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**Decision Process Identify the Problem**

Specify objectives and the criteria for choosing a solution Develop alternatives Analyze and compare alternatives Select the best alternative Implement the chosen alternative Monitor the results The optimal alternative may turn out to be less than optimum. There is usually a certain amount of backtracking and feedback, especially in terms of developing and analyzing alternatives.

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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. Suboptimization The result of different departments each attempting to reach a solution that is optimum for that department. manager's ego and unwillingness to admit a mistake. departmentalize decisions. bounded rationality: the limitations on decision making caused by costs, human abilities, time, technology, and availability of information.

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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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**Possible future demand***

Payoff Table Possible future demand* Present value in $ millions

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**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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**Decision Making under Certainty**

Possible future demand* Choose the alternative that has the best payoff under that state of nature. Present value in $ millions

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**Decision Making under Risk**

Possible future demand* 10 10.5 3 0.3 0.5 0.2 Present value in $ millions

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**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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**Decision Making under Uncertainty: Maximin (Pessimistic)**

Possible future demand* Present value in $ millions

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**Decision Making under Uncertainty: Maximax (Optimistic)**

Possible future demand* Present value in $ millions

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**Decision Making under Uncertainty: Laplace**

Possible future demand* 10 10.33 4.67 Present value in $ millions

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**Decision Making under Uncertainty: Minimax Regret**

Possible future demand* 2 6 3 4 14 10 Present value in $ millions

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**Format of a Decision Tree**

Decision Point 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 Chance Event

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**Decision Tree Example Chance Event Decision Point Low demand, 0.4 $40**

Do nothing, $40 Build small x x Overtime, $50 High demand, 0.6 Expand, $55 Low demand, 0.4 x Build large Do nothing, ($10) Reduce prices, $50 High demand, 0.6 $70 Chance Event Decision Point

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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 Expected payoff Expected payoff perfect information under certainty under risk _ =

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**Expected Value of Perfect Information Example**

Possible future demand* 10 10.5 3 0.3 0.5 0.2 Present value in $ millions

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**Sensitivity Analysis Payoff Table**

Alternative States of Nature State 1 State 2 Probability P1 P2 A 4 12 B 16 2 C 8

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Sensitivity Analysis #1 Payoff #2 Payoff 16 14 12 10 8 6 4 2 B 16 14 12 10 8 6 4 2 A C B best C best A best Sensitivity analysis: determine the range of probability for which an alternative has the best expected payoff

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Chapter 3 Decision Analysis.

Chapter 3 Decision Analysis.

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