perform the tasks at hand. One
important characteristic of assignment problems is that only one job (or worker) is assigned to
one machine (or project).
Each assignment problem uses a table. The numbers in the table will be the costs or times
associated with each particular assignment. For example, if First Printing has three available
typesetters (A, B, and C) and three new jobs to be completed, its table might appear as follows.
The dollar entries represent the firm’s estimate of what it will cost for each job to be completed
by each typesetter.
Assignment method
A special class of linear
programming models that
involves assigning tasks or
jobs to resources.
3Opportunity costs are those profits forgone or not obtained.
Typesetter
Job A B C
R-34 $11 $14 $ 6
S-66 $ 8 $10 $11
T-50 $ 9 $12 $ 7
LO3: Apply the
assignment method for
loading jobs
The assignment method involves adding and subtracting appropriate numbers in the table to
find the lowest opportunity cost3 for each assignment. There are four steps to follow:
1. Subtract the smallest number in each row from every number in that row and then, from the
resulting matrix, subtract the smallest number in each column from every number in that

476 PART 3 Managing Operations
column. This step has the effect of reducing the numbers in the table until a series of zeros,
meaning zero opportunity costs, appear. Even though the numbers change, this reduced
problem is equivalent to the original one, and the same solution will be optimal.
2. Draw the minimum number of vertical and horizontal straight lines necessary to cover all
zeros in the table. If the number of lines equals either the number of rows or the number of
columns in the table, then we can make an optimal assignment (see step 4). If the number of
lines is less than the number of rows or columns, we proceed to step 3.
3. Subtract the smallest number not covered by a line from every other uncovered number. Add
the same number to any number(s) lying at the intersection of any two lines. Do not change
the value of the numbers that are covered by only one line. Return to step 2 and continue
until an optimal assignment is possible.
4. Optimal assignments will always be at zero locations in the table. One systematic way of
making a valid assignment is first to select a row or column that contains only one zero
square. We can make an assignment to that square and then draw lines through its row and
column. From the uncovered rows and columns, we choose another row or column in which
there is only one zero square. We make that assignment and continue the procedure until we
have assigned each person or machine to one task.
Example 4 shows how to use the assignment method.
EXAMPLE 4 �
Assignment method
First Printing wants to find the minimum total cost assignment of 3 jobs to 3 typesetters.
APPROACH � The cost table shown earlier in this section is repeated here, and steps 1 through 4
are applied.
TYPESETTER
A B C
JOB
R-34 $11 $14 $ 6
S-66 $ 8 $10 $11
T-50 $ 9 $12 $ 7
TYPESETTER
A B C
JOB
R-34 5 8 0
S-66 0 2 3
T-50 2 5 0
TYPESETTER
B CA
JOB
R-34 5 6 0
S-66 0 0 3
T-50 2 3 0
SOLUTION �
STEP 1A: Using the previous table, subtract the smallest number in each row from every number in
the row. The result is shown in the table on the left.
STEP 1B: Using the above left table, subtract the smallest number in each column from every num-
ber in the column. The result is shown in the table on the right.
STEP 2: Draw the minimum number of vertical and horizontal straight lines needed to cover all
zeros. Because two lines suffice, the solution is not optimal.
TYPESETTER
A B C
JOB
R-34 5 6 0
S-66 0 0 3
T-50 2 3 0
Smallest uncovered number
AUTHOR COMMENT
You can also tackle
assignment problems with
our Excel OM or POM
software or with Excel’s
Solver add-in.

Chapter 15 Short-Term Scheduling 477
STEP 3: Subtract the smallest uncovered number (2 in this table) from every other uncovered
number and add it to numbers at the intersection of two lines.
Return to step 2. Cover the zeros with straight lines again.
Because three lines are necessary, an optimal assignment can be made (see step 4). Assign R-34 to
person C, S-66 to person B, and T-50 to person A. Referring to the original cost table, we see that:
INSIGHT � If we had assigned S-66 to typesetter A, we could not assign T-50 to a zero location.
LEARNING EXERCISE � If it costs $10 for Typesetter C to complete Job R-34 (instead of $6),
how does the solution change? [Answer: R-34 to A, S-66 to B, T-50 to C: cost = $28.]
RELATED PROBLEMS � 15.3, 15.4, 15.5, 15.6, 15.7, 15.8, 15.9
EXCEL OM Data File Ch15Ex4.xls can be found at www.pearsonhighered.com/heizer.
Minimum cost = $6 + $10 + $9 = $25
TYPESETTER
A B C
JOB
R-34 3 4 0
S-66 0 0 5
T-50 0 1 0
TYPESETTER
A B C
JOB
R-34 3 4 0
S-66 0 0
010T-50
Some assignment problems entail maximizing profit, effectiveness, or payoff of an assignment of
people to tasks or of jobs to machines. An equivalent minimization problem can be obtained by
converting every number in the table to an opportunity loss. To convert a maximizing problem to
an equivalent minimization problem, we create a minimizing table by subtracting every number
in the original payoff table from the largest single number in that table. We then proceed to step
1 of the four-step assignment method. Minimizing the opportunity loss produces the same
assignment solution as the original maximization problem.
The problem of scheduling major
league baseball umpiring crews
from one series of games to the
next is complicated by many
restrictions on travel, ranging from
coast-to-coast time changes,
airline flight schedules, and night
games running late. The league
strives to achieve these two
conflicting objectives: (1) balance
crew assignments relatively evenly
among all teams over the course
of a season and (2) minimize travel
costs. Using the assignment
problem formulation, the time it
takes the league to generate a
schedule has been significantly
decreased, and the quality of the
schedule has improved.

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478 PART 3 Managing Operations
SEQUENCING JOBS
Scheduling provides a basis for assigning jobs to work centers. Loading is a capacity-control
technique that highlights overloads and underloads. Sequencing (also referred to as dispatch-
ing) specifies the order in which jobs should be done at each center. For example, sup-
pose that 10 patients are assigned to a medical clinic for treatment. In what order should
they be treated? Should the first patient to be served be the one who arrived first or the
one who needs emergency treatment? Sequencing methods provide such guidelines. These
methods are referred to as priority rules for sequencing or dispatching jobs to work
centers.
Priority Rules for Dispatching Jobs
Priority rules provide guidelines for the sequence in which jobs should be worked. The rules are
especially applicable for process-focused facilities such as clinics, print shops, and manufactur-
ing job shops. We will examine a few of the most popular priority rules. Priority rules try to min-
imize completion time, number of jobs in the system, and job lateness while maximizing facility
utilization.
The most popular priority rules are:
• FCFS: first come, first served. The first job to arrive at a work center is processed first.
• SPT: shortest processing time. The shortest jobs are handled first and completed.
• EDD: earliest due date. The job with the earliest due date is selected first.
• LPT: longest processing time. The longer, bigger jobs are often very important and are
selected first.
Example 5 compares these rules.
Sequencing
Determining the order in which
jobs should be done at each
work center.
Priority rules
Rules used to determine the
sequence of jobs in process-
oriented facilities.
First come, first served
(FCFS)
Jobs are completed in the
order they arrived.
Shortest processing
time (SPT)
Jobs with the shortest processing
times are assigned first.
Earliest due date (EDD)
Earliest due date jobs are
performed first.
Longest processing
time (LPT)
Jobs with the longest processing
time are completed first.
EXAMPLE 5 �
Priority rules for
dispatching
Five architectural rendering jobs are waiting to be assigned at Avanti Sethi Architects. Their work (pro-
cessing) times and due dates are given in the following table. The firm wants to determine the sequence
of processing according to (1) FCFS, (2) SPT, (3) EDD, and (4) LPT rules. Jobs were assigned a letter
in the order they arrived.
AUTHOR COMMENT
Once jobs are loaded,
managers must decide the
sequence in which they are
to be completed.
APPROACH � Each of the four priority rules is examined in turn. Four measures of effectiveness
can be computed for each rule and then compared to see which rule is best for the company.
SOLUTION �
1. The FCFS sequence shown in the next table is simply A–B–C–D–E. The “flow time” in the sys-
tem for this sequence measures the time each job spends waiting plus time being processed. Job B,
for example, waits 6 days while job A is being processed, then takes 2 more days of operation time
itself; so it will be completed in 8 days—which is 2 days later than its due date.
JOB WORK FLOW JOB DUE JOB
JOB SEQUENCE (PROCESSING) TIME TIME DATE LATENESS
A 6 6 8 0
B 2 8 6 2
C 8 16 18 0
D 3 19 15 4
E 9 28 23 5
28 77 11
JOB WORK JOB DUE
(PROCESSING) TIME DATE
JOB (DAYS) (DAYS)
A 6 8
B 2 6
C 8 18
D 3 15
E 9 23

Chapter 15 Short-Term Scheduling 479
The first-come, first-served rule results in the following measures of effectiveness:
a.
b.
c.
d.
2. The SPT rule shown in the next table results in the sequence B–D–A–C–E. Orders are sequenced
according to processing time, with the highest priority given to the shortest job.
Average job lateness =
Total late days
Number of jobs
=
11
5
= 2.2 days
=
77 days
28 days
= 2.75 jobs
Average number of jobs in the system =
Sum of total flow time
Total job work 1processing2 time
=
28
77
= 36.4%
Utilization metric =
Total job work 1processing2 time
Sum of total flow time
=
77 days
5
= 15.4 days
Average completion time =
Sum of total flow time
Number of jobs
JOB WORK FLOW JOB DUE JOB
JOB SEQUENCE (PROCESSING) TIME TIME DATE LATENESS
B 2 2 6 0
D 3 5
A 6 11 8 3
C 8 19 18 1
E 9 28 23 5
28 65 9
15 0
Measurements of effectiveness for SPT are:
a. Average completion time
b.
c. Average number of jobs in the system
d. Average job lateness
3. The EDD rule shown in the next table gives the sequence B–A–D–C–E. Note that jobs are ordered
by earliest due date first.
=
9
5
= 1.8 days
=
65
28
= 2.32 jobs
Utilization metric =
28
65
= 43.1%
=
65
3
= 13 days
JOB WORK FLOW JOB DUE JOB
JOB SEQUENCE (PROCESSING) TIME TIME DATE LATENESS
B 2 2 6 0
A 6 8 8 0
D 3 11 15 0
C 8 19 18 1
E 9 28 23 5
28 68 6
Measurements of effectiveness for EDD are:
a. Average completion time
b. Utilization metric
c. Average number of jobs in the system
d. Average job lateness =
6
5
= 1.2 days
=
68
28
= 2.43 jobs
=
28
68
= 41.2%
=
68
5
= 13.6 days
LO4: Name and describe
each of the priority
sequencing rules

480 PART 3 Managing Operations
4. The LPT rule shown in the next table results in the order E–C–A–D–B.
Measures of effectiveness for LPT are:
a. Average completion time
b. Utilization metric
c. Average number of jobs in the system
d. Average job lateness
The results of these four rules are summarized in the following table:
=
48
5
= 9.6 days
=
103
28
= 3.68 jobs
=
28
103
= 27.2%
=
103
5
= 20.6 days
JOB WORK FLOW JOB DUE JOB
JOB SEQUENCE (PROCESSING) TIME TIME DATE LATENESS
E 9 9 23 0
C 8 17 18 0
A 6 23 8 15
D 3 26 15 11
B 2 28 6 22
28 103 48
AVERAGE AVERAGE NUMBER AVERAGE
COMPLETION
UTILIZATION
METRIC OF JOBS IN LATENESS
RULE TIME (DAYS) (%) SYSTEM (DAYS)
FCFS 15.4 36.4 2.75 2.2
SPT 13.0 43.1 2.32 1.8
EDD 13.6 41.2 2.43 1.2
LPT 20.6 27.2 3.68 9.6
INSIGHT � LPT is the least effective measurement for sequencing for the Avanti Sethi firm. SPT
is superior in 3 measures, and EDD is superior in the fourth (average lateness).
LEARNING EXERCISE � If job A takes 7 days (instead of 6), how do the 4 measures of effec-
tiveness change under the FCFS rule? [Answer: 16.4 days, 35.4%, 2.83 jobs, 2.8 days late.]
RELATED PROBLEMS � 15.10, 15.12a–d, 15.13, 15.14
EXCEL OM Data File Ch15Ex5.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 15.1 This example is further illustrated in Active Model 15.1 at www.pearsonhighered.com/heizer.
The results in Example 5 are typically true in the real world also. No one sequencing rule always
excels on all criteria. Experience indicates the following:
1. Shortest processing time is generally the best technique for minimizing job flow and
minimizing the average number of jobs in the system. Its chief disadvantage is that long-
duration jobs may be continuously pushed back in priority in favor of short-duration
jobs. Customers may view this dimly, and a periodic adjustment for longer jobs must be
made.
2. First come, first served does not score well on most criteria (but neither does it score par-
ticularly poorly). It has the advantage, however, of appearing fair to customers, which is
important in service systems.
3. Earliest due date minimizes maximum tardiness, which may be necessary for jobs that
have a very heavy penalty after a certain date. In general, EDD works well when lateness is
an issue.

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Chapter 15 Short-Term Scheduling 481
Your doctor may use a first-come,
first-served priority rule satisfactorily.
However, such a rule may be less
than optimal for this emergency
room. What priority rule might be
best, and why? What priority rule is
often used on TV hospital dramas?
Critical Ratio
Another type of sequencing rule is the critical ratio. The critical ratio (CR) is an index number
computed by dividing the time remaining until due date by the work time remaining. As opposed
to the priority rules, critical ratio is dynamic and easily updated. It tends to perform better than
FCFS, SPT, EDD, or LPT on the average job-lateness criterion.
The critical ratio gives priority to jobs that must be done to keep shipping on schedule. A job
with a low critical ratio (less than 1.0) is one that is falling behind schedule. If CR is exactly 1.0, the
job is on schedule. A CR greater than 1.0 means the job is ahead of schedule and has some slack.
The formula for critical ratio is:
Example 6 shows how to use the critical ratio.
CR =
Time remaining
Workdays remaining
=
Due date – Today’s date
Work 1lead2 time remaining
� EXAMPLE 6
Critical ratio
APPROACH � Zyco wants to compute the critical ratios, using the formula for CR.
SOLUTION �
Today is day 25 on Zyco Medical Testing Laboratories’ production schedule. Three jobs are on order,
as indicated here:
Job Due Date Workdays Remaining
A 30 4
B 28 5
C 27 2
Job Critical Ratio Priority Order
A 130 – 252>4 = 1.25 3
B 128 – 252>5 = .60 1
C 127 – 252>2 = 1.00 2
INSIGHT � Job B has a critical ratio of less than 1, meaning it will be late unless expedited. Thus,
it has the highest priority. Job C is on time and job A has some slack. Once job B has been completed,
we would recompute the critical ratios for jobs A and C to determine whether their priorities have
changed.
Critical ratio (CR)
A sequencing rule that is an
index number computed by
dividing the time remaining
until due date by the work
time remaining.

482 PART 3 Managing Operations
LEARNING EXERCISE � Today is day 24 (a day earlier) on Zyco’s schedule. Recompute the
CRs and determine the priorities. [Answer: 1.5, 0.8, 1.5; B is still number 1, but now jobs A and C are
tied for second.]
RELATED PROBLEMS � 15.11, 15.12e, 15.16
In most production scheduling systems, the critical-ratio rule can help do the following:
1. Determine the status of a specific job.
2. Establish relative priority among jobs on a common basis.
3. Relate both make-to-stock and make-to-order jobs on a common basis.
4. Adjust priorities (and revise schedules) automatically for changes in both demand and job
progress.
5. Dynamically track job progress.
Sequencing N Jobs on Two Machines:
Johnson’s Rule
The next step in complexity is the case in which N jobs (where N is 2 or more) must go through
two different machines or work centers in the same order. This is called the N/2 problem.
Johnson’s rule can be used to minimize the processing time for sequencing a group of jobs
through two work centers. It also minimizes total idle time on the machines. Johnson’s rule
involves four steps:
1. All jobs are to be listed, and the time that each requires on a machine is to be shown.
2. Select the job with the shortest activity time. If the shortest time lies with the first machine,
the job is scheduled first. If the shortest time lies with the second machine, schedule the job
last. Ties in activity times can be broken arbitrarily.
3. Once a job is scheduled, eliminate it.
4. Apply Steps 2 and 3 to the remaining jobs, working toward the center of the sequence.
Example 7 shows how to apply Johnson’s rule.
Johnson’s rule
An approach that minimizes
processing time for sequencing
a group of jobs through two
work centers while minimizing
total idle time in the work
centers.
EXAMPLE 7 �
Johnson’s rule
Five specialty jobs at a La Crosse, Wisconsin, tool and die shop must be processed through two work
centers (drill press and lathe). The time for processing each job follows:
Work (processing) Time for Jobs (hours)
Job
Work Center 1
(drill press)
Work Center 2
(lathe)
A 5 2
B 3 6
C 8 4
D 10 7
E 7 12
The owner, Niranjan Pati, wants to set the sequence to minimize his total processing time for the five jobs.
APPROACH � Pati applies the four steps of Johnson’s rule.
SOLUTION �
1. The job with the shortest processing time is A, in work center 2 (with a time of 2 hours). Because
it is at the second center, schedule A last. Eliminate it from consideration.
A

Chapter 15 Short-Term Scheduling 483
2. Job B has the next shortest time (3 hours). Because that time is at the first work center, we sched-
ule it first and eliminate it from consideration.
AB
3. The next shortest time is job C (4 hours) on the second machine. Therefore, it is placed as late as
possible.
4. There is a tie (at 7 hours) for the shortest remaining job. We can place E, which was on the first
work center, first. Then D is placed in the last sequencing position.
The sequential times are:
C AB
B E D C A
Work center 1 3 7 10 8 5
Work center 2 6 12 7 4 2
The time-phased flow of this job sequence is best illustrated graphically:
Thus, the five jobs are completed in 35 hours.
INSIGHT � The second work center will wait 3 hours for its first job, and it will also wait 1 hour
after completing job B.
LEARNING EXERCISE � If job C takes 8 hours in work center 2 (instead of 4 hours), what
sequence is best? [Answer: B–E–C–D–A.]
RELATED PROBLEMS � 15.15, 15.17, 15.18
EXCEL OM Data File Ch15Ex7.xls can be found at www.pearsonhighered.com/heizer.
Time 0 1 3
B
105 7 11 12 13 17 19 21 22 23 25 27 29 31 33 35
E D C A
= Idle = Job completed
Time 0 3 10 20 28 33
9
Work
center
1
Work
center
2
B E D C A
B E D C A
LO5: Use Johnson’s rule
Limitations of Rule-Based Dispatching Systems
The scheduling techniques just discussed are rule-based techniques, but rule-based systems have
a number of limitations. Among these are the following:
1. Scheduling is dynamic; therefore, rules need to be revised to adjust to changes in orders,
process, equipment, product mix, and so forth.

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484 PART 3 Managing Operations
Finite capacity
scheduling (FCS)
Computerized short-term
scheduling that overcomes the
disadvantage of rule-based
systems by providing the user
with graphical interactive
computing.
LO6: Define finite capacity
scheduling
4Finite capacity scheduling (FCS) systems go by a number of names, including finite scheduling and advance planning
systems (APS). The name manufacturing execution systems (MES) may also be used, but MES tends to suggest an
emphasis on the reporting system from shop operations back to the scheduling activity.
2. Rules do not look upstream or downstream; idle resources and bottleneck resources in other
departments may not be recognized.
3. Rules do not look beyond due dates. For instance, two orders may have the same due date.
One order involves restocking a distributor and the other is a custom order that will shut
down the customer’s factory if not completed. Both may have the same due date, but clearly
the custom order is more important.
Despite these limitations, schedulers often use sequencing rules such as SPT, EDD, or critical
ratio. They apply these methods at each work center and then modify the sequence to deal with
a multitude of real-world variables. They may do this manually or with finite capacity scheduling
software.
FINITE CAPACITY SCHEDULING (FCS)
Short-term scheduling is also called finite capacity scheduling.4 Finite capacity scheduling
(FCS) overcomes the disadvantages of systems based exclusively on rules by providing the
scheduler with interactive computing and graphic output. In dynamic scheduling environments
such as job shops (with a high variety, low volume, and shared resources) we expect
changes—but changes disrupt schedules. Therefore, operations managers are moving toward
FCS systems that allow virtually instantaneous change by the operator. Improvements in com-
munication on the shop floor are also enhancing the accuracy and speed of information neces-
sary for effective control in job shops. Computer-controlled machines can monitor events and
collect information in near real-time. This means the scheduler can make schedule changes
based on up-to-the-minute information. These schedules are often displayed in Gantt chart
form. In addition to including priority rule options, many of the current FCS systems also
combine an “expert system” or simulation techniques and allow the scheduler to assign costs
to various options. The scheduler has the flexibility to handle any situation, including order,
labor, or machine changes.
This Lekin® finite capacity
scheduling software
presents a schedule of
the five jobs and the two
work centers shown in
Example 7 (pages 482–483)
in Gantt chart form. The
software is capable of
using a variety of priority
rules, several shop types,
up to 50 jobs, 20 work
centers, and 100 machines
to generate a schedule.
The Lekin software is
available for free at
www.stern.nyc.edu/
om/software/lekin/
download/html and
can solve many of the
problems in the Lecture
Guide & Activities Manual.

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www.stern.nyc.edu/om/software/lekin/download/html

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Chapter 15 Short-Term Scheduling 485
Setups and
run time
Interactive Finite Capacity Scheduling
MRP Data Routing files;
work center
information
Priority
rules
• Expert systems
• Simulation
models
• Master
schedule
• BOM
• Inventory
Tooling and
other resources
Maintenance
Job
A
B
C
D
Day
1
Day
2
Day
3
Day
4
Day
5
Day
6
Day
7
Day
8
� FIGURE 15.5
Finite Capacity Scheduling
Systems Combine MRP and
Shop Floor Production Data
to Generate a Gantt Chart
That Can Be Manipulated
by the User on a Computer
Screen
The initial data for finite scheduling systems is often the output from an MRP system. The
output from MRP systems is traditionally in weekly “buckets” that have no capacity constraint.
These systems just tell the planner when the material is needed, ignoring the capacity issue.
Because infinite-size buckets are unrealistic and inadequate for detail scheduling, MRP data
require refinement. MRP output is combined with routing files, due dates, capacity of work cen-
ters, tooling, and other resource availability to provide the data needed for effective FCS. These
are the same data needed in any manual system, but FCS software formalizes them, speeds
analysis, and makes changes easier. The combining of MRP and FCS data, priority rules, models
to assist analysis, and Gantt chart output is shown in Figure 15.5.
Finite capacity scheduling allows delivery requirements to be based on today’s conditions and
today’s orders, not according to some predefined rule. The scheduler determines what constitutes
a “good” schedule. FCS software packages such as Lekin, ProPlanner, Preactor, Asprova, Tactic,
and Jobplan are currently used at over 60% of U.S. plants.
SCHEDULING REPETITIVE FACILITIES
The scheduling goals defined at the beginning of this chapter are also appropriate for repetitive
production. You may recall from Chapter 7 that repetitive producers make standard products
from modules. The usual approach is to develop a forward-looking schedule on a balanced
assembly line. (Refer to Table 15.2 on page 472).
Repetitive producers want to satisfy customer demands, lower inventory investment, and
reduce the batch (or lot) size, with existing equipment and processes. A technique to move
toward these goals is to use a level-material-use schedule. Level material use means frequent,
high-quality, small lot sizes that contribute to just-in-time production. This is exactly what
world-class producers such as Harley-Davidson, John Deere, and Johnson Controls do. The
advantages of level material use are:
1. Lower inventory levels, which releases capital for other uses
2. Faster product throughput (that is, shorter lead times)
3. Improved component quality and hence improved product quality
4. Reduced floor-space requirements
5. Improved communication among employees because they are closer together (which can
result in improved teamwork and esprit de corps)
6. Smoother production process because large lots have not “hidden” the problems
Suppose a repetitive producer runs large monthly batches: With a level-material-use schedule,
management would move toward shortening this monthly cycle to a weekly, daily, or even
hourly cycle.
Level material use
The use of frequent, high-
quality, small lot sizes that
contribute to just-in-time
production.
AUTHOR COMMENT
Repetitive producers create
forward-looking schedules
with level material use.

486 PART 3 Managing Operations
One way to develop a level-material-use schedule is to first determine the minimum lot size
that will keep the production process moving. This is illustrated in the next chapter, “JIT and
Lean Operations.”
SCHEDULING SERVICES
Scheduling service systems differs from scheduling manufacturing systems in several ways:
• In manufacturing, the scheduling emphasis is on machines and materials; in services, it is on
staffing levels.
• Inventories can help smooth demand for manufacturers, but many service systems do not
maintain inventories.
• Services are labor intensive, and the demand for this labor can be highly variable.
• Legal considerations, such as wage and hour laws and union contracts that limit hours worked
per shift, week, or month, constrain scheduling decisions.
• Because services usually schedule people rather than material, behavioral, social, seniority,
and status issues complicate scheduling.
The following examples note the complexity of scheduling services.
Hospitals A hospital is an example of a service facility that may use a scheduling system every
bit as complex as one found in a job shop. Hospitals seldom use a machine shop priority system
such as first-come, first-served (FCFS) for treating emergency patients. However, they do schedule
products (such as surgeries) just like a factory, and capacities must meet wide variations in demand.
Banks Cross training of the workforce in a bank allows loan officers and other managers to
provide short-term help for tellers if there is a surge in demand. Banks also employ part-time per-
sonnel to provide a variable capacity.
Retail Stores Scheduling optimization systems, such as Workbrain, Cybershift, and Kronos,
are used at retailers including Walmart, Payless Shoes, Target, and Radio Shack. These systems
track individual store sales, transactions, units sold, and customer traffic in 15-minute increments
to create work schedules. Walmart’s 1.3 million and Target’s 350,000 employees used to take
thousands of managers’ hours to schedule; now staffing is drawn up nationwide in a few hours,
and customer checkout experience has improved dramatically.
Airlines Airlines face two constraints when scheduling flight crews: (1) a complex set of FAA
work-time limitations and (2) union contracts that guarantee crew pay for some number of hours
each day or each trip. Airline planners must build crew schedules that meet or exceed crews’ pay
guarantees. Planners must also make efficient use of their other expensive resource: aircraft.
These schedules are typically built using linear programming models. The OM in Action box
“Scheduling Aircraft Turnaround” details how very short-term schedules (20 minutes) can help
an airline become more efficient.
AUTHOR COMMENT
Scheduling people to
perform services can be
even more complex than
scheduling machines.
Good scheduling in the
health care industry can
help keep nurses happy
and costs contained.
Here, nurses in Boston
protest nurse-staffing
levels in Massachusetts
hospitals. Shortages of
qualified nurses is a
chronic problem.

Chapter 15 Short-Term Scheduling 487
24/7 Operations Emergency hotlines, police/fire departments, telephone operations, and mail-
order businesses (such as L.L. Bean) schedule employees 24 hours a day, 7 days a week. To allow
management flexibility in staffing, sometimes part-time workers can be employed. This provides
both benefits (in using odd shift lengths or matching anticipated workloads) and difficulties (from
the large number of possible alternatives in terms of days off, lunch hour times, rest periods, start-
ing times). Most companies use computerized scheduling systems to cope with these complexities.
The OM in Action box “Scheduling for Peaks by Swapping Employees” provides yet another
example of flexibility in scheduling.
� US Airways has cut the turnaround time on commercial flights from the
current 45 minutes to 20 minutes for Boeing 737s. To the right is a list of
procedures that must be completed before the flight can depart:
1
1
2
2
3
3
4
4
5
5
6
6
Ticket agent takes flight plan to pilot, who loads information into
aircraft computer. About 130 passengers disembark from the plane.
Workers clean trash cans, seat pockets, lavatories, etc.
Catering personnel board plane and replenish supply of drinks
and ice.
A fuel truck loads up to 5,300 gallons of fuel into aircraft’s wings.
Baggage crews unload up to 4,000 pounds of luggage and 2,000
pounds of freight. “Runners” rush the luggage to baggage claim
area in terminal.
Ramp agents, who help park aircraft upon arrival, “push” plane back
away from gate.
Airlines that face increasingly difficult financial futures have
recently discovered the importance of efficient scheduling
of ground turnaround activities for flights. For some low-
cost, point-to-point carriers like Southwest Airlines,
scheduling turnarounds in 20 minutes has been standard
policy for years. Yet for others, like Continental, United, and
US Airways, the approach is new. This figure illustrates
how US Airways deals with speedier schedules. Now its
planes average seven trips a day, instead of six, meaning
the carrier can sell tens of thousands more seats a day.
And with this improved scheduling, its punctuality moved
from near the bottom in 2007 to virtually tie Southwest for
first place 2 years later.
OM in Action � Scheduling Aircraft Turnaround
When calls to Choice Hotel International’s reservation
line surged after a recent ad campaign, Choice V.P. Don
Brockwell found his call center short-staffed. So he quickly
arranged to add 20 agents per shift—but not by hiring or
calling a temp service. Instead, the additional workers were
employees of 1-800-Flowers.com. Choice and Flowers’s
unusual deal helps both reduce reliance on outsourcers. It
also bolsters recruiting and retention because call center
workers have more varied work and are less subject to a
seasonal business cycle.
The deal works in part because Choice’s high season
is mid-May through early October, while Flowers’s call
volume increases between October and May, with surges
at Christmas, Valentine’s Day, and Mother’s Day. The
companies typically lend each other as many as 100
employees, for weeks at
a time, in the three call
centers they share.
But some workers
might even change
assignments in the
middle of a shift. Most
employees like the
variety. “When you sit
down and sell hotel rooms for 8 hours a day, selling flowers
is a nice break,” says Rick Hilliner, a former teacher, now at
the Grand Junction, Colorado, center.
Sources: The Wall Street Journal (April 10, 2006): B3; and Call Center
Magazine (March 2005): 18–24.
Sources: US Airways, Boeing, The Wall Street Journal (January 6, 2009):
D8; and Aviation Week & Space Technology (January 29, 2001): 50.
OM in Action � Scheduling for Peaks by Swapping Employees

488 PART 3 Managing Operations
EXAMPLE 8 �
Cyclical scheduling
Hospital administrator Doris Laughlin wants to staff the oncology ward using a standard 5-day work-
week with two consecutive days off, but also wants to minimize the staff. However, as in most hospi-
tals, she faces an inconsistent demand. Weekends have low usage. Doctors tend to work early in the
week, and patients peak on Wednesday then taper off.
APPROACH � Doris must first establish staffing requirements. Then the following five-step
process is applied.
SOLUTION �
1. Determine the necessary daily staffing requirements. Doris has done this:
Employee 1
Employee 2
Employee 3
Employee 4
Employee 5
Employee 6
Employee 7
Capacity
(measured in
number of
employees)
Excess capacity
5
4
3
2
1
1
5
4
3
2
1
1
6
5
4
3
2
1
5
4
3
2
2
1
4
3
2
2
2
1
3
3
3
3
2
1
1
3
3
3
2
1
0
5
0
5
0
6
0
5
0
4
0
3
1
3
0
MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY
Day Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Staff
required 5 5 6 5 4 3 3
Scheduling Service Employees
with Cyclical Scheduling
A number of techniques and algorithms exist for scheduling service-sector employees such as
police officers, nurses, restaurant staff, tellers, and retail sales clerks. Managers, trying to set a
timely and efficient schedule that keeps personnel happy, can spend substantial time each month
developing employee schedules. Such schedules often consider a fairly long planning period
(say, 6 weeks). One approach that is workable yet simple is cyclical scheduling.
Cyclical Scheduling Cyclical scheduling with inconsistent staffing needs is often the case in
services such as restaurants and police work. Here the objective focuses on developing a sched-
ule with the minimum number of workers. In these cases, each employee is assigned to a shift
and has time off. Let’s look at Example 8.
LO7: Use the cyclical
scheduling technique
2. Identify the two consecutive days that have the lowest total requirement and circle these. Assign
these two days off to the first employee. In this case, the first employee has Saturday and Sunday
off because 3 plus 3 is the lowest sum of any 2 days. In the case of a tie, choose the days with the
lowest adjacent requirement, or by first assigning Saturday and Sunday as an “off” day. If there are
more than one, make an arbitrary decision.
3. We now have an employee working each of the uncircled days; therefore, make a new row for the
next employee by subtracting 1 from the first row (because one day has been worked)—except for
the circled days (which represent the days not worked) and any day that has a zero. That is, do not
subtract from a circled day or a day that has a value of zero.
4. In the new row, identify the two consecutive days that have the lowest total requirement and circle
them. Assign the next employee to the remaining days.
5. Repeat the process (steps 3 and 4) until all staffing requirements are met.

Chapter 15 Short-Term Scheduling 489
Doris needs six full-time employees to meet the staffing needs and one employee to work Saturday.
Notice that capacity (number of employees) equals requirements, provided an employee works
overtime on Saturday, or a part-time employee is hired for Saturday.
INSIGHT � Doris has implemented an efficient scheduling system that accommodates 2 consecu-
tive days off for every employee.
LEARNING EXERCISE � If Doris meets the staffing requirement for Saturday with a full-time
employee, how does she schedule that employee? [Answer: That employee can have any 2 days off,
except Saturday, and capacity will exceed requirements by 1 person each day the employee works
(except Saturday).]
RELATED PROBLEMS � 15.19, 15.20
Using the approach in Example 8, Colorado General Hospital saved an average of 10 to 15 hours
a month and found these added advantages: (1) no computer was needed, (2) the nurses were
happy with the schedule, (3) the cycles could be changed seasonally to accommodate avid skiers,
and (4) recruiting was easier because of predictability and flexibility. This approach yields an
optimum, although there may be multiple optimal solutions.
Other cyclical scheduling techniques have been developed to aid service scheduling. Some
approaches use linear programming: This is how Hard Rock Cafe schedules its services (see the
Video Case Study in the Lecture Guide & Activities Manual). There is a natural bias in schedul-
ing to use tools that are understood and yield solutions that are accepted.
CHAPTER SUMMARY
Scheduling involves the timing of operations to achieve the
efficient movement of units through a system. This chapter
addressed the issues of short-term scheduling in process-
focused, repetitive, and service environments. We saw that
process-focused facilities are production systems in which
products are made to order and that scheduling tasks in
them can become complex. Several aspects and approaches
to scheduling, loading, and sequencing of jobs were intro-
duced. These ranged from Gantt charts and the assignment
method of scheduling to a series of priority rules, the
critical-ratio rule, Johnson’s rule
for sequencing, and finite capac-
ity scheduling.
Service systems generally dif-
fer from manufacturing systems.
This leads to the use of first-come,
first-served rules and appointment and reservation sys-
tems, as well as to heuristics and linear programming
approaches for matching capacity to demand in service
environments.
Key Terms
Forward scheduling (p. 470)
Backward scheduling (p. 470)
Loading (p. 472)
Input–output control (p. 472)
ConWIP cards (p. 473)
Gantt charts (p. 474)
Assignment method (p. 475)
Sequencing (p. 478)
Priority rules (p. 478)
First come, first served (FCFS) (p. 478)
Shortest processing time (SPT) (p. 478)
Earliest due date (EDD) (p. 478)
Longest processing time (LPT) (p. 478)
Critical ratio (CR) (p. 481)
Johnson’s rule (p. 482)
Finite capacity scheduling (FCS) (p. 484)
Level material use (p. 485)
Using Software for Short-Term Scheduling
In addition to the commercial software we noted in this chapter, short-term scheduling problems can be
solved with the Excel OM software that comes free at our website www.pearsonhighered.com/heizer.
POM for Windows also includes a scheduling module. The use of each of these programs is explained next.

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490 PART 3 Managing Operations
X Using Excel OM
Excel OM has two modules that help solve short-term scheduling problems: Assignment and Job Shop
Scheduling. The Assignment module is illustrated in Programs 15.1 and 15.2. The input screen, using
the Example 4 data, appears first, as Program 15.1. Once the data are all entered, we choose the Tools
command, followed by the Solver command. Excel’s Solver uses linear programming to optimize
assignment problems. The constraints are also shown in Program 15.1. We then select the Solve
command and the solution appears in Program 15.2.
Excel OM’s Job Shop Scheduling module is illustrated in Program 15.3. Program 15.3 uses
Example 5’s data. Because jobs are listed in the sequence in which they arrived (see column A), the
results are for the FCFS rule. Program 15.3 also shows some of the formulas (columns F, G, H, I, J)
used in the calculations.
To solve with the SPT rule, we need four intermediate steps: (1) Select (that is, highlight) the data in
columns A, B, C for all jobs; (2) invoke the Data command; (3) invoke the Sort command; and (4) sort
by Time (column C) in ascending order. To solve for EDD, step 4 changes to sort by Due Date (column
D) in ascending order. Finally, for an LPT solution, step 4 becomes sort by Due Date (column D) in
descending order.
B22 is where we placed our total
costs on the data screen.
We need to create row and
column totals in order to
create the constraints.
The assignments will be filled in
by Excel’s Solver.
Copy the names from the above
table.
These are the cells that we will ask
Excel’s Solver to fill in for us.
These are the constraints for the linear
programming representation of the
assignment problem. Nonnegativity
constraints have been added through
the Options button.
In Excel 2007, Solver is in the Analysis section of the Data tab.
In prior versions Solver is on the Tools menu. If Solver is not
available please visit www.prenhall.com/weiss.
Use the SUMPRODUCT function to calculate the total
cost. Notice that this function is multiplying the data
table by the assignment table.
� PROGRAM 15.1 Excel OM’s Assignment Module Using Example 4’s Data
After entering the problem data in the yellow area, select Tools, then Solver.

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Chapter 15 Short-Term Scheduling 491
Solver has filled in the assignments with 1s.
It is important to check the statement
made by the Solver. In this case, it
says that Solver found a solution. In
other problems, this may not be the
case. For some problems there may be
no feasible solution, and for others
more iterations may be required.
� PROGRAM 15.2 Excel OM Output Screen for Assignment Problem Described in Program 15.1
An IF function is used
to determine whether
or not the job was late.
= IF(I13–D13>=0,
I13–D13,0)
The results are for an
FCFS schedule. To
create other results,
sort cells A9 through
D13 based on a new
criterion.
= AVERAGE(H9:H13)
Calculate the slack as = D9 – C9.
In this example, all
work begins on Day
1 and all jobs are
available on Day 1.
The completion times
and the flow times are
identical since work
begins on Day 1= H14/C14
� PROGRAM 15.3 Excel OM’s Job Shop Scheduling Module Applied to Example 5’s Data
P Using POM For Windows
POM for Windows can handle both categories of scheduling problems we see in this chapter. Its
Assignment module is used to solve the traditional one-to-one assignment problem of people to tasks,
machines to jobs, and so on. Its Job Shop Scheduling module can solve a one- or two-machine job-shop
problem. Available priority rules include SPT, FCFS, EDD, and LPT. Each can be examined in turn once
the data are all entered. Refer to Appendix IV for specifics regarding POM for Windows.

492 PART 3 Managing Operations
Solved Problems Virtual Office Hours help is available at www.myomlab.com
� SOLVED PROBLEM 15.1
King Finance Corporation, headquartered in New York, wants to
assign three recently hired college graduates, Julie Jones, Al
Smith, and Pat Wilson, to regional offices. However, the firm also
has an opening in New York and would send one of the three there
if it were more economical than a move to Omaha, Dallas, or
Miami. It will cost $1,000 to relocate Jones to New York, $800 to
relocate Smith there, and $1,500 to move Wilson. What is the
optimal assignment of personnel to offices?
� SOLUTION
(a) The cost table has a fourth column to represent New York. To
“balance” the problem, we add a “dummy” row (person) with
a zero relocation cost to each city.
(b) Subtract the smallest number in each row and cover all zeros
(column subtraction of each column’s zero will give the same
numbers and therefore is not necessary):
OFFICE
OMAHA MIAMI DALLAS
HIREE
Jones $800 $1,100 $1,200
Smith $500 $1,600 $1,300
Wilson $500 $1,000 $2,300
OFFICE
OMAHA MIAMI DALLAS NEW YORK
HIREE
Jones $800 $1,100 $1,200 $1,000
Smith $500 $1,600 $1,300 $ 800
Wilson $500 $1,000 $2,300 $1,500
Dummy 0 0 0 0
OFFICE
OMAHA MIAMI DALLAS NEW YORK
HIREE
Jones 0 300 400 200
Smith 0 1,100 800 300
Wilson 0 500 1,800 1,000
Dummy 0 0 0 0
(c) Only 2 lines cover, so subtract the smallest uncovered number
(200) from all uncovered numbers, and add it to each square
where two lines intersect. Then cover all zeros:
(d) Only 3 lines cover, so subtract the smallest uncovered number
(100) from all uncovered numbers, and add it to each square
where two lines intersect. Then cover all zeros:
(e) Still only 3 lines cover, so subtract the smallest uncovered
number (100) from all uncovered numbers, add it to squares
where two lines intersect, and cover all zeros:
(f) Because it takes four lines to cover all zeros, an optimal
assignment can be made at zero squares. We assign:
Wilson to Omaha
Jones to Miami
Dummy (no one) to Dallas
Smith to New York
= $2,400
Cost = $500 + $1,100 + $0 + $800
� SOLVED PROBLEM 15.2
A defense contractor in Dallas has six jobs awaiting processing.
Processing time and due dates are given in the table. Assume that
jobs arrive in the order shown. Set the processing sequence
according to FCFS and evaluate.
OFFICE
OMAHA MIAMI DALLAS NEW YORK
HIREE
Jones 0 100 200 0
Smith 0 900 600 100
Wilson 0 300 1,600 800
Dummy 200 0 0 0
OFFICE
OMAHA MIAMI DALLAS NEW YORK
HIREE
Jones 0 0 100 0
Smith 0 800 500 100
Wilson 0 200 1,500 800
Dummy 300 0 0 100
OFFICE
OMAHA MIAMI DALLAS NEW YORK
HIREE
Jones 100 0 100 0
Smith 0 700 400 0
Wilson 0 100 1,400 700
Dummy 400 0 0 100
JOB PROCESSING JOB DUE
JOB TIME (DAYS) DATE (DAYS)
A 6 22
B 12 14
C 14 30
D 2 18
E 10 25
F 4 34

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Chapter 15 Short-Term Scheduling 493
1.
2.
3.
4. Utilization = 48>182 = 26.4%
Average job lateness = 55>6 = 9.16 days
Average number of jobs in system = 182>48 = 3.79 jobs
Average completion time = 182>6 = 30.33 days
� SOLUTION
FCFS has the sequence A–B–C–D–E–F.
JOB
JOB PROCESSING
SEQUENCE TIME FLOW TIME DUE DATE JOB LATENESS
A 6 6 22 0
B 12 18 14 4
C 14 32 30 2
D 2 34 18 16
E 10 44 25 19
F 4 48 34 14
48 182 55
� SOLVED PROBLEM 15.3
The Dallas firm in Solved Problem 15.2 also wants to consider job sequencing by the SPT priority rule. Apply SPT to
the same data and provide a recommendation.
� SOLUTION
SPT has the sequence D–F–A–E–B–C.
1.
2.
3.
4.
SPT is superior to FCFS in this case on all four measures. If we were to also analyze EDD, we
would, however, find its average job lateness to be lowest at 5.5 days. SPT is a good
recommendation. SPT’s major disadvantage is that it makes long jobs wait, sometimes for a long
time.
Utilization = 48>124 = 38.7%
Average job lateness = 38>6 = 6.33 days
Average number of jobs in system = 124>48 = 2.58 jobs
Average completion time = 124>6 = 20.67 days
JOB
JOB PROCESSING
SEQUENCE TIME FLOW TIME DUE DATE JOB LATENESS
D 2 2 18 0
F 4 6 34 0
A 6 12 22 0
E 10 22 25 0
B 12 34 14 20
C 14 48 30 18
48 124 38
� SOLVED PROBLEM 15.4
Use Johnson’s rule to find the optimum sequence for processing
the jobs shown through two work centers. Times at each center are
in hours.
JOB WORK CENTER 1 WORK CENTER 2
A 6 12
B 3 7
C 18 9
D 15 14
E 16 8
F 10 15

494 PART 3 Managing Operations
� SOLVED PROBLEM 15.5
Illustrate the throughput time and idle time at the two work centers in Solved Problem 15.4 by constructing a time-
phased chart.
� SOLUTION
� SOLUTION
The sequential times are:
B A F D C E
Work center 1 3 0 15 18 16
Work center 2 7 12
6
15
1
14 9 8
0 10
A
37 51 52 68 76
D E
Idle time
0 9 19 52 68
22
Work
center
1
Work
center
2
B A D C E
B F E
3
3
F
A
B F
34
D C
61
C
Bibliography
Baker, Kenneth A., and Dan Trietsch. Principles of Sequencing
and Scheduling. New York: Wiley (2009).
Bard, Jonathan F. “Staff Scheduling in High Volume Service
Facilities with Downgrading.” IIE Transactions 36 (2004):
985–997.
Bolander, Steven, and Sam G. Taylor. “Scheduling Techniques: A
Comparison of Logic.” Production and Inventory
Management Journal (1st Quarter 2000): 1–5.
Cayirli, Tugba, and Emre Veral. “Outpatient Scheduling in Health
Care: A Review of Literature.” Production and Operations
Management 12, no. 4 (Winter 2003): 519–549.
Chapman, Stephen. Fundamentals of Production Planning and
Control. Upper Saddle River, NJ: Prentice Hall (2006).
Deng, Honghui, Q. Wang, G. K. Leong, and S. X. Sun. “Usage of
Opportunity Cost to Maximize Performance in Revenue
Management.” Decision Sciences 38, no. 4 (November 2008):
737–758.
Dietrich, Brenda, G. A. Paleologo, and L. Wynter. “Revenue
Management in Business Services.” Production and
Operations Management 17, no. 4 (July–August 2008):
475–480.
Farmer, Adam, Jeffrey S. Smith, and Luke T. Miller. “Scheduling
Umpire Crews for Professional Tennis Tournaments.”
Interfaces 37, no. 2 (March–April 2007): 187–196.
Geraghty, Kevin. “Revenue Management and Digital Marketing.”
OR/MS Today 35, no. 6 (December 2008): 22–28.
Kellogg, Deborah L., and Steven Walczak. “Nurse Scheduling.”
Interfaces 37, no. 4 (July–August 2007): 355–369.
Lopez, P., and F. Roubellat. Production Scheduling. New York:
Wiley (2008).
Mondschein, S. V., and G. Y. Weintraub. “Appointment Policies
in Service Operations.” Production and Operations
Management 12, no. 2 (Summer 2003): 266–286.
Morton, Thomas E., and David W. Pentico. Heuristic Scheduling
Systems. New York: Wiley (1993).
Pinedo, M. Scheduling: Theory, Algorithms, and Systems, 2nd ed.
Upper Saddle River, N.J.: Prentice Hall (2002).
Plenert, Gerhard, and Bill Kirchmier. Finite Capacity Scheduling.
New York: Wiley (2000).
Render, B., R. M. Stair, and M. Hanna. Quantitative Analysis for
Management, 10th ed. Upper Saddle River, NJ: Prentice Hall
(2009).
�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Payroll Planning, Inc.: Describes setting a schedule for handling the accounting for dozens of client firms.

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JIT and
Lean Operations
Chapter Outline
GLOBAL COMPANY PROFILE: TOYOTA MOTOR
CORPORATION
Just-in-Time, the Toyota Production System,
and Lean Operations 498
Just-in-Time (JIT) 500
JIT Layout 503
JIT Inventory 504
JIT Scheduling 506
JIT Quality 510
Toyota Production System (TPS) 511
Lean Operations 512
Lean Operations in Services 513
� Design of Goods and Services
� Managing Quality
� Process Strategy
� Location Strategies
� Layout Strategies
� Human Resources
� Supply-Chain Management
� Inventory Management
� Independent Demand
� Dependent Demand
� JIT and Lean Operations
� Scheduling
� Maintenance
495

GLOBAL COMPANY PROFILE: TOYOTA MOTOR CORPORATION
ACHIEVING COMPETITIVE ADVANTAGE WITH LEAN OPERATIONS
AT TOYOTA MOTOR CORPORATION
• Central to JIT is a philosophy of continued problem
solving. In practice, JIT means making only what is
needed, when it is needed. JIT provides an
excellent vehicle for finding and eliminating
problems because problems are easy to find in a
system that has no slack. When excess inventory is
eliminated, quality, layout, scheduling, and supplier
issues become immediately evident—as does
excess production.
• Central to TPS is employee learning and a
continuing effort to create and produce products
under ideal conditions. Ideal conditions exist only
when facilities, machines, and people are brought
T
oyota Motor Corporation, with annual sales of
over 9 million cars and trucks, is the largest
vehicle manufacturer in the world. Two
techniques, just-in-time (JIT) and the Toyota
Production System (TPS), have been instrumental in
this post-WWII growth. Toyota, with a wide range of
vehicles, competes head-to-head with successful long-
established companies in Europe and the U.S. Taiichi
Ohno, a former vice president of Toyota, created the
basic framework for the world’s most discussed
systems for improving productivity, JIT and TPS. These
two concepts provide much of the foundation for lean
operations:
6
5
4
3
21
1
2
3
4
5
6
7
8
9 11
10 12 13 14
14
13
12
11
10
9
8
7
Reception
entrance
Land available for
Toyota expansion
Large supplier sites
for future expansion.
Main assembly complex
Tundras are built here.
Toyota Logistics Services coordinates
the shipment of finished Tundras by
truck or rail.
Supplier buildings
surround main
assembly complex.
Completed
trucks exit here
Railway lines bring in engines from
a Toyota plant in Alabama, axles
from a supplier in Arkansas, and
ship out finished trucks.
Tundras go from main assembly
complex to test track or to staging
area where they are shipped by
truck or rail.
Metalsa
Truck frames
Kautex
Fuel tanks
Tenneco Automotive
Exhaust systems
Curtis-Maruyasu America Inc.
Tubing
Millenium Steel Service Texas LLC
Steel processing
Green Metals Inc.
Scrap steel recycling
Avanzar Interior Technologies
Seats and interior parts
Toyotetsu Texas
Stamped parts
Futaba Industrial Texas Corp.
Stamped Parts
14 Suppliers outside the main plant
Outside: Toyota has a 2,000-acre site with 14 of the 21 onsite suppliers, adjacent rail lines, and near-by interstate highway. The site
provides expansion space for both Toyota and for its suppliers — and provides an environment for Just-in-time.
Reyes-Amtex
Interior parts
Toyoda-Gosei Texas LLC
Interior/exterior parts
Vutex Inc.
Assembly services
Takumi Stamping Texas Inc.
Stamped Parts
MetoKote
E-coater
496

together, adding value without waste. Waste
undermines productivity by diverting resources
to excess inventory, unnecessary processing, and
poor quality. Respect for people, extensive training,
cross-training, and standard work practices of
empowered employees focusing on driving out
waste are fundamental to TPS.
Toyota’s latest implementation of TPS and JIT
is present at its new San Antonio plant, the largest
Toyota land site for an automobile assembly plant in
the U.S. Interestingly, despite its annual production
capability of 200,000 Tundra pick-up trucks, the
building itself is one of the smallest in the industry.
Modern automobiles have 30,000 parts, but at Toyota,
independent suppliers combine many of these parts
into sub-assemblies. Twenty-one of these suppliers
are onsite at the San Antonio facility and transfer
components to the assembly line on a JIT basis.
Operations such as these taking place in the new
San Antonio plant are why Toyota continues to perform
near the top in quality and maintain the lowest labor-
hour assembly time in the industry. JIT, TPS, and lean
operations work—and they provide a competitive
advantage at Toyota Motor Corporation.
122
3
4
5
6
7
Level Schedules
models mixed on
production lines
to meet customer
orders.
JIT
parts and supplies
delivered just as
needed in the
quantity needed.
Standard Work
Practices
rigorous, agreed
upon, documented
procedures for
production.
Andon
problem display board
that communicates
abnormalities.
Minimal machines
Proprietary machines
designed for specific
Toyota applications.
Pull System
units produced only
when more production
is needed.
Jidoka
machines with built-in
devices for monitoring
performance and
making judgements.
Assembly Components
placed in cab for easy
access rather than on
shelves adjacent to the
assembly line.
Respect for People
employees treated as
knowledge workers.
Empowered Employees
can stop production, ideas
solicited, quality circles,
etc.
Kaizen Area
an area where suggestions
are tested and evaluated.
Kanban
signal that indicates
production of small
batches of
components.
Toyota’s San Antonio plant has about 2 million interior sq. ft., providing facilities within the final assembly building for 7 of the 21 onsite
suppliers, and capacity to build 200,000 pick-up trucks annually. But most importantly, Toyota practices the world-class Toyota Production
System and expects its suppliers to do the same thing wherever they are.
7 Suppliers inside the main plant
AGC Automotive Americas
Glass assemblies
ARK Inc.
Industrial waste management, recycling
HERO Assemblers LLP
Assembly of tire on to wheel
HERO Logistics LLP
Logistics
PPG Industries Inc.
Glass assemblies
Reyes Automotive Group
Interior/exterior parts
Tokai Rika
Functional parts
1
TOYOTA MOTOR CORPORATION �
497

498 PART 3 Managing Operations
JUST-IN-TIME, THE TOYOTA PRODUCTION SYSTEM,
AND LEAN OPERATIONS
As shown in the Global Company Profile, the Toyota Production System (TPS) contributes to a
world-class operation at Toyota Motor Corporation. In this chapter, we discuss JIT, TPS, and
lean operations as approaches to continuing improvement that drive out waste and lead to world-
class organizations.
Just-in-time (JIT) is an approach of continuous and forced problem solving via a focus on
throughput and reduced inventory. The Toyota Production System (TPS), with its emphasis on
continuous improvement, respect for people, and standard work practices, is particularly suited
for assembly lines. Lean operations supplies the customer with exactly what the customer
wants when the customer wants it, without waste, through continuous improvement. Lean oper-
ations are driven by workflow initiated by the “pull” of the customer’s order. When implemented
as a comprehensive manufacturing strategy, JIT, TPS, and lean systems sustain competitive
advantage and result in increased overall returns.
If there is any distinction between JIT, TPS, and lean operations, it is that:
• JIT emphasizes forced problem solving.
• TPS emphasizes employee learning and empowerment in an assembly-line environment.
• Lean operations emphasize understanding the customer.
However, in practice, there is little difference, and the terms are often used interchangeably.
Leading organizations use the approaches and techniques that make sense for them. In this chap-
ter, we use the term lean operations to encompass all of the related approaches and techniques.
Regardless of the label put on operations improvement, good production systems require that
managers address three issues that are pervasive and fundamental to operations management:
eliminate waste, remove variability, and improve throughput. We first introduce these three
issues and then discuss the major attributes of JIT, TPS, and lean operations. Finally, we look at
lean operations applied to services.
Eliminate Waste
Traditional producers have limited goals—accepting, for instance, the production of some defec-
tive parts and some inventory. Lean producers set their sights on perfection; no bad parts, no
inventory, only value-added activities, and no waste. Any activity that does not add value in the
eyes of the customer is a waste. The customer defines product value. If the customer does not want
to pay for it, it is a waste. Taiichi Ohno, noted for his work on the Toyota Production System, iden-
tified seven categories of waste. These categories have become popular in lean organizations and
cover many of the ways organizations waste or lose money. Ohno’s seven wastes are:
• Overproduction: Producing more than the customer orders or producing early (before it is
demanded) is waste. Inventory of any kind is usually a waste.
• Queues: Idle time, storage, and waiting are wastes (they add no value).
• Transportation: Moving material between plants or between work centers and handling more
than once is waste.
• Inventory: Unnecessary raw material, work-in-process (WIP), finished goods, and excess
operating supplies add no value and are wastes.
LO2: Define the seven
wastes and the 5Ss
Seven wastes
Overproduction
Queues
Transportation
Inventory
Motion
Overprocessing
Defective product
LO1: Define just-in-time, TPS, and lean
operations 498
LO2: Define the seven wastes and
the 5Ss 498
LO3: Explain JIT partnerships 502
LO4: Determine optimal setup time 505
Chapter 16 Learning Objectives
LO5: Define kanban 508
LO6: Compute the required number
of kanbans 510
LO7: Explain the principles of the Toyota
Production System 511
Lean operations
Eliminates waste through a
focus on exactly what the
customer wants.
Toyota Production
System (TPS)
Focus on continuous
improvement, respect for
people, and standard work
practices.
Just-in-time (JIT)
Continuous and forced problem
solving via a focus on
throughput and reduced
inventory.
LO1: Define just-in-time,
TPS, and lean operations
AUTHOR COMMENT
World-class firms
everywhere are using
these three techniques.

Chapter 16 JIT and Lean Operations 499
• Motion: Movement of equipment or people that adds no value is waste.
• Overprocessing: Work performed on the product that adds no value is waste.
• Defective product: Returns, warranty claims, rework, and scrap are a waste.
A broader perspective—one that goes beyond immediate production—suggests that other
resources, such as energy, water, and air, are often wasted but should not be. Efficient, sustain-
able production minimizes inputs and maximizes outputs, wasting nothing.
For over a century, managers have pursued “housekeeping” for a neat, orderly, and efficient
workplace and as a means of reducing waste. Operations managers have embellished “housekeep-
ing” to include a checklist—now known as the 5Ss.1 The Japanese developed the initial 5Ss. Not
only are the 5Ss a good checklist for lean operations, they also provide an easy vehicle with which
to assist the culture change that is often necessary to bring about lean operations. The 5Ss follow:
• Sort/segregate: Keep what is needed and remove everything else from the work area; when in
doubt, throw it out. Identify non-value items and remove them. Getting rid of these items
makes space available and usually improves work flow.
• Simplify/straighten: Arrange and use methods analysis tools (see Chapter 7 and Chapter 10)
to improve work flow and reduce wasted motion. Consider long-run and short-run ergonomic
issues. Label and display for easy use only what is needed in the immediate work area. For
examples of visual displays see Chapter 10, Figure 10.8.
• Shine/sweep: Clean daily; eliminate all forms of dirt, contamination, and clutter from the
work area.
• Standardize: Remove variations from the process by developing standard operating proce-
dures and checklists; good standards make the abnormal obvious. Standardize equipment and
tooling so that cross-training time and cost are reduced. Train and retrain the work team so
that when deviations occur, they are readily apparent to all.
• Sustain/self-discipline: Review periodically to recognize efforts and to motivate to sustain
progress. Use visuals wherever possible to communicate and sustain progress.
U.S. managers often add two additional Ss that contribute to establishing and maintaining a
lean workplace:
• Safety: Build good safety practices into the above five activities.
• Support/maintenance: Reduce variability, unplanned downtime, and costs. Integrate daily
shine tasks with preventive maintenance.
The Ss provide a vehicle for continuous improvement with which all employees can identify.
Operations managers need think only of the examples set by a well-run hospital emergency room
or the spit-and-polish of a fire department for a benchmark. Offices and retail stores, as well as
manufacturers, have successfully used the 5Ss in their respective efforts to eliminate waste and
move to lean operations. A place for everything and everything in its place does make a differ-
ence in a well-run office. And retail stores successfully use the Ss to reduce misplaced merchan-
dise and improve customer service. An orderly workplace reduces waste so that assets are
released for other, more productive, purposes.
Remove Variability
Managers seek to remove variability caused by both internal and external factors. Variability is
any deviation from the optimum process that delivers perfect product on time, every time.
Variability is a polite word for problems. The less variability in a system, the less waste in the
system. Most variability is caused by tolerating waste or by poor management. Among the many
sources of variability are:
• Poor production processes that allow employees and suppliers to produce improper quantities
or late or non-conforming units
• Unknown customer demands
• Incomplete or inaccurate drawings, specifications, and bills of material
5Ss
A lean production checklist:
Sort
Simplify
Shine
Standardize
Sustain
1The term 5S comes from the Japanese words seiri (sort and clear out), seiton (straighten and configure), seiso (scrub
and cleanup), seiketsu (maintain sanitation and cleanliness of self and workplace), and shitsuke (self-discipline and
standardization of these practices).
Variability
Any deviation from the optimum
process that delivers perfect
product on time, every time.

500 PART 3 Managing Operations
Both JIT and inventory reduction are effective tools for identifying causes of variability. The pre-
cise timing of JIT makes variability evident, just as reducing inventory exposes variability. The
removal of variability allows managers to move good materials on schedule, add value at each
step of the production process, drive down costs, and win orders.
Improve Throughput
Throughput time is the time that it takes to move an order from receipt to delivery. Each minute
products remain on the books, costs accumulate and competitive advantage is lost. The time that
an order is in the shop is called manufacturing cycle time. This is the time between the arrival
of raw materials and the shipping of finished product. For example, phone-system manufacturer
Northern Telecom now has materials pulled directly from qualified suppliers to the assembly
line. This effort has reduced a segment of Northern’s manufacturing cycle time from 3 weeks to
just 4 hours, the incoming inspection staff from 47 to 24, and problems on the shop floor caused
by defective materials by 97%. Driving down manufacturing cycle time can make a major
improvement in throughput.
A technique for increasing throughput is a pull system. A pull system pulls a unit to where it
is needed just as it is needed. Pull systems are a standard tool of JIT systems. Pull systems use
signals to request production and delivery from supplying stations to stations that have produc-
tion capacity available. The pull concept is used both within the immediate production process
and with suppliers. By pulling material through the system in very small lots—just as it is
needed—waste and inventory are removed. As inventory is removed, clutter is reduced, problems
become evident, and continuous improvement is emphasized. Removing the cushion of inven-
tory also reduces both investment in inventory and manufacturing cycle time. A push system
dumps orders on the next downstream workstation, regardless of timeliness and resource avail-
ability. Push systems are the antithesis of JIT. Pulling material through a production process as it
is needed rather than in a “push” mode typically lowers cost and improves schedule perfor-
mance, enhancing customer satisfaction.
JUST-IN-TIME (JIT)
With its forced problem solving via a focus on rapid throughput and reduced inventory, JIT pro-
vides a powerful strategy for improving operations. With JIT, materials arrive where they are
needed only when they are needed. When good units do not arrive just as needed, a “problem” has
been identified. By driving out waste and delay in this manner, JIT reduces costs associated with
excess inventory, cuts variability and waste, and improves throughput. JIT is a key ingredient of
lean operations and is particularly helpful in supporting strategies of rapid response and low cost.
Throughput time
The time required to move
orders through the production
process, from receipt to
delivery.
Manufacturing cycle
time
The time between the arrival of
raw materials and the shipping
of finished products.
Pull system
A concept that results in
material being produced only
when requested and moved to
where it is needed just as it is
needed.
AUTHOR COMMENT
JIT places added demands
on performance, but that
is why it pays off.
Many services have adopted JIT techniques as a
normal part of their business. Restaurants like Olive
Garden and Red Lobster expect and receive JIT
deliveries. Both buyer and supplier expect fresh,
high-quality produce delivered without fail just when
it is needed. The system doesn’t work any other way.

Chapter 16 JIT and Lean Operations 501
JIT partnerships
Partnerships of suppliers and
purchasers that remove waste
and drive down costs for mutual
benefits.
Consignment inventory
An arrangement in which the
supplier maintains title to the
inventory until it is used.
Every moment material is held, an activity that adds value should be occurring. Consequently, as
Figure 16.1 suggests, JIT often yields a competitive advantage.
Effective JIT requires a meaningful buyer–supplier partnership.
JIT Partnerships
A JIT partnership exists when a supplier and a purchaser work together with open communica-
tion and a goal of removing waste and driving down costs. Close relationships and trust are crit-
ical to the success of JIT. Figure 16.2 shows the characteristics of JIT partnerships. Some
specific goals of JIT partnerships are:
• Removal of unnecessary activities, such as receiving, incoming inspection, and paperwork
related to bidding, invoicing, and payment.
• Removal of in-plant inventory by delivery in small lots directly to the using department as
needed.
• Removal of in-transit inventory by encouraging suppliers to locate nearby and provide fre-
quent small shipments. The shorter the flow of material in the resource pipeline, the less
inventory. Inventory can also be reduced through a technique known as consignment.
Consignment inventory (see the OM in Action box “Lean Production at Cessna Aircraft”), a
variation of vendor-managed inventory (Chapter 11), means the supplier maintains the title to
the inventory until it is used. For instance, an assembly plant may find a hardware supplier
that is willing to locate its warehouse where the user currently has its stockroom. In this
manner, when hardware is needed, it is no farther than the stockroom. Schedule and produc-
tion information must be shared with the consignment supplier, or inventory holding costs
will just be transferred from the buyer to the supplier, with no net cost reduction. Another
option is to have the supplier ship to other, perhaps smaller, purchasers from the “stockroom.”
• Obtain improved quality and reliability through long-term commitments, communication,
and cooperation.
Layout: Work-cells; Group technology; Flexible machinery; Organized
workplace; Reduced space for inventory.
Few vendors; Supportive supplier relationships;
Quality deliveries on time, directly to work areas.
Suppliers:
JIT TECHNIQUES:
Inventory: Small lot sizes; Low setup time; Specialized parts bins
Zero deviation from schedules; Level schedules;
Suppliers informed of schedules; Kanban techniques
Scheduling:
Scheduled; Daily routine; Operator involvement
Statistical process control; Quality suppliers; Quality within the firm
Preventive maintenance:
Quality production:
Empowered and cross-trained employees; Training support;
Few job classifications to ensure flexibility of employees
Employee
empowerment:
Support of management, employees, and suppliers
Rapid throughput frees assets
Quality improvement reduces waste
Cost reduction adds pricing flexibility
Variability reduction
Rework reduction
Commitment:
WHICH RESULTS IN:
WHICH WINS ORDERS BY:
Faster response to the
customer at lower cost
and higher quality—
A Competitive Advantage
� FIGURE 16.1
JIT Contributes to
Competitive Advantage

502 PART 3 Managing Operations
Leading organizations view suppliers as extensions of their own organizations and expect suppliers
to be fully committed to improvement. Such relationships require a high degree of respect by both
supplier and purchaser. Supplier concerns can be significant; Harley-Davidson, for example, ini-
tially had difficulty implementing JIT because supplier issues outweighed the perceived benefits.
Concerns of Suppliers
Successful JIT partnerships require that supplier concerns be addressed. These concerns include:
1. Diversification: Suppliers may not want to tie themselves to long-term contracts with one
customer. The suppliers’ perception is that they reduce their risk if they have a variety of
customers.
2. Scheduling: Many suppliers have little faith in the purchaser’s ability to produce orders to a
smooth, coordinated schedule.
Mutual
Understanding
and
Trust
Suppliers
Locate near buyer
Extend JIT techniques to their suppliers
Include packaging and routing details
Detail ID and routing labels
Focus on core competencies
Quantities
Produce small lots
Deliver with little overage and underage
Meet mutually developed quality requirements
Produce with zero defects
Shipping
Seek joint scheduling and shipping efficiencies
Consider third-party logistics
Use advance shipping notice (ASN)
Ship frequent small orders
Buyers
Share customer preferences and demand forecasts
Minimize product specifications and encourage innovation
Support supplier innovation and price competitiveness
Develop long-term relationships
Focus on core competencies
Process orders with minimal paperwork (use EDI or Internet)
� FIGURE 16.2 Characteristics of JIT Partnerships
Third, the company
used group technology
and manufacturing cells
to move away from a
batch process that
resulted in large
inventories and unsold
planes. Now, Cessna
pulls product through its
plant only when a
specific order is placed.
These commitments to manufacturing efficiency are
part of the lean operations that has made Cessna the
world’s largest manufacturer of single-engine aircraft.
Sources: www.cessna.com (2007); Strategic Finance (November 2002):
32; Purchasing (September 4, 2003): 25–30; and Fortune (May 1, 2000):
1222B.
OM in Action � Lean Production at Cessna Aircraft
LO3: Explain JIT
partnerships
When Cessna Aircraft opened its new plant in
Independence, Kansas, it saw the opportunity to switch
from a craftwork mentality producing small single-engine
planes to a lean manufacturing system. In doing so,
Cessna adopted three lean practices.
First, Cessna set up consignment- and vendor-
managed inventories with several of its suppliers. Blanket
purchase orders allow Honeywell, for example, to maintain
a 30-day supply of avionic parts onsite. Other vendors were
encouraged to use a nearby warehouse to keep parts that
could then be delivered daily to the production line.
Second, Cessna managers committed to cross-training,
in which team members learn the duties of other team
members and can shift across assembly lines as needed.
To develop these technical skills, Cessna brought in 60
retired assembly-line workers to mentor and teach new
employees. Employees were taught to work as a team
and to assume responsibility for their team’s quality.

www.cessna.com

Chapter 16 JIT and Lean Operations 503
� TABLE 16.1
JIT Layout Tactics
Build work cells for
families of products
Include a large number of
operations in a small
area
Minimize distance
Design little space for
inventory
Improve employee
communication
Use poka-yoke devices
Build flexible or movable
equipment
Cross-train workers to add
flexibility
3. Lead time: Engineering or specification changes can play havoc with JIT because of inade-
quate lead time for suppliers to implement the necessary changes.
4. Quality: Suppliers’capital budgets, processes, or technology may limit ability to respond to
changes in product and quality.
5. Lot sizes: Suppliers may see frequent delivery in small lots as a way to transfer buyers’
holding costs to suppliers.
JIT LAYOUT
JIT layouts reduce another kind of waste—movement. The movement of material on a factory
floor (or paper in an office) does not add value. Consequently, managers want flexible layouts
that reduce the movement of both people and material. JIT layouts place material directly in the
location where needed. For instance, an assembly line should be designed with delivery points
next to the line so material need not be delivered first to a receiving department and then moved
again. This is what VF Corporation’s Wrangler Division in Greensboro, North Carolina, did;
denim is now delivered directly to the line. Toyota has gone one step farther and places hardware
and components in the chassis of each vehicle moving down the assembly line. This is not only
convenient, but it allows Toyota to save space and opens areas adjacent to the assembly line pre-
viously occupied by shelves. When a layout reduces distance, firms often save labor and space
and may have the added bonus of eliminating potential areas for accumulation of unwanted
inventory. Table 16.1 provides a list of JIT layout tactics.
Distance Reduction
Reducing distance is a major contribution of work cells, work centers, and focused factories (see
Chapter 9). The days of long production lines and huge economic lots, with goods passing
through monumental, single-operation machines, are gone. Now firms use work cells, often
arranged in a U shape, containing several machines performing different operations. These work
cells are often based on group technology codes (as discussed in Chapter 5). Group technology
codes help identify components with similar characteristics so we can group them into families.
Once families are identified, work cells are built for them. The result can be thought of as a small
product-oriented facility where the “product” is actually a group of similar products—a family of
products. The cells produce one good unit at a time, and ideally they produce the units only after
a customer orders them.
Increased Flexibility
Modern work cells are designed so they can be easily rearranged to adapt to changes in volume,
product improvements, or even new designs. Almost nothing in these new departments is bolted
down. This same concept of layout flexibility applies to office environments. Not only is most office
furniture and equipment movable, but so are office walls, computer connections, and telecommuni-
cations. Equipment is modular. Layout flexibility aids the changes that result from product and
process improvements that are inevitable with a philosophy of continuous improvement.
Impact on Employees
JIT layouts allow cross-trained employees to bring flexibility and efficiency to the work cell.
Employees working together can tell each other about problems and opportunities for improve-
ment. When layouts provide for sequential operations, feedback can be immediate. Defects are
waste. When workers produce units one at a time, they test each product or component at each
subsequent production stage. Machines in work cells with self-testing poka-yoke functions
detect defects and stop automatically when they occur. Before JIT, defective products were
replaced from inventory. Because surplus inventory is not kept in JIT facilities, there are no such
buffers. Getting it right the first time is critical.
Reduced Space and Inventory
Because JIT layouts reduce travel distance, they also reduce inventory by removing space for
inventory. When there is little space, inventory must be moved in very small lots or even single
units. Units are always moving because there is no storage. For instance, each month Security

504 PART 3 Managing Operations
� TABLE 16.2
JIT Inventory Tactics
Use a pull system to
move inventory
Reduce lot size
Develop just-in-time
delivery systems with
suppliers
Deliver directly to the
point of use
Perform to schedule
Reduce setup time
Use group technology
� FIGURE 16.3 High levels of inventory hide problems (a), but as we reduce inventory, problems are exposed
(b), and finally after reducing inventory and removing problems we have lower inventory, lower costs, and smooth
sailing (c).
Inventory level
Scrap
Setup
time
Late deliveries
Quality
problems
Process
downtime
(a)
Inventory
level
(c)
Inventory
level
Scrap
Setup
time
Late deliveries
Quality
problems
Process
downtime
(b)
No scrap
Setup time
reduced
No late
deliveries
Quality
problems
removed Process
downtime
removed
Pacific Corporation’s focused facility sorts 7 million checks, processes 5 million statements,
and mails 190,000 customer statements. With a JIT layout, mail processing time has been
reduced by 33%, salary costs by tens of thousands of dollars per year, floor space by 50%, and
in-process waiting lines by 75% to 90%. Storage, including shelves and drawers, has been
removed.
JIT INVENTORY
Inventories in production and distribution systems often exist “just in case” something goes
wrong. That is, they are used just in case some variation from the production plan occurs. The
“extra” inventory is then used to cover variations or problems. Effective inventory tactics
require “just in time,” not “just in case.” Just-in-time inventory is the minimum inventory
necessary to keep a perfect system running. With just-in-time inventory, the exact amount of
goods arrives at the moment it is needed, not a minute before or a minute after. Some useful
JIT inventory tactics are shown in Table 16.2 and discussed in more detail in the following
sections.
Reduce Inventory and Variability
Operations managers move toward JIT by first removing inventory. The idea is to eliminate variabil-
ity in the production system hidden by inventory. Reducing inventory uncovers the “rocks” in Figure
16.3(a) that represent the variability and problems currently being tolerated. With reduced inventory,
management chips away at the exposed problems. After the lake is lowered, managers make addi-
tional cuts in inventory and continue to chip away at the next level of exposed problems (see Figure
16.3[b,c]). Ultimately, there will be virtually no inventory and no problems (variability).
Dell estimates that the rapid changes in technology costs % to 2% of its inventory’s value
each week. Shigeo Shingo, co-developer of the Toyota JIT system, says, “Inventory is evil.” He
is not far from the truth. If inventory itself is not evil, it hides evil at great cost.
Reduce Lot Sizes
Just-in-time has also come to mean elimination of waste by reducing investment in inventory.
The key to JIT is producing good product in small lot sizes. Reducing the size of batches can be
a major help in reducing inventory and inventory costs. As we saw in Chapter 12, when inventory
usage is constant, the average inventory level is the sum of the maximum inventory plus the min-
imum inventory divided by 2. Figure 16.4 shows that lowering the order size increases the num-
ber of orders but drops inventory levels.
Ideally, in a JIT environment, order size is one and single units are being pulled from one
adjacent process to another. More realistically, analysis of the process, transportation time, and
containers used for transport are considered when determining lot size. Such analysis typically
results in a small lot size but a lot size larger than one. Once a lot size has been determined, the
1
2
Just-in-time inventory
The minimum inventory
necessary to keep a perfect
system running.
AUTHOR COMMENT
Accountants book inventory
as an asset, but operations
managers know it is costly.
Inventory
“Inventory is evil.”
S. Shingo

Chapter 16 JIT and Lean Operations 505
� FIGURE 16.4
Frequent Orders Reduce
Average Inventory
A lower order size increases
the number of orders and
total ordering cost but
reduces average inventory
and total holding cost.
200
100
In
ve
n
to
ry
Time
Q1 When average order size = 200
average inventory is 100
Q2 When average order size = 100
average inventory is 50
EOQ production order quantity model can be modified to determine the desired setup time. We
saw in Chapter 12 that the production order quantity model takes the form:
(16-1)
where
Example 1 shows how to determine the desired setup time.
H = Holding cost
p = Daily productionS = Setup cost
d = Daily demandD = Annual demand
Q* =
A
2DS
H31 – 1d>p24
� EXAMPLE 1
Determining
optimal setup
time
Crate Furniture, Inc., a firm that produces rustic furniture, desires to move toward a reduced lot size.
Crate Furniture’s production analyst, Aleda Roth, determined that a 2-hour production cycle would be
acceptable between two departments. Further, she concluded that a setup time that would accommo-
date the 2-hour cycle time should be achieved.
APPROACH � Roth developed the following data and procedure to determine optimum setup
time analytically:
Annual demand 400,000 units
Daily demand 400,000 per 250 days 1,600 units per day
Daily production rate 4,000 units per day
EOQ desired 400 (which is the 2-hour demand; that is, 1,600 per day per four
2-hour periods)
Holding cost $20 per unit per year
Setup cost (to be determined)
SOLUTION � Roth determines that the cost, on an hourly basis, of setting up equipment is $30.
Further, she computes that the setup cost per setup should be:
(16-2)
= 0.08 hour, or 4.8 minutes
= $2.40>1$30 per hour2
Setup time = $2.40>1hourly labor rate2
=
13,200,000210.62
800,000
= $2.40
=
140022120211 – 1,600>4,0002
2(400,000)
S =
(Q2)(H)(1 – d>p)
2D
Q2 =
2DS
H11 – d>p2
Q =
A
2DS
H11 – d>p2
S =
=H =
=Q =
=p =
==d =
=D =
LO4: Determine optimal
setup time

Only two changes need to be made for small-lot material flow to work. First, material handling
and work flow need to be improved. With short production cycles, there can be very little wait
time. Improving material handling is usually easy and straightforward. The second change is more
challenging, and that is a radical reduction in setup times. We discuss setup reduction next.
Reduce Setup Costs
Both inventory and the cost of holding it go down as the inventory-reorder quantity and the max-
imum inventory level drop. However, because inventory requires incurring an ordering or setup
cost that must be applied to the units produced, managers tend to purchase (or produce) large
orders. With large orders, each unit purchased or ordered absorbs only a small part of the setup
cost. Consequently, the way to drive down lot sizes and reduce average inventory is to reduce
setup cost, which in turn lowers the optimum order size.
The effect of reduced setup costs on total cost and lot size is shown in Figure 16.5.
Moreover, smaller lot sizes hide fewer problems. In many environments, setup cost is highly
correlated with setup time. In a manufacturing facility, setups usually require a substantial
amount of preparation. Much of the preparation required by a setup can be done prior to shut-
ting down the machine or process. Setup times can be reduced substantially, as shown in
Figure 16.6. For instance, in Kodak’s Guadalajara, Mexico, plant a team reduced the setup
time to change a bearing from 12 hours to 6 minutes! This is the kind of progress that is typi-
cal of world-class manufacturers.
Just as setup costs can be reduced at a machine in a factory, setup time can also be reduced
during the process of getting the order ready. It does little good to drive down factory setup time
from hours to minutes if orders are going to take 2 weeks to process or “set up” in the office. This
is exactly what happens in organizations that forget that JIT concepts have applications in offices
as well as in the factory. Reducing setup time (and cost) is an excellent way to reduce inventory
investment and to improve productivity.
JIT SCHEDULING
Effective schedules, communicated both within the organization and to outside suppliers, sup-
port JIT. Better scheduling also improves the ability to meet customer orders, drives down
inventory by allowing smaller lot sizes, and reduces work-in-process. For instance, Ford
506 PART 3 Managing Operations
INSIGHT � Now, rather than produce components in large lots, Crate Furniture can produce in a
2-hour cycle with the advantage of an inventory turnover of four per day.
LEARNING EXERCISE � If labor cost goes to $40 per hour, what should be the setup time?
[Answer: .06 hour, or 3.6 minutes.]
RELATED PROBLEMS � 16.8, 16.9, 16.10
AUTHOR COMMENT
Reduced lot sizes must
be accompanied by reduced
setup times.
AUTHOR COMMENT
Effective scheduling is
required for effective use of
capital and personnel.
C
o
st
Holding cost
T2
S2
T1
S1
Sum of ordering
and holding cost
Setup cost curves (S1, S2)
Lot size
� FIGURE 16.5
Lower Setup Costs Will
Lower Total Cost
More frequent orders require
reducing setup costs;
otherwise, inventory costs will
rise. As the setup costs are
lowered (from S1 to S2), total
inventory costs also fall (from
T1 to T2).

Chapter 16 JIT and Lean Operations 507
Motor Company now ties some suppliers to its final assembly schedule. Ford communicates
its schedules to bumper manufacturer Polycon Industries from the Ford Oakville production
control system. The scheduling system describes the style and color of the bumper needed for
each vehicle moving down the final assembly line. The scheduling system transmits the infor-
mation to portable terminals carried by Polycon warehouse personnel who load the bumpers
onto conveyors leading to the loading dock. The bumpers are then trucked 50 miles to the Ford
plant. Total time is 4 hours. However, as we saw in our opening Global Company Profile,
Toyota has moved its seat supplier inside the new Tundra plant; this has driven down delivery
time even further.
Table 16.3 suggests several items that can contribute to achieving these goals, but two tech-
niques (in addition to communicating schedules) are paramount. They are level schedules and
kanban.
Level Schedules
Level schedules process frequent small batches rather than a few large batches. Because this
technique schedules many small lots that are always changing, it has on occasion been called
“jelly bean” scheduling. Figure 16.7 contrasts a traditional large-lot approach using large batches
with a JIT level schedule using many small batches. The operations manager’s task is to make
and move small lots so the level schedule is economical. This requires success with the issues
discussed in this chapter that allow small lots. As lots get smaller, the constraints may change and
become increasingly challenging. At some point, processing a unit or two may not be feasible.
90 min
60 min
40 min
25 min
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
15 min
13 min
Train operators and standardize
work procedures (save 2 minutes)
Repeat cycle until subminute
setup is achieved
Use one-touch system to eliminate
adjustments (save 10 minutes)
Separate setup into preparation and actual setup,
doing as much as possible while the
machine/process is operating
(save 30 minutes)
Initial Setup Time
Move material closer and
improve material handling
(save 20 minutes)
Standardize and
improve tooling
(save 15 minutes)
� FIGURE 16.6
Steps for Reducing
Setup Times
Reduced setup times are
a major JIT component.
� TABLE 16.3
JIT Scheduling Tactics
Communicate schedules
to suppliers
Make level schedules
Freeze part of the
schedule
Perform to schedule
Seek one-piece-make
and one-piece-move
Eliminate waste
Produce in small lots
Use kanbans
Make each operation
produce a perfect part
Level schedules
Scheduling products so that
each day’s production meets the
demand for that day.
AA BBB C AA BBB C AA BBB C AA BBB C AA BBB C AA BBB C AA BBB C AA BBB C
AAAAAA BBBBBBBBB CCC AAAAAA BBBBBBBBB CCC AAAAAA BBBBBBBBB CCC
JIT Level Material-Use Approach
Large-Lot Approach
Time
� FIGURE 16.7 Scheduling Small Lots of Parts A, B, and C Increases Flexibility to Meet Customer
Demand and Reduces Inventory
The JIT approach to scheduling produces just as many of each model per time period as the large-lot approach,
provided that setup times are lowered.

The constraint may be the way units are sold and shipped (four to a carton), or an expensive paint
changeover (on an automobile assembly line), or the proper number of units in a sterilizer (for a
food-canning line).
The scheduler may find that freezing the portion of the schedule closest to due dates allows
the production system to function and the schedule to be met. Freezing means not allowing
changes to be part of the schedule. Operations managers expect the schedule to be achieved with
no deviations from the schedule.
Kanban
One way to achieve small lot sizes is to move inventory through the shop only as needed rather
than pushing it on to the next workstation whether or not the personnel there are ready for it. As
noted earlier, when inventory is moved only as needed, it is referred to as a pull system, and the
ideal lot size is one. The Japanese call this system kanban. Kanbans allow arrivals at a work cen-
ter to match (or nearly match) the processing time.
Kanban is a Japanese word for card. In their effort to reduce inventory, the Japanese use sys-
tems that “pull” inventory through work centers. They often use a “card” to signal the need for
another container of material—hence the name kanban. The card is the authorization for the next
container of material to be produced. Typically, a kanban signal exists for each container of
items to be obtained. An order for the container is then initiated by each kanban and “pulled”
from the producing department or supplier. A sequence of kanbans “pulls” the material through
the plant.
The system has been modified in many facilities so that even though it is called a kanban, the
card itself does not exist. In some cases, an empty position on the floor is sufficient indication
that the next container is needed. In other cases, some sort of signal, such as a flag or rag (Figure
16.8) alerts that it is time for the next container.
When there is visual contact between producer and user, the process works like this:
1. The user removes a standard-size container of parts from a small storage area, as shown in
Figure 16.8.
2. The signal at the storage area is seen by the producing department as authorization to replen-
ish the using department or storage area. Because there is an optimum lot size, the produc-
ing department may make several containers at a time.
Figure 16.9 shows how a kanban works, pulling units as needed from production. This system
is similar to the resupply that occurs in your neighborhood supermarket: The customer buys;
the stock clerk observes the shelf or receives notice from the end-of-day sales list and
restocks. When the limited supply, if any, in the store’s storage is depleted, a “pull” signal is
sent to the warehouse, distributor, or manufacturer for resupply, usually that night. The com-
plicating factor in a manufacturing firm is the time needed for actual manufacturing (produc-
tion) to take place.
508 PART 3 Managing Operations
Kanban
The Japanese word for card,
which has come to mean
“signal”; a kanban system
moves parts through production
via a “pull” from a signal.
A kanban need not be as formal as
signal lights or empty carts. The cook
in a fast-food restaurant knows that
when six cars are in line, eight meat
patties and six orders of french fries
should be cooking.
LO5: Define kanban

Chapter 16 JIT and Lean Operations 509
Several additional points regarding kanbans may be helpful:
• When the producer and user are not in visual contact, a card can be used; otherwise, a light or
flag or empty spot on the floor may be adequate.
• Because a pull station may require several resupply components, several kanban pull tech-
niques can be used for different products at the same pull station.
• Usually, each card controls a specific quantity of parts, although multiple card systems are
used if the producing work cell produces several components or if the lot size is different from
the move size.
• In an MRP system (see Chapter 14), the schedule can be thought of as a “build” authorization
and the kanban as a type of “pull” system that initiates the actual production.
• The kanban cards provide a direct control (limit) on the amount of work-in-process between
cells.
• If there is an immediate storage area, a two-card system may be used—one card circulates
between user and storage area, and the other circulates between the storage area and the pro-
ducing area.
Determining the Number of Kanban Cards or Containers The number of kanban
cards, or containers, in a JIT system sets the amount of authorized inventory. To determine the
number of containers moving back and forth between the using area and the producing areas,
management first sets the size of each container. This is done by computing the lot size, using a
X201
Y302
Z405
Z405
Y302
X201
Signal marker hanging on post
for part Z405 shows that
production should start for that
part. The post is located so that
workers in normal locations can
easily see it.
Signal marker on stack of boxes.
Part numbers mark location of
specific part.
� FIGURE 16.8
Diagram of Outbound
Stockpoint with
Warning-Signal Marker
Work
cell
Finished
goods
Final
assembly
Information
flow
Material
flow
Legend:
Material/Parts
Supplier Customer
order
Kanban card
(pull signal)
Kanban card
(pull signal)
Kanban card
(pull signal)
� FIGURE 16.9 Kanban Signals “Pull” Material Through the Production Process
As a customer “pulls” an order from finished goods, a signal (kanban card) is sent to the final assembly area. Final assembly produces and
resupplies finished goods. When final assembly needs components, it sends a signal to its supplier, a work cell. The work cell, in turn, sends a
signal to the material/parts supplier.

510 PART 3 Managing Operations
model such as the production order quantity model (discussed in Chapter 12 and shown again on
pages 505–506 in Equation [16-1]). Setting the number of containers involves knowing (1) lead
time needed to produce a container of parts and (2) the amount of safety stock needed to account
for variability or uncertainty in the system. The number of kanban cards is computed as follows:
(16-3)
Example 2 illustrates how to calculate the number of kanbans needed.
Number of kanbans 1containers2 =
Demand during lead time + Safety stock
Size of container
EXAMPLE 2 �
Determining the
number of kanban
containers
Hobbs Bakery produces short runs of cakes that are shipped to grocery stores. The owner, Ken Hobbs,
wants to try to reduce inventory by changing to a kanban system. He has developed the following data
and asked you to finish the project.
APPROACH � Having determined that the EOQ size is 250, we then determine the number of
kanbans (containers) needed.
SOLUTION �
INSIGHT � Once the reorder point is hit, five containers should be released.
LEARNING EXERCISE � If lead time drops to 1 day, how many containers are needed?
[Answer: 3.]
RELATED PROBLEMS � 16.1, 16.2, 16.3, 16.4, 16.5, 16.6
Demand during lead time + Safety stock
Container size
=
1,000 + 250
250
= 5
Number of kanbans 1containers2 needed =
Safety stock = 250
Lead time * Daily demand = 2 days * 500 cakes = 1,000
Demand during lead time =
Container size 1determined on a production order size EOQ basis2 = 250 cakes
Safety stock = 12 day
Production lead time = Wait time + Material handling time + Processing time = 2 days
Daily demand = 500 cakes
Advantages of Kanban Containers are typically very small, usually a matter of a few hours’
worth of production. Such a system requires tight schedules. Small quantities must be produced sev-
eral times a day. The process must run smoothly with little variability in quality of lead time because
any shortage has an almost immediate impact on the entire system. Kanban places added emphasis on
meeting schedules, reducing the time and cost required by setups, and economical material handling.
Whether it is called kanban or something else, the advantages of small inventory and pulling
material through the plant only when needed are significant. For instance, small batches allow
only a very limited amount of faulty or delayed material. Problems are immediately evident.
Numerous aspects of inventory are bad; only one aspect—availability—is good. Among the bad
aspects are poor quality, obsolescence, damage, occupied space, committed assets, increased
insurance, increased material handling, and increased accidents. Kanban systems put downward
pressure on all these negative aspects of inventory.
In-plant kanban systems often use standardized, reusable containers that protect the specific
quantities to be moved. Such containers are also desirable in the supply chain. Standardized con-
tainers reduce weight and disposal costs, generate less wasted space in trailers, and require less
labor to pack, unpack, and prepare items.
JIT QUALITY
The relationship between JIT and quality is a strong one. They are related in three ways. First,
JIT cuts the cost of obtaining good quality. This saving occurs because scrap, rework, inventory
investment, and damage costs are buried in inventory. JIT forces down inventory; therefore,
fewer bad units are produced and fewer units must be reworked. In short, whereas inventory
hides bad quality, JIT immediately exposes it.
AUTHOR COMMENT
Good quality costs less.
LO6: Compute the
required number of kanbans

Chapter 16 JIT and Lean Operations 511
Second, JIT improves quality. As JIT shrinks queues and lead time, it keeps evidence of errors
fresh and limits the number of potential sources of error. In effect, JIT creates an early warning
system for quality problems so that fewer bad units are produced and feedback is immediate.
This advantage can accrue both within the firm and with goods received from outside vendors.
Finally, better quality means fewer buffers are needed and, therefore, a better, easier-to-
employ JIT system can exist. Often the purpose of keeping inventory is to protect against unreli-
able quality. If consistent quality exists, JIT allows firms to reduce all costs associated with
inventory. Table 16.4 suggests some requirements for quality in a JIT environment.
TOYOTA PRODUCTION SYSTEM (TPS)
Toyota Motor’s Eiji Toyoda and Taiichi Ohno are given credit for the Toyota Production System
(TPS) (see the Global Company Profile that opens this chapter). Three core components of TPS
are continuous improvement, respect for people, and standard work practice.
Continuous Improvement
Continuous improvement under TPS means building an organizational culture and instilling in
its people a value system stressing that processes can be improved—indeed, that improvement is
an integral part of every employee’s job. This process is formalized in TPS by kaizen, the
Japanese word for change for the good, or what is more generally known as continuous improve-
ment. In application, it means making a multitude of small or incremental changes as one seeks
ellusive perfection. (See the OM in Action box “Kaizen at Ducati.”). Instilling the mantra of con-
tinuous improvement begins at recruiting and continues through extensive and continuing train-
ing. One of the reasons continuous improvement works at Toyota, we should note, is because of
another core value at Toyota, Toyota’s respect for people.
Respect for People
At Toyota, people are recruited, trained, and treated as knowledge workers. Aided by aggressive
cross-training and few job classifications, TPS engages the mental as well as physical capacities
of employees in the challenging task of improving operations. Employees are empowered. They
are empowered to make improvements. They are empowered to stop machines and processes
when quality problems exist. Indeed, empowered employees are a necessary part of TPS. This
means that those tasks that have traditionally been assigned to staff are moved to employees.
Toyota recognizes that employees know more about their jobs than anyone else. TPS respects
employees by giving them the opportunity to enrich both their jobs and their lives.
Standard Work Practice
Standard work practice at Toyota includes these underlying principles:
• Work is completely specified as to content, sequence, timing, and outcome.
• Internal and external customer–supplier connections are direct, specifying personnel,
methods, timing, and quantity.
This auto plant, like most JIT facilities, empowers
employees so they can stop the entire production line
by pulling the overhead cord if any quality problems
are spotted.
� TABLE 16.4
JIT Quality Tactics
Use statistical process
control
Empower employees
Build fail-safe methods
(poka-yoke,
checklists, etc.)
Expose poor quality
with small lot JIT
Provide immediate
feedback
AUTHOR COMMENT
TPS brings the entire
person to work.
LO7: Explain the principles
of the Toyota Production
System
Kaizen
A focus on continuous
improvement.

512 PART 3 Managing Operations
• Product and service flows are to be simple and direct. Goods and services are directed to a
specific person or machine.
• Improvements in the system must be made in accordance with the “scientific method,” at the
lowest possible level in the organization.2
TPS requires that activities, connections, and flows include built-in tests to automatically signal
problems. Any gap between what is expected and what occurs becomes immediately evident.
The education and training of Toyota’s employees and the responsiveness of the system to prob-
lems make the seemingly rigid system flexible and adaptable to changing circumstances. The
result is ongoing improvements in reliability, flexibility, safety, and efficiency.
LEAN OPERATIONS
Lean production can be thought of as the end result of a well-run OM function. While JIT and
TPS tend to have an internal focus, lean production begins externally with a focus on the cus-
tomer. Understanding what the customer wants and ensuring customer input and feedback are
starting points for lean production. Lean operations means identifying customer value by analyz-
ing all the activities required to produce the product and then optimizing the entire process from
the customer’s perspective.
Building a Lean Organization
The transition to lean production is difficult. Building an organizational culture where learning,
empowerment, and continuous improvement are the norm is a challenge. However, organiza-
tions that focus on JIT, quality, and employee empowerment are often lean producers. Such
firms drive out activities that do not add value in the eyes of the customer: they include leaders
like United Parcel Service, Harley-Davidson, and, of course, Toyota. Even traditionally craft-
oriented organizations such as Louis Vuitton (see the OM in Action box) find improved produc-
tivity with lean operations. Lean operations adopt a philosophy of minimizing waste by striving
for perfection through continuous learning, creativity, and teamwork. They tend to share the fol-
lowing attributes:
• Use JIT techniques to eliminate virtually all inventory.
• Build systems that help employees produce a perfect part every time.
• Reduce space requirements by minimizing travel distance.
2Adopted from Steven J. Spear, “Learning to Lead at Toyota,” Harvard Business Review 82, no. 5 (May 2004): 78–86;
and Steven Spear and H. Kent Bowen, “Decoding the DNA of the Toyota Production System,” Harvard Business
Review 77, no. 5 (September–October 1999): 97–106.
Ducati Motor Holding SpA of Bologna, Italy, is a motorcycle
racing company whose engineering department replicates
its high-performance racing machines for street use. But the
production department is more interested in replicating lean
thinking and adapting the Toyota Production System to
Ducati’s culture. Ducati has trained everyone throughout
the plant, not just assembly specialists, in lean thinking. One
result is a total productive maintenance effort and improved
material flow that has increased machine reliability 12%
and cut hourly costs 23%.
Another successful approach to drive out waste was
Ducati’s 4-week workshop approach to kaizen. This is a
longer commitment to kaizen teams than is typical, as
kaizens are not designed for perfection on the first pass;
progress, not perfection, is the objective. This approach
provided an opportunity for incremental but significant
changes. The kaizen
workshops also
provided the added
advantage of bringing
people together in
cross-functional teams,
which reinforces a
culture of continuous improvement.
The astounding results at Ducati would make Eiji
Toyoda and Taiichi Ohno, founders of TPS, proud.
Production costs are down by 25%, manufacturing cycle
time has been reduced by 50%, and quality before delivery
has been increased by 70%.
Sources: The Wall Street Journal (March 26, 2008): D1, D5; Target 27, no. 4
(2007): 10–15; and www.oracle.com.
OM in Action � Kaizen at Ducati
AUTHOR COMMENT
Lean drives out non-
value-added activities.

www.oracle.com

Chapter 16 JIT and Lean Operations 513
• Develop partnerships with suppliers, helping them to understand the needs of the ultimate
customer.
• Educate suppliers to accept responsibility for satisfying end customer needs.
• Eliminate all but value-added activities. Material handling, inspection, inventory, and rework
are the likely targets because these do not add value to the product.
• Develop employees by constantly improving job design, training, employee commitment,
teamwork, and empowerment.
• Make jobs challenging, pushing responsibility to the lowest level possible.
• Build worker flexibility through cross-training and reducing job classifications.
Success requires the full commitment and involvement of managers, employees, and suppliers.
The rewards that lean producers reap are spectacular. Lean producers often become benchmark
performers.
LEAN OPERATIONS IN SERVICES
The features of lean operations apply to services just as they do in other sectors. (See the OM in
Action box “Toyota University Teaches Lean Thinking.”) Here are some examples applied to
suppliers, layout, inventory, and scheduling in the service sector.
Suppliers As we have noted, virtually every restaurant deals with its suppliers on a JIT basis.
Those that do not are usually unsuccessful. The waste is too evident—food spoils, and customers
complain or get sick.
Layouts Lean layouts are required in restaurant kitchens, where cold food must be served cold
and hot food hot. McDonald’s, for example, has reconfigured its kitchen layout at great expense to
drive seconds out of the production process, thereby speeding delivery to customers. With the new
process, McDonald’s can produce made-to-order hamburgers in 45 seconds. Layouts also make a
difference in airline baggage claim, where customers expect their bags just-in-time.
Inventory Stockbrokers drive inventory down to nearly zero every day. Most sell and buy
orders occur on an immediate basis because an unexecuted sell or buy order is not acceptable to
the client. A broker may be in serious trouble if left holding an unexecuted trade. Similarly,
McDonald’s reduces inventory waste by maintaining a finished-goods inventory of only 10 min-
utes; after that, it is thrown away. Hospitals, such as Arnold Palmer (described in this chapter’s
LVMH Moet Hennessy Louis Vuitton is the world’s largest
luxury-goods company. Its Louis Vuitton unit, responsible
for half of the company’s profit, makes very upscale
handbags and enjoys a rich markup on sales of about
$5 billion. The return-on-investment is excellent, but sales
could be even better: the firm often can’t match production
to the sales pace of a successful new product. In the high
fashion business that is all about speed-to-market, this is
bad news; a massive overhaul was in order.
Changes on the factory floor were key to the overhaul.
The traditional approach to manufacturing at Louis Vuitton
was batch production: craftsmen, working on partially
completed handbags, performed specialized tasks such
as cutting, gluing, sewing, and assembly. Carts moved
batches of semi-finished handbags on to the next
workstation. It took 20 to 30 workers 8 days to make a
handbag. And defects were high. Lean manufacturing
looked like the way to go.
Craftsmen were retrained to do multiple tasks in small
U-shaped work cells. Each work cell now contains 6 to
12 cross-trained
workers and the
necessary sewing
machines and work
tables. Consistent with
one-piece flow, the
work is passed through
the cell from worker
to worker. The system
reduces inventory
and allows workers
to detect flaws earlier.
Rework under the old system was sometimes as high as
50% and internal losses as high as 4%. Returns are down
by two-thirds. The system has not only improved
productivity and quality, it also allows Louis Vuitton to
respond to the market faster—with daily scheduling as
opposed to weekly scheduling.
Sources: The Wall Street Journal (October 9, 2006): A1, A15 and (January
31, 2006): A1, A13.
OM in Action � Going Lean at Louis Vuitton
AUTHOR COMMENT
JIT, TPS, and lean began
in factories but are now
also used in services
throughout the world.
VIDEO 16.1
JIT at Arnold Palmer Hospital

514 PART 3 Managing Operations
Video Case Study in the Lecture Guide & Activities Manual), manage JIT inventory and low safety
stocks for many items. Even critical supplies such as pharmaceuticals may be held to low levels by
developing community networks as backup systems. In this manner, if one pharmacy runs out of
a needed drug, another member of the network can supply it until the next day’s shipment arrives.
Scheduling At airline ticket counters, the focus of the system is on adjusting to customer
demand. But rather than being accommodated by inventory availability, demand is satisfied by
personnel. Through elaborate scheduling, ticket counter personnel show up just-in-time to cover
peaks in customer demand. In other words, rather than “things” inventoried, personnel are sched-
uled. At a salon, the focus is only slightly different: the customer and the staff are scheduled to
assure prompt service. At McDonald’s and Walmart, scheduling of personnel is down to
15-minute increments, based on precise forecasting of demand. Additionally, at McDonald’s,
production is done in small lots to ensure that fresh, hot hamburgers are delivered just-in-time. In
short, both personnel and production are scheduled to meet specific demand. Notice that in
all three of these lean organizations—the airline ticket counter, the salon, and McDonald’s—
scheduling is a key ingredient. Excellent forecasts drive those schedules. Those forecasts may be
very elaborate, with seasonal, daily, and even hourly components in the case of the airline ticket
counter (holiday sales, flight time, etc.), seasonal and weekly components at the salon (holidays
and Fridays create special problems), and down to a few minutes (to respond to the daily meal
cycle) at McDonald’s.
To deliver goods and services to customers under continuously changing demand, suppliers
need to be reliable, inventories lean, cycle times short, and schedules nimble. A lean focus
engages and empowers employees to create and deliver the customer’s perception of value, elim-
inating whatever does not contribute to this goal. Lean operations are currently being developed
with great success in many firms, regardless of their products. Lean techniques are widely used
in both goods-producing and service-producing firms; they just look different.
Lean operations take on an unusual form in an
operating room. McKesson-General, Baxter
International, and many other hospital suppliers
provide surgical supplies for hospitals on a JIT
basis. (1) They deliver prepackaged surgical
supplies based on hospital operating schedules,
and (2) the surgical packages themselves are
prepared so supplies are available in the sequence
in which they will be used during surgery.
Based in Gardenia, California, Toyota University teaches its
employees the Toyota Production System. But Toyota has
also opened its door to others. As a public service, Toyota
has been teaching lean thinking classes to the Los Angeles
Police Department and the U.S. military. Classes begin, as
one might expect, with a car-building exercise. Using model
cars and desks as workstations and delivery areas, students
begin with a focus on fast throughput and high production
goals. This results in a “push” system, with lots of work-in-
process piling up, lots of defects to be reworked, and too
many of the wrong kind of cars on the “dealer’s” lot.
The exercise is then revised, and students are taught to
respond to orders and to form kaizen (continuous
improvement) teams. The revised exercise then uses a
“pull” system that responds to orders and fixes even the
most minor problems immediately. With a focus only on
filling orders and “pulling” demand through the production
process with no defects, a faster, more efficient production
line is formed.
Instructor Matthew May’s observation about adapting
lean methods beyond the factory: “If you can do it with
LAPD, you can do it anywhere.”
Sources: The Wall Street Journal (March 5, 2007): B1, B4; and www.
isosupport.com.
OM in Action � Toyota University Teaches Lean Thinking

www.isosupport.com

www.isosupport.com

Chapter 16 JIT and Lean Operations 515
CHAPTER SUMMARY
JIT, TPS, and lean operations are philosophies of continuous
improvement. Lean operations focus on customer desires,
TPS focuses on respect for people and standard work prac-
tices, and JIT focuses on driving out waste by reducing inven-
tory. But all three approaches reduce waste in the production
process. And because waste is found in anything that does not
add value, organizations that implement these techniques are
adding value more efficiently than
other firms. The expectation of these
systems is that empowered employ-
ees work with committed manage-
ment to build systems that respond to
customers with ever-lower cost and
higher quality.
Key Terms
Just-in-time (JIT) (p. 498)
Toyota Production System (TPS) (p. 498)
Lean operations (p. 498)
Seven wastes (p. 498)
5Ss (p. 499)
Variability (p. 499)
Throughput time (p. 500)
Manufacturing cycle time (p. 500)
Pull system (p. 500)
JIT partnerships (p. 501)
Consignment inventory (p. 501)
Just-in-time inventory (p. 504)
Level schedules (p. 507)
Kanban (p. 508)
Kaizen (p. 511)
Solved Problems Virtual Office Hours help is available at www.myomlab.com
� SOLVED PROBLEM 16.1
Krupp Refrigeration, Inc., is trying to reduce inventory and wants
you to install a kanban system for compressors on one of its
assembly lines. Determine the size of the kanban and the number
of kanbans (containers) needed.
Safety stock = 12 day’s production of compressors
Lead time = 3 days
* daily usage of 100 compressors2
Annual usage = 25,000 150 weeks * 5 days each
Daily production = 200 compressors
Annual holding cost per compressor = $100
Setup cost = $10
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� SOLUTION
First, we must determine kanban container size. To do this, we determine the production order quantity (see dis-
cussion in Chapter 12 or Equation [16-1]), which determines the kanban size:
So the production order size and the size of the kanban container .
Then we determine the number of kanbans:
=
300 + 100
100
=
400
100
= 4 containers
Number of kanbans =
Demand during lead time + Safety stock
Size of container
Safety stock = 100 1= 12 * daily production of 2002
Demand during lead time = 300 1= 3 days * daily usage of 1002
= 100= 210,000 = 100 compressors.
Q*p =
Q
2DS
Ha1 –
d
p
b
=
Q
2125,00021102
Ha1 –
d
p
b
=
Q
500,000
100a1 –
100
200
b
=
A
500,000
50

www.myomlab.com

516 PART 3 Managing Operations
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Together. New York: The Free Press (2005).

Maintenance
and Reliability
Chapter Outline
GLOBAL COMPANY PROFILE: ORLANDO UTILITIES
COMMISSION
The Strategic Importance
of Maintenance and Reliability 520
Reliability 521
Maintenance 524
Total Productive Maintenance 528
Techniques for Enhancing
Maintenance 529
� Design of Goods and Services
� Managing Quality
� Process Strategy
� Location Strategies
� Layout Strategies
� Human Resources
� Supply-Chain Management
� Inventory Management
� Scheduling
� Maintenance
517

GLOBAL COMPANY PROFILE: ORLANDO UTILITIES COMMISSION
MAINTENANCE PROVIDES A COMPETITIVE ADVANTAGE
FOR ORLANDO UTILITIES COMMISSION
T
he Orlando Utilities Commission (OUC) owns
and operates power plants that supply power to
two central Florida counties. Every year, OUC
takes each one of its power-generating units
off-line for 1 to 3 weeks to perform maintenance work.
Additionally, each unit is also taken off-line every
3 years for a complete overhaul and turbine generator
inspection. Overhauls are scheduled for spring and
fall, when the weather is mildest and demand for
power is low. These overhauls last from 6 to 8 weeks.
Units at OUC’s Stanton Energy Center require that
maintenance personnel perform approximately 12,000
repair and preventive maintenance tasks a year. To
accomplish these tasks efficiently, many of these jobs
are scheduled daily via a computerized maintenance
management program. The computer generates
preventive maintenance work orders and lists of
required materials.
Every day that a plant is down for maintenance
costs OUC about $110,000 extra for the replacement
The Stanton Energy Center in Orlando.
cost of power that must be generated elsewhere.
However, these costs pale beside the costs associated
with a forced outage. An unexpected outage could cost
OUC an additional $350,000 to $600,000 each day!
Scheduled overhauls are not easy; each one has
1,800 distinct tasks and requires 72,000 labor-hours.
But the value of preventive maintenance was
illustrated by the first overhaul of a new turbine
generator. Workers discovered a cracked rotor blade,
which could have destroyed a $27 million piece of
equipment. To find such cracks, which are invisible to
the naked eye, metals are examined using dye tests,
X-rays, and ultrasound.
At OUC, preventive maintenance is worth its
weight in gold. As a result, OUC’s electric distribution
system has been ranked number one in the
Southeast U.S. by PA Consulting Group—a leading
consulting firm. Effective maintenance provides a
competitive advantage for the Orlando Utilities
Commission.
518

� Two employees are on scaffolding near the top of Stanton Energy
Center’s 23-story high boiler, checking and repairing super heaters.
� This inspector is examining a low-pressure
section of turbine. The tips of these turbine
blades will travel at supersonic speeds of
1,300 miles per hour when the plant is in
operation. A crack in one of the blades can
cause catastrophic failure.
� Maintenance of capital-intensive facilities requires good planning to minimize
downtime. Here, turbine overhaul is under way. Organizing the thousands of
parts and pieces necessary for a shutdown is a major effort.
ORLANDO UTILITIES COMMISSION �
519

LO1: Describe how to improve system
reliability 521
LO2: Determine system reliability 522
LO3: Determine mean time between
failures (MTBF) 523
LO4: Distinguish between preventive and
breakdown maintenance 524
520 PART 3 Managing Operations
Chapter 17 Learning Objectives
THE STRATEGIC IMPORTANCE OF MAINTENANCE
AND RELIABILITY
Managers at Orlando Utilities Commission (OUC), the subject of the chapter-opening Global
Company Profile, fight for reliability to avoid the undesirable results of equipment failure. At
OUC, a generator failure is very expensive for both the company and its customers. Power outages
are instantaneous, with potentially devastating consequences. Similarly, managers at Frito-Lay,
Walt Disney Company, and United Parcel Service (UPS) are intolerant of failures or breakdowns.
Maintenance is critical at Frito-Lay to achieve high plant utilization and excellent sanitation. At
Disney, sparkling-clean facilities and safe rides are necessary to retain its standing as one of the
most popular vacation destinations in the world. Likewise, UPS’s famed maintenance strategy
keeps its delivery vehicles operating and looking as good as new for 20 years or more.
These companies, like most others, know that poor maintenance can be disruptive, inconve-
nient, wasteful, and expensive in dollars and even in lives. As Figure 17.1 illustrates, the interde-
pendency of operator, machine, and mechanic is a hallmark of successful maintenance and
reliability. Good maintenance and reliability management enhances a firm’s performance and
protects its investment.
The objective of maintenance and reliability is to maintain the capability of the system. Good
maintenance removes variability. Systems must be designed and maintained to reach expected
performance and quality standards. Maintenance includes all activities involved in keeping a
system’s equipment in working order. Reliability is the probability that a machine part or prod-
uct will function properly for a specified time under stated conditions.
In this chapter, we examine four important tactics for improving the reliability and mainte-
nance not only of products and equipment but also of the systems that produce them. The four
tactics are organized around reliability and maintenance.
The reliability tactics are:
1. Improving individual components
2. Providing redundancy
Maintenance
The activities involved in
keeping a system’s equipment
in working order.
Reliability
The probability that a machine
part or product will function
properly for a specified time
under stated conditions.
LO5: Describe how to improve
maintenance 525
LO6: Compare preventive and breakdown
maintenance costs 527
LO7: Define autonomous maintenance 528
AUTHOR COMMENT
If the system is not reliable,
everything else is
more difficult.
AUTHOR COMMENT
Employee commitment
makes a big difference.
Maintenance and Reliability
Procedures
Results
Reduced inventory
Improved quality
Improved capacity
Reputation for quality
Continuous improvement
Reduced variability
Clean and lubricate
Monitor and adjust
Make minor repairs
Keep computerized records
Employee Involvement
Partnering with maintenance
personnel
Skill training
Reward system
Employee empowerment
� FIGURE 17.1
Good Maintenance and
Reliability Management
Requires Employee
Involvement and Good
Procedures
VIDEO 17.1
Maintenance Drives Profits
at Frito-Lay

Chapter 17 Maintenance and Reliability 521
The maintenance tactics are:
1. Implementing or improving preventive maintenance
2. Increasing repair capabilities or speed
Variability corrupts processes and creates waste. The operations manager must drive out variabil-
ity: Designing for reliability and managing for maintenance are crucial ingredients for doing so.
RELIABILITY
Systems are composed of a series of individual interrelated components, each performing a spe-
cific job. If any one component fails to perform, for whatever reason, the overall system (for
example, an airplane or machine) can fail. First, we discuss improving individual components,
and then we discuss providing redundancy.
Improving Individual Components
Because failures do occur in the real world, understanding their occurrence is an important relia-
bility concept. We now examine the impact of failure in a series. Figure 17.2 shows that as the
number of components in a series increases, the reliability of the whole system declines very
quickly. A system of interacting parts, each of which has a 99.5% reliability, has an over-
all reliability of 78%. If the system or machine has 100 interacting parts, each with an individual
reliability of 99.5%, the overall reliability will be only about 60%!
To measure reliability in a system in which each individual part or component may have its
own unique rate of reliability, we cannot use the reliability curve in Figure 17.2. However, the
method of computing system reliability ( ) is simple. It consists of finding the product of indi-
vidual reliabilities as follows:
(17-1)
where
and so on.
Equation (17-1) assumes that the reliability of an individual component does not depend on
the reliability of other components (that is, each component is independent). Additionally, in this
equation as in most reliability discussions, reliabilities are presented as probabilities. Thus, a .90
reliability means that the unit will perform as intended 90% of the time. It also means that it will
fail of the time. We can use this method to evaluate the reliability of a
service or a product, such as the one we examine in Example 1.
1 – .90 = .10 = 10%
R2 = reliability of component 2
R1 = reliability of component 1
Rs = R1 * R2 * R3 * Á * Rn
Rs
n = 50
Average reliability of each component (percent)
100 98 97 96
0
20
40
60
80
100
R
e
lia
b
ili
ty
o
f
th
e
s
ys
te
m
(
p
e
rc
e
n
t)

99
n = 300
n
= 400
n = 100
n = 50
n = 10
n = 1
n = 200
� FIGURE 17.2
Overall System Reliability as
a Function of Number of n
Components (Each with the
Same Reliability) and
Component Reliability with
Components in a Series
LO1: Describe how to
improve system reliability
AUTHOR COMMENT
Designing for reliability is an
excellent place to start
reducing variability.

522 PART 3 Managing Operations
EXAMPLE 1 �
Reliability in a
series
The National Bank of Greeley, Colorado, processes loan applications through three clerks set up in
series, with reliabilities of .90, .80, and .99. It wants to find the system reliability.
APPROACH � Apply Equation (17-1) to solve for Rs.
R3R2R1
.90 .80 .99 RS
SOLUTION � The reliability of the loan process is:
INSIGHT � Because each clerk in the series is less than perfect, the error probabilities are cumu-
lative and the resulting reliability for this series is .713, which is less than any one clerk.
LEARNING EXERCISE � If the lowest-performing clerk (.80) is replaced by a clerk perform-
ing at .95 reliability, what is the new expected reliability? [Answer: .846.]
RELATED PROBLEMS � 17.1, 17.2, 17.5, 17.11
EXCEL OM Data File Ch17Ex1.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 17.1 This example is further illustrated in Active Model 17.1 at www.pearsonhighered.com/heizer.
Rs = R1 * R2 * R3 = 1.9021.8021.992 = .713, or 71.3%
Component reliability is often a design or specification issue for which engineering design
personnel may be responsible. However, supply-chain personnel may be able to improve compo-
nents of systems by staying abreast of suppliers’ products and research efforts. Supply-chain
personnel can also contribute directly to the evaluation of supplier performance.
The basic unit of measure for reliability is the product failure rate (FR). Firms
producing high-technology equipment often provide failure-rate data on their products. As
shown in Equations (17-2) and (17-3), the failure rate measures the percent of failures among
the total number of products tested, FR(%), or a number of failures during a period of time,
FR(N):
(17-2)
(17-3)
Perhaps the most common term in reliability analysis is the mean time between failures
(MTBF), which is the reciprocal of FR(N):
(17-4)
In Example 2, we compute the percentage of failure FR(%), number of failures FR(N), and mean
time between failures (MTBF).
MTBF =
1
FR1N2
FR1N2 =
Number of failures
Number of unit-hours of operation time
FR1%2 =
Number of failures
Number of units tested
* 100%
EXAMPLE 2 �
Determining mean
time between
failures
Twenty air-conditioning systems designed for use by astronauts in NASA’s space shuttles were oper-
ated for 1,000 hours at NASA’s Huntsville, Alabama, test facility. Two of the systems failed during the
test—one after 200 hours and the other after 600 hours.
APPROACH � To determine the percent of failures [FR(%)], the number of failures per unit of
time [FR(N )], and the mean time between failures (MTBF), we use Equations (17-2), (17-3), and
(17-4), respectively.
Mean time between
failures (MTBF)
The expected time between a
repair and the next failure of a
component, machine, process,
or product.
LO2: Determine system
reliability

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Chapter 17 Maintenance and Reliability 523
SOLUTION � Percentage of failures:
Number of failures per operating hour:
where Total time (1,000 hr)(20 units)
20,000 unit-hour
Nonoperating time 800 hr for 1st failure + 400 hr for 2nd failure
1,200 unit-hour
Operating time Total time Nonoperating time
.000106 failure/unit-hour
Because
If the typical space shuttle trip lasts 6 days, NASA may be interested in the failure rate per trip:
INSIGHT � Mean time between failures (MTBF) is the standard means of stating reliability.
LEARNING EXERCISE � If non-operating time drops to 800, what is the new MTBF?
[Answer: 9,606 hr.]
RELATED PROBLEMS � 17.6, 17.7
= .0153 failure/trip
= 1.00010621242162
Failure rate = 1Failures/unit-hr2124 hr>day216 days>trip2
MTBF =
1
.000106
= 9,434 hr
MTBF =
1
FR1N2
=
FR1N2 =
2
20,000 – 1,200
=
2
18,800
-=
=
=
=
=
FR1N2 =
Number of failures
Operating time
FR1%2 =
Number of failures
Number of units tested
=
2
20
1100%2 = 10%
If the failure rate recorded in Example 2 is too high, NASA will have to either increase the relia-
bility of individual components, and thus of the system, or install several backup air-conditioning
units on each space shuttle. Backup units provide redundancy.
Providing Redundancy
To increase the reliability of systems, redundancy is added. The technique here is to “back up”
components with additional components. This is known as putting units in parallel and is a stan-
dard operations management tactic. Redundancy is provided to ensure that if one component
fails, the system has recourse to another. For instance, say that reliability of a component is .80
and we back it up with another component with reliability of .80. The resulting reliability is the
probability of the first component working plus the probability of the backup (or parallel) com-
ponent working multiplied by the probability of needing the backup component ( ).
Therefore:
1.82 + 31.82 * 11 – .824 = .8 + .16 = .96
£Probabilityof firstcomponent
working
≥ + C£Probabilityof secondcomponent
working
≥ * £Probabilityof needing second
component
≥S =
1 – .8 = .2
LO3: Determine mean time
between failures (MTBF)
Redundancy
The use of components in
parallel to raise reliability.

524 PART 3 Managing Operations
EXAMPLE 3 �
Reliability with a
parallel process
The National Bank is disturbed that its loan-application process has a reliability of only .713 (see
Example 1) and would like to improve this situation.
APPROACH � The bank decides to provide redundancy for the two least reliable clerks.
SOLUTION � This procedure results in the following system:
INSIGHT � By providing redundancy for two clerks, National Bank has increased reliability of
the loan process from .713 to .94.
LEARNING EXERCISE � What happens when the bank replaces both clerks with one new
clerk who has a reliability of .90. [Answer: ]
RELATED PROBLEMS � 17.8, 17.9, 17.10, 17.12, 17.13, 17.14, 17.16, 17.18
EXCEL OM Data File Ch17Ex3.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL 17.2 This example is further illustrated in Active Model 17.2 at www.pearsonhighered.com/heizer.
Rs = .88.
R2
= .99 * .96 * .99 = .94
= 3.9 + 1.921.124 * 3.8 + 1.821.224 * .99
0.90 : 0.80 : 0.99 = 3.9 + .911 – .9)4 * 3.8 + .811 – .824 * .99
R1 R2 R3
0.90 0.80
T T
Example 3 shows how redundancy can improve the reliability of the loan process presented in
Example 1.
MAINTENANCE
There are two types of maintenance: preventive maintenance and breakdown maintenance.
Preventive maintenance involves performing routine inspections and servicing and keeping
facilities in good repair. These activities are intended to build a system that will find potential
failures and make changes or repairs that will prevent failure. Preventive maintenance is much
more than just keeping machinery and equipment running. It also involves designing technical
and human systems that will keep the productive process working within tolerance; it allows the
system to perform. The emphasis of preventive maintenance is on understanding the process and
keeping it working without interruption. Breakdown maintenance occurs when equipment fails
and must be repaired on an emergency or priority basis.
Implementing Preventive Maintenance
Preventive maintenance implies that we can determine when a system needs service or will need
repair. Therefore, to perform preventive maintenance, we must know when a system requires ser-
vice or when it is likely to fail. Failures occur at different rates during the life of a product. A
high initial failure rate, known as infant mortality, may exist for many products.1 This is why
many electronic firms “burn in” their products prior to shipment: That is to say, they execute a
variety of tests (such as a full wash cycle at Whirlpool) to detect “startup” problems prior to ship-
ment. Firms may also provide 90-day warranties. We should note that many infant mortality fail-
ures are not product failures per se, but rather failure due to improper use. This fact points up the
importance in many industries of operations management’s building an after-sales service sys-
tem that includes installing and training.
Once the product, machine, or process “settles in,” a study can be made of the MTBF (mean
time between failures) distribution. Such distributions often follow a normal curve. When these
distributions exhibit small standard deviations, then we know we have a candidate for preventive
maintenance, even if the maintenance is expensive.
AUTHOR COMMENT
Even the most
reliable systems require
maintenance.
LO4: Distinguish between
preventive and breakdown
maintenance
Preventive maintenance
A plan that involves routine
inspections, servicing, and
keeping facilities in good
repair to prevent failure.
Breakdown
maintenance
Remedial maintenance that
occurs when equipment fails
and must be repaired on an
emergency or priority basis.
Infant mortality
The failure rate early in the life
of a product or process.
1Infant mortality failures often follow a negative exponential distribution.

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Chapter 17 Maintenance and Reliability 525
Once our firm has a candidate for preventive maintenance, we want to determine when pre-
ventive maintenance is economical. Typically, the more expensive the maintenance, the nar-
rower must be the MTBF distribution (that is, have a small standard deviation). In addition, if
the process is no more expensive to repair when it breaks down than the cost of preventive
maintenance, perhaps we should let the process break down and then do the repair. However,
the consequence of the breakdown must be fully considered. Even some relatively minor
breakdowns have catastrophic consequences. (See the OM in Action box “Preventive
Maintenance Saves Lives” on the next page). At the other extreme, preventive maintenance
costs may be so incidental that preventive maintenance is appropriate even if the MTBF distri-
bution is rather flat (that is, it has a large standard deviation). In any event, consistent with job
enrichment practices, machine operators must be held responsible for preventive maintenance
of their own equipment and tools.
With good reporting techniques, firms can maintain records of individual processes,
machines, or equipment. Such records can provide a profile of both the kinds of maintenance
required and the timing of maintenance needed. Maintaining equipment history is an important
part of a preventive maintenance system, as is a record of the time and cost to make the repair.
Such records can also provide information about the family of equipment and suppliers.
Reliability and maintenance are of such importance that most systems are now computerized.
Figure 17.3 shows the major components of such a system with files to be maintained on the left
and reports generated on the right.
Both Boeing and General Motors are pursuing competitive advantage via their reliability
and maintenance information systems. Boeing can now monitor the health of an airplane in flight
and relay relevant information in real time to the ground, providing a head start on reliability and
maintenance issues. Similarly, General Motors, with its On Star wireless satellite service, alerts
car owners to 1,600 possible diagnostic failures, such as faulty airbags sensor or even the need
for an oil change. For GM, the service provides immediate data that its engineers can use to jump
on quality issues before customers even notice a problem. This has saved the firm an estimated
$100 million in warranty costs by catching problems early.
Figure 17.4(a) shows a traditional view of the relationship between preventive maintenance
and breakdown maintenance. In this view, operations managers consider a balance between the
two costs. Allocating more resources to preventive maintenance will reduce the number of break-
downs. At some point, however, the decrease in breakdown maintenance costs may be less than
the increase in preventive maintenance costs. At this point, the total cost curve begins to rise.
Beyond this optimal point, the firm will be better off waiting for breakdowns to occur and repair-
ing them when they do.
Inventory and
purchasing reports
Computer
Repair
history file Data entry
• Work requests
• Purchase requests
• Time reporting
• Contract work
Data Files Output Reports
Equipment
parts list
Equipment
history reports
Costs analysis
(Actual vs. standard)
Work orders
• Preventive
maintenance
• Scheduled
downtime
• Emergency
maintenance
Maintenance and
work order schedule
Equipment file
with parts list
Inventory of
spare parts
Personnel data with
skills, wages, etc.
� FIGURE 17.3
A Computerized Maintenance
System
LO5: Describe how to
improve maintenance

526 PART 3 Managing Operations
Unfortunately, cost curves such as in Figure 17.4(a) seldom consider the full costs of a break-
down. Many costs are ignored because they are not directly related to the immediate breakdown.
For instance, the cost of inventory maintained to compensate for downtime is not typically con-
sidered. Moreover, downtime can have a devastating effect on safety and morale. Employees
may also begin to believe that performance to standard and maintaining equipment are not
important. Finally, downtime adversely affects delivery schedules, destroying customer relations
and future sales. When the full impact of breakdowns is considered, Figure 17.4(b) may be a bet-
ter representation of maintenance costs. In Figure 17.4(b), total costs are at a minimum when the
system does not break down.
Assuming that all potential costs associated with downtime have been identified, the operations
staff can compute the optimal level of maintenance activity on a theoretical basis. Such analysis,
of course, also requires accurate historical data on maintenance costs, breakdown probabilities,
Flight 5481’s trip was short. It lasted 70 seconds. The
flight left the Charlotte Airport, bound for Greenville/
Spartanburg, but seconds after lift-off, the nose of the
aircraft pitched upward, the plane rolled, and, moments
later, slammed into the corner of a maintenance facility at
the airport. The Beech 1900D commuter plane carried 21
people to their death. The following are selected comments
from the final moments of the flight:
8:47:02—Co-pilot Jonathan Gibbs: “Wuh.”
8:47:03—Capt. Katie Leslie: “Help me. . . . You got it?”
8:47:05—Gibbs: “Oh (expletive). Push down.”
8:47:12—Leslie: “Push the nose down.”
8:47:14—Leslie: “Oh my God.”
8:47:16—Leslie (calling to controllers): “We have an
emergency for Air Midwest fifty-four eighty-one.”
8:47:18—Faint voice from passenger area: “Daddy.”
8:47:26—Leslie: “Oh my God, ahh.”
8:47:26—Gibbs: “Uh, uh, God, ahh (expletive).”
8:47:28 End of recording
The National Transportation Safety Board’s focus in this
situation is a preventive maintenance error made two days
prior to the crash. The mechanic and a supervisor skipped
at least 12 steps required in the maintenance of the
tension of the pitch-control cables during the Detail 6 check
that include the pitch of the control cable tension. Data
show that the control column position changed during the
maintenance and the plane lost about two-thirds down-
elevator capability. Investigators believe that the aircraft
would have been flyable with fully functioning controls had
it been given proper preventive maintenance. Maintenance
can improve quality, reduce costs, and win orders. It can
also be a matter of life and death.
Sources: Aviation Week and Space Technology (May 26, 2003): 52; USA
Today (May 21, 2003): 8A; and The Wall Street Journal (May 21, 2003): D3
and (May 20, 2003): D1, D3.
OM in Action � Preventive Maintenance Saves Lives
Total
costs
Preventive
maintenance
costs
Preventive
maintenance
costs
Breakdown
maintenance
costs
Maintenance commitment
(a) Traditional View of Maintenance (b) Full Cost View of Maintenance
Optimal point (lowest-
cost maintenance policy)
C
o
st
s
Total
costs
Full cost of
breakdowns
Maintenance commitment
Optimal point (lowest-
cost maintenance policy)
C
o
st
s
� FIGURE 17.4 Maintenance Costs
AUTHOR COMMENT
When all breakdown costs
are considered, much
more maintenance may
be advantageous.

Chapter 17 Maintenance and Reliability 527
� EXAMPLE 4
Comparing
preventive and
breakdown
maintenance
costs
Farlen & Halikman is a CPA firm specializing in payroll preparation. The firm has been successful in
automating much of its work, using high-speed printers for check processing and report preparation.
The computerized approach, however, has problems. Over the past 20 months, the printers have broken
down at the rate indicated in the following table:
Number of
Breakdowns
Number of Months That
Breakdowns Occurred
0 2
1 8
2 6
3 4
Total: 20
Each time the printers break down, Farlen & Halikman estimates that it loses an average of $300 in pro-
duction time and service expenses. One alternative is to purchase a service contract for preventive
maintenance. Even if Farlen & Halikman contracts for preventive maintenance, there will still be
breakdowns, averaging one breakdown per month. The price for this service is $150 per month.
APPROACH � To determine if the CPA firm should follow a “run until breakdown” policy or
contract for preventive maintenance, we follow a 4-step process:
STEP 1. Compute the expected number of breakdowns (based on past history) if the firm continues
as is, without the service contract.
STEP 2. Compute the expected breakdown cost per month with no preventive maintenance contract.
STEP 3. Compute the cost of preventive maintenance.
STEP 4. Compare the two options and select the one that will cost less.
SOLUTION �
STEP 1.
Number of Number of
Breakdowns Frequency Breakdowns Frequency
0 2
1 3 4>20 = 0.28>20 = .4
6>20 = 0.32>20 = .1
STEP 2.
STEP 3.
= $450>month
= 11 breakdown>month21$3002 + $150>month
a
Preventive
maintenance cost
b = £Cost of expectedbreakdowns if service
contract signed
≥ + aCost of
service contract
b
= $480>month
= 11.621$3002
Expected breakdown cost = a
Expected number
of breakdowns
b * a
Cost per
breakdown
b
= 1.6 breakdowns/month
= 0 + .4 + .6 + .6
= 1021.12 + 1121.42 + 1221.32 + 1321.22
a
Expected number
of breakdowns
b = aBaNumber ofbreakdownsb * aCorrespondingfrequency bR
LO6: Compare preventive
and breakdown maintenance
costs
and repair times. Example 4 shows how to compare preventive and breakdown maintenance costs
to select the least expensive maintenance policy.

528 PART 3 Managing Operations
STEP 4. Because it is less expensive overall to hire a maintenance service firm ($450) than to not do
so ($480), Farlen & Halikman should hire the service firm.
INSIGHT � Determining the expected number of breakdowns for each option is crucial to making
a good decision. This typically requires good maintenance records.
LEARNING EXERCISE � What is the best decision if the preventive maintenance contract cost
increases to $195 per month? [Answer: At per month, “run until breakdown”
becomes less expensive (assuming that all costs are included in the $300 per breakdown cost).]
RELATED PROBLEMS � 17.3, 17.4, 17.17
$4951= $300 + $1952
Using variations of the technique shown in Example 4, operations managers can examine main-
tenance policies.
Increasing Repair Capabilities
Because reliability and preventive maintenance are seldom perfect, most firms opt for some level
of repair capability. Enlarging or improving repair facilities can get the system back in operation
faster. A good maintenance facility should have these six features:
1. Well-trained personnel
2. Adequate resources
3. Ability to establish a repair plan and priorities2
4. Ability and authority to do material planning
5. Ability to identify the cause of breakdowns
6. Ability to design ways to extend MTBF
However, not all repairs can be done in the firm’s facility. Managers must, therefore, decide
where repairs are to be performed. Figure 17.5 provides a continuum of options and how they
rate in terms of speed, cost, and competence. Moving to the right in Figure 17.5 may improve the
competence of the repair work, but at the same time it increases costs and replacement time.
Autonomous Maintenance
Preventive maintenance policies and techniques must include an emphasis on employees accept-
ing responsibility for the “observe, check, adjust, clean, and notify” type of equipment mainte-
nance. Such policies are consistent with the advantages of employee empowerment. This
approach is known as autonomous maintenance. Employees can predict failures, prevent
breakdowns, and prolong equipment life. With autonomous maintenance, the manager is making
a step toward both employee empowerment and maintaining system performance.
TOTAL PRODUCTIVE MAINTENANCE
Many firms have moved to bring total quality management concepts to the practice of preventive
maintenance with an approach known as total productive maintenance (TPM). It involves the
concept of reducing variability through autonomous maintenance and excellent maintenance
practices. Total productive maintenance includes:
• Designing machines that are reliable, easy to operate, and easy to maintain
• Emphasizing total cost of ownership when purchasing machines, so that service and mainte-
nance are included in the cost
• Developing preventive maintenance plans that utilize the best practices of operators, mainte-
nance departments, and depot service
• Training for autonomous maintenance so operators maintain their own machines and partner
with maintenance personnel
2You may recall from our discussion of network planning in Chapter 3 that DuPont developed the critical path method
(CPM) to improve the scheduling of maintenance projects.
LO7: Define autonomous
maintenance
AUTHOR COMMENT
Maintenance improves
productivity.
Total productive
maintenance (TPM)
Combines total quality
management with a strategic
view of maintenance from
process and equipment design
to preventive maintenance.
Autonomous
maintenance
Operators partner with
maintenance personnel to
observe, check, adjust, clean,
and notify.

Chapter 17 Maintenance and Reliability 529
Operator
(autonomous maintenance)
Increasing Operator Ownership Increasing Complexity
Manufacturer’s
field service
Depot service
(return equipment)
Competence is higher as we
move to the right.
Maintenance
department
Preventive
maintenance costs less and
is faster the more we move to the left.
� FIGURE 17.5
The Operations Manager
Determines How
Maintenance Will Be
Performed
High utilization of facilities, tight scheduling, low inventory, and consistent quality demand
reliability. Total productive maintenance is the key to reducing variability and improving
reliability.
TECHNIQUES FOR ENHANCING MAINTENANCE
Three techniques have proven beneficial to effective maintenance: simulation, expert systems,
and sensors.
Simulation Because of the complexity of some maintenance decisions, computer simulation
is a good tool for evaluating the impact of various policies. For instance, operations personnel
can decide whether to add more staff by determining the trade-offs between machine reliability
and the costs of additional labor. Management can also simulate the replacement of parts that
have not yet failed as a way of preventing future breakdowns. Simulation via physical models
can also be useful. For example, a physical model can vibrate an airplane to simulate thousands
of hours of flight time to evaluate maintenance needs.
Expert Systems OM managers use expert systems (that is, computer programs that mimic
human logic) to assist staff in isolating and repairing various faults in machinery and equipment.
For instance, General Electric’s DELTA system asks a series of detailed questions that aid the
user in identifying a problem. DuPont uses expert systems to monitor equipment and to train
repair personnel.
Automated Sensors Sensors warn when production machinery is about to fail or is becom-
ing damaged by heat, vibration, or fluid leaks. The goal of such procedures is not only to avoid
failures but also to perform preventive maintenance before machines are damaged.
AUTHOR COMMENT
Both OM techniques and
the physical sciences can
improve maintenance.
Operations managers focus on design improvements and
backup components to improve reliability. Reliability improve-
ments also can be obtained through the use of preventive main-
tenance and excellent repair facilities.
Firms give employees “ownership” of their equipment.
When workers repair or do preventive maintenance on their
own machines, breakdowns are less common. Well-trained
and empowered employees ensure reliable systems through
preventive maintenance. In turn, reli-
able, well-maintained equipment not
only provides higher utilization but also
improves quality and performance to
schedule. Top firms build and maintain
systems that drive out variability so that cus-
tomers can rely on products and services to be produced to
specifications and on time.
CHAPTER SUMMARY
Key Terms
Maintenance (p. 520)
Reliability (p. 520)
Mean time between failures
(MTBF) (p. 522)
Redundancy (p. 523)
Preventive maintenance (p. 524)
Breakdown maintenance (p. 524)
Infant mortality (p. 524)
Autonomous maintenance (p. 528)
Total productive maintenance
(TPM) (p. 528)

530 PART 3 Managing Operations
`Using Software to Solve Reliability Problems
Solved Problems Virtual Office Hours help is available at www.myomlab.com
� SOLVED PROBLEM 17.1
The semiconductor used in the Sullivan Wrist Calculator has five
circuits, each of which has its own reliability rate. Component 1
has a reliability of .90; component 2, .95; component 3, .98; com-
ponent 4, .90; and component 5, .99. What is the reliability of one
semiconductor?
� SOLUTION
= .7466
= 1.9021.9521.9821.9021.992
Semiconductor reliability, Rs = R1 * R2 * R3 * R4 * R5
R4 R5R2 R3
.95 .98 .90 .99.90
R1
.90 .90
� SOLVED PROBLEM 17.2
A recent engineering change at Sullivan Wrist Calculator places a backup component in each of the two
least reliable transistor circuits. The new circuits will look like the following:
� SOLUTION
= .903
= .99 * .95 * .98 * .99 * .99
= 3.9 + .094 * .95 * .98 * 3.9 + .094 * .99
Reliability = 3.9 + 11 – .92 * .94 * .95 * .98 * 3.9 + 11 – .92 * .94 * .99
PX Excel OM and POM for Windows may be used to solve reliability problems. The reliability module allow us to enter
(1) number of systems (components) in the series (1 through 10); (2) number of backup, or parallel, components (1 through 12);
and (3) component reliability for both series and parallel data.
What is the reliability of the new system?
Bibliography
Bauer, Eric, X. Zhang, and D. A. Kimber. Practical System
Reliability. New York: Wiley (2009).
Blank, Ronald. The Basics of Reliability. University Park, IL:
Productivity Press (2004).
Cua, K. O., K. E. McKone, and R. G. Schroeder. “Relationships
between Implementation of TQM, JIT, and TPM and
Manufacturing Performance.” Journal of Operations
Management 19, no. 6 (November 2001): 675–694.
Finigen, Tim, and Jim Humphries. “Maintenance Gets Lean.” IE
Industrial Systems 38, no. 10 (October 2006): 26–31.
Sova, Roger, and Lea A. P. Tonkin. “Total Productive Maintenance
at Crown International.” Target: Innovation at Work 19, no. 1
(1st Quarter 2003): 41–44.
Stephens, M. P. Productivity and Reliability-Based Maintenance
Management. Upper Saddle River, NJ: Prentice Hall (2004).
Weil, Marty. “Beyond Preventive Maintenance.” APICS 16, no. 4
(April 2006): 40–43.
�Additional Case Studies: Visit www.myomlab.com or www.pearsonhighered.com/heizer for these free case studies:
Cartak’s Department Store: Requires the evaluation of the impact of an additional invoice verifier.
Worldwide Chemical Company: The maintenance department in this company is in turmoil.

www.myomlab.com

www.myomlab.com

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QUANTITATIVE MODULE
Decision-Making Tools
Module Outline
The Decision Process in Operations 532
Fundamentals of Decision Making 533
Decision Tables 534
Types of Decision-Making
Environments 534
Decision Trees 538
PART FOUR
Quantitative Modules (A–F)
531

532 PART 4 Quantitative Modules
LO1: Create a simple decision tree 534
LO2: Build a decision table 534
LO3: Explain when to use each of the three
types of decision-making environments 534
LO4: Calculate an expected monetary
value (EMV) 536
Module A Learning Objectives
THE DECISION PROCESS IN OPERATIONS
Operations managers are not gamblers. But they are decision makers. To achieve the goals of
their organizations, managers must understand how decisions are made and know which deci-
sion-making tools to use. To a great extent, the success or failure of both people and companies
depends on the quality of their decisions. Overcoming uncertainty is a manager’s challenge.
What makes the difference between a good decision and a bad decision? A “good” decision—
one that uses analytic decision making—is based on logic and considers all available data and
possible alternatives. It also follows these six steps:
1. Clearly define the problem and the factors that influence it.
2. Develop specific and measurable objectives.
*To see the details of Phillips’s decision, see Example A9.
Source: Based on Business 2.0 (November 2003): 128–134.
77 7
7
Of all the hands he might have
opened with, 80% are likely worse
than 5 5 or very high face cards.
That means there’s an 80% chance
he’ll fold and I’ll collect $99,000.
If I raise him “all in,” he’ll
have to either bet all
$422,000 of his chips or
fold. My guess is, he’ll fold
unless he has a 5 5 or
better, or very high face
cards.
So my overall
expected value
is $71,570, or
nearly 5% of all
chips in the
game. I’m
going for it. *
“ALL IN”
A call would put $853,000 on the
table. Hmmm. But if I read him right,
there’s only a 20% probability his
cards are good enough for him to
call, and even then, there’s a 45%
chance my 7s win.
T.J. probably has
good cards, or
he wouldn’t have
opened. But he
doesn’t know I
have a pair of 7s.
WILL T.J. FOLD?
WOULD YOU GO ALL IN?
At the Legends of Poker tournament in Los Angeles, veteran T.J. Cloutier opens with a $60,000 bet. (Antes and
required bets of $39,000 are already on the table.) Former Go2net CTO Paul Phillips ponders going “all in”—betting
virtually all his chips. Using decision theory, here’s how he decided.
WHAT IF HE CALLS?
LO5: Compute the expected value of
perfect information (EVPI) 537
LO6: Evaluate the nodes in a decision tree 539
LO7: Create a decision tree with sequential
decisions 540

Module A Decision-Making Tools 533
AUTHOR COMMENT
This section uses a
decision tree to introduce
the terminology of
decision theory.
3. Develop a model—that is, a relationship between objectives and variables (which are mea-
surable quantities).
4. Evaluate each alternative solution based on its merits and drawbacks.
5. Select the best alternative.
6. Implement the decision and set a timetable for completion.
Throughout this book, we have introduced a broad range of mathematical models and tools that
help operations managers make better decisions. Effective operations depend on careful decision
making. Fortunately, there are a whole variety of analytic tools to help make these decisions.
This module introduces two of them—decision tables and decision trees. They are used in a wide
number of OM situations, ranging from new-product analysis (Chapter 5), to capacity planning
(Supplement 7), to location planning (Chapter 8), to scheduling (Chapter 15), and to mainte-
nance planning (Chapter 17).
FUNDAMENTALS OF DECISION MAKING
Regardless of the complexity of a decision or the sophistication of the technique used to analyze
it, all decision makers are faced with alternatives and “states of nature.” The following notation
will be used in this module:
1. Terms:
a. Alternative—A course of action or strategy that may be chosen by a decision maker
(e.g., not carrying an umbrella tomorrow).
b. State of nature—An occurrence or a situation over which the decision maker has little
or no control (e.g., tomorrow’s weather).
2. Symbols used in a decision tree:
a. □—decision node from which one of several alternatives may be selected.
b. �—a state-of-nature node out of which one state of nature will occur.
To present a manager’s decision alternatives, we can develop decision trees using the above sym-
bols. When constructing a decision tree, we must be sure that all alternatives and states of nature
are in their correct and logical places and that we include all possible alternatives and states
of nature.
� EXAMPLE A1
A simple
decision tree
Getz Products Company is investigating the possibility of producing and marketing backyard storage
sheds. Undertaking this project would require the construction of either a large or a small manufactur-
ing plant. The market for the product produced—storage sheds—could be either favorable or unfavor-
able. Getz, of course, has the option of not developing the new product line at all.
APPROACH � Getz decides to build a decision tree.
SOLUTION � Figure A.1 illustrates Getz’s decision tree.
Unfavorable market
Favorable market
A decision node A state of nature node
1
2
Unfavorable market
Favorable market
Co
nst
ruc
t
lar
ge
pla
nt
Do nothing
Construct
small plant
� FIGURE A.1
Getz Products Decision Tree

534 PART 4 Quantitative Modules
DECISION TABLES
We may also develop a decision or payoff table to help Getz Products define its alternatives.
For any alternative and a particular state of nature, there is a consequence or outcome, which is
usually expressed as a monetary value. This is called a conditional value. Note that all of the
alternatives in Example A2 are listed down the left side of the table, that states of nature (out-
comes) are listed across the top, and that conditional values (payoffs) are in the body of the
decision table.
Decision table
A tabular means of analyzing
decision alternatives and states
of nature.
LO2: Build a decision table
EXAMPLE A2 �
A decision table
Getz Products now wishes to organize the following information into a table. With a favorable market,
a large facility will give Getz Products a net profit of $200,000. If the market is unfavorable, a $180,000
net loss will occur. A small plant will result in a net profit of $100,000 in a favorable market, but a net
loss of $20,000 will be encountered if the market is unfavorable.
APPROACH � These numbers become conditional values in the decision table. We list alterna-
tives in the left column and states of nature across the top of the table.
SOLUTION � The completed table is shown in Table A.1.
�TABLE A.1
Decision Table with
Conditional Values for
Getz Products
States of Nature
Alternatives Favorable Market Unfavorable Market
Construct large plant $200,000
Construct small plant $100,000
Do nothing $ 0 $ 0
– $ 20,000
– $180,000
INSIGHT � The toughest part of decision tables is obtaining the data to analyze.
LEARNING EXERCISE � In Examples A3 and A4, we see how to use decision tables to make
decisions.
AUTHOR COMMENT
Decision tables force logic
into decision making.
TYPES OF DECISION-MAKING ENVIRONMENTS
The types of decisions people make depend on how much knowledge or information they have
about the situation. There are three decision-making environments:
• Decision making under uncertainty
• Decision making under risk
• Decision making under certainty
AUTHOR COMMENT
Depending on the certainty
of information, there are
three approaches in
decision theory.
LO3: Explain when to
use each of the three types
of decision-making
environments
INSIGHT � We never want to overlook the option of “doing nothing” as that is usually a possible
decision.
LEARNING EXERCISE � Getz now considers constructing a medium-sized plant as a fourth
option. Redraw the tree in Figure A.1 to accommodate this. [Answer: Your tree will have a new node
and branches between “Construct large plant” and “Construct small plant.”]
RELATED PROBLEMS � A.2e, A.8b, A.14a, A.15a, A.17a, A.18
LO1: Create a simple
decision tree

Module A Decision-Making Tools 535
� EXAMPLE A3
A decision table
analysis under
uncertainty
Getz Products Company would like to apply each of these three approaches now.
APPROACH � Given Getz’s decision table of Example A2, he determines the maximax, max-
imin, and equally likely decision criteria.
SOLUTION � Table A.2 provides the solution.
� TABLE A.2
Decision Table for Decision
Making under Uncertainty
States of Nature
Favorable Unfavorable Maximum Minimum Row
Alternatives Market Market in Row in Row Average
Construct large
plant $200,000 $200,000 $10,000
Construct small
plant $100,000 $100,000 $40,000
Do nothing $ 0 $ 0 $ 0 $ 0 $ 0
Maximax Maximin Equally
likely
– $ 20,000– $ 20,000
– $180,000– $180,000
1. The maximax choice is to construct a large plant. This is the maximum of the maximum number
within each row, or alternative.
2. The maximin choice is to do nothing. This is the maximum of the minimum number within each
row, or alternative.
3. The equally likely choice is to construct a small plant. This is the maximum of the average out-
come of each alternative. This approach assumes that all outcomes for any alternative are
equally likely.
INSIGHT � There are optimistic decision makers (“maximax”) and pessimistic ones (“maximin”).
Maximax and maximin present best case–worst case planning scenarios.
LEARNING EXERCISE � Getz reestimates the outcome for constructing a large plant when
the market is favorable and raises it to $250,000. What numbers change in Table A.2? Do the decisions
change? [Answer: The maximax is now $250,000, and the row average is $35,000 for large plant. No
decision changes.]
RELATED PROBLEMS � A.1, A.2b–d, A.4, A.6
Maximax
A criterion that finds an
alternative that maximizes
the maximum outcome.
Maximin
A criterion that finds an
alternative that maximizes the
minimum outcome.
Equally likely
A criterion that assigns equal
probability to each state of
nature.
Decision Making under Uncertainty
When there is complete uncertainty as to which state of nature in a decision environment may
occur (i.e., when we cannot even assess probabilities for each possible outcome), we rely on
three decision methods:
1. Maximax: This method finds an alternative that maximizes the maximum outcome for every
alternative. First, we find the maximum outcome within every alternative, and then we pick
the alternative with the maximum number. Because this decision criterion locates the alterna-
tive with the highest possible gain, it has been called an “optimistic” decision criterion.
2. Maximin: This method finds the alternative that maximizes the minimum outcome for every
alternative. First, we find the minimum outcome within every alternative, and then we pick
the alternative with the maximum number. Because this decision criterion locates the alter-
native that has the least possible loss, it has been called a “pessimistic” decision criterion.
3. Equally likely: This method finds the alternative with the highest average outcome. First,
we calculate the average outcome for every alternative, which is the sum of all outcomes
divided by the number of outcomes. We then pick the alternative with the maximum number.
The equally likely approach assumes that each state of nature is equally likely to occur.

536 PART 4 Quantitative Modules
Decision Making under Risk
Decision making under risk, a more common occurrence, relies on probabilities. Several possi-
ble states of nature may occur, each with an assumed probability. The states of nature must be
mutually exclusive and collectively exhaustive and their probabilities must sum to 1.1 Given a
decision table with conditional values and probability assessments for all states of nature, we can
determine the expected monetary value (EMV) for each alternative. This figure represents the
expected value or mean return for each alternative if we could repeat this decision (or similar
types of decisions) a large number of times.
The EMV for an alternative is the sum of all possible payoffs from the alternative, each
weighted by the probability of that payoff occurring:
Example A4 illustrates how to compute the maximum EMV.
* 1Probability of last state of nature2
+ Á + 1Payoff of last state of nature2
* 1Probability of 2nd state of nature2
+ 1Payoff of 2nd state of nature2
* 1Probability of 1st state of nature2
EMV1Alternative i2 = 1Payoff of 1st state of nature2
Expected monetary
value (EMV)
The expected payout or value of
a variable that has different
possible states of nature, each
with an associated probability.
LO4: Calculate an
expected monetary value
(EMV)
1To review these other statistical terms, refer to Tutorial 1, “Statistical Review for Managers” at www.pearsonhighered.
com/heizer.
EXAMPLE A4 �
Expected monetary
value
Getz would like to find the EMV for each alternative.
APPROACH � Getz Products’ operations manager believes that the probability of a favorable
market is exactly the same as that of an unfavorable market; that is, each state of nature has a .50
chance of occurring. He can now determine the EMV for each alternative (see Table A.3):
� TABLE A.3
Decision Table for
Getz Products
States of Nature
Alternatives Favorable Market Unfavorable Market
Construct large plant ( ) $200,000
Construct small plant ( ) $100,000
Do nothing ( ) $ 0 $ 0
Probabilities .50 .50
A3
– $ 20,000A2
– $180,000A1
SOLUTION �
1. EMV
2. EMV
3. EMV
INSIGHT � The maximum EMV is seen in alternative . Thus, according to the EMV decision
criterion, Getz would build the small facility.
LEARNING EXERCISE � What happens to the three EMVs if Getz increases the conditional
value on the “large plant/favorable market” result to $250,000? [Answer: EMV . No
change in decision.]
RELATED PROBLEMS � A.2e, A.3a, A.5a, A.7a, A.8, A.9a, A.10, A.11, A.12, A.14a,b,
A.16a, A.22
EXCEL OM Data File ModAExA4.xls can be found at www.pearsonhighered.com/heizer.
(A1) = $35,000
A2
1A32 = 1.521$02 + 1.521$02 = $0
1A22 = 1.521$100,0002 + 1.521– $20,0002 = $40,000
1A12 = 1.521$200,0002 + 1.521– $180,0002 = $10,000

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Module A Decision-Making Tools 537
Decision Making under Certainty
Now suppose that the Getz operations manager has been approached by a marketing research
firm that proposes to help him make the decision about whether to build the plant to produce
storage sheds. The marketing researchers claim that their technical analysis will tell Getz with
certainty whether the market is favorable for the proposed product. In other words, it will change
Getz’s environment from one of decision making under risk to one of decision making under
certainty. This information could prevent Getz from making a very expensive mistake. The mar-
keting research firm would charge Getz $65,000 for the information. What would you recom-
mend? Should the operations manager hire the firm to make the study? Even if the information
from the study is perfectly accurate, is it worth $65,000? What might it be worth? Although some
of these questions are difficult to answer, determining the value of such perfect information can
be very useful. It places an upper bound on what you would be willing to spend on information,
such as that being sold by a marketing consultant. This is the concept of the expected value of
perfect information (EVPI), which we now introduce.
Expected Value of Perfect Information (EVPI)
If a manager were able to determine which state of nature would occur, then he or she would
know which decision to make. Once a manager knows which decision to make, the payoff
increases because the payoff is now a certainty, not a probability. Because the payoff will
increase with knowledge of which state of nature will occur, this knowledge has value.
Therefore, we now look at how to determine the value of this information. We call this difference
between the payoff under perfect information and the payoff under risk the expected value of
perfect information (EVPI).
To find the EVPI, we must first compute the expected value with perfect information
(EVwPI), which is the expected (average) return if we have perfect information before a deci-
sion has to be made. To calculate this value, we choose the best alternative for each state of
nature and multiply its payoff times the probability of occurrence of that state of nature:
In Example A5 we use the data and decision table from Example A4 to examine the expected
value of perfect information.
* 1Probability of last state of nature2
+ Á + 1Best outcome for last state of nature2
* 1Probability of 2nd state of nature2
+ 1Best outcome for 2nd state of nature2
* 1Probability of 1st state of nature2
perfect information 1EVwPI2 = 1Best outcome or consequence for 1st state of nature2
Expected value with
EVPI = Expected value with perfect information – Maximum EMV
AUTHOR COMMENT
EVPI places an upper limit
on what you should pay
for information.
Expected value of per-
fect information (EVPI)
The difference between the
payoff under perfect
information and the payoff
under risk.
Expected value with
perfect information
(EVwPI)
The expected (average) return if
perfect information is available.
LO5: Compute the
expected value of perfect
information (EVPI)
� EXAMPLE A5
Expected value
of perfect
information
The Getz operations manager would like to calculate the maximum that he would pay for information—
that is, the expected value of perfect information, or EVPI.
APPROACH � Referring to Table A.3 in Example 4, the follows a two-stage process. First, the expected
value with perfect information (EVwPI) is computed. Then, using this information, the EVPI is calculated.
SOLUTION �
1. The best outcome for the state of nature “favorable market” is “build a large facility” with
a payoff of $200,000. The best outcome for the state of nature “unfavorable market”
is “do nothing” with a payoff of $0. Expected value with perfect information
. Thus, if we had perfect information, we would
expect (on the average) $100,000 if the decision could be repeated many times.
1$200,000210.502 + 1$0210.502 = $100,000
=

538 PART 4 Quantitative Modules
DECISION TREES
Decisions that lend themselves to display in a decision table also lend themselves to display in a
decision tree. We will therefore analyze some decisions using decision trees. Although the use of
a decision table is convenient in problems having one set of decisions and one set of states of
nature, many problems include sequential decisions and states of nature.
When there are two or more sequential decisions, and later decisions are based on the out-
come of prior ones, the decision tree approach becomes appropriate. A decision tree is a
graphic display of the decision process that indicates decision alternatives, states of nature and
their respective probabilities, and payoffs for each combination of decision alternative and state
of nature.
Expected monetary value (EMV) is the most commonly used criterion for decision tree analy-
sis. One of the first steps in such analysis is to graph the decision tree and to specify the mone-
tary consequences of all outcomes for a particular problem.
Analyzing problems with decision trees involves five steps:
1. Define the problem.
2. Structure or draw the decision tree.
3. Assign probabilities to the states of nature.
4. Estimate payoffs for each possible combination of decision alternatives and states of nature.
5. Solve the problem by computing the expected monetary values (EMV) for each state-of-
nature node. This is done by working backward—that is, by starting at the right of the tree
and working back to decision nodes on the left.
AUTHOR COMMENT
Decision trees can become
complex, so we illustrate
three of them in this section.
Decision tree
A graphical means of analyzing
decision alternatives and states
of nature.
When Tomco Oil had to decide which of its new Kentucky lease areas to drill for oil, it turned to decision tree analysis. The 74 different
factors, including geological, engineering, economic, and political factors, became much clearer. Decision tree software such as DPL
(shown here), Tree Plan, and Supertree allow decision problems to be analyzed with less effort and greater depth than ever before.
2. The maximum EMV is $40,000 for , which is the expected outcome without perfect informa-
tion. Thus:
INSIGHT � The most Getz should be willing to pay for perfect information is $60,000. This con-
clusion, of course, is again based on the assumption that the probability of each state of nature is 0.50.
LEARNING EXERCISE � How does the EVPI change if the “large plant/favorable market”
conditional value is $250,000? [Answer: EVPI ]
RELATED PROBLEMS � A.3b, A.5b, A.7, A.9, A.14, A.16
= $85,000.
= $100,000 – $40,000 = $60,000
EVPI = EVwPI – Maximum EMV
A2

Module A Decision-Making Tools 539
� EXAMPLE A6
Solving a tree
for EMV
Getz wants to develop a completed and solved decision tree.
APPROACH � The payoffs are placed at the right-hand side of each of the tree’s branches (see
Figure A.2). The probabilities (first used by Getz in Example A4) are placed in parentheses next to
each state of nature. The expected monetary values for each state-of-nature node are then calculated
and placed by their respective nodes. The EMV of the first node is $10,000. This represents the branch
from the decision node to “construct a large plant.” The EMV for node 2, to “construct a small plant,”
is $40,000. The option of “doing nothing” has, of course, a payoff of $0.
SOLUTION � The branch leaving the decision node leading to the state-of-nature node with the
highest EMV will be chosen. In Getz’s case, a small plant should be built.
Unfavorable market (.5)
Favorable market (.5)
1
2
Unfavorable market (.5)
Favorable market (.5)
Co
nst
ruc
t la
rge
pla
nt
Do nothing
Construct small plant
EMV for node 1
= $10,000
EMV for node 2
= $40,000
= (.5) ($200,000) + (.5) (–$180,000)
= (.5) ($100,000) + (.5) (–$20,000)
Payoffs
$200,000
–$180,000
$100,000
20,000–$
$0
� FIGURE A.2
Completed and Solved
Decision Tree for Getz
Products
INSIGHT � This graphical approach is an excellent way for managers to understand all the
options in making a major decision. Visual models are often preferred over tables.
LEARNING EXERCISE � Correct Figure A.2 to reflect a $250,000 payoff for “construct large
plant/favorable market.” [Answer: Change one payoff and recompute the EMV for node 1.]
RELATED PROBLEMS � A.2e, A.8b, A.14a,b, A.17, A.18
EXCEL OM Data File ModAExA6.xls can be found at www.pearsonhighered.com/heizer.
LO6: Evaluate the nodes
in a decision tree
A More Complex Decision Tree
When a sequence of decisions must be made, decision trees are much more powerful tools than
are decision tables. Let’s say that Getz Products has two decisions to make, with the second deci-
sion dependent on the outcome of the first. Before deciding about building a new plant, Getz has
the option of conducting its own marketing research survey, at a cost of $10,000. The informa-
tion from this survey could help it decide whether to build a large plant, to build a small plant, or
not to build at all. Getz recognizes that although such a survey will not provide it with perfect
information, it may be extremely helpful.
Getz’s new decision tree is represented in Figure A.3 of Example A7. Take a careful look at
this more complex tree. Note that all possible outcomes and alternatives are included in their
logical sequence. This procedure is one of the strengths of using decision trees. The manager is
forced to examine all possible outcomes, including unfavorable ones. He or she is also forced to
make decisions in a logical, sequential manner.

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540 PART 4 Quantitative Modules
EXAMPLE A7 �
A decision tree with
sequential decisions
Getz Products wishes to develop the new tree for this sequential decision.
APPROACH � Examining the tree in Figure A.3, we see that Getz’s first decision point is whether
to conduct the $10,000 market survey. If it chooses not to do the study (the lower part of the tree), it can
either build a large plant, a small plant, or no plant. This is Getz’s second decision point. If the decision
is to build, the market will be either favorable (.50 probability) or unfavorable (also .50 probability).
The payoffs for each of the possible consequences are listed along the right-hand side. As a matter of
fact, this lower portion of Getz’s tree is identical to the simpler decision tree shown in Figure A.2.
S
ur
ve
y
re
su
lts
fa
vo
ra
bl
e
First Decision
Point
Second Decision
Point
1
2
3 Unfavorable market
Favorable marketLa
rge
pl
an
t
No plant
Small
plant
Unfavorable market
Favorable market
$190,000
–$190,000
$ 90,000
–$ 30,000
–$ 10,000
Payoffs
4
5 Unfavorable market
Favorable market
La
rge
pl
an
t
Small
plant
Unfavorable market
Favorable market
$190,000
–$190,000
$ 90,000
–$ 30,000
–$ 10,000
6
7 Unfavorable market
Favorable market
Small
plant
Unfavorable market
Favorable market
$200,000
–$180,000
$100,000
20,000–$
$0
S
urvey
results
negative
C
o
n
d
u
ct
m
a
rk
e
t
su
rv
e
y
D
o not conduct survey
$
1
0
6
,4
0
0
$
4
9
,2
0
0
$
2
,4
0
0
$
4
0
,0
0
0
$49,200
No plant
No plant
La
rge
pl
an
t
$106,400
$63,600
–$87,400
$2,400
$10,000
$40,000
(.22)
(.78)
(.22)
(.78)
(.73)
(.27)
(.73)
(.27)
(.5)
(.5)
(.5)
(.5)
(.
45
)
(.55)
� FIGURE A.3
Getz Products Decision
Tree with Probabilities and
EMVs Shown
LO7: Create a decision
tree with sequential
decisions
AUTHOR COMMENT
The short parallel lines
mean “prune” that branch, as
it is less favorable than
another available option and
may be dropped.
SOLUTION � The upper part of Figure A.3 reflects the decision to conduct the market survey.
State-of-nature node number 1 has 2 branches coming out of it. Let us say there is a 45% chance that
the survey results will indicate a favorable market for the storage sheds. We also note that the probabil-
ity is .55 that the survey results will be negative.
The rest of the probabilities shown in parentheses in Figure A.3 are all conditional probabilities. For
example, .78 is the probability of a favorable market for the sheds given a favorable result from the
market survey. Of course, you would expect to find a high probability of a favorable market given that
the research indicated that the market was good. Don’t forget, though: There is a chance that Getz’s
$10,000 market survey did not result in perfect or even reliable information. Any market research study
is subject to error. In this case, there remains a 22% chance that the market for sheds will be unfavor-
able given positive survey results.
Likewise, we note that there is a 27% chance that the market for sheds will be favorable given neg-
ative survey results. The probability is much higher, .73, that the market will actually be unfavorable
given a negative survey.

Module A Decision-Making Tools 541
Finally, when we look to the payoff column in Figure A.3, we see that $10,000—the cost of the
marketing study—has been subtracted from each of the top 10 tree branches. Thus, a large plant con-
structed in a favorable market would normally net a $200,000 profit. Yet because the market study was
conducted, this figure is reduced by $10,000. In the unfavorable case, the loss of $180,000 would
increase to $190,000. Similarly, conducting the survey and building no plant now results in a
payoff.
With all probabilities and payoffs specified, we can start calculating the expected monetary value of
each branch. We begin at the end or right-hand side of the decision tree and work back toward the ori-
gin. When we finish, the best decision will be known.
1. Given favorable survey results:
The EMV of no plant in this case is . Thus, if the survey results are favorable, a large
plant should be built.
2. Given negative survey results:
The EMV of no plant is again for this branch. Thus, given a negative survey result,
Getz should build a small plant with an expected value of $2,400.
3. Continuing on the upper part of the tree and moving backward, we compute the expected value
of conducting the market survey:
4. If the market survey is not conducted:
The EMV of no plant is $0. Thus, building a small plant is the best choice, given the marketing
research is not performed.
5. Because the expected monetary value of conducting the survey is $49,200—vs. an EMV of
$40,000 for not conducting the study—the best choice is to seek marketing information. If the
survey results are favorable, Getz Products should build the large plant; if they are unfavorable,
it should build the small plant.
INSIGHT � You can reduce complexity in a large decision tree by viewing and solving a number
of smaller trees—start at the end branches of a large one. Take one decision at a time.
LEARNING EXERCISE � Getz estimates that if he conducts a market survey, there is really
only a 35% chance the results will indicate a favorable market for the sheds. How does the tree change?
[Answer: The EMV of conducting the survey , so Getz should not do it now.]
RELATED PROBLEMS � A.13, A.18, A.19, A.20, A.21, A.23
= $38,800
EMV 1node 72 = 1.5021$100,0002 + 1.5021– $20,0002 = $40,000
EMV 1node 62 = 1.5021$200,0002 + 1.5021– $180,0002 = $10,000
EMV 1node 12 = 1.4521$106,4002 + 1.5521$2,4002 = $49,200
– $10,000
EMV 1node 52 = 1.2721$90,0002 + 1.7321– $30,0002 = $2,400
EMV 1node 42 = 1.2721$190,0002 + 1.7321– $190,0002 = – $87,400
– $10,000
EMV 1node 32 = 1.7821$90,0002 + 1.2221– $30,0002 = $63,600
EMV 1node 22 = 1.7821$190,0002 + 1.2221– $190,0002 = $106,400
– $10,000
Using Decision Trees in Ethical Decision Making
Decision trees can also be a useful tool to aid ethical corporate decision making. The decision
tree illustrated in Example A8, developed by Harvard Professor Constance Bagley, provides
guidance as to how managers can both maximize shareholder value and behave ethically. The
tree can be applied to any action a company contemplates, whether it is expanding operations in
a developing country or reducing a workforce at home.

542 PART 4 Quantitative Modules
EXAMPLE A8 �
Ethical decision
making
Smithson Corp. is opening a plant in Malaysia, a country with much less stringent environmental laws
than the U.S., its home nation. Smithson can save $18 million in building the manufacturing facility—
and boost its profits—if it does not install pollution-control equipment that is mandated in the U.S. but
not in Malaysia. But Smithson also calculates that pollutants emitted from the plant, if unscrubbed,
could damage the local fishing industry. This could cause a loss of millions of dollars in income as well
as create health problems for local inhabitants.
APPROACH � Smithson decides to build a decision tree to model the problem.
SOLUTION � Figure A.4 outlines the choices management can consider. For example, if in man-
agement’s best judgment the harm to the Malaysian community by building the plant will be greater
than the loss in company returns, the response to the question “Is it ethical?” will be no.
Now, say Smithson proposes building a somewhat different plant, one with pollution controls,
despite a negative impact on company returns. That decision takes us to the branch “Is it ethical not to
take action?” If the answer (for whatever reason) is no, the decision tree suggests proceeding with the
plant but notifying the Smithson Board, shareholders, and others about its impact.
Ye
s
Ye
s
Y
es
No
No
N
o
Action outcome
Do it
Don’t do it
Don’t do it
Is action
legal?
Does action
maximize
company
returns?
Ye
s
No
Don’t do it
Do it
but notify
appropriate
parties
Is it ethical?
(Weigh the effect
on employees,
customers, suppliers,
community versus
shareholder benefit.)
Is it ethical not to take
action? (Weigh the
harm to shareholders
versus benefits to
other stakeholders.)
� FIGURE A.4
Smithson’s Decision Tree
for Ethical Dilemma
Source: Modified from Constance E.
Bagley, “The Ethical Leader’s Decision
Tree,” Harvard Business Review
(January–February 2003): 18–19.
INSIGHT � This tree allows managers to view the options graphically. This is a good way to start
the process.
Ethical decisions can be quite complex: What happens, for example, if a company builds a
polluting plant overseas, but this allows the company to sell a life-saving drug at a lower cost
around the world? Does a decision tree deal with all possible ethical dilemmas? No—but it does
provide managers with a framework for examining those choices.
The Poker Decision Process
We opened Module A with ex-dot-commer Paul Phillips’s decision to go “all in” at the Legends
of Poker tournament in Los Angeles. Example A9 shows how he computed the expected value.
Homework Problem A.24 gives you a chance to create a decision tree for this process.
EXAMPLE A9 �
Phillips’s Poker
Decision
As on the first page in this module, Paul Phillips is deciding whether to bet all his chips against poker
star T.J. Cloutier. Phillips holds a pair of 7s. Phillips reasons that T.J. will fold (with 80% probability)
if he does not have a pair of 5s or better, or very high cards like a Jack, Queen, King, or Ace. But he
also figures that a call would put $853,000 into the pot and surmises that even then, there is 45%
chance his pair of 7s will win.
APPROACH � Phillips does an expected monetary analysis.

Module A Decision-Making Tools 543
This module examines two of the most widely used decision
techniques—decision tables and decision trees. These tech-
niques are especially useful for making decisions under risk.
Many decisions in research and development, plant and
equipment, and even new buildings and structures can be
analyzed with these decision models. Problems in inventory
control, aggregate planning, mainte-
nance, scheduling, and production con-
trol also lend themselves to decision
table and decision tree applications.
MODULE SUMMARY
Key Terms
Decision table (p. 534)
Maximax (p. 535)
Maximin (p. 535)
Equally likely (p. 535)
Expected monetary value (EMV) (p. 536)
Expected value of perfect information
(EVPI) (p. 537)
Expected value with perfect information
(EVwPI) (p. 537)
Decision tree (p. 538)
SOLUTION � If T.J. folds,
If T.J. calls,
INSIGHT � The overall EMV of $71,570 indicates that if this decision were to be made many
times, the average payoff would be large. So Phillips decides to bet almost all of his chips. As it turns
out, T.J. was holding a pair of Jacks. Even though Phillips’s decision in this instance did not work out,
his analysis and procedure was the correct one.
LEARNING EXERCISE � What would happen if the amount of money already in the pot were
only $39,000? [Answer: The overall EMV = $23,570.]
RELATED PROBLEM � A.24
Overall EMV = $79,200 – $7,630 = $71,570
= .20 3– $38,1504 = – $7,630
= .20 3$383,850 – $422,0004
EMV = .20 31.4521$853,0002 – Phillips’s bet of $422,0004
= $79,200
EMV = 1.8021$99,0002
the chance T.J. will call
The amount of money already
in the pot
Using Software for Decision Models
Analyzing decision tables is straightforward with Excel, Excel OM, and POM for Windows. When decision
trees are involved, Excel OM or commercial packages such as DPL, Tree Plan, and Supertree provide flexi-
bility, power, and ease. POM for Windows will also analyze trees but does not have graphic capabilities.
X Using Excel OM
Excel OM allows decision makers to evaluate decisions quickly and to perform sensitivity analysis on
the results. Program A.1 uses the Getz data to illustrate input, output, and selected formulas needed to
compute the EMV and EVPI values.

544 PART 4 Quantitative Modules
= MAX(B8:C8)
Compute the EMV for each alternative using = SUMPRODUCT(B$7:C$7, B8:C8).
= MIN(B8:C8)
Find the best outcome
for each measure using
= MAX(G8:G10).
= SUMPRODUCT(B$7:C$7,
B14:C14) = E14 – E11
To calculate the EVPI,
find the best outcome
for each scenario.
= MAX(B8:B10)
Program A.2 uses Excel OM to create the decision tree for Getz Products shown earlier in Example A6.
The tool to create the tree is seen in the window on the right.
Use this Decision Tree
Creation window to
create the tree.
Expected profit for
the small plant
= F15*F13 + F19*F17
Maximum Profit
= MAX(D7 + C9, D15 + C1)
Use the branch that
leads to node 3 in
order to achieve the
maximum profit.
P Using POM for Windows
POM for Windows can be used to calculate all of the information described in the decision tables and
decision trees in this module. For details on how to use this software, please refer to Appendix IV.
� PROGRAM A.1 Using Excel OM to Compute EMV and Other Measures for Getz
� PROGRAM A.2 Getz Products’ Decision Tree Using Excel OM

Module A Decision-Making Tools 545
Solved Problems Virtual Office Hours help is available at www.myomlab.com
� SOLVED PROBLEM A.1
Stella Yan Hua is considering the possibility of opening a small
dress shop on Fairbanks Avenue, a few blocks from the university.
She has located a good mall that attracts students. Her options are
to open a small shop, a medium-sized shop, or no shop at all. The
market for a dress shop can be good, average, or bad. The proba-
bilities for these three possibilities are .2 for a good market, .5 for
an average market, and .3 for a bad market. The net profit or loss
for the medium-sized or small shops for the various market condi-
tions are given in the following table. Building no shop at all
yields no loss and no gain. What do you recommend?
� SOLUTION
The problem can be solved by computing the expected monetary value (EMV) for each alternative:
As you can see, the best decision is to build the medium-sized shop. The EMV for this alternative is
$19,500.
EMV 1No shop2 = 1.221$02 + 1.521$02 + 1.321$02 = $0
EMV 1Medium-sized shop2 = 1.221$100,0002 + 1.521$35,0002 + 1.321- $60,0002 = $19,500
EMV 1Small shop2 = 1.221$75,0002 + 1.521$25,0002 + 1.321- $40,0002 = $15,500
� SOLVED PROBLEM A.2
T.S. Amer’s Ski Shop in Nevada has a 100-day season. T.S. has
established the probability of various store traffic, based on histor-
ical records of skiing conditions, as indicated in the table to the
right. T.S. has four merchandising plans, each focusing on a popu-
lar name brand. Each plan yields a daily net profit as noted in the
table. He also has a meteorologist friend, who for a small fee, will
accurately tell tomorrow’s weather so T.S. can implement one of
his four merchandising plans.
a) What is the expected monetary value (EMV) under risk?
b) What is the expected value with perfect information (EVwPI)?
c) What is the expected value of perfect information (EVPI)?
� SOLUTION
a) The highest expected monetary value under risk is:
So the maximum EMV = $55
b) The expected value with perfect information is:
c) The expected value of perfect information is:
EVPI = EVwPI – Maximum EMV = 61 – 55 = $6
= 10 + 23 + 12 + 16 = $61
EVwPI = .201502 + .251922 + .301402 + .251642
EMV 1Columbia2 = .201452 + .251722 + .301102 + .251602 = $45
EMV 1Cloud Veil2 = .201352 + .251802 + .301402 + .251642 = $55
EMV 1North Face2 = .201502 + .251842 + .301102 + .251522 = $47
EMV 1Patagonia2 = .201402 + .251922 + .301202 + .251482 = $49
States of Nature
Good Average Bad
Market Market Market
Alternatives ($) ($) ($)
Small shop 75,000 25,000 –40,000
Medium-sized
shop 100,000 35,000 –60,000
No shop 0 0 0
Probabilities .20 .50 .30
Traffic in Store Because
Decision Alternatives of Ski Conditions
(merchandising plan (states of nature)
focusing on:) 1 2 3 4
Patagonia $40 92 20 48
North Face 50 84 10 52
Cloud Veil 35 80 40 64
Columbia 45 72 10 60
Probabilities .20 .25 .30 .25

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546 PART 4 Quantitative Modules
� FIGURE A.5 Demand at Ravinder Nath’s Supermarket
� SOLVED PROBLEM A.3
Daily demand for cases of Tidy Bowl cleaner at Ravinder Nath’s
Supermarket has always been 5, 6, or 7 cases. Develop a decision
tree that illustrates her decision alternatives as to whether to stock
5, 6, or 7 cases.
� SOLUTION
The decision tree is shown in Figure A.5
Demand is 5 cases
Demand is 6 cases
Demand is 7 cases
Demand is 5 cases
Demand is 6 casesStock 6 cases
Demand is 7 cases
Demand is 5 cases
Demand is 6 cases
Demand is 7 cases
St
oc
k 5
ca
se
s
Stock 7 cases
�Additional Case Studies: Visit www.myomlab.com or www.pearsonhighered.com/heizer for these additional free case studies:
Arctic, Inc.: A refrigeration company has several major options with regard to capacity and expansion.
Ski Right Corp.: Which of four manufacturers should be selected to manufacture ski helmets?
Bibliography
Balakrishnan, R., B. Render, and R. M. Stair Jr. Managerial
Decision Modeling with Spreadsheets, 2nd ed. Upper Saddle
River, NJ: Prentice Hall (2007).
Buchannan, Leigh, and Andrew O’Connell. “A Brief History of
Decision Making.” Harvard Business Review 84, no. 1
(January 2006): 32–41.
Hammond, J. S., R. L. Kenney, and H. Raiffa. “The Hidden Traps
in Decision Making.” Harvard Business Review 84, no. 1
(January 2006): 118–126.
Keefer, Donald L. “Balancing Drug Safety and Efficacy for a
Go/No-Go Decision.” Interfaces 34, no. 2 (March–April
2004): 113–116.
Miller, C. C., and R. D. Ireland. “Intuition in Strategic Decision
Making.” Academy of Management Executive 19, no. 1
(February 2005): 19.
Parmigiani, G., and L. Inoue. Decision Theory: Principles and
Approaches. New York: Wiley (2010).
Raiffa, H., and R. Schlaifer. Applied Statistical Decision Theory.
New York: Wiley (2000).
Render, B., R. M. Stair Jr., and M. Hanna. Quantitative Analysis
for Management, 10th ed. Upper Saddle River, NJ: Prentice
Hall (2009).

www.myomlab.com

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Module Outline
Why Use Linear Programming? 548
Requirements of a Linear
Programming Problem 549
Formulating Linear Programming
Problems 549
Graphical Solution to a Linear
Programming Problem 550
Sensitivity Analysis 555
Solving Minimization Problems 557
Linear Programming Applications 559
The Simplex Method of LP 562
QUANTITATIVE MODULE
Linear Programming
547

548 PART 4 Quantitative Modules
The storm front closed in quickly
on Chicago’s O’Hare Airport,
shutting it down without warning.
The heavy thunderstorms,
lightning, and poor visibility sent
American Airlines passengers and
ground crew scurrying. Because
American Airlines uses linear
programming (LP) to schedule
flights, hotels, crews, and
refueling, LP has a direct impact
on profitability. If American gets a
major weather disruption at one
of its hubs, a lot of flights may get
canceled, which means a lot of
crews and airplanes in the wrong
places. LP is the tool that helps
airlines such as American unsnarl
and cope with this weather mess.
LO1: Formulate linear programming
models, including an objective
function and constraints 550
LO2: Graphically solve an LP problem
with the iso-profit line method 552
LO3: Graphically solve an LP problem
with the corner-point method 554
Module B Learning Objectives
LO4: Interpret sensitivity analysis and
shadow prices 555
LO5: Construct and solve a
minimization problem 558
LO6: Formulate production-mix, diet,
and labor scheduling problems 559
WHY USE LINEAR PROGRAMMING?
Many operations management decisions involve trying to make the most effective use of an orga-
nization’s resources. Resources typically include machinery (such as planes, in the case of an air-
line), labor (such as pilots), money, time, and raw materials (such as jet fuel). These resources
may be used to produce products (such as machines, furniture, food, or clothing) or services
(such as airline schedules, advertising policies, or investment decisions). Linear programming
(LP) is a widely used mathematical technique designed to help operations managers plan and
make the decisions necessary to allocate resources.
A few examples of problems in which LP has been successfully applied in operations man-
agement are:
1. Scheduling school buses to minimize the total distance traveled when carrying students
2. Allocating police patrol units to high crime areas to minimize response time to 911 calls
3. Scheduling tellers at banks so that needs are met during each hour of the day while
minimizing the total cost of labor
4. Selecting the product mix in a factory to make best use of machine- and labor-hours avail-
able while maximizing the firm’s profit
5. Picking blends of raw materials in feed mills to produce finished feed combinations at
minimum cost
6. Determining the distribution system that will minimize total shipping cost from several
warehouses to various market locations
7. Developing a production schedule that will satisfy future demands for a firm’s product and
at the same time minimize total production and inventory costs
8. Allocating space for a tenant mix in a new shopping mall so as to maximize revenues to the
leasing company (see the OM in Action box “Using LP to Select Tenants in a Shopping Mall”)
Linear programming
(LP)
A mathematical technique
designed to help operations
managers plan and make
decisions relative to the trade-
offs necessary to allocate
resources.

Module B Linear Programming 549
Homart Development Company is one of the largest
shopping-center developers in the U.S. When starting a
new center, Homart produces a tentative floor plan, or
“footprint,” for the mall. This plan outlines sizes, shapes,
and spaces for large department stores. Leasing
agreements are reached with the two or three major
department stores that will become anchor stores in the
mall. The anchor stores are able to negotiate highly
favorable occupancy agreements. Homart’s profits come
primarily from the rent paid by the nonanchor tenants—the
smaller stores that lease space along the aisles of the mall.
The decision as to allocating space to potential tenants is,
therefore, crucial to the success of the investment.
The tenant mix describes the desired stores in the mall
by their size, general location, and type of merchandise or
service provided. For example, the mix might specify two
small jewelry stores in a central section of the mall and a
medium-size shoe store and a large restaurant in one of
the side aisles. In the past, Homart developed a plan for
tenant mix using “rules of thumb” developed over years of
experience in mall development.
Now, to improve its bottom line in an increasingly
competitive marketplace, Homart treats the tenant-mix
problem as an LP model. First, the model assumes that
tenants can be classified into categories according to the
type of merchandise or service they provide. Second, the
model assumes that for each store type, store sizes can be
estimated by distinct category. For example, a small jewelry
store is said to contain about 700 square feet and a large
one about 2,200 square feet. The tenant-mix model is a
powerful tool for enhancing Homart’s mall planning and
leasing activities.
Sources: Journal of Retail and Leisure Property (October 2006): 270–278;
Real Estate Review (Spring 1995): 52–56; and Interfaces (March–April
1988): 1–9.
OM in Action � Using LP to Select Tenants in a Shopping Mall
REQUIREMENTS OF A LINEAR PROGRAMMING PROBLEM
All LP problems have four requirements: an objective, constraints, alternatives, and linearity:
1. LP problems seek to maximize or minimize some quantity (usually profit or cost). We refer
to this property as the objective function of an LP problem. The major objective of a typi-
cal firm is to maximize dollar profits in the long run. In the case of a trucking or airline dis-
tribution system, the objective might be to minimize shipping costs.
2. The presence of restrictions, or constraints, limits the degree to which we can pursue our
objective. For example, deciding how many units of each product in a firm’s product line to
manufacture is restricted by available labor and machinery. We want, therefore, to maximize
or minimize a quantity (the objective function) subject to limited resources (the constraints).
3. There must be alternative courses of action to choose from. For example, if a company pro-
duces three different products, management may use LP to decide how to allocate among
them its limited production resources (of labor, machinery, and so on). If there were no alter-
natives to select from, we would not need LP.
4. The objective and constraints in linear programming problems must be expressed in terms of
linear equations or inequalities.
FORMULATING LINEAR PROGRAMMING PROBLEMS
One of the most common linear programming applications is the product-mix problem. Two or
more products are usually produced using limited resources. The company would like to deter-
mine how many units of each product it should produce to maximize overall profit given its lim-
ited resources. Let’s look at an example.
Shader Electronics Example
The Shader Electronics Company produces two products: (1) the Shader x-pod, a portable music
player, and (2) the Shader BlueBerry, an internet-connected color telephone. The production
process for each product is similar in that both require a certain number of hours of electronic
work and a certain number of labor-hours in the assembly department. Each x-pod takes 4 hours
of electronic work and 2 hours in the assembly shop. Each BlueBerry requires 3 hours in elec-
tronics and 1 hour in assembly. During the current production period, 240 hours of electronic
time are available, and 100 hours of assembly department time are available. Each x-pod sold
yields a profit of $7; each BlueBerry produced may be sold for a $5 profit.
AUTHOR COMMENT
Here we set up an LP
example that we will follow
for most of this module.
Objective function
A mathematical expression
in linear programming that
maximizes or minimizes some
quantity (often profit or cost,
but any goal may be used).
Constraints
Restrictions that limit the
degree to which a manager
can pursue an objective.
ACTIVE MODEL B.1 This
example is further illustrated in
Active Model B.1 at www.pearson
highered.com/heizer.

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550 PART 4 Quantitative Modules
Now we can create the LP objective function in terms of and
Our next step is to develop mathematical relationships to describe the two constraints in this
problem. One general relationship is that the amount of a resource used is to be less than or equal
to ( ) the amount of resource available.
First constraint: Electronic time used is Electronic time available.
Second constraint: Assembly time used is Assembly time available.
Both these constraints represent production capacity restrictions and, of course, affect the
total profit. For example, Shader Electronics cannot produce 70 x-pods during the production
period because if both constraints will be violated. It also cannot make
x-pods and BlueBerrys. This constraint brings out another important aspect of linear
programming; that is, certain interactions will exist between variables. The more units of one
product that a firm produces, the fewer it can make of other products.
GRAPHICAL SOLUTION TO A LINEAR
PROGRAMMING PROBLEM
The easiest way to solve a small LP problem such as that of the Shader Electronics Company
is the graphical solution approach. The graphical procedure can be used only when there are
two decision variables (such as number of x-pods to produce, and number of BlueBerrys
to produce, ). When there are more than two variables, it is not possible to plot the solution
on a two-dimensional graph; we then must turn to more complex approaches described later in
this module.
Graphical Representation of Constraints
To find the optimal solution to a linear programming problem, we must first identify a set, or
region, of feasible solutions. The first step in doing so is to plot the problem’s constraints on
a graph.
The variable (x-pods, in our example) is usually plotted as the horizontal axis of the graph,
and the variable (BlueBerrys) is plotted as the vertical axis. The complete problem may be
restated as:
Maximize profit = $7X1 + $5X2
X2
X1
X2
X1,
X2 = 10
X1 = 50X1 = 70,
2X1 + 1X2 … 100 1hours of assembly time2
…
4X1 + 3X2 … 240 1hours of electronic time2
…
…
Maximize profit = $7X1 + $5X2
X2:X1
LO1: Formulate linear
programming models,
including an objective
function and constraints
Graphical solution
approach
A means of plotting a solution
to a two-variable problem on a
graph.
Decision variables
Choices available to a decision
maker.
Shader’s problem is to determine the best possible combination of x-pods and BlueBerrys to
manufacture to reach the maximum profit. This product-mix situation can be formulated as a lin-
ear programming problem.
We begin by summarizing the information needed to formulate and solve this problem (see
Table B.1). Further, let’s introduce some simple notation for use in the objective function and
constraints. Let:
X2 = number of BlueBerrys to be produced
X1 = number of x-pods to be produced
Hours Required to Produce One Unit
x-pods BlueBerrys
Department 1X12 1X22 Available Hours This Week
Electronic 4 3 240
Assembly 2 1 100
Profit per unit $7 $5
�TABLE B.1
Shader Electronics Company
Problem Data

Module B Linear Programming 551
Subject to the constraints:
(These last two constraints are also called nonnegativity constraints).
The first step in graphing the constraints of the problem is to convert the constraint
inequalities into equalities (or equations).
The equation for constraint A is plotted in Figure B.1 and for constraint B in Figure B.2.
To plot the line in Figure B.1, all we need to do is to find the points at which the line
intersects the and axes. When (the location where the line
touches the axis), it implies that and that Likewise, when we
see that and that Thus, constraint A is bounded by the line running from
to The shaded area represents all points that satisfy
the original inequality.
Constraint B is illustrated similarly in Figure B.2. When then and when
then Constraint B, then, is bounded by the line between
and The shaded area represents the original inequality.
Figure B.3 shows both constraints together. The shaded region is the part that satisfies both
restrictions. The shaded region in Figure B.3 is called the area of feasible solutions, or simply the
feasible region. This region must satisfy all conditions specified by the program’s constraints
and is thus the region where all constraints overlap. Any point in the region would be a feasible
solution to the Shader Electronics Company problem. Any point outside the shaded area would
represent an infeasible solution. Hence, it would be feasible to manufacture 30 x-pods and 20
BlueBerrys but it would violate the constraints to produce 70 x-pods and
40 BlueBerrys. This can be seen by plotting these points on the graph of Figure B.3.
Iso-Profit Line Solution Method
Now that the feasible region has been graphed, we can proceed to find the optimal solution to
the problem. The optimal solution is the point lying in the feasible region that produces the
highest profit.
1X1 = 30, X2 = 202,
1X1 = 50, X2 = 02.
1X1 = 0, X2 = 1002X1 = 50.X2 = 0,
X2 = 100;X1 = 0,
1X1 = 60, X2 = 02.1X1 = 0, X2 = 802
X1 = 60.4X1 = 240
X2 = 0,X2 = 80.3X2 = 240X2
X1 = 0X2X14X1 + 3X2 = 240
Constraint B: 2X1 + 1X2 = 100
Constraint A: 4X1 + 3X2 = 240
X2 Ú 0 1number ofBlueBerrysproduced is greater thanor equal to 02
X1 Ú 0 1number ofx-podsproduced is greater thanor equal to 02
2X1 + 1X2 … 100 1assembly constraint2
4X1 + 3X2 … 240 1electronics constraint2
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Number of x-pods
X1
X2
20
40
60
80
100
Constraint A
(X1 = 0, X2 = 80)
(X1 = 60, X2 = 0)
20 40 60 80 100
� FIGURE B.1 Constraint A
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Number of x-pods
X1
X2
20
40
60
80
100
Constraint B
(X1 = 0, X2 = 100)
(X1 = 50, X2 = 0)
20 40 60 80 100
� FIGURE B.2 Constraint B
Feasible region
The set of all feasible
combinations of decision
variables.

552 PART 4 Quantitative Modules
Once the feasible region has been established, several approaches can be taken in solving for
the optimal solution. The speediest one to apply is called the iso-profit line method.1
We start by letting profits equal some arbitrary but small dollar amount. For the Shader
Electronics problem, we may choose a profit of $210. This is a profit level that can easily be
obtained without violating either of the two constraints. The objective function can be written as
This expression is just the equation of a line; we call it an iso-profit line. It represents all com-
binations (of ) that will yield a total profit of $210. To plot the profit line, we proceed
exactly as we did to plot a constraint line. First, let and solve for the point at which the
line crosses the axis:
Then let and solve for
We can now connect these two points with a straight line. This profit line is illustrated in Figure
B.4. All points on the line represent feasible solutions that produce a profit of $210.
We see, however, that the iso-profit line for $210 does not produce the highest possible profit
to the firm. In Figure B.5, we try graphing three more lines, each yielding a higher profit. The
middle equation, was plotted in the same fashion as the lower line.
When
When
Again, any combination of x-pods and BlueBerrys on this iso-profit line will produce
a total profit of $280.
1X221X12
X1 = 40 x-pods
$280 = $7X1 + $5102
X2 = 0:
X2 = 56 BlueBerrys
$280 = $7102 + $5X2
X1 = 0:
$280 = $7X1 + $5X2,
X1 = 30 x-pods
$210 = $7X1 + $5102
X1:X2 = 0
X2 = 42 BlueBerrys
$210 = $7102 + $5X2
X2
X1 = 0
X1, X2
$210 = 7X1 + 5X2.
20 40 100
Number of x-pods
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X 1
X 2
40
60
80
100
Electronics (constraint A)
Assembly (constraint B)
60 80
Feasible
region
20
� FIGURE B.3
Feasible Solution Region for
the Shader Electronics
Company Problem
1Iso means “equal” or “similar.” Thus, an iso-profit line represents a line with all profits the same, in this case $210.
LO2: Graphically solve
an LP problem with the
iso-profit line method
Iso-profit line method
An approach to solving a linear
programming maximization
problem graphically.

Module B Linear Programming 553
Note that the third line generates a profit of $350, even more of an improvement. The farther
we move from the 0 origin, the higher our profit will be. Another important point to note is that
these iso-profit lines are parallel. We now have two clues as to how to find the optimal solution
to the original problem. We can draw a series of parallel profit lines (by carefully moving our
ruler in a plane parallel to the first profit line). The highest profit line that still touches some point
of the feasible region will pinpoint the optimal solution. Notice that the fourth line ($420) is too
high to count because it does not touch the feasible region.
The highest possible iso-profit line is illustrated in Figure B.6. It touches the tip of the feasi-
ble region at the corner point and yields a profit of $410.
Corner-Point Solution Method
A second approach to solving linear programming problems employs the corner-point method.
This technique is simpler in concept than the iso-profit line approach, but it involves looking at
the profit at every corner point of the feasible region.
The mathematical theory behind linear programming states that an optimal solution to any
problem (that is, the values of that yield the maximum profit) will lie at a corner point, orX1, X2
1X1 = 30, X2 = 402
20 40 100
Number of x-pods
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X 1
X 2
40
60
80
100
(30, 0)
$210 = $7
60 80
(0, 42)
20
X1 + $5X2
� FIGURE B.4 A Profit Line of $210 Plotted for the Shader
Electronics Company
20 40 100
Number of x-pods
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X1
X 2
40
60
80
100
60 80
20
$350 = $7X1+ $5X2
$280 = $7X1 + $5X2
$210 = $7X1 + $5X2
$420 = $7X1 + $5X2
� FIGURE B.5 Four Iso-Profit Lines Plotted for the Shader
Electronics Company
20 40 100
Number of x-pods
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X1
X 2
40
60
80
100
60 80
20
Maximum profit line
$410 = $7X1 + $5X2
Optimal solution point
(X1 = 30, X2 = 40)
� FIGURE B.6
Optimal Solution for the
Shader Electronics Problem
Corner-point method
A method for solving graphical
linear programming problems.

554 PART 4 Quantitative Modules
extreme point, of the feasible region. Hence, it is necessary to find only the values of the vari-
ables at each corner; the maximum profit or optimal solution will lie at one (or more) of them.
Once again we can see (in Figure B.7) that the feasible region for the Shader Electronics Company
problem is a four-sided polygon with four corner, or extreme, points. These points are labeled �, �,
�, and � on the graph. To find the values producing the maximum profit, we find out
what the coordinates of each corner point are, then determine and compare their profit levels:
We skipped corner point � momentarily because to find its coordinates accurately, we will have
to solve for the intersection of the two constraint lines. As you may recall from algebra, we can
apply the method of simultaneous equations to the two constraint equations:
To solve these equations simultaneously, we multiply the second equation by –2:
and then add it to the first equation:
or:
Doing this has enabled us to eliminate one variable, and to solve for We can now substi-
tute 40 for in either of the original constraint equations and solve for Let us use the first
equation. When then:
X1 = 30
4X1 = 120
4X1 + 120 = 240
4X1 + 31402 = 240
X2 = 40,
X1.X2
X2.X1,
X2 = 40
+ 1X2 = 40
– 4X1 – 2X2 = – 200
+ 4X1 + 3X2 = 240
– 212X1 + 1X2 = 1002 = – 4X1 – 2X2 = – 200
2X1 + 1X2 = 100 1assembly time2
4X1 + 3X2 = 240 1electronics time2
Point �: 1X1 = 50, X2 = 02 Profit $71502 + $5102 = $350
Point �: 1X1 = 0, X2 = 802 Profit $7102 + $51802 = $400
Point �: 1X1 = 0, X2 = 02 Profit $7102 + $5102 = $0
1X1, X22
20 40 100
Number of x-pods
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X 2
40
60
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100
60 80
20
1
4
3
2
� FIGURE B.7
The Four Corner Points of the
Feasible Region
AUTHOR COMMENT
We named the decision
variables X1 and X 2 here, but
any notations (e.g., x-p and
B or X and Y ) would do
as well.
LO3: Graphically solve an
LP problem with the corner-
point method

Module B Linear Programming 555
Thus, point � has the coordinates We can compute its profit level to com-
plete the analysis:
Because point � produces the highest profit of any corner point, the product mix of
x-pods and BlueBerrys is the optimal solution to the Shader Electronics problem. This
solution will yield a profit of $410 per production period; it is the same solution we obtained
using the iso-profit line method.
SENSITIVITY ANALYSIS
Operations managers are usually interested in more than the optimal solution to an LP problem.
In addition to knowing the value of each decision variable (the ) and the value of the objective
function, they want to know how sensitive these answers are to input parameter changes. For
example, what happens if the coefficients of the objective function are not exact, or if they
change by 10% or 15%? What happens if right-hand-side values of the constraints change?
Because solutions are based on the assumption that input parameters are constant, the subject of
sensitivity analysis comes into play. Sensitivity analysis, or postoptimality analysis, is the study
of how sensitive solutions are to parameter changes.
There are two approaches to determining just how sensitive an optimal solution is to changes.
The first is simply a trial-and-error approach. This approach usually involves resolving the entire
problem, preferably by computer, each time one input data item or parameter is changed. It can
take a long time to test a series of possible changes in this way.
The approach we prefer is the analytic postoptimality method. After an LP problem has been
solved, we determine a range of changes in problem parameters that will not affect the optimal
solution or change the variables in the solution. This is done without resolving the whole prob-
lem. LP software, such as Excel’s Solver or POM for Windows, has this capability. Let us exam-
ine several scenarios relating to the Shader Electronics example.
Program B.1 is part of the Excel Solver computer-generated output available to help a deci-
sion maker know whether a solution is relatively insensitive to reasonable changes in one or
more of the parameters of the problem. (The complete computer run for these data, including
input and full output, is illustrated in Programs B.2 and B.3 later in this module.)
Xis
X2 = 40
X1 = 30
Point �: 1X1 = 30, X2 = 402 Profit = $71302 + $51402 = $410
1X1 = 30, X2 = 402.
Parameter
Numerical value that is given in
a model.
Sensitivity analysis
An analysis that projects how
much a solution may change if
there are changes in the
variables or input data.
AUTHOR COMMENT
Here we look at the sensitivity
of the final answers to
changing inputs.
The solution values for the variables
appear. We should make 30 X-pods
and 40 BlueBerrys.
We will use 240 hours and 100 hours
of Electronics and Assembly time,
respectively.
If we use 1 more Electronics hours our profit will
increase by $1.50. This is true for up to 60 more
hours.The profit will fall by $1.50 for each Electronics
hour less than 240 hours, down as low as 200 hours.
� PROGRAM B.1
Sensitivity Analysis for
Shader Electronics Using
Excel’s Solver
LO4: Interpret sensitivity
analysis and shadow prices

556 PART 4 Quantitative Modules
Sensitivity Report
The Excel Sensitivity Report for the Shader Electronics example in Program B.1 has two distinct
components: (1) a table titled Adjustable Cells and (2) a table titled Constraints. These tables
permit us to answer several what-if questions regarding the problem solution.
It is important to note that while using the information in the sensitivity report to answer
what-if questions, we assume that we are considering a change to only a single input data value
at a time. That is, the sensitivity information does not always apply to simultaneous changes in
several input data values.
The Adjustable Cells table presents information regarding the impact of changes to the objec-
tive function coefficients (i.e., the unit profits of $7 and $5) on the optimal solution. The
Constraints table presents information related to the impact of changes in constraint right-hand-
side (RHS) values (i.e., the 240 hours and 100 hours) on the optimal solution. Although different
LP software packages may format and present these tables differently, the programs all provide
essentially the same information.
Changes in the Resources or
Right-Hand-Side Values
The right-hand-side values of the constraints often represent resources available to the firm. The
resources could be labor-hours or machine time or perhaps money or production materials avail-
able. In the Shader Electronics example, the two resources are hours available of electronics time
and hours of assembly time. If additional hours were available, a higher total profit could be real-
ized. How much should the company be willing to pay for additional hours? Is it profitable to
have some additional electronics hours? Should we be willing to pay for more assembly time?
Sensitivity analysis about these resources will help us answer these questions.
If the right-hand side of a constraint is changed, the feasible region will change (unless the
constraint is redundant), and often the optimal solution will change. In the Shader example, there
were 100 hours of assembly time available each week and the maximum possible profit was
$410. If the available assembly hours are increased to 110 hours, the new optimal solution seen
in Figure B.8(a) is (45,20) and the profit is $415. Thus, the extra 10 hours of time resulted in an
increase in profit of $5 or $0.50 per hour. If the hours are decreased to 90 hours as shown in
Figure B.8(b), the new optimal solution is (15,60) and the profit is $405. Thus, reducing the
hours by 10 results in a decrease in profit of $5 or $0.50 per hour. This $0.50 per hour change in
profit that resulted from a change in the hours available is called the shadow price, or dual value.
The shadow price for a constraint is the improvement in the objective function value that results
from a one-unit increase in the right-hand side of the constraint.
20 40 60 80 100
100
(a)
80
60
40
20
0
Changed assembly constraint from 2X1 + 1X2 = 100
to 2X1 + 1X2 = 110
Electronics constraint
is unchanged3
4
2
1
Corner point 3 is still optimal, but
values at this point are now X1 = 45,
X2 = 20, with a profit = $415.
X1
X2
� FIGURE B.8 Shader Electronics Sensitivity Analysis on Right-Hand-Side (RHS) Resources
100
20 40 60 80 100
80
60
40
20
0
Changed assembly constraint from 2X1 + 1X2 = 100
to 2X1 + 1X2 = 90
Electronics constraint
is unchanged
Corner point 3 is still optimal, but
values at this point are now X1 = 15,
X2 = 60, with a profit = $405.3
4
2
1
X1
X2(b)
Shadow price
(or dual value)
The value of one additional unit
of a scarce resource in LP.

Module B Linear Programming 557
Validity Range for the Shadow Price Given that Shader Electronics’ profit increases by
$0.50 for each additional hour of assembly time, does it mean that Shader can do this indefi-
nitely, essentially earning infinite profit? Clearly, this is illogical. How far can Shader increase its
assembly time availability and still earn an extra $0.50 profit per hour? That is, for what level of
increase in the RHS value of the assembly time constraint is the shadow price of $0.50 valid?
The shadow price of $0.50 is valid as long as the available assembly time stays in a range
within which all current corner points continue to exist. The information to compute the upper
and lower limits of this range is given by the entries labeled Allowable Increase and Allowable
Decrease in the Sensitivity Report in Program B.1. In Shader’s case, these values show that the
shadow price of $0.50 for assembly time availability is valid for an increase of up to 20 hours
from the current value and a decrease of up to 20 hours. That is, the available assembly time can
range from a low of 80 to a high of 120 for the shadow price of
$0.50 to be valid. Note that the allowable decrease implies that for each hour of assembly time
that Shader loses (up to 20 hours), its profit decreases by $0.50.
Changes in the Objective Function Coefficient
Let us now focus on the information provided in Program B.1 titled Adjustable Cells. Each row
in the Adjustable Cells table contains information regarding a decision variable (i.e., x-pods or
BlueBerrys) in the LP model.
Allowable Ranges for Objective Function Coefficients As the unit profit contribution
of either product changes, the slope of the iso-profit lines we saw earlier in Figure B.5 changes.
The size of the feasible region, however, remains the same. That is, the locations of the corner
points do not change.
The limits to which the profit coefficient of x-pods or BlueBerrys can be changed without
affecting the optimality of the current solution is revealed by the values in the Allowable Increase
and Allowable Decrease columns of the Sensitivity Report in Program B.1. The allowable
increase in the objective function coefficient for BlueBerrys is only $0.25. In contrast, the allow-
able decrease is $1.50. Hence, if the unit profit of BlueBerrys drops to $4 (i.e., a decrease of $1
from the current value of $5), it is still optimal to produce 30 x-pods and 40 BlueBerrys. The total
profit will drop to $370 (from $410) because each BlueBerry now yields less profit (of $1 per
unit). However, if the unit profit drops below $3.50 per BlueBerry (i.e., a decrease of more than
$1.50 from the current $5 profit), the current solution is no longer optimal. The LP problem will
then have to be resolved using Solver, or other software, to find the new optimal corner point.
SOLVING MINIMIZATION PROBLEMS
Many linear programming problems involve minimizing an objective such as cost instead of
maximizing a profit function. A restaurant, for example, may wish to develop a work schedule to
meet staffing needs while minimizing the total number of employees. Also, a manufacturer may
seek to distribute its products from several factories to its many regional warehouses in such a
way as to minimize total shipping costs.
Minimization problems can be solved graphically by first setting up the feasible solution
region and then using either the corner-point method or an iso-cost line approach (which is anal-
ogous to the iso-profit approach in maximization problems) to find the values of and that
yield the minimum cost.
Example B1 shows how to solve a minimization problem.
X2X1
1= 100 + 2021= 100 – 202
Iso-cost
An approach to solving a linear
programming minimization
problem graphically.
AUTHOR COMMENT
LP problems can be
structured to minimize costs
as well as maximize profits.
� EXAMPLE B1
A minimization
problem with two
variables
Cohen Chemicals, Inc., produces two types of photo-developing fluids. The first, a black-and-white
picture chemical, costs Cohen $2,500 per ton to produce. The second, a color photo chemical, costs
$3,000 per ton.
Based on an analysis of current inventory levels and outstanding orders, Cohen’s production man-
ager has specified that at least 30 tons of the black-and-white chemical and at least 20 tons of the color
chemical must be produced during the next month. In addition, the manager notes that an existing
inventory of a highly perishable raw material needed in both chemicals must be used within 30 days.
To avoid wasting the expensive raw material, Cohen must produce a total of at least 60 tons of the
photo chemicals in the next month.

558 PART 4 Quantitative Modules
APPROACH � Formulate this information as a minimization LP problem.
Let:
Subject to:
SOLUTION � To solve the Cohen Chemicals problem graphically, we construct the problem’s
feasible region, shown in Figure B.9.
X1, X2 Ú 0 nonnegativity requirements
X1 + X2 Ú 60 tons total
X2 Ú 20 tons of color chemical
X1 Ú 30 tons of black-and-white chemical
Objective: Minimize cost = $2,500X1 + $3,000X2
X2 = number of tons of color photo chemical produced
X1 = number of tons of black-and-white photo chemical produced
0
X 1
X2
Feasible
region
X1 = 30 X2 = 20
10
10
20
30
40
50
20 30 40 50 60
60
X1 + X2 = 60
b
a
� FIGURE B.9
Cohen Chemicals’ Feasible
Region
Minimization problems are often unbounded outward (that is, on the right side and on the top), but
this characteristic causes no problem in solving them. As long as they are bounded inward (on the left
side and the bottom), we can establish corner points. The optimal solution will lie at one of the corners.
In this case, there are only two corner points, a and b, in Figure B.9. It is easy to determine that at
point a, and and that at point b, and The optimal solution is
found at the point yielding the lowest total cost.
Thus:
The lowest cost to Cohen Chemicals is at point a. Hence the operations manager should produce 40
tons of the black-and-white chemical and 20 tons of the color chemical.
INSIGHT � The area is either not bounded to the right or above in a minimization problem (as it is
in a maximization problem).
LEARNING EXERCISE � Cohen’s second constraint is recomputed and should be
Does anything change in the answer? [Answer: Now and ]
RELATED PROBLEMS � B.3, B.5, B.6, B.11, B.12, B.22, B.24
EXCEL OM Data File ModBExB1.xls can be found at www.pearsonhighered.com/heizer.
Total cost = $157,500.X1 = 45, X2 = 15,
X2 Ú 15.
= $165,000
= 2,5001302 + 3,0001302
Total cost at b = 2,500X1 + 3,000X2
= $160,000
= 2,5001402 + 3,0001202
Total cost at a = 2,500X1 + 3,000X2
X2 = 30.X1 = 30X2 = 20,X1 = 40
LO5: Construct and solve
a minimization problem

www.pearsonhighered.com/heizer

Module B Linear Programming 559
LINEAR PROGRAMMING APPLICATIONS
The foregoing examples each contained just two variables ( and ). Most real-world prob-
lems contain many more variables, however. Let’s use the principles already developed to formu-
late a few more-complex problems. The practice you will get by “paraphrasing” the following
LP situations should help develop your skills for applying linear programming to other common
operations situations.
Production-Mix Example
Example B2 involves another production-mix decision. Limited resources must be allocated
among various products that a firm produces. The firm’s overall objective is to manufacture the
selected products in such quantities as to maximize total profits.
X2X1
LO6: Formulate
production-mix, diet, and
labor scheduling problems
� EXAMPLE B2
A production-mix
problem
It has been said that an airline seat is the most perishable
commodity in the world. Each time an airliner takes off
with an empty seat, a revenue opportunity is lost forever.
For Continental, Delta, and Southwest, which each flies
thousands of flight legs per day on hundreds of planes,
the schedule is their very heartbeat. These schedules, all
developed with massive LP models (Southwest’s program
has 90,000 constraints and 2 million variables), assign
aircraft to specific routes and assign pilots and flight
attendants to each of these aircraft.
One flight leg for Continental might consist of a Boeing
777 assigned to fly at 7:05 A.M. from Houston to Chicago to
arrive at 9:15 A.M. Continental’s problem, like that of Delta
and every other competitor, is to match planes such as
737s, 767s, or 777s to flight legs such as Houston–Chicago
and to fill seats with paying passengers. And when
schedule disruptions occur due to a hurricane (like Katrina
in 2005), mechanical problems, or crew unavailability,
planes and people are often in the wrong place.
That is why Continental runs its OptSolver, Delta its
ColdStart, and Southwest its ILOG Optimizer every day.
These LP models include constraints such as aircraft
availability, maintenance needs, crew training requirements,
arrival/departure needs, and so on. The airlines’ objectives
are to minimize a combination of operating costs and lost
passenger revenue. The savings from LP have been in the
$100s of millions per year at these airlines.
Sources: Computers & Operations Research (June 2009): 2031; Aviation
Daily (June 16, 2009): 6; Interfaces (July–August 2004): 253–271; and
www.blogsouthwest.com (2010).
OM in Action � Continental, Delta, and Southwest Save $100s of Millions with LP
AUTHOR COMMENT
Now we look at three larger
problems—ones that have
more than two decision
variables each and therefore
are not graphed.
Failsafe Electronics Corporation primarily manufactures four highly technical products, which it sup-
plies to aerospace firms that hold NASA contracts. Each of the products must pass through the follow-
ing departments before they are shipped: wiring, drilling, assembly, and inspection. The time
requirements in each department (in hours) for each unit produced and its corresponding profit value
are summarized in this table:
The production time available in each department each month and the minimum monthly production
requirement to fulfill contracts are as follows:
Department
Product Wiring Drilling Assembly Inspection Unit Profit
XJ201 .5 3 2 .5 $ 9
XM897 1.5 1 4 1.0 $12
TR29 1.5 2 1 .5 $15
BR788 1.0 3 2 .5 $11
Department
Capacity
(hours) Product
Minimum
Production Level
Wiring 1,500 XJ201 150
Drilling 2,350 XM897 100
Assembly 2,600 TR29 200
Inspection 1,200 BR788 400

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560 PART 4 Quantitative Modules
APPROACH � Formulate this production-mix situation as an LP problem. The production man-
ager first specifies production levels for each product for the coming month. He lets:
SOLUTION � The LP formulation is:
INSIGHT � There can be numerous constraints in an LP problem. The constraint right-hand sides
may be in different units, but the objective function uses one common unit—dollars of profit, in this
case. Because there are more than two decision variables, this problem is not solved graphically.
LEARNING EXERCISE � Solve this LP problem as formulated. What is the solution?
[Answer: ]
RELATED PROBLEMS � B.7, B.8, B.10, B.19, B.20, B.21, B.23, B.28, B.29
X1 = 150, X2 = 300, X3 = 200, X4 = 400.
X1,X2,X3,X4 Ú 0
X4 Ú 400 units of BR788
X3 Ú 200 units of TR29
X2 Ú 100 units of XM897
X1 Ú 150 units of XJ201
.5X1 + 1X2 + .5X3 + .5X4 … 1,200 hours of inspection
2X1 + 4X2 + 1X3 + 2X4 … 2,600 hours of assembly available
3X1 + 1X2 + 2X3 + 3X4 … 2,350 hours of drilling available
subject to: .5X1 + 1.5X2 + 1.5X3 + 1X4 … 1,500 hours of wiring available
Objective: Maximize profit = 9X1 + 12X2 + 15X3 + 11X4
X4 = number of units of BR788 produced
X3 = number of units of TR29 produced
X2 = number of units of XM897 produced
X1 = number of units of XJ201 produced
Diet Problem Example
Example B3 illustrates the diet problem, which was originally used by hospitals to determine the
most economical diet for patients. Known in agricultural applications as the feed-mix problem,
the diet problem involves specifying a food or feed ingredient combination that will satisfy stated
nutritional requirements at a minimum cost level.
EXAMPLE B3 �
A diet problem
The Feed ’N Ship feedlot fattens cattle for local farmers and ships them to meat markets in Kansas City
and Omaha. The owners of the feedlot seek to determine the amounts of cattle feed to buy to satisfy
minimum nutritional standards and, at the same time, minimize total feed costs.
Each grain stock contains different amounts of four nutritional ingredients: A, B, C, and D. Here are
the ingredient contents of each grain, in ounces per pound of grain:
The cost per pound of grains X, Y, and Z is $0.02, $0.04, and $0.025, respectively. The minimum
requirement per cow per month is 64 ounces of ingredient A, 80 ounces of ingredient B, 16 ounces of
ingredient C, and 128 ounces of ingredient D.
The feedlot faces one additional restriction—it can obtain only 500 pounds of stock Z per month
from the feed supplier, regardless of its need. Because there are usually 100 cows at the Feed ’N Ship
feedlot at any given time, this constraint limits the amount of stock Z for use in the feed of each cow to
no more than 5 pounds, or 80 ounces, per month.
Feed
Ingredient Stock X Stock Y Stock Z
A 3 oz 2 oz 4 oz
B 2 oz 3 oz 1 oz
C 1 oz 0 oz 2 oz
D 6 oz 8 oz 4 oz

Module B Linear Programming 561
APPROACH � Formulate this as a minimization LP problem.
SOLUTION �
Objective: Minimize cost
subject to: Ingredient A requirement:
Ingredient B requirement:
Ingredient C requirement:
Ingredient D requirement:
Stock Z limitation:
The cheapest solution is to purchase 40 pounds of grain at a cost of $0.80 per cow.
INSIGHT � Because the cost per pound of stock X is so low, the optimal solution excludes grains
Y and Z.
LEARNING EXERCISE � The cost of a pound of stock X just increased by 50%. Does this
affect the solution? [Answer: Yes, when the cost per pound of grain X is $0.03, pounds,
pounds, and per cow.]
RELATED PROBLEMS � B.6, B.30
cost = $1.12X3 = 0,X2 = 16
X1 = 16
X1,
X1,X2,X3 Ú 0
X3 … 5
6X1 + 8X2 + 4X3 Ú 128
1X1 + 0X2 + 2X3 Ú 16
2X1 + 3X2 + 1X3 Ú 80
3X1 + 2X2 + 4X3 Ú 64
= .02X1 + .04X2 + .025X3
X3 = number of pounds of stock Z purchased per cow each month
X2 = number of pounds of stock Y purchased per cow each month
Let: X1 = number of pounds of stock X purchased per cow each month
Labor Scheduling Example
Labor scheduling problems address staffing needs over a specific time period. They are espe-
cially useful when managers have some flexibility in assigning workers to jobs that require over-
lapping or interchangeable talents. Large banks and hospitals frequently use LP to tackle their
labor scheduling. Example B4 describes how one bank uses LP to schedule tellers.
Mexico City Bank of Commerce and Industry is a busy bank that has requirements for between 10 and
18 tellers depending on the time of day. Lunchtime, from noon to 2 P.M., is usually heaviest. The table
below indicates the workers needed at various hours that the bank is open.
The bank now employs 12 full-time tellers, but many people are on its roster of available part-time
employees. A part-time employee must put in exactly 4 hours per day but can start anytime between
9 A.M. and 1 P.M. Part-timers are a fairly inexpensive labor pool because no retirement or lunch benefits
are provided them. Full-timers, on the other hand, work from 9 A.M. to 5 P.M. but are allowed 1 hour for
lunch. (Half the full-timers eat at 11 A.M., the other half at noon.) Full-timers thus provide 35 hours per
week of productive labor time.
By corporate policy, the bank limits part-time hours to a maximum of 50% of the day’s total requirement.
Part-timers earn $6 per hour (or $24 per day) on average, whereas full-timers earn $75 per day in
salary and benefits on average.
APPROACH � The bank would like to set a schedule, using LP, that would minimize its total
manpower costs. It will release 1 or more of its full-time tellers if it is profitable to do so.
We can let:
full-time tellers
part-timers starting at 9 A.M. (leaving at 1 P.M.)
part-timers starting at 10 A.M. (leaving at 2 P.M.)
part-timers starting at 11 A.M. (leaving at 3 P.M.)
part-timers starting at noon (leaving at 4 P.M.)
part-timers starting at 1 P.M. (leaving at 5 P.M.)P5 =
P4 =
P3 =
P2 =
P1 =
F =
Time Period
Number of
Tellers Required Time Period
Number of
Tellers Required
9 A.M.–10 A.M. 10 1 P.M.–2 P.M. 18
10 A.M.–11 A.M. 12 2 P.M.–3 P.M. 17
11 A.M.–Noon 14 3 P.M.–4 P.M. 15
Noon–1 P.M. 16 4 P.M.–5 P.M. 10
� EXAMPLE B4
Scheduling bank
tellers

562 PART 4 Quantitative Modules
SOLUTION � Objective function:
Constraints: For each hour, the available labor-hours must be at least equal to the required labor-hours:
(9 A.M. to 10 A.M. needs)
(10 A.M. to 11 A.M. needs)
(11 A.M. to noon needs)
(noon to 1 P.M. needs)
(1 P.M. to 2 P.M. needs)
(2 P.M. to 3 P.M. needs)
(3 P.M. to 4 P.M. needs)
(4 P.M. to 5 P.M. needs)
Only 12 full-time tellers are available, so:
Part-time worker-hours cannot exceed 50% of total hours required each day, which is the sum of the
tellers needed each hour:
or:
There are two alternative optimal schedules that Mexico City Bank can follow. The first is to employ only
10 full-time tellers ( ) and to start 7 part-timers at 10 A.M. ( ), 2 part-timers at 11 A.M. and
noon ( and ), and 3 part-timers at 1 P.M. ( ). No part-timers would begin at 9 A.M.P5 = 3P4 = 2P3 = 2
P2 = 7F = 10
F,P1,P2,P3,P4,P5 Ú 0
4P1 + 4P2 + 4P3 + 4P4 + 4P5 … 0.5011122
41P1 + P2 + P3 + P4 + P52 … .50110 + 12 + 14 + 16 + 18 + 17 + 15 + 102
F … 12
F + P5 Ú 10
F + P4 + P5 Ú 15
F + P3 + P4 + P5 Ú 17
F + P2 + P3 + P4 + P5 Ú 18
1
2F + P1 + P2 + P3 + P4 Ú 16
1
2F + P1 + P2 + P3 Ú 14
F + P1 + P2 Ú 12
F + P1 Ú 10
Minimize total daily manpower cost = $75F + $241P1 + P2 + P3 + P4 + P52
THE SIMPLEX METHOD OF LP
Most real-world linear programming problems have more than two variables and thus are too
complex for graphical solution. A procedure called the simplex method may be used to find the
optimal solution to such problems. The simplex method is actually an algorithm (or a set of
instructions) with which we examine corner points in a methodical fashion until we arrive at the
best solution—highest profit or lowest cost. Computer programs (such as Excel OM and POM for
Windows) and Excel spreadsheets are available to solve linear programming problems via the
simplex method.
For details regarding the algebraic steps of the simplex algorithm, see Tutorial 3 at our text
website or refer to a management science textbook.2
Simplex method
An algorithm for solving linear
programming problems of all
sizes.
2See, for example, Barry Render, Ralph M. Stair, and Michael Hanna, Quantitative Analysis for Management, 10th ed.
(Pearson Education, Inc., Upper Saddle River, NJ, 2009): Chapters 7–9; or Raju Balakrishnan, Barry Render, and
Ralph M. Stair, Managerial Decision Modeling with Spreadsheets, 2nd ed. (Pearson Education, Inc., Upper Saddle
River, NJ, 2007): Chapters 2–4.
The second solution also employs 10 full-time tellers, but starts 6 part-timers at 9 A.M. ( ),
1 part-timer at 10 A.M. ( ), 2 part-timers at 11 A.M. and noon ( and ), and 3 part-
timers at 1 P.M. ( ). The cost of either of these two policies is $1,086 per day.
INSIGHT � It is not unusual for multiple optimal solutions to exist in large LP problems. In this
case, it gives management the option of selecting, at the same cost, between schedules. To find an alter-
nate optimal solution, you may have to enter the constraints in a different sequence.
LEARNING EXERCISE � The bank decides to give part-time employees a raise to $7 per hour.
Does the solution change? [Answer: Yes,
]
RELATED PROBLEM � B.18
P4 = 5, P5 = 0.
cost = $1,142, F = 10, P1 = 6, P2 = 1, P3 = 2,
P5 = 3
P4 = 2P3 = 2P2 = 1
P1 = 6

Module B Linear Programming 563
This module introduces a special kind of model, linear pro-
gramming. LP has proven to be especially useful when try-
ing to make the most effective use of an organization’s
resources.
The first step in dealing with LP models is problem for-
mulation, which involves identifying and creating an objec-
tive function and constraints. The second step is to solve the
problem. If there are only two decision variables, the prob-
lem can be solved graphically, using the
corner-point method or the iso-
profit/iso-cost line method. With either
approach, we first identify the feasible
region, then find the corner point yielding
the greatest profit or least cost. LP is used in
a wide variety of business applications, as the examples and
homework problems in this module reveal.
Key Terms
Linear programming (LP) (p. 548)
Objective function (p. 549)
Constraints (p. 549)
Graphical solution approach (p. 550)
Decision variables (p. 550)
Feasible region (p. 551)
Iso-profit line method (p. 552)
Corner-point method (p. 553)
Parameter (p. 555)
Sensitivity analysis (p. 555)
Shadow price (or dual value) (p. 556)
Iso-cost (p. 557)
Simplex method (p. 562)
Using Software to Solve LP Problems
All LP problems can be solved with the simplex method, using software such as Excel OM and POM for
Windows or Excel. This approach produces valuable economic information such as the shadow price, or
dual value, and provides complete sensitivity analysis on other inputs to the problems. Excel uses
Solver, which requires that you enter your own constraints. Excel OM and POM for Windows require
only that demand data, supply data, and shipping costs be entered. In the following section we illustrate
how to create an Excel spreadsheet for LP problems.
X Using Excel Spreadsheets
Excel offers the ability to analyze linear programming problems using built-in problem-solving tools.
Excel’s tool is named Solver. Solver is limited to 200 changing cells (variables), each with 2 boundary
constraints and up to 100 additional constraints. These capabilities make Solver suitable for the solution
of complex, real-world problems.
We use Excel to set up the Shader Electronics problem in Program B.2. The objective and constraints
are repeated here:
Objective function: Maximize profit =
21x-pods2 + 11BlueBerry2 … 100
Subject to: 41x-pods2 + 31BlueBerrys2 … 240
$71No. of x-pods2 + $51No. of BlueBerrys2
MODULE SUMMARY
� PROGRAM B.2
Using Excel to Formulate
the Shader Electronics
Problem

564 PART 4 Quantitative Modules
� PROGRAM B.3
Excel Solution to Shader
Electronics LP Problem
The Excel screen in Program B.3 shows Solver’s solution to the Shader Electronics Company problem.
Note that the optimal solution is now shown in the changing cells (cells B8 and C8, which served as the
variables). The Reports selection performs more extensive analysis of the solution and its environment.
Excel’s sensitivity analysis capability was illustrated earlier in Program B.1.
PX Using Excel OM and POM for Windows
Excel OM and POM for Windows can handle relatively large LP problems. As output, the software pro-
vides optimal values for the variables, optimal profit or cost, and sensitivity analysis. In addition, POM
for Windows provides graphical output for problems with only two variables.
Solved Problems Virtual Office Hours help is available at www.myomlab.com
� SOLVED PROBLEM B.1
Smith’s, a Niagara, New York, clothing manufacturer that pro-
duces men’s shirts and pajamas, has two primary resources avail-
able: sewing-machine time (in the sewing department) and
cutting-machine time (in the cutting department). Over the next
month, owner Barbara Smith can schedule up to 280 hours of
work on sewing machines and up to 450 hours of work on cutting
machines. Each shirt produced requires 1.00 hour of sewing time
and 1.50 hours of cutting time. Producing each pair of pajamas
requires .75 hour of sewing time and 2 hours of cutting time.
To express the LP constraints for this problem mathemati-
cally, we let:
X2 = number of pajamas produced
X1 = number of shirts produced
� SOLUTION
First constraint: hours of sewing-machine time
available—our first scarce resource
Second constraint: hours of cutting-machine time
available—our second scarce resource
Note: This means that each pair of pajamas takes 2 hours of the cutting resource.
Smith’s accounting department analyzes cost and sales figures and states that each shirt produced will
yield a $4 contribution to profit and that each pair of pajamas will yield a $3 contribution to profit.
This information can be used to create the LP objective function for this problem:
Objective function: Maximize total contribution to profit = $4X1 + $3X2
1.5X1 + �X2 … 450
1X1 + .75X2 … 280
Computations
Value Cell Excel Formula Action
Left Hand Side D4 =SUMPRODUCT($B$8:$C$8,B4:C4) Copy to D5:D6
Slack G5 =F5–D5 Copy to G6
Select Tools, Solver
Set Solver parameters as displayed
Press Solve

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Module B Linear Programming 565
� SOLVED PROBLEM B.2
We want to solve the following LP problem for Kevin Caskey
Wholesale Inc. using the corner-point method:
� SOLUTION
Figure B.10 illustrates these constraints:
Corner-point a:
Corner-point b:
Corner-point d:
Corner-point c is obtained by solving equations
and simultaneously. Multiply the second equa-
tion by and add it to the first.
And or or
Corner-point c:
Hence the optimal solution is:
1×1 = 18, x2 = 42 Profit = $190
Profit = 91182+ 7142= $1901X1 = 18,X2 = 42
X1 = 18X1 + 12 = 30X1 + 3142 = 30
Thus X2 = 4.
– 5X2 = – 20
– 2X1 + 6X2 = – 60
2X1 + 1X2 = 40
– 2
X1 + 3X2 = 30
2X1 + 1X2 = 40
1X1 = 20, X2 = 02 Profit = 91202 + 7102 = $180
1X1 = 0, X2 = 102 Profit = 9102 + 71102 = $70
1X1 = 0, X2 = 02 Profit = 0
X1,X2 Ú 0
X1 + 3X2 … 30
Constraints: 2X1 + 1X2 … 40
Objective: Maximize profit = $9X1 + $7X2
0
X 1
X 2
10
10
20
30
40
20 30 40
dc
a
b
� FIGURE B.10 K. Caskey Wholesale, Inc.’s Feasible Region
subject to these constraints:
Figure B.11 illustrates these constraints.
1
2X1 Ú 1
1
2 oz 1ingredientA constraint2
4X1 + 3X2 Ú 48 oz 1ingredientB constraint2
5X1 + 10X2 Ú 90 oz 1ingredientA constraint2
Composition of Each Pound of Feed
Ingredient `
Brand Y
Feed
Brand Z
Feed
Minimum Monthly
Requirement
A 5 oz 10 oz 90 oz
B 4 oz 3 oz 48 oz
C .5 oz 0 1.5 oz
Cost/lb $.02 $.03
� SOLVED PROBLEM B.3
Holiday Meal Turkey Ranch is considering buying two different
types of turkey feed. Each feed contains, in varying proportions,
some or all of the three nutritional ingredients essential for fatten-
ing turkeys. Brand Y feed costs the ranch $.02 per pound. Brand Z
costs $.03 per pound. The rancher would like to determine the
lowest-cost diet that meets the minimum monthly intake require-
ment for each nutritional ingredient.
The following table contains relevant information about the
composition of brand Y and brand Z feeds, as well as the minimum
monthly requirement for each nutritional ingredient per turkey.
� SOLUTION
If we let:
then we may proceed to formulate this linear programming prob-
lem as follows:
Objective: Minimize cost 1in cents2 = 2X1 + 3X2
X2 = number of pounds of brand Z feed purchased
X1 = number of pounds of brand Y feed purchased Pounds of brand
P
o
u
n
d
s
o
f
b
ra
n
d
0
X1
X2
5 10 15 20
Y
Z
5
10
15
20
b
Feasible region
Ingredient C constraint
Ingredient B constraint
Ingredient A constraint
c
a
� FIGURE B.11 Feasible Region for the Holiday Meal Turkey
Ranch Problem

Bibliography
Bard, J. F. “Staff Scheduling in High Volume Services with
Downgrading.” IIE Transactions 36 (October 2004): 985.
Brown, G., R. F. Dell, and A. M. Newman. “Optimizing Military
Capital Planning.” Interfaces 34, no. 6 (November–December
2004): 415–425.
daSilva, C. G., et al. “An Interactive Decision Support System for
an Aggregate Planning Production Model.” Omega 34 (April
2006): 167.
Denton, Brian T. “AusWest Timbers Uses an Optimization Model
to Improve Its Manufacturing Process.” Interfaces 38, no. 4
(July–August 2008): 341–344.
Duran, G., et al. “Scheduling the Chilean Soccer League by Integer
Programming.” Interfaces 37, no. 6 (November–December
2007): 539–555.
Harrod, Steven. “A Spreadsheet-Based, Matrix Formulation Linear
Programming Lesson.” Decision Sciences Journal of
Innovative Education 7, no. 1 (January 2009): 249.
Martin, C. H. “Ohio University’s College of Business Uses Integer
Programming to Schedule Classes.” Interfaces 34
(November–December 2004): 460–465.
Neureuther, B. D., G. G. Polak, and N. R. Sanders. “A Hierarchical
Production Plan for a Make-to-Order Steel Fabrication Plant.”
Production Planning & Control 15 (April 2004): 324.
Pasupathy, K., and A. Medina-Borja. “Integrating Excel, Access,
and Visual Basic to Deploy Performance Measurement and
Evaluation at the American Red Cross.” Interfaces 38, no. 4
(July–August 2008): 324–340.
Render, B., R. M. Stair, and Michael Hanna. Quantitative Analysis
for Management, 10th ed. Upper Saddle River, NJ: Prentice
Hall (2009).
Render, B., R. M. Stair, and R. Balakrishman. Managerial
Decision Modeling with Spreadsheets, 2nd ed. Upper Saddle
River, NJ: Prentice Hall (2007).
Sodhi, M. S., and S. Norri. “A Fast and Optimal Modeling
Approach Applied to Crew Rostering at London
Underground.” Annals of OR 127 (March 2004): 259.
Taylor, Bernard. Introduction to Management Science, 10th ed.
Upper Saddle River, NJ: Prentice Hall (2011).
�Additional Case Studies: Visit www.myomlab.com or www.pearsonhighered.com/heizer for these additional free case studies:
Chase Manhattan Bank: This scheduling case involves finding the optimal number of full-time versus part-time employees at a bank.
Coastal States Chemical: The company must prepare for a shortage of natural gas.
566 PART 4 Quantitative Modules
Pounds of brand
P
o
u
n
d
s
o
f
b
ra
n
d
0
X 1
X2
5 10 15 20 25
Y
Z
5
10
15
20
Feasible region (shaded area)
(X1 = 8.4, X2 = 4.8)
X
1
31.2¢ = 2
+ 3X
2
54¢ = 2X
1 + 3X
2 iso-cost line
Direction of decreasing cost
� FIGURE B.12
Graphical Solution to the
Holiday Meal Turkey
Ranch Problem Using the
Iso-Cost Line
AUTHOR COMMENT
Note that the last line parallel
to the 54¢ iso-cost line
that touches the feasible
region indicates the optimal
corner point.
The iso-cost line approach may be used to solve LP mini-
mization problems such as that of the Holiday Meal Turkey
Ranch. As with iso-profit lines, we need not compute the cost at
each corner point, but instead draw a series of parallel cost lines.
The last cost point to touch the feasible region provides us with the
optimal solution corner.
For example, we start in Figure B.12 by drawing a 54¢ cost
line, namely, Obviously, there are many points54 = 2X1 + 3X2.
in the feasible region that would yield a lower total cost. We pro-
ceed to move our iso-cost line toward the lower left, in a plane par-
allel to the 54¢ solution line. The last point we touch while still in
contact with the feasible region is the same as corner point b of
Figure B.11. It has the coordinates ( ) and an
associated cost of 31.2 cents.
X1 = 8.4, X2 = 4.8

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QUANTITATIVE MODULE
Transportation Models
Module Outline
Transportation Modeling 568
Developing an Initial Solution 570
The Stepping-Stone Method 572
Special Issues in Modeling 575
567

568 PART 4 Quantitative Modules
LO1: Develop an initial solution to a transportation
model with the northwest-corner and
intuitive lowest-cost methods 570
LO2: Solve a problem with the stepping-stone
method 572
Module C Learning Objectives
TRANSPORTATION MODELING
Because location of a new factory, warehouse, or distribution center is a strategic issue with sub-
stantial cost implications, most companies consider and evaluate several locations. With a wide
variety of objective and subjective factors to be considered, rational decisions are aided by a
number of techniques. One of those techniques is transportation modeling.
The transportation models described in this module prove useful when considering alternative
facility locations within the framework of an existing distribution system. Each new potential
plant, warehouse, or distribution center will require a different allocation of shipments, depend-
ing on its own production and shipping costs and the costs of each existing facility. The choice of
a new location depends on which will yield the minimum cost for the entire system.
Transportation modeling finds the least-cost means of shipping supplies from several ori-
gins to several destinations. Origin points (or sources) can be factories, warehouses, car rental
agencies like Avis, or any other points from which goods are shipped. Destinations are any points
that receive goods. To use the transportation model, we need to know the following:
1. The origin points and the capacity or supply per period at each.
2. The destination points and the demand per period at each.
3. The cost of shipping one unit from each origin to each destination.
The transportation model is actually a class of the linear programming models discussed in
Quantitative Module B. As it is for linear programming, software is available to solve transportation
The problem facing rental companies like Avis, Hertz, and National is cross-country travel. Lots of it.
Cars rented in New York end up in Chicago, cars from L.A. come to Philadelphia, and cars from
Boston come to Miami. The scene is repeated in over 100 cities around the U.S. As a result, there
are too many cars in some cities and too few in others. Operations managers have to decide how
many of these rentals should be trucked (by costly auto carriers) from each city with excess
capacity to each city that needs more rentals. The process requires quick action for the most
economical routing; so rental car companies turn to transportation modeling.
Transportation
modeling
An iterative procedure for
solving problems that involves
minimizing the cost of shipping
products from a series of
sources to a series of
destinations.
LO3: Balance a transportation problem 576
LO4: Solve a problem with degeneracy 577

Module C Transportation Models 569
Albuquerque
(300 units
required)
Des Moines
(100 units
capacity)
Evansville
(300 units
capacity)
Cleveland
(200 units
required)
Boston
(200 units
required)
Fort Lauderdale
(300 units
capacity)
� FIGURE C.1
Transportation Problem
� TABLE C.1
Transportation Costs per
Bathtub for Arizona Plumbing
To
From Albuquerque Boston Cleveland
Des Moines $5 $4 $3
Evansville $8 $4 $3
Fort Lauderdale $9 $7 $5
problems. To fully use such programs, though, you need to understand the assumptions that underlie
the model. To illustrate one transportation problem, in this module we look at a company called
Arizona Plumbing, which makes, among other products, a full line of bathtubs. In our example, the
firm must decide which of its factories should supply which of its warehouses. Relevant data for
Arizona Plumbing are presented in Table C.1 and Figure C.1. Table C.1 shows, for example, that it
costs Arizona Plumbing $5 to ship one bathtub from its Des Moines factory to its Albuquerque ware-
house, $4 to Boston, and $3 to Cleveland. Likewise, we see in Figure C.1 that the 300 units required
by Arizona Plumbing’s Albuquerque warehouse may be shipped in various combinations from its
Des Moines, Evansville, and Fort Lauderdale factories.
From
To
Des Moines
$5
Albuquerque
Evansville
$8
Fort Lauderdale
$9
$4
Boston
$4
$7
$3
Cleveland
$3
$5
Factory
capacity
Warehouse
requirement 300 200 200 700
300
300
100
Cleveland
warehouse demand
Cost of shipping 1 unit from Fort
Lauderdale factory to Boston warehouse
Total demand
and total supply
Cell
representing
a possible
source-to-
destination
shipping
assignment
(Evansville
to Cleveland)
Des Moines
capacity
constraint
� FIGURE C.2
Transportation Matrix for
Arizona Plumbing
The first step in the modeling process is to set up a transportation matrix. Its purpose is to
summarize all relevant data and to keep track of algorithm computations. Using the information
displayed in Figure C.1 and Table C.1, we can construct a transportation matrix as shown in
Figure C.2.

570 PART 4 Quantitative Modules
DEVELOPING AN INITIAL SOLUTION
Once the data are arranged in tabular form, we must establish an initial feasible solution to the
problem. A number of different methods have been developed for this step. We now discuss two
of them, the northwest-corner rule and the intuitive lowest-cost method.
The Northwest-Corner Rule
The northwest-corner rule requires that we start in the upper-left-hand cell (or northwest cor-
ner) of the table and allocate units to shipping routes as follows:
1. Exhaust the supply (factory capacity) of each row (e.g., Des Moines: 100) before moving
down to the next row.
2. Exhaust the (warehouse) requirements of each column (e.g., Albuquerque: 300) before mov-
ing to the next column on the right.
3. Check to ensure that all supplies and demands are met.
Example C1 applies the northwest-corner rule to our Arizona Plumbing problem.
LO1: Develop an initial
solution to a transportation
model with the northwest-
corner and intuitive lowest-
cost methods
Northwest-corner rule
A procedure in the
transportation model where one
starts at the upper-left-hand cell
of a table (the northwest corner)
and systematically allocates
units to shipping routes.
EXAMPLE C1 �
The northwest-
corner rule
Arizona Plumbing wants to use the northwest-corner rule to find an initial solution to its problem.
APPROACH � Follow the 3 steps listed above. See Figure C.3.
SOLUTION � To make the initial solution, these five assignments are made:
1. Assign 100 tubs from Des Moines to Albuquerque (exhausting Des Moines’s supply).
2. Assign 200 tubs from Evansville to Albuquerque (exhausting Albuquerque’s demand).
3. Assign 100 tubs from Evansville to Boston (exhausting Evansville’s supply).
4. Assign 100 tubs from Fort Lauderdale to Boston (exhausting Boston’s demand).
5. Assign 200 tubs from Fort Lauderdale to Cleveland (exhausting Cleveland’s demand and
Fort Lauderdale’s supply).
AUTHOR COMMENT
Here are two ways of finding
an initial solution.
From
To
(D) Des Moines
(E) Evansville
(F) Fort Lauderdale
$9
$4 $3
(A)
Albuquerque
(B)
Boston
(C)
Cleveland
$3
Factory
capacity
Warehouse
requirement 300 200 200 700
300
300
100
200
100
100
100
200
Means that the firm is shipping 100
bathtubs from Fort Lauderdale to Boston
$5
$8 $4
$7 $5
� FIGURE C.3
Northwest-Corner Solution to
Arizona Plumbing Problem
� TABLE C.2
Computed Shipping Cost Route
From To Tubs Shipped Cost per Unit Total Cost
D A 100 $5 $ 500
E A 200 8 1,600
E B 100 4 400
F B 100 7 700
F C 200 5
Total: $4,200
$1,000
The total cost of this shipping assignment is $4,200 (see Table C.2).

Module C Transportation Models 571
INSIGHTS � The solution given is feasible because it satisfies all demand and supply constraints.
The northwest-corner rule is easy to use, but it totally ignores costs, and therefore should only be con-
sidered as a starting position.
LEARNING EXERCISE � Does the shipping assignment change if the cost from Des Moines
to Albuquerque increases from $5 per unit to $10 per unit? Does the total cost change? [Answer: This
initial assignment is the same, but cost = $4,700.]
RELATED PROBLEMS � C.1a, C.3a, C.9, C.11, C.12
The Intuitive Lowest-Cost Method
The intuitive method makes initial allocations based on lowest cost. This straightforward
approach uses the following steps:
1. Identify the cell with the lowest cost. Break any ties for the lowest cost arbitrarily.
2. Allocate as many units as possible to that cell without exceeding the supply or demand.
Then cross out that row or column (or both) that is exhausted by this assignment.
3. Find the cell with the lowest cost from the remaining (not crossed out) cells.
4. Repeat steps 2 and 3 until all units have been allocated.
Intuitive method
A cost-based approach to
finding an initial solution to a
transportation problem.
� EXAMPLE C2
The intuitive
lowest-cost
approach
Arizona Plumbing now wants to apply the intuitive lowest-cost approach.
APPROACH � Apply the 4 steps listed above to the data in Figure C.2.
SOLUTION � When the firm uses the intuitive approach on the data (rather than the northwest-
corner rule) for its starting position, it obtains the solution seen in Figure C.4.
1D to C2 1E to C2 1E to B2 1F to A2
The total cost of this approach = $311002 + $311002 + $412002 + $913002 = $4,100.
INSIGHT � This method’s name is appropriate as most people find it intuitively correct to include
costs when making an initial assignment.
LEARNING EXERCISE � If the cost per unit from Des Moines to Cleveland is not
$3, but rather $6, does this initial solution change? [Answer: Yes, now
Others unchanged at zero. Total cost stays the same.]
RELATED PROBLEMS � C.1b, C.2, C.3b
E – B = 100, E – C = 200, F – A = 300.
D – B = 100, D – C = 0,
While the likelihood of a minimum-cost solution does improve with the intuitive method, we
would have been fortunate if the intuitive solution yielded the minimum cost. In this case, as in the
northwest-corner solution, it did not. Because the northwest-corner and the intuitive lowest-cost
� FIGURE C.4 Intuitive Lowest-Cost Solution to Arizona Plumbing Problem
From
To
(D) Des Moines
(E) Evansville
(F) Fort Lauderdale
Warehouse requirement
(A)
Albuquerque
(B)
Boston
(C)
Cleveland
300 200 200 700
300
300
100
100
100
200
$9
$8
$5 $4
$4
$7
$3
$3
$5
300
Factory
capacity
Second, cross out column C after
entering 100 units in this $3 cell
because column C is satisfied.
First, cross out top row (D) after
entering 100 units in $3 cell
because row D is satisfied.
Finally, enter 300 units in the only
remaining cell to complete
the allocations.
Third, cross out row E and column B after
entering 200 units in this $4 cell because
a total of 300 units satisfies row E and
column B.

572 PART 4 Quantitative Modules
Stepping-stone method
An iterative technique for
moving from an initial feasible
solution to an optimal solution
in the transportation method.
approaches are meant only to provide us with a starting point, we often will have to employ an
additional procedure to reach an optimal solution.
THE STEPPING-STONE METHOD
The stepping-stone method will help us move from an initial feasible solution to an optimal solu-
tion. It is used to evaluate the cost effectiveness of shipping goods via transportation routes not
currently in the solution. When applying it, we test each unused cell, or square, in the transporta-
tion table by asking: What would happen to total shipping costs if one unit of the product (for
example, one bathtub) was tentatively shipped on an unused route? We conduct the test as follows:
1. Select any unused square to evaluate.
2. Beginning at this square, trace a closed path back to the original square via squares that are
currently being used (only horizontal and vertical moves are permissible). You may, how-
ever, step over either an empty or an occupied square.
3. Beginning with a plus ( ) sign at the unused square, place alternating minus signs and plus
signs on each corner square of the closed path just traced.
4. Calculate an improvement index by first adding the unit-cost figures found in each square
containing a plus sign and then by subtracting the unit costs in each square containing a
minus sign.
5. Repeat steps 1 through 4 until you have calculated an improvement index for all unused
squares. If all indices computed are greater than or equal to zero, you have reached an optimal
solution. If not, the current solution can be improved further to decrease total shipping costs.
Example C3 illustrates how to use the stepping-stone method to move toward an optimal solu-
tion. We begin with the northwest-corner initial solution developed in Example C1.
+
LO2: Solve a problem with
the stepping-stone method
EXAMPLE C3 �
Checking unused
routes with the
stepping-stone
method
Arizona Plumbing wants to evaluate unused shipping routes.
APPROACH � Start with Example C1’s Figure C.3 and follow the 5 steps listed above. As you
can see, the four currently unassigned routes are Des Moines to Boston, Des Moines to Cleveland,
Evansville to Cleveland, and Fort Lauderdale to Albuquerque.
SOLUTION � Steps 1 and 2. Beginning with the Des Moines–Boston route, first trace a closed
path using only currently occupied squares (see Figure C.5). Place alternating plus and minus signs in
the corners of this path. In the upper-left square, for example, we place a minus sign because we have
subtracted 1 unit from the original 100. Note that we can use only squares currently used for shipping
to turn the corners of the route we are tracing. Hence, the path Des Moines–Boston to Des
Moines–Albuquerque to Fort Lauderdale–Albuquerque to Fort Lauderdale–Boston to Des
Moines–Boston would not be acceptable because the Fort Lauderdale–Albuquerque square is empty. It
turns out that only one closed route exists for each empty square. Once this one closed path is identi-
fied, we can begin assigning plus and minus signs to these squares in the path.
Step 3. How do we decide which squares get plus signs and which squares get minus signs? The
answer is simple. Because we are testing the cost-effectiveness of the Des Moines–Boston shipping
route, we try shipping 1 bathtub from Des Moines to Boston. This is 1 more unit than we were sending
between the two cities, so place a plus sign in the box. However, if we ship 1 more unit than before
from Des Moines to Boston, we end up sending 101 bathtubs out of the Des Moines factory. Because
the Des Moines factory’s capacity is only 100 units, we must ship 1 bathtub less from Des Moines to
Albuquerque. This change prevents us from violating the capacity constraint.
To indicate that we have reduced the Des Moines–Albuquerque shipment, place a minus sign in its
box. As you continue along the closed path, notice that we are no longer meeting our Albuquerque
warehouse requirement for 300 units. In fact, if we reduce the Des Moines–Albuquerque shipment to
99 units, we must increase the Evansville–Albuquerque load by 1 unit, to 201 bathtubs. Therefore,
place a plus sign in that box to indicate the increase. You may also observe that those squares in which
we turn a corner (and only those squares) will have plus or minus signs.
Finally, note that if we assign 201 bathtubs to the Evansville–Albuquerque route, then we must
reduce the Evansville–Boston route by 1 unit, to 99 bathtubs, to maintain the Evansville factory’s
capacity constraint of 300 units. To account for this reduction, we thus insert a minus sign in the
Evansville–Boston box. By so doing, we have balanced supply limitations among all four routes on the
closed path.

Module C Transportation Models 573
Step 4. Compute an improvement index for the Des Moines–Boston route by adding unit costs in
squares with plus signs and subtracting costs in squares with minus signs.
This means that for every bathtub shipped via the Des Moines–Boston route, total transportation costs
will increase by $3 over their current level.
Let us now examine the unused Des Moines–Cleveland route, which is slightly more difficult to
trace with a closed path (see Figure C.6). Again, notice that we turn each corner along the path only at
squares on the existing route. Our path, for example, can go through the Evansville–Cleveland box but
cannot turn a corner; thus we cannot place a plus or minus sign there. We may use occupied squares
only as stepping-stones:
Des Moines – Cleveland index = $3 – $5 + $8 – $4 + $7 – $5 = + $4
Des Moines – Boston index = $4 – $5 + $8 – $4 = + $3
Result of proposed shift in allocation = 1 $4 – 1 $5 + 1 $8 – 1 $4 = + $3
Evaluation of Des Moines to Boston square
From
To
(D) Des Moines
(E) Evansville
(F) Fort Lauderdale
$9
$3
(A)
Albuquerque
(B)
Boston
(C)
Cleveland
$3
Factory
capacity
Warehouse
requirement 300 200 200 700
300
300
100
200
100
100
100
200
$5
$8 $4
$7 $5
$4Start
201
200
$8 99 $4
99
$5 1 $4100
100
� � ��
� FIGURE C.5
Stepping-Stone Evaluation
of Alternative Routes for
Arizona Plumbing
From
To
(D) Des Moines
(E) Evansville
(F) Fort Lauderdale
$9
(A)
Albuquerque
(B)
Boston
(C)
Cleveland
$3
Warehouse
requirement 300 200 200 700
300
300
100
200
100
100
100
200
$5
$7 $5
$4 Start $3
$8 $4
Factory
capacity
� FIGURE C.6
Testing Des Moines to
Cleveland

574 PART 4 Quantitative Modules
Again, opening this route fails to lower our total shipping costs.
Two other routes can be evaluated in a similar fashion:
INSIGHT � Because this last index is negative, we can realize cost savings by using the (currently
unused) Fort Lauderdale–Albuquerque route.
LEARNING EXERCISE � What would happen to total cost if Arizona used the shipping route
from Des Moines to Cleveland? [Answer: Total cost of the current solution would increase by $400.]
RELATED PROBLEMS � C.1c, C.3c, C.7, C.8, C.10, C.13, C.15, C.16, C.17
EXCEL OM Data File ModCExC3.xls can be found at www.pearsonhighered.com/heizer.
1Closed path = FA – FB + EB – EA2
Fort Lauderdale – Albuquerque index = $9 – $7 + $4 – $8 = – $2
1Closed path = EC – EB + FB – FC2
Evansville – Cleveland index = $3 – $4 + $7 – $5 = + $1
In Example C3, we see that a better solution is indeed possible because we can calculate a nega-
tive improvement index on one of our unused routes. Each negative index represents the amount
by which total transportation costs could be decreased if one unit was shipped by the
source–destination combination. The next step, then, is to choose that route (unused square) with
the largest negative improvement index. We can then ship the maximum allowable number of
units on that route and reduce the total cost accordingly.
What is the maximum quantity that can be shipped on our new money-saving route? That
quantity is found by referring to the closed path of plus signs and minus signs drawn for the route
and then selecting the smallest number found in the squares containing minus signs. To obtain a
new solution, we add this number to all squares on the closed path with plus signs and subtract it
from all squares on the path to which we have assigned minus signs.
One iteration of the stepping-stone method is now complete. Again, of course, we must test to
see if the solution is optimal or whether we can make any further improvements. We do this by
evaluating each unused square, as previously described. Example C4 continues our effort to help
Arizona Plumbing arrive at a final solution.
EXAMPLE C4 �
Improvement
indices
Arizona Plumbing wants to continue solving the problem.
APPROACH � Use the improvement indices calculated in Example C3. We found in Example C3
that the largest (and only) negative index is on the Fort Lauderdale–Albuquerque route (which is the
route depicted in Figure C.7).
SOLUTION � The maximum quantity that may be shipped on the newly opened route, Fort
Lauderdale–Albuquerque (FA), is the smallest number found in squares containing minus signs—
in this case, 100 units. Why 100 units? Because the total cost decreases by $2 per unit shipped, we
know we would like to ship the maximum possible number of units. Previous stepping-stone calcu-
lations indicate that each unit shipped over the FA route results in an increase of 1 unit shipped
from Evansville (E) to Boston (B) and a decrease of 1 unit in amounts shipped both from F to B
(now 100 units) and from E to A (now 200 units). Hence, the maximum we can ship over the FA
route is 100 units. This solution results in zero units being shipped from F to B. Now we take the
following four steps:
1. Add 100 units (to the zero currently being shipped) on route FA.
2. Subtract 100 from route FB, leaving zero in that square (though still balancing the row total for F).
3. Add 100 to route EB, yielding 200.
4. Finally, subtract 100 from route EA, leaving 100 units shipped.
Note that the new numbers still produce the correct row and column totals as required. The new solu-
tion is shown in Figure C.8.
Total shipping cost has been reduced by (100 units) ($2 saved per unit) = $200 and
is now $4,000. This cost figure, of course, can also be derived by multiplying the cost of
*

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shipping each unit by the number of units transported on its respective route, namely:
INSIGHT � Looking carefully at Figure C.8, however, you can see that it, too, is not yet
optimal. Route EC (Evansville–Cleveland) has a negative cost improvement index of –$1.
LEARNING EXERCISE � Find the final solution for this route on your own. [Answer:
Programs C.1 and C.2, at the end of this module, provide an Excel OM solution.]
RELATED PROBLEMS � C.4, C.6, C.7, C.8, C.10, C.13, C.15, C.16, C.17
Closed path = EC – EA + FA – FC.
1001$52 + 1001$82 + 2001$42 + 1001$92 + 2001$52 = $4,000.
From
To
(D) Des Moines
(E) Evansville
(F) Fort Lauderdale
Warehouse demand
(A)
Albuquerque
(B)
Boston
(C)
Cleveland
$3
300 200 200 700
300
300
100
200
100
100
200
$5
$7 $5
$4 $3
$8 $4
$9
100
Factory
capacity
� FIGURE C.7
Transportation Table:
Route FA
From
To
(D) Des Moines
(E) Evansville
(F) Fort Lauderdale
Warehouse demand
(A)
Albuquerque
(B)
Boston
(C)
Cleveland
$3
300 200 200 700
300
300
100100
100
100
200
200
$5
$7 $5
$4 $3
$8 $4
$9
Factory
capacity
� FIGURE C.8
Solution at Next Iteration
(Still Not Optimal)
AUTHOR COMMENT
FA has a negative index:
FA (�9) to FB (�7) to EB
(�4) to EA (�8) � �$2
SPECIAL ISSUES IN MODELING
Demand Not Equal to Supply
A common situation in real-world problems is the case in which total demand is not equal to
total supply. We can easily handle these so-called unbalanced problems with the solution pro-
cedures that we have just discussed by introducing dummy sources or dummy destinations.
If total supply is greater than total demand, we make demand exactly equal the surplus by cre-
ating a dummy destination. Conversely, if total demand is greater than total supply, we intro-
duce a dummy source (factory) with a supply equal to the excess of demand. Because these
units will not in fact be shipped, we assign cost coefficients of zero to each square on the
dummy location. In each case, then, the cost is zero. Example C5 demonstrates the use of a
dummy destination.
Dummy sources
Artificial shipping source points
created when total demand is
greater than total supply to
effect a supply equal to the
excess of demand over supply.
AUTHOR COMMENT
Let’s look at what happens
when two issues arise:
an unbalanced problem
and degeneracy.
Dummy destinations
Artificial destination points
created when the total supply is
greater than the total demand;
they serve to equalize the total
demand and supply.
Module C Transportation Models 575

576 PART 4 Quantitative Modules
EXAMPLE C5 �
Adjusting for unequal
supply and demand
with a dummy column
Arizona Plumbing decides to increase the production in its Des Moines factory from 100 tubs to 250
bathtubs. This increases supply over demand and creates an unbalanced problem.
APPROACH � To reformulate this unbalanced problem, we refer back to the data presented in
Example C1 and present the new matrix in Figure C.9. First, we use the northwest-corner rule to find
the initial feasible solution. Then, once the problem is balanced, we can proceed to the solution in the
normal way.
SOLUTION �
Total cost = 2501$52 + 501$82 + 2001$42 + 501$32 + 1501$52 + 150102 = $3,350
From
To
(D) Des Moines
(E) Evansville
(F) Fort Lauderdale
(A)
Albuquerque
(B)
Boston
(C)
Cleveland
$3
Dummy
50
250
200
150
$7 $5
$4
$8 $4
$9
50
Factory
capacity
Warehouse
requirement 300 200 200 850
300
300
250
New
Des Moines
capacity
150
150
$5 $3
0
0
0
� FIGURE C.9
Northwest-Corner Rule
with Dummy
INSIGHT � Excel OM and POM for Windows software automatically perform the balance for
you. But if you are solving by hand, be careful to decide first whether a dummy row (source) or a
dummy column (destination) is needed.
LEARNING EXERCISE � Arizona instead increases Des Moines’s capacity to 350 tubs.
Does the initial northwest-corner solution change? [Answer: Yes, now
]
RELATED PROBLEMS � C.5, C.9, C.14
EXCEL OM Data File ModCExC5.xls can be found at www.pearsonhighered.com/heizer.
F – C = 50, F – Dummy = 250. Cost = $3,000.E – B = 150, E – C = 150,
D – A = 300, D – B = 50,
Degeneracy
To apply the stepping-stone method to a transportation problem, we must observe a rule about
the number of shipping routes being used: The number of occupied squares in any solution (ini-
tial or later) must be equal to the number of rows in the table plus the number of columns minus 1.
Solutions that do not satisfy this rule are called degenerate.
Degeneracy occurs when too few squares or shipping routes are being used. As a result, it
becomes impossible to trace a closed path for one or more unused squares. The Arizona
Plumbing problem we just examined was not degenerate, as it had 5 assigned routes (3 rows or
factories columns or warehouses ).
To handle degenerate problems, we must artificially create an occupied cell: That is, we place
a zero or a very small amount (representing a fake shipment) in one of the unused squares and then
treat that square as if it were occupied. Remember that the chosen square must be in such a posi-
tion as to allow all stepping-stone paths to be closed. We illustrate this procedure in Example C6.
– 1+ 3
LO3: Balance a
transportation problem
Degeneracy
An occurrence in transportation
models in which too few
squares or shipping routes are
being used, so that tracing a
closed path for each unused
square becomes impossible.
Martin Shipping Company has three warehouses from which it supplies its three major retail customers
in San Jose. Martin’s shipping costs, warehouse supplies, and customer demands are presented in the
transportation table in Figure C.10. It wants to make an initial shipping assignment.
EXAMPLE C6 �
Dealing with
degeneracy

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Module C Transportation Models 577
APPROACH � To make the initial shipping assignments in that table, we apply the northwest-
corner rule.
SOLUTION � The initial solution is degenerate because it violates the rule that the number of
used squares must equal the number of rows plus the number of columns minus 1. To correct the prob-
lem, we may place a zero in the unused square that permits evaluation of all empty cells. Some exper-
imenting may be needed because not every cell will allow tracing a closed path for the remaining cells.
Also, we want to avoid placing the 0 in a cell that has a negative sign in a closed path. No reallocation
will be possible if we do this.
For this example, we try the empty square that represents the shipping route from Warehouse 2 to
Customer 1. Now we can close all stepping-stone paths and compute improvement indices.
INSIGHT � We must always check the unused squares in a transportation solution to make sure
the Number of rows Number of columns 1 = Number of occupied squares.
LEARNING EXERCISES � Explain why the “zero” cannot be placed in the Warehouse
3–Customer 1 square. [Answer: The route, Warehouse 1–Customer 2, cannot be closed now.] Why did
this problem become degenerate? [Answer: Our first assignment, 100 units to the Warehouse
1–Customer 1 cell, fully met both the first row and first columns’ needs in one cell.]
RELATED PROBLEMS � C.11, C.12
EXCEL OM Data File ModCExC6.xls can be found at www.pearsonhighered.com/heizer.
-+
From
To
Warehouse 1
Warehouse 2
Warehouse 3
Customer demand
Customer
1
Customer
2
Customer
3
Warehouse
supply
100 100 100 300
80
120
100
0
100
100
80
$8
$10 $7
$2 $6
$10 $9
$7
20
$9
� FIGURE C.10
Martin’s Northwest-Corner
Rule
LO4: Solve a problem with
degeneracy
The transportation model, a form of linear programming, is
used to help find the least-cost solutions to systemwide ship-
ping problems. The northwest-corner method (which begins
in the upper-left corner of the transportation table) or the
intuitive lowest-cost method may be used for finding an initial
feasible solution. The stepping-stone algorithm is then used
for finding optimal solutions. Unbalanced problems are
those in which the total demand and total supply are not
equal. Degeneracy refers to the case in
which the number of rows + the num-
ber of columns – 1 is not equal to the
number of occupied squares. The trans-
portation model approach is one of the four
location models described earlier in Chapter
8. Additional solution techniques are presented in Tutorial 4 at
our free website, www.pearsonhighered.com/heizer.
MODULE SUMMARY
Key Terms
Transportation modeling (p. 568)
Northwest-corner rule (p. 570)
Intuitive method (p. 571)
Stepping-stone method (p. 572)
Dummy sources (p. 575)
Dummy destinations (p. 575)
Degeneracy (p. 576)

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www.pearsonhighered.com/heizer

578 PART 4 Quantitative Modules
Using Software to Solve Transportation Problems
Excel, Excel OM, and POM for Windows may all be used to solve transportation problems. Excel uses
Solver, which requires that you enter your own constraints. Excel OM also uses Solver but is prestruc-
tured so that you need enter only the actual data. POM for Windows similarly requires that only demand
data, supply data, and shipping costs be entered.
X Using Excel OM
Excel OM’s Transportation module uses Excel’s built-in Solver routine to find optimal solutions to trans-
portation problems. Program C.1 illustrates the input data (from Arizona Plumbing) and total-cost formu-
las. To reach an optimal solution, we must go to Excel’s Tools bar, request Solver, then select Solve. In
Excel 2007, Solver is in the Analysis section of the Data tab. The output appears in Program C.2.
� PROGRAM C.1 Excel OM Input Screen and Formulas, Using Arizona Plumbing Data
P Using POM for Windows
The POM for Windows Transportation module can solve both maximization and minimization problems
by a variety of methods. Input data are the demand data, supply data, and unit shipping costs. See
Appendix IV for further details.
Enter the origin and
destination names,
the shipping costs,
and the total supply
and demand figures.
Our target cell is the total cost cell (B21), which
we wish to minimize by changing the shipment
cells (B16 through D18). The constraints ensure
that the number shipped is equal to the number
demanded and that we don’t ship more units
than we have on hand.
The total shipments to and from each
location are calculated here.
These are the cells in which Solver will place the shipments.
In Excel 2007, Solver is in the Analysis section of the Data tab.
In the prior Excel version Solver is on the Tools menu. If Solver
is not available please visit www.prenhall.com/weiss.
Nonnegativity
constaints have been
added through the
Options button
The total cost is created here by
multiplying the data table by the
shipment table using the
SUMPRODUCT function.

www.prenhall.com/weiss

Module C Transportation Models 579
� PROGRAM C.2 Output from Excel OM with Optimal Solution to Arizona Plumbing Problem
Solved Problems Virtual Office Hours help is available at www.myomlab.com
pertinent production and distribution costs as well as plant capac-
ities and distribution demands.
Which of the new locations, in combination with the exist-
ing plants and distribution centers, yields a lower cost for the
firm?
� SOLVED PROBLEM C.1
� TABLE C.3
Production Costs, Distribution
Costs, Plant Capabilities, and
Market Demands for Williams
Auto Top Carriers
To Distribution Centers
Los New Normal Unit
From Plants Angeles York Production Production Cost
Existing plants
Atlanta $8 $5 600 $6
Tulsa $4 $7 900 $5
Proposed locations
New Orleans $5 $6 500 $4 (anticipated)
Houston $4 $6a 500 $3 (anticipated)
Forecast demand 800 1,200 2,000
aIndicates distribution cost (shipping, handling, storage) will be $6 per carrier between Houston and New York.
� SOLUTION
To answer this question, we must solve two transportation prob-
lems, one for each combination. We will recommend the location
that yields a lower total cost of distribution and production in
combination with the existing system.
We begin by setting up a transportation table that represents
the opening of a third plant in New Orleans (see Figure C.11).
Then we use the northwest-corner method to find an initial solution.
The total cost of this first solution is $23,600. Note that the cost of
each individual “plant-to-distribution-center” route is found by
adding the distribution costs (in the body of Table C.3) to the
respective unit production costs (in the right-hand column of
Table C.3). Thus, the total production-plus-shipping cost of one
Williams Auto Top Carriers currently maintains plants in Atlanta
and Tulsa to supply auto top carriers to distribution centers in Los
Angeles and New York. Because of expanding demand, Williams
has decided to open a third plant and has narrowed the choice to
one of two cities—New Orleans and Houston. Table C.3 provides

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580 PART 4 Quantitative Modules
From
To
Atlanta
Tulsa
New Orleans
Demand
Los Angeles New York
Production
capacity
800 1,200 2,000
500
900
600
200 700
$14
500
$9
$9
$11
$12
$10
600
� FIGURE C.11
Initial Williams
Transportation Table
for New Orleans
Because the firm can save $6 for every unit shipped from Atlanta to
New York, it will want to improve the initial solution and send as
many units as possible (600, in this case) on this currently unused
route (see Figure C.12). You may also want to confirm that the total
cost is now $20,000, a savings of $3,600 over the initial solution.
Next, we must test the two unused routes to see if their
improvement indices are also negative numbers:
Index for Atlanta–Los Angeles
Index for New Orleans–Los Angeles
Because both indices are greater than zero, we have already
reached our optimal solution for the New Orleans location. If
Williams elects to open the New Orleans plant, the firm’s total
production and distribution cost will be $20,000.
This analysis, however, provides only half the answer to
Williams’s problem. The same procedure must still be followed to
determine the minimum cost if the new plant is built in Houston.
Determining this cost is left as a homework problem. You can help
provide complete information and recommend a solution by solving
Problem C.8 (in the Lecture Guide & Activities Manual ).
= $9 – $10 + $12 – $9 = $2
= $14 – $11 + $12 – $9 = $6
auto top carrier from Atlanta to Los Angeles is $14 ($8 for shipping
plus $6 for production).
Is this initial solution (in Figure C.11) optimal? We can use the
stepping-stone method to test it and compute improvement indices
for unused routes:
Improvement index for Atlanta–New York route
Improvement index for New Orleans–Los Angeles route
= $2
– $91Tulsa – Los Angeles2
+ $121Tulsa – New York2
– $10 1New Orleans – New York2
= + $9 1New Orleans – Los Angeles2
= – $6
+ $91Tulsa – Los Angeles2 – $121Tulsa – New York2
= + $11 1Atlanta – New York2 – $14 1Atlanta – Los Angeles2
= $23,600
= $8,400 + $1,800 + $8,400 + $5,000
+ 1700 units * $122 + 1500 units * $102
Total cost = 1600 units * $142 + 1200 units * $92
From
To
Atlanta
Tulsa
New Orleans
Demand
Los Angeles New York
800 1,200 2,000
500
900
600
800 100
$14
500
600
$9
$9
$11
$12
$10
Production
capacity
� FIGURE C.12
Improved Transportation
Table for Williams

Module C Transportation Models 581
� SOLVED PROBLEM C.2
structure the same decision analysis using linear programming (LP),
which we explained in detail in Quantitative Module B.
� SOLUTION
Using the data in Figure C.11 (p. 580), we write the objective function and constraints as follows:
Subject to:
XAtl,NY + XTul,NY + XNO,NY Ú 1200 1New York demand constraint2
XAtl,LA + XTul,LA + XNO,LA Ú 800 1Los Angeles demand constraint2
XNO,LA + XNO,NY … 500 1production capacity at New Orleans2
XTul,LA + XTul,NY … 900 1production capacity at Tulsa2
XAtl,LA + XAtl,NY … 600 1production capacity at Atlanta2
Minimize total cost = $14XAtl,LA + $11XAtl,NY + $9XTul,LA + $12XTul,NY + $9XNO,LA + $10XNO,NY
Bibliography
Balakrishnan, R., B. Render, and R. M. Stair. Managerial Decision
Modeling with Spreadsheets, 2nd. ed. Upper Saddle River, NJ:
Prentice Hall (2007).
Drezner, Z. Facility Location: A Survey of Applications and
Methods. Secaucus, NJ: Springer-Verlag (1995).
Koksalan, M., and H. Sural. “Efes Beverage Group Makes
Location and Distribution Decisions for Its Malt Plants.”
Interfaces 29 (March–April 1999): 89–103.
Ping, J., and K. F. Chu. “A Dual-Matrix Approach to the
Transportation Problem.” Asia-Pacific Journal of Operations
Research 19 (May 2002): 35–46.
Render, B., R. M. Stair, and M. Hanna. Quantitative Analysis for
Management, 10th ed. Upper Saddle River, NJ: Prentice Hall
(2009).
Schmenner, R. W. “Look Beyond the Obvious in Plant Location.”
Harvard Business Review 57, no. 1 (January–February 1979):
126–132.
Taylor, B. Introduction to Management Science, 10th ed., Upper
Saddle River, NJ: Prentice Hall (2011).
�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
Consolidated Bottling (B): This case involves determining where to add bottling capacity.
In Solved Problem C.1, we examined the Williams Auto Top Carriers
problem by using a transportation table. An alternative approach is to

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Module Outline
Queuing Theory 584
Characteristics of a
Waiting-Line System 585
Queuing Costs 589
The Variety of Queuing Models 590
Other Queuing Approaches 601
QUANTITATIVE MODULE
Waiting-Line Models
583

584 PART 4 Quantitative Modules
Paris’s EuroDisney,
Tokyo’s Disney Japan, and
the U.S.’s Disney World
and Disneyland all have
one feature in common—
long lines and seemingly
endless waits. However,
Disney is one of the
world’s leading companies
in the scientific analysis of
queuing theory. It analyzes
queuing behaviors and
can predict which rides
will draw what length
crowds. To keep visitors
happy, Disney makes lines
appear to be constantly
moving forward, entertains
people while they wait,
and posts signs telling
visitors how many minutes
until they reach each ride.
LO1: Describe the characteristics
of arrivals, waiting lines, and
service systems 585
LO2: Apply the single-channel queuing
model equations 590
LO3: Conduct a cost analysis for
a waiting line 593
Module D Learning Objectives
LO4: Apply the multiple-channel
queuing model formulas 593
LO5: Apply the constant-service-time
model equations 597
LO6: Perform a limited-population
model analysis 599
QUEUING THEORY
The body of knowledge about waiting lines, often called queuing theory, is an important part of
operations and a valuable tool for the operations manager. Waiting lines are a common situa-
tion—they may, for example, take the form of cars waiting for repair at a Midas Muffler Shop,
copying jobs waiting to be completed at a Kinko’s print shop, or vacationers waiting to enter the
Space Mountain ride at Disney. Table D.1 lists just a few OM uses of waiting-line models.
Waiting-line models are useful in both manufacturing and service areas. Analysis of queues in
terms of waiting-line length, average waiting time, and other factors helps us to understand ser-
vice systems (such as bank teller stations), maintenance activities (that might repair broken
machinery), and shop-floor control activities. Indeed, patients waiting in a doctor’s office and
broken drill presses waiting in a repair facility have a lot in common from an OM perspective.
Both use human and equipment resources to restore valuable production assets (people and
machines) to good condition.
�TABLE D.1
Common Queuing Situations
Situation Arrivals in Queue Service Process
Supermarket Grocery shoppers Checkout clerks at cash register
Highway toll booth Automobiles Collection of tolls at booth
Doctor’s office Patients Treatment by doctors and nurses
Computer system Programs to be run Computer processes jobs
Telephone company Callers Switching equipment forwards calls
Bank Customers Transactions handled by teller
Machine maintenance Broken machines Repair people fix machines
Harbor Ships and barges Dock workers load and unload
Queuing theory
A body of knowledge about
waiting lines.
Waiting line (queue)
Items or people in a line
awaiting service.

Module D Waiting-Line Models 585
CHARACTERISTICS OF A WAITING-LINE SYSTEM
In this section, we take a look at the three parts of a waiting-line, or queuing, system (as shown
in Figure D.1):
1. Arrivals or inputs to the system: These have characteristics such as population size, behav-
ior, and a statistical distribution.
2. Queue discipline, or the waiting line itself: Characteristics of the queue include whether it is
limited or unlimited in length and the discipline of people or items in it.
3. The service facility: Its characteristics include its design and the statistical distribution of
service times.
We now examine each of these three parts.
Arrival Characteristics
The input source that generates arrivals or customers for a service system has three major
characteristics:
1. Size of the arrival population
2. Behavior of arrivals
3. Pattern of arrivals (statistical distribution)
Size of the Arrival (Source) Population Population sizes are considered either unlim-
ited (essentially infinite) or limited (finite). When the number of customers or arrivals on hand
at any given moment is just a small portion of all potential arrivals, the arrival population is
considered unlimited, or infinite. Examples of unlimited populations include cars arriving at
a big-city car wash, shoppers arriving at a supermarket, and students arriving to register for
classes at a large university. Most queuing models assume such an infinite arrival population.
An example of a limited, or finite, population is found in a copying shop that has, say, eight
copying machines. Each of the copiers is a potential “customer” that may break down and
require service.
Pattern of Arrivals at the System Customers arrive at a service facility either according
to some known schedule (for example, one patient every 15 minutes or one student every half
hour) or else they arrive randomly. Arrivals are considered random when they are independent of
one another and their occurrence cannot be predicted exactly. Frequently in queuing problems,
the number of arrivals per unit of time can be estimated by a probability distribution known as
LO1: Describe the
characteristics of arrivals,
waiting lines, and service
systems
AUTHOR COMMENT
Every queuing system
has 3 parts.
Service
facility
Arrivals to the system
Dave’s
Car Wash
Enter
In the system Exit the system
Exit
1st
St.
3rd
St.
2nd
St.
1st
St.
3rd
St.
2nd
St.
Queue (waiting line)Arrivals
from the
general population . . .
Population of
dirty cars
Ave.
A
Ave.
B
Ave.
C
Ave.
A
Ave.
B
Ave.
D
SW St.
SE St.
NW St.
NE St.
Exit the system
Arrival Characteristics
• Size of arrival population
• Behavior of arrivals
• Statistical distribution of arrivals
Waiting-Line Characteristics Service Characteristics
• Service design
• Statistical distribution of service
• Limited vs. unlimited
• Queue discipline
� FIGURE D.1 Three Parts of a Waiting Line, or Queuing System, at Dave’s Car Wash
Unlimited, or infinite,
population
A queue in which a virtually
unlimited number of people or
items could request the services,
or in which the number of
customers or arrivals on hand at
any given moment is a very small
portion of potential arrivals.
Limited, or finite,
population
A queue in which there are only
a limited number of potential
users of the service.

586 PART 4 Quantitative Modules
the Poisson distribution.1 For any given arrival time (such as 2 customers per hour or 4 trucks
per minute), a discrete Poisson distribution can be established by using the formula:
(D-1)
where probability of x arrivals
number of arrivals per unit of time
average arrival rate
(which is the base of the natural logarithms)
With the help of the table in Appendix II, which gives the value of for use in the Poisson dis-
tribution, these values are easy to compute. Figure D.2 illustrates the Poisson distribution for
and This means that if the average arrival rate is customers per hour, the
probability of 0 customers arriving in any random hour is about 13%, probability of 1 customer is
about 27%, 2 customers about 27%, 3 customers about 18%, 4 customers about 9%, and so on.
The chances that 9 or more will arrive are virtually nil. Arrivals, of course, are not always Poisson
distributed (they may follow some other distribution). Patterns, therefore, should be examined to
make certain that they are well approximated by Poisson before that distribution is applied.
Behavior of Arrivals Most queuing models assume that an arriving customer is a patient
customer. Patient customers are people or machines that wait in the queue until they are served
and do not switch between lines. Unfortunately, life is complicated by the fact that people have
been known to balk or to renege. Customers who balk refuse to join the waiting line because it is
too long to suit their needs or interests. Reneging customers are those who enter the queue but
then become impatient and leave without completing their transaction. Actually, both of these
situations just serve to highlight the need for queuing theory and waiting-line analysis.
Waiting-Line Characteristics
The waiting line itself is the second component of a queuing system. The length of a line can be
either limited or unlimited. A queue is limited when it cannot, either by law or because of physi-
cal restrictions, increase to an infinite length. A small barbershop, for example, will have only a
limited number of waiting chairs. Queuing models are treated in this module under an assump-
tion of unlimited queue length. A queue is unlimited when its size is unrestricted, as in the case
of the toll booth serving arriving automobiles.
A second waiting-line characteristic deals with queue discipline. This refers to the rule by
which customers in the line are to receive service. Most systems use a queue discipline known as
l = 2l = 4.l = 2
e–l
e = 2.7183
l =
x =
P1x2 =
P1x2 =
e–llx
x!
for x = 0, 1, 2, 3, 4, Á
1When the arrival rates follow a Poisson process with mean arrival rate, the time between arrivals follows a negative expo-
nential distribution with mean time between arrivals of The negative exponential distribution, then, is also representative
of a Poisson process but describes the time between arrivals and specifies that these time intervals are completely random.
1>l.
l,
Poisson distribution
A discrete probability
distribution that often describes
the arrival rate in queuing
theory.
0
Distribution for
0.05
P
ro
b
a
b
ili
ty
0.10
0.15
0.20
0.25
2 3 4 5 6 7 8 9
� = 2
PProbability = (x ) =
e �
x !
−� x
X X0
Distribution for
0.05
P
ro
b
a
b
ili
ty
0.10
0.15
0.20
0.25
2 3 4 5 6 7 8 9
� = 4
10 1111
� FIGURE D.2
Two Examples of the Poisson
Distribution for Arrival Times
AUTHOR COMMENT
Notice that even though the
mean arrival rate might be
per hour, there is still a
small chance that as many as
9 customers arrive in an hour.
l = 2

Module D Waiting-Line Models 587
the first-in, first-out (FIFO) rule. In a hospital emergency room or an express checkout line at
a supermarket, however, various assigned priorities may preempt FIFO. Patients who are criti-
cally injured will move ahead in treatment priority over patients with broken fingers or noses.
Shoppers with fewer than 10 items may be allowed to enter the express checkout queue (but are
then treated as first-come, first-served). Computer-programming runs also operate under priority
scheduling. In most large companies, when computer-produced paychecks are due on a specific
date, the payroll program gets highest priority.2
Service Characteristics
The third part of any queuing system are the service characteristics. Two basic properties are
important: (1) design of the service system and (2) the distribution of service times.
Basic Queuing System Designs Service systems are usually classified in terms of their
number of channels (e.g., number of servers) and number of phases (e.g., number of service
stops that must be made). See Figure D.3. A single-channel queuing system, with one server, is
2The term FIFS (first-in, first-served) is often used in place of FIFO. Another discipline, LIFS (last-in, first-served),
also called last-in, first-out (LIFO), is common when material is stacked or piled so that the items on top are used first.
First-in, first-out
(FIFO) rule
A queue discipline in which the
first customers in line receive
the first service.
Single-channel queuing
system
A service system with one line
and one server.
Multichannel, multiphase system
Departures
after service
Queue
Single-channel, single-phase system
Departures
after service
Single-channel, multiphase system
Queue
Queue
Multichannel, single-phase system
Departures
after service
Queue
Arrivals
Arrivals
Arrivals
Arrivals
Some college
registrations
Most bank and post
office service windows
A family
dentist’s office
Example
A McDonald’s
dual-window
drive-through
Phase 1
service
facility
Channel 1
Phase 1
service
facility
Channel 2
Phase 2
service
facility
Channel 1
Phase 2
service
facility
Channel 2
Service
facility
Departures
after service
Service
facility
Channel 3
Service
facility
Channel 2
Service
facility
Channel 1
Phase 1
service
facility
Phase 2
service
facility
� FIGURE D.3 Basic Queuing System Designs

588 PART 4 Quantitative Modules
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00
0.1
00
P
ro
b
a
b
ili
ty
t
h
a
t
se
rv
ic
e
t
im
e
≥
t
Time t (hours)
Probability that service time is greater than t = e–μt for t ≥ 0
Average service rate, or average number served per time unit (μ) = 3
customers per hour ⇒ Average service time = 20 minutes (or 1/3 hour)
per customer
μ = Average service rate (average number served per time unit)
e = 2.7183 (the base of natural logarithms)
Average service rate (μ) =
1 customer per hour
� FIGURE D.4
Two Examples of the
Negative Exponential
Distribution for Service
Times
AUTHOR COMMENT
Although Poisson and
exponential distributions are
commonly used to describe
arrival rates and service
times, other probability
distributions are valid in
some cases.
typified by the drive-in bank with only one open teller. If, on the other hand, the bank has several
tellers on duty, with each customer waiting in one common line for the first available teller, then
we would have a multiple-channel queuing system. Most banks today are multichannel service
systems, as are most large barbershops, airline ticket counters, and post offices.
In a single-phase system, the customer receives service from only one station and then exits
the system. A fast-food restaurant in which the person who takes your order also brings your food
and takes your money is a single-phase system. So is a driver’s license agency in which the per-
son taking your application also grades your test and collects your license fee. However, say the
restaurant requires you to place your order at one station, pay at a second, and pick up your food
at a third. In this case, it is a multiphase system. Likewise, if the driver’s license agency is large
or busy, you will probably have to wait in one line to complete your application (the first service
stop), queue again to have your test graded, and finally go to a third counter to pay your fee. To
help you relate the concepts of channels and phases, Figure D.3 presents these four possible
channel configurations.
Service Time Distribution Service patterns are like arrival patterns in that they may be either
constant or random. If service time is constant, it takes the same amount of time to take care of
each customer. This is the case in a machine-performed service operation such as an automatic car
wash. More often, service times are randomly distributed. In many cases, we can assume that ran-
dom service times are described by the negative exponential probability distribution.
Figure D.4 shows that if service times follow a negative exponential distribution, the probabil-
ity of any very long service time is low. For example, when an average service time is 20 minutes
(or three customers per hour), seldom if ever will a customer require more than 1.5 hours in the
service facility. If the mean service time is 1 hour, the probability of spending more than 3 hours
in service is quite low.
Measuring a Queue’s Performance
Queuing models help managers make decisions that balance service costs with waiting-line
costs. Queuing analysis can obtain many measures of a waiting-line system’s performance,
including the following:
1. Average time that each customer or object spends in the queue.
2. Average queue length.
3. Average time that each customer spends in the system (waiting time plus service time).
4. Average number of customers in the system.
5. Probability that the service facility will be idle.
6. Utilization factor for the system.
7. Probability of a specific number of customers in the system.
Multiple-channel
queuing system
A service system with one
waiting line but with several
servers.
Single-phase system
A system in which the customer
receives service from only one
station and then exits the
system.
Multiphase system
A system in which the customer
receives services from several
stations before exiting the
system.
Negative exponential
probability distribution
A continuous probability
distribution often used to
describe the service time in a
queuing system.

Module D Waiting-Line Models 589
QUEUING COSTS
As described in the OM in Action box “Zero Wait Time Guarantee at this Michigan Hospital’s
ER,” operations managers must recognize the trade-off that takes place between two costs: the
cost of providing good service and the cost of customer or machine waiting time. Managers want
queues that are short enough so that customers do not become unhappy and either leave without
buying or buy but never return. However, managers may be willing to allow some waiting if it is
balanced by a significant savings in service costs.
One means of evaluating a service facility is to look at total expected cost. Total cost is the
sum of expected service costs plus expected waiting costs.
As you can see in Figure D.5, service costs increase as a firm attempts to raise its level of ser-
vice. Managers in some service centers can vary capacity by having standby personnel and
machines that they can assign to specific service stations to prevent or shorten excessively long
lines. In grocery stores, for example, managers and stock clerks can open extra checkout coun-
ters. In banks and airport check-in points, part-time workers may be called in to help. As the level
of service improves (that is, speeds up), however, the cost of time spent waiting in lines
AUTHOR COMMENT
The 2 costs we consider here
are cost of servers and cost
of lost time waiting.
Other hospitals smirked when in 2000, Michigan’s
Oakwood Healthcare chain rolled out an emergency room
(ER) guarantee that promised a written apology and movie
tickets to patients not seen by a doctor within 30 minutes.
Even employees cringed at what sounded like a cheap
marketing ploy.
But if you have visited an ER lately and watched some
patients wait for hours on end—the official average wait is
47 minutes—you can understand why Oakwood’s patient
satisfaction levels have soared. The 30-minute guarantee
was such a huge success that fewer than 1% of the
191,000 ER patients asked for free tickets in 2002. The
following year, Oakwood upped the stakes again, offering
a 15-minute guarantee. Then, in 2006, Oakwood started its
Zero Wait Program in the ERs. Patients who enter any
Oakwood emergency department are immediately cared
for by a healthcare professional.
Oakwood’s CEO even extended the ER guarantee to on-
time surgery, 45-minute meal service orders, and other custom
room services. “Medicine is a service business,” says Larry
Alexander, the head of an
ER in Sanford, Florida. “And
people are in the mindset of
the fast-food industry.”
How did Oakwood make
good on its promise to
eliminate the ER queue? It
first studied queuing theory,
then reengineered its billing,
records, and lab operations
to drive down service time.
Then, to improve service
capability, Oakwood
upgraded its technical staff.
Finally, it replaced its ER
physicians with a crew
willing to work longer hours.
Sources: PR Newswire (November 21, 2006); and Crain’s Detroit Business
(March 4, 2002): 1.
OM in Action � Zero Wait Time Guarantee at This Michigan Hospital’s ER
Minimum
total
cost
High level
of service
Optimal
service level
Cost of waiting time
Cost of providing service
Total expected cost
Cost
Low level
of service
� FIGURE D.5
The Trade-Off Between
Waiting Costs and
Service Costs
AUTHOR COMMENT
Different organizations value
their customers’ times
differently.

590 PART 4 Quantitative Modules
decreases. (Refer to Figure D.5.) Waiting cost may reflect lost productivity of workers while
tools or machines await repairs or may simply be an estimate of the cost of customers lost
because of poor service and long queues. In some service systems (for example, an emergency
ambulance service), the cost of long waiting lines may be intolerably high.
THE VARIETY OF QUEUING MODELS
A wide variety of queuing models may be applied in operations management. We will introduce
you to four of the most widely used models. These are outlined in Table D.2, and examples of
each follow in the next few sections. More complex models are described in queuing theory
textbooks3 or can be developed through the use of simulation (the topic of Module F). Note that
all four queuing models listed in Table D.2 have three characteristics in common. They all
assume:
1. Poisson distribution arrivals
2. FIFO discipline
3. A single-service phase
In addition, they all describe service systems that operate under steady, ongoing conditions. This
means that arrival and service rates remain stable during the analysis.
Model A (M/M/1): Single-Channel Queuing
Model with Poisson Arrivals and Exponential
Service Times
The most common case of queuing problems involves the single-channel, or single-server, wait-
ing line. In this situation, arrivals form a single line to be serviced by a single station (see Fig-
ure D.3 on p. 587). We assume that the following conditions exist in this type of system:
1. Arrivals are served on a first-in, first-out (FIFO) basis, and every arrival waits to be served,
regardless of the length of the line or queue.
2. Arrivals are independent of preceding arrivals, but the average number of arrivals (arrival
rate) does not change over time.
3. Arrivals are described by a Poisson probability distribution and come from an infinite (or
very, very large) population.
4. Service times vary from one customer to the next and are independent of one another, but
their average rate is known.
� TABLE D.2 Queuing Models Described in This Chapter
Model
Name
(technical name
in parentheses) Example
Number
of
Channels
Number
of
Phases
Arrival
Rate
Pattern
Service
Time
Pattern
Population
Size
Queue
Discipline
A Single-channel
system (M/M/1)
Information
counter at
department store
Single Single Poisson Exponential Unlimited FIFO
B Multichannel
(M/M/S)
Airline ticket
counter
Multi-
channel
Single Poisson Exponential Unlimited FIFO
C Constant service
(M/D/1)
Automated
car wash
Single Single Poisson Constant Unlimited FIFO
D Limited
population
(finite population)
Shop with only a
dozen machines
that might break
Single Single Poisson Exponential Limited FIFO
3See, for example John F. Shortle, et al. Fundamentals of Queuing Theory, 4th ed. New York: Wiley (2008).
LO2: Apply the single-
channel queuing model
equations
AUTHOR COMMENT
This is the main section of
Module D. We illustrate 4
important queuing models.

Module D Waiting-Line Models 591
4In queuing notation, the first letter refers to the arrivals (where M stands for Poisson distribution); the second letter
refers to service (where M is again a Poisson distribution, which is the same as an exponential rate for service—and a D
is a constant service rate); the third symbol refers to the number of servers. So an M/D/1 system (our Model C) has
Poisson arrivals, constant service, and one server.
The giant Moscow
McDonald’s boasts 900
seats, 800 workers, and
$80 million in annual sales
(vs. less than $2 million in
a U.S. outlet). Americans
would balk at the average
waiting time of 45 minutes,
but Russians are used
to such long lines.
McDonald’s represents
good service in Moscow.
� TABLE D.3
Queuing Formulas for Model A:
Single-Channel System, Also
Called M/M/1
mean number of arrivals per time periodl =
mean number of people or items served per time periodm =
average number of units (customers) in the system (waiting and being served)
=
l
m – l
Ls =
average time a unit spends in the system (waiting time plus service time)
=
1
m – l
Ws =
average number of units waiting in the queue
=
l2
m1m – l2
Lq =
average time a unit spends waiting in the queue
=
l
m1m – l2
=
Lq
l
Wq =
utilization factor for the system
=
l
m
r =
probability of 0 units in the system (that is, the service unit is idle)
= 1 –
l
m
P0 =
probability of more than k units in the system, where n is the number of units in the system
= ¢l
m
≤k+ 1Pn7k =
5. Service times occur according to the negative exponential probability distribution.
6. The service rate is faster than the arrival rate.
When these conditions are met, the series of equations shown in Table D.3 can be developed.
Examples D1 and D2 illustrate how Model A (which in technical journals is known as the M/M/1
model) may be used.4

592 PART 4 Quantitative Modules
Probability of More Than k Cars in the System
k Pn 7 k = 12>32k + 1
0 .667 ; Note that this is equal to 1 – P0 = 1 – .33 = .667.
1 .444
2 .296
3 .198 ; Implies that there is a 19.8% chance that more than 3 cars are in the system.
4 .132
5 .088
6 .058
7 .039
INSIGHT � Recognize that arrival and service times are converted to the same rate. For example,
a service time of 20 minutes is stated as an average rate of 3 mufflers per hour. It’s also important to
differentiate between time in the queue and time in the system.
LEARNING EXERCISE � If cars/hour instead of the current 3 arrivals, what are the
new values of and [Answer: 1 car, 30 min., .5 cars, 15 min., 50%, .50.]
RELATED PROBLEMS � D.1, D.2, D.3, D.4, D.6, D.7, D.8, D.9a–e, D.10, D.11a–c,
D.12a–d.
EXCEL OM Data File ModDExD1.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL D.1 This example is further illustrated in Active Model D.1 at www.pearsonhighered.com/heizer.
P0?Wq,Lq,Ws,Ls,
m = 4
Once we have computed the operating characteristics of a queuing system, it is often
important to do an economic analysis of their impact. Although the waiting-line model
Tom Jones, the mechanic at Golden Muffler Shop, is able to install new mufflers at an average rate of 3
per hour (or about 1 every 20 minutes), according to a negative exponential distribution. Customers
seeking this service arrive at the shop on the average of 2 per hour, following a Poisson distribution.
They are served on a first-in, first-out basis and come from a very large (almost infinite) population of
possible buyers.
We would like to obtain the operating characteristics of Golden Muffler’s queuing system.
APPROACH � This is a single-channel (M/M/1) system and we apply the formulas in
Table D.3
SOLUTION �
= .33 probability there are 0 cars in the system
P0 = 1 –
l
m
= 1 –
2
3
= 66.6% of time mechanic is busy
r =
l
m
=
2
3
= 40-minute average waiting time per car
Wq =
l
m(m – l)
=
2
3(3 – 2)
=
2
3
hour
= 1.33 car waiting in line, on average
Lq =
l2
m1m – l2
=
22
313 – 22
=
4
3(1)
=
4
3
= 1-hour average time in the system
Ws =
1
m – l
=
1
3 – 2
= 1
= 2 cars in the system, on average
Ls =
l
m – l
=
2
3 – 2
=
2
1
m = 3 cars serviced per hour
l = 2 cars arriving per hour
EXAMPLE D1 �
A single-channel
queue

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Module D Waiting-Line Models 593
� EXAMPLE D2
Economic
analysis of
example D1
LO3: Conduct a cost
analysis for a waiting line
Golden Muffler Shop’s owner is interested in cost factors as well as the queuing parameters computed
in Example D1. He estimates that the cost of customer waiting time, in terms of customer dissatisfac-
tion and lost goodwill, is $10 per hour spent waiting in line. Jones, the mechanic, is paid $7 per hour.
APPROACH � First, compute the average daily customer waiting time, then the daily salary for
Jones, and finally the total expected cost.
SOLUTION � Because the average car has a hour wait and because there are approxi-
mately 16 cars serviced per day (2 arrivals per hour times 8 working hours per day), the total number of
hours that customers spend waiting each day for mufflers to be installed is:
Hence, in this case:
The only other major cost that Golden’s owner can identify in the queuing situation is the salary of
Jones, the mechanic, who earns $7 per hour, or $56 per day. Thus:
This approach will be useful in Solved Problem D.2 on page 603.
INSIGHT � and are the two most important queuing parameters when it comes to cost
analysis. Calculating customer wait times, we note, is based on average time waiting in the queue
times the number of arrivals per hour times the number of hours per day. This is because this exam-
ple is set on a daily basis. This is the same as using since
LEARNING EXERCISE � If the customer waiting time is actually $20 per hour and Jones gets
a salary increase to $10 per hour, what are the total daily expected costs? [Answer: $293.34.]
RELATED PROBLEMS � D.12e–f, D.13, D.22, D.23, D.24
Lq = Wql.Lq,
1l2
1Wq2
WqLq
= $162.67 per day
Total expected costs = $106.67 + $56
Customer waiting-time cost = $10 a10
2
3
b = $106.67 per day
2
3
1162 =
32
3
= 10
2
3
hour
1Wq2
2
3-
Model B (M/M/S): Multiple-Channel Queuing Model
Now let’s turn to a multiple-channel queuing system in which two or more servers or channels
are available to handle arriving customers. We still assume that customers awaiting service form
one single line and then proceed to the first available server. Multichannel, single-phase waiting
lines are found in many banks today: A common line is formed, and the customer at the head of
the line proceeds to the first free teller. (Refer to Figure D.3 on p. 587 for a typical multichannel
configuration.)
The multiple-channel system presented in Example D3 again assumes that arrivals follow a
Poisson probability distribution and that service times are exponentially distributed. Service is
first-come, first-served, and all servers are assumed to perform at the same rate. Other assump-
tions listed earlier for the single-channel model also apply.
The queuing equations for Model B (which also has the technical name M/M/S) are shown
in Table D.4. These equations are obviously more complex than those used in the single-
channel model; yet they are used in exactly the same fashion and provide the same type of
information as the simpler model. (Note: The POM for Windows and Excel OM software
described later in this chapter can prove very useful in solving multiple-channel, as well as
other, queuing problems.)
LO4: Apply the multiple-
channel queuing model
formulas
described previously is valuable in predicting potential waiting times, queue lengths, idle
times, and so on, it does not identify optimal decisions or consider cost factors. As we saw
earlier, the solution to a queuing problem may require management to make a trade-off
between the increased cost of providing better service and the decreased waiting costs
derived from providing that service.
Example D2 examines the costs involved in Example D1.

594 PART 4 Quantitative Modules
The probability that there are zero people or units in the system is:
The average number of people or units in the system is:
The average time a unit spends in the waiting line and being serviced (namely, in the system) is:
The average number of people or units in line waiting for service is:
The average time a person or unit spends in the queue waiting for service is:
Wq = Ws –
1
m
=
Lq
l
Lq = Ls –
l
m
Ws =
m1l/m2M
1M – 12!1Mm – l22
P0 +
1
m
=
Ls
l
Ls =
lm1l/m2M
1M – 12!1Mm – l22
P0 +
l
m
P0 =
1B aM- 1
n= 0
1
n!
a
l
m
b
nR + 1
M!
a
l
m
b
M Mm
Mm – l
for Mm 7 l
m = average service rate at each channel
l = average arrival rate
M = number of channels open
At this Costco in
Washington state: To
shorten queues, each
register is staffed with two
servers. This approach,
along with new checkout
technology, has enabled
Costco to increase from
37 to 45 customers per
register each hour.
�TABLE D.4
Queuing Formulas for Model B:
Multichannel System, Also
Called M/M/S

Module D Waiting-Line Models 595
� EXAMPLE D3
A multiple-channel
queue
We can summarize the characteristics of the two-channel model in Example D3 and compare
them to those of the single-channel model in Example D1 as follows:
The increased service has a dramatic effect on almost all characteristics. For instance, note that
the time spent waiting in line drops from 40 minutes to only 2.5 minutes.
Use of Waiting Line Tables Imagine the work a manager would face in dealing with
or 5 channel waiting line models if a computer was not readily available. The arith-
metic becomes increasingly troublesome. Fortunately, much of the burden of manually examining
M = 3, 4,
Single Channel Two Channels
P0 .33 .5
Ls 2 cars .75 car
Ws 60 minutes 22.5 minutes
Lq 1.33 cars .083 car
Wq 40 minutes 2.5 minutes
The Golden Muffler Shop has decided to open a second garage bay and hire a second mechanic to
handle installations. Customers, who arrive at the rate of about per hour, will wait in a single
line until 1 of the 2 mechanics is free. Each mechanic installs mufflers at the rate of about
per hour.
The company wants to find out how this system compares with the old single-channel waiting-line
system.
APPROACH � Compute several operating characteristics for the channel system, using
the equations in Table D.4, and compare the results with those found in Example D1.
SOLUTION �
Then:
INSIGHT � It is very interesting to see the big differences in service performance when an addi-
tional server is added.
LEARNING EXERCISE � If per hour, instead of what are the new values for
and [Answers: 0.6, .53 cars, 16 min, .033 cars, 1 min.]
RELATED PROBLEMS � D.7h, D.9f, D.11d, D.15, D.20
EXCEL OM Data File ModDExD3.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL D.2 This example is further illustrated in Active Model D.2 at www.pearsonhighered.com/heizer.
Wq?P0, Ls, Ws, Lq,
m = 3,m = 4
= 2.5 minutes average time a car spends in the queue (waiting)
Wq =
Lq
l
=
.083
2
= .0415 hour
= .083 average number of cars in the queue (waiting)
Lq = Ls –
l
m
=
3
4
–
2
3
=
9
12
–
8
12
=
1
12
= 22.5 minutes average time a car spends in the system
Ws =
Ls
l
=
3/4
2
=
3
8
hour
= .75 average number of cars in the system
Ls =
12213212/322
1!32132 – 242
a
1
2
b +
2
3
=
8 / 3
16
a
1
2
b +
2
3
=
3
4
= .5 probability of zero cars in the system
=
1
1 +
2
3
+
1
2
a
4
9
ba
6
6 – 2
b
=
1
1 +
2
3
+
1
3
=
1
2
P0 =
1Ba1
n= 0
1
n!
a
2
3
b
nR + 1
2!
a
2
3
b
2 2132
2132 – 2
M = 2
m = 3
l = 2

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596 PART 4 Quantitative Modules
Poisson Arrivals, Exponential Service Times
Number of Service Channels, M
r 1 2 3 4 5
.10 .0111
.15 .0264 .0008
.20 .0500 .0020
.25 .0833 .0039
.30 .1285 .0069
.35 .1884 .0110
.40 .2666 .0166
.45 .3681 .0239 .0019
.50 .5000 .0333 .0030
.55 .6722 .0449 .0043
.60 .9000 .0593 .0061
.65 1.2071 .0767 .0084
.70 1.6333 .0976 .0112
.75 2.2500 .1227 .0147
.80 3.2000 .1523 .0189
.85 4.8166 .1873 .0239 .0031
.90 8.1000 .2285 .0300 .0041
.95 18.0500 .2767 .0371 .0053
1.0 .3333 .0454 .0067
1.2 .6748 .0904 .0158
1.4 1.3449 .1778 .0324 .0059
1.6 2.8444 .3128 .0604 .0121
1.8 7.6734 .5320 .1051 .0227
2.0 .8888 .1739 .0398
2.2 1.4907 .2770 .0659
2.4 2.1261 .4305 .1047
2.6 4.9322 .6581 .1609
2.8 12.2724 1.0000 .2411
3.0 1.5282 .3541
3.2 2.3856 .5128
3.4 3.9060 .7365
3.6 7.0893 1.0550
3.8 16.9366 1.5184
4.0 2.2164
4.2 3.3269
4.4 5.2675
4.6 9.2885
4.8 21.6384
Alaska National Bank is trying to decide how many drive-in teller windows to open on a busy Saturday.
CEO Ted Eschenbach estimates that customers arrive at a rate of about per hour, and that each
teller can service about customers per hour.
APPROACH � Ted decides to use Table D.5 to compute and
SOLUTION � The ratio is Turning to the table, under Ted sees
that if only service window is open, the average number of customers in line will be 8.1. If two
windows are open, drops to .2285 customers, to .03 for tellers, and to .0041 for
tellers. Adding more open windows at this point will result in an average queue length of 0.
M = 4M = 3Lq
M = 1
r = .90,r = l>m = 1820 = .90.
Wq.Lq
m = 20
l = 18
EXAMPLE D4 �
Use of waiting line
tables
multiple channel queues can be avoided by using Table D.5. This table, the result
of hundreds of computations, represents the relationship between three things: (1) a ratio we
call ([rho] which is simple to find—it’s just ), (2) number of service channels open,
and (3) the average number of customers in the queue, Lq (which is what we’d like to find). For
any combination of the ratio and or 5 open service channels, you can quickly
look in the body of the table to read off the appropriate value for
Example D4 illustrates the use of Table D.5.
Lq.
M = 1, 2, 3, 4,r
l>mr
�TABLE D.5
Values of Lq for M = 1 – 5
Service Channels and Selected
Values of R � L/M

Long check-in lines (left photo) such as at Los Angeles International (LAX) are a common airport sight. This is an M/M/S model—
passengers wait in a single queue for one of several agents. But at Anchorage International Airport (right photo), Alaska Air has
jettisoned the traditional wall of ticket counters. Instead, 1.2 million passengers per year use self-service check-in machines and
staffed “bag drop” stations. Looking nothing like a typical airport, the new system doubled the airline’s check-in capacity, and cut
staff needs in half, all while speeding travelers through in less than 15 minutes, even during peak hours.
Module D Waiting-Line Models 597
It is also a simple matter to compute the average waiting time in the queue, since
When one channel is open, Wq � 8.1 customers/(18 customers per hour) � .45 hours � 27 minutes
waiting time; when two tellers are open, Wq � .2285 customers/(18 customers per hour) � .0127
hours and so on.
INSIGHT � If a computer is not readily available, Table D.5 makes it easy to find and to then
compute Table D.5 is especially handy to compare for different numbers of servers (M).
LEARNING EXERCISE � The number of customers arriving on a Thursday afternoon at
Alaska National is 15/hour. The service rate is still 20 customers/hour. How many people are in the
queue if there are 1, 2, or 3 servers? [Answer: 2.25, .1227, .0147.]
RELATED PROBLEM � D.5
LqWq.
Lq
� 34 minute;
Wq = Lq>l.Wq,
You might also wish to check the calculations in Example D3 against tabled values just to
practice the use of Table D.5. You may need to interpolate if your exact value is not found in the
first column. Other common operating characteristics besides are published in tabular form in
queuing theory textbooks.
Model C (M/D/1): Constant-Service-Time Model
Some service systems have constant, instead of exponentially distributed, service times. When
customers or equipment are processed according to a fixed cycle, as in the case of an automatic
car wash or an amusement park ride, constant service times are appropriate. Because constant
rates are certain, the values for and are always less than they would be in Model
A, which has variable service rates. As a matter of fact, both the average queue length and the
average waiting time in the queue are halved with Model C. Constant-service-model formulas
are given in Table D.6. Model C also has the technical name M/D/1 in the literature of queuing
theory.
WsLq, Wq, Ls,
Lq
LO5: Apply the
constant-service-time
model equations

598 PART 4 Quantitative Modules
Inman Recycling, Inc., collects and compacts aluminum cans and glass bottles in Reston, Louisiana. Its
truck drivers currently wait an average of 15 minutes before emptying their loads for recycling. The
cost of driver and truck time while they are in queues is valued at $60 per hour. A new automated com-
pactor can be purchased to process truckloads at a constant rate of 12 trucks per hour (that is, 5 minutes
per truck). Trucks arrive according to a Poisson distribution at an average rate of 8 per hour. If the new
compactor is put in use, the cost will be amortized at a rate of $3 per truck unloaded.
APPROACH � CEO Tony Inman hires a summer college intern to conduct an analysis to evaluate
the costs versus benefits of the purchase. The intern uses the equation for in Table D.6.
SOLUTION �
INSIGHT � Constant service times, usually attained through automation, help control the vari-
ability inherent in service systems. This can lower average queue length and average waiting time.
Note the 2 in the denominator of the equations for and in Table D.6.
LEARNING EXERCISE � With the new constant-service-time system, what are the average
waiting time in the queue, average number of trucks in the system, and average waiting time in the sys-
tem? [Answer: 0.0833 hours, 1.33, 0.1667 hours.]
RELATED PROBLEMS � D.14, D.16, D.21
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ACTIVE MODEL D.3 This example is further illustrated in Active Model D.3 at www.pearsonhighered.com/heizer.
WqLq
New savings = $ 7/trip
Cost of new equipment amortized: = $ 3/trip
Savings with new equipment = $151current system2 – $51new system2 = $10/trip
Waiting cost/trip with new compactor = 11/12 hr wait21$60/hr cost2 = $5/trip
Average waiting time in queue = Wq =
l
2m1m – l2
=
8
21122112 – 82
=
1
12
hr
New system: l = 8 trucks/hr arriving m = 12 trucks/hr served
Current waiting cost/trip = 11/4 hr waiting now21$60/hr cost2 = $15>trip
Wq
Little’s Law
A practical and useful relationship in queuing for any system in a steady state is called Little’s
Law. A steady state exists when a queuing system is in its normal operating condition (e.g., after
customers waiting at the door when a business opens in the morning are taken care of). Little’s
Law can be written as either:
(D-2)
or:
(D-3)Lq = lWq 1which is the same as Wq = Lq/l2
L = lW 1which is the same as W = L/l2
EXAMPLE D5 �
A constant-service
model
Example D5 gives a constant-service-time analysis.
Average length of queue: Lq =
l2
2m1m – l2
Average waiting time in queue: Wq =
l
2m1m – l2
Average number of customers in system: Ls = Lq +
l
m
Average time in system: Ws = Wq +
1
m
TABLE D.6 �
Queuing Formulas for Model C:
Constant Service, Also Called
M/D/1

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Module D Waiting-Line Models 599
LO6: Perform a limited-
population model analysis
5Limited, or finite, queuing tables are available to handle arrival populations of up to 250. Although there is no
definite number that we can use as a dividing point between limited and unlimited populations, the general rule of
thumb is this: If the number in the queue is a significant proportion of the arrival population, use a limited population
queuing model. For a complete set of N-values, see L. G. Peck and R. N. Hazelwood, Finite Queuing Tables (New
York: Wiley, 1958).
Service factor: X =
T
T + U
Average number running: J = NF11 – X2
Average number waiting: L = N11 – F2 Average number being serviced: H = FNX
Average waiting time: W =
L1T + U2
N – L
=
T11 – F2
XF
Number in population: N = J + L + H
Notation
D = probability that a unit will have to wait in queue N = number of potential customers
F = efficiency factor T = average service time
H = average number of units being served U = average time between unit service
J = average number running requirements
L = average number of units waiting for service W = average time a unit waits in line
M = number of service channels X = service factor
Source: Based on L. G. Peck and R. N. Hazelwood, Finite Queuing Tables (New York: Wiley, 1958).
� TABLE D.7
Queuing Formulas and
Notation for Model D:
Limited-Population
Formulas
The advantage of these formulas is that once two of the parameters are known, the other one can
easily be found. This is important because in certain waiting-line situations, one of these might
be easier to determine than the other.
Little’s Law is also important because it makes no assumptions about the probability distribu-
tions for arrivals and service times, the number of servers, or service priority rules. The law
applies to all the queuing systems discussed in this module, except the limited-population model,
which we discuss next.
Model D: Limited-Population Model
When there is a limited population of potential customers for a service facility, we must con-
sider a different queuing model. This model would be used, for example, if we were consider-
ing equipment repairs in a factory that has 5 machines, if we were in charge of maintenance for
a fleet of 10 commuter airplanes, or if we ran a hospital ward that has 20 beds. The limited-
population model allows any number of repair people (servers) to be considered.
This model differs from the three earlier queuing models because there is now a dependent rela-
tionship between the length of the queue and the arrival rate. Let’s illustrate the extreme situation:
If your factory had five machines and all were broken and awaiting repair, the arrival rate would
drop to zero. In general, then, as the waiting line becomes longer in the limited population model,
the arrival rate of customers or machines drops.
Table D.7 displays the queuing formulas for the limited-population model. Note that they
employ a different notation than Models A, B, and C. To simplify what can become time-
consuming calculations, finite queuing tables have been developed that determine D and F. D
represents the probability that a machine needing repair will have to wait in line. F is a waiting-
time efficiency factor. D and F are needed to compute most of the other finite model formulas.
A small part of the published finite queuing tables is illustrated in this section. Table D.8 (on
p. 601) provides data for a population of 5
To use Table D.8, we follow four steps:
1. Compute X (the service factor), where
2. Find the value of X in the table and then find the line for M (where M is the number of
service channels).
3. Note the corresponding values for D and F.
4. Compute L, W, J, H, or whichever are needed to measure the service system’s performance.
X = T/1T + U2.
N = 5.
Example D6 illustrates these steps.

600 PART 4 Quantitative Modules
6. The cost analysis follows:
Number of
Technicians
Average Number
Printers Down
1N – J2
Average Cost/Hr.
for Downtime
1N – J21$120/hr2
Cost/Hr. for
Technicians
1at $25/hr2
Total
Cost/Hr.
1 .64 $76.80 $25.00 $101.80
2 .46 $55.20 $50.00 $105.20
INSIGHT � This analysis suggests that having only one technician on duty will save a few dollars
per hour This may seem like a small amount, but it adds up to over
$7,000 per year.
LEARNING EXERCISE � DOE has just replaced its printers with a new model that seems to break
down after about 18 hours of use. Recompute the costs. [Answer: For and
total For and total ]
RELATED PROBLEMS � D.17, D.18, D.19
EXCEL OM Data File ModDExD6.xls can be found at www.pearsonhighered.com/heizer.
cost/hr = $111.56.M = 2, F = .997, J = 4.487,cost/hr = $112.00.
M = 1, F = .95, J = 4.275,
1$105.20 – $101.80 = $3.402.
This isn’t Disney World,
where waits are made
tolerable—or even fun—
via amusements and
entertainment. This
long line of frustrated
customers is the Chicago
office of the Consulate of
Mexico, where immigrants
apply for ID cards that
are considered legal
documents. How could
the principles in this
module be used to
improve this queuing
system?
EXAMPLE D6 �
A limited-population
model
Past records indicate that each of the 5 massive laser computer printers at the U.S. Department of
Energy (DOE), in Washington, DC, needs repair after about 20 hours of use. Breakdowns have been
determined to be Poisson distributed. The one technician on duty can service a printer in an average of
2 hours, following an exponential distribution. Printer downtime costs $120 per hour. Technicians are
paid $25 per hour. Should the DOE hire a second technician?
APPROACH � Assuming the second technician can also repair a printer in an average of 2 hours,
we can use Table D.8 (because there are N = 5 machines in this limited population) to compare the
costs of 1 versus 2 technicians.
SOLUTION �
1. First, we note that hours and hours.
2. Then, (close to .090 [to use for determining D and F]).
3. For server, and
4. For servers, and
5. The average number of printers working is
For this is
For it is J = 1521.998211 – .0912 = 4.54.M = 2,
J = 1521.960211 – .0912 = 4.36.M = 1,
J = NF11 – X2.
F = .998.D = .044M = 2
F = .960.D = .350M = 1
X =
T
T + U
=
2
2 + 20
=
2
22
= .091
U = 20T = 2

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� TABLE D.8 Finite Queuing Tables for a Population of N � 5*
X M D F X M D F X M D F X M D F X M D F
.012 1 .048 .999 1 .404 .945 1 .689 .801 .330 4 .012 .999 3 .359 .927
.019 1 .076 .998 .110 2 .065 .996 .210 3 .032 .998 3 .112 .986 .520 2 .779 .728
.025 1 .100 .997 1 .421 .939 2 .211 .973 2 .442 .904 1 .988 .384
.030 1 .120 .996 .115 2 .071 .995 1 .713 .783 1 .902 .583 .540 4 .085 .989
.034 1 .135 .995 1 .439 .933 .220 3 .036 .997 .340 4 .013 .999 3 .392 .917
.036 1 .143 .994 .120 2 .076 .995 2 .229 .969 3 .121 .985 2 .806 .708
.040 1 .159 .993 1 .456 .927 1 .735 .765 2 .462 .896 1 .991 .370
.042 1 .167 .992 .125 2 .082 .994 .230 3 .041 .997 1 .911 .569 .560 4 .098 .986
.044 1 .175 .991 1 .473 .920 2 .247 .965 .360 4 .017 .998 3 .426 .906
.046 1 .183 .990 .130 2 .089 .933 1 .756 .747 3 .141 .981 2 .831 .689
.050 1 .198 .989 1 .489 .914 .240 3 .046 .996 2 .501 .880 1 .993 .357
.052 1 .206 .988 .135 2 .095 .993 2 .265 .960 1 .927 .542 .580 4 .113 .984
.054 1 .214 .987 1 .505 .907 1 .775 .730 .380 4 .021 .998 3 .461 .895
.056 2 .018 .999 .140 2 .102 .992 .250 3 .052 .995 3 .163 .976 2 .854 .670
1 .222 .985 1 .521 .900 2 .284 .955 2 .540 .863 1 .994 .345
.058 2 .019 .999 .145 3 .011 .999 1 .794 .712 1 .941 .516 .600 4 .130 .981
1 .229 .984 2 .109 .991 .260 3 .058 .994 .400 4 .026 .977 3 .497 .883
.060 2 .020 .999 1 .537 .892 2 .303 .950 3 .186 .972 2 .875 .652
1 .237 .983 .150 3 .012 .999 1 .811 .695 2 .579 .845 1 .996 .333
.062 2 .022 .999 2 .115 .990 .270 3 .064 .994 1 .952 .493 .650 4 .179 .972
1 .245 .982 1 .553 .885 2 .323 .944 .420 4 .031 .997 3 .588 .850
.064 2 .023 .999 .155 3 .013 .999 1 .827 .677 3 .211 .966 2 .918 .608
1 .253 .981 2 .123 .989 .280 3 .071 .993 2 .616 .826 1 .998 .308
.066 2 .024 .999 1 .568 .877 2 .342 .938 1 .961 .471 .700 4 .240 .960
1 .260 .979 .160 3 .015 .999 1 .842 .661 .440 4 .037 .996 3 .678 .815
.068 2 .026 .999 2 .130 .988 .290 4 .007 .999 3 .238 .960 2 .950 .568
1 .268 .978 1 .582 .869 3 .079 .992 2 .652 .807 1 .999 .286
.070 2 .027 .999 .165 3 .016 .999 2 .362 .932 1 .969 .451 .750 4 .316 .944
1 .275 .977 2 .137 .987 1 .856 .644 .460 4 .045 .995 3 .763 .777
.075 2 .031 .999 1 .597 .861 .300 4 .008 .999 3 .266 .953 2 .972 .532
1 .294 .973 .170 3 .017 .999 3 .086 .990 2 .686 .787 .800 4 .410 .924
.080 2 .035 .998 2 .145 .985 2 .382 .926 1 .975 .432 3 .841 .739
1 .313 .969 1 .611 .853 1 .869 .628 .480 4 .053 .994 2 .987 .500
.085 2 .040 .998 .180 3 .021 .999 .310 4 .009 .999 3 .296 .945 .850 4 .522 .900
1 .332 .965 2 .161 .983 3 .094 .989 2 .719 .767 3 .907 .702
.090 2 .044 .998 1 .638 .836 2 .402 .919 1 .980 .415 2 .995 .470
1 .350 .960 .190 3 .024 .998 1 .881 .613 .500 4 .063 .992 .900 4 .656 .871
.095 2 .049 .997 2 .117 .980 .320 4 .010 .999 3 .327 .936 3 .957 .666
1 .368 .955 1 .665 .819 3 .103 .988 2 .750 .748 2 .998 .444
.100 2 .054 .997 .200 3 .028 .998 2 .422 .912 1 .985 .399 .950 4 .815 .838
1 .386 .950 .200 2 .194 .976 1 .892 .597 .520 4 .073 .991 3 .989 .631
.105 2 .059 .997
*See notation in Table D.7.
Module D Waiting-Line Models 601
OTHER QUEUING APPROACHES
Many practical waiting-line problems that occur in service systems have characteristics like
those of the four mathematical models already described. Often, however, variations of these
specific cases are present in an analysis. Service times in an automobile repair shop, for example,
tend to follow the normal probability distribution instead of the exponential. A college registra-
tion system in which seniors have first choice of courses and hours over other students is an
example of a first-come, first-served model with a preemptive priority queue discipline. A phys-
ical examination for military recruits is an example of a multiphase system, one that differs from
the single-phase models discussed earlier in this module. A recruit first lines up to have blood
drawn at one station, then waits for an eye exam at the next station, talks to a psychiatrist at the
third, and is examined by a doctor for medical problems at the fourth. At each phase, the recruit
must enter another queue and wait his or her turn. Many models, some very complex, have been
developed to deal with situations such as these.
AUTHOR COMMENT
When the assumptions of the 4
models we just introduced do
not hold true, there are other
approaches still available to us.

602 PART 4 Quantitative Modules
Queues are an important part of the world of operations man-
agement. In this module, we describe several common queuing
systems and present mathematical models for analyzing them.
The most widely used queuing models include Model A, the
basic single-channel, single-phase system with Poisson arrivals
and exponential service times; Model B, the multichannel
equivalent of Model A; Model C, a constant-service-rate
model; and Model D, a limited-population system. All four
models allow for Poisson arrivals, first-in, first-out service, and
a single-service phase. Typical operating characteristics we
examine include average time spent
waiting in the queue and system, aver-
age number of customers in the queue
and system, idle time, and utilization rate.
A variety of queuing models exists for
which all the assumptions of the traditional
models need not be met. In these cases, we use more complex
mathematical models or turn to a technique called simulation.
The application of simulation to problems of queuing systems
is addressed in Module F.
Key Terms
Queuing theory (p. 584)
Waiting line (queue) (p. 584)
Unlimited, or infinite, population (p. 585)
Limited, or finite, population (p. 585)
Poisson distribution (p. 586)
First-in, first-out (FIFO) rule (p. 587)
Single-channel queuing system (p. 587)
Multiple-channel queuing system (p. 588)
Single-phase system (p. 588)
Multiphase system (p. 588)
Negative exponential probability
distribution (p. 588)
MODULE SUMMARY
Using Software to Solve Queuing Problems
Both Excel OM and POM for Windows may be used to analyze all but the last two homework problems in the Lecture Guide &
Activities Manual for this module.
X Using Excel OM
Excel OM’s Waiting-Line program handles all four of the models developed in this module. Program D.1 illustrates our first
model, the M/M/1 system, using the data from Example D1.
� PROGRAM D.1 Using Excel OM for Queuing
Example D1’s (Golden Muffler Shop) data are illustrated in the M/M/1 model.
Sample Calculations
Probability
=1–B7/B8
=B16*B$7/B$8
=B17*B$7/B$8
Cumulative
Probability
=1–B7/B8
=C16+B17
=C17+B18
Enter the arrival rate
and service rate in
column B. Be sure
that you enter rates
rather than times.
Calculating
Parameters
=B7/B8
=B7^2/(B8*(B8–B7))
=B7(B8–B7)
=B7/(B8*(B8–B7))
=1/(B8–B7)
=1 – E7
P Using POM For Windows
There are several POM for Windows queuing models from which to select in that program’s Waiting-Line
module. The program can include an economic analysis of cost data, and, as an option, you may display
probabilities of various numbers of people/items in the system. See Appendix IV for further details.

Module D Waiting-Line Models 603
Solved Problems Virtual Office Hours help is available www.myomlab.com
� SOLVED PROBLEM D.1
Sid Das Brick Distributors currently employs 1 worker whose job
is to load bricks on outgoing company trucks. An average of 24
trucks per day, or 3 per hour, arrive at the loading platform,
according to a Poisson distribution. The worker loads them at a
rate of 4 trucks per hour, following approximately the exponential
distribution in his service times.
Das believes that adding an additional brick loader will sub-
stantially improve the firm’s productivity. He estimates that a two-
person crew loading each truck will double the loading rate ( )
from 4 trucks per hour to 8 trucks per hour. Analyze the effect on
the queue of such a change and compare the results to those
achieved with one worker. What is the probability that there will
be more than 3 trucks either being loaded or waiting?
m
� SOLUTION
These results indicate that when only one loader is employed, the average truck must wait three quarters
of an hour before it is loaded. Furthermore, there is an average of 2.25 trucks waiting in line to be
loaded. This situation may be unacceptable to management. Note also the decline in queue size after the
addition of a second loader.
Number of Brick Loaders
1 2
Truck arrival rate ( )l 3/hr 3/hr
Loading rate ( )m 4/hr 8/hr
Average number in system ( )Ls 3 trucks .6 truck
Average time in system ( )Ws 1 hr .2 hr
Average number in queue ( )Lq 2.25 trucks .225 truck
Average time in queue ( )Wq .75 hr .075 hr
Utilization rate ( )r .75 .375
Probability system empty ( )P0 .25 .625
Probability of More than Trucks in Systemk
Probability n 7 k
k One Loader Two Loaders
0 .75 .375
1 .56 .141
2 .42 .053
3 .32 .020
� SOLVED PROBLEM D.2
Truck drivers working for Sid Das (see Solved Problem D.1) earn
an average of $10 per hour. Brick loaders receive about $6 per
hour. Truck drivers waiting in the queue or at the loading platform
are drawing a salary but are productively idle and unable to gener-
ate revenue during that time. What would be the hourly cost sav-
ings to the firm if it employed 2 loaders instead of 1?
Referring to the data in Solved Problem D.1, we note that the
average number of trucks in the system is 3 when there is only 1
loader and 6 when there are 2 loaders.
� SOLUTION
Number of Loaders
1 2
Truck driver idle time costs
* 1Hourly rate24 = 1321$102 =
[1Average number of trucks2
$30 $ 6 = 1.621$102
Loading costs 6 12 = 1221$62
Total expected cost per hour $36 $18
The firm will save $18 per hour by adding another loader.
� SOLVED PROBLEM D.3
Sid Das is considering building a second platform or gate to speed
the process of loading trucks. This system, he thinks, will be even
more efficient than simply hiring another loader to help out on the
first platform (as in Solved Problem D.1).
Assume that the worker at each platform will be able to load
4 trucks per hour each and that trucks will continue to arrive at the
rate of 3 per hour. Then apply the appropriate equations to find the
waiting line’s new operating conditions. Is this new approach
indeed speedier than the other two that Das has considered?

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604 PART 4 Quantitative Modules
� SOLUTION
Looking back at Solved Problem D.1, we see that although length of the queue and average time in the
queue are lowest when a second platform is open, the average number of trucks in the system and average
time spent waiting in the system are smallest when two workers are employed at a single platform. Thus, we
would probably recommend not building a second platform.
Wq =
.123
3
= .041 hr
Lq = .873 – 3/4 = .123
Ws =
.873
3
= .291 hr
Ls =
314213/422
112!18 – 322
1.45452 +
3
4
= .873
=
1
1 +
3
4
+
1
2
a
3
4
b
2
a
8
8 – 3
b
= .4545
P0 =
1Ba1
n= 0
1
n!
a
3
4
b
nR + 1
2!
a
3
4
b
2 2142
2142 – 3
� SOLVED PROBLEM D.4
St. Elsewhere Hospital’s cardiac care unit (CCU) has 5 beds,
which are virtually always occupied by patients who have just
undergone major heart surgery. Two registered nurses are on duty
in the CCU in each of the three 8-hour shifts. About every 2 hours
(following a Poisson distribution), one of the patients requires a
nurse’s attention. The nurse will then spend an average of 30 min-
utes (exponentially distributed) assisting the patient and updating
medical records regarding the problem and care provided.
Because immediate service is critical to the 5 patients, two
important questions are: What is the average number of patients
being attended by the nurses? What is the average time that a
patient spends waiting for one of the nurses to arrive?
� SOLUTION
X =
T
T + U
=
30
30 + 120
= .20
U = 120 minutes
T = 30 minutes
M = 2 nurses
N = 5 patients
From Table D.8 (p. 601), with and we see that:
=
30 (1 – .976)
(.20)(.976)2
= 3.69 minutes
W = average waiting time for a nurse =
T11 – F2
XF
= 1.97621521.202 = .98 L 1 patient at any given time
H = average number being attended to = FNX
F = .976
M = 2,X = .20
�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this additional free case study:
Pantry Shopper: The case requires the redesign of a checkout system for a supermarket.
Bibliography
Canonaco, P., et al. “A Queuing Network Model for the
Management of Berth Crane Operations.” Computers &
Operations Research 35, no. 8 (August 2008): 2432.
Cochran, J. K., and K. Roche. “A Queueing-Based Decision
Support Methodology to Estimate Hospital Inpatient Bed
Demand.” Journal of the Operational Research Society 59,
no. 11 (November 2008): 1471–1483.
Gross, Donald, John F. Shortle, James M. Thompson, and Carl M.
Harris. Fundamentals of Queuing Theory, 4th ed. New York:
Wiley (2008).
Parlar, M., and M. Sharafali. “Dynamic Allocation of Airline
Check-In Counters: A Queueing Optimization Problem.”
Management Science 54, no. 8 (August 2008):
1410–1425.
Prabhu, N. U. Foundations of Queuing Theory. Dordecht,
Netherlands: Kluwer Academic Publishers (1997).
Ramaswami, V., et al. “Ensuring Access to Emergency Services in
the Presence of Long Internet Dial-Up Calls.” Interfaces 35,
no. 5 (September–October 2005): 411–425.
Render, B., R. M. Stair, and R. Balakrishnan. Managerial Decision
Modeling with Spreadsheets, 2nd ed. Upper Saddle River, NJ:
Prentice Hall (2007).
Render, B., R. M. Stair, and M. Hanna. Quantitative Analysis for
Management, 10th ed. Upper Saddle River, NJ: Prentice Hall
(2009).
Stanford, D. A., E. Renouf, and V. C. McAlister. “Waiting for Liver
Transplantation in Canada.” Health Care Management
Science 11, no. 2 (June 2008): 196–208.

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QUANTITATIVE MODULE
Learning Curves
Module Outline
What Is a Learning Curve? 606
Learning Curves in Services and
Manufacturing 607
Applying the Learning Curve 608
Strategic Implications of
Learning Curves 611
Limitations of Learning Curves 612
605

606 PART 4 Quantitative Modules
LO1: Define learning curve 607
LO2: Use the arithmetic concept to
estimate times 608
LO3: Compute learning curve effects with
the logarithmic and learning-curve
coefficient approaches 609
Module E Learning Objectives
WHAT IS A LEARNING CURVE?
Most organizations learn and improve over time. As firms and employees perform a task over and
over, they learn how to perform more efficiently. This means that task times and costs decrease.
Learning curves are based on the premise that people and organizations become better at
their tasks as the tasks are repeated. A learning curve graph (illustrated in Figure E.1) displays
labor-hours per unit versus the number of units produced. From it we see that the time needed to
produce a unit decreases, usually following a negative exponential curve, as the person or com-
Medical procedures such
as heart surgery follow a
learning curve. Research
indicates that the death
rate from heart transplants
drops at a 79% learning
curve, a learning rate not
unlike that in many
industrial settings. It
appears that as doctors
and medical teams
improve with experience,
so do your odds as a
patient. If the death rate is
halved every three
operations, practice may
indeed make perfect.
C
o
st
/
t
im
e
p
e
r
re
p
e
tit
io
n
0 Number of repetitions (volume)
� FIGURE E.1
The Learning-Curve Effect
States That Time per
Repetition Decreases as the
Number of Repetitions
Increases
LO4: Describe the strategic implications of
learning curves 611
Learning curves
The premise that people and
organizations get better at their
tasks as the tasks are repeated;
sometimes called experience
curves.

pany produces more units. In other words, it takes less time to complete each additional unit a
firm produces. However, we also see in Figure E.1 that the time savings in completing each sub-
sequent unit decreases. These are the major attributes of the learning curve.
Learning curves were first applied to industry in a report by T. P. Wright of Curtis-Wright
Corp. in 1936.1 Wright described how direct labor costs of making a particular airplane
decreased with learning, a theory since confirmed by other aircraft manufacturers. Regardless of
the time needed to produce the first plane, learning curves are found to apply to various cate-
gories of air frames (e.g., jet fighters versus passenger planes versus bombers). Learning curves
have since been applied not only to labor but also to a wide variety of other costs, including
material and purchased components. The power of the learning curve is so significant that it
plays a major role in many strategic decisions related to employment levels, costs, capacity, and
pricing.
The learning curve is based on a doubling of production: That is, when production doubles,
the decrease in time per unit affects the rate of the learning curve. So, if the learning curve is an
80% rate, the second unit takes 80% of the time of the first unit, the fourth unit takes 80% of the
time of the second unit, the eighth unit takes 80% of the time of the fourth unit, and so forth. This
principle is shown as:
(E-1)
where T � unit cost or unit time of the first unit
L � learning curve rate
n � number of times T is doubled
If the first unit of a particular product took 10 labor-hours, and if a 70% learning curve is pres-
ent, the hours the fourth unit will take require doubling twice—from 1 to 2 to 4. Therefore, the
formula is:
LEARNING CURVES IN SERVICES AND MANUFACTURING
Different organizations—indeed, different products—have different learning curves. The rate of
learning varies depending on the quality of management and the potential of the process and
product. Any change in process, product, or personnel disrupts the learning curve. Therefore,
caution should be exercised in assuming that a learning curve is continuing and permanent.
As you can see in Table E.1, industry learning curves vary widely. The lower the number (say
70% compared to 90%), the steeper the slope and the faster the drop in costs. By tradition, learn-
ing curves are defined in terms of the complements of their improvement rates. For example, a
70% learning curve implies a 30% decrease in time each time the number of repetitions is dou-
bled. A 90% curve means there is a corresponding 10% rate of improvement.
Stable, standardized products and processes tend to have costs that decline more steeply than
others. Between 1920 and 1955, for instance, the steel industry was able to reduce labor-hours
per unit to 79% each time cumulative production doubled.
Learning curves have application in services as well as industry. As was noted in the caption
for the opening photograph, 1-year death rates of heart transplant patients at Temple University
Hospital follow a 79% learning curve. The results of that hospital’s 3-year study of 62 patients
receiving transplants found that every three operations resulted in a halving of the 1-year death
rate. As more hospitals face pressure from both insurance companies and the government to enter
fixed-price negotiations for their services, their ability to learn from experience becomes increas-
ingly critical. In addition to having applications in both services and industry, learning curves are
useful for a variety of purposes. These include:
1. Internal: Labor forecasting, scheduling, establishing costs and budgets.
2. External: Supply-chain negotiations (see the SMT case study in the Lecture Guide &
Activities Manual).
3. Strategic: Evaluation of company and industry performance, including costs and pricing.
Hours required for unit 4 = 10 * 1.722 = 4.9 hours
T * Ln = Time required for the nth unit
Module E Learning Curves 607
AUTHOR COMMENT
Learning is a universal
concept, but rates of learning
differ widely.
LO1: Define learning curve
1T. P. Wright, “Factors Affecting the Cost of Airplanes,” Journal of the Aeronautical Sciences (February 1936).

608 PART 4 Quantitative Modules
The consequences of learning curves can be far-reaching. For example, there are major prob-
lems in scheduling if the learning improvement is not considered: labor and plants may sit idle a
portion of the time. Firms may also refuse more work because they ignore their own efficiency
improvements.
APPLYING THE LEARNING CURVE
A mathematical relationship enables us to express the time required to produce a certain unit.
This relationship is a function of how many units have been produced before the unit in question
and how long it took to produce them. To gain a mastery of this relationship, we will work
through learning curves scenarios using three different approaches: arithmetic analysis, logarith-
mic analysis, and learning-curve coefficients.
Arithmetic Approach
The arithmetic approach is the simplest approach to learning-curve problems. As we noted at the
beginning of this module, each time production doubles, labor per unit declines by a constant
factor, known as the learning rate. So, if we know that the learning rate is 80% and that the first
unit produced took 100 hours, the hours required to produce the 2nd, 4th, 8th, and 16th units are
as follows:
AUTHOR COMMENT
Here are the three ways
of solving learning
curve problems.
LO2: Use the arithmetic
concept to estimate times
Nth Unit Produced Hours for Nth Unit
1 100.0
2
4
8
16 41.0 = 1.8 * 51.22
51.2 = 1.8 * 642
64.0 = 1.8 * 802
80.0 = 1.8 * 1002
As long as we wish to find the hours required to produce N units and N is one of the doubled val-
ues, then this approach works. Arithmetic analysis does not tell us how many hours will be
needed to produce other units. For this flexibility, we must turn to the logarithmic approach.
Learning-
Curve
Cumulative Slope
Example Improving Parameter Parameter (%)
1. Model-T Ford production Price Units produced 86
2. Aircraft assembly Direct labor-hours per unit Units produced 80
3. Equipment maintenance Average time to replace a Number of replacements 76
at GE group of parts
4. Steel production Production worker labor-hours Units produced 79
per unit produced
5. Integrated circuits Average price per unit Units produced 72a
6. Handheld calculator Average factory selling price Units produced 74
7. Disk memory drives Average price per bit Number of bits 76
8. Heart transplants 1-year death rates Transplants completed 79
9. Cesarean section baby Average operation time Number of surgeries 93
deliveries
aConstant dollars.
Sources: W. Y. Fok, L. Y. S. Chan, and T. K. H. Chung. “The Effect of Learning Curves on the Outcome of a Caesarean Section.”
BSOG (November 2006): 1259–1263; James A. Cunningham, “Using the Learning Curve as a Management Tool,” IEEE Spectrum
(June 1980): 45. © 1980 IEEE; and Davis B. Smith and Jan L. Larsson, “The Impact of Learning on Cost: The Case of Heart
Transplantation.” Hospital and Health Services Administration (Spring 1989): 85–97.
TABLE E.1 �
Examples of Learning-Curve
Effects

Module E Learning Curves 609
Logarithmic Approach
The logarithmic approach allows us to determine labor for any unit, , by the formula:
(E-2)
where time for the Nth unit
hours to produce the first unit
b (log of the learning rate)/(log 2) = slope of the learning curve
Some of the values for b are presented in Table E.2. Example E1 shows how this formula
works.
=
T1 =
TN =
TN = T11Nb2
TN
� TABLE E.2
Learning-Curve Values of b
Learning
Rate (%) b
70
75
80
85
90 – .152
– .234
– .322
– .415
– .515
� EXAMPLE E1
Using logs to
compute learning
curves
The learning rate for a typical CPA to conduct a dental practice audit is 80%. Greg Lattier, a new grad-
uate of Lee College, completed his first audit in 100 hours. If the dental offices he audits are about the
same, how long should he take to finish his third job?
APPROACH � We will use the logarithmic approach in Equation (E-2).
SOLUTION �
INSIGHT � Greg improved quickly from his first to his third audit. An 80% rate means that from
just the first to second jobs, his time decreased by 20%.
LEARNING EXERCISE � If Greg’s learning rate was only 90%, how long would the third
audit take? [Answer: 84.621 hours.]
RELATED PROBLEMS � E.1, E.2, E.9, E.10, E.11, E.16
EXCEL OM Data File ModEExE1.xls can be found at www.pearsonhighered.com/heizer.
= 1100213 – .3222 = 70.2 labor-hours
= 1100213log.8>log 22
T3 = 1100 hours213b2
TN = T11Nb2
The logarithmic approach allows us to determine the hours required for any unit produced, but
there is a simpler method.
Learning-Curve Coefficient Approach
The learning-curve coefficient technique is embodied in Table E.3 and the following equation:
(E-3)
where number of labor-hours required to produce the Nth unit
number of labor-hours required to produce the first unit
learning-curve coefficient found in Table E.3
The learning-curve coefficient, C, depends on both the learning rate (70%, 75%, 80%, and so on)
and the unit number of interest.
Example E2 uses the preceding equation and Table E.3 to calculate learning-curve effects.
C =
T1 =
TN =
TN = T1C
LO3: Compute learning-
curve effects with the
logarithmic and learning-
curve coefficient approaches
� EXAMPLE E2
Using learning-
curve coefficients
It took a Korean shipyard 125,000 labor-hours to produce the first of several tugboats that you expect
to purchase for your shipping company, Great Lakes, Inc. Boats 2 and 3 have been produced by the
Koreans with a learning factor of 85%. At $40 per hour, what should you, as purchasing agent, expect
to pay for the fourth unit?
APPROACH � First, search Table E.3 for the fourth unit and a learning rate of 85%. The learning-
curve coefficient, C, is .723.

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610 PART 4 Quantitative Modules
� TABLE E.3 Learning-Curve Coefficients, Where Coefficient, C = N (log of learning rate/log 2)
70% 75% 80% 85% 90%
Unit Total Unit Total Unit Total Unit Total Unit Total
Unit Number Time Co- Time Co- Time Co- Time Co- Time Co- Time Co- Time Co- Time Co- Time Co- Time Co-
(N) efficient efficient efficient efficient efficient efficient efficient efficient efficient efficient
1 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
2 .700 1.700 .750 1.750 .800 1.800 .850 1.850 .900 1.900
3 .568 2.268 .634 2.384 .702 2.502 .773 2.623 .846 2.746
4 .490 2.758 .562 2.946 .640 3.142 .723 3.345 .810 3.556
5 .437 3.195 .513 3.459 .596 3.738 .686 4.031 .783 4.339
6 .398 3.593 .475 3.934 .562 4.299 .657 4.688 .762 5.101
7 .367 3.960 .446 4.380 .534 4.834 .634 5.322 .744 5.845
8 .343 4.303 .422 4.802 .512 5.346 .614 5.936 .729 6.574
9 .323 4.626 .402 5.204 .493 5.839 .597 6.533 .716 7.290
10 .306 4.932 .385 5.589 .477 6.315 .583 7.116 .705 7.994
11 .291 5.223 .370 5.958 .462 6.777 .570 7.686 .695 8.689
12 .278 5.501 .357 6.315 .449 7.227 .558 8.244 .685 9.374
13 .267 5.769 .345 6.660 .438 7.665 .548 8.792 .677 10.052
14 .257 6.026 .334 6.994 .428 8.092 .539 9.331 .670 10.721
15 .248 6.274 .325 7.319 .418 8.511 .530 9.861 .663 11.384
16 .240 6.514 .316 7.635 .410 8.920 .522 10.383 .656 12.040
17 .233 6.747 .309 7.944 .402 9.322 .515 10.898 .650 12.690
18 .226 6.973 .301 8.245 .394 9.716 .508 11.405 .644 13.334
19 .220 7.192 .295 8.540 .388 10.104 .501 11.907 .639 13.974
20 .214 7.407 .288 8.828 .381 10.485 .495 12.402 .634 14.608
25 .191 8.404 .263 10.191 .355 12.309 .470 14.801 .613 17.713
30 .174 9.305 .244 11.446 .335 14.020 .450 17.091 .596 20.727
35 .160 10.133 .229 12.618 .318 15.643 .434 19.294 .583 23.666
40 .150 10.902 .216 13.723 .305 17.193 .421 21.425 .571 26.543
45 .141 11.625 .206 14.773 .294 18.684 .410 23.500 .561 29.366
50 .134 12.307 .197 15.776 .284 20.122 .400 25.513 .552 32.142
SOLUTION � To produce the fourth unit, then, takes:
To find the cost, multiply by $40:
INSIGHT � The learning-curve coefficient approach is very easy to apply. If we had not factored
learning into our cost estimates, the price would have been
LEARNING EXERCISE � If the learning factor improved to 80%, how would the cost change?
[Answer: It would drop to $3,200,000.]
RELATED PROBLEMS � E.1, E.2, E.3a, E.5a,c, E.6a,b, E.9, E.10, E.11, E.14, E.16, E.22
EXCEL OM Data File ModEExE2.xls can be found at www.pearsonhighered.com/heizer.
ACTIVE MODEL E.1 This example is further illustrated in Active Model E.1 at www.pearsonhighered.com/heizer.
first boat2 = $6,000,000.
125,000 hours * $40 per hour 1same as the
90,375 hours * $40 per hour = $3,615,000
= 90,375 hours
T4 = 1125,000 hours21.7232
TN = T1C
Table E.3 also shows cumulative values. These allow us to compute the total number of hours
needed to complete a specified number of units. Again, the computation is straightforward. Just
multiply the table coefficient value by the time required for the first unit. Example E3 illustrates
this concept.

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Module E Learning Curves 611
� EXAMPLE E3
Using cumulative
coefficients
Example E2 computed the time to complete the fourth tugboat that Great Lakes plans to buy. How long
will all four boats require?
APPROACH � We look at the “Total Time Coefficient” column in Table E.3 and find that the
cumulative coefficient for 4 boats with an 85% learning factor is 3.345.
SOLUTION � The time required is:
INSIGHT � For an illustration of how Excel OM can be used to solve Examples E2 and E3, see
Program E.1 at the end of this module.
LEARNING EXERCISE � What is the value of if the learning factor is 80% instead of 85%?
[Answer: 392,750 hours.]
RELATED PROBLEMS � E.3b, E.4, E.5b,c, E.6c, E.7, E.15, E.19, E.20a
T4
T4 = 1125,000213.3452 = 418,125 hours in total for all 4 boats
TN = T1C
Using Table E.3 requires that we know how long it takes to complete the first unit. Yet, what hap-
pens if our most recent or most reliable information available pertains to some other unit? The
answer is that we must use these data to find a revised estimate for the first unit and then apply
the table coefficient to that number. Example E4 illustrates this concept.
� EXAMPLE E4
Revising
learning-curve
estimates
Great Lakes, Inc., believes that unusual circumstances in producing the first boat (see Example E2)
imply that the time estimate of 125,000 hours is not as valid a base as the time required to produce the
third boat. Boat number 3 was completed in 100,000 hours. It wants to solve for the revised estimate
for boat number 1.
APPROACH � We return to Table E.3, with a unit value of N = 3 and a learning-curve coefficient
of C = .773 in the 85% column.
SOLUTION � To find the revised estimate, divide the actual time for boat number 3, 100,000
hours, by C = .773:
So, 129,366 hours is the new (revised) estimate for boat 1.
INSIGHT � Any change in product, process, or personnel will change the learning curve. The new
estimate for boat 1 suggests that related cost and volume estimates need to be revised.
LEARNING EXERCISE � Boat 4 was just completed in 90,000 hours. Great Lakes thinks the
85% learning rate is valid but isn’t sure about the 125,000 hours for the first boat. Find a revised esti-
mate for boat 1. [Answer: 124,481, suggesting that boat 1’s time was fairly accurate after all.]
RELATED PROBLEMS � E.8, E.12, E.13, E.17, E.18, E.20b, E.21, E.23
EXCEL OM Data File ModEExE4.xls can be found at www.pearsonhighered.com/heizer.
100,000
.773
= 129,366 hours
STRATEGIC IMPLICATIONS OF LEARNING CURVES
So far, we have shown how operations managers can forecast labor-hour requirements for a prod-
uct. We have also shown how purchasing agents can determine a supplier’s cost, knowledge that
can help in price negotiations. Another important application of learning curves concerns strategic
planning.
An example of a company cost line and industry price line are so labeled in Figure E.2. These
learning curves are straight because both scales are log scales. When the rate of change is con-
stant, a log-log graph yields a straight line. If an organization believes its cost line to be the
“company cost” line, and the industry price is indicated by the dashed horizontal line, then the
LO4: Describe the
strategic implications of
learning curves

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612 PART 4 Quantitative Modules
Accumulated volume (log scale)
Gross profit
margin
Industry price
Loss
P
ri
ce
p
e
r
u
n
it
(l
o
g
s
ca
le
)
Com
pany cost
(c)
(b)
(a)
� FIGURE E.2
Industry Learning Curve for
Price Compared with
Company Learning Curve for
Cost
company must have costs at the points below the dashed line (for example, point a or b) or else
operate at a loss (point c).
Lower costs are not automatic; they must be managed down. When a firm’s strategy is to pur-
sue a curve steeper than the industry average (the company cost line in Figure E.2), it does this by:
1. Following an aggressive pricing policy
2. Focusing on continuing cost reduction and productivity improvement
3. Building on shared experience
4. Keeping capacity growing ahead of demand
Costs may drop as a firm pursues the learning curve, but volume must increase for the learning
curve to exist. Moreover, managers must understand competitors before embarking on a learning-
curve strategy. Weak competitors are undercapitalized, stuck with high costs, or do not under-
stand the logic of learning curves. However, strong and dangerous competitors control their
costs, have solid financial positions for the large investments needed, and have a track record of
using an aggressive learning-curve strategy. Taking on such a competitor in a price war may help
only the consumer.
LIMITATIONS OF LEARNING CURVES
Before using learning curves, some cautions are in order:
• Because learning curves differ from company to company, as well as industry to industry,
estimates for each organization should be developed rather than applying someone else’s.
• Learning curves are often based on the time necessary to complete the early units; therefore,
those times must be accurate. As current information becomes available, reevaluation is
appropriate.
• Any changes in personnel, design, or procedure can be expected to alter the learning
curve, causing the curve to spike up for a short time, even if it is going to drop in the long
run.
• While workers and processes may improve, the same learning curves do not always apply to
indirect labor and material.
• The culture of the workplace, as well as resource availability and changes in the process, may
alter the learning curve. For instance, as a project nears its end, worker interest and effort may
drop, curtailing progress down the curve.
AUTHOR COMMENT
Both the vertical and
horizontal axes of this figure
are log scales. This is known
as a log-log graph.
AUTHOR COMMENT
Determining accurate rates
of learning requires
careful analysis.

Module E Learning Curves 613
The learning curve is a powerful tool for the operations man-
ager. This tool can assist operations managers in determining
future cost standards for items produced as well as purchased.
In addition, the learning curve can provide understanding
about company and industry performance. We saw three
approaches to learning curves: arith-
metic analysis, logarithmic analysis,
and learning-curve coefficients found in
tables. Software can also help analyze
learning curves.
MODULE SUMMARY
Key Term
Learning curves (p. 606)
Using Software for Learning Curves
Excel, Excel OM, and POM for Windows may all be used in analyzing learning curves. You can use the
ideas in the following section on Excel OM to build your own Excel spreadsheet if you wish.
X Using Excel OM
Program E.1 shows how Excel OM develops a spreadsheet for learning-curve calculations. The input
data come from Examples E2 and E3. In cell B7, we enter the unit number for the base unit (which does
not have to be 1), and in B8, we enter the time for this unit.
=SUM($B$16:B16)
These are used for computations. Do not touch these cells. In cell B11, the time for the first
unit is computed, allowing us to use initial units other than unit 1. In cell B12, the power to be
raised to is computed, making the formulas in the rest of column B much simpler.
=$B$11*POWER(1,$B$12)
� PROGRAM E.1 Excel OM’s Learning-Curve Module, Using Data from Examples E2 and E3
P Using POM for Windows
The POM for Windows Learning Curve module computes the length of time that future units will take,
given the time required for the base unit and the learning rate (expressed as a number between 0 and 1).
As an option, if the times required for the first and Nth units are already known, the learning rate can be
computed. See Appendix IV for further details.

614 PART 4 Quantitative Modules
� SOLUTION
from Table E.3, coefficient for 80% unit time
a)
b) Total time for the first 11 units = (56 hours)(6.777) = 379.5 hours
from Table E.3, coefficient for 80% total time
c) To find the time for units 12 through 15, we take the total cumulative time for units 1 to 15 and
subtract the total time for units 1 to 11, which was computed in part (b). Total time for the
first So, the time for units 12 through 15 is
(This figure could also be confirmed by computing the times for units
12, 13, 14, and 15 separately using the unit-time coefficient column and then adding them.) Expected
cost for units 12 through 15 = 197.1 hours21$30 per hour2 = $2,913.
476.6 – 379.5 = 97.1 hours.
15 units = 156 hours218.5112 = 476.6 hours.
T11 = 156 hours21.4622 = 25.9 hours
TN = T1C
� SOLVED PROBLEM E.2
If the first time you performed a job took 60 minutes, how long
will the eighth job take if you are on an 80% learning curve?
� SOLUTION
Three doublings from 1 to 2 to 4 to 8 implies Therefore,
we have:
or, using Table E.3, we have Therefore:
60 * .512 = 30.72 minutes
C = .512.
60 * 1.823 = 60 * .512 = 30.72 minutes
.83.
� SOLVED PROBLEM E.1
Digicomp produces a new telephone system with built-in TV
screens. Its learning rate is 80%.
a) If the first one took 56 hours, how long will it take Digicomp to
make the eleventh system?
Solved Problems Virtual Office Hours help is available at www.myomlab.com
b) How long will the first 11 systems take in total?
c) As a purchasing agent, you expect to buy units 12 through 15
of the new phone system. What would be your expected cost
for the units if Digicomp charges $30 for each labor-hour?
Bibliography
Boh,W. F., S. A. Slaughter, and J. A. Espinosa. “Learning from
Experience in Software Development.” Management Science
53, no. 8 (August 2007): 1315–1332.
Couto, J. P., and J. C. Teixeira. “Using a Linear Model for
Learning Curve Effect on Highrise Floor Construction.”
Construction Management & Economics 23 (May 2005): 355.
McDonald, A., and L. Schrattenholzer. “Learning Curves and
Technology Assessment.” International Journal of Technology
Management 23 (2002): 718.
Morrison, J. Bradley. “Putting the Learning Curve into Context.”
Journal of Business Research 61, no. 1 (November 2008): 1182.
Ngwenyama, O., A. Guergachi, and T. McLaren. “Using
the Learning Curve to Maximize IT Productivity.”
International Journal of Production Economics 105, no. 2
(February 2007): 524.
Smunt, T. L., and C. A. Watts. “Improving Operations Planning
with Learning Curves.” Journal of Operations Management
21 (January 2003): 93.
Weston, M. Learning Curves. New York: Crown Publishing
(2000).

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QUANTITATIVE MODULE
Simulation
615
Module Outline
What Is Simulation? 616
Advantages and Disadvantages
of Simulation 617
Monte Carlo Simulation 618
Simulation of a Queuing Problem 621
Simulation and Inventory Analysis 623

616 PART 4 Quantitative Modules
LO1: List the advantages and disadvantages
of modeling with simulation 617
LO2: Perform the five steps in a
Monte Carlo simulation 618
Module F Learning Objectives
When Bay Medical Center faced severe overcrowding at its outpatient clinic, it turned to computer simulation to
try to reduce bottlenecks and improve patient flow. A simulation language called Micro Saint analyzed current
data relating to patient service times between clinic rooms. By simulating different numbers of doctors and
staff, simulating the use of another clinic for overflow, and simulating a redesign of the existing clinic, Bay
Medical Center was able to make decisions based on an understanding of both costs and benefits. This
resulted in better patient service at lower cost.
Source: Micro Analysis and Design Simulation Software, Inc., Boulder, CO.
LO3: Simulate a queuing problem 621
LO4: Simulate an inventory problem 623
LO5: Use Excel spreadsheets to create a
simulation 627
WHAT IS SIMULATION?
Simulation models abound in our world. The city of Atlanta, for example, uses them to control
traffic. Europe’s Airbus Industries uses them to test the aerodynamics of proposed jets. The U.S.
Army simulates war games on computers. Business students use management gaming to simu-
late realistic business competition. And thousands of organizations like Bay Medical Center
develop simulation models to help make operations decisions.
Most of the large companies in the world use simulation models. Table F.1 lists just a few
areas in which simulation is now being applied.
Simulation is the attempt to duplicate the features, appearance, and characteristics of a real
system. In this module, we will show how to simulate part of an operations management system
by building a mathematical model that comes as close as possible to representing the reality of
� TABLE F.1
Some Applications of
Simulation
Ambulance location and dispatching Bus scheduling
Assembly-line balancing Design of library operations
Parking lot and harbor design Taxi, truck, and railroad dispatching
Distribution system design Production facility scheduling
Scheduling aircraft Plant layout
Labor-hiring decisions Capital investments
Personnel scheduling Production scheduling
Traffic-light timing Sales forecasting
Voting pattern prediction Inventory planning and control
Simulation
The attempt to duplicate the
features, appearance, and
characteristics of a real system,
usually via a computerized
model.

Module F Simulation 617
the system. The model will then be used to estimate the effects of various actions. The idea
behind simulation is threefold:
1. To imitate a real-world situation mathematically
2. Then to study its properties and operating characteristics
3. Finally to draw conclusions and make action decisions based on the results of the simulation
In this way, a real-life system need not be touched until the advantages and disadvantages of a
major policy decision are first measured on the model.
To use simulation, an OM manager should:
1. Define the problem.
2. Introduce the important variables associated with the problem.
3. Construct a numerical model.
4. Set up possible courses of action for testing by specifying values of variables.
5. Run the experiment.
6. Consider the results (possibly modifying the model or changing data inputs).
7. Decide what course of action to take.
These steps are illustrated in Figure F.1.
The problems tackled by simulation may range from very simple to extremely complex, from
bank-teller lines to an analysis of the U.S. economy. Although small simulations can be con-
ducted by hand, effective use of the technique requires a computer. Large-scale models, simulat-
ing perhaps years of business decisions, are virtually all handled by computer.
In this module, we examine the basic principles of simulation and then tackle some problems in
the areas of waiting-line analysis and inventory control. Why do we use simulation in these areas
when mathematical models described in other chapters can solve similar problems? The answer is
that simulation provides an alternative approach for problems that are very complex mathemati-
cally. It can handle, for example, inventory problems in which demand or lead time is not constant.
ADVANTAGES AND DISADVANTAGES OF SIMULATION
Simulation is a tool that has become widely accepted by managers for several reasons. The main
advantages of simulation are as follows:
1. Simulation is relatively straightforward and flexible.
2. It can be used to analyze large and complex real-world situations that cannot be solved by
conventional operations management models.
3. Real-world complications can be included that most OM models cannot permit. For exam-
ple, simulation can use any probability distribution the user defines; it does not require stan-
dard distributions.
4. “Time compression” is possible. The effects of OM policies over many months or years can
be obtained by computer simulation in a short time.
5. Simulation allows “what-if?” types of questions. Managers like to know in advance what
options will be most attractive. With a computerized model, a manager can try out several
policy decisions within a matter of minutes.
6. Simulations do not interfere with real-world systems. It may be too disruptive, for example,
to experiment physically with new policies or ideas in a hospital or manufacturing plant.
7. Simulation can study the interactive effects of individual components or variables in order to
determine which ones are important.
The main disadvantages of simulation are as follows:
1. Good simulation models can be very expensive; they may take many months to develop.
2. It is a trial-and-error approach that may produce different solutions in repeated runs. It does
not generate optimal solutions to problems (as does linear programming).
3. Managers must generate all of the conditions and constraints for solutions that they want to
examine. The simulation model does not produce answers without adequate, realistic input.
4. Each simulation model is unique. Its solutions and inferences are not usually transferable to
other problems.
Define problem
Introduce variables
Construct model
Conduct simulation
Examine results
Select best course
Specify values
of variables
� FIGURE F.1
The Process of Simulation
AUTHOR COMMENT
There are many reasons
it’s better to simulate a
real-world system than
to experiment with it.
LO1: List the advantages
and disadvantages of
modeling with simulation

618 PART 4 Quantitative Modules
MONTE CARLO SIMULATION
When a system contains elements that exhibit chance in their behavior, the Monte Carlo
method of simulation may be applied. The basis of Monte Carlo simulation is experimentation
on chance (or probabilistic) elements by means of random sampling.
The technique breaks down into five simple steps:
1. Setting up a probability distribution for important variables.
2. Building a cumulative probability distribution for each variable.
3. Establishing an interval of random numbers for each variable.
4. Generating random numbers.
5. Actually simulating a series of trials.
Let’s examine these steps in turn.
Step 1. Establishing Probability Distributions. The basic idea in the Monte Carlo simula-
tion is to generate values for the variables making up the model under study. In real-world sys-
tems, a lot of variables are probabilistic in nature. To name just a few: inventory demand; lead
time for orders to arrive; times between machine breakdowns; times between customer arrivals at
a service facility; service times; times required to complete project activities; and number of
employees absent from work each day.
One common way to establish a probability distribution for a given variable is to examine histori-
cal outcomes. We can find the probability, or relative frequency, for each possible outcome of a vari-
able by dividing the frequency of observation by the total number of observations. Here’s an example.
The daily demand for radial tires at Barry’s Auto Tire over the past 200 days is shown in
columns 1 and 2 of Table F.2. Assuming that past arrival rates will hold in the future, we can con-
vert this demand to a probability distribution by dividing each demand frequency by the total
demand, 200. The results are shown in column 3.
Step 2. Building a Cumulative Probability Distribution for Each Variable. The conver-
sion from a regular probability distribution, such as in column 3 of Table F.2, to a cumulative
probability distribution is an easy job. In column 4, we see that the cumulative probability for
LO2: Perform the five
steps in a Monte Carlo
simulation
Computer simulation models have
been developed to address a variety
of productivity issues at fast-food
restaurants such as Burger King. In one,
the ideal distance between the drive-
through order station and the pickup
window was simulated. For example,
because a longer distance reduced
waiting time, 12 to 13 additional
customers could be served per hour—
a benefit of about $20,000 in extra
sales per restaurant per year. In another
simulation, a second drive-through
window was considered. This model
predicted a sales increase of 15%.
Cumulative probability
distribution
The accumulation of individual
probabilities of a distribution.
� TABLE F.2
Demand for Barry’s Auto Tire
(1) (2) (3) (4)
Probability of Cumulative
Demand for Tires Frequency Occurrence Probability
0 10 .05
1 20 .15
2 40 .35
3 60 .65
4 40 .85
5 30 1.00
200 days 200>200 = 1.00
30>200 = .15
40>200 = .20
60>200 = .30
40>200 = .20
20>200 = .10
10>200 = .05
AUTHOR COMMENT
This approach is named
after the random behavior
of a roulette wheel.
AUTHOR COMMENT
To establish a probability
distribution for tires, we
assume that historical
demand is a good indicator
of future demand.
Monte Carlo method
A simulation technique that
uses random elements when
chance exists in their behavior.

Module F Simulation 619
each level of demand is the sum of the number in the probability column (column 3) added to the
previous cumulative probability.
Step 3. Setting Random-Number Intervals. Once we have established a cumulative proba-
bility distribution for each variable in the simulation, we must assign a set of numbers to repre-
sent each possible value or outcome. These are referred to as random-number intervals.
Basically, a random number is a series of digits (say, two digits from 01, 02, . . . , 98, 99, 00)
that have been selected by a totally random process—a process in which each random number
has an equal chance of being selected.
If, for example, there is a 5% chance that demand for Barry’s radial tires will be 0 units per day,
then we will want 5% of the random numbers available to correspond to a demand of 0 units. If a
total of 100 two-digit numbers is used in the simulation, we could assign a demand of 0 units to the
first 5 random numbers: 01, 02, 03, 04, and 05.1 Then a simulated demand for 0 units would be cre-
ated every time one of the numbers 01 to 05 was drawn. If there is also a 10% chance that demand
for the same product will be 1 unit per day, we could let the next 10 random numbers (06, 07, 08,
09, 10, 11, 12, 13, 14, and 15) represent that demand—and so on for other demand levels.
Similarly, we can see in Table F.3 that the length of each interval on the right corresponds to
the probability of 1 of each of the possible daily demands. Thus, in assigning random numbers to
1Alternatively, we could have assigned the random numbers 00, 01, 02, 03, and 04 to represent a demand of 0 units.
The 2 digits 00 can be thought of as either 0 or 100. As long as 5 numbers out of 100 are assigned to the 0 demand, it
does not make any difference which 5 they are.
Random-number
intervals
A set of numbers to represent
each possible value or outcome
in a computer simulation.
Random number
A series of digits that have been
selected by a totally random
process.
� TABLE F.3
The Assignment of Random-
Number Intervals for Barry’s
Auto Tire
Cumulative Interval of
Daily Demand Probability Probability Random Numbers
0 .05 .05 01 through 05
1 .10 .15 06 through 15
2 .20 .35 16 through 35
3 .30 .65 36 through 65
4 .20 .85 66 through 85
5 .15 1.00 86 through 00
AUTHOR COMMENT
You may start random
number intervals at either 01
or 00, but the text starts at 01
so that the top of each range
is the cumulative probability.
� TABLE F.4 Table of 2-Digit Random Numbers
52 06 50 88 53 30 10 47 99 37 66 91 35 32 00 84 57 07
37 63 28 02 74 35 24 03 29 60 74 85 90 73 59 55 17 60
82 57 68 28 05 94 03 11 27 79 90 87 92 41 09 25 36 77
69 02 36 49 71 99 32 10 75 21 95 90 94 38 97 71 72 49
98 94 90 36 06 78 23 67 89 85 29 21 25 73 69 34 85 76
96 52 62 87 49 56 59 23 78 71 72 90 57 01 98 57 31 95
33 69 27 21 11 60 95 89 68 48 17 89 34 09 93 50 44 51
50 33 50 95 13 44 34 62 64 39 55 29 30 64 49 44 30 16
88 32 18 50 62 57 34 56 62 31 15 40 90 34 51 95 26 14
90 30 36 24 69 82 51 74 30 35 36 85 01 55 92 64 09 85
50 48 61 18 85 23 08 54 17 12 80 69 24 84 92 16 49 59
27 88 21 62 69 64 48 31 12 73 02 68 00 16 16 46 13 85
45 14 46 32 13 49 66 62 74 41 86 98 92 98 84 54 33 40
81 02 01 78 82 74 97 37 45 31 94 99 42 49 27 64 89 42
66 83 14 74 27 76 03 33 11 97 59 81 72 00 64 61 13 52
74 05 81 82 93 09 96 33 52 78 13 06 28 30 94 23 37 39
30 34 87 01 74 11 46 82 59 94 25 34 32 23 17 01 58 73
59 55 72 33 62 13 74 68 22 44 42 09 32 46 71 79 45 89
67 09 80 98 99 25 77 50 03 32 36 63 65 75 94 19 95 88
60 77 46 63 71 69 44 22 03 85 14 48 69 13 30 50 33 24
60 08 19 29 36 72 30 27 50 64 85 72 75 29 87 05 75 01
80 45 86 99 02 34 87 08 86 84 49 76 24 08 01 86 29 11
53 84 49 63 26 65 72 84 85 63 26 02 75 26 92 62 40 67
69 84 12 94 51 36 17 02 15 29 16 52 56 43 26 22 08 62
37 77 13 10 02 18 31 19 32 85 31 94 81 43 31 58 33 51
Source: Reprinted from A Million Random Digits with 100,000 Normal Deviates (New York: The Free Press, 1995). Used by permission.

620 PART 4 Quantitative Modules
EXAMPLE F1 �
Simulating demand
Barry’s Auto Tire wants to simulate 10 days of demand for radial tires.
APPROACH � Earlier, we went through Steps 1 and 2 in the Monte Carlo method (in Table F.2)
and Step 3 (in Table F.3). Now we need to generate random numbers (Step 4) and simulate demand
(Step 5).
SOLUTION � We select the random numbers needed from Table F.4, starting in the upper-left-
hand corner and continuing down the first column and record the corresponding daily demand:
the daily demand for 3 radial tires, the range of the random-number interval (36 through 65) cor-
responds exactly to the probability (or proportion) of that outcome. A daily demand for 3 radial
tires occurs 30% of the time. All of the 30 random numbers greater than 35 up to and including
65 are assigned to that event.
Step 4. Generating Random Numbers. Random numbers may be generated for simulation
problems in two ways. If the problem is large and the process under study involves many simu-
lation trials, computer programs are available to generate the needed random numbers. If the
simulation is being done by hand, the numbers may be selected from a table of random digits.
Step 5. Simulating the Experiment. We may simulate outcomes of an experiment by sim-
ply selecting random numbers from Table F.4. Beginning anywhere in the table, we note the
interval in Table F.3 into which each number falls. For example, if the random number chosen is
81 and the interval 66 through 85 represents a daily demand for 4 tires, then we select a demand
of 4 tires. Example F1 carries the simulation further.
INSIGHT � It is interesting to note that the average demand of 3.9 tires in this 10-day simulation dif-
fers substantially from the expected daily demand, which we may calculate from the data in Table F.3:
However, if this simulation was repeated hundreds or thousands of times, the average simulated
demand would be nearly the same as the expected demand.
LEARNING EXERCISE � Resimulate the 10 days, this time with random numbers from col-
umn 2 of Table F.4. What is the average daily demand? [Answer: 2.5.]
RELATED PROBLEMS � F.1, F.2, F.3, F.4, F.5, F.7, F.9, F.10, F.14, F.20
= 2.95 tires
= 0 + .1 + .4 + .9 + .8 + .75
= 1.052102 + 1.102112 + 1.202122 + 1.302132 + 1.202142 + 1.152152
Expected demand = a
5
i= 1
1probability of i units2 * 1demand of i units2
Day Random Simulated
Number Number Daily Demand
1 52 3
2 37 3
3 82 4
4 69 4
5 98 5
6 96 5
7 33 2
8 50 3
9 88 5
10 90 5
39 Total 10-day demand
39/10 = 3.9 = tires average daily demand
Naturally, it would be risky to draw any hard and fast conclusions about the operation of a
firm from only a short simulation like Example F1. Seldom would anyone actually want to go
to the effort of simulating such a simple model containing only one variable. Simulating by

Module F Simulation 621
The animation on the computer screen is not encouraging.
Starbucks is running a digital simulation of customers order-
ing new warm sandwiches and pastries at a “virtual” store.
At first, things seem to go well, as animated workers
rush around, preparing orders. But then they can’t keep up.
Soon the customers are stacking up in line, and the goal of
serving each person in less than 3 minutes is blown. The
line quickly reaches the point at which customers decide
the snack or drink isn’t worth the wait—called the “balking
point” in queuing theory.
Fortunately for Starbucks, the customers departing
without their frappuchinos and decaf slim lattes are digital.
The simulation software is helping operations managers
find out what caused the backup before the scene repeats
itself in the real world.
OM in Action � Simulation Software Takes the Kinks out of Starbucks’s Lines
LO3: Simulate a queuing
problem
AUTHOR COMMENT
Using simulation is often
the best way to model
a complex system.
� EXAMPLE F2
A barge-
unloading
simulation with
two variables
Following long trips down the Mississippi River from industrial midwestern cities, fully loaded barges
arrive at night in New Orleans. Barges are unloaded on a first-in, first-out basis. Any barges not
unloaded on the day of arrival must wait until the following day. However, tying up barges in dock is an
expensive proposition, and the superintendent cannot ignore the angry phone calls from barge owners
reminding him that “time is money!” He decides that before going to the Port of New Orleans
controller to request additional unloading crews, he should conduct a simulation study of arrivals,
unloadings, and delays. A 100-day simulation would be ideal, but for purposes of illustration, the
superintendent can begin with a shorter 15-day analysis.
APPROACH � Follow the 5 steps in Monte Carlo simulation: (1) establish probability distribu-
tions for the important variables (i.e., barge arrivals and barge unloadings); (2 and 3) create cumulative
distributions and random number intervals for each variable; (4) draw random numbers from Table F.4;
and (5) simulate the experiment.
SOLUTION � The number of barges docking on any given night ranges from 0 to 5. The probabil-
ity of 0, 1, 2, 3, 4, and 5 arrivals is displayed in Table F.5. In the same table, we establish cumulative
probabilities and corresponding random-number intervals for each possible value.
� TABLE F.5
Overnight Barge Arrival Rates
and Random-Number Intervals
Number of Cumulative Random-Number
Arrivals Probability Probability Interval
0 .13 .13 01 through 13
1 .17 .30 14 through 30
2 .15 .45 31 through 45
3 .25 .70 46 through 70
4 .20 .90 71 through 90
5 .10 1.00 91 through 00
1.00
Simulation
software is also
used to find the
point where capital
expenditures will
pay off. In large
chains such as
Starbucks, adding
even a minor piece
of equipment can
add up. A $200 blender in each of Starbucks’s more than
10,000 stores globally can cost the firm $2 million.
Sources: The Wall Street Journal (August 4, 2009): A1, A10; and Business
Wire (February 13, 2006): 1 and (June 15, 2005): 1.
hand does, however, demonstrate the important principles involved and may be useful in
small-scale studies.
SIMULATION OF A QUEUING PROBLEM
An important use of simulation is in the analysis of waiting-line problems. As we saw in Module D,
the assumptions required for solving queuing problems are quite restrictive. For most realistic queuing
systems, simulation may be the only approach available as we see in the Starbucks OM in Action box.
Example F2 illustrates the use of simulation for a large unloading dock and its associated
queue. Arrivals of barges at the dock are not Poisson-distributed, and unloading rates (service
times) are not exponential or constant. As such, the mathematical waiting-line models of Module
D cannot be used.

622 PART 4 Quantitative Modules
The dock superintendent believes that the number of barges unloaded also tends to vary from day to
day. In Table F.6, the superintendent provides information from which we can create a probability
distribution for the variable daily unloading rate. As we just did for the arrival variable, we can set up
an interval of random numbers for the unloading rates.
� TABLE F.6
Unloading Rates and Random-
Number Intervals
Daily Cumulative Random-Number
Unloading Rates Probability Probability Interval
1 .05 .05 01 through 05
2 .15 .20 06 through 20
3 .50 .70 21 through 70
4 .20 .90 71 through 90
5 .10 1.00 91 through 00
1.00
AUTHOR COMMENT
The relationship between
these random-number
intervals and cumulative
probability is that the top end
of each interval is equal to
the cumulative probability
percentage. Random numbers are drawn from the top row of Table F.4 to generate daily arrival rates. To create
daily unloading rates, they are drawn from the second row of Table F.4. Table F.7 shows the day-to-day
port simulation.
� TABLE F.7
Queuing Simulation of Port of
New Orleans Barge Unloadings
(1) (2) (3) (4) (5) (6) (7)
Number Number Total
Delayed from Random of Nightly to Be Random Number
Day Previous Day Number Arrivals Unloaded Number Unloaded
1 a 52 3 3 37 3
2 0 06 0 0 63 0 b
3 0 50 3 3 28 3
4 0 88 4 4 02 1
5 3 53 3 6 74 4
6 2 30 1 3 35 3
7 0 10 0 0 24 0 c
8 0 47 3 3 03 1
9 2 99 5 7 29 3
10 4 37 2 6 60 3
11 3 66 3 6 74 4
12 2 91 5 7 85 4
13 3 35 2 5 90 4
14 1 32 2 3 73 3 d
15 0 00 5 5 59 3
20 41 39
Total delays Total arrivals Total unloadings
aWe can begin with no delays from the previous day. In a long simulation, even if we started with five overnight delays, that
initial condition would be averaged out.
bThree barges could have been unloaded on day 2. Yet because there were no arrivals and no backlog existed, zero unloadings
took place.
cThe same situation as noted in footnote b takes place.
dThis time, 4 barges could have been unloaded, but because only 3 were in queue, the number unloaded is recorded as 3.
�
INSIGHT � The superintendent will likely be interested in at least three useful and important
pieces of information:
= 2.60 unloadings per day
Average number of barges unloaded each day =
39 unloadings
15 days
= 2.73 arrivals per night
Average number of nightly arrivals =
41 arrivals
15 days
= 1.33 barges delayed per day
¢Average number of barges
delayed to the next day
≤ = 20 delays
15 days

Module F Simulation 623
LO4: Simulate an
inventory problem
The simulation in Table F.7 by itself provides interesting data, but these three averages are management
information to help make decisions.
LEARNING EXERCISE � If the random numbers for day 15 were 03 and 93 (instead of 00 and
59), how would these 3 averages change? [Answer: They would be 1.33 (unchanged), 2.4, and 2.4.]
RELATED PROBLEMS � F.6, F.8, F.15, F.19, F.21
When the data from Example F2 are analyzed in terms of delay costs, idle labor costs, and the cost
of hiring extra unloading crew, the dock superintendent and port controller can make a better
staffing decision. They may even choose to resimulate the process assuming different unloading
rates that correspond to increased crew sizes. Although simulation cannot guarantee an optimal
solution to problems such as this, it can be helpful in re-creating a process and identifying good
decision alternatives.
SIMULATION AND INVENTORY ANALYSIS
In Chapter 12, we introduced inventory models. The commonly used EOQ models are based on
the assumption that both product demand and reorder lead time are known, constant values. In
most real-world inventory situations, though, demand and lead time are variables, so accurate
analysis becomes extremely difficult to handle by any means other than simulation.
In this section, we present an inventory problem with two decision variables and two proba-
bilistic components. The owner of the hardware store in Example F3 would like to establish
order quantity and reorder point decisions for a particular product that has probabilistic (uncer-
tain) daily demand and reorder lead time. He wants to make a series of simulation runs, trying
out various order quantities and reorder points, to minimize his total inventory cost for the item.
Inventory costs in this case will include ordering, holding, and stockout costs.
AUTHOR COMMENT
Most real-world inventory
systems have probabilistic
events and benefit from a
simulation approach.
� EXAMPLE F3
An inventory
simulation with
two variables
Simkin’s Hardware Store, in Reno, sells the Ace model electric drill. Daily demand for this particular
product is relatively low but subject to some variability. Lead times tend to be variable as well. Mark
Simkin wants to develop a simulation to test an inventory policy of ordering 10 drills, with a reorder
point of 5. In other words, every time the on-hand inventory level at the end of the day is 5 or less,
Simkin will call his supplier that evening and place an order for 10 more drills. Simkin notes that if the
lead time is 1 day, the order will not arrive the next morning but rather at the beginning of the follow-
ing workday. Stockouts become lost sales, not backorders.
APPROACH � Simkin wants to follow the 5 steps in the Monte Carlo simulation process.
SOLUTION � Over the past 300 days, Simkin has observed the sales shown in column 2 of Table
F.8. He converts this historical frequency into a probability distribution for the variable daily demand
(column 3). A cumulative probability distribution is formed in column 4 of Table F.8. Finally, Simkin
establishes an interval of random numbers to represent each possible daily demand (column 5).
� TABLE F.8
Probabilities and Random-
Number Intervals for Daily Ace
Drill Demand
(1) (2) (3) (4) (5)
Demand for Cumulative Interval of
Ace Drill Frequency Probability Probability Random Numbers
0 15 .05 .05 01 through 05
1 30 .10 .15 06 through 15
2 60 .20 .35 16 through 35
3 120 .40 .75 36 through 75
4 45 .15 .90 76 through 90
5 30 .10 1.00 91 through 00
300 days 1.00
When Simkin places an order to replenish his inventory of drills, there is a delivery lag of from 1 to
3 days. This means that lead time may also be considered a probabilistic variable. The number of days
that it took to receive the past 50 orders is presented in Table F.9. In a fashion similar to the creation of

624 PART 4 Quantitative Modules
the demand variable, Simkin establishes a probability distribution for the lead time variable (column 3
of Table F.9), computes the cumulative distribution (column 4), and assigns random-number intervals
for each possible time (column 5).
� TABLE F.9
Probabilities and Random-
Number Intervals for Reorder
Lead Time
(1) (2) (3) (4) (5)
Lead Time Cumulative Random-Number
(days) Frequency Probability Probability Interval
1 10 .20 .20 01 through 20
2 25 .50 .70 21 through 70
3 15 .30 1.00 71 through 00
50 orders 1.00
The entire process is simulated in Table F.10 for a 10-day period. We assume that beginning inventory
(column 3) is 10 units on day 1. We took the random numbers (column 4) from column 2 of Table F.4.
�TABLE F.10
Simkin Hardware’s First
Inventory Simulation. Order
Quantity = 10 Units; Reorder
Point = 5 units
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Units Beginning Random Ending Lost Random Lead
Day Received Inventory Number Demand Inventory Sales Order? Number Time
1 10 06 1 9 0 No
2 0 9 63 3 6 0 No
3 0 6 57 3 3 a 0 Yes 02 b 1
4 0 3 94 c 5 0 2 No d
5 10 e 10 52 3 7 0 No
6 0 7 69 3 4 0 Yes 33 2
7 0 4 32 2 2 0 No
8 0 2 30 2 0 0 No
9 10 f 10 48 3 7 0 No
10 0 7 88 4 3 0 Yes 14 1
Totals: 41 2
aThis is the first time inventory dropped to the reorder point of five drills. Because no prior order was outstanding, an
order is placed.
bThe random number 02 is generated to represent the first lead time. It was drawn from column 2 of Table F.4 as the
next number in the list being used. A separate column could have been used from which to draw lead-time random
numbers if we had wanted to do so, but in this example, we did not do so.
cAgain, notice that the random digits 02 were used for lead time (see footnote b). So the next number in the
column is 94.
dNo order is placed on day 4 because there is an order outstanding from the previous day that has not yet arrived.
eThe lead time for the first order placed is 1 day, but as noted in the text, an order does not arrive the next
morning but rather the beginning of the following day. Thus, the first order arrives at the start of day 5.
fThis is the arrival of the order placed at the close of business on day 6. Fortunately for Simkin, no lost sales
occurred during the 2-day lead time before the order arrived.
Table F.10 was filled in by proceeding 1 day (or line) at a time, working from left to right. It is a
four-step process:
1. Begin each simulated day by checking to see whether any ordered inventory has just arrived. If
it has, increase current inventory by the quantity ordered (10 units, in this case).
2. Generate a daily demand from the demand probability distribution for the selected random number.
3. Compute: Ending inventory = Beginning inventory minus Demand. If on-hand inventory is
insufficient to meet the day’s demand, satisfy as much demand as possible and note the number
of lost sales.
4. Determine whether the day’s ending inventory has reached the reorder point (5 units). If it has,
and if there are no outstanding orders, place an order. Lead time for a new order is simulated for
the selected random number corresponding to the distribution in Table F.9.
INSIGHTS � Simkin’s inventory simulation yields some interesting results. The average daily
ending inventory is:
Average ending inventory =
41 total units
10 days
= 4.1 units>day

Module F Simulation 625
We also note the average lost sales and number of orders placed per day:
LEARNING EXERCISE � How would these 3 averages change if the random numbers for day
10 were 04 and 93 instead of 88 and 14? [Answer: 4.5, .2 (no change), and .2.]
RELATED PROBLEMS � F.11, F.16a
Average number of orders placed =
3 orders
10 days
= .3 orders>day
Average lost sales =
2 sales lost
10 days
= .2 units>day
Example F4 shows how these data can be useful in studying the inventory costs of the policy
being simulated.
� EXAMPLE F4
Adding costs to
Example F3
Simkin wants to put a cost on the ordering policy simulated in Example F3.
APPROACH � Simkin estimates that the cost of placing each order for Ace drills is $10, the hold-
ing cost per drill held at the end of each day is $.50, and the cost of each lost sale is $8. This informa-
tion enables us to compute the total daily inventory cost.
SOLUTION � Here are the three cost components:
INSIGHT � This cost will help Simkin decide if the order policy is a good one.
LEARNING EXERCISE � If the cost of placing an order is really $20 (instead of $10), what is
the correct total daily inventory cost? [Answer: $9.65.]
RELATED PROBLEMS � F.12, F.13, F.16b, F.17, F.18
ROP = 5Q = 10,
Total daily inventory cost = Daily order cost + Daily holding cost + Daily stockout cost = $6.65
= $8 per lost sale * .2 lost sales per day = $1.60
Daily stockout cost = 1Cost per lost sale2 * 1Average number of lost sales per day2
= 50¢ per unit per day * 4.1 units per day = $2.05
Daily holding cost = 1Cost of holding 1 unit for 1 day2 * 1Average ending inventory2
= $10 per order * .3 order per day = $3
Daily order cost = 1Cost of placing 1 order2 * 1Number of orders placed per day2
Now that we have worked through Examples F3 and F4, we want to emphasize something very
important: This simulation should be extended many more days before we draw any conclusions
as to the cost of the order policy being tested. If a hand simulation is being conducted, 100 days
would provide a better representation. If a computer is doing the calculations, 1,000 days would
be helpful in reaching accurate cost estimates. (Moreover, remember that even with a 1,000-day
simulation, the generated distribution should be compared with the desired distribution to ensure
valid results.)
Let us say that Simkin does complete a 1,000-day simulation of the policy from Example
F3 (order quantity = 10 drills, reorder point = 5 drills). Does this complete his analysis? The
answer is no—this is just the beginning! Simkin must now compare this potential strategy
with other possibilities. For example, what about order quantity = 10, reorder point = 4? Or
order quantity = 12, reorder point = 6? Or order quantity = 14, reorder point = 5? Perhaps
every combination of values—of order quantity from 6 to 20 drills and reorder points from
3 to 10—should be simulated. After simulating all reasonable combinations of order
quantities and reorder points, Simkin would likely select the pair yielding the lowest total
inventory cost. Problem F.12 in the Lecture Guide & Activities Manual gives you a chance to
help Simkin begin this series of comparisons.

626 PART 4 Quantitative Modules
Simulation involves building mathematical models that
attempt to act like real operating systems. In this way, a
real-world situation can be studied without imposing on the
actual system. Although simulation models can be devel-
oped manually, simulation by computer is generally more
desirable. The Monte Carlo approach uses random numbers
to represent variables, such as inven-
tory demand or people waiting in line,
which are then simulated in a series of
trials. Simulation is widely used as an
operations tool because its advantages
usually outweigh its disadvantages.
MODULE SUMMARY
Key Terms
Simulation (p. 616)
Monte Carlo method (p. 618)
Cumulative probability distribution (p. 618)
Random-number intervals (p. 619)
Random number (p. 619)
Using Software in Simulation
Computers are critical in simulating complex tasks. They can generate random numbers, simulate thou-
sands of time periods in a matter of seconds or minutes, and provide management with reports that
improve decision making. A computer approach is almost a necessity in order to draw valid conclusions
from a simulation.
Computer programming languages can help the simulation process. General-purpose languages,
such as BASIC or C��, constitute one approach. Special-purpose simulation languages, such as GPSS
and SIMSCRIPT, have a few advantages: (1) they require less programming time for large simulations,
(2) they are usually more efficient and easier to check for errors, and (3) random-number generators are
already built in as subroutines.
Commercial, easy-to-use prewritten simulation programs are also available. Some are generalized to
handle a wide variety of situations ranging from queuing to inventory. These include programs such as
Extend, Modsim, Witness, MAP/1, Enterprise Dynamics, Simfactory, ProModel, Micro Saint, and
ARENA. The OM in Action box above, “Simulating Jackson Memorial Hospital’s Operating Rooms,”
described one application of ARENA software.
Spreadsheet software such as Excel can also be used to develop simulations quickly and easily.
Such packages have built-in random-number generators and develop outputs through “data-fill” table
commands.
Miami’s Jackson Memorial Hospital, Florida’s largest with
1,576 inpatient beds, is also one of the U.S.’s finest. In
1996, it received the highest accreditation score of any
public-sector hospital in the country. Jackson’s operations
management team is constantly seeking ways of
increasing hospital efficiency, and the construction
of new operating rooms (ORs) prompted the development
of a simulation of the existing 31 ORs.
The OR section of the hospital includes a patient
holding area and a patient recovery area, both of which
were experiencing problems owing to ineffective
scheduling of OR services. A simulation study, modeled
using the ARENA software package, sought to maximize
use of OR rooms and staff. Inputs to the model included
(1) the amount of time a patient waits in the holding area,
(2) the specific process the patient undergoes, (3) the staff
schedule, (4) room availability, and (5) time of day.
The first hurdle that the management team had to deal
with at Jackson was the vast number of records to review
to extract the information necessary for the simulation
model. The second hurdle was the quality of the data. A
thorough analysis of the records determined which were
good and which had to be discarded. In the end, Jackson’s
carefully screened databases led to a good set of data
inputs for the model. The simulation model then
successfully developed five measures of performance:
(1) number of procedures a day, (2) average case time,
(3) staff utilization, (4) room utilization, and (5) average
waiting time in the holding area.
Sources: Knight Ridder Tribune Business Service (May 3, 2004): 1;
and M. A. Centeno et al., “Challenges of Simulating Hospital Facilities,”
Proceedings of the 12th Annual Conference of the Production and
Operations Management Society (March 2001).
OM in Action � Simulating Jackson Memorial Hospital’s Operating Rooms

Module F Simulation 627
LO5: Use Excel
spreadsheets to create a
simulation
Use the RAND function
to generate random
numbers between 0
and 1.
Use the VLOOKUP function to
determine the number of tires
sold based on the random
number generated and the
probability table in C4:E9.
Use the FREQUENCY function to create a frequency
table based on the simulation runs in column I.
� PROGRAM F.1 Using Excel to Simulate Tire Demand for Barry’s Auto Tire Shop
The output shows a simulated average of 3.2 tires per day (in cell I14).
X Using Excel Spreadsheets
The ability to generate random numbers and then “look up” these numbers in a table to associate them
with a specific event makes spreadsheets excellent tools for conducting simulations. Program F.1 illus-
trates an Excel simulation for Example F1.
Notice that the cumulative probabilities are calculated in column E of Program F.1. This procedure
reduces the chance of error and is useful in larger simulations involving more levels of demand.
The function in column I looks up the random number (generated in column H) in the
leftmost column of the defined lookup table ($A$4:$B$9). The function moves down-
ward through this column until it finds a cell that is bigger than the random number. It then goes to the
previous row and gets the value from column B of the table.
In column H, for example, the first random number shown is .716. Excel looked down the left-hand
column of the lookup table ($A$4:$B$9) of Program F.1 until it found .85. From the previous row it
retrieved the value in column B which is 4. Pressing the F9 function key recalculates the random num-
bers and the simulation.
= VLOOKUP
= VLOOKUP
Value Cell Excel Formula Action
Cumulative probability A4 =0
Cumulative probability A5 =A4+D4 Copy to A6:A9
Random Number H4 =RAND() Copy to H5:H13
Demand I4 =VLOOKUP(H4,$A$4:$B$9,2,TRUE) Copy to I5:I13
Average I14 =AVERAGE(I4:I13)
Frequency C18 =FREQUENCY($I$4:$I$13,$B$18:$B$23) Array copy to C19:C23
Total C24 =SUM(C18:C23)
Percentage D18 =C18/$C$24 Copy to D19:D23
Average simulated
demand D25 =SUMPRODUCT(B18:B23,D18:D23)
Cumulative Percentage E18 =D18
Cumulative Percentage E19 =E18+D19 Copy to E20:E23
Press the F9 Key to simulate

628 PART 4 Quantitative Modules
Solved Problems Virtual Office Hours help is available at www.myomlab.com
a) If Higgins maintains a constant supply of 8 water heaters in any
given week, how many times will he stockout during a 20-week
simulation? We use random numbers from the 7th column of
Table F.4 (on p. 619), beginning with the random digit 10.
b) What is the average number of sales per week over the 20-
week period?
c) Using an analytic nonsimulation technique, determine the
expected number of sales per week. How does this compare
with the answer in part (b)?
� SOLVED PROBLEM F.1
Higgins Plumbing and Heating maintains a stock of 30-gallon
water heaters that it sells to homeowners and installs for them.
Owner Jim Higgins likes the idea of having a large supply on hand
to meet any customer demand. However, he also recognizes that it
is expensive to do so. He examines water heater sales over the past
50 weeks and notes the following:
Water Heater Number of Weeks This
Sales per Week Number Was Sold
4 6
5 5
6 9
7 12
8 8
9 7
10 3
50 weeks total data
� SOLUTION
Cumulative Random-Number
Heater Sales Probability Probability Intervals
4 .12 .12 01 through 12
5 .10 .22 13 through 22
6 .18 .40 23 through 40
7 .24 .64 41 through 64
8 .16 .80 65 through 80
9 .14 .94 81 through 94
10 .06 1.00 95 through 00
1.00
a)
Random Simulated Random Simulated
Week Number Sales Week Number Sales
1 10 4 11 08 4
2 24 6 12 48 7
3 03 4 13 66 8
4 32 6 14 97 10
5 23 6 15 03 4
6 59 7 16 96 10
7 95 10 17 46 7
8 34 6 18 74 8
9 34 6 19 77 8
10 51 7 20 44 7
PX Using POM for Windows and Excel OM
POM for Windows and Excel OM are capable of handling any simulation that contains only one random
variable, such as Example F1. For further details, please refer to Appendix IV.

www.myomlab.com

Module F Simulation 629
� SOLVED PROBLEM F.2
Random numbers may be used to simulate continuous distribu-
tions. As a simple example, assume that fixed cost equals $300,
profit contribution equals $10 per item sold, and you expect an
equally likely chance of 0 to 99 units to be sold. That is, profit
equals , where X is the number sold. The mean
amount you expect to sell is 49.5 units.
a) Calculate the expected value.
b) Simulate the sale of 5 items, using the following double-digit
randomly-selected numbers of items sold:
37 77 13 10 85
c) Calculate the expected value of part (b) and compare with the
results of part (a).
– $300 + $10X
� SOLUTION
a) Expected value
b)
c) The mean of these simulated sales is $144. If the sample size
were larger, we would expect the two values to be closer.
– 300 + $101852 = $550
– 300 + $101102 = – $200
– 300 + $101132 = – $170
– 300 + $101772 = $470
– 300 + $101372 = $70
= – 300 + 10149.52 = $195
With a supply of 8 heaters, Higgins will stock out three times during the 20-week period (in weeks 7, 14,
and 16).
b) Average sales by simulation
c) Using expected values, we obtain:
With a longer simulation, these two approaches will lead to even closer values.
+ .14192 + .061102 = 6.88 heaters
+ .18162 + .24172 + .16182
E 1sales2 = .1214 heaters2 + .10152
= total sales>20 weeks = 135>20 = 6.75 per week
Bibliography
Al-Zubaidi, H., and D. Tyler. “A Simulation Model of Quick
Response Replenishment of Seasonal Clothing.” International
Journal of Retail and Distribution Management 32 (2004):
320.
Balakrishnan, R., B. Render, and R. M. Stair. Managerial Decision
Modeling with Spreadsheets, 2nd ed. Upper Saddle River, NJ:
Prentice Hall (2007).
Banks, Jerry and Randall R. Gibson “The ABC’s of Simulation
Practice.” Analytics (Spring 2009): 16–23.
Banks, J., J. S. Carson, B. L. Nelson, and D. M. Nicol. Discrete-
Event System Simulation, 5th ed. Upper Saddle River, NJ:
Prentice Hall (2010).
Gavirneni, S., D. J. Morrice, and P. Mullarkey. “Simulation Helps
Maxager Shorten Its Sales.” Interfaces 2 (March–April 2004):
87–96.
Huang, H. C., et al. “Sim Man—A Simulation Model for
Workforce Capacity Planning.” Computers & Operations
Research 196, no. 3 (August 1, 2009): 1147.
Kelton, W. D., R. P. Sadowski, and N. B. Swets, Simulation with
Arena, 5th ed. New York: McGraw-Hill (2010).
Law, A. Simulation Modeling and Analysis, 4th ed. New York:
McGraw-Hill (2007).
Render, B., R. M. Stair, and M. Hanna. Quantitative Analysis for
Management, 10th ed. Upper Saddle River, NJ: Prentice Hall
(2009).
Rossetti, Manuel D. Simulation Modeling and ARENA. New York:
Wiley (2009).
Saltzman, Robert M., and Vijay Mehrotra. “A Call Center Uses
Simulation to Drive Strategic Change.” Interfaces 31, no. 3
(May–June 2001): 87–101.
Sud, V. P., et al. Reducing Flight Delays Through Better Traffic
Management. Interfaces 39, no. 1 (January/February 2009):
35–51.
Taylor, S. J .E., et al. “Simulation Modelling Is 50.” The Journal of
the Operational Research Society 60, no. S1 (May 2009):
S69–S13.
Thompson, G. M., and R. Verma. “Computer Simulation in
Hospitality Teaching, Practice and Research.” Cornell Hotel
and Restaurant Administration Quarterly 44 (April 2003): 85.
�Additional Case Study: Visit www.myomlab.com or www.pearsonhighered.com/heizer for this free case study:
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A1
APPENDIX I
Normal Curve Areas
APPENDIX II
Values of e for Use in the Poisson
Distribution
APPENDIX III
Table of Random Numbers
APPENDIX IV
Using Excel OM and POM
for Windows
–l
Appendices

A2 Appendix I
1.55
1.55
Standard Deviations
0
Mean Z
Area is
.93943
To find the area under the normal curve, you can apply either Table I.1 or Table I.2. In Table I.1, you must know how many
standard deviations that point is to the right of the mean. Then, the area under the normal curve can be read directly from the
normal table. For example, the total area under the normal curve for a point that is 1.55 standard deviations to the right of the
mean is .93943.
TABLE I.1
Z .00 .01 .02 .03 .04 .05 .06 .07 .08 .09
.0 .50000 .50399 .50798 .51197 .51595 .51994 .52392 .52790 .53188 .53586
.1 .53983 .54380 .54776 .55172 .55567 .55962 .56356 .56749 .57142 .57535
.2 .57926 .58317 .58706 .59095 .59483 .59871 .60257 .60642 .61026 .61409
.3 .61791 .62172 .62552 .62930 .63307 .63683 .64058 .64431 .64803 .65173
.4 .65542 .65910 .66276 .66640 .67003 .67364 .67724 .68082 .68439 .68793
.5 .69146 .69497 .69847 .70194 .70540 .70884 .71226 .71566 .71904 .72240
.6 .72575 .72907 .73237 .73565 .73891 .74215 .74537 .74857 .75175 .75490
.7 .75804 .76115 .76424 .76730 .77035 .77337 .77637 .77935 .78230 .78524
.8 .78814 .79103 .79389 .79673 .79955 .80234 .80511 .80785 .81057 .81327
.9 .81594 .81859 .82121 .82381 .82639 .82894 .83147 .83398 .83646 .83891
1.0 .84134 .84375 .84614 .84849 .85083 .85314 .85543 .85769 .85993 .86214
1.1 .86433 .86650 .86864 .87076 .87286 .87493 .87698 .87900 .88100 .88298
1.2 .88493 .88686 .88877 .89065 .89251 .89435 .89617 .89796 .89973 .90147
1.3 .90320 .90490 .90658 .90824 .90988 .91149 .91309 .91466 .91621 .91774
1.4 .91924 .92073 .92220 .92364 .92507 .92647 .92785 .92922 .93056 .93189
1.5 .93319 .93448 .93574 .93699 .93822 .93943 .94062 .94179 .94295 .94408
1.6 .94520 .94630 .94738 .94845 .94950 .95053 .95154 .95254 .95352 .95449
1.7 .95543 .95637 .95728 .95818 .95907 .95994 .96080 .96164 .96246 .96327
1.8 .96407 .96485 .96562 .96638 .96712 .96784 .96856 .96926 .96995 .97062
1.9 .97128 .97193 .97257 .97320 .97381 .97441 .97500 .97558 .97615 .97670
2.0 .97725 .97784 .97831 .97882 .97932 .97982 .98030 .98077 .98124 .98169
2.1 .98214 .98257 .98300 .98341 .98382 .98422 .98461 .98500 .98537 .98574
2.2 .98610 .98645 .98679 .98713 .98745 .98778 .98809 .98840 .98870 .98899
2.3 .98928 .98956 .98983 .99010 .99036 .99061 .99086 .99111 .99134 .99158
2.4 .99180 .99202 .99224 .99245 .99266 .99286 .99305 .99324 .99343 .99361
2.5 .99379 .99396 .99413 .99430 .99446 .99461 .99477 .99492 .99506 .99520
2.6 .99534 .99547 .99560 .99573 .99585 .99598 .99609 .99621 .99632 .99643
2.7 .99653 .99664 .99674 .99683 .99693 .99702 .99711 .99720 .99728 .99736
2.8 .99744 .99752 .99760 .99767 .99774 .99781 .99788 .99795 .99801 .99807
2.9 .99813 .99819 .99825 .99831 .99836 .99841 .99846 .99851 .99856 .99861
3.0 .99865 .99869 .99874 .99878 .99882 .99886 .99899 .99893 .99896 .99900
3.1 .99903 .99906 .99910 .99913 .99916 .99918 .99921 .99924 .99926 .99929
3.2 .99931 .99934 .99936 .99938 .99940 .99942 .99944 .99946 .99948 .99950
3.3 .99952 .99953 .99955 .99957 .99958 .99960 .99961 .99962 .99964 .99965
3.4 .99966 .99968 .99969 .99970 .99971 .99972 .99973 .99974 .99975 .99976
3.5 .99977 .99978 .99978 .99979 .99980 .99981 .99981 .99982 .99983 .99983
3.6 .99984 .99985 .99985 .99986 .99986 .99987 .99987 .99988 .99988 .99989
3.7 .99989 .99990 .99990 .99990 .99991 .99991 .99992 .99992 .99992 .99992
3.8 .99993 .99993 .99993 .99994 .99994 .99994 .99994 .99995 .99995 .99995
3.9 .99995 .99995 .99996 .99996 .99996 .99996 .99996 .99996 .99997 .99997
APPENDIX I N O R M A L C U R V E A R E A S

Appendix I A3
TABLE I.2
Z .00 .01 .02 .03 .04 .05 .06 .07 .08 .09
0.0 .00000 .00399 .00798 .01197 .01595 .01994 .02392 .02790 .03188 .03586
0.1 .03983 .04380 .04776 .05172 .05567 .05962 .06356 .06749 .07142 .07535
0.2 .07926 .08317 .08706 .09095 .09483 .09871 .10257 .10642 .11026 .11409
0.3 .11791 .12172 .12552 .12930 .13307 .13683 .14058 .14431 .14803 .15173
0.4 .15542 .15910 .16276 .16640 .17003 .17364 .17724 .18082 .18439 .18793
0.5 .19146 .19497 .19847 .20194 .20540 .20884 .21226 .21566 .21904 .22240
0.6 .22575 .22907 .23237 .23565 .23891 .24215 .24537 .24857 .25175 .25490
0.7 .25804 .26115 .26424 .26730 .27035 .27337 .27637 .27935 .28230 .28524
0.8 .28814 .29103 .29389 .29673 .29955 .30234 .30511 .30785 .31057 .31327
0.9 .31594 .31859 .32121 .32381 .32639 .32894 .33147 .33398 .33646 .33891
1.0 .34134 .34375 .34614 .34850 .35083 .35314 .35543 .35769 .35993 .36214
1.1 .36433 .36650 .36864 .37076 .37286 .37493 .37698 .37900 .38100 .38298
1.2 .38493 .38686 .38877 .39065 .39251 .39435 .39617 .39796 .39973 .40147
1.3 .40320 .40490 .40658 .40824 .40988 .41149 .41309 .41466 .41621 .41174
1.4 .41924 .42073 .42220 .42364 .42507 .42647 .42786 .42922 .43056 .43189
1.5 .43319 .43448 .43574 .43699 .43822 .43943 .44062 .44179 .44295 .44408
1.6 .44520 .44630 .44738 .44845 .44950 .45053 .45154 .45254 .45352 .45449
1.7 .45543 .45637 .45728 .45818 .45907 .45994 .46080 .46164 .46246 .46327
1.8 .46407 .46485 .46562 .46638 .46712 .46784 .46856 .46926 .46995 .47062
1.9 .47128 .47193 .47257 .47320 .47381 .47441 .47500 .47558 .47615 .47670
2.0 .47725 .47778 .47831 .47882 .47932 .47982 .48030 .48077 .48124 .48169
2.1 .48214 .48257 .48300 .48341 .48382 .48422 .48461 .48500 .48537 .48574
2.2 .48610 .48645 .48679 .48713 .48745 .48778 .48809 .48840 .48870 .48899
2.3 .48928 .48956 .48983 .49010 .49036 .49061 .49086 .49111 .49134 .49158
2.4 .49180 .49202 .49224 .49245 .49266 .49286 .49305 .49324 .49343 .49361
2.5 .49379 .49396 .49413 .49430 .49446 .49461 .49477 .49492 .49506 .49520
2.6 .49534 .49547 .49560 .49573 .49585 .49598 .49609 .49621 .49632 .49643
2.7 .49653 .49664 .49674 .49683 .49693 .49702 .49711 .49720 .49728 .49736
2.8 .49744 .49752 .49760 .49767 .49774 .49781 .49788 .49795 .49801 .49807
2.9 .49813 .49819 .49825 .49831 .49836 .49841 .49846 .49851 .49856 .49861
3.0 .49865 .49869 .49874 .49878 .49882 .49886 .49889 .49893 .49897 .49900
3.1 .49903 .49906 .49910 .49913 .49916 .49918 .49921 .49924 .49926 .49929
0 1.55
Mean Z
Area shaded
is .43943
1.55 Standard
Deviations
As an alternative to Table I.1, the numbers in Table I.2 represent the proportion of the total area away from the mean, , to
one side. For example, the area between the mean and a point that is 1.55 standard deviations to its right is .43943.
m

A4 Appendix III
APPENDIX III TA B L E O F R A N D O M N U M B E R S
APPENDIX II VA L U E S O F e F O R U S E
I N T H E P O I S S O N D I S T R I B U T I O N
–l
Values of
.0 1.0000 1.6 .2019 3.1 .0450 4.6 .0101
.1 .9048 1.7 .1827 3.2 .0408 4.7 .0091
.2 .8187 1.8 .1653 3.3 .0369 4.8 .0082
.3 .7408 1.9 .1496 3.4 .0334 4.9 .0074
.4 .6703 2.0 .1353 3.5 .0302 5.0 .0067
.5 .6065 2.1 .1225 3.6 .0273 5.1 .0061
.6 .5488 2.2 .1108 3.7 .0247 5.2 .0055
.7 .4966 2.3 .1003 3.8 .0224 5.3 .0050
.8 .4493 2.4 .0907 3.9 .0202 5.4 .0045
.9 .4066 2.5 .0821 4.0 .0183 5.5 .0041
1.0 .3679 2.6 .0743 4.1 .0166 5.6 .0037
1.1 .3329 2.7 .0672 4.2 .0150 5.7 .0033
1.2 .3012 2.8 .0608 4.3 .0136 5.8 .0030
1.3 .2725 2.9 .0550 4.4 .0123 5.9 .0027
1.4 .2466 3.0 .0498 4.5 .0111 6.0 .0025
1.5 .2231
e–lle–lle–lle–ll
e–l
52 06 50 88 53 30 10 47 99 37 66 91 35 32 00 84 57 07
37 63 28 02 74 35 24 03 29 60 74 85 90 73 59 55 17 60
82 57 68 28 05 94 03 11 27 79 90 87 92 41 09 25 36 77
69 02 36 49 71 99 32 10 75 21 95 90 94 38 97 71 72 49
98 94 90 36 06 78 23 67 89 85 29 21 25 73 69 34 85 76
96 52 62 87 49 56 59 23 78 71 72 90 57 01 98 57 31 95
33 69 27 21 11 60 95 89 68 48 17 89 34 09 93 50 44 51
50 33 50 95 13 44 34 62 64 39 55 29 30 64 49 44 30 16
88 32 18 50 62 57 34 56 62 31 15 40 90 34 51 95 26 14
90 30 36 24 69 82 51 74 30 35 36 85 01 55 92 64 09 85
50 48 61 18 85 23 08 54 17 12 80 69 24 84 92 16 49 59
27 88 21 62 69 64 48 31 12 73 02 68 00 16 16 46 13 85
45 14 46 32 13 49 66 62 74 41 86 98 92 98 84 54 33 40
81 02 01 78 82 74 97 37 45 31 94 99 42 49 27 64 89 42
66 83 14 74 27 76 03 33 11 97 59 81 72 00 64 61 13 52
74 05 81 82 93 09 96 33 52 78 13 06 28 30 94 23 37 39
30 34 87 01 74 11 46 82 59 94 25 34 32 23 17 01 58 73
59 55 72 33 62 13 74 68 22 44 42 09 32 46 71 79 45 89
67 09 80 98 99 25 77 50 03 32 36 63 65 75 94 19 95 88
60 77 46 63 71 69 44 22 03 85 14 48 69 13 30 50 33 24
60 08 19 29 36 72 30 27 50 64 85 72 75 29 87 05 75 01
80 45 86 99 02 34 87 08 86 84 49 76 24 08 01 86 29 11
53 84 49 63 26 65 72 84 85 63 26 02 75 26 92 62 40 67
69 84 12 94 51 36 17 02 15 29 16 52 56 43 26 22 08 62
37 77 13 10 02 18 31 19 32 85 31 94 81 43 31 58 33 51
Source: Excerpted from A Million Random Digits with 100,000 Normal Deviates, The Free Press (1955): 7, with
permission of the RAND Corporation.

Appendix IV A5
� PROGRAM IV.1A Excel OM Modules Menu in Add-Ins Tab in Excel 2007
APPENDIX IV U S I N G E X C E L O M A N D P O M
F O R W I N D O W S
Two approaches to computer-aided decision making are provided with this text: Excel OM and
POM (Production and Operations Management) for Windows. These are the two most user-
friendly software packages available to help you learn and understand operations management.
Both programs can be used either to solve homework problems identified with a computer logo
or to check answers you have developed by hand. Both software packages use the standard
Windows interface and run on any IBM-compatible PC operating Windows XP or better.
EXCEL OM
Excel OM has also been designed to help you to better learn and understand both OM and Excel.
Even though the software contains 24 modules and more than 50 submodules, the screens for
every module are consistent and easy to use. Modules can be accessed through either of two
menus that are added to Excel. The Heizer menu lists the modules in chapter order as illustrated
for Excel 2007 in Program IV.1a. The Excel OM menu lists the modules in alphabetical order, as
illustrated for earlier versions of Excel in Program IV.1b. This software is provided at no cost to
purchasers of this textbook at our Web sites, www.pearsonhighered.com/heizer and www.
myomlab.com. Excel 2000 or better must be on your PC.
To install Excel OM, after the web page opens, click on the Software option on the left hand
side, click on Excel OM (version 3) and follow the instructions. Default values have been
assigned in the setup program, but you may change them if you like. The default folder into
which the program will be installed is named C:\ProgramFiles\ExcelOM3, and the default name

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A6 Appendix IV
for the program group placed in the START menu is Excel OM 3. Generally speaking, it is sim-
ply necessary to click NEXT each time the installation asks a question.
Starting the Program To start Excel OM, double-click on the Excel OM 3 shortcut placed
on the desktop during installation. Alternatively, you may click on START, PROGRAMS,
EXCEL OM 3. In Excel 2007 the Excel OM menu will appear in the Add-Ins tab of the Excel
2007 ribbon as displayed in Program IV.1a, while in earlier versions of Excel the Excel OM
menu will appear in the main menu of Excel as displayed in Program IV.1b.
If you have Excel 2007 and do not see an Add-Ins Tab on the Ribbon or do not see Excel
OM 3 on this tab as displayed in Program IV.1a, then your Excel 2007 security settings need to
be revised to enable Excel OM 3. Please consult the Excel 2007 instructions at the support site,
www.prenhall.com/weiss.
Excel OM serves two purposes in the learning process. First, it can simply help you solve
homework problems. You enter the appropriate data, and the program provides numerical solu-
tions. POM for Windows operates on the same principle. However, Excel OM allows for a sec-
ond approach; that is, noting the Excel formulas used to develop solutions and modifying them
to deal with a wider variety of problems. This “open” approach enables you to observe, under-
stand, and even change the formulas underlying the Excel calculations, hopefully conveying
Excel’s power as an OM analysis tool.
POM FOR WINDOWS
POM for Windows is decision support software that is also offered free to students who
purchased this text and is available at our Web sites www.pearsonhighered.com/heizer and
� PROGRAM IV.1B Excel OM Modules Menu in Main Excel Menu for Versions of Excel Prior to Excel 2007

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Appendix IV A7
� PROGRAM IV.2 POM for Windows Module List
Instruction notes are
here to help explain
what to do next.
www.myomlab.com. Program IV.2 shows a list of 24 OM modules on the Web site that will be
installed on your hard drive. Once you follow the standard setup instructions, a POM for
Windows program icon will be added to your start menu and desktop. The program may be
accessed by double-clicking on the icon. Updates to POM for Windows are available on the
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Name Index
Bridger, R. S., 331
Brockman, Beverly K., 152
Broedner, P., 21
Brown, G.R., 566
Brown, Mark G., 175
Buboltz, W.C., 309n
Buchannan, Leigh, 546
Burke, Robert, 515
Burt, D. N., 405
Caiola, Gene, 463
Camevalli, J. A., 152
Campbell, Omar, 121
Canonaco, P, 604
Cavanagh, R. R., 175
Cayirli, Tugba, 494
Centeno, M.A., 626
Chambers, Chester, 250
Champy, James, 369
Chan, L.Y.S., 608
Chang, Y, 226
Chankong, V, 302
Chapman, Steven N., 405, 494
Chen, Fangruo, 431
Cheng, H. K., 250
Chopra, Sunil, 250, 358, 405
Chowdhury, S., 165n
Chu, K., 581
Chua, R. C. H. 175, 199
Chung, T.K.H., 608
Chung-Yee Lee, 369
Cleland. D. L., 81
Clive, L. M., 405
Cochran, J.K., 604
Colville, G., 431
Combs, James, G., 358
Conway, Richard W., 405
Couto, J. P., 614
Cox, Jeff, 237, 237n
Crandall, Richard E. 463
Crook, T. Russell, 358
Crosby, Philip B., 175, 158n, 159
Crotts, J. C., 45
Cua, Kristy O., 530
Cunningham, James, 608
Dada, Maqbool, 405
Dahlgaard, J. J., 199
daSilva, C.G., 566
Davenport, T. H., 226
Davis, Stanley B. 199
Debo, L. G., 226
DeFeo, J.A., 175, 199
DeHoratius, N., 379n
DeJong.A. K., 331
Dell, F., 566
Dellande, S., 45
Deltas, G. 175
De Matteis, J. J., 449n
Deming, W. Edwards, 9, 158, 159n, 160,
160n, 161n, 178n
Deng, Honghui, 494
Denton, Brian T., 271, 566
DeRuyter, K., 331
Deshmukh, S. 250
Dibbern, J., 369
Dickson, D. R., 45
Diebold, F. X., 121
Dietrich, Brenda, 494
Dogan, K., 250
Doll, William, 134n
Drezner, Zvi, 271, 581
Duran, G., 566
Duray, R., 226
Einicki, R.A., 250
Elg, M., 199
Elnekave, M., 331
Eppinger, S., 152
Ernst, David, 152
Espinosa, J.A., 614
Evans, J. R., 175
Farmer, Adam, 494
Feigenbaum, Armand. V., 159, 175
Ferguson, M., 226
Fildes, Robert, 121
Finigen, Tim, 530
Fisher, M. L., 337
Fisscher, O., 158n
Fitzsimmons, James, 265n
Fleut, Nicholas, 302
Flinchbauh, Jamie, 515
Florida, R., 271
Flynn, Barbara B., 45
Flynn, E. J., 45
Ford, Henry, 9
Ford, R. C., 45
Fok, W.Y., 608
Fornell, C. 363n
Francis, R. L., 302
Freivaids, A., 331
Friedman, Thomas, 45, 369
Fry, P., 101n
Galt, J., 121
Gantt, Henry L., 9
Gardiner, Stanley C., 250
Gattiker, Thomas, 463
Gavirneni, S., 629
Georgoff, D. M., 121
Geraghty, Kevin, 494
Gerwin, Donald, 152
Gianipero, L. C., 358
Abbernathy, Frederick H., 405
Adenso-Diaz, B., 463
Aft, Larry, 331
Akturk. M.S., 302
Al-Zubzici, H., 629
Ambec, Stefan, 152
Anthony, T. E, 70n
Anupindi, Ravi, 250
Arnold, J.R., 405
Aron, R., 369
Ashkenas, R. N., 81
Ata, Asad, 250
Atamturk. A., 250
Bagley, Constance, 541
Baker, Kenneth A., 494
Bakir, S.T., 199
Balakrishnan, R., 81, 121, 562n, 566,
581, 604, 629, 546
Ballou, Ronald H., 271
Bamford, James, 152
Banks, Jerry, 629
Barba-Gutierrez, Y., 463
Barber, Felix, 331
Bard, J., 494, 566
Barnes, R. M., 331
Bartness, Andrew D., 271
Baruch,Keren, 405
Bassett, Glenn, 425
Bauer, Eric, 530
Beatty, Richard W., 331
Becker, Brian E., 331
Beckman, S. L., 45
Bell, Steve, 463
Benton, W. C., 405
Berenson, Mark L., 121
Berry, W.L., 226, 405, 431
Berry, Leonard L., 174
Besterfield, Dale, H., 175, 199
Billington, P., 397n
Birchfield, C., 302
Birchfield, J. C., 302
Blackburn, Joseph, 358
Blackstone, John H., 250
Blank, Ronald, 530
Blecker, Thorsten, 358
Bolander, Steven, 463, 494
Boh, W. F., 614
Bowen, H. Kent, 512n
Bowers, John, 250
Bowman, E. H., 422, 422n
Boyd, L. H., 250
Boyer, Kenneth K, 358
Bradley, James R., 405
Bradley, Morrison J., 614
Brandl, Dennis, 250
Bravard, J., 369
I1
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I2 Name Index
Inoue, L. 546
Ireland, L. R., 42
Ireland, R.D., 42, 81, 546
Jack, Eric P., 250
Jacobs, F. R., 405, 431
Jain, Chaman L., 121
Jayaraman, V., 358
Jennings, Daniel F, 463
Johnson, Alan, 302
Johnson, M. Eric, 216, 226
Johnson, Steven, 331
Jones, Daniel T., 516
Jonsson, Patrik, 250
Joshi, M. P., 45
Juran, J. M., 158, 158n, 159, 167
Kahn, Judd, 45, 369
Kanet, J., 463
Kanter, Rosabeth, 129n
Kao, John, 129n
Kaplan, Robert. S., 45
Karason, O.Y., 302
Karlos, A. 81
Kathuria, R., 45
Kator, C., 302
Keating, B., 121
Kee, Micah R., 302
Keefer, Donald L., 546
Keeps, David, A., 302
Kekre, Sunder, 250
Kellogg, Deborah L., 494
Kelly, J. E., 55
Kelton, W. D., 629
Kennedy. M., 271
Kenny, R.L., 546
Keren, Baruch, 405
Kersten, Wolfgang, 358
Kerzner, H., 81
Keyte, Beau, 515
Khanna, M., 175
Kimber, D. A., 530
Kimes, Sheryl, 265n, 431
King-Metters, K., 431
Kinkel, S., 21
Kirchmier, Bill, 494
Klamroth, K., 271
Klassen, R., 358
Koehn, D., 158n
Koh, S. C. L., 463
Kohne, E. J. 358
Koksalan, M., 581
Konz, S., 331
Kopezak, Laura Rock, 216
Koronacki, J., 178n
Koufteros, Xenophon, 134n
Krehbiel, Tim, 121
Kreipl, Stephan, 358
Krishnan, M.S., 363n
Krishnan, V., 152
Krupp, James A. G., 463
Kuo, C., 262n
Labach, Elaine J., 212
Laborde, J., 369
Lai, P., 175
Langella, I. M., 226
Lanoie, Paul, 152
Larson, E. W., 81
Larson, Jan, 608
Larson, S., 302
Law, A. 629
Lawrence, Barry F, 463
Lawrence, M., 121
Lay, G., 21
Lee, Hau L., 369
Leidner, Dorothy, 369
Lemmink, J., 331
Leonard, M., 121
Leong, G. K., 358, 494
Leppa, Carol J., 516
Levine, David M., 121
Lewis, Mike, 45
Lewis, William W., 21
Lian, Z., 405
Liker, Jeffrey K, 515
Lin, H. 199
Lindner, C. A., 324n
Lindsay, William M., 175
Ling, F.Y.Y., 81
Linton, J. D., 358
Liu, X., 405
Loch, Christopher H., 152
Locher, Drew, 515
Lopez, P., 494
MacLean, D., 152
Mahan, Michael, 302
Malykhina, E., 379n
Mantel, S., 81
Maroto, A., 21
Martin, C. H., 566
Marucheck, Ann S., 358
Matta, N. F, 81
Matthes, N., 199
Mattsson, Stig-Arne, 250
Maylor, Harvey, 81
McAllister, V.C., 604
McDonald, A. 614
McDonald, Stan C., 405
McGinnis. L. F., 302
McKone, Kathleen E., 530
McLaren, T., 614
McLeavey, D. W., 397n
Medina-Borja, A. 566
Mehrotra, Vijay, 629
Meindl, Peter, 358
Melby, B. M., 369
Melnyk, Steven A., 291n
Mentzer, John T., 271
Meredith, J. R., 81
Merrick, Amy, 339n
Messel, Gregg, 515
Messner, W., 369
Metters, Richard, 431
Midler, Paul, 369
Miguel, P.A.C., 152
Miller, C.C., 546
Miller, Luke T., 494
Milligan, G.W., 226
Mitra, Amit, 175, 199
Modigliani, Franco, 422n
Gibson, Randall, 629
Gilad, I., 331
Gilbreth, Frank, 9, 317
Gilbreth, Lillian, 9, 317
Gilliland, M., 121
Gilmore, James H., 226
Gitlow, Howard, S., 175
Goetsch, David L., 199
Goldratt, Eliyahu, 237, 237n, 250
Gonul, M.S., 121
Gonzalez-Benito, J., 175
Gonzalez-Benito, O., 175
Goodale, John C., 250
Goodwin, Paul, 121
Graban, Mark, 515
Gray, C. L., 81
Greenwald, Bruce C., 21, 45, 369
Groebner, D., 101n
Gross, Donald, 604
Gross, E. E. Jr., 313
Gryna F. M. 175, 199
Guergachi, A., 614
Gultekin, H., 302
Gupta, M.C., 250
Gupta, S. M., 463
Hackman, J. R., 309, 309n
Hall, Joseph M., 226
Hall, Robert W., 515
Halvey, J. K., 369
Hammond, J.S., 546
Handfield, R. B., 358
Hanke, J. E., 121
Hanna, M., 81, 121, 271, 405, 494, 546,
562, 566, 581, 604, 629
Hansen, Bertrand, 190
Harrington, D. R., 175
Harris, Carl M., 604
Harrod, Stephen, 566
Hazelwood, R.N., 599n
Hegde.V. G., 226
Heinzl, A. 369
Heizer, Jay, 121
Helgadottir, Hilder, 81
Helms, A. S., 324n
Heragu, S. S., 302
Heyer, N., 302
Hill, R. R., 70n
Hirschheim, R., 369
Hitt, M., 42
Hochbaum, D. S., 250
Holt, Charles C., 422n
Hopp, Wallace J., 431
Hoskisson, R. E., 42
Hounshell, D. A., 21
Hu, J., 358
Huang, H. C., 629
Huang, L. 21
Huang, T., 21
Hueter, Jackie, 19
Hult, G. Thomas M., 358
Humphries, Jim, 530
Huselid, Mark A., 331
Immonen, A., 152
Inderfurth, Karl, 226

Name Index I3
Simon, Herbert, 422n
Singh, J. V., 369
Singhal,V. R., 358
Sinha, K. K. 358
Skinner, Wickham, 45
Slack, Nigel, 45
Slaughter, S.A., 614
Smith, Adam, 308
Smith, Bernard, 115n
Smith, Davis, B., 608
Smith Gerald, 191n
Smith, Jeffrey S., 494
Smith, K., 101n
Smunt, T. L., 614
Snir, Eli, M., 250
Sodhi, M.S., 566
Snyder, L. V., 271
Soltani, E., 175
Sorensen, Charles, 9
Sova, Roger, 530
Spear, Steven J., 512n
Spearman, Mark L., 431
Spigener, J.B., 194
Sprague, Linda G., 21
Sridharan, V., 463
Stair, Jr., Ralph, 81, 121, 271,
405, 494, 546, 562, 566,
581, 604, 629
Stanford, D.A., 604
Stanley, L.L., 358
Stanowy, A., 302
Starr, Martin K., 422n
Stephens, M. P., 530
Stern, Scott, 254, 271
Stewart, D. M., 175
Strack, Rainer, 331
Su, J. C. P., 226
Sud. V. P., 629
Summer, M., 463
Summers, Donna, 175, 199
Sun, S. X., 494
Sural, H., 581
Swamidass, Paul M., 226
Swart, W., 19
Swets, N. B., 629
Tabatabai, Bijan, 121
Taguchi, Genichi, 165, 165n
Tallman, Stephen, 271
Tan, K. C, 358
Tangen, S., 21
Taylor, Bernard, 566, 581
Taylor, Frederick W., 9, 21, 311, 317,
321, 463, 494
Taylor, S. J. E., 629
Taylor, Terry A., 431
Teixeira, J. C., 614
Terwiesch, C., 152
Thomas, A., 309n
Thomas, A. R., 369
Thompson, A. A., 444
Thompson, Gary M., 431, 629
Thompson, J. R., 178n
Thompson, James, M., 604
Tibben-Lembke, Ronald S. 250
Tippet, L., 325
Pinedo, Michael, 358, 494
Ping, J., 581
Pinkerton, R., 405
Pisano, Gary P., 152, 358
Plambeck, Erica L., 431
Plenert, Gerhard, 494
Pokladnik, F. M., 70n
Polak, G.G., 566
Porter, Michael, 38n, 45, 254, 271
Prabhu, N. U., 604
Pullman, Madeline E., 250, 431
Pyke. D. F., 431
Raiffa, H., 546
Raman, A. 379n
Ramaswami, C. 604
Raturi, Amitabh S., 350
Reinhardt, Gilles, 405
Render, Barry, 81, 121, 271, 405, 494,
546, 562, 566, 581, 604, 629
Renouf, E., 604
Reynolds, Brian E., 463
Roche, K., 604
Roodbergen, K. J., 302
Rosenfield, D. B., 45
Rosseti, Manuel D., 629
Roth, H. P., 199
Roubellat, F., 494
Rubalcaba, L., 21
Rubin, Paul, 405
Rudberg, Martin, 45
Rugtusanatham, Johnny M., 226
Saad, S.M., 463
Saaksvuori, A., 152
Sadikoglu, E., 331
Sadowski. R.P., 629
Sahay, B. S., 21
Saltzman, Robert M., 629
Salvador, Fabrizio, 226
Salvendy, G., 331
Samaddar, S., 431
San, G., 21
Sanders, N.R., 566
Schlaifer, R., 546
Schmeidler, Neil, 331
Schmenner, Roger, W., 581
Schniederjans, Ashlyn, 360n
Schniederjans, Dara, 360n
Schniederjans, Marc J., 360n
Schonberger, Richard J., 516
Schrattenholzer, L., 614
Schroeder, Roger G., 45, 175, 530
Scudder, Gary, 358
Segerstedt, A., 463
Seider, Warren D., 152
Shah, Pigush, 121
Shannon, P., 101n
Sharafali, M., 604
Shaw, B.W., 324n
Sheen, G., 199
Shewhart, Walter, 9, 161, 161n
Shortle, John F., 590, 604
Siggelkow, Nicolaj, 45
Silver, E. A., 431
Simmons, B., L., 331
Moeeni, R. 226
Moncrief, Stephen, 463
Monczka, R. M., 358
Mondschein, Susana V., 494
Montgomery, D.C., 199
Morgan, James M., 515
Morgan, R., 369
Morgan, Robert M., 152
Morrice, D.M., 629
Morton, Thomas E., 494
Mukhopadhyay, S., 431
Mullarkey, P., 629
Munson, C.L., 358
Murdick, Robert G., 121
Muth, John F, 422n
Muther, Richard, 278
Muthasamy, S. K., 331
Narasimhan, S., 397n
Narayanan, Sriram, 358
Nayebpour, M.P., 158n
Nelson, B.L., 629
Nelson-Peterson, Dana L., 516
Neuman. R. P., 175
Neureuther, B.D., 566
Newman, A.M., 566
Ngwenyama, O. 614
Nicol, D.M., 629
Niebel, B., 321
Niemann, G., 323
Nijhof, A. 158n
Noblitt, James M., 405
Norri, S. 566
Norris, G, 463
Norton, David P., 45
Oates, David, 81
Oberwetter, R. 426n
Ohno, Taiichi, 511
O’Connell, Andrew, 546
Oldham, Greg R., 309, 309n
Olsson, J., 199
Onkal, D., 121
O’Sullivan, Jill, 463
Pagell, Mark, 291n
Palar, M. 604
Paleologo, G. A., 494
Panchalavarapu, P. R., 174, 302
Pande, R. S., 175
Parasuraman, A., 174
Pareto, Vifredo, 167, 375
Parmigiani, F. 546
Parks, Charles M. 516
Partovi, F.Y., 271
Pasupathy, K., 566
Patterson, J.L., 358
Peck, L.G., 599n
Pentico, David, W., 494
Petcavage, S., 405
Peterson, A. P. G, 313
Peterson, R., 431
Pfeffer, Jeffrey, 331
Phillips, P., 175
Phyper, J. D., 152
Pine 11, Joseph, 226

I4 Name Index
Walczak, Steven, 494
Walker, M. R., 55
Wallace, Rusty, 304-306
Walsh, Ellen, 331
Walton, S., 431
Wang, Q., 494
Ward, P. T., 226
Watson, James L., 43n
Watson, Kevin J., 250
Watts, C. A., 614
Webb, L., 369
Weil, Marty, 530
Weintraub, Gabriel Y., 494
Welborn, Cliff, 226
Wemmerlov, U., 302
West, B. M., 45
Weston, M. 614
Wheeler, J. V, 331
Whitaker, J. 363n
White, G., 271
White, J. A., 302
White, R. E., 262n
Whitin, T. M., 463
Whitten, Dwayne, 369
Whitney, Eli, 9
Whybark, D. C., 405, 431
Wichern, D. W., 121
Wiersema, Fred, 129n
Wilkinson, T. J., 369
Wilson, J. H., 121
Winkelspecht, C., 309n
Wisner, Joel D., 358
Witt, Clyde E., 405
Wolf, Martin, 45
Womack, James P., 516
Wren, Daniel A., 21
Wright, T. P., 607, 607n
Wu, Jen-Hur, 463
Wu, Y., 165n
Wyckoff, Daryl, D., 376
Wyne, Mark, A., 26n
Wynter, L., 494
Wysocki, R. K., 81
Yourdon, Edward, 369
Yurklewicz, Jack, 121
Zakaria, Fareed, 45
Zeithaml, Valerie, 174
Zeng, Amy Z., 302
Zhang, X., 530
Zhao, T., 302
Zipkin, Paul, 226
Tokay, L. B., 226
Tolo, B., 331
Tompkins, James A., 302
Ton, Z., 379n
Tonkin, Lea A. P., 530
Toyoda, Eiji, 511
Trietsch, Dan, 494
Tseng, C. L., 302
Tyler, D. 629
Uhich, G., 70n
Ulrich, Karl T., 152
Upton, David M., 302
Urs, Rajiv, 121
van Biema, Michael, 21
van Veen-Dirks, Paula, 516
Van Wassenhove, L. N., 226
Veral, Emre, 494
Verganti, Roberto, 152, 358
Verma, Rohit, 250, 629
Verzuh, Eric, 81
Vis, I. F. A., 302
Vollmann, T. E., 405, 431
Vonderembse, Mark, 134n
Wacker, John, G. 455
Wagner, H. M., 463

General Index
Area under the normal curve, T1–4
to T1–5
ARENA software, 626
Argentina, MERCOSUR and, 27
Ariba, 348
Arithmetic approach, learning curves
and, 608
Arnold Palmer Hospital, 54
managing quality, 154–156
mission statement, 30
Automatic identification systems, (AIS),
218–219
Autonomous maintenance, 528
Automated Storage and Retrieval
Systems (ASRS), 220
Avendra, 348
Average observed time, 318
Average outgoing quality, 195, T2–5
to T2–6
Backflush, MRP and, 447
Backsourcing, 363, 364
Backward integration, 342
Backward pass, 63
Backward scheduling, 470
Balanced flow approach, MRP and, 447
Balancing work cells, 289–291
Balking customers, 586
Banking and theory of constraints, 238
Banks, scheduling for services and, 486
Basic economic order quantity, 381
Basic feasible solution, T3–3
BASIC software, 626
Basic queuing system designs, 587
Basic variables, T3–3
Bechtel, 48–50
Behavior of arrivals, waiting line
and, 585
Benchmarking, 163–164
Benetton, 26, 456
Beta probability distribution, 66
BetzDearborn, Inc., 178
Bias, 114
Bias error, 116
Bills-of-material (BOM), 142, 438–441
Blanket orders, 346
BMW, 138
Boeing Aircraft, 24–25, 26, 146,
294, 338
Borders Books, 207
Bottleneck analysis & theory of
constraints, 234–238
management of, 237–238
Brazil, MERCOSUR and, 27
Breakdown maintenance, 524
Break-even analysis, 238–242
algebraic approach, 239
assumptions and, 239
contribution and, 239
definition, 238
fixed costs, 239
graphic approach, 239
multiproduct case and, 240–242
objective of, 238
revenue function, 239
single-product case and, 240
variable costs, 239
Bristol-Myers Squibb, 138
Bucketless systems, MRP and, 447
Buckets, MRP and, 446
Build an organization, 41
ABC analysis, 375–377
Acceptable quality level (AQL), 194
Acceptance sampling, 193–195, T2–1
to T2–7
average outgoing quality (AOQL),
195, T2–5 to T2–6
operating characteristic (OC) curve
and, 194–195
sampling plans, T2–2
Accurate inventory records, MRP and, 441
Accurate pull data, 345
Activity charts, job design and, 315
Activity map, 40
Activity-on-Arrow (AOA), 55–56, 60
Activity-on-Node (AON), 55–59
Activity times, variability in, 65–71
Adaptive smoothing, 115
Advanced shipping notice (ASN), 347
Advantages of ERP Systems, 458
Advantages of Kanban, 510
Advantages of outsourcing, 367
Advantages of simulation, 617
Aggregate planning, 407–432
comparison of planning methods for, 422
methods for, 415–420
nature of, 411–412
planning process and, 410–411
services and, 422–425
strategies for, 412–415
yield management and, 425–428
Aggregate scheduling. See Aggregate
planning
Airfreight, logistics management and, 351
Airline industry,
aggregate planning and, 425
scheduling services in, 466–468,
486–487
Alabama, Auto Industry and, 257
ALDEP, (Automated Layout Design
Program) 287
Algebraic approach, break-even analysis
and, 239–240
Alliances, time-based competition and, 142
Allowable ranges for objective function
coefficients, 557
Amazon.com, 88, 372–374
Ambient conditions, 280–281
American National Standards Institute
(ANSI) 159
American Society for Quality (ASQ), 156
Analysis and design, process strategy
and, 211–214
Anheuser-Busch, 219
APEC, 27
Apple Computer Corp., 54
Applications of L.P., 559–562
Applying the learning curve, 608–611
Appraisal costs, quality and, 158
Approaches to forecasting, 89–90
Arcs, routing and scheduling vehicles
and, T5–3
I5
Note: Page numbers beginning with a T refer to the Online Tutorial chapters that appear on our website www.pearsonhighered.com/heizer.
process focus, 205
Arrival characteristics, waiting line
systems and, 585–586
behavior of arrivals, 586
characteristics of, 586–587
pattern of arrivals and, 585
Poisson distribution, 586
size of, 585
Artifacts, servicescapes and, 280–281
Artificial variables, T3–8
ASRS, 220
Assembly chart, 144
Assembly drawing, 144
Assembly line, product-oriented layout
and, 292
Assembly line balancing, product-
oriented layout and, 293–297
objectives of, 293
Assets committed to inventory,
354–355
Assignable variations, statistical process
control and, 179
Assignment method, loading and,
475–477
Associative forecasting methods:
108–113
regression analysis, 108–110
correlation coefficient for regression
lines, 111–113
linear-regression analysis, 108
regression analysis, 113
standard error of the estimate,
110–111
Associative models, 89–90
Assumptions, break-even analysis and, 239
AT&T, 158
Attract and retain global talent, global
view of operations and, 27–29
Attribute(s):
c-charts, 188–190
control charts for, 186–190
p-charts and, 186–188
versus variables, inspection and, 171
Auctions, online and, 348
Auctions, supply-chain management,
and, 348
Audits & metrics to evaluate
outsourcing, 368
Australia, SEATO and, 27
Automated guided vehicles (AGVs), 220
Automated sensors, maintenance and, 529

www.pearsonhighered.com/heizer

I6 General Index
Building a lean organization, JIT and,
512–513
Building a cumulative probability
distribution, Monte Carlo
Simulation and, 618–619
Building a lean organization, JIT and,
512–513
Build-to-order, 207, 207n
Bullwhip effect, 344
C++ software, 626
Cadbury Schweppes PLC, 134
CAD, 136
Cadillac, 158
CAFTA, 27
Call Center industry, location strategies
and, 266
CAM, 137
Canada, NAFTA and, 27
Canon, 289
Capacity, constraint management and,
227–250
analysis and, 234
applying expected monetary value to
capacity decisions, 243–244
applying investment analysis to
strategy-driven investments,
244–247
Arnold Palmer Hospital, 234
bottleneck analysis and theory of
constraints, 234–238
break-even analysis, 238–242
capacity exceeds demand, 232
considerations and, 231
definition, 228
demand and, 233–234
design and, 35, 228–230
effective capacity and, 229
forecasting and, 87
managing demand, 231–233
planning, MRP and, 453–454
reducing risks with incremental
changes, 242–243
seasonable demands, 232
service sector and, 233–234
strategy and, 230
strategy-driven investments
and, 244
theory of constraints, 237
Capacity analysis, 234
Capacity considerations, 231
Capacity design, OM decisions
and, 39
Capacity investment aspects, 244
Capacity management, service sector
and, 233–234
Capacity options, aggregate strategies
and, 413
Capital, as productivity variable,
16, 17
Carbon footprint, 224
Cartoon industry in Manila, 27
Cash flow in capacity investment
aspects, 244
Caterpillar, 43
Cause and effect diagrams, 167
c-charts, 188–190
Center of gravity method, location
strategies and, 262–263
Central limit theorem, 180–189
Cessna Aircraft, 502
Changes in objective function
coefficient, 557
Changes in resources or right-hand-side
values, L.P. and, 556
Changing processes, process strategy, 211
Channel assembly, supply-chain mgt.
and, 347
Characteristics of a waiting line system,
585–588
Characteristics of vehicle routing and
scheduling problems, T5–3
to T5–5
Chase strategy, aggregate scheduling
and, 414–415
Check sheets, TQM tools and, 166–167
Chile, SEATO and, 27
Chinese postman problem (CPP), T5–4
CIM, 220
Clark and Wright Savings heuristic,
T5–5, T5–7 to T5–8
Classifying routing and scheduling
vehicle problems, T5–3 to T5–4
Closed-loop material requirements
planning, 452
Cluster first, route second approach,
T5–10 to T5–11
Clustering, 258–259
Coefficient approach, learning curve and,
609–611
Coefficient of correlation, 111
Coefficient of determination, 112
Collaborative Planning, Forecasting &
Replenishment (CPFR), 346
Company reputation, quality and, 157
Comparison of aggregate planning
methods, 422
Comparison of process choices, 208–211
Compatible organizational cultures,
organizing the supply
chain and, 344
Comparative advantage, theory of,
362–363
achieving through operations, 31–34
Competing on cost, operations and, 32
differentiation, operations and, 31–32
product strategy options and, 126–127
response, operations, 32–34
Competitive advantage, operations and,
31–34
Amazon.com, 372–374
Arnold Palmer Hospital and, 154–156
Bechtel and, 48–50
Boeing and, 24–25
Darden Restaurants, 334–336
differentiation and, 31–32
Disney World and, 84–86
Federal Express and, 252–253
Frito Lay, 408–410
human resources and, 306–307
McDonalds and, 274–275
Orlando Utilities Commission, 518–520
Product strategy options and, 126–127
Regal Marine and, 124–126
Toyota Motor Corp., 496–498
Wheeled Coach, 434–436
Competitive bidding, 350
Complex decision tree, 539–541
Components:
lead time for, 441
reliability and, 521–523
Computer-aided design (CAD), 136
Computer-aided manufacturing
(CAM), 137
Computer-integrated manufacturing
(CIM), 220
Computer numerical control (CNC), 218
Computer software for process oriented
layouts, 287
Concurrent engineering, 134
Concurrent scheduler approach, T5–13
Configuration management, 145
Considerations for capacity decision, 231
Consignment inventory, JIT and, 501
Constant-service-time model, 597–598
Constant work-in-process (ConWIP), 473
Constraints:
graphical representation of, L.P.
problem and, 550–551
human resource strategy and, 306–307
linear programming and, 549
Consumer market survey, forecasting
and, 89
Consumer’s risk, 194, T2–3 to T2–4
Continental Air Lines, 559
Continuous improvement, TPS and, 511
Continuous probability distributions,
statistical tools and, T1–5 to
T1–8
Contract manufacturing, 361
Contribution, break-even analysis and, 239
Control charts, 169
attributes, 186–190
c-charts, 188–191
defined, 169–170
managerial issues and, 190–191
p-charts, 186–188, 190
patterns on, 190
R-charts, 185
steps to follow in using, 185–186
which chart to use, 190
variables, 180
x̄-chart, 181–184
Control of service inventory, 379
Controlling forecasts, 113–115
Controlling project mgt. and, 54
ConWIP cards, 473
Core competencies, 39–41, 261–263
Core job characteristics, 309
CORELAP (Computerized Relationship
Layout Planning), 287
Corner-point solution method, 553–555
Correlation analysis, forcasting and,
108–113
Costco, 594
Cost(s), competing on, 32
intangible, 257
location and, 254, 257–258
tangible, 257
Cost-based price model, 350
Cost of quality (COQ), 158
Cost of shipping alternatives, 352–353
Cost savings, advantages of outsourcing
and, 367
Cost-time trade-offs, project mgt. and,
71–73
Cp, 191–192
Cpk, 192–193
CPM. See Critical Path Method (CPM)
CRAFT (Computerized Relative
Allocation of Facilities
Techniques), 287
Crashing, project mgt. and, 71–73
Creating future competition, disadvantages
of outsourcing and, 368
Criteria, scheduling and, 470–471
Critical decisions of OM, 7–8
Critical path, 55

General Index I7
Critical path analysis, 60–64
Critical Path Method (CPM), 55–60
activity-on-arrow example, 60
activity-on-node example, 57–59
calculating slack time, 64–65
critique of, 73–74
determining the project schedule,
60–65
dummy activities, 56
framework of, 55
identifying the critical path, 67–68
network diagrams and approaches,
55–56
variability in activity time, 65–71
Critical ratio (CR), sequencing and,
481–482
Critique of PERT and CPM, 73–74
Cross-docking, 282
Crossover charts, 209–210
Culture, location strategy, and, 258
Cultural issues, global view of operations
and, 29
Cumulative probability distribution,
Monte Carlo Simulation and,
618–619
Currency risks, location strategies and, 256
Customer interaction, process design
and, 215–217
Customizing, warehousing layout
and, 282
Cycle counting, inventory management
and, 377–378
Cycle time, focused work center &
focused factory and, 295, 295n
Cyclical variations in data, forecasting
and, 108
Cycles, forecasting and, 90, 108
Cyclical scheduling, 488–489
Dalrymple Bay, capacity and, 232
Darden Restaurants, 267, 334–336
Deadhead time, T5–12
Decision making:
expected value of perfect information
(EVPI), 537–538
expected value with perfect
information (EVwPI), 537–538
under certainty, 537
under risk, 536
types of environments, 534–538
under uncertainty, 535
Decision making tools, 531–546, also
see decision trees
decision tables, 534–538
decision trees, 538–543
fundamentals of, 533–534
process in operations and, 532–533
types of environments, 534–538
Decision tables, 534–538
Decision trees, 538–543
definition, 538
ethical decision making and, 541–542
more complex, 539–541
poker decision process, 532, 542–543
product design and, 149–150
Decision variables, linear programming
and, 550
Decline phase, product life cycle
and, 128
Decomposition of a time series, 90
Defining a product, 142–144
Degeneracy, transportation modeling
and, 576–577
Dell computers, mass customization
and, 207
Delphi method, forecasting and, 89
Delta Airlines, 54, 466–468, 559
Demand exceeds capacity, 231–232
Demand forecasts, 87
Demand management in service sector,
233–234
Demand not equal to supply, transportation
models and, 575–576
Demand options, aggregate strategies
and, 414
Deming’s 14 points, quality and, 160
Dependent inventory model
requirements, 436–441
accurate inventory records and, 441
bills-of-material and, 438–441
lead times for components and, 441
master production schedule and,
436–438
purchase orders outstanding and, 441
Depot node, routing and scheduling
vehicles and, T5–3
Design capacity, 228–230
Design for Manufacture and Assembly
(DFMA), 136
Design of goods and services, 7,
123–152, also see Product
Development
application of decision trees to product
design, 149–150
defining the product, 142–144
documents for production, 144–146
documents of services, 147–148
generating new products, 129–130
goods and services selection, 126–129
issues for product design, 135–137
product development, 130–135
service design, 146–147
time-based competition, 140–142
transition to production, 150
Determinants of service quality, 174
Developing missions and strategies,
30–31
DFMA, 136
DHL, 352
Diet problem, L.P. and, 560–561
Differences between goods and services,
10–11, 37
Differentiation, competitive advantage
and, 31–32
Disadvantages of ERP systems, 458
Disadvantages of outsourcing, 367–368
Disadvantages of simulation, 617
Disaggregation, aggregate planning
and, 412
Discrete probability distributions,
strategic tools and, T1–2 to T1–3
Disney:
experience differentiation and, 32
forecasting and, 84–86, 88
waiting lines and, 584
Dispatching jobs, priority rules and,
478–480
Distance reduction, JIT layout and, 503
Distribution resource planning (DRP),
454–455
Distribution systems, supply-chain
management, 351–352
DMAIC, TQM and, 161–162
Documents:
for production, 144–146
for services, 147–148
Double smoothing, 101
Dow Chemical, 162n
Drop shipping, 347
Dual value, 556
Ducati, Kaizen and, 512
Dummy activities, 56
Dummy destinations, 575
Dummy sources, 575
DuPont, 55, 138, 162n
Dynamics, MRP and, 446
Earliest due date (EDD), 478
Earliest finish time (EF), Critical Path
Analysis and, 61–64
Earliest start time (ES), Critical Path
Analysis and, 61–63
Economic forecasts, 87
Economic order quantity (EOQ) model,
381, 448
lot sizing and, 448–449
minimize costs, 381
production order quantity model,
387–390
quantity discount model, 390–393
robust model, 385–386
Economic part period (EPP), lot sizing,
and, 450
Economics, supply chain, and, 340–341
Effective OM and, 13n
Effective capacity, 228–230
Efficient consumer response (ECR), 458
Efficient, definition of, 13n
Efficiency, OM and, 13n
capacity and, 229
Electronic data interchange (EDI), 347
Electronic ordering and funds
transfer, 347
Eliminate waste, JIT and, 498–499
Employee empowerment, OM and, 12
job expansion and, 309
TQM and, 162–163
Employment stability policies, 307
EMV (Expected Monetary Value), 536
Engineering change notice (ECN), 145
Engineering drawing, 142
Enterprise Dynamics software, 626
Enterprise Resource Planning (ERP),
455, also see Materials
Requirement Planning and ERP
Environmentally friendly designs, ethics,
sustainability and, 138–140
goals, 139
guide lines, 139
laws and industry standards, 139–140
Environmentally sensitive production,
OM and, 12
EOQ (economic order quantity models),
386, 448
E-procurement, 347–349
Equally likely, decision-making under
uncertainty, 535
Ergonomics, work environment and,
311–314
ERP (Enterprise Resource Planning),
455–458
advantages and disadvantages of, 458
objective of, 455
service sector and, 458
Establishing probability distributions,
simulation and, 618
Ethical issues, global view of operations
and, 29
location strategies and, 257

I8 General Index
Ethics:
decision trees in, 541–542
human resources, job design and work
measurement, 328
OM and, 12
outsourcing, 368
project management and, 52
quality management and, 158
social responsibility and, 19
supply chain and, 339–340
European Union (EU), 27, 27n
Evaluating outsourcing risk with factor
rating, 365–366
EVPI (Expected Value of Perfect
Information), 537–538
EVwPI (Expected Value with Perfect
Information), 537–539
Excel:
break-even analysis, 247
forecasting, 118
inventory mgt., 401
linear programming, 563–564
location strategies, 269
outsourcing, 369
simulation and, 627
spreadsheets to determine control
limits for c-chart, 196
Excel OM:
aggregate planning and, 429
breakeven analysis and, 247
decision models and, 543–544
develop x̄-charts, p-charts, c-charts,
OC curves, acceptance sampling
and process capability and, 197
factor rating modules, outsourcing
and, 369
forecasting and, 118
inventory management and, 401–402
layout problems and, 298
learning curves and, 613
location problems and, 269
L.P. problems and, 564
material requirements planning & ERP
and, 459
outsourcing, 369
project scheduling and, 77
reliability and, 530
short-term scheduling and, 490–491
transportation problems, 578–579
using and, A5–A6
waiting line models, 602
Exchange rates, location strategies
and, 256
Expected monetary value (EMV), 536
Expected value:
of discrete probability distribution,
statistical tools and, T1–3
of perfect information (EVPI), 537–538
with perfect information (EVwPI),
537–538
under certainty, 537
Experience differentiation, 32
Expert systems, maintenance and, 529
Exponential smoothing, forecasting
and, 94–98
trend adjustment and, 98–101
Extend software, 626
Extensions of MRP, 451–454
capacity planning, 453–454
closed loop, 452
material requirements planning II,
451–452
External costs, quality and, 158
Fabrication line, production-oriented
layout and, 292
Factor-rating method,
location strategies and, 259–260
evaluating outsourcing risk with, 365
Factors affecting location decisions and,
255–259
Factory flow, 287
Faro Technologies, 195
Fast food restaurants, forecasting and,
116–117
Feasible region, 551
Feasible tour, T5–3
FedEx, 117, 158, 252–253
Feedback to operators, 312
Feed-mix problem, L.P. and, 560–561
Ferrari racing team, 164
FIFS (first in, first served), 587n
Finance/accounting, OM and, 4
Finished goods inventory, 375
Finite arrival population, 585
Finite capacity scheduling (FCS), 446,
484–485
First-come, first-served (FCFS)
system, 478
First-in, first-out (FIFO), 587, 587n
First-in, first-served (FIFS), 537n
First-order smoothing, 101
First Simplex tableau, T3–2 to T3–4
Fish-bone chart, 167
Five forces analysis, 38
5 Ss, lean operations and, 499, 499n
Fixed costs, break-even analysis and, 239
Fixed-period (P) inventory systems,
399–400
Fixed-position layout, 276
Fixed-quantity (Q) inventory system, 399
Flexibility, process strategy and, 218
Flexible manufacturing system
(FMS), 220
Flexible response, 32–33
Flexible workweek, 308
Flexibility, JIT and, 503
Flex-time, work schedules and, 307
Flow charts, 168–169, 211
Flow diagrams, 314–315
Flowers Bakery, 193
Focus forecasting, 115–116
Focused factory, 291
Focused processes, 211
Focused work center, 291
Focusing on core competencies,
advantages of outsourcing
and, 367
Ford, process risk and, 338
Forecasting, 83–121, also see Time-
series forecasting; Associative
forecasting methods
approaches to, 89–90
capacity and, 87
defined, 86
monitoring and controlling forecasts
and, 113–116
product life cycle and, 87
qualitative methods and, 89
quantitative methods and, 89–90
service sector and, 116–117
seven steps in, 88
software in, 118
strategic importance of, 87–88
time horizons and, 86–87
types of, 87
Formulating problems, L.P. and, 549
Forward integration, 342
Forward pass, 61–63
Forward scheduling, 470
Four process strategies, 204–211
mass customization focus, 206–208
process focus, 204–205
product focus, 206
repetitive focus, 205–206
Franz Colruyt, low-cost strategy and, 33
Free float, 64n
Free slack, 64n
Free time, 64n
Frito Lay:
aggregate planning and, 408–410
product focus, 206
resources and, 224
x̄-charts, 187
Functionality, servicescapes and, 280, 281
Functions of inventory, 374–375
Fundamentals of decision making,
533–534
Future time horizon, forecasting and,
86–87
Gaining outside expertise, advantages of
outsourcing and, 367
Gaining outside technology, advantages
of outsourcing and, 367
Gantt charts, 474–475
load chart, 474
project scheduling and, 53
schedule chart, 474–475
General Electric, 161
Generating new products, 129–130
Generating random numbers, 620
Geographic information systems (GISs),
location strategies and, 267–268
Glidden Paints, 111
Global company profiles:
Amazon.com, 372–374
Arnold Palmer Hospital, 154–156
Bechtel Group, 48–50
Boeing Aircraft, 24–25
Darden Restaurants, 334–336
Delta Airlines, 466–468
Disney World, 84–86
FedEx, 252–253
Frito-Lay, 408–410
Hard Rock Cafe, 2–3
Harley Davidson, 202–204
McDonald’s, 274–275
NASCAR Racing Team, 304–306
Orlando Utilities Commission, 518–520
Regal Marine, 124–126
Toyota Motor Corp., 496–498
Wheeled Coach, 434–436
Global focus, OM and, 12
Global implications, impact of culture
and ethics and, 29
quality and, 157
Global operations. See Operations
Strategy in a global environment
Global operations strategy options, 42–44
Global view of operations, 26–29
Goals, ethical, environmentally-friendly
design and, 139
Goods and services:
competitive advantages and,
126–127
design of, 123–152
global operations and, 35
organizing and, 4–5
product-by-value analysis, 128–129

General Index I9
product life cycles, 127–128
product strategy options, 126–127
service selection and, 126
Goods, differences from services, 41
GPSS, special-purpose simulation
language, 626
Graphic approach, break-even analysis
and, 239
Graphical methods for aggregate
scheduling, 415–420
Graphical representation of constraints,
L.P. and, 550–551
Graphical solution approach, L.P. and,
550–555
Graphical techniques, 415
Green disassembly lines, 292
Gross material requirements plan, MRP
and, 442
Group technology, 144
Growth of services, OM and, 11
Growth phase, product life cycle and, 128
Guidelines, ethical, environmentally-
friendly design and, 139
Haier, 26
Hard Rock Cafe, 2–4
environmental (political) risks, 338
layout and, 280
mission statement, 30
Pareto charts and, 167–168
Harley-Davidson, 202–204
Hawthorne studies, 309
Hercules Incorporated, 178
Hertz Car Rental, 426, 568
Heuristic, assembly-line balancing
and, 296
Histogram, 169
Historical experience, labor standards
and, 317
Holding costs, 380
Honda, 40
Honeywell, 161
Hong Kong, SEATO and, 27
Hospitals, also see Arnold Palmer Hospital
aggregate planning and, 424
MRP and, 454
scheduling services and, 486
Hotel site selection, location strategies
and, 265–266
Hotels, MRP and, 454
House of quality, 131
Human resource, job design, work
management and, 303–331
competitive advantage for, 306–307
ergonomics and the work environment,
311–314
ethics and the work environment, 328
OM and, 35
job design and, 308–311
labor planning and, 307–308
labor standards, 317–328
methods analysis, 314–315
objective of, 306
service processes and, 217
visual work place, 315–316
Human resources, forecasting and, 87
Hyundai Shipyard, 67
IBM, 360
Impact on employees, JIT layout and, 503
Implementing Six Sigma, 162
Implications of quality, 157
Importance of inventory, 374–375
Importance of project management, 50
Importance of short-term scheduling, 468
Improve supply chain, global view of
operations and, 27–28
Improving individual components,
reliability and, 521–523
Improving operations and service,
advantages of outsourcing
and, 367
Incentives:
job design and, 311
managing the supply chain, 344
Increased flexibility, JIT layout and, 503
Increased transportation costs,
disadvantages of outsourcing, 367
Increasing repair capabilities,
maintenance and, 528
Independent demand, inventory models
and, 380–393
basic economic order quantity (EOQ)
model, 381
minimizing costs, 381–386
production order quantity model,
387–390
quantity discount models, 390–393
reorder points, 386–387
Industry standard, design of goods and
services and, 139–140
Infant mortality, 524, 524n
Infinite arrival population, 585
Initial solution, transportation models,
and, 570–572
Innovation, location and, 254–255
Input-output control, loading jobs
and, 472
Inspection:
attributes versus variables, 171–172
definition, 170
quality management and, 170–172
service industry and, 171
source and, 171
when and where, 170–171
Intangible costs, location strategies
and, 257
Integrate OM with other activities, 41
Integrated supply chain, 345–347
Intermittent facilities, 471
Intermittent processes, 204
Internal benchmarking, 162–163
Internal failure, quality and, 158
International business, 42
International quality standards,
159–160
International strategy, global operations
and, 42
Introductory phase, product life cycle
and, 128
Intuitive lowest-cost method, 571–572
Intuitive method, 571
Inventory analysis, simulation and,
623–625
Inventory, lean operations in services
and, 371–405, 513
Inventory management, 371–405, also
see Independent demand
fixed-period (P) systems and,
399–400
functions of, 374–375
importance of, 374–375
inventory models for independent
demand, 380–393
just-in-time, 504–506
Kanban, 508–510
managing and, 375–379
models, 380
OM and, 35
other probabilistic models, 396–398
probabilistic models and safety stock,
393–397
single-period model, 398–399
Inventory turnover, 354
Inventory types, 375
Investment analysis, capacity planning
and, 244–247
Ishikawa diagrams, 167
ISO, 552n
ISO9000, 159
ISO14000, 138, 159–160
ISO24700, 159–160
ISO-cost line, 557
Isometric drawing, 144
ISO-profit line solution method,
551–553
Issues in:
integrated supply chain, 344–345
operations strategy, 36–39
product design, 135–137
short-term scheduling, 468–471
Jackson Memorial Hospital, 626
Japan, SEATO and, 27
JC Penney, 346
Job characteristics, 309
Job classifications, 308
Job design, 308–311
definition, 308
human resource strategy, OM and, 35
job expansion, 308–309
labor specialization, 308
limitations of job expansion, 310
motivation and incentive systems
and, 311
OM decisions and, 35, 37
psychological components of, 309
self-directed teams, 310
Job enlargement, human resource
strategy and, 309
Job expansion, 308–309
Job enrichment, 309
Job lots, 284
Job rotation, 309
Job shops, facilities, 471
scheduling, 471n
Job specialization, 308
John Deere, 378, 414
John Hopkins Hospital, 172
Johnson Electric Holdings, LTD., 34
Johnson’s rule, sequencing and,
482–483
Joint ventures, time-based competition
and, 142
supply-chain mgt. and, 343
Jury of executive opinion, 89
Just-in-time:
MRP and, 446–447
supply-chain mgt. and, 353–354
TQM and, 164–165
Just-in-time and lean operations,
495–516
concerns of suppliers, 502–503
definition, 498
inventory and, 504–506
just-in-time, 500–503
Kanban, 508–510
layout and, 503–504
lean operations, 512–514

I10 General Index
Just-in-time and lean operations (continued)
material requirements planning and,
446–447
partnerships, 501–502
quality and, 510–511
scheduling and, 506–510
services, 513–514
Toyota production system and,
498–500, 511–512
Just-in-time (JIT) inventory, 504–506
reduce inventory, 504
reduce lot sizes, 504–506
reduce setup costs, 506
reduce variability, 504
Just-in-time partnerships, 501–502
concerns of suppliers, 502–503
Just-in-time performance, OM and, 12
Kaizen, JIT and, 511
Kanban, JIT and, 508–510
advantages of, 510
definition, 508
number of cards or containers and,
509–510
Keiretsu networks, 343
Key success factors (KSFs), 39–41
Kindle, 88
Kits, BOMs and, 440
Kitted material, MRP and, 440
Knowledge-based pay systems, 311
Knowledge society, 17
Komatsu, 42
Krispy Kreme, 232
Labor productivity of, location strategies
and, 256
as productivity variable, 16–17
Labor planning, human resources and,
307–308
Labor scheduling example, L.P. and,
561–562
Labor specialization, job design and, 308
Labor standards, 317–328
historical experience, 317
predetermined time standards, 322–325
time studies, 317–322
work sampling, 325–328
Large lots, integrated supply-chain, 344
Last-in, first-out, (LIFO), 587n
Last in, first-served (LIFS), 587n
Latest finish time (LF), 61, 63–64
Latest start time (LS), 61, 63–64
Laws, design of goods and services and,
139–140
Layout, types of, 276–278
Layout design, OM decisions and, 35
service processes and, 217
Layout strategies, 273–302
fixed-position layout, 282–283
just-in-time and, 513
office layout and, 278–279
process-oriented layout and, 276, 283
repetitive and product-oriented layout
and, 292–297
retail layout and, 279–281
servicescapes, 280–281
services, lean operations, and, 513–514
strategic importance of, 276
types of, 276–278
warehouse and storage layouts and,
281–283
work cells, 288–291
La-Z-Boy, 318
Lead time:
inventory model and, 386, 396–397
MRP and, 441
Leaders in quality, 158, 159
Lean operations, just-in-time and,
498–500
Lean operations, in services and,
513–514
Learn to improve operations, global view
of operations and, 27, 29
Learning-curve coefficient approach,
609–611
Learning curves, 605–614
applying, 608–611
definition, 606–607
limitations of, 612
services and manufacturing and,
607–608
strategic implication of, 611–612
Least-squares method, trend projections
and, 103
Level material use, 485–486
Level schedules, JIT and, 507
Level scheduling, 415
Level strategy, aggregate planning and, 415
Life cycle, strategy and, 128
Life cycle assessment, 139
Life cycle perspectives, systems and,
138–140
Limitations of:
job expansion, 310
learning curves, 612
rule-based dispatching systems, 483–484
Limited arrival population, 585
Linear decision rule (LDR), aggregate
planning and, 422
Linear programming (L.P.), 547–566,
also see Simplex method of
linear programming
applications and, 559–562
changes in the objective function
coefficient and, 557
corner-point method and, 553–555
definition of, 548
diet problem and, 560–561
feed-mix problem and, 560–561
formulating problem and, 549
graphical solution to, 550–555
iso-profit line solution method and,
551–553
labor scheduling and, 561–562
minimization problems and, 557–558
production mix example and, 559–560
requirements of a programming
problem and, 549
sensitivity analysis, 555–557
why we use LP, 548
Shader Electronics Co. example,
549–557
simplex method of, 562
Linear regression analysis, forecasting
and, 108–110
Little’s Law, 598–599
L.L. Bean, 163, 163n
Load reports, 453
Loading jobs, short term scheduling and,
472–477
assignment method, 475–477
Gantt charts, 474–475
input-output control, 472–473
Local optimization, managing the supply
chain and, 344
Location:
costs and, 254
innovation and, 254–255
Location decisions, factors affecting in,
255–259
Location selection, OM decision and, 35
Location strategies, 251–271
factors affecting location decisions,
255–259
methods of evaluating location
alternatives, 259–264
objective of, 254
service location strategy, 264–268
strategic importance of, 254–255
transportation model, 263–264
Locational break-even analysis, 260–261
Logarithmic approach, learning curves
and, 609
Logistics mgt., supply chain mgt. and,
350–354
Longest processing time (LPT), 478
Long-range forecast, 86–87
Loss of control, disadvantages of
outsourcing and, 367
Lot-for-lot, 447–448
Lot size reduction, integrated supply
chain and, 345
Lot sizing decision, 447
Lot sizing summary, 451
Lot sizing techniques, MRP and,
447–451
economic order quantity, 448–449
economic part period (EPP), 450
lot-for-lot, 447–448
part period balancing (PPB),
449–450
Wagner-Whitin algorithm, 451
Lot tolerance percent defective
(LTPD), 195
Louis Vuitton, 513
Low-cost leadership, 32
Low-level coding, MRP and, 440–441
Machine technology, 218
Maintenance and reliability, 517–530,
also see Reliability
automated sensors, 529
defined, 520
expert systems applied to, 529
increasing repair capabilities, 528
objective of, 520
OM and, 35
preventive maintenance, 524–528
reliability, 520–524
simulation and, 529
strategic importance of, 520–521
techniques for enhancing and, 529
total productive maintenance,
528–529
Maintenance/repair/operating (MROs)
inventories and, 375
Make-or-buy decisions, 143, 341
Malcolm Baldrige National Quality
Awards, 158
Management, MRP and, 446–447
dynamics of, 446
JIT and, 446
supply-chain and, 336–338
Management as productivity
variable, 16, 17
Management coefficients model,
aggregate planning, 422
Management process, OM and, 7

General Index I11
Managerial issues, control charts and,
190–191
Managing demand, capacity and,
231–233
Managing quality, 153–175, also see
Total Quality Management
cost of, 158
defining, 156–159
demand, capacity and, 233–234
ethics and, 158
implications of, 157
international quality standards,
159–160
role of inspection, 170–172
services and, 172–174
strategy and, 156
tools of TQM, 166–170
total quality mgt., 160–166
Manila, cartoon industry in, 27
Manufacturability, product development
and, 134
Manufacturing, learning curve and,
607–608
Manufacturing cycle time, 500
MAP/1 software, 626
Maquiladoras, 27, 257
Market-based price model, 350
Marketing, OM and, 4
Markets, global view of operations and,
26–29
Mass customization, OM and, 12
process strategy and, 206–208
Master production schedule 412,
436–438
Material handling costs, 281
Material requirements planning
(MRP) and Enterprise
resource planning (ERP),
433–465, also see Dependent
inventory model requirements
capacity planning and, 453
closed loop, 452
defined, 436
dependent demand, 436
dependent inventory model
requirements and, 436–441
distribution resource planning (DRP)
and, 454–455
dynamics, 446
enterprise resource planning (ERP),
455–458
extensions of, 451–454
JIT and, 446–447
lot-sizing techniques and, 447–451
management and, 446–447
services and, 454–455
structure for, 441–445
Material requirements planning II (MRP
II), 451–454
Mathematical approaches, aggregate
planning and, 420–422
Maturity phase, product life cycle
and, 128
Maximax, decision-making under
uncertainty and, 535
Maximin, decision-making under
uncertainty, 535
Maximization problems, linear
programming and, T3–7
McDonald’s Corp., 146, 274–275, 591
Mean absolute deviation (MAD), 95–96
Mean absolute percent error (MAPE),
97–98
Mean chart limits, setting of, 181–184
Mean squared error (MSE), 97
Mean time between failures (MTBF),
522–523
Measurement, productivity and, 14–16
Measuring:
forecast error, 95–98
queue performance, 588
supply chain performance, 354–356
Medium-range forecast, 86–87
Mercedes Benz, 163
Merck mission statement, 30
MERCOSUR, 27
Methods analysis, 314–315
Methods for aggregate planning,
415–420
Methods Time Measurement (MTM),
323–325
Methods Time Measurement
Association, 323n
Mexico, NAFTA, 27
Miami Heat Game, 58
Michelin, 127
Micro Saint software, 626
Microsoft Corp., 54, 141, 360
Microsoft Project, project mgt. and,
74–77
entering data, 74–75
PERT analysis, 76
tracking the time status of
a project, 76
viewing the project schedule, 75–76
Milliken, 158
Milton Bradley, 390
Minimal-cost-flow problem, T5–13
Minimization problems, L.P. and,
557–558, T3–7 to T3–8
Minimizing costs, independent demand
inventory and, 381–386
Minimum cost of insertion technique,
T5–10
Miscellaneous services, aggregate
planning and, 424
Mission, global view of operations and,
30–31
Mixed strategy, aggregate planning
and, 415
Mixing options, aggregate planning and,
414–415
MNC (Multinational Corp.), 42
Models, inventory and, 380
Modi method (modified distribution):
how to use, T4 –1 to T4–4
solving a problem, T4–2 to T4–4
transportation problems and, T4–2
to T4–4
Modsim software, 626
Modular bills, MRP and, 440
Modular design, product development
and, 135
Modules, repetitive focus and, 205
Moment-of-truth, service design and,
147–148
Monte Carlo method, 618
Monte Carlo simulation, 618–621
Monitoring forecasts, 113–116
Most likely time, PERT and, 66
Motivation, incentive systems, 311
Motivation system, job design and, 311
Motorola, 158, 161, 360
Moving averages, quantitative
forecasting and, 91–94
MROs, 375
MRP. See Material requirements planning
MSDS, 328, 328n
Mrs. Field’s Cookies, 281
Multidomestic strategy, global
operations and, 43
Multifactor productivity, 15–16
Multinational corporation (MNC), 42
Multiphase system, 588
Multiple-channel queuing model, 588
Multiple regression, 113
Multiple regression analysis, 113
Multiple traveling salesmen problem
(MTSP), T5–4, T5–8
Multiproduct case, break-even analysis
and, 240–242
Mutual agreement on goals, managing
the supply chain and, 344
NAFTA (North American Free Trade
Agreement), 27
Naive approach, quantitative forecasting
and, 90–91
NASA, 363
NASCAR, 304–306
National, rental company and, 568
National chains, aggregate planning
and, 424
Natural variations, statistical process
control and, 179
Nature of aggregate planning, 411–412
Nearest neighbor procedure, T5–5
to T5–7
Nearshoring, 365
Negative exponential probability
distribution, 588
Negative impact on employees,
disadvantages of outsourcing
and, 368
Negotiation strategies, vendor selection
and, 350
Net material requirements plan, MRP
and, 443–445
Net present value, strategy-driven
investments and, 224–227
Network diagrams and approaches,
project management and, 55–60
Networks, routing and scheduling
vehicles and, T5–3
New Guinea, SEATO and, 27
New product opportunities, 129
importance of, 129–130
New trends in OM, 12–13
New Zealand, SEATO and, 27
Nike, 127
Nissan, 448
Nodes, routing and scheduling vehicles
and, T5–3
Non-basic variables, T3–3
Normal curve areas, A2–A3, T1–4
to T1–7
Normal time, labor standards and, 318
North American Free Trade Agreement
(NAFTA), 27
Northwest corner rule, transportation
models and, 570–571, 577
Objective function, L.P. problems
and, 549
Objective function coefficients,
allowable ranges and, 557
Objective of human resource
strategy, 306

I12 General Index
Objectives of routing and scheduling
vehicle problems, T5–2
Offshoring, 360
Office layout, 276, 278–279
Office relationships chart, 278
O’Hare Airport, 548
Olive Garden Restaurant, also see
Darden Restaurants
forecasting, 91
JIT, 500
OM in Action:
Assembly lines to green disassembly
lines, 292
Auto industry in Alabama, 257
Backsourcing to Small-Town,
U.S.A., 364
Banking and theory of constraints
(TOC), 238
B-2 Bomber, job design and, 314
Benetton, ERP software and, 456
Borders Books, process strategy
and, 207
Cadbury Schweppes PLC, designing
Trident Splash, 134
Cell-Phone Industry, chasing fads
in, 141
Cessna Aircraft, 502
Continental Air Line, scheduling
planes, L.P. and, 559
Dalrymple Bay, capacity and, 232
Delta Airlines
project mgt. and, 54
scheduling planes, LP and, 559
DHL, supply chain and, 352
FedEx, forecasting and, 117
Franz Colruyt, low-cost strategy and, 33
Going Global to Compete, 28
Going Lean at Louis Vuitton, 513
Hospital benchmarks against Ferrari
Racing Team, 164
Hotel industry, technology changes
and, 222
Incentives to unsnarl traffic jams in the
OR, 311
Inventory accuracy at Milton
Bradley, 390
JC Penney, supply chain and, 346
Jackson Memorial Hospital, simulation
and, 626
Johnson Electric Holdings, Ltd.,
response strategy and, 34
Kaizen at Ducati, 512
Lean Production at Cessna Aircraft, 502
Mass customization for straight
teeth, 216
Miami Heat Game, 58
Milton Bradley inventory management
and, 390
Olive Garden, forecasting and, 91
Preventive maintenance saves
lives, 526
Quality Coils, Inc., 256
Radio Frequency Tags, supply chain
and, 345
Rebuilding the Pentagon after 9/11,
project mgt. and, 74
Red Lobster Restaurant, forecasting
and, 91
Richey International’s Spies, 174
Roses, supply-chain management
and, 338
Saving Seconds at Retail Boosts
Productivity, 318
Scheduling aircraft turnaround, 487
Scheduling workers who fall asleep,
short-term scheduling, 471
Scheduling for peaks by swapping
employees, 487
Shopping mall, linear programming
and, 549
66, 207, 896 bottles of beer on the
wall, 400
Smooth FM Radio, process
strategy, 207
Snapper, aggregate planning and, 412
Southwest Air Lines, scheduling
airplanes and, 559
Starbucks Coffee, location
strategy and, 264
productivity and, 14
simulation and, 621
Subaru, ISO 14001 and, 160
Taco Bell, productivity and lower costs
and, 19
Tightest Ship in Shipping Business,
323
Toyota University teaches lean
thinking, 514
Unisys Corp., SPC and, 188
U.S. cartoon production in Manila,
global view of operations, 27
Wal-Mart:
inventory management and, 377
link to China, 367
Zero wait time guarantee in Michigan’s
ER, 589
Work Cells Increase Productivity at
Canon, 289
Workers falling asleep, scheduling
and, 471
Yield management at Hertz, aggregate
planning and, 426
On-line auctions, 348
On-line catalogues, 347–348
One-sided window, T5–12
Operating characteristics (OC) curves,
194–195, T2–2 to T2–3
Operations and productivity, 1–21
Operations chart, job design and, 315
Operations decisions, 35, 532–533
Operations layout strategy. See Layout
strategy, T2–2 to T2–3
Operations management:
decision process in, 532–533
definition, 4
ethics and social responsibility, 19
heritage of, 8–10
Hard Rock Café and, 2–3
job opportunities in, 7–8
management process, 7
new trends, 12–13
organizing to produce goods &
services, 4–5
productivity challenge, 13–18
reasons to study, 6–7
service sector, 10–12
significant events in, 9
ten strategy decisions, 1, 7
where OM jobs are, 7
why study, 6–7
Operations strategy in a global
environment, 23–45
competitive advantage through
operations, 31–34
developing missions and strategies,
30–31
global strategy, 43
global view, 26–29
issues in, 36–39
strategy development and
implementation, 39–41
strategy options, 42–44
ten strategic OM decisions, 35–36
Operator input to machines, 312
Opportunities in an integrated supply
chain, 345–347
Opportunities to improve service
processes, 217
Opportunity cost, assignment method
and, 475, 475n
Optimistic time in PERT, 66
Ordering cost, 380
Organizing to produce goods and
services, 4–5
Organizing product development,
133–134
Origin points, transportation modeling
and, 568
Orlando Utilities Commission, 518–520
OSHA, 328, 328n
Outsource providers, rating and, 366
Outsourcing as a Supply Chain Strategy,
341, 359–369
audits and metrics to evaluate and, 368
advantages, 367–368
disadvantages, 367–368
ethical issues in, 368
evaluating risk with factor rate,
365–366
risks in, 363–364
strategic planning and core
competencies, 361–363
types of, 361
What is outsourcing?, 360–361
p-chart, 186–188, 190
P system, 399–400
Paddy-Hopkirk Factory, 314–315
Paladin Software Corp., 54
Paraguay, MERCOSUR and, 27
Parameter, sensitivity analysis and, 555
Parametric Technology Corp., 146n
Pareto charts, 167–168
Part period balancing (PPB), lot sizing
and, 449–450
Partial tour, T5–6
Partnering relationships, supply chain
strategies and, 12
Partnerships, JIT and, 501–502
“Pass-through facilities,” supply-chain
mgt. and, 347
Path, T5–6
PDCA, 161
Pegging, 446
Pentagon after 9/11, 74
Perpetual inventory system, 399
Personal ethics, 339
PERT. See Project management
Pessimistic time estimate, PERT and, 66
Phantom bills of material, MRP and, 440
Philippines, cartoon industry and, 27
Pilferage, 379
Pipelines, logistics management
and, 351
Pivot column, T3–4
Pivot number, T3–4
Pivot row, T3–4
Plan-Do-Check-Act (PDCA), 161

General Index I13
Planned order receipt, MRP and, 443
Planned order release, MRP and, 443
Planning bills, MRP and, 440
Planning horizons, aggregate planning
and, 410–411
Planning process, aggregate planning
and, 410–411
Poisson distribution, 586, A2
Poisson table, A4
Poka-yoke, 171
Political risk, location strategy and, 258
Poker decision process, decision tree
and, 542–543
POM for Windows: A6–A7
aggregate planning, 429
breakeven analysis, 247
decision table and trees, 544
forecasting, 118
inventory problems, 402
layout strategy, 299
learning curves, 613
linear programming, 564
location problems, 269
material requirements planning (MRP),
459–460
outsourcing as a supply chain strategy,
369
project scheduling, 78
reliability problems, 530
scheduling, 492
simulation, 628
SPC control charts, OC curves,
acceptance sampling & process
capability, 197
transportation problems, 578
use of, A6–A7
waiting line, 602
Portion control standards, 143
Postponement, process strategy
and, 207
supply-chain mgt. and, 346–347
Predetermined time standards, 322–325
Prevention costs, quality and, 158
Preventive maintenance, 524–528
Primavera Systems, Inc., 54
Priority rules, 478–480
Probabilistic inventory models and safety
stock, 393–398
service level and, 393
Process analysis, design and, 211–214
Process capability, SPC and, 191–193
definition, 191
index and, 192
ratio and, 191–192
Process charts, 213–214, 315
Process choices, comparison of, 208–211
Process control, 219
Process cycle time, 235
Process design, OM and, 35
customer interaction and, 215–217
Process focus, process strategies and,
204–205
Process-focused facilities, 471–472
Process mapping, 211–212
Process-oriented layout, 276, 283–288
computer software for, 287
focused work center and focused
factory, 291
work cells and, 288–291
Process redesign, 223
Process strategy, 204–226
analysis and design, 211–214
defined, 204
four process strategies, 204–211
process redesign, 223
production technology, 218
selection of equipment and technology,
217–218
service process design and, 214–217
sustainability, 223–225
technology in services, 221–222
Process time of a station, 235
Process time of a system, 235
Producer’s risk, 194, T2–3 to T2–4
Product-by-value analysis, 128–129
Product decision, 126, T2–3 to T2–4
Product design issues, 135–138
computer-aided design (CAD), 136
computer-aided manufacturing
(CAM), 137
environmentally friendly designs,
138–140
ethics and, 138–140
modular design, 135
robust design, 135
sustainability, 138–140
value analysis, 137
virtual reality technology, 137
Product development, 130–135, also see
Design of Goods and Services
development system, 130–131
importance of, 129–130
issues for design and, 135–137
manufacturability and value
engineering, 134–135
organizing for, 133–134
quality function deployment (QFD),
131–133
systems, life cycle perspectives and,
138–140
teams and, 134
Product failure rate (FR), reliability
and, 522
Product focus, 206
Product focused facilities, 472
Product generation, new
opportunities, 129
Product liability, quality and, 157
Product life cycle, 87, 127
management and, 145–146
strategy and, 128
Product Life-Cycle Management (PLM),
145–146
Product-mix problem, linear
programming and, 559–560
Product-oriented layout, 277, 292–297
assembly line balancing and, 293–297
Production, defined, 4
Production order quantity model,
387–390
Production/operations, OM and, 4
Production technology, 218–220
automated guided vehicles (AGV), 220
automatic identification system (AIS),
218–219
automated storage & retrieval system
(ASRS), 220
computer-integrated manufacturing
(CIM), 220
flexible manufacturing system
(FMS), 220
machine technology, 218
process control, 219
radio frequency identification, 218–219
robots, 220
vision systems, 219–220
Productivity, defined, 12
single factor, 15
multifactor, 15
Productivity challenge and OM, 13–16
defined, 13
measurement of, 14–16
service sector and, 18
variables, 16
Productivity variables, 16–18
Project crashing and cost-time trade-offs,
71–73
Project completion probability, 68–71
Project controlling, 54
Project management, 47–81
activity-on-arrow example, 55–56, 60
activity-on-node example, 55–59
calculating slack time, 64–65
cost-time trade-offs, 71–73
CPM in. See Critical Path Method
crashing, 71–73
critical path analysis, 60
critique of PERT & CPM, 73–74
determining the project schedule,
60–65
dummy activity, 56
framework of PERT & CPM, 55
identifying the critical path, 64–65
importance of, 50
Microsoft Project, 54, 65
network diagrams and approaches, 55
PERT, 55
PERT/CPM in, 55
probability of project completion,
68–71
project controlling, 54
project crashing, 71–73
project planning, 50–53
project scheduling and, 53
techniques of, 55–60
time estimates in, 66–67
variability in activity times, 65–71
Project manager, 51–52
Project organization, 50
Project planning, 50–53
Project scheduling, 53
ProModel software, 626
Proplanner, 146n
Provide better goods and services,
global view of operations
and, 27, 28
Providing redundancy, reliability and,
523–524
Proximity to competitors, location
strategies and, 258–259
Proximity to markets, location strategies
and, 258
Proximity to suppliers, location strategies
and, 258
Psychological components, job design
and, 309
Pull data, 345
Pull system, 500
Purchase orders outstanding, MRP
and, 441
Purchase technology by acquiring firm,
141–142
Q systems, 399
Qualitative forecasting methods, 89
Delphi method, 89
Jury of executive opinion, 89
sales force composite, 89
consumer market survey, 89

I14 General Index
Quality, also see Statistical Process
Control; Total Quality
Management (TQM)
cost of, 158
defining, 156
ethics and, 158
implications of, 157
International Quality Standards, 159–160
just-in-time and, 510–511
Malcolm Baldrige National Quality
Award, 158
OM decisions and, 35
Quality circle, 162
Quality Coils, Inc., 256
Quality Function Deployment (QFD),
131–133
Quality loss function (QLF), 165
Quality robust, 165
Quantitative forecasts, 89–90
Quantity discount models, inventory
management and, 390–393
Queue(s), limited and unlimited, 585
Queue costs, 589–590
Queuing models, variety of, 590–601,
also see waiting line models
Model A (M/M/l): single channel with
Poisson arrivals/exponential
service times, 590–593
Model B (M/M/S): multiple-channel
queuing model, 593–597
Model C (M/D/l): constant-service-
time model, 597–598
Model D: limited-population model,
599–601
Queuing problems, simulation of,
621–623
Queuing theory, 584
Quick response, 32, 34
Radio frequency identification (RFID),
218–219
Railroads, logistics management
and, 351
Random number, 619
table of, A4
Random number intervals, Monte Carlo
simulation and, 619
Random stocking, warehouse layout
and, 282
Random variations, time series
forecasting and, 90
Range chart limits, setting of, 185
using of, 185–186
Rapid product development, OM and, 12
Rating International Risk Factors, 365
Rating outsource providers, 366
Raw material inventory, 375
R-chart, 180
Real-Time inventory tracking, 348–349
Record accuracy, inventory management
and, 377
Recycle, sustainability and, 223
Red Lobster Restaurants, also see
Darden restaurants
forecasting, 91
JIT, 500
time study, 319
Reduce costs, global view of operations
and, 27–28
Reduce lot sizes, JIT and, 504–505
Reduce inventory, JIT and, 504–506
Reduce setup costs, JIT and, 506
Reduce variability, JIT inventory
and, 504
Reduced space and inventory, JIT and,
503–504
Reducing risk with incremental changes,
242–243
Redundancy, reliability and, 523–524
Regal Marine, 124–126
Regression and correlation analysis,
forecasting and, 108–116
Regulations, sustainability and, 224
Reliable response, 32–34
Reliability, 521–524, also see
Maintenance
improving individual components and,
521–522
providing redundancy and, 523–524
strategic importance of, 520–521
Remington Rand, 55
Reneging customers, 586
Reorder point (ROP) inventory mgt. and,
386–387
Repetitive focus, process strategy and,
205–206
Repetitive facilities, scheduling and, 485
Repetitive layout, 292–297
Reputation, sustainability and, 224–225
Requirements of a L.P. problem, 549
Requirements of work cells, 288–289
Resources, sustainability and, 223
Resources view, operations strategy
and, 36
Respect for people, TPS and, 511
Response, competitive advantage
and, 32–34
Restaurants, aggregate planning and, 424
MRP and, 454
Retail layout, 276, 279–280
Retail stores, scheduling services
and, 486
Revenue function, break-even analysis
and, 239
Revenue management, aggregate
planning and, 425
RFID, 218–219
RFQs (requests for quotes), 348
Richey International, 174
Right-hand/left-hand chart, 315
Right-hand-side values, L.P. and,
556–557
Risk, supply-chain and, 337–338
Risks in outsourcing, 363–364
Ritz-Carlton Hotels, 158
Robots, 220
Robust design, product development
and, 135
Robust model, inventory management
and, 385
Role of inspection, 170–172
Route sheet, 144
Routing vehicles, T5–4
Routing service vehicles, T5–5 to T5–11
Run test, charts and, 191
Rusty Wallace’s NASCAR Racing Team,
304–306
Safety stock, inventory mgt. and, 387
Safety stock, MRP and, 445
Sales force composite, forecasting
and, 89
Samples, SPC and, 179
SAP PLM, 146n
Scatter diagrams, TQM tools and, 167
Scheduling. See Short-term Scheduling
and Loading Jobs
aggregate planning and, 410
by simulation, 422
criteria, 470–471
decisions, 410
just-in-time and, 514
lean operations in services and, 514
OM decisions and, 35
service vehicles, T5–11 to T5–13
vehicles, T5–4
SCOR, 356
Seasonal demands, capacity and, 232
Seasonal variations in data, 103–108
Seasonality, time series and, 90
SEATO, 27
Second-order smoothing, 101
Security, JIT, supply chain mgt. and,
353–354
Selection of equipment and technology,
process strategy and, 217–218
Self-directed teams, 310
Sensitivity analysis, L.P. and, 555–557
Sensitivity Report, 556
Sequencing, jobs in work centers,
478–484
critical ratio and, 481–482
definition, 478
Johnson’s rule and, 482–483
limitations of rule-based dispatching
systems, 483–484
priority rules for dispatching jobs,
478–480
Sequential sampling, T2–2
Service(s), also see Service Sector
aggregate planning and, 422–425
defined, 10
ERP and, 458
design of, goods and, 7, 146–148
differences from goods and, 10–11
documents for, 147–148
growth of, 11
lean operations in, 513–514
learning curves in, 607–608
MRP and, 454–455
pay in, 11
scheduling and, 486
service blueprinting, process strategy
and, 214
service characteristics, waiting line
system and, 587–588
service time distribution, waiting line
system and, 588
total quality management, services
and, 172–174
Service industry inspection, 171
Service level, probabilistic models
and, 393
Service location strategy, 264–268
Service pay, 11
Service processes, 217
Service recovery, 173–174
Service sector:
defined, 10
demand and capacity mgt. in, 233–234
ERP and, 458
forecasting and, 116–117
operations in, 10–12
productivity and, 18
TQM in, 172–174
Service vehicle scheduling, T5–11
to T5–13
Servicescapes, 280

General Index I15
Setup cost, 380
Setup time, 380
Seven steps in forecasting, 88
Seven tools of TQM, 166–170
Seven wastes, lean operations and,
498–499
Shader Electronics, L.P. problem
example, 549–557, T3–1
to T3–7
Shadow price, 556
Sherwin Williams, 127
Shipping alternatives, cost of, 352–353
Shortest processing time (SPT), 478
Short-range forecast, 86–87
Short-term scheduling, 465–494, also see
Scheduling
airlines, 466–468
cyclical scheduling, service employees
and, 488–489
finite capacity (FCS) and, 484–485
forward and backward scheduling, 470
importance of, 468
issues, and, 468–471
limitations of rule-based dispatching
systems, 483–484
loading jobs, 472–473
process focused facilities and, 471–472
repetitive facilities, and, 485–486
sequencing, jobs in work centers,
478–484
services and, 486–489
strategic importance of, 468
Shrinkage, 379
Siemans, 18
Signs, symbols, artifacts, 280–281
Simplex method, definition, T3–2
Simplex method of L.P., 562, T3–1
to T3–10
artificial and surplus variable, T3–7
converting constraints to equations,
T3–2
setting up first simplex table, T3–2
to T3–4
simplex solution procedures, T3–4
to T3–6
solving minimization problems, T3–7
to T3–8
summary of simplex steps for
maximization problems, T3–6
Simfactory software, 626
SIMSCRIPT software, 626
Simulation, 615–629, T3–1 to T3–10
advantages and disadvantages, 617
defined, 616–617
experiment and, 620–621
inventory analysis and, 623–625
maintenance and, 529
Monte Carlo, 618–621
queuing problem and, 621–623
software and, 626
Single channel queuing model/poisson
arrivals/exponential service
times, 590–593
Single-channel queuing system, 587
Single factor productivity, 15
Single sampling, T2–2
Single-period inventory model, 398–399
Single-phase system, 588
Single-product case, break-even analysis
and, 240
Single stage control of replenishment,
345–346
Six Sigma, 161–162, 162n
Slack time, 64–65
Slack variables, simplex method
and, T3–2
Slotting fees, 279–280
Small bucket approach, MRP and,
446–447
Smooth FM Radio, 207
Smoothing constant, 94–95
Snapper Lawn Mowers, 412
Social responsibility, OM and, 19
Solving routing and scheduling vehicle
problems, T5–4
SONY, 26, 360, 362
Source inspection, 191
Sources, transportation models and, 568
South Korea, SEATO and, 27
Southwest Airlines, 41, 310
Spatial layout, functionality and, 280–281
Special considerations for service
process design, 214–217
Special issues in modeling, 575–577
Special packaging, 347
Specialty retail shops, forecasting
and, 116
Staffing an organization, 41
Staffing work cells, 289–291
Standard error of estimate, 110–111
Standard for the exchange of product
data (STEP), 136
Standard normal distribution, T1–5
to T1–7
Standard normal table, A2–A3, T1–5
to T1–7
Standard time, labor standards and,
319–322
Standard work practice, TPS and,
511–512
Standardization, supply-chain mgt.
and, 346
Starbucks Coffee:
location strategy and, 264
productivity and, 14
simulation and, 621
Statistical process control (SPC), 178–199
acceptance sampling, 178, 193–194
assignable variations, 179
attributes for, 186–190
c-charts, 188–190
central limit theorem and, 180–181
control charts, 178
control charts for variables, 180
definition, 178
managerial issues and control charts,
190–191
mean chart limits, 181–184
natural variations, 179
patterns on control charts, 190–191
p-charts, 186–188, 190
process capability, 191–193
R-chart, 180
samples, 179
setting mean chart limits, 181–184
setting range chart limits and, 185
using ranges and mean charts, 185
variables for, 180
which chart to use, 190
x̄-chart, 180–190
Statistical tools for managers, T1–1
to T1–8
continuous probability distributions,
T1–4 to T1–7
discrete probability distribution, T1–2
to T1–4
expected value of a discrete probability
distribution, T1–3
variance of a discrete probability
distribution, T1–3 to T1–4
Steelcase, 277
Stepping-stone method, 572–575
Steps in forecasting, 88
Strategic importance:
of forecasting, 87–88
of layout decisions, 276
of learning curves, 611–612
of location, 254–255
of maintenance and reliability,
520–521
of short term scheduling, 468
of supply-chain management,
336–338
Strategic OM decisions, 35–36
Strategic planning and core
competencies, 361–363
Strategy, aggregate planning, 412–415
capacity and, 230
competitive advantages and,
126–127
definition, 34–35
development and implementation,
39–41
global operation options and, 42
human resource, 306
international, 42
issues in operations and, 36–39
life cycle and, 128
multidomestic, 43
operations in a global environment,
23–45
outsourcing, 359–369
process, 201–226
quality and, 156
supply chain, 341–343
transnational, 43
Strategy developing and, 30
Strategy driven investment, applying
investment analysis, 244
Structure for MRP, 441–445
Subaru, 160
Subtours, T5–8
Super Fast Pizza, 33
Supermarket MRP and, 447
Suppliers, lean operations in services
and, 513
Supply-chain management, 333–358
definition, 336
economics and, 340–341
E-procurement and, 347–349
ethics and, 339–340
forecasting and, 88
logistics management, 350–354
managing and, 343–347
measuring performance and,
354–356
objective of, 337
OM and, 35
partnering and, 12
performance and, 354
risk, 337–338
strategic importance of, 336–338
strategies and, 341–343
suppliers, few, many and, 341
sustainability and, 339
vendor selection and, 349–350
Supply Chain Operations Reference
model (SCOR), 356
Surplus variables, T3–7

I16 General Index
Sustainability:
carbon footprint, 272
environmentally friendly designs and,
138–140
recycle, 223
resources, 223
regulations, 224
reputation, 224–225
supply-chain mgt. and, 339–340
Wal-mart and, 367
SWOT analysis, 39
Symantec Corp., 54
Symbols:
decision trees and, 53
servicescapes and, 280–281
System nervousness, 446
Systems and life cycle perspectives, 138
TacoBell, 19, 76, 127
Taguchi concepts, 165
Takt time, 290, 290n, 295n
Takumi, 158
Tangible costs, location strategies and, 257
Target oriented quality, 165
Techniques for enhancing
maintenance, 529
Techniques of project mgt., 55–60
Technological forecasts, 87
Ten OM strategy decisions, 39
Texas Instruments and, 158
Theory of comparative advantage,
362–363
Theory of constraints (TOC), capacity
and constraint mgt., 237
banking and, 238
Therbligs, 323
Third-Party logistics, 351–352
3-D object modeling, 136
Three time estimates in PERT, 66–67
Throughput time, 500
Time-based competition, product
development and, 140–142
alliances, 142
joint ventures, 142
purchasing technology by buying
a firm, 141–142
Time fences, 446
Time-function mapping, process
analysis, design and, 211–212
Time horizons, 86–87
Time Measurement Units (TMUs), 323
Time series forecasting, 90–118
cycles in, 90
cyclical variations in data, 108
decomposition of time series and, 90
exponential smoothing and, 94–98
exponential smoothing with trend
adjustment, 98–101
measuring forecast error, 95–98
moving averages and, 91–94
naive approach to, 90
random variations and, 90
seasonal variations in data, 103–108
seasonality, 90
smoothing constant, 94–95
trend and, 90
trend projections and, 101–103
Time studies, labor standards and, 317–322
Times series models, 89–90
Tools of total quality management,
166–170
cause & effect diagrams, 167
check sheets, 166–167
flow charts, 168–169
histogram, 169
knowledge of, 166
Pareto charts, 167–168
scatter diagrams, 167
statistical process control, 169–170
Total factor productivity, 15
Total productive maintenance (TPM),
528–529
Total quality management (TQM),
160–166
benchmarking, 163–164
continuous improvement, 161
definition, 160
employee empowerment, 162–163
inspection, 170–172
just-in-time, 164–165
services, 172–174
Six Sigma, 161–162
Taguchi concepts, 165
tools of, 166–170
Total slack time, 65
Tour, T5–15
Toyota Motor Corp., 496–498
Toyota Production System, 498–500,
511–512
Toyota University, 514
TQM. See Total quality management
(TQM)
Tracking signal, 114
Transition to production, 150
Transnational strategy, global operations
and, 43–44
Transportation, location strategies and,
263–264
Transportation matrix, 569
Transportation method of linear
programming, 420–422
Transportation models, 567–581
initial solution and, 570–572
location and strategies and, 263–264
special issues in, 575–577
stepping-stone method and, 572–575
transportation modeling, 568–569
Transportation problems, MODI and VAM
methods and, T4–1 to T4–10
MODI method, T4–2 to T4–4
VOGEL’s approximation method
(VAM), T4–4 to T4–7
Traveling salesman problem (TSP),
T5–4, T5–5 to T5–8
Trend projections, forecasting and, 90,
101–103
Trucking, logistics management and, 351
Trust, managing the supply chain
and, 344
24/7 operations, scheduling services
and, 487
Two-sided window, T5–12
Type I error, 195
Type II error, 195
Types of decision making environments,
534–538
Types of forecasts, 87
inventory, 375
layouts, 276–278
outsourcing, 361
UGS Corp., 146n, 288
Understand markets, global view of
operations and, 27, 28
Undirected arcs, routing and scheduling
vehicles, T5–3
Unisys Corp., 188
Unlimited arrival population, 585
UPS (United Parcel Service), 173, 423
U.S., NAFTA CAFTA and, 27
U.S. Steel, 470
Using, ExcelOM, A5–A6
Using POM for Windows, A6–A7
Uruguay, MERCOSUR and, 27
Utilization, capacity and, 229
Validity range for the shadow price,
L.P. and, 557
Value analysis, 137
Value-chain analysis, 38
Value engineering, product development
and, 134–135
Value stream mapping, 212–213
Values, location strategy and, 258
Variability, lean operations and, 499–500
Variability in activity times, project mgt.
and, 65–71
probability of project completion, 68–71
three time estimates in PERT, 66–67
Variable(s) control charts for, 180
Variable costs, break-even analysis
and, 239
Variable demand, probabilistic models,
and 396
Variable inspection, 171
Variance of a discrete probability
distribution, statistical tools and,
T1–3 to T1–4
Variety of queuing models, 590–601
Vehicle routing and scheduling, T5–1
to T5–18
characteristics of problems and, T5–3
to T5–5
introduction, T5–2
objectives of routing and scheduling
problems, T5–2
other problems, T5–13 to T5–14
routing service vehicles, T5–5 to
T5–11
scheduling service vehicles, T5–11
to T5–13
Vendor:
development, 350
evaluation, 349
managed inventory (VMI), 346
selection, supply-chain mgt. and,
349–350
Vertical integration, supply-chain mgt.
and, 342–343
Virtual companies, supply chain
strategies and, 343
Virtual reality technology, 137
Vision systems, production technology
and, 219–220
Visual workplace, job design and,
315–316
Vogel’s approximation method (VAM),
transportation problems and,
T4–4 to T4–7
Volvo, 26
Wagner-Whitin algorithm, lot sizing
and, 451
Waiting line models, 583–604, also see
Queuing models
characteristics of waiting line system,
586–587
measuring queue performance and, 588

General Index I17
other queuing approaches, 601
queuing costs, 589–590
queuing models, varieties of, 590–601
queuing theory, 584
service characteristics and, 587–588
Waiting lines, 584
Wal-Mart, also see OM in Action
inventory and, 277
retail layout and, 280
sustainability and, 367
Warehousing layout, 276, 281–283
crossdocking, 282
customizing, 282
objective, 281
random stocking, 282
Waste elimination, JIT philosophy and,
498–499
Waterways, logistics mgt. and, 351
Westminster Software, Inc., 54
What is a learning curve?, 606–607
What is simulation?, 616–617
Wheeled Coach, 434–436
Where are OM jobs?, 7
Why study OM, 6–7
Witness software, 626
Work balance chart, 290
Work breakdown structure (WBS),
project mgt. and, 52–53
Work cells, layout and, 277
focused work center and focused
factory, 291
requirements of, 288–289
scheduling and, 472
staffing and balancing, 289–291
Work environment, job design and,
312–314
Work-in-process (WIP) inventory, 375
Work measurement (Labor Standards),
317–328
historical experience and, 317
predetermined time standards and,
322–325
time studies and, 317–322
work sampling and, 325–328
Work order, 144
Work rules, human resources and, 308
Work schedules, labor planning and,
307–308
World Trade Organization (WTO), 27
x̄-charts, 180, 190
Xerox, 158, 163, 164
Yield management, aggregate planning
and, 425–428
Zero defects, 161

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P1
Photo Credits
CHAPTER 1: p. 2 (top): Andre Jenny/Alamy Images; p. 2 (bottom): Hard Rock
Café; p. 3: Hard Rock Café; p. 9: From the Collections of Henry Ford Museum &
Greenfield Village; p. 14: © Marc Asnin/CORBIS, all rights reserved; p. 17
(left): TEK Image/Photo Researchers, Inc.; p. 17 (right): John McLean/Photo
Researchers, Inc.; p. 18: Siemens press picture, Courtesy of Siemens AG,
Munich/Berlin.
CHAPTER 2: p. 24: Boeing Commerical Airplane Group; p. 25: Boeing
Commercial Airplane Group; p. 27: © Disney Enterprises, Inc.; p. 29: Kraipit
Phanvut/SIPA Press; p. 33: AP Wide World Photos; p. 40: Courtesy of American
Honda Motor Co., Inc.; p. 40 (top middle): Julie Lucht/Shutterstock; p. 40
(bottom middle): Courtesy of www.HondaNews.com; p. 43 (left): Copyright
© 1997 Komatsu Ltd. All rights reserved; p. 43 (right): Reprinted courtesy of
Caterpillar Inc.
CHAPTER 3: p. 48 (left): © Bechtel Corporation; p. 48 (right): Q&A Photos,
Ltd., www.qaphotos.com; p. 49: Bill Pogue/Getty Images Inc. – Stone Allstock;
p. 49 (bottom right): Joe Cavaretta/AP Wide World Photos; p. 49 (bottom left):
Thomas Hartwell/U.S. Agency for International Development (USAID); p. 54
(top): Getty Images, Inc.; p. 54 (bottom left): Jonathan Bailey Associates; p. 54
(bottom right): Courtesy of Jonathan Bailey Associates; p. 58: Jeffrey Allan
Salter/Redux Pictures; p. 65: Hard Rock Café; p. 67: Paul Chesley/Getty Images
Inc. — Stone Allstock; p. 74: Mai/Mai/Getty Images/Time Life Pictures; p. 76:
David Young-Wolff/PhotoEdit Inc.
CHAPTER 4: p. 84 (top): Kelly-Mooney Photography/CORBIS/© Disney
Enterprises, Inc.; p. 84 (bottom): Jeff Greenberg/PhotoEdit, Inc./© Disney
Enterprises, Inc.; p. 85: Peter Cosgrove/AP Wide World Photos/Disney characters
© Disney Enterprises, Inc. Used by permission from Disney Enterprises, Inc.;
p. 85 (top right): © Kevin Fleming/CORBIS, all rights reserved/Used by permission
from Disney Enterprises, Inc.; p. 85 (bottom): Getty Images, Inc./Disney characters
© Disney Enterprise, Inc. Used by permission from Disney Enterprises, Inc.; p. 88:
Wikipedia, The Free Encyclopedia; p. 91: Fred Prouser/CORBIS-NY;
p. 104: Courtesy of Yamaha Motor media; p. 111: ICI Paints; p. 117: Anton
Vengo/SuperStock, Inc.
CHAPTER 5: p. 124: Regal Marine Industries, Inc.; p. 125: Regal Marine
Industries, Inc.; p. 127 (left): John Acurso. NIKE and the Swoosh Design logo
are trademarks of Nike, Inc. and its affiliates. Used by permission; p. 127 (mid-
dle): © FranAois Grelet/Michelin/Newscom; p. 127 (right): Dutch Boy
Paints/Sherwin Williams; p. 136 (left): Maximilian Stock LTD/Phototake NYC;
p. 136 (middle): Courtesy of Silicon Graphics, Inc.; p. 136 (right): Maximilian
Stock LTD/Phototake NYC; p. 137: Courtesy 3D Systems; p. 138: BMW of
North America, LLC; p. 140 (left): Dainis Derics/Shutterstock; p. 140 (right):
Eugene Hoshiko/AP Wide World Photos; p. 143: David Murray © Dorling
Kindersley; p. 146 (left): J.R. Simplot Company; p. 146 (right): David R.
Frazier/David R. Frazier Photolibrary, Inc.
CHAPTER 6: p. 154: Jonathan Bailey Associates; p. 155 (top): Courtesy of
Cardinal Health; p. 155 (middle and bottom): Jonathan Bailey Associates;
p. 160: Courtesy of Subaru of Indiana Automotive, Inc. p. 163: TRW
Automotive; p. 164: Clive Mason/Allsport Concepts/Getty Images; p. 171:
Ralf-Finn Hestoft/CORBIS-NY; p. 172 (left): Jonathan Nourok/PhotoEdit, Inc.;
p. 172 (right): © David Joel/Getty Images; p. 173: Ann States Photography.
SUPPLEMENT 6: p. 178: Courtesy of BetzDearborn, A Division of Hercules
Incorporated; p. 187 (left): Donna McWilliam/AP Wide World Photos; p. 187
(right): Richard Pasley Photography; p. 189: © Charles O’Rear/CORBIS, all rights
reserved; p. 193: Georgia Institute of Technology; p. 195: Faro Technologies.
CHAPTER 7: p. 202: © Igor Lubnevskiy/Alamy; p. 203 (top left): Courtesy of
Harley-Davidson; page 203 (bottom right): Catherine Karnow/Woodfin Camp &
Associates, Inc.; p. 203 (top right): Dave Bartruff/Stock Boston; p. 203 (bottom
left): Steven Rubin/The Image Works; p. 205 (left): Brasiliao/Shutterstock; p.
205 (middle, left): 300 dpi/Shutterstock; p. 205 (middle, right): Tund/
Shutterstock; p. 205: (right): Archman/Shutterstock; p. 216: Courtesy of align-
tech.com; p. 219: Courtesy of Anheuser-Busch, Inc.; p. 221 (top left): Getty
Images, Inc. – Stone Allstock; p. 221 (top right): G2 Classic, Gensym
Corporation; p. 221 (middle left): Ron Sully/Omnica Corporation; p. 221
(bottom middle): Tate Carlson/Stockphoto.com; p. 221 (bottom right):
iStockphoto.com; p. 221 (bottom left): Courtesy of Diamond Phoenix
Corporation; p. 224 (left): Kruell/laif/Redux Pictures; p. 224 (right):
Courtesy of RF Technologies, Inc.
SUPPLEMENT 7: p. 228: John Garrett/Getty Images, Inc.-Stone Allstock;
p. 232: Chitose Suzuki/AP Wide World Photos; p. 233 (left): © Bob Krist/
CORBIS; p. 233 (right): Michelangelo Gisone/AP Wide World Photos; p. 234:
© Lester Lefkowitz/CORBIS, all rights reserved; p. 240: Getty Images; p. 241:
Jupiter Images Royalty Free.
CHAPTER 8: p. 252 (top): Chris Sorensen Photography; p. 252 (bottom): AP
Wide World Photos; p. 253 (top): Jon Riley/Southern Stock/Photolibrary.com;
p. 253 (middle): Matt York/AP Wide World Photos; p. 253 (bottom): Shi
Li/shzq/ImagineChina.com; p. 257: Allen Tannenbaum; p. 265 (left): Monica
Lewis/True Bethel Baptist Church; p. 265 (right): Courtesy of Jay Heizer;
p. 267: MayInfo Corporation.
CHAPTER 9: p. 274: Rick Wiliking/CORBIS-NY; p. 275: Callie Lipkin
Photography, Inc. p. 275 (top): Nancy Siesel/NYT Pictures; p. 277: Chuck
Keeler/Getty Images, Inc. – Stone Allstock; p. 280 (top): Courtesy of walmart-
facts.com/www.walmartfacts.com/articles/4939.aspx; p. 280 (bottom): Courtesy
of Hard Rock Café; p. 281: Fabian Bimmer/AP Wide World Photos; p. 283
(top): Craig Ruttle/AP Wide World Photos; p. 283 (middle): Dick Blume/The
Image Works; p. 283 (bottom): CORBIS-NY; p. 288: UGS; p. 294: Boeing
Commercial Airplane Group; p. 297: Cary Wolinsky/Stock Boston.
CHAPTER 10: p. 304: John Raoux/The Orlando Sentinel; p. 310 (left): Pam
Francis/Southwest Airlines Co.: p. 310 (right): Courtesy of Southwest Airlines;
p. 312 (top): Andy Freeberg Photography; p. 312 (bottom): Scott Hirko/
iStockphoto.com; p. 313 (left): Chad Ehlers/Stock Connection; p. 313
(right): © NUFEA/Boeing; p. 318 (top): AP Wide World Photos; p. 318
(bottom): Tony Freeman/PhotoEdit Inc.; p. 321: Laubrass, Inc.; p. 323: F.
Hoffmann/The Images Works; p. 325: Samuel Ashfield/Photo Researchers, Inc.
CHAPTER 11: p. 334: Courtesy of Darden Corporation: p. 335: Courtesy of
Darden Corporation; p. 336 (top left): Bill Stormont/CORBIS-NY;
p. 336 (top middle): Susan Van Etten/PhotoEdit Inc.; p. 336 (middle bottom):
David de Lossy, Ghislain & Marie/Getty Images Inc. – Image Bank;
p. 336 (middle): Getty Images/Digial Vision; p. 336 (middle): Michael
Newman/PhotoEdit Inc.; p. 336 (top right): Jose Manuel Riberio,
REUTERS/CORBIS-NY; p. 336 (middle right): Peter Byron/PhotoEdit Inc.;
p. 336 (bottom right): Richard Levine/Alamy.com; p. 338: Courtesy of
Jackson & Perkins; p. 348: Courtesy of Ariba, Inc.; p. 351: South Carolina
State Port Authority; p. 352: Francesco Broli; p. 353: Courtesy of Federal
Express Corporation.
SUPPLEMENT 11: p. 360: Keith Dannemiller/Alamy Images; p. 363:
NASA/Associated Press; p. 364: © Sherwin Crasto/Reuters/CORBIS, all rights
reserved; p. 366: A. Ramey/PhotoEdit Inc.
CHAPTER 12: p. 372 (top): Marilyn Newton; p. 372 (middle and bottom):
David Burnett/Contact Press Images, Inc.; p. 373 (top): David Burnett/Contact
Press Images, Inc.; p. 373 (bottom): Contact Press Images, Inc.; p. 374: Anna
Sheveleva/Shutterstock; p. 378: Courtesy of Deere & Company, Moline, IL,
USA; p. 379: McKesson Corporation; p. 385: AP Wide World Photos; p. 388:
Telegraph Colour Library/Lester Lefkowitz/Getty Images, Inc. – Taxi; p. 390:
Anthony Labbe Photography; p. 400: Richard Levine/Alamy.com.
CHAPTER 13: p. 408–409: Frito Lay Corporation; p. 411: Courtesy of
Simplicity Manufacturing, Inc.; p. 413 (top left) Getty Images–Stockbyte,
Royalty Free; p. 413 (top right): Courtesy of OSA (National Organization for
Automotive Safety and Victim’s Aid). Copyright 2003. All rights reserved.
Reprinted with permission; p. 413 (left bottom): Ron Sherman/Creative
Eye/MIRA.com; p. 413 (middle right): Mark Richards/PhotoEdit Inc.; p. 413
(middle right): Michael Newman/PhotoEdit Inc.; p. 413 (bottom right): GmbH
& Co. KG/Alamy Images; p. 414: John Deere & Company; p. 423: Greg
Foster/Gregory Foster, Inc.; p. 426: Getty Images.
CHAPTER 14: p. 434 (left): Collins Industries, Inc.; p. 434 (right): Wheeled
Coach Industries Incorporated; p. 435 (left): Wheeled Coach Industries
Incorporated; p. 435 (right): Collins Industries, Inc.; p. 448: John Russell/AP
Wide World Photos: p. 451: Courtesy of User Solutions, Inc.
CHAPTER 15: p. 466: Courtesy of Delta Air Lines; p. 467 (top left): Mike
Segar/CORBIS-NY; p. 467 (bottom): Etienne de Malglaive/ZUMA Press-
Gamma; p. 467 (top right): AP Wide World Photos; p. 469 (top): Michael
Newman/Photo Edit Inc.; p. 469 (bottom): Peter Endig/Landov Media; p. 470:
Tom Carroll/Phototake NYC; p. 477: PCN Photography; p. 481: Charles
Gupton/Charles Gupton Photography; p. 486: Patricia McDonnell/AP Wide
World Photos; p. 487: (c) Courtesy of Choice Hotels International.
CHAPTER 16: p. 497: © Bob Daemmrich/CORBIS, all rights reserved; p. 500:
© Culinary Institute of America; p. 502: Cessna Aircraft Company; p. 508:
Donna Shader; p. 511: New United Motor Manufacturing, Inc. (NUMMI);
p. 512: Gerardo Burgos Galindo/Shutterstock; p. 513: Colin Young-
Wolff/PhotoEdit Inc.; p. 514: Courtesy of Cardinal Health, Inc.

www.HondaNews.com

www.qaphotos.com

www.walmartfacts.com/articles/4939.aspx

P2 Photo Credits
CHAPTER 17: p. 518: Orlando Utilities Commission; p. 519: Orlando Utilities
Commission.
MODULE A: p. 532: Shutterstock; p. 538 (left): EyeWire Collection/Getty
Images – Photodisc-Royalty Free; p. 538 (right): Syncopation Software.
MODULE B: p. 548: Harry M. Walker.
MODULE C: p. 568: Shutterstock.
MODULE D: p. 584: © Disney Enterprise, Inc./Jeff Greenberg/PhotoEdit;
p. 589: Ric Feld/AP Wide World Photos; p. 591: Roy/EXPLORER/Photo
Researchers, Inc. p. 594: Courtesy of Costco Wholesale; p. 597 (left): David
Young-Wolff/PhotoEdit Inc.; p. 597 (right): Stephen Brashear/AP Wide World
Photos; p. 600: Stephen J. Carrera/AP Wide World Photos.
MOUDLE E: p. 606: Dick Blume/The Image Works.
MODULE F: p. 616 (right): Micro Analysis & Design Simulation Software,
Inc.; p. 616 (left): Department of Health & Social Services, Christine Lynch/AP
Wide World Photos; p. 618: Donna Shader; p. 621: Mark Lennihan/AP Wide
World Photos.

Table of Contents
About the Authors
Preface
PART ONE: Introduction to Operations Management
1. Operations and Productivity
Global Company Profile: Hard Rock Cafe
What Is Operations Management?
Organizing to Produce Goods and Services
Why Study OM?
What Operations Managers Do
The Heritage of Operations Management
Operations in the Service Sector
Exciting New Trends in Operations Management
The Productivity Challenge
Ethics and Social Responsibility
Chapter Summary
Key Terms
Solved Problems
Bibliography
2. Operations Strategy in a Global Environment
Global Company Profile: Boeing
A Global View of Operations
Developing Missions and Strategies
Achieving Competitive Advantage Through Operations
Ten Strategic OM Decisions
Issues in Operations Strategy
Strategy Development and Implementation
Global Operations Strategy Options
Chapter Summary
Key Terms
Solved Problems
Bibliography
3. Project Management
Global Company Profile: Bechtel Group
The Importance of Project Management
Project Planning
Project Scheduling
Project Controlling
Project Management Techniques: PERT and CPM
Determining the Project Schedule
Variability in Activity Times
Cost–Time Trade-Offs and Project Crashing
A Critique of PERT and CPM
Using Microsoft Project to Manage Projects
Chapter Summary
Key Terms
Using Software to Solve Project Management Problems
Solved Problems
Bibliography
4. Forecasting
Global Company Profile: Walt Disney Parks & Resorts
What Is Forecasting?
The Strategic Importance of Forecasting
Seven Steps in the Forecasting System
Forecasting Approaches
Time-Series Forecasting
Associative Forecasting Methods: Regression and Correlation Analysis
Monitoring and Controlling Forecasts
Forecasting in the Service Sector
Chapter Summary
Key Terms
Using Software in Forecasting
Solved Problems
Bibliography

PART TWO: Designing Operations
5. Design of Goods and Services
Global Company Profile: Regal Marine
Goods and Services Selection
Generating New Products
Product Development
Issues for Product Design
Ethics, Environmentally-Friendly Designs, and Sustainability
Time-Based Competition
Defining a Product
Documents for Production
Service Design
Application of Decision Trees to Product Design
Transition to Production
Chapter Summary
Key Terms
Solved Problems
Bibliography
6. Managing Quality
Global Company Profile: Arnold Palmer Hospital
Quality and Strategy
Defining Quality
International Quality Standards
Total Quality Management
Tools of TQM
The Role of Inspection
TQM in Services
Chapter Summary
Key Terms
Bibliography
Supplement 6: Statistical Process Control
Statistical Process Control (SPC)
Process Capability
Acceptance Sampling
Supplement Summary
Key Terms
Using Software for SPC
Solved Problems
Bibliography
7 Process Strategy and Sustainability
Global Company Profile: Harley-Davidson
Four Process Strategies
Process Analysis and Design
Special Considerations for Service Process Design
Selection of Equipment and Technology
Production Technology
Technology in Services
Process Redesign
Sustainability
Chapter Summary
Key Terms
Solved Problems
Bibliography
Supplement 7: Capacity and Constraint Management
Capacity
Bottleneck Analysis and the Theory of Constraints
Break-Even Analysis
Reducing Risk with Incremental Changes
Applying Expected Monetary Value (EMV) to Capacity Decisions
Applying Investment Analysis to Strategy-Driven Investments
Supplement Summary
Key Terms
Using Software for Break-Even Analysis
Solved Problems
Bibliography
8. Location Strategies
Global Company Profile: FedEx
The Strategic Importance of Location
Factors That Affect Location Decisions
Methods of Evaluating Location Alternatives
Service Location Strategy
Chapter Summary
Key Terms
Using Software to Solve Location Problems
Solved Problems
Bibliography
9. Layout Strategies
Global Company Profile: McDonald’s
The Strategic Importance of Layout Decisions
Types of Layout
Office Layout
Retail Layout
Warehousing and Storage Layouts
Fixed-Position Layout
Process-Oriented Layout
Work Cells
Repetitive and Product-Oriented Layout
Chapter Summary
Key Terms
Using Software to Solve Layout Problems
Solved Problems
Bibliography
10. Human Resources, Job Design, and Work Measurement
Global Company Profile: Rusty Wallace’s NASCAR Racing Team
Human Resource Strategy for Competitive Advantage
Labor Planning
Job Design
Ergonomics and the Work Environment
Methods Analysis
The Visual Workplace
Labor Standards
Ethics
Chapter Summary
Key Terms
Solved Problems
Bibliography

PART THREE: Managing Operations
11. Supply-Chain Management
Global Company Profile: Darden Restaurants
The Supply Chain’s Strategic Importance
Ethics and Sustainability
Supply-Chain Economics
Supply-Chain Strategies
Managing the Supply Chain
E-Procurement
Vendor Selection
Logistics Management
Measuring Supply-Chain Performance
Chapter Summary
Key Terms
Solved Problems
Bibliography
Supplement 11: Outsourcing as a Supply-Chain Strategy
What Is Outsourcing?
Strategic Planning and Core Competencies
Risks of Outsourcing
Evaluating Outsourcing Risk with Factor Rating
Advantages and Disadvantages of Outsourcing
Audits and Metrics to Evaluate Performance
Ethical Issues in Outsourcing
Supplement Summary
Key Terms
Using Software to Solve Outsourcing Problems
Bibliography
12. Inventory Management
Global Company Profile: Amazon.com
The Importance of Inventory
Managing Inventory
Inventory Models
Inventory Models for Independent Demand
Probabilistic Models and Safety Stock
Single-Period Model
Fixed-Period (P) Systems
Chapter Summary
Key Terms
Using Software to Solve Inventory Problems
Solved Problems
Bibliography
13. Aggregate Planning
Global Company Profile: Frito-Lay
The Planning Process
The Nature of Aggregate Planning
Aggregate Planning Strategies
Methods for Aggregate Planning
Aggregate Planning in Services
Yield Management
Chapter Summary
Key Terms
Using Software for Aggregate Planning
Solved Problems
Bibliography
14. Material Requirements Planning (MRP) and ERP
Global Company Profile: Wheeled Coach
Dependent Demand
Dependent Inventory Model Requirements
MRP Structure
MRP Management
Lot-Sizing Techniques
Extensions of MRP
MRP in Services
Enterprise Resource Planning (ERP)
Chapter Summary
Key Terms
Using Software to Solve MRP Problems
Solved Problems
Bibliography
15. Short-Term Scheduling
Global Company Profile: Delta Air Lines
The Importance of Short-Term Scheduling
Scheduling Issues
Scheduling Process-Focused Facilities
Loading Jobs
Sequencing Jobs
Finite Capacity Scheduling (FCS)
Scheduling Repetitive Facilities
Scheduling Services
Chapter Summary
Key Terms
Using Software for Short-Term Scheduling
Solved Problems
Bibliography
16. JIT and Lean Operations
Global Company Profile: Toyota Motor Corporation
Just-in-Time, the Toyota Production System, and Lean Operations
Just-in-Time (JIT)
JIT Layout
JIT Inventory
JIT Scheduling
JIT Quality
Toyota Production System
Lean Operations
Lean Operations in Services
Chapter Summary
Key Terms
Solved Problems
Bibliography
17. Maintenance and Reliability
Global Company Profile: Orlando Utilities Commission
The Strategic Importance of Maintenance and Reliability
Reliability
Maintenance
Total Productive Maintenance
Techniques for Enhancing Maintenance
Chapter Summary
Key Terms
Using Software to Solve Reliability Problems
Solved Problems
Bibliography

PART FOUR: Quantitative Modules
A. Decision-Making Tools
The Decision Process in Operations
Fundamentals of Decision Making
Decision Tables
Types of Decision-Making Environments
Decision Trees
Module Summary
Key Terms
Using Software for Decision Models
Solved Problems
Bibliography
B. Linear Programming
Why Use Linear Programming?
Requirements of a Linear Programming Problem
Formulating Linear Programming Problems
Graphical Solution to a Linear Programming Problem
Sensitivity Analysis
Solving Minimization Problems
Linear Programming Applications
The Simplex Method of LP
Module Summary
Key Terms
Using Software to Solve LP Problems
Solved Problems
Bibliography
C. Transportation Models
Transportation Modeling
Developing an Initial Solution
The Stepping-Stone Method
Special Issues in Modeling
Module Summary
Key Terms
Using Software to Solve Transportation Problems
Solved Problems
Bibliography
D. Waiting-Line Models
Queuing Theory
Characteristics of a Waiting-Line System
Queuing Costs
The Variety of Queuing Models
Other Queuing Approaches
Module Summary
Key Terms
Using Software to Solve Queuing Problems
Solved Problems
Bibliography
E. Learning Curves
What Is a Learning Curve?
Learning Curves in Services and Manufacturing
Applying the Learning Curve
Strategic Implications of Learning Curves
Limitations of Learning Curves
Module Summary
Key Terms
Using Software for Learning Curves
Solved Problems
Bibliography
F. Simulation
What Is Simulation?
Advantages and Disadvantages of Simulation
Monte Carlo Simulation
Simulation of a Queuing Problem
Simulation and Inventory Analysis
Module Summary
Key Terms
Using Software in Simulation
Solved Problems
Bibliography

Appendices
Indices
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
R
S
T
U
V
W
Y
Z
General Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
Y
Z
Photo Credits

Chapter 8_Location Strategies

9/19/12
1
8 – 1
8
PowerPoint presentation to accompany
Heizer and Render
Operations Management, 10e
Principles of Operations Management, 8e

PowerPoint slides by Jeff Heyl
Prof.Vivek Veeraiah, MG 6303
Operations Management
Location Strategies
8 – 2
Outline
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Global Company Profile:
FedEx
u  The Strategic Importance of
Location
8 – 3
Outline  –  Continued
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Factors That Affect Location
Decisions
u Labor Productivity
u Exchange Rates and Currency Risks
u Costs
u Political Risk, Values, and Culture
u Proximity to Markets
u Proximity to Suppliers
u Proximity to Competitors (Clustering)
8 – 4
Outline  –  Continued
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Methods of Evaluating Location
Alternatives
u The Factor-Rating Method
u Locational Break-Even Analysis
u Center-of-Gravity Method
u Transportation Model
8 – 5
Outline  –  Continued
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Service Location Strategy
u How Hotel Chains Select Sites
u The Call Center Industry
u Geographic Information Systems
8 – 6
Learning  Objectives
When you complete this chapter you should be able
to:
Prof.Vivek Veeraiah, MG 6303
Operations Management
1.  Identify and explain seven major factors
that effect location decisions
2.  Compute labor productivity
3.  Apply the factor-rating method
4.  Complete a locational break-even
analysis graphically and mathematically

9/19/12
2
8 – 7
Learning  Objectives
When you complete this chapter you should be able
to:
Prof.Vivek Veeraiah, MG 6303
Operations Management
5.  Use the center-of-gravity method
6.  Understand the differences between
service and industrial-sector location
strategies
8 – 8
Federal  Express
•  Central hub concept
o  Enables service to more locations with
fewer aircraft
o  Enables matching of aircraft flights with
package loads
o  Reduces mishandling and delay in
transit because there is total control of
packages from pickup to delivery
Prof.Vivek Veeraiah, MG 6303
Operations Management
8 – 9
Location  Strategy
Prof.Vivek Veeraiah, MG 6303
Operations Management
The objective of location strategy is
to maximize the benefit of location
to the firm
8 – 10
Location  Strategy
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  One of the most important decisions a
firm makes
u  Increasingly global in nature
u  Significant impact on fixed and
variable costs
u  Decisions made relatively infrequently
u  The objective is to maximize the
benefit of location to the firm
8 – 11
Location  and  Costs
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Location decisions based on low
cost require careful consideration
u  Once in place, location-related
costs are fixed in place and
difficult to reduce
u  Determining optimal facility
location is a good investment
8 – 12
Location  and  Innovation
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Cost is not always the most important
aspect of a strategic decision
u  Four key attributes when strategy is
based on innovation
u High-quality and specialized inputs
u An environment that encourages
investment and local rivalry
u A sophisticated local market
u Local presence of related and
supporting industries

9/19/12
3
8 – 13
Location  Decisions
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Long-term decisions
u  Decisions made infrequently
u  Decision greatly affects both fixed
and variable costs
u  Once committed to a location,
many resource and cost issues
are difficult to change
8 – 14
Location  Decisions
Prof.Vivek Veeraiah, MG 6303
Operations Management
Country Decision Key Success Factors
1.  Political risks, government
rules, attitudes, incentives
2.  Cultural and economic
issues
3.  Location of markets
4.  Labor talent, attitudes,
productivity, costs
5.  Availability of supplies,
communications, energy
6.  Exchange rates and
currency risks Figure 8.1
8 – 15
Location  Decisions
Prof.Vivek Veeraiah, MG 6303
Operations Management
Region/
Community
Decision
Key Success Factors
1.  Corporate desires
2.  Attractiveness of region
3.  Labor availability and costs
4.  Costs and availability of utilities
5.  Environmental regulations
6.  Government incentives and
fiscal policies
7.  Proximity to raw materials and
customers
8.  Land/construction costs
MN
WI
MI
IL IN OH
Figure 8.1
8 – 16
Location  Decisions
Prof.Vivek Veeraiah, MG 6303
Operations Management
Site Decision Key Success Factors
1.  Site size and cost
2.  Air, rail, highway, and
waterway systems
3.  Zoning restrictions
4.  Proximity of services/
supplies needed
5.  Environmental impact
issues
Figure 8.1
8 – 17
Global  Competitiveness  
Index  of  Countries
Prof.Vivek Veeraiah, MG 6303
Operations Management
Country 2009 Rank 2005 Rank
Switzerland 1 4
USA 2 1
Japan 8 10
Canada 9 13
UK 13 9
Israel 27 23
China 29 48
Italy 48 38
India 49 22
Mexico 60 59
Russia 63 53 Table 8.1
8 – 18
Factors  That  Affect    
Location  Decisions
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Labor productivity
u  Wage rates are not the only cost
u  Lower productivity may increase total cost
Labor cost per day
Productivity (units per day)
= Cost per unit
Connecticut
= $1.17 per unit
$70
60 units
Juarez
= $1.25 per unit
$25
20 units

9/19/12
4
8 – 19
Factors  That  Affect    
Location  Decisions
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Exchange rates and currency risks
u  Can have a significant impact on costs
u  Rates change over time
u  Costs
u  Tangible – easily measured costs such as
utilities, labor, materials, taxes
u  Intangible – less easy to quantify and
include education, public transportation,
community, quality-of-life
8 – 20
Factors  That  Affect    
Location  Decisions
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Exchange rates and currency risks
u  Can have a significant impact on cost
structure
u  Rates change over time
u  Costs
u  Tangible – easily measured costs such as
utilities, labor, materials, taxes
u  Intangible – less easy to quantify and
include education, public transportation,
community, quality-of-life
Location
decisions based
on costs alone
can create
difficult ethical
situations
8 – 21
Factors  That  Affect    
Location  Decisions
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Political risk, values, and culture
u  National, state, local governments
attitudes toward private and intellectual
property, zoning, pollution, employment
stability may be in flux
u  Worker attitudes towards turnover, unions,
absenteeism
u  Globally cultures have different attitudes
towards punctuality, legal, and ethical
issues
8 – 22
Ranking  Corruption
Prof.Vivek Veeraiah, MG 6303
Operations Management
Rank Country 2009 CPI Score (out of 10)
1 New Zealand 9.4
2 Demark 9.3
3 Singapore, Sweden 9.2
5 Switzerland 9.0
8 Australia, Canada, Iceland 8.7
12 Hong Kong 8.2
14 Germany 8.0
17 Japan, UK 7.7
19 USA 7.5
37 Taiwan 5.6
39 South Korea 5.5
56 Malaysia 4.5
79 China 3.6
89 Mexico 3.3
146 Russia 2.2
Least
Corrupt
Most
Corrupt
8 – 23
Factors  That  Affect    
Location  Decisions
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Proximity to markets
u  Very important to services
u  JIT systems or high transportation costs
may make it important to manufacturers
u  Proximity to suppliers
u  Perishable goods, high transportation
costs, bulky products
8 – 24
Factors  That  Affect    
Location  Decisions
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Proximity to competitors
u  Called clustering
u  Often driven by resources such as natural,
information, capital, talent
u  Found in both manufacturing and service
industries

9/19/12
5
8 – 25
Clustering  of  Companies
Prof.Vivek Veeraiah, MG 6303
Operations Management
Industry Locations Reason for clustering
Wine making Napa Valley (US)
Bordeaux region
(France)
Natural resources of
land and climate
Software firms Silicon Valley,
Boston, Bangalore
(India)
Talent resources of
bright graduates in
scientific/technical
areas, venture
capitalists nearby
Race car
builders
Huntington/North
Hampton region
(England)
Critical mass of talent
and information
Table 8.3 8 – 26
Clustering  of  Companies
Prof.Vivek Veeraiah, MG 6303
Operations Management
Industry Locations Reason for clustering
Theme parks
(Disney World,
Universal
Studios)
Orlando, Florida A hot spot for
entertainment, warm
weather, tourists, and
inexpensive labor
Electronics
firms
Northern Mexico NAFTA, duty free
export to US
Computer
hardware
manufacturers
Singapore, Taiwan High technological
penetration rate and
per capita GDP, skilled/
educated workforce
with large pool of
engineers
Table 8.3
8 – 27
Clustering  of  Companies
Prof.Vivek Veeraiah, MG 6303
Operations Management
Industry Locations Reason for clustering
Fast food
chains
(Wendy’s,
McDonald’s,
Burger King,
and Pizza Hut)
Sites within 1 mile
of each other
Stimulate food sales,
high traffic flows
General aviation
aircraft
(Cessna,
Learjet, Boeing)
Wichita, Kansas Mass of aviation skills
Orthopedic
device
manufacturing
Warsaw, Indiana Ready supply of skilled
workers, strong U.S.
market
Table 8.3 8 – 28
Factor-­‐‑Rating  Method
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Popular because a wide variety of factors
can be included in the analysis
u  Six steps in the method
1.  Develop a list of relevant factors called key
success factors
2.  Assign a weight to each factor
3.  Develop a scale for each factor
4.  Score each location for each factor
5.  Multiply score by weights for each factor for
each location
6.  Recommend the location with the highest
point score
8 – 29
Factor-­‐‑Rating  Example
Prof.Vivek Veeraiah, MG 6303
Operations Management
Key Scores
Success (out of 100) Weighted Scores
Factor Weight France Denmark France Denmark
Labor
availability
and attitude .25 70 60 (.25)(70) = 17.5 (.25)(60) = 15.0
People-to-
car ratio .05 50 60 (.05)(50) = 2.5 (.05)(60) = 3.0
Per capita
income .10 85 80 (.10)(85) = 8.5 (.10)(80) = 8.0
Tax structure .39 75 70 (.39)(75) = 29.3 (.39)(70) = 27.3
Education
and health .21 60 70 (.21)(60) = 12.6 (.21)(70) = 14.7
Totals 1.00 70.4 68.0
Table 8.4
8 – 30
Locational    
Break-­‐‑Even  Analysis
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Method of cost-volume analysis used for
industrial locations
u  Three steps in the method
1.  Determine fixed and variable costs for
each location
2.  Plot the cost for each location
3.  Select location with lowest total cost for
expected production volume

9/19/12
6
8 – 31
Locational  Break-­‐‑Even  
Analysis  Example
Prof.Vivek Veeraiah, MG 6303
Operations Management
Three locations:
Akron $30,000 $75 $180,000
Bowling Green $60,000 $45 $150,000
Chicago $110,000 $25 $160,000
Fixed Variable Total
City Cost Cost Cost
Total Cost = Fixed Cost + (Variable Cost x Volume)
Selling price = $120
Expected volume = 2,000 units
8 – 32
Locational  Break-­‐‑Even  
Analysis  Example
Prof.Vivek Veeraiah, MG 6303
Operations Management
–
$180,000 –
–
$160,000 –
$150,000 –
–
$130,000 –
–
$110,000 –
–
–
$80,000 –
–
$60,000 –
–
–
$30,000 –
–
$10,000 –
–
A
nn
ua
l c
os
t
| | | | | | |
0 500 1,000 1,500 2,000 2,500 3,000
Volume
Akron
lowest
cost
Bowling Green
lowest cost
Chicago
lowest
cost
Chica
go co
st cu
rve
Ak
ro
n c
os
t
cu
rv
e
Bo
wli
ng
Gre
en
cos
t cu
rve
Figure 8.2
8 – 33
Center-­‐‑of-­‐‑Gravity  
Method
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Finds location of distribution
center that minimizes distribution
costs
u  Considers
u Location of markets
u Volume of goods shipped to those
markets
u Shipping cost (or distance)
8 – 34
Center-­‐‑of-­‐‑Gravity  
Method
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Place existing locations on a
coordinate grid
u Grid origin and scale is arbitrary
u Maintain relative distances
u  Calculate X and Y coordinates for
‘center of gravity’
u Assumes cost is directly
proportional to distance and
volume shipped
8 – 35
Center-­‐‑of-­‐‑Gravity  
Method
Prof.Vivek Veeraiah, MG 6303
Operations Management
x – coordinate =
∑dixQi
∑Qi
i
i
∑diyQi
∑Qi
i
i
y – coordinate =
where dix = x-coordinate of location i
diy = y-coordinate of location i
Qi = Quantity of goods moved to
or from location i
8 – 36
Center-­‐‑of-­‐‑Gravity  
Method
Prof.Vivek Veeraiah, MG 6303
Operations Management
North-South
East-West
120 –
90 –
60 –
30 –
– | | | | | |
30 60 90 120 150 Arbitrary
origin
Chicago (30, 120)
New York (130, 130)
Pittsburgh (90, 110)
Atlanta (60, 40)
Figure 8.3

9/19/12
7
8 – 37
Center-­‐‑of-­‐‑Gravity  
Method
Prof.Vivek Veeraiah, MG 6303
Operations Management
Number of Containers
Store Location Shipped per Month
Chicago (30, 120) 2,000
Pittsburgh (90, 110) 1,000
New York (130, 130) 1,000
Atlanta (60, 40) 2,000
x-coordinate =
(30)(2000) + (90)(1000) + (130)(1000) + (60)(2000)
2000 + 1000 + 1000 + 2000
= 66.7
y-coordinate =
(120)(2000) + (110)(1000) + (130)(1000) + (40)(2000)
2000 + 1000 + 1000 + 2000
= 93.3
8 – 38
Center-­‐‑of-­‐‑Gravity  
Method
Prof.Vivek Veeraiah, MG 6303
Operations Management
North-South
East-West
120 –
90 –
60 –
30 –
– | | | | | |
30 60 90 120 150 Arbitrary
origin
Chicago (30, 120)
New York (130, 130)
Pittsburgh (90, 110)
Atlanta (60, 40)
Center of gravity (66.7, 93.3)
+
Figure 8.3
8 – 39
Transportation  Model
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Finds amount to be shipped from
several points of supply to several
points of demand
u  Solution will minimize total
production and shipping costs
u  A special class of linear
programming problems
8 – 40
Worldwide  Distribution  of  
Volkswagens  and  Parts
Prof.Vivek Veeraiah, MG 6303
Operations Management
Figure 8.4
8 – 41
Service  Location  
Strategy
1.  Purchasing power of customer-drawing area
2.  Service and image compatibility with
demographics of the customer-drawing area
3.  Competition in the area
4.  Quality of the competition
5.  Uniqueness of the firm’s and competitors’
locations
6.  Physical qualities of facilities and neighboring
businesses
7.  Operating policies of the firm
8.  Quality of management
Prof.Vivek Veeraiah, MG 6303
Operations Management 8 – 42
Location  Strategies
Prof.Vivek Veeraiah, MG 6303
Operations Management
Table 8.6
Service/Retail/Professional Location Goods-Producing Location
Revenue Focus Cost Focus
Volume/revenue
Drawing area; purchasing power
Competition; advertising/pricing

Physical quality
Parking/access; security/lighting;
appearance/image

Cost determinants
Rent
Management caliber
Operations policies (hours, wage
rates)
Tangible costs
Transportation cost of raw material
Shipment cost of finished goods
Energy and utility cost; labor; raw
material; taxes, and so on

Intangible and future costs
Attitude toward union
Quality of life
Education expenditures by state
Quality of state and local
government

9/19/12
8
8 – 43
Location  Strategies
Prof.Vivek Veeraiah, MG 6303
Operations Management
Table 8.6
Service/Retail/Professional Location Goods-Producing Location
Techniques Techniques
Regression models to determine
importance of various factors
Factor-rating method
Traffic counts
Demographic analysis of drawing area
Purchasing power analysis of area
Center-of-gravity method
Geographic information systems
Transportation method
Factor-rating method
Locational break-even analysis
Crossover charts
8 – 44
Location  Strategies
Prof.Vivek Veeraiah, MG 6303
Operations Management
Table 8.6
Service/Retail/Professional Location Goods-Producing Location
Assumptions Assumptions
Location is a major determinant of
revenue
High customer-contact issues are
critical
Costs are relatively constant for a
given area; therefore, the revenue
function is critical
Location is a major determinant of
cost
Most major costs can be identified
explicitly for each site
Low customer contact allows focus
on the identifiable costs
Intangible costs can be evaluated
8 – 45
How  Hotel  Chains  Select  Sites
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Location is a strategically important
decision in the hospitality industry
u  La Quinta started with 35 independent
variables and worked to refine a
regression model to predict profitability
u  The final model had only four variables
u  Price of the inn
u  Median income levels
u  State population per inn
u  Location of nearby colleges
r2 = .51
51% of the
profitability is
predicted by
just these four
variables!
8 – 46
The  Call  Center  Industry
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Requires neither face-to-face
contact nor movement of materials
u  Has very broad location options
u  Traditional variables are no longer
relevant
u  Cost and availability of labor may
drive location decisions
8 – 47
Geographic  Information  
Systems  (GIS)
Prof.Vivek Veeraiah, MG 6303
Operations Management
u  Important tool to help in location analysis
u  Enables more complex demographic
analysis
u  Available data bases include
u  Detailed census data
u  Detailed maps
u  Utilities
u  Geographic features
u  Locations of major services
8 – 48
Geographic  Information  
Systems  (GIS)
Prof.Vivek Veeraiah, MG 6303
Operations Management

9/19/12
9
8 – 49
Prof.Vivek Veeraiah, MG 6303
Operations Management
All rights reserved. No part of this publication may be reproduced, stored in a retrieval
system, or transmitted, in any form or by any means, electronic, mechanical, photocopying,
recording, or otherwise, without the prior written permission of the publisher.
Printed in the United States of America.

Chapter 9 Layout Strategies课件

9/19/12
1
9 – 1 © 2011 Pearson Education, Inc. publishing as Prentice Hall
9 Layout Strategies
PowerPoint presentation to accompany
Heizer and Render
Operations Management, 10e
Principles of Operations Management, 8e

PowerPoint slides by Jeff Heyl
9 – 2 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Outline
u  Global Company Profile:
McDonald’s
u  The Strategic Importance of
Layout Decisions
u  Types of Layout
u  Office Layout
9 – 3 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Outline – Continued
u  Retail Layout
u  Servicescapes
u  Warehousing and Storage Layouts
u  Cross-Docking
u  Random Docking
u  Customizing
u  Fixed-Position Layout
9 – 4 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Outline – Continued
u  Process-Oriented Layout
u  Computer Software for Process-
Oriented Layouts
u  Work Cells
u  Requirements of Work Cells
u  Staffing and Balancing Work Cells
u  The Focused Work Center and the
Focused Factory
9 – 5 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Outline – Continued
u  Repetitive and Product-Oriented
Layout
u  Assembly-Line Balancing
9 – 6 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Learning Objectives
When you complete this chapter, you
should be able to:
1.  Discuss important issues in office layout
2.  Define the objectives of retail layout
3.  Discuss modern warehouse management
and terms such as ASRS, cross-docking,
and random stocking
4.  Identify when fixed-position layouts are
appropriate

9/19/12
2
9 – 7 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Learning Objectives
When you complete this chapter, you
should be able to:
5.  Explain how to achieve a good process-
oriented facility layout
6.  Define work cell and the requirements of
a work cell
7.  Define product-oriented layout
8.  Explain how to balance production flow
in a repetitive or product-oriented facility
9 – 8 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Innovations at McDonald’s
u  Indoor seating (1950s)
u  Drive-through window (1970s)
u  Adding breakfast to the menu
(1980s)
u  Adding play areas (late 1980s)
u  Redesign of the kitchens (1990s)
u  Self-service kiosk (2004)
u  Now three separate dining sections
9 – 9 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Innovations at McDonald’s
u  Indoor seating (1950s)
u  Drive-through window (1970s)
u  Adding breakfast to the menu
(1980s)
u  Adding play areas (late 1980s)
u  Redesign of the kitchens (1990s)
u  Self-service kiosk (2004)
u  Now three separate dining sections
Six out of the
seven are
layout
decisions!
9 – 10 © 2011 Pearson Education, Inc. publishing as Prentice Hall
McDonald’s New Layout
u  Seventh major innovation
u  Redesigning all 30,000 outlets around
the world
u  Three separate dining areas
u  Linger zone with comfortable chairs and
Wi-Fi connections
u  Grab and go zone with tall counters
u  Flexible zone for kids and families
u  Facility layout is a source of
competitive advantage
9 – 11 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Strategic Importance of
Layout Decisions
The objective of layout strategy
is to develop an effective and
efficient layout that will meet the
firm’s competitive requirements
9 – 12 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Layout Design
Considerations
u  Higher utilization of space, equipment,
and people
u  Improved flow of information, materials,
or people
u  Improved employee morale and safer
working conditions
u  Improved customer/client interaction
u  Flexibility

9/19/12
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9 – 13 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Types of Layout
1.  Office layout
2.  Retail layout
3.  Warehouse layout
4.  Fixed-position layout
5.  Process-oriented layout
6.  Work-cell layout
7.  Product-oriented layout
9 – 14 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Types of Layout
1.  Office layout: Positions workers,
their equipment, and spaces/offices
to provide for movement of
information
2.  Retail layout: Allocates shelf space
and responds to customer behavior
3.  Warehouse layout: Addresses trade-
offs between space and material
handling
9 – 15 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Types of Layout
4.  Fixed-position layout: Addresses the
layout requirements of large, bulky
projects such as ships and
buildings
5.  Process-oriented layout: Deals with
low-volume, high-variety production
(also called job shop or intermittent
production)
9 – 16 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Types of Layout
6.  Work cell layout: Arranges
machinery and equipment to focus
on production of a single product or
group of related products
7.  Product-oriented layout: Seeks the
best personnel and machine
utilizations in repetitive or
continuous production
9 – 17 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Layout Strategies
Objectives Examples
Office Locate workers requiring
frequent contact close to one
another
Allstate Insurance
Microsoft Corp.
Retail Expose customer to high-
margin items
Kroger’s Supermarket
Walgreen’s
Bloomingdale’s
Warehouse
(storage)
Balance low cost storage
with low-cost material
handling
Federal-Mogul’s warehouse
The Gap’s distribution center
Project (fixed
position)
Move material to the limited
storage areas around the site
Ingall Ship Building Corp.
Trump Plaza
Pittsburgh Airport
Table 9.1
9 – 18 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Layout Strategies
Objectives Examples
Job Shop
(process
oriented)
Manage varied material flow
for each product
Arnold Palmer Hospital
Hard Rock Cafe
Olive Garden
Work Cell
(product
families)
Identify a product family,
build teams, cross train team
members
Hallmark Cards
Wheeled Coach
Standard Aero
Repetitive/
Continuous
(product
oriented)
Equalize the task time at each
workstation
Sony’s TV assembly line
Toyota Scion
Table 9.1

9/19/12
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9 – 19 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Good Layouts Consider
u  Material handling equipment
u  Capacity and space requirements
u  Environment and aesthetics
u  Flows of information
u  Cost of moving between various
work areas
9 – 20 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Office Layout
u  Grouping of workers, their equipment, and
spaces to provide comfort, safety, and
movement of information
u  Movement of
information is main
distinction
u  Typically in state of
flux due to frequent
technological
changes
9 – 21 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Relationship Chart
Figure 9.1
9 – 22 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Supermarket Retail Layout
u  Objective is to maximize
profitability per square foot of
floor space
u  Sales and profitability vary
directly with customer exposure
9 – 23 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Five Helpful Ideas for
Supermarket Layout
1.  Locate high-draw items around the
periphery of the store
2.  Use prominent locations for high-impulse
and high-margin items
3.  Distribute power items to both sides of an
aisle and disperse them to increase
viewing of other items
4.  Use end-aisle locations
5.  Convey mission of store through careful
positioning of lead-off department
9 – 24 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Store Layout
Figure 9.2

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9 – 25 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Retail Slotting
u  Manufacturers pay fees to retailers
to get the retailers to display (slot)
their product
u  Contributing factors
u  Limited shelf space
u  An increasing number of new
products
u  Better information about sales
through POS data collection
u  Closer control of inventory
9 – 26 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Retail Store Shelf Space
Planogram
u  Computerized
tool for shelf-
space
management
u  Generated from
store’s scanner
data on sales
u  Often supplied
by manufacturer
5 facings
S
ham
poo
S
ham
poo
S
ham
poo
S
ham
poo
S
ham
poo
C
onditioner
C
onditioner
S
ham
poo
S
ham
poo
S
ham
poo
S
ham
poo
C
onditioner
2 ft.
9 – 27 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Servicescapes
1.  Ambient conditions – background
characteristics such as lighting, sound,
smell, and temperature
2.  Spatial layout and functionality – which
involve customer
circulation path planning,
aisle characteristics, and
product grouping
3.  Signs, symbols, and
artifacts – characteristics
of building design that
carry social significance
9 – 28 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Warehousing and Storage
Layouts
u  Objective is to optimize trade-offs
between handling costs and costs
associated with warehouse space
u  Maximize the total “cube” of the
warehouse – utilize its full volume
while maintaining low material
handling costs
u  Minimize damage and spoilage
9 – 29 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Warehousing and Storage
Layouts
u  All costs associated with the transaction
u  Incoming transport
u  Storage
u  Finding and moving material
u  Outgoing transport
u  Equipment, people, material, supervision,
insurance, depreciation
Material Handling Costs
9 – 30 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Warehousing and Storage
Layouts
u  Warehouse density tends to vary
inversely with the number of different
items stored
u  Automated Storage and
Retrieval Systems (ASRSs)
can significantly improve
warehouse productivity by
an estimated 500%
u  Dock location is a key
design element

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9 – 31 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Cross-Docking
u  Materials are moved directly from
receiving to shipping and are not placed
in storage in the warehouse
u  Requires tight
scheduling and
accurate shipments,
bar code or RFID
identification used for
advanced shipment
notification as
materials
are unloaded
9 – 32 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Random Stocking
u  Typically requires automatic identification
systems (AISs) and effective information
systems
u  Random assignment of stocking locations
allows more efficient use of space
u  Key tasks
1.  Maintain list of open locations
2.  Maintain accurate records
3.  Sequence items to minimize travel, pick time
4.  Combine picking orders
5.  Assign classes of items to particular areas
9 – 33 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Customizing
u  Value-added activities performed at
the warehouse
u  Enable low cost and rapid response
strategies
u  Assembly of components
u  Loading software
u  Repairs
u  Customized labeling and packaging
9 – 34 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Shipping and receiving docks
Office
C
us
to
m
iz
at
io
n
Conveyor
Storage racks
Staging
Warehouse Layout
Traditional Layout
9 – 35 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Warehouse Layout
Cross-Docking Layout
Shipping and receiving docks
O
ff
ic
e
Shipping and receiving docks
9 – 36 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Fixed-Position Layout
u  Product remains in one place
u  Workers and equipment come to site
u  Complicating factors
u  Limited space at site
u  Different materials
required at different
stages of the project
u  Volume of materials
needed is dynamic

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9 – 37 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Alternative Strategy
u  As much of the project as possible
is completed off-site in a product-
oriented facility
u  This can
significantly
improve
efficiency but
is only possible
when multiple
similar units need to be created
9 – 38 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Process-Oriented Layout
u  Like machines and equipment are
grouped together
u  Flexible and capable of handling a
wide variety of products or
services
u  Scheduling can be difficult and
setup, material handling, and
labor costs can be high
9 – 39 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Surgery
Radiology
ER
triage
room
ER Beds Pharmacy
Emergency room admissions
Billing/exit
Laboratories
Process-Oriented Layout
Patient A – broken leg
Patient B – erratic heart
pacemaker
Figure 9.3
9 – 40 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Process-Oriented Layout
u  Like machines and equipment are
grouped together
u  Flexible and capable of handling a
wide variety of products or
services
u  Scheduling can be difficult and
setup, material handling, and
labor costs can be high
9 – 41 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Layout at Arnold Palmer Hospital
Central break
and medical
supply rooms
Local linen
supply
Local
nursing pod
Pie-shaped
rooms
Central nurses
station
9 – 42 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Process-Oriented Layout
u  Arrange work centers so as to
minimize the costs of material
handling
u  Basic cost elements are
u  Number of loads (or people)
moving between centers
u  Distance loads (or people) move
between centers

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9 – 43 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Process-Oriented Layout
Minimize cost = ∑ ∑ Xij Cij
n

i = 1
n

j = 1
where n = total number of work centers or
departments
i, j = individual departments
Xij = number of loads moved from
department i to department j
Cij = cost to move a load between
department i and department j

9 – 44 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Process Layout Example
1.  Construct a “from-to matrix”
2.  Determine the space requirements
3.  Develop an initial schematic diagram
4.  Determine the cost of this layout
5.  Try to improve the layout
6.  Prepare a detailed plan
Arrange six departments in a factory to
minimize the material handling costs.
Each department is 20 x 20 feet and the
building is 60 feet long and 40 feet wide.
9 – 45 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Department Assembly Painting Machine Receiving Shipping Testing
(1) (2) Shop (3) (4) (5) (6)
Assembly (1)
Painting (2)
Machine Shop (3)
Receiving (4)
Shipping (5)
Testing (6)
Number of loads per week

50 100 0 0 20
30 50 10 0
20 0 100
50 0
0

Process Layout Example
Figure 9.4
9 – 46 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Area 1 Area 2 Area 3

Area 4 Area 5 Area 6
60’
40’
Process Layout Example

Receiving Shipping Testing
Department Department Department
(4) (5) (6)
Figure 9.5
Assembly Painting Machine Shop
Department Department Department
(1) (2) (3)
9 – 47 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Process Layout Example
Interdepartmental Flow Graph
Figure 9.6
100
50
20
50
50
20
10
100
30 Machine
Shop (3)
Testing
(6)
Shipping
(5)
Receiving
(4)
Assembly
(1)
Painting
(2)
9 – 48 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Process Layout Example
Cost = $50 + $200 + $40
(1 and 2) (1 and 3) (1 and 6)

+ $30 + $50 + $10
(2 and 3) (2 and 4) (2 and 5)

+ $40 + $100 + $50
(3 and 4) (3 and 6) (4 and 5)

= $570
Cost = ∑ ∑ Xij Cij
n

i = 1
n

j = 1

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9 – 49 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Process Layout Example
Revised Interdepartmental Flow Graph
Figure 9.7
30
50
20
50
10 20
50 100
100 Machine
Shop (3)
Testing
(6)
Shipping
(5)
Receiving
(4)
Painting
(2)
Assembly
(1)
9 – 50 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Process Layout Example
Cost = $50 + $100 + $20
(1 and 2) (1 and 3) (1 and 6)

+ $60 + $50 + $10
(2 and 3) (2 and 4) (2 and 5)

+ $40 + $100 + $50
(3 and 4) (3 and 6) (4 and 5)

= $480
Cost = ∑ ∑ Xij Cij
n

i = 1
n

j = 1
9 – 51 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Area 1 Area 2 Area 3

Area 4 Area 5 Area 6
60’
40’
Process Layout Example

Receiving Shipping Testing
Department Department Department
(4) (5) (6)
Figure 9.8
Painting Assembly Machine Shop
Department Department Department
(2) (1) (3)
9 – 52 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Computer Software
u  Graphical approach only works for
small problems
u  Computer programs are available to
solve bigger problems
u  CRAFT
u  ALDEP
u  CORELAP
u  Factory Flow
9 – 53 © 2011 Pearson Education, Inc. publishing as Prentice Hall
CRAFT Example
Figure 9.9
TOTAL COST 20,100
EST. COST REDUCTION .00
ITERATION 0
(a)
A A A A B B
A A A A B B
D D D D D D
C C D D D D
F F F F F D
E E E E E D
TOTAL COST 14,390
EST. COST REDUCTION 70
ITERATION 3
(b)
D D D D B B
D D D D B B
D D D E E E
C C D E E F
A A A A A F
A A A F F F
9 – 54 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Computer Software
u  Three dimensional visualization
software allows managers to view
possible layouts and assess process,
material
handling,
efficiency,
and safety
issues

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Work Cells
u  Reorganizes people and machines
into groups to focus on single
products or product groups
u  Group technology identifies
products that have similar
characteristics for particular cells
u  Volume must justify cells
u  Cells can be reconfigured as
designs or volume changes
9 – 56 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Advantages of Work Cells
1.  Reduced work-in-process inventory
2.  Less floor space required
3.  Reduced raw material and finished
goods inventory
4.  Reduced direct labor
5.  Heightened sense of employee
participation
6.  Increased use of equipment and
machinery
7.  Reduced investment in machinery and
equipment
9 – 57 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Requirements of Work Cells
1.  Identification of families of products
2.  A high level of training, flexibility
and empowerment of employees
3.  Being self-contained, with its own
equipment and resources
4.  Test (poka-yoke) at each station in
the cell
9 – 58 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Improving Layouts Using
Work Cells
Current layout – workers in
small closed areas.
Improved layout – cross-trained
workers can assist each other.
May be able to add a third worker
as additional output is needed.
Figure 9.10 (a)
9 – 59 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Improving Layouts Using
Work Cells
Current layout – straight
lines make it hard to balance
tasks because work may not
be divided evenly
Improved layout – in U
shape, workers have better
access. Four cross-trained
workers were reduced.
Figure 9.10 (b)
U-shaped line may reduce employee movement
and space requirements while enhancing
communication, reducing the number of
workers, and facilitating inspection
9 – 60 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Staffing and Balancing
Work Cells
Determine the takt time
Takt time =
Total work time available
Units required
Determine the number
of operators required
Workers required =
Total operation time required
Takt time

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9 – 61 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Staffing Work Cells Example
600 Mirrors per day required
Mirror production scheduled for 8 hours per day
From a work balance
chart total
operation time
= 140 seconds
S
ta
nd
ar
d
tim
e
re
qu
ir
ed

Operations
Assemble Paint Test Label Pack for
shipment
60
50
40
30
20
10
0
9 – 62 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Staffing Work Cells Example
600 Mirrors per day required
Mirror production scheduled for 8 hours per day
From a work balance
chart total
operation time
= 140 seconds
Takt time = (8 hrs x 60 mins) / 600 units
= .8 mins = 48 seconds
Workers required =
Total operation time required
Takt time
= 140 / 48 = 2.91
9 – 63 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Work Balance Charts
u  Used for evaluating operation
times in work cells
u  Can help identify bottleneck
operations
u  Flexible, cross-trained employees
can help address labor
bottlenecks
u  Machine bottlenecks may require
other approaches
9 – 64 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Focused Work Center and
Focused Factory
u  Focused Work Center
u  Identify a large family of similar products
that have a large and stable demand
u  Moves production from a general-purpose,
process-oriented facility to a large work cell
u  Focused Factory
u  A focused work cell in a separate facility
u  May be focused by product line, layout,
quality, new product introduction, flexibility,
or other requirements
9 – 65 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Focused Work Center and
Focused Factory
Table 9.2
Work Cell Focused Work Center Focused Factory
Description: Work cell is
a temporary product-
oriented arrangement
of machines and
personnel in what is
ordinarily a process-
oriented facility
A focused work center is
a permanent product-
oriented arrangement
of machines and
personnel in what is
ordinarily a process-
oriented facility
A focused factory is a
permanent facility to
produce a product or
component in a
product-oriented
facility. Many focused
factories currently
being built were
originally part of a
process-oriented
facility
Example: A job shop
with machinery and
personnel rearranged
to produce 300 unique
control panels
Example: Pipe bracket
manufacturing at a
shipyard
Example: A plant to
produce window
mechanism for
automobiles
9 – 66 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Repetitive and Product-
Oriented Layout
1.  Volume is adequate for high equipment
utilization
2.  Product demand is stable enough to justify high
investment in specialized equipment
3.  Product is standardized or approaching a phase
of life cycle that justifies investment
4.  Supplies of raw materials and components are
adequate and of uniform quality
Organized around products or families of
similar high-volume, low-variety products

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Product-Oriented Layouts
u  Fabrication line
u  Builds components on a series of machines
u  Machine-paced
u  Require mechanical or engineering changes
to balance
u  Assembly line
u  Puts fabricated parts together at a series of
workstations
u  Paced by work tasks
u  Balanced by moving tasks
Both types of lines must be balanced so that the
time to perform the work at each station is the same
9 – 68 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Product-Oriented Layouts
1.  Low variable cost per unit
2.  Low material handling costs
3.  Reduced work-in-process inventories
4.  Easier training and supervision
5.  Rapid throughput
Advantages
1.  High volume is required
2.  Work stoppage at any point ties up the
whole operation
3.  Lack of flexibility in product or production
rates
Disadvantages
9 – 69 © 2011 Pearson Education, Inc. publishing as Prentice Hall
McDonald’s Assembly Line
Figure 9.12
9 – 70 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Disassembly Lines
u  Disassembly is being considered in new
product designs
u  “Green” issues and recycling standards are
important consideration
u  Automotive
disassembly
is the 16th
largest
industry in
the US
9 – 71 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Assembly-Line Balancing
u  Objective is to minimize the imbalance
between machines or personnel while
meeting required output
u  Starts with the precedence
relationships
u  Determine cycle time
u  Calculate theoretical
minimum number of
workstations
u  Balance the line by
assigning specific
tasks to workstations
9 – 72 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Wing Component Example
This means that
tasks B and E
cannot be done
until task A has
been completed
Performance Task Must Follow
Time Task Listed
Task (minutes) Below
A 10 —
B 11 A
C 5 B
D 4 B
E 12 A
F 3 C, D
G 7 F
H 11 E
I 3 G, H
Total time 66

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Wing Component Example
Performance Task Must Follow
Time Task Listed
Task (minutes) Below
A 10 —
B 11 A
C 5 B
D 4 B
E 12 A
F 3 C, D
G 7 F
H 11 E
I 3 G, H
Total time 66 I
G F
C
D
H
B
E
A
10
11 12
5
4 3
7 11 3
Figure 9.13
9 – 74 © 2011 Pearson Education, Inc. publishing as Prentice Hall
I
G F
C
D
H
B
E
A
10
11 12
5
4 3
7 11 3
Figure 9.13
Performance Task Must Follow
Time Task Listed
Task (minutes) Below
A 10 —
B 11 A
C 5 B
D 4 B
E 12 A
F 3 C, D
G 7 F
H 11 E
I 3 G, H
Total time 66
Wing Component Example
480 available
mins per day
40 units required
Cycle time =
Production time
available per day
Units required per day
= 480 / 40
= 12 minutes per unit
Minimum
number of
workstations
=
∑ Time for task i
Cycle time
n
i = 1
= 66 / 12
= 5.5 or 6 stations
9 – 75 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Wing Component Example
I
G F
C
D
H
B
E
A
10
11 12
5
4 3
7 11 3
Figure 9.13
Performance Task Must Follow
Time Task Listed
Task (minutes) Below
A 10 —
B 11 A
C 5 B
D 4 B
E 12 A
F 3 C, D
G 7 F
H 11 E
I 3 G, H
Total time 66
480 available
mins per day
40 units required
Cycle time = 12 mins
Minimum
workstations = 5.5 or 6
Line-Balancing Heuristics
1. Longest task time Choose the available task
with the longest task time
2. Most following tasks Choose the available task
with the largest number of
following tasks
3. Ranked positional
weight
Choose the available task for
which the sum of following
task times is the longest
4. Shortest task time Choose the available task
with the shortest task time
5. Least number of
following tasks
Choose the available task
with the least number of
following tasks
Table 9.4
9 – 76 © 2011 Pearson Education, Inc. publishing as Prentice Hall
480 available
mins per day
40 units required
Cycle time = 12 mins
Minimum
workstations = 5.5 or 6
Performance Task Must Follow
Time Task Listed
Task (minutes) Below
A 10 —
B 11 A
C 5 B
D 4 B
E 12 A
F 3 C, D
G 7 F
H 11 E
I 3 G, H
Total time 66 Station
1
Wing Component Example
Station
2
Station 3
Station 3
Station
4
Station
5
Station 6 Station 6
I
G F
H
C
D
B
E
A
10 11
12
5
4
3 7
11
3
Figure 9.14
9 – 77 © 2011 Pearson Education, Inc. publishing as Prentice Hall
Performance Task Must Follow
Time Task Listed
Task (minutes) Below
A 10 —
B 11 A
C 5 B
D 4 B
E 12 A
F 3 C, D
G 7 F
H 11 E
I 3 G, H
Total time 66
Wing Component Example
480 available
mins per day
40 units required
Cycle time = 12 mins
Minimum
workstations = 5.5 or 6
Efficiency =
∑ Task times
(Actual number of workstations) x (Largest cycle time)
= 66 minutes / (6 stations) x (12 minutes)
= 91.7%
9 – 78 © 2011 Pearson Education, Inc. publishing as Prentice Hall
All rights reserved. No part of this publication may be reproduced, stored in a retrieval
system, or transmitted, in any form or by any means, electronic, mechanical, photocopying,
recording, or otherwise, without the prior written permission of the publisher.
Printed in the United States of America.

chapter 1_operations and productivity

8/17/12
1
1 – 1
1
PowerPoint presentation to accompany
Heizer and Render
Operations Management, 10e
Principles of Operations Management, 8e

PowerPoint slides by Jeff Heyl
MG 6303 Prof. Vivek Veeraiah
Operations and
Productivity
1 – 2
Outline
•  Global Company Profile: Hard Rock Cafe
MG 6303 Prof. Vivek Veeraiah
u  What Is Operations Management?
u  Organizing to Produce Goods and
Services
u  Why Study OM?
u  What Operations Managers Do
1 – 3
Outline – Continued
MG 6303 Prof. Vivek Veeraiah
u  The Heritage of Operations
Management
u  Operations in the Service Sector
u Differences between Goods and
Services
u Growth of Services
u Service Pay
u  Exciting New Trends in Operations
Management
1 – 4
Outline – Continued
•  The Productivity Challenge
o  Productivity Measurement
o  Productivity Variables
o  Productivity and the Service Sector
•  Ethics and Social Responsibility
MG 6303 Prof. Vivek Veeraiah
1 – 5
Learning Objectives
MG 6303 Prof. Vivek Veeraiah
When you complete this chapter
you should be able to:
1.  Define operations management
2.  Explain the distinction between
goods and services
3.  Explain the difference between
production and productivity
1 – 6
Learning Objectives
MG 6303 Prof. Vivek Veeraiah
When you complete this chapter
you should be able to:
4.  Compute single-factor
productivity
5.  Compute multifactor productivity
6.  Identify the critical variables in
enhancing productivity

8/17/12
2
1 – 7
The Hard Rock Cafe
MG 6303 Prof. Vivek Veeraiah
u First opened in 1971
u  Now – 129 restaurants in over 40 countries
u Rock music memorabilia
u Creates value in the form of good food
and entertainment
u 3,500+ custom meals per day in Orlando
u How does an item get on the menu?
u Role of the Operations Manager
1 – 8
What Is
Operations
Management?
Production is the creation of goods and
services
MG 6303 Prof. Vivek Veeraiah
Operations management (OM) is
the set of activities that create
value in the form of goods and
services by transforming inputs
into outputs
1 – 9
Organizing to Produce
Goods and Services
•  Essential functions:
1.  Marketing – generates demand
2.  Production/operations – creates the product
3.  Finance/accounting – tracks how well the organization is doing,
pays bills, collects the money
MG 6303 Prof. Vivek Veeraiah 1 – 10
Organizational Charts
MG 6303 Prof. Vivek Veeraiah
Operations
Teller
Scheduling
Check Clearing
Collection
Transaction
processing
Facilities design/
layout
Vault operations
Maintenance
Security
Finance
Investments
Security
Real estate
Accounting
Auditing
Marketing
Loans
Commercial
Industrial
Financial
Personal
Mortgage
Trust Department
Commercial Bank
Figure 1.1(A)
1 – 11
Organizational Charts
MG 6303 Prof. Vivek Veeraiah
Operations
Ground support
equipment
Maintenance
Ground Operations
Facility
maintenance
Catering
Flight Operations
Crew scheduling
Flying
Communications
Dispatching
Management science
Finance/
accounting
Accounting
Payables
Receivables
General Ledger
Finance
Cash control
International
exchange
Airline
Figure 1.1(B)
Marketing
Traffic
administration
Reservations
Schedules
Tariffs (pricing)
Sales
Advertising
1 – 12
Marketing
Sales
promotion
Advertising
Sales
Market
research
Organizational Charts
MG 6303 Prof. Vivek Veeraiah
Operations
Facilities
Construction; maintenance
Production and inventory control
Scheduling; materials control
Quality assurance and control
Supply-chain management
Manufacturing
Tooling; fabrication; assembly
Design
Product development and design
Detailed product specifications
Industrial engineering
Efficient use of machines, space,
and personnel
Process analysis
Development and installation of
production tools and equipment
Finance/
accounting
Disbursements/
credits
Receivables
Payables
General ledger
Funds Management
Money market
International
exchange
Capital requirements
Stock issue
Bond issue
and recall
Manufacturing
Figure 1.1(C)

8/17/12
3
1 – 13
Why Study OM?
1.  OM is one of three major functions of any
organization, we want to study how people organize
themselves for productive enterprise
MG 6303 Prof. Vivek Veeraiah
2.  We want (and need) to know how
goods and services are produced
3.  We want to understand what
operations managers do
4.  OM is such a costly part of an
organization
1 – 14
Options for Increasing
Contribution
MG 6303 Prof. Vivek Veeraiah

Table 1.1
Sales $100,000 $150,000 $100,000 $100,000
Cost of Goods – 80,000 – 120,000 – 80,000 – 64,000
Gross Margin 20,000 30,000 20,000 36,000
Finance Costs – 6,000 – 6,000 – 3,000 – 6,000
Subtotal 14,000 24,000 17,000 30,000
Taxes at 25% – 3,500 – 6,000 – 4,250 – 7,500
Contribution $ 10,500 $ 18,000 $ 12,750 $ 22,500
Finance/
Marketing Accounting OM
Option Option Option

Increase Reduce Reduce
Sales Finance Production
Current Revenue 50% Costs 50% Costs 20%
1 – 15
What
Operations
Managers Do
u  Planning
u  Organizing
u  Staffing
u  Leading
u  Controlling
MG 6303 Prof. Vivek Veeraiah
Basic Management Functions
1 – 16
Ten  Critical  Decisions
MG 6303 Prof. Vivek Veeraiah
Ten Decision Areas Chapter(s)
1.  Design of goods and services 5
2.  Managing quality 6, Supplement 6
3.  Process and capacity 7, Supplement 7
design
4.  Location strategy 8
5.  Layout strategy 9
6.  Human resources and 10
job design
7.  Supply-chain 11, Supplement 11
management
8.  Inventory, MRP, JIT 12, 14, 16
9.  Scheduling 13, 15
10.  Maintenance 17 Table 1.2
1 – 17
The Critical Decisions
1.  Design of goods and services
o  What good or service should we offer?
o  How should we design these products and services?
2.  Managing quality
o  How do we define quality?
o  Who is responsible for quality?
MG 6303 Prof. Vivek Veeraiah
Table 1.2 (cont.)
1 – 18
The Critical Decisions
3.  Process and capacity design
o  What process and what capacity will these products require?
o  What equipment and technology is necessary for these processes?
4.  Location strategy
o  Where should we put the facility?
o  On what criteria should we base the location decision?
MG 6303 Prof. Vivek Veeraiah
Table 1.2 (cont.)

8/17/12
4
1 – 19
The Critical Decisions
5.  Layout strategy
o  How should we arrange the facility?
o  How large must the facility be to meet our plan?
6.  Human resources and job design
o  How do we provide a reasonable work environment?
o  How much can we expect our employees to produce?
MG 6303 Prof. Vivek Veeraiah
Table 1.2 (cont.)
1 – 20
The Critical Decisions
7.  Supply-chain management
o  Should we make or buy this component?
o  Who should be our suppliers and how can we integrate them into our
strategy?
8.  Inventory, material requirements planning, and JIT
o  How much inventory of each item should we have?
o  When do we re-order?
MG 6303 Prof. Vivek Veeraiah
Table 1.2 (cont.)
1 – 21
The Critical Decisions
9.  Intermediate and short–term scheduling
o  Are we better off keeping people on the payroll during slowdowns?
o  Which jobs do we perform next?
10.  Maintenance
o  How do we build reliability into our processes?
o  Who is responsible for maintenance?
MG 6303 Prof. Vivek Veeraiah
Table 1.2 (cont.)
1 – 22
Where are the OM
Jobs?
•  Technology/methods
•  Facilities/space utilization
•  Strategic issues
•  Response time
•  People/team development
•  Customer service
•  Quality
•  Cost reduction
•  Inventory reduction
•  Productivity improvement
MG 6303 Prof. Vivek Veeraiah
1 – 23
Opportuniti
es
MG 6303 Prof. Vivek Veeraiah
Figure 1.2
1 – 24
New Challenges in OM
MG 6303 Prof. Vivek Veeraiah
u Global focus
u Just-in-time
u Supply-chain
partnering
u Rapid product
development,
alliances
u Mass
customization
u Empowered
employees, teams
To From
u Local or national focus
u Batch shipments
u Low bid purchasing

u Lengthy product
development

u Standard products

u Job specialization

8/17/12
5
1 – 25
Characteristics of Goods
MG 6303 Prof. Vivek Veeraiah
u Tangible product
u Consistent product
definition
u Production usually
separate from
consumption
u Can be inventoried
u Low customer
interaction
1 – 26
Characteristics of
Service
MG 6303 Prof. Vivek Veeraiah
u Intangible product
u Produced and
consumed at same time
u Often unique
u High customer
interaction
u Inconsistent product
definition
u Often knowledge-based
u Frequently dispersed
1 – 27
Industry and Services as
Percentage of GDP
MG 6303 Prof. Vivek Veeraiah
Services Manufacturing
A
us
tr
al
ia

C
an
ad
a
C
hi
na

C
ze
ch
R
ep

Fr
an
ce

G
er
m
an
y
H
on
g
K
on
g
Ja
pa
n
M
ex
ic
o
R
us
si
an
F
ed

S
ou
th
A
fr
ic
a
S
pa
in

U
K

U
S

90 −
80 −
70 −
60 −
50 −
40 −
30 −
20 −
10 −
0 −
1 – 28
Goods  and  Services
MG 6303 Prof. Vivek Veeraiah
Automobile
Computer
Installed carpeting
Fast-food meal
Restaurant meal/auto repair
Hospital care
Advertising agency/
investment management
Consulting service/
teaching
Counseling
Percent of Product that is a Good Percent of Product that is a Service
100% 75 50 25 0 25 50 75 100%
| | | | | | | | |
1 – 29
120 –
100 –
80 –
60 –
40 –
20 –
0 – | | | | | | |
1950 1970 1990 2010 (est)
1960 1980 2000
E
m
pl
oy
m
en
t (
m
ill
io
ns
)
Manufacturing  and  
Service  Employment
MG 6303 Prof. Vivek Veeraiah
Figure 1.4 (A)
Manufacturing
Service
1 – 30
Manufacturing  
Employment  and  
Production
MG 6303 Prof. Vivek Veeraiah
Figure 1.4 (B)
40 –
30 –
20 –
10 –
0 – | | | | | | |
1950 1970 1990 2010 (est)
1960 1980 2000
– 150
– 125
– 100
– 75
– 50
– 25
– 0
E
m
pl
oy
m
en
t (
m
ill
io
ns
)
In
de
x:
1
99
7
=
10
0
Manufacturing
employment
(left scale)
Industrial
production
(right scale)

8/17/12
6
1 – 31
Development  of  the    
Service  Economy
MG 6303 Prof. Vivek Veeraiah Figure 1.4 (C)
United States
Canada
France
Italy
Britain
Japan
W. Germany
1970 2010 (est)
| | | | |
40 50 60 70 80
Percent
1 – 32
Organizations in Each
Sector
Service Sector Example
% of all
Jobs
Education,
Legal, Medical,
other
San Diego Zoo, Arnold
Palmer Hospital
25.8
Trade (retail,
wholesale)
Walgreen’s, Wal-Mart,
Nordstrom’s
14.9
Utilities,
Transportation
Pacific Gas & Electric,
American Airlines
5.2
Professional and
Business
Services
Snelling and Snelling, Waste
Management, Inc.
10.7
MG 6303 Prof. Vivek Veeraiah
Table 1.3
1 – 33
Organizations in Each
Sector
Service Sector Example
% of all
Jobs
Finance,
Information,
Real Estate
Citicorp, American Express,
Prudential, Aetna
9.6
Food, Lodging,
Entertainment
Olive Garden, Motel 6, Walt
Disney
8.5
Public
Administration
U.S., State of Alabama, Cook
County
4.6

Total 78.8
MG 6303 Prof. Vivek Veeraiah
Table 1.3
1 – 34
Organizations in Each
Sector
Other Sectors Example
% of all
Jobs
Manufacturing
Sector
General Electric, Ford,
U.S. Steel, Intel
11.2
Construction
Sector
Bechtel, McDermott 8.1
Agriculture
Sector
King Ranch 1.4
Mining Sector Homestake Mining 0.5
Total 21.2
MG 6303 Prof. Vivek Veeraiah
Table 1.3
1 – 35
Changing Challenges
MG 6303 Prof. Vivek Veeraiah
Traditional
Approach
Reasons for
Change
Current
Challenge
Ethics and
regulations
not at the
forefront
Public concern over
pollution, corruption,
child labor, etc.
High ethical and
social
responsibility;
increased legal
and professional
standards
Local or
national
focus
Growth of reliable, low
cost communication
and transportation
Global focus,
international
collaboration
Lengthy
product
development
Shorter life cycles;
growth of global
communication; CAD,
Internet
Rapid product
development;
design
collaboration
Figure 1.5
1 – 36
Changing Challenges
MG 6303 Prof. Vivek Veeraiah
Traditional
Approach
Reasons for
Change
Current
Challenge
Low cost
production,
with little
concern for
environment;
free
resources
(air, water)
ignored
Public sensitivity to
environment; ISO 14000
standard; increasing
disposal costs
Environmentally
sensitive
production; green
manufacturing;
sustainability
Low-cost
standardized
products
Rise of consumerism;
increased affluence;
individualism
Mass
customization
Figure 1.5

8/17/12
7
1 – 37
Changing Challenges
MG 6303 Prof. Vivek Veeraiah
Traditional
Approach
Reasons for
Change
Current
Challenge
Emphasis on
specialized,
often manual
tasks
Recognition of the
employee’s total
contribution; knowledge
society
Empowered
employees;
enriched jobs
“In-house”
production;
low-bid
purchasing
Rapid technological
change; increasing
competitive forces
Supply-chain
partnering; joint
ventures, alliances
Large lot
production
Shorter product life
cycles; increasing need
to reduce inventory
Just-In-Time
performance;
lean; continuous
improvement
Figure 1.5
1 – 38
New Trends in OM
•  Ethics
•  Global focus
•  Environmentally sensitive production
•  Rapid product development
•  Environmentally sensitive production
•  Mass customization
•  Empowered employees
•  Supply-chain partnering
•  Just-in-time performance
MG 6303 Prof. Vivek Veeraiah
1 – 39
Productivity Challenge
MG 6303 Prof. Vivek Veeraiah
Productivity is the ratio of outputs (goods
and services) divided by the inputs
(resources such as labor and capital)
The objective is to improve productivity!
Important Note!
Production is a measure of output
only and not a measure of efficiency
1 – 40
Feedback loop
Outputs
Goods
and
services
Transformation
The U.S. economic system
transforms inputs to outputs
at about an annual 2.5%
increase in productivity per
year. The productivity
increase is the result of a
mix of capital (38% of 2.5%),
labor (10% of 2.5%), and
management (52% of 2.5%).
The Economic System
MG 6303 Prof. Vivek Veeraiah
Inputs
Labor,
capital,
management
Figure 1.6
1 – 41
Improving Productivity at
Starbucks
MG 6303 Prof. Vivek Veeraiah
A team of 10 analysts
continually look for ways
to shave time. Some
improvements:
Stop requiring signatures
on credit card purchases
under $25
Saved 8 seconds
per transaction
Change the size of the ice
scoop
Saved 14 seconds
per drink
New espresso machines Saved 12 seconds
per shot
1 – 42
Improving Productivity at
Starbucks
MG 6303 Prof. Vivek Veeraiah
A team of 10 analysts
continually look for ways
to shave time. Some
improvements:
Stop requiring signatures
on credit card purchases
under $25
Saved 8 seconds
per transaction
Change the size of the ice
scoop
Saved 14 seconds
per drink
New espresso machines Saved 12 seconds
per shot
Operations improvements have
helped Starbucks increase yearly
revenue per outlet by $200,000 to
$940,000 in six years.
Productivity has improved by 27%,
or about 4.5% per year.

8/17/12
8
1 – 43
u  Measure of process improvement
u  Represents output relative to input
u  Only through productivity increases
can our standard of living improve
Productivity
MG 6303 Prof. Vivek Veeraiah
Productivity =
Units produced
Input used
1 – 44
Productivity Calculations
MG 6303 Prof. Vivek Veeraiah
Productivity =
Units produced
Labor-hours used
= = 4 units/labor-hour
1,000
250
Labor Productivity
One resource input ð single-factor productivity
1 – 45
Multi-Factor Productivity
MG 6303 Prof. Vivek Veeraiah
Output
Labor + Material + Energy
+ Capital + Miscellaneous
Productivity =
u Also known as total factor productivity
u Output and inputs are often expressed
in dollars
Multiple resource inputs ð multi-factor productivity
1 – 46
Collins Title Productivity
MG 6303 Prof. Vivek Veeraiah
Staff of 4 works 8 hrs/day 8 titles/day
Payroll cost = $640/day Overhead = $400/day
Old System:
= Old labor productivity
8 titles/day
32 labor-hrs
1 – 47
Collins Title Productivity
MG 6303 Prof. Vivek Veeraiah
Staff of 4 works 8 hrs/day 8 titles/day
Payroll cost = $640/day Overhead = $400/day
Old System:
8 titles/day
32 labor-hrs
= Old labor productivity = .25 titles/labor-hr
1 – 48
Collins Title Productivity
MG 6303 Prof. Vivek Veeraiah
Staff of 4 works 8 hrs/day 8 titles/day
Payroll cost = $640/day Overhead = $400/day
Old System:
14 titles/day Overhead = $800/day
New System:
8 titles/day
32 labor-hrs
= Old labor productivity
= New labor productivity
= .25 titles/labor-hr
14 titles/day
32 labor-hrs

8/17/12
9
1 – 49
Collins Title Productivity
MG 6303 Prof. Vivek Veeraiah
Staff of 4 works 8 hrs/day 8 titles/day
Payroll cost = $640/day Overhead = $400/day
Old System:
14 titles/day Overhead = $800/day
New System:
8 titles/day
32 labor-hrs
= Old labor productivity = .25 titles/labor-hr
14 titles/day
32 labor-hrs
= New labor productivity = .4375 titles/labor-hr
1 – 50
Collins Title Productivity
MG 6303 Prof. Vivek Veeraiah
Staff of 4 works 8 hrs/day 8 titles/day
Payroll cost = $640/day Overhead = $400/day
Old System:
14 titles/day Overhead = $800/day
New System:
= Old multifactor productivity
8 titles/day
$640 + 400
1 – 51
Collins Title Productivity
MG 6303 Prof. Vivek Veeraiah
Staff of 4 works 8 hrs/day 8 titles/day
Payroll cost = $640/day Overhead = $400/day
Old System:
14 titles/day Overhead = $800/day
New System:
8 titles/day
$640 + 400
= Old multifactor productivity = .0077 titles/dollar
1 – 52
Collins Title Productivity
MG 6303 Prof. Vivek Veeraiah
Staff of 4 works 8 hrs/day 8 titles/day
Payroll cost = $640/day Overhead = $400/day
Old System:
14 titles/day Overhead = $800/day
New System:
8 titles/day
$640 + 400
= Old multifactor productivity
= New multifactor productivity
= .0077 titles/dollar
14 titles/day
$640 + 800
1 – 53
Collins Title Productivity
MG 6303 Prof. Vivek Veeraiah
Staff of 4 works 8 hrs/day 8 titles/day
Payroll cost = $640/day Overhead = $400/day
Old System:
14 titles/day Overhead = $800/day
New System:
8 titles/day
$640 + 400
14 titles/day
$640 + 800
= Old multifactor productivity
= New multifactor productivity
= .0077 titles/dollar
= .0097 titles/dollar
1 – 54
Measurement Problems
MG 6303 Prof. Vivek Veeraiah
1.  Quality may change while the
quantity of inputs and outputs
remains constant
2.  External elements may cause an
increase or decrease in
productivity
u  Precise units of measure may be
lacking

8/17/12
10
1 – 55
Productivity Variables
MG 6303 Prof. Vivek Veeraiah
1.  Labor – contributes
about 10% of the
annual increase
2.  Capital – contributes
about 38% of the
annual increase
3.  Management –
contributes about
52% of the annual
increase
1 – 56
Key Variables for
Improved Labor
Productivity
MG 6303 Prof. Vivek Veeraiah
1.  Basic education appropriate for the
labor force
2.  Diet of the labor force
3.  Social overhead that makes labor
available
u Challenge is in maintaining and
enhancing skills in the midst of rapidly
changing technology and knowledge
1 – 57
Labor Skills
MG 6303 Prof. Vivek Veeraiah
About half of the 17-year-olds in the U.S. cannot
correctly answer questions of this type
Figure 1.7
1 – 58
Investment  and  
Productivity  
MG 6303 Prof. Vivek Veeraiah
10
8
6
4
2
0
P
er
ce
nt
in
cr
ea
se
in
p
ro
du
ct
iv
ity

Percentage investment
10 15 20 25 30 35
1 – 59
Service Productivity
MG 6303 Prof. Vivek Veeraiah
1.  Typically labor intensive
2.  Frequently focused on unique individual
attributes or desires
3.  Often an intellectual task performed by
professionals
4.  Often difficult to mechanize
5.  Often difficult to evaluate for quality
1 – 60
Productivity at Taco Bell
MG 6303 Prof. Vivek Veeraiah
Improvements:
u  Revised the menu
u  Designed meals for easy preparation
u  Shifted some preparation to suppliers
u  Efficient layout and automation
u  Training and employee empowerment
u  New water and energy saving grills

8/17/12
11
1 – 61
Productivity at Taco Bell
MG 6303 Prof. Vivek Veeraiah
Improvements:
þ Revised the menu
þ Designed meals for easy preparation
þ Shifted some preparation to suppliers
þ Efficient layout and automation
þ Training and employee empowerment
þ New water and energy saving grills
Results:
u  Preparation time cut to 8 seconds
u  Management span of control increased
from 5 to 30
u  In-store labor cut by 15 hours/day
u  Stores handle twice the volume with half
the labor
u  Conserve 300 million gallons of water and
200 million KwH of electricity each year
saving $17 million annually
1 – 62
Ethics and
Social Responsibility
MG 6303 Prof. Vivek Veeraiah
Challenges facing
operations managers:
u Developing and producing safe,
quality products
u Maintaining a clean environment
u Providing a safe workplace
u Honoring stakeholder commitments

Chapter 2_Operations Strategy in a Global Environment

8/17/12
1
2 – 1
2
PowerPoint
 presenta-on
 to
 accompany
 
 
Heizer
 and
 Render
 
 
Opera-ons
 Management,
 10e
 
 
Principles
 of
 Opera-ons
 Management,
 8e
 

 
PowerPoint
 slides
 by
 Jeff
 Heyl
 
Operations Strategy in a
Global Environment
2 – 2
Outline
u  Global Company Profile: Boeing
u  A Global View of Operations
u  Cultural and Ethical Issues
u  Developing Missions And
Strategies
u  Mission
u  Strategy
2 – 3
Outline – Continued
u  Achieving Competitive Advantage
Through Operations
u  Competing On Differentiation
u  Competing On Cost
u  Competing On Response
u  Ten Strategic OM Decisions
2 – 4
Outline – Continued
u  Strategy Development and
Implementation
u  Key Success Factors and Core
Competencies
u  Build and Staff the Organization
u  Integrate OM with Other Activities
2 – 5
Outline – Continued
u  Global Operations Strategy
Options
u  International Strategy
u  Multidomestic Strategy
u  Global Strategy
u  Transnational Strategy
2 – 6
Learning Objectives
1.  Define mission and strategy
2.  Identify and explain three strategic
approaches to competitive
advantage
3.  Identify and define the 10 decisions
of operations management
When you complete this chapter you
should be able to:

8/17/12
2
2 – 7
Learning Objectives
4.  Understand the significant key
success factors and core
competencies
5.  Identify and explain four global
operations strategy options
When you complete this chapter you
should be able to:
2 – 8
Some Boeing Suppliers (787)
Firm Country Component

Latecoere France Passenger doors

Labinel France Wiring

Dassault France Design and
PLM software

Messier-Bugatti France Electric brakes

Thales France Electrical power
conversion system
and integrated
standby flight display

Messier-Dowty France Landing gear structure

Diehl Germany Interior lighting

2 – 9
Some Boeing Suppliers (787)
Firm Country Component

Cobham UK Fuel pumps and valves

Rolls-Royce UK Engines

Smiths Aerospace UK Central computer
system

BAE SYSTEMS UK Electronics

Alenia Aeronautics Italy Upper center
fuselage &
horizontal stabilizer

Toray Industries Japan Carbon fiber for
wing and tail units

2 – 10
Some Boeing Suppliers (787)
Firm Country Component

Fuji Heavy Japan Center wing box
Industries

Kawasaki Heavy Japan Forward fuselage,
Industries fixed section of wing,
landing gear well

Teijin Seiki Japan Hydraulic actuators

Mitsubishi Heavy Japan Wing box
Industries

Chengdu Aircraft China Rudder
Group

Hafei Aviation China Parts
2 – 11
Some Boeing Suppliers (787)
Firm Country Component

Korean Aviation South Wingtips
Korea

Saab Sweden Cargo access doors
2 – 12
Global Strategies
•  Boeing – sales and production are worldwide
•  Benetton – moves inventory to stores around
the world faster than its competition by
building flexibility into design, production, and
distribution
•  Sony – purchases components from
suppliers in Thailand, Malaysia, and around
the world

8/17/12
3
2 – 13
Global Strategies
•  Volvo – considered a Swedish company but
until recently was controlled by an American
company, Ford. The current Volvo S40 is
built in Belgium and shares its platform with
the Mazda 3 built in Japan and the Ford
Focus built in Europe.
•  Haier – A Chinese company, produces
compact refrigerators (it has one-third of the
US market) and wine cabinets (it has half of
the US market) in South Carolina
2 – 14
35 –
30 –
25 –
20 –
15 –
10 –
5 –
0 – | | | | | | | | | | |
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 (est*)
Year
P
er
ce
nt

Growth of World Trade
Figure 2.1
Collapse of the
Berlin Wall
2 – 15
Reasons to Globalize
Reasons to Globalize
1.  Reduce costs (labor, taxes, tariffs, etc.)
2.  Improve supply chain
3.  Provide better goods and services
4.  Understand markets
5.  Learn to improve operations
6.  Attract and retain global talent
Tangible
Reasons
Intangible
Reasons
2 – 16
Reduce Costs
•  Foreign locations with lower wage rates can lower
direct and indirect costs
•  Maquiladoras
•  World Trade Organization (WTO)
•  North American Free Trade Agreement (NAFTA)
•  APEC, SEATO, MERCOSUR, CAFTA
•  European Union (EU)
2 – 17
Improve the Supply Chain
•  Locating facilities closer to unique resources
•  Auto design to California
•  Athletic shoe production to China
•  Perfume manufacturing in France
2 – 18
Provide Better Goods
and Services
•  Objective and subjective characteristics of goods
and services
•  On-time deliveries
•  Cultural variables
•  Improved customer service

8/17/12
4
2 – 19
Understand Markets
•  Interacting with foreign customers and suppliers can
lead to new opportunities
•  Cell phone
design from
Europe
•  Cell phone
fads from
Japan
•  Extend the product life cycle
2 – 20
Learn to Improve Operations
•  Remain open to the free flow of ideas
•  General Motors partnered with a Japanese auto
manufacturer to learn new approaches to production
and inventory control
•  Equipment and layout have been improved using
Scandinavian ergonomic competence
2 – 21
Attract and Retain Global Talent
•  Offer better employment opportunities
•  Better growth opportunities and insulation against
unemployment
•  Relocate unneeded personnel to more prosperous
locations
2 – 22
Cultural and Ethical Issues
•  Cultures can be quite different
•  Attitudes can be quite different towards
u Punctuality
u Lunch breaks
u Environment
u Intellectual
property
u Thievery
u Bribery
u Child labor
2 – 23
Companies Want To Consider
•  National literacy rate
•  Rate of innovation
•  Rate of technology
change
•  Number of skilled
workers
•  Political stability
•  Product liability laws
•  Export restrictions
•  Variations in language
u  Work ethic
u  Tax rates
u  Inflation
u  Availability of raw
materials
u  Interest rates
u  Population
u  Number of miles of
highway
u  Phone system
2 – 24
Match Product & Parent
•  Braun Household
Appliances
•  Firestone Tires
•  Godiva Chocolate
•  Haagen-Dazs Ice
Cream
•  Jaguar Autos
•  MGM Movies
•  Lamborghini Autos
•  Alpo Petfoods
1.  Volkswagen
2.  Bridgestone
3.  Campbell Soup
4.  Tata Motors Limited
5.  Proctor and Gamble
6.  Nestlé
7.  Pillsbury
8.  Sony

8/17/12
5
2 – 25
Match Product & Parent
•  Braun Household
Appliances
•  Firestone Tires
•  Godiva Chocolate
•  Haagen-Dazs Ice
Cream
•  Jaguar Autos
•  MGM Movies
•  Lamborghini Autos
•  Alpo Petfoods
1.  Volkswagen
2.  Bridgestone
3.  Campbell Soup
4.  Tata Motors Limited
5.  Proctor and Gamble
6.  Nestlé
7.  Pillsbury
8.  Sony
2 – 26
Match Product & Country
•  Braun Household
Appliances
•  Firestone Tires
•  Godiva Chocolate
•  Haagen-Dazs Ice
Cream
•  Jaguar Autos
•  MGM Movies
•  Lamborghini Autos
•  Alpo Pet Foods
1.  Great Britain
2.  Germany
3.  Japan
4.  United States
5.  Switzerland
6.  India
2 – 27
Match Product & Country
•  Braun Household
Appliances
•  Firestone Tires
•  Godiva Chocolate
•  Haagen-Dazs Ice
Cream
•  Jaguar Autos
•  MGM Movies
•  Lamborghini Autos
•  Alpo Pet Foods
1.  Great Britain
2.  Germany
3.  Japan
4.  United States
5.  Switzerland
6.  India
2 – 28
Developing Missions
and Strategies
Mission statements tell an
organization where it is going
The Strategy tells the
organization how to get there
2 – 29
Mission
u  Mission – where are
you going?
u  Organization’s
purpose for being
u  Answers ‘What do
we provide society?’
u  Provides boundaries
and focus
2 – 30
Merck
The mission of Merck is to provide society with superior
products and services—innovations and solutions that
improve the quality of life and satisfy customer needs—to
provide employees with meaningful work and advancement
opportunities and investors with a superior rate of return.
Figure 2.2

8/17/12
6
2 – 31
Hard Rock Cafe
Our Mission: To spread the spirit of Rock ’n’ Roll by delivering
an exceptional entertainment and dining experience. We are
committed to being an important, contributing member of our
community and offering the Hard Rock family a fun, healthy,
and nurturing work environment while ensuring our long-term
success.
Figure 2.2
2 – 32
Arnold Palmer Hospital
Arnold Palmer Hospital for
Children provides state-of-the-art,
family centered healthcare
focused on restoring the joy of
childhood in an environment of
compassion, healing, and hope.
Figure 2.2
2 – 33
Benefit to
Society
Mission
Factors Affecting Mission
Philosophy
and Values
Profitability
and Growth Environment
Customers Public Image
2 – 34
Sample Missions
Sample Company Mission
To manufacture and service an innovative, growing, and
profitable worldwide microwave communications business
that exceeds our customers’ expectations.
Sample Operations Management Mission
To produce products consistent with the company’s mission
as the worldwide low-cost manufacturer.
Figure 2.3
2 – 35
Sample Missions
Figure 2.3
Sample OM Department Missions
Product design To design and produce products and
services with outstanding quality and
inherent customer value.
Quality management To attain the exceptional value that is
consistent with our company mission and
marketing objectives by close attention to
design, procurement, production, and field
service operations
Process design To determine, design, and produce the
production process and equipment that will
be compatible with low-cost product, high
quality, and good quality of work life at
economical cost.
2 – 36
Sample Missions
Figure 2.3
Sample OM Department Missions
Location To locate, design, and build efficient and
economical facilities that will yield high
value to the company, its employees, and the
community.
Layout design To achieve, through skill, imagination, and
resourcefulness in layout and work methods,
production effectiveness and efficiency
while supporting a high quality of work life.
Human resources To provide a good quality of work life, with
well-designed, safe, rewarding jobs, stable
employment, and equitable pay, in exchange
for outstanding individual contribution from
employees at all levels.

8/17/12
7
2 – 37
Sample Missions
Figure 2.3
Sample OM Department Missions
Supply-chain
management
To collaborate with suppliers to develop
innovative products from stable, effective,
and efficient sources of supply.
Inventory To achieve low investment in inventory
consistent with high customer service levels
and high facility utilization.
Scheduling To achieve high levels of throughput and
timely customer delivery through effective
scheduling.
Maintenance To achieve high utilization of facilities and
equipment by effective preventive
maintenance and prompt repair of facilities
and equipment.
2 – 38
Strategic Process
Marketing Operations Finance/ Accounting
Functional
Area Missions
Organization’s
Mission
2 – 39
Strategy
u Action plan to
achieve mission
u Functional areas
have strategies
u Strategies exploit
opportunities and
strengths, neutralize
threats, and avoid
weaknesses
2 – 40
Strategies
 for
 Competitive
 
Advantage
 
•  Differentiation – better, or at least different
•  Cost leadership – cheaper
•  Response – rapid response
2 – 41
Competing on Differentiation
Uniqueness can go beyond both the physical
characteristics and service attributes to encompass
everything that impacts customer’s perception of value
u  Safeskin gloves – leading edge products
u  Walt Disney Magic Kingdom –
experience differentiation
u  Hard Rock Cafe – dining experience
2 – 42
Competing on Cost
Provide the maximum value as perceived by customer.
Does not imply low quality.
u  Southwest Airlines – secondary
airports, no frills service, efficient
utilization of equipment
u  Wal-Mart – small overhead, shrinkage,
distribution costs
u  Franz Colruyt – no bags, low light, no
music, doors on freezers

8/17/12
8
2 – 43
Competing on Response
•  Flexibility is matching market changes in
design innovation and volumes
•  A way of life at Hewlett-Packard
•  Reliability is meeting schedules
•  German machine industry
•  Timeliness is quickness
in design, production,
and delivery
•  Johnson Electric,
Pizza Hut, Motorola
2 – 44
OM’s Contribution to Strategy
Product
Quality
Process
Location
Layout
Human
resource
Supply chain
Inventory
Scheduling
Maintenance
DIFFERENTIATION
Innovative design … Safeskin’s innovative gloves
Broad product line … Fidelity Security’s mutual funds
After-sales service … Caterpillar’s heavy equipment
service
Experience … Hard Rock Café’s dining
experience

COST LEADERSHIP
Low overhead … Franz-Colruyt’s warehouse-type
stores
Effective capacity
use … Southwest Airline’s
aircraft utilization
Inventory
management … Wal Mart’s sophisticated
distribution system

RESPONSE
Flexibility … Hewlett-Packard’s response to
volatile world market
Reliability … FedEx’s “absolutely, positively,
on time”
Quickness … Pizza Hut’s 5-minute guarantee
at lunchtime
Figure 2.4
10 Operations Competitive
Decisions Approach Example Advantage
Response
(faster)
Cost
leadership
(cheaper)
Differentiation
(better)
2 – 45
Managing Global Service
Operations
u Capacity planning
u Location planning
u Facilities design and layout
u Scheduling
Requires a different perspective on:
2 – 46
10 Strategic OM Decisions
1.  Goods and service
design
2.  Quality
3.  Process and
capacity design
4.  Location selection
5.  Layout design
6.  Human resources
and job design
7.  Supply-chain
management
8.  Inventory
9.  Scheduling
10.  Maintenance
2 – 47
Goods and Services and
the 10 OM Decisions
Operations
Decisions Goods Services
Goods and
service
design
Product is usually
tangible
Product is not
tangible
Quality Many objective
standards
Many subjective
standards
Process
and
capacity
design
Customers not
involved
Customer may be
directly involved
Capacity must
match demand
Table 2.1
2 – 48
Goods and Services and
the 10 OM Decisions
Operations
Decisions Goods Services
Location
selection
Near raw
materials and
labor
Near customers
Layout
design
Production
efficiency
Enhances product
and production
Human
resources
and job
design
Technical skills,
consistent labor
standards, output
based wages
Interact with
customers, labor
standards vary
Table 2.1

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9
2 – 49
Goods and Services and
the 10 OM Decisions
Operations
Decisions Goods Services
Supply
chain
Relationship
critical to final
product
Important, but
may not be
critical
Inventory Raw materials,
work-in-process,
and finished
goods may be
held
Cannot be stored
Scheduling Level schedules
possible
Meet immediate
customer demand
Table 2.1
2 – 50
Goods and Services and
the 10 OM Decisions
Operations
Decisions Goods Services
Maintenance Often preventive
and takes place
at production site
Often “repair” and
takes place at
customer’s site
Table 2.1
2 – 51
Operations Strategies of Two
Drug Companies
Brand Name Drugs, Inc. Generic Drug Corp.
Competitive
Advantage Product Differentiation Low Cost
Product
Selection and
Design
Heavy R&D investment;
extensive labs; focus on
development in a broad
range of drug
categories
Low R&D investment;
focus on development
of generic drugs
Quality Major priority, exceed
regulatory requirements
Meets regulatory
requirements on a
country by country
basis
Table 2.2
2 – 52
Operations Strategies of Two
Drug Companies
Brand Name Drugs, Inc. Generic Drug Corp.
Competitive
Advantage Product Differentiation Low Cost
Process Product and modular
process; long
production runs in
specialized facilities;
build capacity ahead of
demand
Process focused;
general processes; “job
shop” approach, short-
run production; focus
on high utilization
Location Still located in the city
where it was founded
Recently moved to low-
tax, low-labor-cost
environment
Table 2.2
2 – 53
Operations Strategies of Two
Drug Companies
Brand Name Drugs, Inc. Generic Drug Corp.
Competitive
Advantage Product Differentiation Low Cost
Scheduling Centralized production
planning
Many short-run
products complicate
scheduling
Layout Layout supports
automated product-
focused production
Layout supports
process-focused “job
shop” practices
Table 2.2
2 – 54
Operations Strategies of Two
Drug Companies
Brand Name Drugs, Inc. Generic Drug Corp.
Competitive
Advantage Product Differentiation Low Cost
Human
Resources
Hire the best;
nationwide searches
Very experienced top
executives; other
personnel paid below
industry average
Supply Chain Long-term supplier
relationships
Tends to purchase
competitively to find
bargains
Table 2.2

8/17/12
10
2 – 55
Operations Strategies of Two
Drug Companies
Brand Name Drugs, Inc. Generic Drug Corp.
Competitive
Advantage Product Differentiation Low Cost
Inventory High finished goods
inventory to ensure all
demands are met
Process focus drives up
work-in-process
inventory; finished
goods inventory tends
to be low
Maintenance Highly trained staff;
extensive parts
inventory
Highly trained staff to
meet changing demand
Table 2.2
2 – 56
Issues In Operations Strategy
•  Resources view
•  Value Chain analysis
•  Porter’s Five Forces model
•  Operating in a system with many external factors
•  Constant change
2 – 57
Strategy
Analysis
SWOT Analysis
Internal
Strengths
Internal
Weaknesses
External
Opportunities
External
Threats
Mission
2 – 58
Product Life Cycle
Best period to
increase market
share

R&D engineering is
critical
Practical to change
price or quality
image

Strengthen niche
Poor time to
change image,
price, or quality

Competitive costs
become critical
Defend market
position
Cost control
critical
Introduction Growth Maturity Decline
C
om
pa
ny
S
tr
at
eg
y/
Is
su
es

Figure 2.5
Internet search engines
Sales
Drive-through
restaurants
CD-ROMs
Analog
TVs
iPods
Boeing 787
LCD &
plasma TVs
Twitter
Avatars
Xbox 360
2 – 59
Product Life Cycle
Product design
and
development
critical
Frequent
product and
process design
changes
Short production
runs
High production
costs
Limited models
Attention to
quality
Introduction Growth Maturity Decline
O
M
S
tr
at
eg
y/
Is
su
es

Forecasting
critical
Product and
process
reliability
Competitive
product
improvements
and options
Increase capacity
Shift toward
product focus
Enhance
distribution
Standardization
Fewer product
changes, more
minor changes
Optimum
capacity
Increasing
stability of
process
Long production
runs
Product
improvement
and cost cutting
Little product
differentiation
Cost
minimization
Overcapacity
in the
industry
Prune line to
eliminate
items not
returning
good margin
Reduce
capacity
Figure 2.5
2 – 60
Strategy Development Process
Determine the Corporate Mission
State the reason for the firm’s existence and identify the
value it wishes to create.
Form a Strategy
Build a competitive advantage, such as low price, design, or
volume flexibility, quality, quick delivery, dependability, after-
sale service, broad product lines.
Analyze the Environment
Identify the strengths, weaknesses, opportunities, and threats.
Understand the environment, customers, industry, and competitors.
Figure 2.6

8/17/12
11
2 – 61
Strategy Development and
Implementation
•  Identify key success factors
•  Build and staff the organization
•  Integrate OM with other activities
The operations manager’s job is to
implement an OM strategy, provide
competitive advantage, and increase
productivity
2 – 62
Key Success Factors
Production/Operations
Figure 2.7
Marketing
Service
Distribution
Promotion
Channels of distribution
Product positioning
(image, functions)
Finance/Accounting
Leverage
Cost of capital
Working capital
Receivables
Payables
Financial control
Lines of credit
Decisions Sample Options Chapter
Product
Quality
Process
Location
Layout
Human resource
Supply chain
Inventory
Schedule
Maintenance
Customized, or standardized
Define customer expectations and how to achieve them
Facility size, technology, capacity
Near supplier or near customer
Work cells or assembly line
Specialized or enriched jobs
Single or multiple suppliers
When to reorder, how much to keep on hand
Stable or fluctuating production rate
Repair as required or preventive maintenance
5
6, S6
7, S7
8
9
10
11, S11
12, 14, 16
13, 15
17
Support a Core Competence and Implement Strategy by
Identifying and Executing the Key Success Factors in the Functional Areas
2 – 63
Courteous, but
Limited Passenger
Service
Standardized
Fleet of Boeing
737 Aircraft
Competitive Advantage:
Low Cost
Lean,
Productive
Employees
Short Haul, Point-to-
Point Routes, Often to
Secondary Airports
High
Aircraft
Utilization
Frequent,
Reliable
Schedules
Figure 2.8
Activity Mapping at
Southwest Airlines
2 – 64
Activity Mapping at
Southwest Airlines
Courteous, but
Limited Passenger
Service
Standardized
Fleet of Boeing
737 Aircraft
Competitive Advantage:
Low Cost
Lean,
Productive
Employees
Short Haul, Point-to-
Point Routes, Often to
Secondary Airports
High
Aircraft
Utilization
Frequent,
Reliable
Schedules
Figure 2.8
Automated ticketing machines
No seat assignments
No baggage transfers
No meals (peanuts)
2 – 65
Activity Mapping at
Southwest Airlines
Courteous, but
Limited Passenger
Service
Standardized
Fleet of Boeing
737 Aircraft
Competitive Advantage:
Low Cost
Lean,
Productive
Employees
Short Haul, Point-to-
Point Routes, Often to
Secondary Airports
High
Aircraft
Utilization
Frequent,
Reliable
Schedules
Figure 2.8
No meals (peanuts)
Lower gate costs at
secondary airports
High number of flights
reduces employee idle time
between flights
2 – 66
Activity Mapping at
Southwest Airlines
Courteous, but
Limited Passenger
Service
Standardized
Fleet of Boeing
737 Aircraft
Competitive Advantage:
Low Cost
Lean,
Productive
Employees
Short Haul, Point-to-
Point Routes, Often to
Secondary Airports
High
Aircraft
Utilization
Frequent,
Reliable
Schedules
Figure 2.8
High number of flights
reduces employee idle time
between flights
Saturate a city with flights,
lowering administrative
costs (advertising, HR, etc.)
per passenger for that city
Pilot training required on
only one type of aircraft
Reduced maintenance
inventory required because
of only one type of aircraft

8/17/12
12
2 – 67
Activity Mapping at
Southwest Airlines
Courteous, but
Limited Passenger
Service
Standardized
Fleet of Boeing
737 Aircraft
Competitive Advantage:
Low Cost
Lean,
Productive
Employees
Short Haul, Point-to-
Point Routes, Often to
Secondary Airports
High
Aircraft
Utilization
Frequent,
Reliable
Schedules
Figure 2.8
Pilot training required on
only one type of aircraft
Reduced maintenance
inventory required because
of only one type of aircraft
Excellent supplier relations
with Boeing has aided
financing
2 – 68
Activity Mapping at
Southwest Airlines
Courteous, but
Limited Passenger
Service
Standardized
Fleet of Boeing
737 Aircraft
Competitive Advantage:
Low Cost
Lean,
Productive
Employees
Short Haul, Point-to-
Point Routes, Often to
Secondary Airports
High
Aircraft
Utilization
Frequent,
Reliable
Schedules
Figure 2.8
Reduced maintenance
inventory required because
of only one type of aircraft
Flexible employees and
standard planes aid
scheduling
Maintenance personnel
trained only one type of
aircraft
20-minute gate turnarounds
Flexible union
contracts
2 – 69
Activity Mapping at
Southwest Airlines
Courteous, but
Limited Passenger
Service
Standardized
Fleet of Boeing
737 Aircraft
Competitive Advantage:
Low Cost
Lean,
Productive
Employees
Short Haul, Point-to-
Point Routes, Often to
Secondary Airports
High
Aircraft
Utilization
Frequent,
Reliable
Schedules
Figure 2.8
Automated ticketing
machines
Empowered employees
High employee
compensation
Hire for attitude, then train
High level of stock
ownership
High number of flights
reduces employee idle time
between flights
2 – 70
Four International Operations
Strategies
C
os
t R
ed
uc
tio
n
C
on
si
de
ra
tio
ns

High
Low
High Low
Local Responsiveness Considerations
(Quick Response and/or Differentiation)
u Standardized product
u Economies of scale
u Cross-cultural learning

Examples:
Texas Instruments
Caterpillar
Otis Elevator
Global Strategy Transnational Strategy
u Move material, people, ideas
across national boundaries
u Economies of scale
u Cross-cultural learning

Examples
Coca-Cola
Nestlé
International Strategy
u Import/export or
license existing
product

Examples
U.S. Steel
Harley Davidson
Multidomestic Strategy
u Use existing
domestic model globally
u Franchise, joint ventures,
subsidiaries

Examples
Heinz The Body Shop
McDonald’s Hard Rock Cafe
Figure 2.9
2 – 71
Some Multinational
Corporations
% Sales % Assets
Outside Outside
Home Home Home % Foreign
Company Country Country Country Workforce
Citicorp USA 34 46 NA
Colgate- USA 72 63 NA
Palmolive
Dow USA 60 50 NA
Chemical
Gillette USA 62 53 NA
Honda Japan 63 36 NA
IBM USA 57 47 51
2 – 72
Some Multinational
Corporations
% Sales % Assets
Outside Outside
Home Home Home % Foreign
Company Country Country Country Workforce
ICI Britain 78 50 NA
Nestle Switzerland 98 95 97
Philips Netherlands 94 85 82
Electronics
Siemens Germany 51 NA 38
Unilever Britain & 95 70 64
Netherlands

8/17/12
13
2 – 73
Four International Operations
Strategies
u International
Strategy
u Global Strategy
u Multidomestic
Strategy
u Transnational
Strategy
2 – 74
Four International Operations
Strategies
C
os
t R
ed
uc
tio
n
C
on
si
de
ra
tio
ns

High
Low
High Low
Local Responsiveness Considerations
(Quick Response and/or Differentiation)
Figure 2.9
u Import/export or
license existing
product

Examples
U.S. Steel
Harley Davidson
International
Strategy
2 – 75
Four International Operations
Strategies
C
os
t R
ed
uc
tio
n
C
on
si
de
ra
tio
ns

High
Low
High Low
Local Responsiveness Considerations
(Quick Response and/or Differentiation)
International Strategy
u Import/export or
license existing
product

Examples
U.S. Steel
Harley Davidson
Figure 2.9
2 – 76
Four International Operations
Strategies
C
os
t R
ed
uc
tio
n
C
on
si
de
ra
tio
ns

High
Low
High Low
Local Responsiveness Considerations
(Quick Response and/or Differentiation)
International Strategy
u Import/export or
license existing
product

Examples
U.S. Steel
Harley Davidson
Figure 2.9
u Standardized
product
u Economies of scale
u Cross-cultural
learning

Examples
Texas Instruments
Caterpillar
Otis Elevator
Global
Strategy
2 – 77
Four International Operations
Strategies
C
os
t R
ed
uc
tio
n
C
on
si
de
ra
tio
ns

High
Low
High Low
Local Responsiveness Considerations
(Quick Response and/or Differentiation)
u Standardized product
u Economies of scale
u Cross-cultural learning

Examples:
Texas Instruments
Caterpillar
Otis Elevator
Global Strategy
International Strategy
u Import/export or
license existing
product

Examples
U.S. Steel
Harley Davidson
Figure 2.9
2 – 78
Four International Operations
Strategies
C
os
t R
ed
uc
tio
n
C
on
si
de
ra
tio
ns

High
Low
High Low
Local Responsiveness Considerations
(Quick Response and/or Differentiation)
u Standardized product
u Economies of scale
u Cross-cultural learning

Examples:
Texas Instruments
Caterpillar
Otis Elevator
Global Strategy
International Strategy
u Import/export or
license existing
product

Examples
U.S. Steel
Harley Davidson
Figure 2.9
u Use existing
domestic model
globally
u Franchise, joint
ventures,
subsidiaries

Examples
Heinz
McDonald’s
The Body Shop
Hard Rock Cafe
Multidomestic
Strategy

8/17/12
14
2 – 79
Four International Operations
Strategies
C
os
t R
ed
uc
tio
n
C
on
si
de
ra
tio
ns

High
Low
High Low
Local Responsiveness Considerations
(Quick Response and/or Differentiation)
u Standardized product
u Economies of scale
u Cross-cultural learning

Examples:
Texas Instruments
Caterpillar
Otis Elevator
Global Strategy
International Strategy
u Import/export or
license existing
product

Examples
U.S. Steel
Harley Davidson
Multidomestic Strategy
u Use existing
domestic model globally
u Franchise, joint ventures,
subsidiaries

Examples
Heinz The Body Shop
McDonald’s Hard Rock Cafe
Figure 2.9
2 – 80
Four International Operations
Strategies
C
os
t R
ed
uc
tio
n
C
on
si
de
ra
tio
ns

High
Low
High Low
Local Responsiveness Considerations
(Quick Response and/or Differentiation)
u Standardized product
u Economies of scale
u Cross-cultural learning

Examples:
Texas Instruments
Caterpillar
Otis Elevator
Global Strategy
International Strategy
u Import/export or
license existing
product

Examples
U.S. Steel
Harley Davidson
Multidomestic Strategy
u Use existing
domestic model globally
u Franchise, joint ventures,
subsidiaries

Examples
Heinz The Body Shop
McDonald’s Hard Rock Cafe
Figure 2.9
u Move material,
people, ideas
across national
boundaries
u Economies of scale
u Cross-cultural
learning

Examples
Coca-Cola
Nestlé
Transnational
Strategy
2 – 81
Four International Operations
Strategies
C
os
t R
ed
uc
tio
n
C
on
si
de
ra
tio
ns

High
Low
High Low
Local Responsiveness Considerations
(Quick Response and/or Differentiation)
u Standardized product
u Economies of scale
u Cross-cultural learning

Examples:
Texas Instruments
Caterpillar
Otis Elevator
Global Strategy Transnational Strategy
u Move material, people, ideas
across national boundaries
u Economies of scale
u Cross-cultural learning

Examples
Coca-Cola
Nestlé
International Strategy
u Import/export or
license existing
product

Examples
U.S. Steel
Harley Davidson
Multidomestic Strategy
u Use existing
domestic model globally
u Franchise, joint ventures,
subsidiaries

Examples
Heinz The Body Shop
McDonald’s Hard Rock Cafe
Figure 2.9
2 – 82
Ranking Corruption
Rank Country 2009 CPI Score (out of 10)
1 New Zealand 9.4
2 Demark 9.3
3 Singapore, Sweden 9.2
5 Switzerland 9.0
8 Australia, Canada, Iceland 8.7
12 Hong Kong 8.2
14 Germany 8.0
17 Japan, UK 7.7
19 USA 7.5
37 Taiwan 5.6
39 South Korea 5.5
56 Malaysia 4.5
79 China 3.6
89 Mexico 3.3
146 Russia 2.2
Least
Corrupt
Most
Corrupt

Chapter 5_Design of Goods and Services

8/19/12
1
5 – 1
5
MG 6303 Prof. Vivek Veeraiah
Design of Goods
and Services
PowerPoint presentation to accompany
Heizer and Render
Operations Management, 10e
Principles of Operations Management, 8e

PowerPoint slides by Jeff Heyl
5 – 2
Product Decision
The objective of the product decision is to develop and implement
a product strategy that meets the demands of the marketplace
with a competitive advantage
MG 6303 Prof. Vivek Veeraiah
5 – 3
Product Decision
u  The good or service the organization provides society
u  Top organizations typically focus on core products
u  Customers buy satisfaction, not just a physical good or particular
service
u  Fundamental to an organization’s strategy with implications
throughout the operations function
MG 6303 Prof. Vivek Veeraiah
5 – 4
Product Strategy Options
MG 6303 Prof. Vivek Veeraiah
u  Differentiation
u  Shouldice Hospital
u  Low cost
u  Taco Bell
u  Rapid response
u  Toyota
5 – 5
Product Life Cycles
MG 6303 Prof. Vivek Veeraiah
u  May be any length from a few
hours to decades
u  The operations function must
be able to introduce new
products successfully
5 – 6
Product Life Cycles
MG 6303 Prof. Vivek Veeraiah
Negative
cash flow
Introduction Growth Maturity Decline
S
al
es
, c
os
t,
an
d
ca
sh
fl
ow

Cost of development and production
Cash
flow
Net revenue (profit)
Sales revenue
Loss
Figure 5.1

8/19/12
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5 – 7
Product Life Cycle
MG 6303 Prof. Vivek Veeraiah
Introductory Phase
u  Fine tuning may warrant
unusual expenses for
1.  Research
2.  Product development
3.  Process modification and
enhancement
4.  Supplier development
5 – 8
Product Life Cycle
MG 6303 Prof. Vivek Veeraiah
Growth Phase
u  Product design begins to
stabilize
u  Effective forecasting of
capacity becomes necessary
u  Adding or enhancing capacity
may be necessary
5 – 9
Product Life Cycle
MG 6303 Prof. Vivek Veeraiah
Maturity Phase
u  Competitors now established
u  High volume, innovative
production may be needed
u  Improved cost control,
reduction in options, paring
down of product line
5 – 10
Product Life Cycle
MG 6303 Prof. Vivek Veeraiah
Decline Phase
u  Unless product makes a
special contribution to the
organization, must plan to
terminate offering
5 – 11
Product-by-Value Analysis
u  Lists products in descending order of their individual
dollar contribution to the firm
u  Lists the total annual dollar contribution of the product
u  Helps management evaluate alternative strategies
MG 6303 Prof. Vivek Veeraiah
5 – 12
Product-by-Value Analysis
MG 6303 Prof. Vivek Veeraiah
Individual
Contribution ($)
Total Annual
Contribution ($)
Love Seat $102 $36,720
Arm Chair $87 $51,765
Foot Stool $12 $6,240
Recliner $136 $51,000
Sam’s Furniture Factory

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3
5 – 13
New Product Opportunities
MG 6303 Prof. Vivek Veeraiah
1.  Understanding the
customer
2.  Economic change
3.  Sociological and
demographic change
4.  Technological change
5.  Political/legal change
6.  Market practice, professional
standards, suppliers, distributors
Brains
tormin
g
is a us
eful too
l
5 – 14
Importance of New
Products
MG 6303 Prof. Vivek Veeraiah
Industry
leader
Top
third
Middle
third
Bottom
third
Figure 5.2a
Percentage of Sales from New Products
50%
40%
30%
20%
10%
Position of Firm in Its Industry
5 – 15
Disney Attendance
MG 6303 Prof. Vivek Veeraiah
Figure 5.2b
50
40
30
20
10
0
M
ill
io
ns
o
f v
is
ito
rs

‘93 ‘95 ‘97 ‘99 ‘01 ‘03 ‘05 ‘07
Magic Kingdom
Disney-Hollywood
Epcot
Animal Kingdom
5 – 16
Cisco Product Revenue
MG 6303 Prof. Vivek Veeraiah
Figure 5.2c
35
30
25
20
15
10
5
0
B
ill
io
ns
o
f d
ol
la
rs

‘02 ‘03 ‘04 ‘05 ‘06 ’07 ‘08
Other
Routers
Switches
5 – 17
Product Development System
MG 6303 Prof. Vivek Veeraiah
Scope of
product
development
team
Scope for
design and
engineering
teams
Evaluation
Introduction
Test Market
Functional Specifications
Design Review
Product Specifications
Customer Requirements
Ability
Ideas
Figure 5.3
5 – 18
Quality Function Deployment
1.  Identify customer wants
2.  Identify how the good/service will satisfy customer
wants
3.  Relate customer wants to product hows
4.  Identify relationships between the firm’s hows
5.  Develop importance ratings
6.  Evaluate competing products
7.  Compare performance to desirable technical attributes
MG 6303 Prof. Vivek Veeraiah

8/19/12
4
5 – 19
Organizing for Product
Development
u  Historically – distinct departments
u  Duties and responsibilities are defined
u  Difficult to foster forward thinking
u  A Champion
u  Product manager drives the product through the product
development system and related organizations
MG 6303 Prof. Vivek Veeraiah
5 – 20
Organizing for Product
Development
u  Team approach
u  Cross functional – representatives from all disciplines or
functions
u  Product development teams, design for manufacturability
teams, value engineering teams
u  Japanese “whole organization” approach
u  No organizational divisions
MG 6303 Prof. Vivek Veeraiah
5 – 21
Manufacturability and
Value Engineering
u  Benefits:
1.  Reduced complexity of products
2.  Reduction of environmental impact
3.  Additional standardization of products
4.  Improved functional aspects of product
5.  Improved job design and job safety
6.  Improved maintainability (serviceability) of the
product
7.  Robust design
MG 6303 Prof. Vivek Veeraiah
5 – 22
Cost Reduction of a Bracket
via Value Engineering
MG 6303 Prof. Vivek Veeraiah
Figure 5.5
5 – 23
Issues for Product
Development
u  Robust design
u  Modular design
u  Computer-aided design (CAD)
u  Computer-aided manufacturing (CAM)
u  Virtual reality technology
u  Value analysis
u  Environmentally friendly design
MG 6303 Prof. Vivek Veeraiah
5 – 24
Robust Design
MG 6303 Prof. Vivek Veeraiah
u  Product is designed so that small
variations in production or
assembly do not adversely affect
the product
u  Typically results in lower cost and
higher quality

8/19/12
5
5 – 25
Modular Design
u  Products designed in easily segmented components
u  Adds flexibility to both production and marketing
u  Improved ability to satisfy customer requirements
MG 6303 Prof. Vivek Veeraiah
5 – 26
Computer Aided Design (CAD)
u  Using computers to
design products and
prepare engineering
documentation
u  Shorter development
cycles, improved
accuracy, lower cost
u  Information and
designs can be
deployed worldwide
MG 6303 Prof. Vivek Veeraiah
5 – 27
Extensions of CAD
u  Design for Manufacturing and Assembly
(DFMA)
u  Solve manufacturing problems during the
design stage
u  3-D Object Modeling
u  Small prototype
development
u  CAD through the
internet
u  International data
exchange through STEP
MG 6303 Prof. Vivek Veeraiah
5 – 28
Computer-Aided
Manufacturing (CAM)
MG 6303 Prof. Vivek Veeraiah
u  Utilizing specialized computers
and program to control
manufacturing equipment
u  Often driven by the CAD system
(CAD/CAM)
5 – 29
Benefits of CAD/CAM
1.  Product quality
2.  Shorter design time
3.  Production cost reductions
4.  Database availability
5.  New range of capabilities
MG 6303 Prof. Vivek Veeraiah
5 – 30
Virtual Reality Technology
u  Computer technology used to develop an interactive, 3-D
model of a product from the basic CAD data
u  Allows people to ‘see’ the finished design before a physical
model is built
u  Very effective in large-scale designs such as plant layout
MG 6303 Prof. Vivek Veeraiah

8/19/12
6
5 – 31
Value Analysis
u  Focuses on design improvement during production
u  Seeks improvements leading either to a better product or a
product which can be produced more economically with less
environmental impact
MG 6303 Prof. Vivek Veeraiah
5 – 32
Ethics, Environmentally
Friendly Designs, and
Sustainability
MG 6303 Prof. Vivek Veeraiah
u  It is possible to enhance productivity
and deliver goods and services in an
environmentally and ethically
responsible manner
u  In OM, sustainability means ecological
stability
u  Conservation and renewal of resources
through the entire product life cycle
5 – 33
Ethics, Environmentally
Friendly Designs, and
Sustainability
MG 6303 Prof. Vivek Veeraiah
u  Design
u  Polyester film and shoes
u  Production
u  Prevention in production and
packaging
u  Destruction
u  Recycling in automobiles
5 – 34
Ethics, Environmentally
Friendly Designs, and
Sustainability
MG 6303 Prof. Vivek Veeraiah
5 – 35
The Ethical Approach
MG 6303 Prof. Vivek Veeraiah
u  Goals
1.  Developing safe end environmentally
sound practices
2.  Minimizing waste of resources
3.  Reducing environmental liabilities
4.  Increasing cost-effectiveness of
complying with environmental
regulations
5.  Begin recognized as a good corporate
citizen
5 – 36
Guidelines for Environmentally
Friendly Designs
1.  Make products recyclable
2.  Use recycled materials
3.  Use less harmful ingredients
4.  Use lighter components
5.  Use less energy
6.  Use less material
MG 6303 Prof. Vivek Veeraiah

8/19/12
7
5 – 37
Laws and Industry Standards
MG 6303 Prof. Vivek Veeraiah
For Design …
u  Food and Drug Administration
u  Consumer Products Safety Commission
u  National Highway Safety Administration
u  Children’s Product Safety Act
5 – 38
Laws and Industry Standards
MG 6303 Prof. Vivek Veeraiah
For Manufacture/Assembly …
u  Occupational Safety and Health
Administration
u  Environmental Protection Agency
u  Professional ergonomic standards
u  State and local laws dealing with
employment standards, discrimination, etc.
5 – 39
Laws and Industry Standards
MG 6303 Prof. Vivek Veeraiah
For Disassembly/Disposal …
u  Vehicle Recycling Partnership
u  Increasingly rigid laws worldwide
5 – 40
Time-Based Competition
MG 6303 Prof. Vivek Veeraiah
u  Product life cycles are becoming
shorter and the rate of
technological change is
increasing
u  Developing new products faster
can result in a competitive
advantage
5 – 41
Product Development
Continuum
MG 6303 Prof. Vivek Veeraiah
Internal Cost of product development Shared
Lengthy Speed of product development Rapid and/
or Existing
High Risk of product development Shared
EXTERNAL DEVELOPMENT STRATEGIES
Alliances
Joint ventures
Purchase technology or expertise
by acquiring the developer
INTERNAL DEVELOPMENT STRATEGIES
Migrations of existing products
Enhancements to existing products
New internally developed products
Figure 5.6
5 – 42
Acquiring Technology
MG 6303 Prof. Vivek Veeraiah
u  By Purchasing a Firm
u  Speeds development
u  Issues concern the fit between the acquired
organization and product and the host
u  Through Joint Ventures
u  Both organizations learn
u  Risks are shared
u  Through Alliances
u  Cooperative agreements between
independent organizations

8/19/12
8
5 – 43
Service Design
MG 6303 Prof. Vivek Veeraiah
u  Service typically includes direct
interaction with the customer
u  Increased opportunity for customization
u  Reduced productivity
u  Cost and quality are still determined at
the design stage
u  Delay customization
u  Modularization
u  Reduce customer interaction, often
through automation
5 – 44
Service Design
MG 6303 Prof. Vivek Veeraiah
Figure 5.12
5 – 45
Service Design
MG 6303 Prof. Vivek Veeraiah
Figure 5.12
5 – 46
Moments of Truth
MG 6303 Prof. Vivek Veeraiah
u  Concept created by Jan Carlzon of
Scandinavian Airways
u  Critical moments between the
customer and the organization that
determine customer satisfaction
u  There may be many of these moments
u  These are opportunities to gain or
lose business
5 – 47
Transition to Production
u  Know when to move to production
u  Product development can be viewed as
evolutionary and never complete
u  Product must move from design to production
in a timely manner
u  Most products have a trial production period
to insure producibility
u  Develop tooling, quality control, training
u  Ensures successful production
MG 6303 Prof. Vivek Veeraiah
5 – 48
Transition to Production
u  Responsibility must also transition as the
product moves through its life cycle
u  Line management takes over from design
u  Three common approaches to managing
transition
u  Project managers
u  Product development teams
u  Integrate product development and
manufacturing organizations
MG 6303 Prof. Vivek Veeraiah