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MGT3303 Michel Leseure Introduction to Decision Analysis Decision analysis studies the process of making difficult decisions The objective is to update,

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Presentation on theme: "MGT3303 Michel Leseure Introduction to Decision Analysis Decision analysis studies the process of making difficult decisions The objective is to update,"— Presentation transcript:

1 MGT3303 Michel Leseure Introduction to Decision Analysis Decision analysis studies the process of making difficult decisions The objective is to update, model, and document the intuition of managers A structured approach to decision making is especially critical in group decision making Key importance in the case of resource decisions (operations strategy)

2 MGT3303 Michel Leseure Why are decisions difficult? Complexity –Simply keeping all the issues in mind at one time is nearly imposible Uncertainty –Making a decision is especially difficult when you are not sure about one decision variable’s state Multiple objectives Different perspectives lead to different conclusions

3 MGT3303 Michel Leseure Example Winter of 1985: –The Oregon Department of Agriculture (ODA) faced an infestation of gypsy moth in Lane County in Western Oregon –Forest industry representative call for an aggressive eradication campaign

4 MGT3303 Michel Leseure Alternatives Use BT, a bacterial insecticide –target specific –ecologically safe –reasonably effective Use Orthene, a chemical spray –registered as acceptable for home garden use –questions about its ultimate ecological effects –possible danger to humans Possible to use both

5 MGT3303 Michel Leseure Opinions Forestry officials: –Argue Orthene is more potent and is necessary to ensure eradication Environmentalists: –Orthene potentially too dangerous Others –Already too late anyway Others... –Still time but decision/implementation has to be made now

6 MGT3303 Michel Leseure Subjective Judgments In decisional contexts such as the one faced by ODA –objective data is lacking –a procedure determining an « optimal » decision derived from objective data is of little use... –personal statements about uncertainty and value become important inputs (no choice...) –Discovering and finalizing these judgments is a key issue in decision analysis –Instead of criticizing them, we will learn how to better assess and use them

7 MGT3303 Michel Leseure The Decision Analysis Process Identify the decision situation and understand objectives Identify alternatives Decompose and model the problem –model of problem structure –model of uncertainty –model of preferences Choose the best alternative Sensitivity analysis Is further analysis needed? yes/no? Implement the chosen alternative

8 MGT3303 Michel Leseure Requisite Decision Models A model can be considered requisite only when no new intuitions emerge about the problem or when it contains everything that is essential for solving the problem

9 MGT3303 Michel Leseure Elements of a Decision Problem Values and objectives Decisions to make Uncertain events Consequences

10 MGT3303 Michel Leseure Values and Objectives Value: used in its general sense « things that matter to you » Objective: Specific thing that you want to achieve The set of objectives taken up together make up the values Values are the reason for making the decision in the first place They define the decision context

11 MGT3303 Michel Leseure Objectives for Boeing’s Supercomputer Supercomputer Objectives Cost Five-year costs Cost of improved performance Performance Speed Throughput Memory Size Disk Size On-site performance User Needs Installation date Roll in/Roll out Ease of Use Software compatibility Mean time between failures Operational Needs Square footage Water cooling Operator tools Telecommuni- -cations Vendor support Management Issue Vendor Health US Ownership Commitment to supercomputer

12 MGT3303 Michel Leseure Decision to Make Given a decisional context, one (or several) decision(s) has to be made In some cases, several decisions may have to be made in a sequence Decision 1Decision 3Decision 2 Time

13 MGT3303 Michel Leseure Uncertain Events –Uncertain events are either linked to chance or are linked to a probability distribution –Uncertain events have outcome –It is important to position uncertain events appropriately between decisions Decision 1Decision 3Decision 2 Time

14 MGT3303 Michel Leseure Consequences After the last decision has been made and the last uncertain event has been resolved, the decision maker’s fate is finally determined Decision 1Decision 3Decision 2 Time Consequence

15 MGT3303 Michel Leseure Example Policy Decision Accident Management Decisions Time Accident Consequence: Cost $ Environmental damage PR damage Cause Weather Location Weather for Cleanup Cost Environmental Damage

16 MGT3303 Michel Leseure Making Choices Decision Trees Example: Texaco vs. Pennzoil Decision trees and expected value –certainty equivalent

17 MGT3303 Michel Leseure Decision Trees Decision trees are a graphical representation of a decision problem Invest Do not invest Venture succeeds Venture fails Typical return earned on less risky investment Funds lost Large return

18 MGT3303 Michel Leseure Decision Tree Forecast Evacuate Stay Storm hits Miami Storm misses Miami SafetyCost Safe Danger High Low

19 MGT3303 Michel Leseure Cash Flows and Probabilities To each branch of the tree, we can attach –a probability –and/or, a cash flow –or any measure replacing monetary values for a specific problem

20 MGT3303 Michel Leseure Case Study: Texaco vs. Pennzoil In early 1984, Pennzoil and Getty Oil agreed to the terms of a merger Before the signature of the formal agreement, Texaco offered Getty a substantially better price, and Gordon Getty (majority stockholder) defected on Pennzoil and sold to Texaco

21 MGT3303 Michel Leseure Case Study: Texaco vs. Pennzoil Pennzoil felt this was unfair practice and filed a lawsuit against Texaco, alleging that Texaco had interfered illegally in the the Pennzoil-Getty negotiations Pennzoil won the case in late 1985 and was awarded $11.1 billion – the largest settlement in the US at this point in time Texaco appealed and the settlement was reduced by $2 billion – but interest and penalty got the amount back to $10.3 billion

22 MGT3303 Michel Leseure Case Study: Texaco vs. Pennzoil Kinnear, Texaco’s CEO, announced that Texaco would file for bankruptcy if Pennzoil obtained court permission to secure the judgment by filing liens against Texaco’s assets Kinnear promised to fight the case all the way to the Supreme Court

23 MGT3303 Michel Leseure Texaco’s Offer In April 1987, just before Pennzoil started filing liens, Texaco offered to pay Pennzoil $2billion to settle the entire case Liedtke, chairman of Pennzoil, announced that his advisors estimated that a settlement of 3-5 billions would be fair What should Liedtke do?

24 MGT3303 Michel Leseure Decision Tree Settlement Amount ($billion) 2 Counteroffer $5 billion 5 Texaco accepts $5 billion Texaco counteroffers $3 billion 3 Accepts $3 billion Refuse Final Court Decision 0 5 10.3 Final Court Decision 0 5 10.3 Texaco refuses counteroffer Accepts $2 billion

25 MGT3303 Michel Leseure Subjective Probabilities In the decision tree, we are missing probability estimates of the each event For this lecture, we will take these probability values for granted

26 MGT3303 Michel Leseure Decision Tree Settlement Amount ($billion) 2 Counteroffer $5 billion 5 Texaco accepts $5 billion Texaco counteroffers $3 billion 3 Accepts $3 billion Refuse Final Court Decision 0 5 10.3 Final Court Decision 0 5 10.3 Texaco refuses counteroffer (0.17) (0.33) (0.50) (0.2) (0.5) (0.3) (0.2) (0.5) (0.3) Accepts $2 billion

27 MGT3303 Michel Leseure Expected Monetary Value Computing an expected monetary value is a way of selecting among risky alternative Computing expected values bring the problem back to a « certainty equivalent » What is the expected value of the court judgment?

28 MGT3303 Michel Leseure Expected Value of the Court Judgment EV = 0.2 * 10.3 + 0.5 * 5 + 0.3 * 0 EV = $ 4.56 billion Final Court Decision 0 5 10.3 (0.5) (0.3) (0.2) It is possible to reduce the tree with this certainty equivalent

29 MGT3303 Michel Leseure Reduced Tree 2 Counteroffer $5 billion 5 Texaco accepts $5 billion Texaco counteroffers $3 billion 3 Accepts $3 billion Refuse Texaco refuses counteroffer (0.17) (0.33) (0.50) 4.56 Eliminated Accepts $2 billion

30 MGT3303 Michel Leseure Expected Monetary Value of the Counteroffer What is the expected monetary value of Pennzoil $5 billion counter offer: EV = P(Texaco accepts) * 5 + P(Texaco refuse) * 4.56 + P(Texaco counteroffers) * 4.56 EV = 4.63 Liedtke should not accept the $2 billion offer, and should counter-offer $5 billion. If Texaco refuses, then the matter should be taken to court

31 MGT3303 Michel Leseure Reducing the Decision Tree In practice, we do not reduce the decision tree but report expected values on the nodes

32 MGT3303 Michel Leseure Resolved Decision Tree 2 4.63 Counteroffer $5 billion 5 Texaco accepts $5 billion Texaco counteroffers $3 billion 3 Accepts $3 billion 4.56 Refuse Final Court Decision 0 5 10.3 4.56 Final Court Decision 0 5 10.3 Texaco refuses counteroffer (0.17) (0.33) (0.50) (0.2) (0.5) (0.3) (0.2) (0.5) (0.3) Accepts $2 billion

33 MGT3303 Michel Leseure Suggested Homework Problem S2-10, p. 70 Problem S2-13, p. 71


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