1 om2, supp. ch. e. decision analysis ©2010 cengage learning. all rights reserved. may not be...

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OM2, Supp. Ch. E. Decision Analysis

©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

DECISION ANALYSIS

SUPPLEMENTARY CHAPTER E

DAVID A. COLLIERAND

JAMES R. EVANS

OM2

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LO1 Describe types of management decisions where decision analysis techniques are useful and the basic elements of a decision problem.

LO2 Explain how to evaluate risk in making decisions and apply decision criteria to select an appropriate decision alternative.

LO3 Describe how to construct simple decision trees and use them to select optimal expected value decisions.

Supplemental Chapter E Learning Outcomes

l e a r n i n g o u t c o m e s

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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 Highin 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.

Supplemental Chapter E. Decision Analysis

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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?

What do you think?


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Decision analysis is the formal study of how people make decisions, particularly when faced with uncertaininformation, 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


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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.


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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.


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Example

Supplemental Chapter E. Decision AnalysisExhibit E.1

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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.


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One Time Decisions Without Event Probabilities

1. Maximax—choose the decision that will maximizethe maximum possible profit among all events. Thisis 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.


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Example


Maximax criterion: choose to build new plant

Maximin criterion: choose to expand existing plant

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ExampleSupplemental Chapter E. Decision Analysis

Opportunity loss criterion: choose to build new plant

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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.

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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 )


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Example


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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.

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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.


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Example


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Optimal Decision Strategy


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New Product Introduction Decision Tree

Exhibit E.6

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Trendy’s Pies Case Study 1. Use these cost, revenue, and probability

estimates along with the decision tree to identify the best decision strategy for Trendy’s Pies.

2. 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?


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