Week 1 discussion
DQ1 Present an example of a business situation that you
believe would lend itself to the use of a quantitative business model. Clearly
explain how the model could be used in this situation.
DQ2 Multiple models are often used in supporting business
decision making. Outline a situation in your organization or industry that
required the need for multiple models. What factors were unique to this
situation? Support your response with rationale from the readings or external
research.
Week 2 discussion
DQ1 Apply a business decision model to something you do
every day, such as select an outfit, order lunch, or determine your exercise
routine. Be creative in your approach. How did you select the model? Include
rationale with support from the readings.
DQ2 Using the decision tree resources available in the Topic
Materials, create a decision tree for the scenario you outlined in Topic 2 DQ
1. Attach the decision tree to your response and include insights in crafting
the decision tree. How would you apply your experience to larger-scale
decisions at an organizational level?
Week 3 discussion
DQ1 Identify two business situations or problems within your
current organization or industry. Articulate how one of these lends itself to a
simple linear regression and how one does not. Why is simple linear regression
appropriate to address one situation or problem but not the other? Support your
ideas with evidence from the readings.
DQ2 You are the vice president of sales for TerraFirma, a
company that manufactures outdoor sporting gear. You receive a report on your
desk one morning that claims little or no relationship between the University
of Michigan Consumer Sentiment Index (CSI) and outdoor sporting gear sales. The
claim is based on a very low R2 of the simple regression model, using these two
variables (CSI and sales). Discuss how you would (or should) react to this
report and why. What clarifying questions might you ask?
Week 4 discussion
DQ1 Provide an example based on your professional experience
of a situation in which using a multiple regression model or nonlinear
regression model may have helped your organization make a better decision.
DQ2 What types of business situations or problems might best
lend themselves to multiple linear regression? What types may not? When do you
anticipate using a multiple linear regression model in your postgraduate,
professional experience? Explain.
Week 5 discussion
DQ1 Discuss the strategic importance of forecasting at your
organization (or one with which you are familiar). What strategic decisions
does it need to make in terms of forecasting? Provide two recent examples. In
your opinion, was this the best way? How could the process be improved?
DQ2 Refer to the Topic Material, “Chapter 1 –
Fundamental Issues in Business Forecasting.” This resource includes a
discussion of unrealistic expectations and forecast accuracy. How have you seen
this demonstrated in your organization or industry? Describe the forecasting
scenario and the “prediction” that did not come true. What
conversations did management have surrounding this issue? How would you
mitigate expectations for a situation like this in the future?
Week 6 discussion
DQ1 Identify two key strategic decisions made by your
current team, department, or organization. How could those decisions have been
enhanced by optimization models? Support your rationale with evidence from
readings or external research.
DQ2 Find a current example of a linear optimization model
used in your industry. Describe the industry’s needs, including any unique
factors, how the linear optimization model was used, and the problem or
challenge it addressed. Would you suggest a different model be used? Why or why
not? Support your response with rationale from the assigned readings.
Week 7 discussion
DQ1 Explain the importance of correctly stating the
objective function and constraints in linear optimization problems. Using
examples from your professional experience, describe the problems that could
result if the objective function and constraints are not stated properly. Why
did these problems arise? Support your anecdotal evidence with support and
rationale from the readings.
DQ2 Describe a workforce scheduling, a blending, and a
logistics problem facing your current organization or industry. What is being
optimized in each of your examples and why? How do linear optimization
techniques differ from decision tree analysis? Which are more applicable to the
examples you identified? Support your response with rationale from the
readings.
Week 8 discussion
DQ1 Describe a current problem facing your department,
organization, or industry that would indicate the need for simulation. What key
factors of this business situation indicate the need for simulation (versus the
other modeling techniques covered in the course)? Support your response with
rationale from the readings.
DQ2 Consider some of the examples you have brought up in
earlier topics. Describe the key differences between simulation models and the
models covered earlier in the course. Outline how the approach to solving this
problem would differ in terms of applying and computing/solving the models.
