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Looking for someone who is good at FORECASTING: Using the University of Phoenix Material: Summer Historical Inventory Generate a frequency distribution of the data using Excel. Generate a normal distribution of the data using with a brief description for both. I have the frequency distribution done except the speaker notes just need the normal with the speaker notes.
>Frequency distribution
Month
Forecast
00
0
0
92
Frequency Distribution
1 2 3 4 5 6 7 8 9 10 11 12 39600 37080 30000 59210 64375 57750 47520 56638 29855 39638 27323 19350
Months
Forecast
Sheet1
Executive Management: Team C
Cherelle Smith, Heather Krawinkel, Ritu Jain, Scott Hrovat and Vickie Dufault
QRB/501
July 8, 2013
Terrance Feravich
University of Phoenix Summer Historical Data – Heather
Customer Needs
Increase Profits
Forecasting
Keep Track
The inventory system Team C has chosen to use is the University of Phoenix Summer Historical Data. With any inventory system a company needs to focus on what it will be selling or purchasing and determine what kind of system will work best. The purpose of an inventory system is to help the company meet their customers needs as well as helping increase profits. The purposes of each inventory system may depend on the company and what the daily demands are. Once a company can determine what the customer needs the management staff can then begin to forecast what the customer will spend in the future months or years.
After the forecasting has been set in place the company can then begin to keep track of the inventory that is coming in and leaving the building. There are different ways of keeping track; by hand or by using technology. Keeping track by hand can be used for small businesses, but it can increase the chances of human error. Using technology such as barcode systems let you quickly scan the product item numbers, while small handheld computers let workers quickly input inventory information” (Teeboom, 20130, p. 1, Para. 5). This then allows for a person to print out specific times during the day or even certain weeks or months to see where the numbers have increased or decreased.
2
Raw Data Used – Heather
The raw data used was from the University of Phoenix Summer Historical Data. It shows the increase or decrease of each month through four years and also includes the forecasted amounts. This data is a good source when it comes to figuring out the busy and slow months for the company.
According to the forecasted amounts each month was above the $40,000 mark and the highest month was May coming in at $64,375. The next highest months were April at $59,210, June at 57,750, and August at $56,638. The last month to come in at a close number was July at $47,520.
The slower months according to the forecasted amounts were January, February, March, September, October, November and December. The amounts were fairly close; January was at $39,600, February was at $37,080 and October was at $39,638. The next lower months were March and September coming in at $30,000 and $29,855. Finally we have November coming in at $27,323 and December with $19,350.
3
Frequency Distribution – Vickie
4
Normal Distribution – Vickie
5
Mode, Median and Mean
Month Year 1 Year 2 Year 3 Year 4 Forecast
1 18,000 45,100 59,800 35,500 39,600
2 19,800 46,530 30,740 51,250 37,080
3 15,700 22,100 47,800 34,400 30,000
4 53,600 41,350 73,890 68,000 59,210
5 83,200 46,000 60,200 68,100 64,375
6 72,900 41,800 55,200 61,100 57,750
7 55,200 39,800 32,180 62,300 47,520
8 57,350 64,100 38,600 66,500 56,638
9 15,400 47,600 25,020 31,400 29,855
10 27,700 43,050 51,300 36,500 39,638
11 21,400 39,300 31,790 16,800 27,323
12 17,100 10,300 31,100 18,900 19,350
Avg. 38,112 43,902 44,802 45,896 42,362
Mean 38,112 43,902 44,802 45,896 42,362
Median 24,550 42,425 43,200 43,875 39,619
Compared to the raw data that was used is the mean, median for each year. There isn’t an mode for each year because sales was at different each month, sales did not occur the same each month.
6
Range and Standard Deviation
Month Year 1 Year 2 Year 3 Year 4 Forecast
1 18,000 45,100 59,800 35,500 39,600
2 19,800 46,530 30,740 51,250 37,080
3 15,700 22,100 47,800 34,400 30,000
4 53,600 41,350 73,890 68,000 59,210
5 83,200 46,000 60,200 68,100 64,375
6 72,900 41,800 55,200 61,100 57,750
7 55,200 39,800 32,180 62,300 47,520
8 57,350 64,100 38,600 66,500 56,638
9 15,400 47,600 25,020 31,400 29,855
10 27,700 43,050 51,300 36,500 39,638
11 21,400 39,300 31,790 16,800 27,323
12 17,100 10,300 31,100 18,900 19,350
Avg. 38,112 43,902 44,802 45,896 42,362
Standard Dev. 24,739 13,336 15,408 19,186 14,596
The standard deviation measure how spread out numbers are. Under each year is the average sales and under that is the standard deviation of each year.
7
Improving Using Frequency Distribution
Frequency Distribution is the representation of either in a graphical or tabular format, this displays the number of observations within a given interval. The intervals must be mutually exclusive and exhaustive. Frequency distributions are usually used within a statistical context. The frequency distribution will make the data easier to understand and more visual for the presentation. Above represents the Frequency of the averages of the summer data for each month.
8
U of P Forecast Summer Data
Forecast Of the Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 39600.0 37080.0 30000.0 59210.0 64375.0 57750.0 47520.0 56638.0 29855.0 39638.0 27323.0 19350.0
Improving Using
Normal Distribution
A function that represents the distribution of many random variables as a symmetrical bell-shaped graph. This gives the inventory a visual of the variables that have a normal deviation in data to show how dramatic the data is from one year to the next.
9
Normal Distribution
Standard Deviation 0.0 24739.0 13336.0 15408.0 19186.0 14596.0 0.0
Central Tendency – Ritu
Dispersion – Ritu
References
Teeboom, L. (2013). How to Design an Inventory Control System. Retrieved July 6, 2013 from http://smallbusiness.chron.com/design-inventory-control-system-40910.html
University of Phoenix Summer Inventory Data. (2011). Retrieved from https://portal.phoenix.edu/classroom/coursematerials/qrb_501/20130528/OSIRIS:44702195
12
Month Year 1 Year 2 Year 3 Year 4 Forecast
1 18,000 45,100 59,800 35,500
39,600
2 19,800 46,530 30,740 51,250
37,080
3 15,700 22,100 47,800 34,400
30,000
4 53,600 41,350 73,890 68,000
59,210
5 83,200 46,000 60,200 68,100
64,375
6 72,900 41,800 55,200 61,100
57,750
7 55,200 39,800 32,180 62,300
47,520
8 57,350 64,100 38,600 66,500
56,638
9 15,400 47,600 25,020 31,400
29,855
10 27,700 43,050 51,300 36,500
39,638
11 21,400 39,300 31,790 16,800
27,323
12 17,100 10,300 31,100 18,900
19,350
Avg. 38,112 43,902 44,802 45,896 42,362
Month
Year 1
Year 2
Year 3
Year 4
Forecast
1
18,000
45,100
59,800
35,500
39,600
2
19,800
46,530
30,740
51,250
37,080
3
15,700
22,100
47,800
34,400
30,000
4
53,600
41,350
73,890
68,000
59,210
5
83,200
46,000
60,200
68,100
64,375
6
72,900
41,800
55,200
61,100
57,750
7
55,200
39,800
32,180
62,300
47,520
8
57,350
64,100
38,600
66,500
56,638
9
15,400
47,600
25,020
31,400
29,855
10
27,700
43,050
51,300
36,500
39,638
11
21,400
39,300
31,790
16,800
27,323
12
17,100
10,300
31,100
18,900
19,350
Avg.
38,112
43,902
44,802
45,896
42,362
