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A buyer for a large department store chain must place orders with an athletic shoe manufacturer six months prior to the time the shoes will be sold in the department stores. In particular, the buyer must decide on November 1 how many pairs of the manufacturer’s newest model of tennis shoes to order for sale during the coming summer season. Assume that each pair of this new brand of tennis shoes costs the department store chain $45 per pair. Furthermore, assume that each pair of these shoes can then be sold to the chain’s customers for $70 per pair. Any pairs of these shoes remaining unsold at the end of the summer season will be sold in a closeout sale next fall for $35 each. The probability distribution of consumer demand for these tennis shoes during the coming summer season has been assessed by market research specialists and is provided
Consumer Demand Probability
1 0.05
2 0.15
3 0.25
4 0.30
5 0.15
6 0.10
. Finally, assume that the department store chain must purchase these tennis shoes from the manufacturer in lots of 100 pairs. a. Create a payoff table that specifies the contribution to profit (in dollars) from the sale of the tennis shoes by this department store chain for each possible purchase decision and each outcome with respect to consumer demand. Build the payoff matrix model in each case. In displayed comment boxes or textboxes, show one or two typical formulae used to compute the payoffs. To enable the interpretation of formulae, your model needs to display row and column headings. As we have discussed, this is easy using the Snipping tool. For problem 42 only, apply the following decision criteria: maximin (pessimist’s criterion), minimax regret (doesn’t use probabilities), EMV and EOL. Write a sentence stating the optimal solution for each of the 4 criteria. The statement should contain the recommended strategy and the value of the payoff. Hint for 42: The optimal EMV solution is to order 700 pairs.

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