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Operations Management
William J. Stevenson 8th edition
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5s Decision Theory CHAPTER
Operations Management, Eighth Edition, by William J. Stevenson Copyright © 2005 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin
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product and service design
Decision Theory Decision Theory represents a general approach to decision making which is suitable for a wide range of operations management decisions, including: Capacity planning product and service design location planning equipment selection
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Decision Theory Elements
A set of possible future conditions exists that will have a bearing on the results of the decision A list of alternatives for the manager to choose from A known payoff for each alternative under each possible future condition
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Decision Theory Process
Identify possible future conditions called states of nature Develop a list of possible alternatives, one of which may be to do nothing Determine the payoff associated with each alternative for every future condition
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Decision Theory Process (Cont’d)
If possible, determine the likelihood of each possible future condition Evaluate alternatives according to some decision criterion and select the best alternative
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Causes of Poor Decisions
Bounded Rationality The limitations on decision making caused by costs, human abilities, time, technology, and availability of information
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Causes of Poor Decisions (Cont’d)
Suboptimization The result of different departments each attempting to reach a solution that is optimum for that department
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Decision Environments
Certainty - Environment in which relevant parameters have known values Risk - Environment in which certain future events have probable outcomes Uncertainty - Environment in which it is impossible to assess the likelihood of various future events
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Decision Making under Uncertainty
Maximin - Choose the alternative with the best of the worst possible payoffs Maximax - Choose the alternative with the best possible payoff Laplace - Choose the alternative with the best average payoff of any of the alternatives Minimax Regret - Choose the alternative that has the least of the worst regrets
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Format of a Decision Tree
Figure 5S.1 State of nature 1 B Payoff 1 State of nature 2 Payoff 2 Payoff 3 2 Choose A’1 Choose A’2 Payoff 6 Payoff 4 Payoff 5 Choose A’3 Choose A’4 Choose A’ 1 Decision Point Chance Event
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Expected Value of Perfect Information
Expected value of perfect information: the difference between the expected payoff under certainty and the expected payoff under risk Expected value of perfect information Expected payoff under certainty Expected payoff under risk - =
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Sensitivity Analysis Example S-8 16 14 12 10 8 6 4 2 16 14 12 10 8 6 4
B C A best C best B best #1 Payoff #2 Payoff 16 14 12 10 8 6 4 2 16 14 12 10 8 6 4 2 Sensitivity analysis: determine the range of probability for which an alternative has the best expected payoff
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Solved Problem 5
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