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PART 1 DECISION MAKING 5S-1 Decision Process Select the best alternative Implement the chosen alternative Monitor the results Identify the Problem Specify objectives and the criteria for choosing a solution Develop alternatives Analyze and compare alternatives

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PART 1 DECISION MAKING 5S-2 Causes of Poor Decisions Bounded Rationality The limitations on decision making caused by costs, human abilities, time, technology, and availability of information. Suboptimization The result of different departments each attempting to reach a solution that is optimum for that department.

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PART 1 DECISION MAKING 5S-3 Decision Theory 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 equipment selection location planning

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PART 1 DECISION MAKING 5S-4 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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PART 1 DECISION MAKING 5S-5 Payoff Table Possible future demand* *Present value in $ millions

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PART 1 DECISION MAKING 5S-6 Decision Making 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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PART 1 DECISION MAKING 5S-7 Decision Making under Certainty Possible future demand* *Present value in $ millions

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PART 1 DECISION MAKING 5S-8 Decision Making under Risk Possible future demand* *Present value in $ millions

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PART 1 DECISION MAKING 5S-9 Decision Making under Uncertainty Maximin (pessimistic) - Choose the alternative with the best of the worst possible payoffs Maximax (optimistic) - 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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PART 1 DECISION MAKING 5S-10 Decision Making under Uncertainty: Maximin (Pessimistic) Possible future demand* *Present value in $ millions

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PART 1 DECISION MAKING 5S-11 Decision Making under Uncertainty: Maximax (Optimistic) Possible future demand* *Present value in $ millions

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PART 1 DECISION MAKING 5S-12 Decision Making under Uncertainty: Laplace Possible future demand* *Present value in $ millions

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PART 1 DECISION MAKING 5S-13 Decision Making under Uncertainty: Minimax Regret Possible future demand* *Present value in $ millions

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PART 1 DECISION MAKING 5S-14 Format of a Decision Tree State of nature 1 B Payoff 1 State of nature 2 Payoff 2 Payoff 3 2 Choose A’ 1 Choose A’ 2 Payoff 6 State of nature 2 2 Payoff 4 Payoff 5 Choose A’ 3 Choose A’ 4 State of nature 1 Choose A’ Choose A’ 2 1 Decision Point Chance Event

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PART 1 DECISION MAKING 5S-15 Decision Tree Example Decision Point Chance Event Build small Build large Low demand, 0.4 High demand, 0.6 Low demand, 0.4 High demand, 0.6 Reduce prices, $50 Overtime, $50 Expand, $55 Do nothing, ($10) Do nothing, $40 $40 $70 x x x

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PART 1 DECISION MAKING 5S-16 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 Expected payoff Expected payoff perfect information under certainty under risk = _

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PART 1 DECISION MAKING 5S-17 Expected Value of Perfect Information Example Possible future demand* *Present value in $ millions

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PART 1 DECISION MAKING 5S-18 Sensitivity Analysis Payoff Table AlternativeStates of Nature State 1State 2 ProbabilityP1P2 A412 B162 C128

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PART 1 DECISION MAKING 5S-19 Sensitivity Analysis A B C A bestC bestB best #1 Payoff#2 Payoff Sensitivity analysis: determine the range of probability for which an alternative has the best expected payoff

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