Predictive Models

For the Essay Questions (drawn only from chapters 9, 12 and 14), answer each of them as completely as you think appropriate. Answers should be coherent. Bulleted items are acceptable if you are providing a list. Each answer will be worth up to 10 points, with 30 points total.

For each essay question, go to where it says “Start here” to begin your essay response.

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Note: I consider these to be open note, open book. Please do not merely lift material directly from the text or slides. Bibliography and attribution are not needed.

EQ1: (From Ch. 9) The diagram below is the completed decision tree used to manually calculate the expected monetary value (EMV) of three possible decisions. Decisions 1 and 2 both have multiple possible outcomes with probabilities of those outcomes occurring and anticipated positive or negative monetary outcomes. Decision 3 represents a known positive outcome with no risk associated with it.

Describe the process whereby the decision tree would have been built, left-to-right, and then the “fold back” process that would have been used to quantify the possible outcomes that led to the choosing of Decision 1. Be sure to describe the mechanics (the parts and their uses) of the decision tree.

EQ2: (From Ch. 12) Describe the two conflicting pressures in play at the heart of essentially every inventory management decision scenario. Explain the concepts and implications of lead time and carrying costs and how they factor into the decision. What implications might considerations of whether your demand is internal or external have in your inventory modeling approach?

Start here

EQ3: (From Ch. 14) Describe the process of taking a set of observations over a given time period, graphing those observations, and then creating trend lines in an attempt to create predictive models (graphical and mathematical). How would you go about determining whether one of your trend lines

was a “better fit” than the other(s)? How would you determine a level of confidence in your predictive models?

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