Operations Management Decision-Making Tools Module A

1. Operations ManagementDecision-Making ToolsModule A © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

2. 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 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

3. 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 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

4. Models, and the Techniques of Scientific Management • 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 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

5. 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 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

6. 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 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

7. 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 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

8. Quantitative Analysis Logic Historical Data Marketing Research Scientific Analysis Modeling Problem Decision Qualitative Analysis Emotions Intuition Personal Experience and Motivation Rumors The Decision-Making Process © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

9. Out-comes States of Nature Alternatives Decision Problem Ways of Displaying a Decision Problem • Decision trees • Decision tables © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

10. Fundamentals of Decision Theory The three types of decision models: • Decision making under uncertainty • Decision making under risk • Decision making under certainty © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

11. Fundamentals of 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 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

12. Decision Table States of Nature Alternatives State 1 State 2 Alternative 1 Outcome 1 Outcome 2 Alternative 2 Outcome 3 Outcome 4 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

13. Decision Making Under 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. © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

14. Example - Decision Making Under Uncertainty Maximax Maximin Equally likely © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

15. Decision Making Under Risk • 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 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

16. Number of states of nature Value of Payoff N  Probability of payoff EMV ( A ) = V P ( V ) * i i i = i 1 = + + + V P ( V ) V P ( V ) V P ( V ) * ... * * 1 1 2 2 N N Alternative i Expected Monetary Value Equation © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

17. Example - Decision Making Under Uncertainty Best choice © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

18. Expected Value of Perfect Information (EVPI) • EVPI places an upper bound on what one would pay for additional information • EVPI is the expected value with perfect information minus the maximum EMV © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

19. Expected Value With Perfect Information (EV|PI) n (Best outcome for the state of nature j) = å EV | PI * P(S ) j = 1 j where j=1 to the number of states of nature, n © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

20. Expected Value of Perfect Information • EVPI = EV|PI - maximum EMV © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

21. Favorable Market ($) Unfavorable Market ($) EMV Construct a large plant $20,000 -$180,000 200,000 Construct a small plant $40,000 $100,000 $20,000 Do nothing $0 $0 $0 0.50 0.50 Expected Value of Perfect Information © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

22. Expected Value of Perfect Information EVPI = expected value with perfect information - max(EMV) = $200,000*0.50 + 0*0.50 - $40,000 = $60,000 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

23. Expected Opportunity Loss • EOL is the cost of not picking the best solution • EOL = Expected Regret © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

24. Computing EOL - The Opportunity Loss Table © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

25. The Opportunity Loss Table - continued © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

26. The Opportunity Loss Table - continued © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

27. Sensitivity Analysis EMV(Large Plant) = $200,000P - (1-P)$180,000 EMV(Small Plant) = $100,000P - $20,000(1-P) EMV(Do Nothing) = $0P + 0(1-P) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

28. Sensitivity Analysis - continued EMV (Small Plant) EMV(Large Plant) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

29. Decision Trees • 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 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

30. Analyzing Problems with Decision 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 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

31. State 1 Outcome 1 1 State 2 Outcome 2 Alternative 1 State 1 Alternative 2 Outcome 3 2 State 2 Outcome 4 Decision Node State of Nature Node Decision Tree © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458