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BUAD306

BUAD306. Chapter 5S – Decision Theory. Why DM is Important. The act of selecting a preferred course of action among alternatives A KEY responsibility of Operations Managers. DM Applications. Some Decision Making techniques can be specific: Capacity planning Location planning Lease/Buy

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BUAD306

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  1. BUAD306 Chapter 5S – Decision Theory

  2. Why DM is Important • The act of selecting a preferred course of action among alternatives • A KEY responsibility of Operations Managers

  3. DM Applications • Some Decision Making techniques can be specific: • Capacity planning • Location planning • Lease/Buy • But in general, we can improve Decision Making by using logical approaches

  4. Reasons for Poor DM • There may be better choices that have not been considered • Information about options may be imperfect • Knowledge of existing circumstances may be imperfect • Past experience may be irrelevant • Prediction of the future may be wrong • Chains of cause and effect are subject to high probability of error • Too much Information • Peer Pressure

  5. How do WE make decisions? • Alternatives? • Likelihoods? • Outcomes?

  6. States of Nature • Possible outcomes that your business may experience • Examples: • Demand: High, Medium, Low • Contracts: Awarded, Not Awarded • Weather: Rainy, Mixed, Dry

  7. Alternatives • Choices the business can make, given the state of nature or other information • Examples: • Demand: Purchase new machinery, Don’t purchase machinery • Contracts: Hire Additional Staff, Don’t Hire • Weather: Invest in Irrigation System, Don’t Invest • Do Nothing

  8. Payoff Table • Present value in $ millions • (Page 180 in text)

  9. Likelihoods of Conditions • Estimates of likelihood • Typically stated in percentages, must total to 1.0 • Based on historical data or subjective • Examples: • Demand: High (50%), Medium (30%), Low (20%) • Weather: Rainy (30%), Mixed (40%), Dry (30%)

  10. Decision Environments • Certainty - Environment in which future events will definitely occur • Uncertainty - Environment in which it is impossible to assess the likelihood of various future events • Risk - Environment in which certain future events have probable outcomes Different environments require different analysis techniques!

  11. DM Under Certainty • When you know for sure which of the future conditions will occur, choose the alternative with the highest payoff!

  12. DM Under Certainty Example We know for sure demand will be a) low, b) moderate, c) high • Present value in $ millions • (Page 180 in text)

  13. DM Under Uncertainty • Maximin  • Maximax  • Laplace  You don’t need to know Minimax Regret or Opportunity Loss Tables.

  14. Maximin  “The best of the worst” • Determine the worst possible payoff for each alternative, then • Choose the alternative that is the “best worst”.

  15. Maximax  “The best of the best” • Determine the best possible payoff for each alternative, then • Choose the alternative that is the “best of the best”.

  16. Laplace  “The best average” • Determine the average payoff for each alternative, then • Choose the alternative that is the “best average”.

  17. Decision Under Uncertainty Example: A Product Manager for a handheld software company is trying to decide whether to create an application for the Droid, iPhone or both devices. The revenue associated with each alternative depends on the demand for the product as noted below. • What is the Maximin choice? • What is the Maximax choice? • What is the LaPlace choice?

  18. Decision Under Uncertainty Example: A Product Manager for a handheld software company is trying to decide whether to create an application for the Droid, iPhone or both devices. The revenue associated with each alternative depends on the demand for the product as noted below.

  19. Example: COST Part A: Maximin, Maximax, Laplace

  20. DM Under Risk • Most typical in business • Incorporates likelihoods into the process • Allows you to weight payoffs by the probability that the state of nature will occur

  21. Expected Monetary Value • The best expected value among the alternatives • Steps: • For each cell in the Payoff Table, multiple the value by the likelihood of that state of nature • Sum up weighted values and selects the best payoff

  22. EMV Example: We have established likelihoods of future demand as follows: Low: .40, Medium, .50, High, .10

  23. EMV Example: Going back to our handheld application example, we now have the following likelihoods of future demand: Low: 30%, Moderate: 50% and High: 20% What are the EMVs for each alternative? EMVDroid EMViPhone EMVCombo

  24. Example: COST Part B: Assume the following likelihoods: X= .5, Y = .2, Z = .3

  25. Expected Value of Perfect Information (EVPI) • What if you could delay your decision until you had more data? Would you?? • How much would you be willing to pay for that extra time? • EVPI allows you to determine that figure

  26. Calculating EVPI • Want to know if the cost of obtaining the perfect information will be less than the expected gain due to delaying your decision. Therefore: EVPI = Expected Payoff Expected Payoff Under Certainty Under Risk (EMV)

  27. EMV Example: Low: .40, Medium, .50, High, .10 EVPI = Expected Payoff __ Expected Payoff Under Certainty Under Risk Expected Payoff Under Certainty: Expected Payoff Under Risk: EVPI =

  28. EMV Example: Going back to our handheld application example, we now have the following likelihoods of future demand: Low: 30%, Moderate: 50% and High: 20% What is the EVPI for this scenario? Expected Payoff Under Certainty = Expected Payoff Under Risk = Expected Value of Perfect Information =

  29. Example: COST Part C: EVPI

  30. Example: Zero EVPI Calculate EMV for each alternative assuming likelihoods of: Fair: .3, Good: .5 and Superior: .2 What is the EVPI for this scenario? What does it mean?

  31. Decision Trees • Schematic representation • Helpful in analyzing sequential decisions • Can see all the options in front of you and compare easily

  32. Decision Tree Lingo • Nodes – • Square Nodes - Make a decision • Round Nodes – Probabilities of events • Branches – contain information re: that decision or state of nature • Right to Left Analysis • Tree Pruning

  33. Should we run the light?

  34. HW #9 • Firm must decide to build: Small, Medium or Large facility. Demand for all sizes could be low (.2) or high (.8). • If build small and demand is low, NPV = $42. If demand is high, can subcontract (NPV = $42) or expand greatly (NPV = $48) • If build medium and demand is low, NPV = $22. If demand is high, can do nothing (NPV = $46) or expand greatly (NPV = $50) • If build large and demand is low, NPV = -$20. If demand is high, NPV = $72.

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