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- 1. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-1 Operations Operations Management Management Decision-Making Tools Decision-Making Tools Module A Module A
- 2. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-2 Outline Outline The Decision Process in Operations Fundamentals of Decision Making Decision Tables Decision Making under Uncertainty Decision Making Under Risk Decision Making under Certainty Expected Value of Perfect Information (EVPI) Decision Trees A More Complex Decision Tree
- 3. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-3 Learning Objectives Learning Objectives When you complete this chapter, you should be able to : Identify or Define: Decision trees and decision tables Highest monetary value Expected value of perfect information Sequential decisions Describe or Explain: Decision making under risk
- 4. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-4 Models, and the Techniques of Models, and the Techniques of Scientific Management Scientific Management Can Help Managers To Can Help Managers To: Gain deeper insight into the nature of business relationships Find better ways to assess values in such relationships; and See a way of reducing, or at least understanding, uncertainty that surrounds business plans and actions
- 5. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-5 Steps to Good Decisions Steps to Good Decisions Define problem and influencing factors Establish decision criteria Select decision-making tool (model) Identify and evaluate alternatives using decision- making tool (model) Select best alternative Implement decision Evaluate the outcome
- 6. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-6 Models Models Are less expensive and disruptive than experimenting with the real world system Allow operations managers to ask “What if” types of questions Are built for management problems and encourage management input Force a consistent and systematic approach to the analysis of problems Require managers to be specific about constraints and goals relating to a problem Help reduce the time needed in decision making
- 7. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-7 Limitations of Models Limitations of Models They may be expensive and time-consuming to develop and test are often misused and misunderstood (and feared) because of their mathematical and logical complexity tend to downplay the role and value of nonquantifiable information often have assumptions that oversimplify the variables of the real world
- 8. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-8 The Decision-Making Process The Decision-Making Process Problem Decision Quantitative Analysis Logic Historical Data Marketing Research Scientific Analysis Modeling Qualitative Analysis Emotions Intuition Personal Experience and Motivation Rumors
- 9. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-9 Decision Problem Alternatives States of Nature Out- comes Decision trees Decision tables Ways of Displaying Ways of Displaying a Decision Problem a Decision Problem
- 10. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-10 Fundamentals of Fundamentals of Decision Theory Decision Theory The three types of decision models: Decision making under uncertainty Decision making under risk Decision making under certainty
- 11. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-11 Fundamentals of Fundamentals of Decision Theory - continued Decision Theory - continued Terms: Alternative: course of action or choice State of nature: an occurrence over which the decision maker has no control Symbols used in decision tree: A 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
- 12. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-12 Decision Table Decision Table States of Nature Alternatives State 1 State 2 Alternative 1 Outcome 1 Outcome 2 Alternative 2 Outcome 3 Outcome 4
- 13. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-13 Decision Making Under Decision Making Under Uncertainty Uncertainty Maximax - Choose the alternative that maximizes the maximum outcome for every alternative (Optimistic criterion) Maximin - Choose the alternative that maximizes the minimum outcome for every alternative (Pessimistic criterion) Equally likely - chose the alternative with the highest average outcome.
- 14. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-14 Example - Decision Making Under Example - Decision Making Under Uncertainty Uncertainty States of Nature Alternatives Favorable Market Unfavorable Market Maximum in Row Minimum in Row Row Average Construct large plant $200,000 -$180,000 $200,000 -$180,000 $10,000 Construct small plant $100,000 -$20,000 $100,000 -$20,000 $40,000 $0 $0 $0 $0 $0 Maximax Maximin Equally likely
- 15. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-15 Probabilistic decision situation States of nature have probabilities of occurrence Select alternative with largest expected monetary value (EMV) EMV = Average return for alternative if decision were repeated many times Decision Making Under Risk Decision Making Under Risk
- 16. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-16 Expected Monetary Value Expected Monetary Value Equation Equation Probability of payoff EMV A V P V V P V V P V V P V i i i i N N ( ( ) ( ) ( ) ( ) ) = N = * = * + * + + * 1 1 1 2 2 Number of states of nature Number of states of nature Value of Payoff Alternative i ...
- 17. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-17 Example - Decision Making Under Example - Decision Making Under Uncertainty Uncertainty States of Nature Alternatives Favorable Market P(0.5) Unfavorable Market P(0.5) Expected value Construct large plant $200,000 -$180,000 $10,000 Construct small plant $100,000 -$20,000 $40,000 Do nothing $0 $0 $0 Best choice
- 18. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-18 Expected Value of Perfect Expected Value of Perfect Information ( Information (EVPI) ) EVPI EVPI places an upper bound on what one would pay for additional information EVPI EVPI is the expected value with perfect information minus the maximum EMV
- 19. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-19 Expected Value With Perfect Expected Value With Perfect Information ( Information (EV|PI) ) ) P(S * j PI | EV n j where j=1 to the number of states of nature, n (Best outcome for the state of nature j)
- 20. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-20 Expected Value of Perfect Expected Value of Perfect Information Information EVPI EVPI = EV|PI EV|PI - maximum EMV EMV
- 21. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-21 Expected Value of Perfect Expected Value of Perfect Information Information State of Nature Alternative Probabilities Construct a large plant Construct a small plant Do nothing 200,000 -$180,000 $0 Favorable Market ($) Unfavorable Market ($) 0.50 0.50 EMV $40,000 $100,000 $20,000 $0 $0 $20,000
- 22. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-22 Expected Value of Perfect Expected Value of Perfect Information Information EVPI EVPI = expected value with perfect information - max(EMV EMV) = $200,000*0.50 + 0*0.50 - $40,000 = $60,000
- 23. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-23 Expected Opportunity Loss Expected Opportunity Loss EOL EOL is the cost of not picking the best solution EOL EOL = Expected Regret
- 24. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-24 Computing EOL - The Opportunity Computing EOL - The Opportunity Loss Table Loss Table State of Nature Alternative Favorable Market ($) Unfavorable Market ($) Large Plant 200,000 - 200,000 0 - (-180,000) Small Plant 200,000 - 100,000 0 -(-20,000) Do Nothing 200,000 - 0 0-0 Probabilities 0.50 0.50
- 25. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-25 The Opportunity Loss Table - The Opportunity Loss Table - continued continued State of Nature Alternative Favorable Market ($) Unfavorable Market ($) Large Plant 0 180,000 Small Plant 100,000 20,000 Do Nothing 200,000 0 Probabilities 0.50 0.50
- 26. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-26 The Opportunity Loss Table - The Opportunity Loss Table - continued continued Alternative EOL Large Plant (0.50)*$0 + (0.50)*($180,000) $90,000 Small Plant (0.50)*($100,000) + (0.50)(*$20,000) $60,000 Do Nothing (0.50)*($200,000) + (0.50)*($0) $100,000
- 27. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-27 Sensitivity Analysis Sensitivity Analysis EMV(Large Plant) = $200,000P P - (1-P 1-P)$180,000 EMV(Small Plant) = $100,000P P - $20,000(1-P 1-P) EMV(Do Nothing) = $0P P + 0(1-P 1-P)
- 28. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-28 Sensitivity Analysis - continued Sensitivity Analysis - continued -200000 -150000 -100000 -50000 0 50000 100000 150000 200000 250000 0 0.2 0.4 0.6 0.8 1 Values of P E M V Values Point 1 Point 2 EMV (Small Plant) EMV(Large Plant)
- 29. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-29 Graphical display of decision process Used for solving problems With 1 set of alternatives and states of nature, decision tables can be used also With several sets of alternatives and states of nature (sequential decisions), decision tables cannot be used EMV is criterion most often used Decision Trees Decision Trees
- 30. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-30 Analyzing Problems with Decision Analyzing Problems with Decision Trees Trees Define the problem Structure or draw the decision tree Assign probabilities to the states of nature Estimate payoffs for each possible combination of alternatives and states of nature Solve the problem by computing expected monetary values for each state-of-nature node
- 31. PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-31 Decision Tree Decision Tree 1 2 State 1 State 2 State 1 State 2 Alternative 1 Alternative 2 Decision Decision Node Node Outcome 1 Outcome 1 Outcome 2 Outcome 2 Outcome 3 Outcome 3 Outcome 4 Outcome 4 State of Nature Node State of Nature Node
Editor's Notes
- #4: A point to make here is that the importance of models lies in the insight they provide, not the numbers. This issue merits discussion!
- #5: A major point to make here is that this process should be considered iterative. Once we have actually built and exercised the model, we may realize we did not understand the problem in the first place!
- #6: Several points: - good models are indeed built for the use of managers; the best are built by the managers who will use them. - different people solve the same problem in different ways - the optimal model will therefore be tailored to the problem and the individual solving it. - models are most important for the insight they allow you to gain, not for the numbers they produce. - models are for helping you understand problems, not for telling you what to do.
- #7: Examples are helpful here: - cost of some models > $1,000,000 - many models employ probability and statistics, linear and non-linear programming; some employ chaos theory, and fuzzy logic - how do you represent the role of consumer bias in a computer? - assumptions of linearity, of continuity, of boundedness, etc.
- #9: It can be useful here to explain what decision trees and decision tables are, and then ask students to suggest some problems that lend themselves to the use of one or the other of these models.
- #10: It is usually worthwhile developing the difference between risk and uncertainty.