operations management decision making tools module a l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Operations Management Decision-Making Tools Module A PowerPoint Presentation
Download Presentation
Operations Management Decision-Making Tools Module A

Loading in 2 Seconds...

play fullscreen
1 / 29

Operations Management Decision-Making Tools Module A - PowerPoint PPT Presentation


  • 738 Views
  • Uploaded on

Operations Management Decision-Making Tools Module A. Outline. T he 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 )

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

Operations Management Decision-Making Tools Module A


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
  1. Operations ManagementDecision-Making ToolsModule A © 2004 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 © 2004 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 • Decision making under uncertainty • Decision making under risk © 2004 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 © 2004 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 © 2004 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 © 2004 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 © 2004 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 © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  9. Outcomes States of Nature Alternatives Decision Problem Ways of Displaying a Decision Problem • Decision trees • Decision tables © 2004 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 © 2004 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 © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  12. Favorable market A state of nature node 1 Unfavorable market Construct large plant Favorable market A decision node Construct small plant 2 Unfavorable market Do nothing Getz Products Decision Tree © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  13. Decision Table States of Nature State 1 State 2 Alternatives Outcome 1 Outcome 2 Alternative 1 Outcome 3 Outcome 4 Alternative 2 © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  14. 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. © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  15. States of Nature Alternatives Favorable Unfavorable Maximum Minimum Row Market Market in Row in Row Average Construct $200,000 - $180,000 $200,000 - $180,000 $10,000 large plant Construct $100,000 - $20,000 $100,000 - $20,000 $40,000 small plant $0 $ 0 $0 $0 $0 Do nothing Maximax Maximin Equally likely Example - Decision Making Under Uncertainty © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  16. The Decisions • The maximax choice is to construct a large plant. This is the maximum of the maximum number within each row or alternative. • The maximin choice is to do nothing. This is the maximum of the minimum number within each row or alternative. • The equally likely choice is to construct a small plant. This is the maximum of the average outcomes of each alternative. This approach assumes that all outcomes for any alternative are equally likely. © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  17. 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 © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  18. Number of states of nature Value of Payoff N  Probability of payoff EMV ( A ) = X P ( X ) * j i i = i 1 = + + + X P ( X ) X P ( X ) X P ( X ) * ... * * 1 1 2 2 N N Alternative i Expected Monetary Value Equation © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  19. States of Nature Alternatives Favorable Unfavorable Expected Market Market P(0.5) value P(0.5) Construct $200,000 -$180,000 $10,000 large plant Construct $100,000 -$20,000 $40,000 Best choice small plant Do nothing $0 $0 $0 Example - Decision Making Under Uncertainty © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  20. Expected Value of Perfect Information (EVPI) • EVPI places an upper bound on what one would pay for additional information • EVPIis the expected value with certainty minus the maximum EMV © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  21. Expected Value With Perfect Information (EV|PI) © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  22. Expected Value of Perfect Information EVPI = Expected value under Certainty - maximum EMV © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  23. Expected Value of Perfect Information 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 © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  24. Expected Value of Perfect Information EVPI = expected value with perfect information - max(EMV) = $200,000*0.50 + 0*0.50 - $40,000 = $60,000 © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  25. Decision Trees • Graphical display of decision process • Used for solving problems • With one 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 © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  26. 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 © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  27. 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 © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  28. EMV for node 1 = $10,000 Favorable market (0.5) 1 Unfavorable market (0.5) Construct large plant Favorable market (0.5) Construct small plant 2 Unfavorable market (0.5) Do nothing EMV for node 2 = $40,000 Getz Products Decision TreeCompleted and Solved Payoffs $200,000 -$180,000 $100,000 -20,000 0 © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

  29. Fav. Mkt (0.78) 2nd decision point $106,000 1st decision point $190,000 -$190,000 $90,000 $30,000 $10,000 2 Unfav. Mkt (0.22) Large plant $63,600 Small plant $106,400 Fav. Mkt (0.78) 3 Unfav. Mkt (0.22) Sur. Res. Pos. (.45) No plant -$87,400 Fav. Mkt (0.27) $190,000 -$190,000 $90,000 $30,000 $10,000 1 4 Unfav. Mkt (0.73) Sur. Res. Neg. (.55) Large plant Survey Fav. Mkt (0.27) $2,400 Small plant $2,400 5 Unfav. Mkt (0.73) No plant $49,200 $10,000 Fav. Mkt (0.5) $200,000 -$180,000 $100,000 $20,000 $0 6 Unfav. Mkt (0.5) No survey Large plant $40,000 Fav. Mkt (0.5) Small plant $40,000 7 Unfav. Mkt (0.5) No plant Getz Products Decision Tree with Probabilities and EMVs Shown © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458