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Quan 2020 Case Study Guidelines

Case studies are difficult for many students because they involve some uncertainty; that is, there is usually not a single correct answer or approach. This, however, is a very common situation in the “real world,” where your boss or colleagues may ask you to investigate a problem and come up with solutions, or you may, on your own initiative, choose to tackle an analysis. In many cases the problem itself is not even clearly or completely defined, and that definition becomes your first order of business.

Cases are thus included in this course in order to give you practice with real-world type analysis problems. They involve a bit of role-playing; in most cases they involve a fictional customer that you are tasked with providing analysis and answers to specific questions. Keep this in mind – the quality of your work should be consistent with what you would provide to a real customer. The following list is based on problems that have been revealed from past student work on similar cases. You can help ensure better grades on your case studies if you review these notes before you begin work on each case, and as you near the end of your work.

1. Put yourself in your customer’s place – create an analysis that you, as a customer, would be pleased to receive.

2. Thoroughly read all of the case material, and note very carefully any specific questions! Believe it or not, a common mistake is to not answer the questions that are asked. As your customer, I will be very unhappy with your work if you do not answer the questions I am paying you to answer.

3. Before you even start your analysis, consider the organization of the report you will send to the customer. This can be very simple. Typically, it will be something like this:

Supporting Data (typically an Appendix)

The introduction should outline the problem and briefly describe your approach. The analysis section should detail the quantitative methods you used to come up with your answers. In this section you should include key formulas, tables, and graphs that support your answers. In the conclusions section you should summarize the results of your analysis, and include answers to the specific questions asked. Finally, the appendix should include more details of the mathematics you employed. For example it might contain supporting tables or graphs, detailed calculations, or results of alternative methods that support your conclusions.

4. Every table and chart in your report (including the Appendix) should be numbered and labeled, so it can easily be referenced in your paper, or by the reader. A common mistake is to include a bunch of tables and charts in the appendix, but not label them, thus making them almost useless to the customer.

5. Don’t assume that your customer is an expert on the techniques you are using. For example, if you talk about forecasting methods, briefly explain them, and any special related terms you use. Your goal should be to create an analysis that is easy for the customer to follow, understand, and agree with!

6. Your analysis section should include key results (NUMBERS!) that support your conclusions. After all, this is a course about numerical methods for decision making – the outputs of those methods are numbers (for example, forecasted sales, simulated inventory, calculated production). Your results will not be very credible if you make statements like, “We believe you should increase your production by 10%.” They will be much more credible if you can say instead, “Based on the results of 1000 simulation runs, increasing production by 10% will result in an average revenue increase of $50,000 per quarter.”

7. You will work on cases in teams – typically of three or four students. Organize your project so that the responsibilities of each team member are clear. Make good use of the time provided in class to work on the cases, and use e-mail, etc. to make efficient use of your time outside of class.

8. EDIT, EDIT, EDIT. I often receive papers that include spelling errors, nonsense “sentences,” duplicated paragraphs, conclusions that are inconsistent with the analysis, questions not answered, etc. Remember – you are submitting your analysis to an imaginary customer (who happens to be me). Your customer will be inclined to dismiss your analysis (and never pay you again) if it is incomplete, unprofessional, hard to follow, or filled with obvious errors. Most of these issues can be caught and corrected if you thoroughly edit your paper before turning it in. It’s a good idea to select one member of your group to play the role of the customer, and thoroughly read and edit your report.

Required Text: Managerial Decision Modeling with Spreadsheets, 3rd edition;
Balakrishnan, Render, and Stair; Prentice Hall (2013);
ISBN # 978-0-13-611583-0

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Category: Sample Questions