Part 1
Prepare:
- Complete this self-inventoryLinks to an external site. to explore your personal decision-making style.
- Revisit the case study from the Learn section this week: Organizational Decision Making: The Case of CoolToys, Inc.
Discuss:
Answer the following questions about the decision-making self-inventory:
- What category or categories did you align with based on your results?
- What are you most confident in and what would you like to work on regarding your decision-making abilities?
In the case study, CoolToys’ leadership is considering forgoing profitability in the short term for future growth opportunities. In the business world you will be tasked with making strategic decisions.
- What stands out to you as the most important data set in the case study to inform CoolToys’ leadership about their product mix decision? Why?
Part 2
Provide a substantive response to one of your classmates (Pamela Are).
Hello Everyone!
The category that I align most with after doing the self-inventory was the Systematic category. I can say that this is true because I do like to look at the whole picture when making big decisions. I was not always like this though, when I was younger, I was much more of an impulsive decision maker, and it led me to making bad decisions and having outcomes that were not in my best interest. Because of this I had to teach myself to become more of a thinker and to be less impulsive because sometimes we can trade what we want now or feels good now for what we want or where we want to be in the long run. I have had to teach myself to be a strategic thinker and decision maker and think that it suits me well. Of course, different situations call for different decision-making processes but when it comes to big decisions, I am definitely systematic in my approach. I honestly feel like I use all 5 of the decision-making styles at different times because different situations call for different ways of decision making without a doubt.
I am most confident in a systematic approach when it comes to making big decisions without a doubt. I guess it makes sense to me to make sure I do my research to make the soundest decision before I pull the trigger on a major decision. This way I can feel more confident in the outcome. As far as what I would like to work on is being less dependent. The reason that I say that is at times if something is not my strong suit and I don’t know much about something I tend to ask someone that knows more than me to either get educated on it or to see if their input is the same as mine or if my thoughts on it were wrong altogether before I make a decision. So, I could work on being less dependent and trusting in myself more so that I don’t come off as being unsure or incompetent.
What stands out to me as the most important data set to inform CoolToys’ leadership if they are considering foregoing short term profit for future growth opportunities is the Product Selection with Costing and Machine Data if I had to choose one. The reason I choose this one is because to me it gives me the most information including the cost to make each product, what each product sells for, and the amount of time it takes to make each unit for each item. With this data I can determine the profit margin for each unit of each product and the amount of time it takes to produce each unit of each item. If an item is not making much profit compared to cost and time the item better be a huge seller or it is not worth the cost of production, and we need to look into ways to lower cost, increase price point, ways to speed up production, or to discontinue the item. Or if an item has a large profit margin and fast production but is not a big seller, we can use the machine to produce the certain amount of that item daily to meet demand and then spend the rest of the day with the machine meeting the demand of other items. But with the Product Selection with Costing Machine Data along with the Marketing and Sales Data I would be able to make decisions on what the demand for each product item are and what items are growing in popularity and what items are losing interest. Using the data from both sources they can work together to make informed decisions on what items are profitable to produce and what the demand for each item is for each item so we can best utilize the machines to meet demand and find ways to improve production and lower production costs at the same time. The data all works hand in hand to find out the best product mix today and going forward.