DECISION ANALYSIS

1. OM2 SUPPLEMENTARY CHAPTER E DECISION ANALYSIS DAVID A. COLLIER AND JAMES R. EVANS

2. Supplemental Chapter E Learning Outcomes l e a r n i n g o u t c o m e s LO1Describe types of management decisions where decision analysis techniques are useful and the basic elements of a decision problem. LO2Explain how to evaluate risk in making decisions and apply decision criteria to select an appropriate decision alternative. LO3Describe how to construct simple decision trees and use them to select optimal expected value decisions.

3. Supplemental Chapter E. Decision Analysis What do you think we should do? We’re down by 10 with 5 minutes left—plenty of time to get the ball back,” pondered Ken Kendall, head coach of West High in talking to offensive coach Craig Russell. West was facing fourth down and short yardage for another first down from their opponent’s 9-yard line. “Should we try for the first down or go for the field goal?” Craig noted that statistically a run is better than a field goal attempt inside the 10-yard line. Ken wasn’t so sure, trying to weigh the risk of not getting the first down or a touchdown instead of an almost sure field goal.

4. Supplemental Chapter E. Decision Analysis What do you think? Describe a situation in your personal or work life where you need to make an important decision. What criteria will you use? How will you make the decision?

5. Supplemental Chapter E. Decision Analysis • Decision analysis is the formal study of how people • make decisions, particularly when faced with uncertain • information, as well as a collection of techniques to support the analysis of decision problems. • Applications: • Product selection • Facility capacity expansion and location • Inventory analysis • Technology and process selection

6. Supplemental Chapter E. Decision Analysis • Applying Decision Analysis Tools • Decision analysis techniques apply when decisions • Are important • Are probably unique • Allow some time for study • Are complex • Involve uncertainty and risk • Uncertainty refers to not knowing what will happen in the future. Risk is the uncertainty associated with an undesirable outcome, such as financial loss.

7. Supplemental Chapter E. Decision Analysis • Structuring Decision Problems • Decision alternatives represent the choices that a decision maker can make. • Events represent the future outcomes that can occur after a decision is made and that are not under the control of the decision maker • A numerical value associated with a decision coupled with some event is called a payoff. • For many situations, we can estimate probabilities of events.

8. Exhibit E.1 Supplemental Chapter E. Decision Analysis Example

9. Supplemental Chapter E. Decision Analysis • Selecting Decision Alternatives • For one-time decisions, managers must take into account the risk associated with making the wrong decision. • For decisions that are repeated over and over, managers can choose decisions based on the expected payoffs that might occur.

10. Supplemental Chapter E. Decision Analysis One Time Decisions Without Event Probabilities 1. Maximax—choose the decision that will maximize the maximum possible profit among all events. This is an aggressive, or risk-taking, approach. 2. Maximin—choose the decision that will maximize the minimum possible profit among all events. This is a conservative, or risk-averse, approach. 3. Minimax regret—choose the decision that will minimize the maximum opportunity loss associated with the events.

11. Supplemental Chapter E. Decision Analysis Example Maximax criterion: choose to build new plant Maximin criterion: choose to expand existing plant

12. Supplemental Chapter E. Decision Analysis Example Opportunity loss criterion: choose to build new plant

13. Supplemental Chapter E. Decision Analysis Cost Payoffs 1. Minimin—choose the decision that will minimize the minimum possible cost among all events. 2. Minimax—choose the decision that will minimize the maximum possible cost among all events. 3. Minimax regret—choose the decision that will minimize the maximum opportunity loss associated with the events. When the output measure is cost is that the “best” payoff is the lowest cost, not the highest profit.

14. Supplemental Chapter E. Decision Analysis Repeated Decisions With Event Probabilities The expected value approach is to select the decision alternative with the best expected payoff. P(sj ) = probability of occurrence for event sj N = number of events EV (di) = ΣjP(sj )V (di, sj)

15. Exhibit E.3 Supplemental Chapter E. Decision Analysis • Example

16. Supplemental Chapter E. Decision Analysis Expected Value of Perfect Information The expected value of perfect information, or EVPI, is the difference between the expected payoff under perfect information and the expected payoff of the optimal decision without perfect information.

17. Supplemental Chapter E. Decision Analysis Decision Trees A decision tree is a graphical schematic of the logical order with which decisions are made and events occur. Nodes refer to the intersections, or junction points, of the tree. Arcs are the connectors between the nodes. Arcs are sometimes called branches.

18. Exhibit E.4 Supplemental Chapter E. Decision Analysis Example

19. Exhibit E.7 Supplemental Chapter E. Decision Analysis Optimal Decision Strategy

20. Exhibit E.6 Supplemental Chapter E. Decision Analysis New Product Introduction Decision Tree

21. Supplemental Chapter E. Decision Analysis Trendy’s Pies Case Study Use these cost, revenue, and probability estimates along with the decision tree to identify the best decision strategy for Trendy’s Pies. Suppose that Trendy is concerned about her probability estimates of the consumer response to the regional test market. Although her estimates are .7 for a high response and .3 for a low response, she is not very confident of these values. Determine how the decision strategy would change if the probability of a high response varies from .1 to .9 in increments of .1. How sensitive is the best strategy in Question 1 to this probability assumption?