Operations Management Module A – Decision-Making Tools Outline Outline – Continued

Operations Management Module A – Decision-Making Tools Outline Outline – Continued www.phwiki.com

Operations Management Module A – Decision-Making Tools Outline Outline – Continued

Erdmann, Nancy, Executive Editor/Garden Editor has reference to this Academic Journal, PHwiki organized this Journal Operations Management Module A – Decision-Making Tools © 2006 Prentice Hall, Inc. PowerPoint presentation to accompany Heizer/Render Principles of Operations Management, 6e Operations Management, 8e Outline The Decision Process in Operations Fundamentals of Decision Making Decision Tables Outline – Continued Types of Decision-Making Environments Decision Making Under Uncertainty Decision Making Under Risk Decision Making Under Certainty Expected Value of Perfect In as long as mation (EVPI)

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Outline – Continued Decision Trees A More Complex Decision Tree Using Decision Trees in Ethical Decision Making Learning Objectives When you complete this module, you should be able to: Identify or Define: Decision trees in addition to decision tables Highest monetary value Expected value of perfect in as long as mation Sequential decisions Learning Objectives When you complete this chapter, you should be able to: Describe or Explain: Decision making under risk Decision making under uncertainty Decision making under certainty

The Decision Process in Operations Clearly define the problems in addition to the factors that influence it Develop specific in addition to measurable objectives Develop a model Evaluate each alternative solution Select the best alternative Implement the decision in addition to set a timetable as long as completion Fundamentals of Decision Making Terms: Alternative—a course of action or strategy that may be chosen by the decision maker State of nature—an occurrence or a situation over which the decision maker has little or no control Fundamentals of Decision Making Symbols used in a decision tree: —decision node from which one of several alternatives may be selected —a state-of-nature node out of which one state of nature will occur

Decision Tree Example Figure A.1 Decision Table Example Table A.1 Decision-Making Environments Decision making under uncertainty Complete uncertainty as to which state of nature may occur Decision making under risk Several states of nature may occur Each has a probability of occurring Decision making under certainty State of nature is known

Uncertainty Maximax Find the alternative that maximizes the maximum outcome as long as every alternative Pick the outcome with the maximum number Highest possible gain Uncertainty Maximin Find the alternative that maximizes the minimum outcome as long as every alternative Pick the outcome with the minimum number Least possible loss Uncertainty Equally likely Find the alternative with the highest average outcome Pick the outcome with the maximum number Assumes each state of nature is equally likely to occur

Uncertainty Example Maximax choice is to construct a large plant Maximin choice is to do nothing Equally likely choice is to construct a small plant Risk Each possible state of nature has an assumed probability States of nature are mutually exclusive Probabilities must sum to 1 Determine the expected monetary value (EMV) as long as each alternative Expected Monetary Value

EMV Example EMV(A1) = (.5)($200,000) + (.5)(-$180,000) = $10,000 EMV(A2) = (.5)($100,000) + (.5)(-$20,000) = $40,000 EMV(A3) = (.5)($0) + (.5)($0) = $0 Table A.3 EMV Example EMV(A1) = (.5)($200,000) + (.5)(-$180,000) = $10,000 EMV(A2) = (.5)($100,000) + (.5)(-$20,000) = $40,000 EMV(A3) = (.5)($0) + (.5)($0) = $0 Best Option Table A.3 Certainty Is the cost of perfect in as long as mation worth it Determine the expected value of perfect in as long as mation (EVPI)

Expected Value of Perfect In as long as mation EVPI is the difference between the payoff under certainty in addition to the payoff under risk EVPI Example The best outcome as long as the state of nature “favorable market” is “build a large facility” with a payoff of $200,000. The best outcome as long as “unfavorable” is “do nothing” with a payoff of $0. EVPI Example The maximum EMV is $40,000, which is the expected outcome without perfect in as long as mation. Thus: = $100,000 – $40,000 = $60,000 The most the company should pay as long as perfect in as long as mation is $60,000

Erdmann, Nancy Phoenix Home & Garden Executive Editor/Garden Editor www.phwiki.com

Decision Trees In as long as mation in decision tables can be displayed as decision trees A decision tree is a graphic display of the decision process that indicates decision alternatives, states of nature in addition to their respective probabilities, in addition to payoffs as long as each combination of decision alternative in addition to state of nature Appropriate as long as showing sequential decisions Decision Trees Decision Trees Define the problem Structure or draw the decision tree Assign probabilities to the states of nature Estimate payoffs as long as each possible combination of decision alternatives in addition to states of nature Solve the problem by working backward through the tree computing the EMV as long as each state-of-nature node

Decision Tree Example Figure A.2 Complex Decision Tree Example Figure A.3 Complex Example Given favorable survey results EMV(2) = (.78)($190,000) + (.22)(-$190,000) = $106,400 EMV(3) = (.78)($90,000) + (.22)(-$30,000) = $63,600 The EMV as long as no plant = -$10,000 so, if the survey results are favorable, build the large plant

Decision Trees in Ethical Decision Making Is action legal Figure A.4

Erdmann, Nancy Executive Editor/Garden Editor

Erdmann, Nancy is from United States and they belong to Phoenix Home & Garden and they are from  Scottsdale, United States got related to this Particular Journal. and Erdmann, Nancy deal with the subjects like Gardening

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