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OPERATIONS MANAGEMENT Quantitative Module A - Decision Making Tools

OPERATIONS MANAGEMENT Quantitative Module A - Decision Making Tools .

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OPERATIONS MANAGEMENT Quantitative Module A - Decision Making Tools

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  1. OPERATIONS MANAGEMENTQuantitative Module A - Decision Making Tools Classroom discussion questions to accompany Heizer/Render Principles of Operations Management, 6e and Operations Management, 8e (to be used in conjunction with wireless polling devices or other classroom interaction activities) Jay Heizer Barry Render Module A

  2. A “good” decision is one that: • Is made by the most powerful decision maker. • Uses analytical decision making. • Relies mostly on folklore and tradition. • Has good outcomes. Module A

  3. A course of action that can be selected by a decision maker is referred to as: • A state of nature. • An alternative. • An outcome. • A conditional value. Module A

  4. Which of the following is considered an “optimistic” decision criterion? • Maximax. • Maximin. • Equally likely. Module A

  5. Which of the following is considered a “pessimistic” decision criterion? • Maximax. • Maximin. • Equally likely. Module A

  6. EMV is a measure associated with: • Decision making under uncertainty. • Decision making under certainty. • Decision making under risk. Module A

  7. Which is not an attribute of a state of nature for decision making under risk? • The states of nature must be mutually exclusive. • The states of nature must be collectively exhaustive. • Their probabilities are computed through experimentation. • Their probabilities must add up to 1. Module A

  8. A decision tree analysis is most appropriate when the problem has: • One set of decisions. • One set of states of nature. • Sequential decisions. • Sequential states of nature. • Sequential decisions and sequential states of nature. Module A

  9. All of the following steps are taken to analyze decision problems except: • Define the problem. • Structure a decision tree. • Assign probabilities to the alternatives. • Estimate payoffs for each possible alternative/state of nature combination. • Solve the problem by computing expected monetary values for each state of nature node. Module A

  10. The Expected Value of Perfect Information (EVPI) is the: • Payoff for a decision under perfect information. • Payoff under minimum risk. • Average expected payoff. • Difference between the payoff under certainty and the payoff under risk. Module A

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