Bài giảng Operations Management - Module A: Decision-Making Tools

Operations ManagementDecision-Making ToolsModule A1OutlineThe Decision Process in OperationsFundamentals of Decision MakingDecision TablesDecision Making under UncertaintyDecision Making Under RiskDecision Making under CertaintyExpected Value of Perfect Information (EVPI)Decision TreesA More Complex Decision Tree2Learning ObjectivesWhen you complete this chapter, you should be able to:Identify or Define: Decision trees and decision tablesHighest monetary valueExpected value of perfect informationSequential decisionsDescribe or Explain:Decision making under riskDecision making under uncertaintyDecision making under risk3Models, and the Techniques of Scientific ManagementCan Help Managers To: Gain deeper insight into the nature of business relationshipsFind better ways to assess values in such relationships; andSee a way of reducing, or at least understanding, uncertainty that surrounds business plans and actions4Steps to Good DecisionsDefine problem and influencing factorsEstablish decision criteriaSelect decision-making tool (model)Identify and evaluate alternatives using decision-making tool (model)Select best alternativeImplement decisionEvaluate the outcome5ModelsAre less expensive and disruptive than experimenting with the real world systemAllow operations managers to ask “What if” types of questionsAre built for management problems and encourage management inputForce a consistent and systematic approach to the analysis of problemsRequire managers to be specific about constraints and goals relating to a problemHelp reduce the time needed in decision making6Limitations of ModelsTheymay be expensive and time-consuming to develop and testare often misused and misunderstood (and feared) because of their mathematical and logical complexitytend to downplay the role and value of nonquantifiable informationoften have assumptions that oversimplify the variables of the real world7The Decision-Making ProcessProblemDecisionQuantitative AnalysisLogicHistorical DataMarketing ResearchScientific AnalysisModelingQualitative AnalysisEmotionsIntuitionPersonal Experience and MotivationRumors8Decision Problem AlternativesStates of NatureOutcomesDecision treesDecision tablesWays of Displaying a Decision Problem9Fundamentals of Decision TheoryThe three types of decision models:Decision making under uncertaintyDecision making under risk Decision making under certainty10Fundamentals of Decision Theory - continuedTerms:Alternative: course of action or choiceState of nature: an occurrence over which the decision maker has no controlSymbols used in decision tree:A decision node from which one of several alternatives may be selectedA state of nature node out of which one state of nature will occur11Getz Products Decision Tree12Unfavorable marketUnfavorable marketFavorable marketFavorable marketConstruct small plantConstruct large plantDo nothingA decision nodeA state of nature node12Decision TableStates of NatureAlternativesState 1State 2Alternative 1Outcome 1Outcome 2Alternative 2Outcome 3Outcome 413Decision Making Under UncertaintyMaximax - 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.14Example - Decision Making Under 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 MaximaxMaximinEqually likelyDo nothing15The 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.16Probabilistic decision situationStates of nature have probabilities of occurrenceSelect alternative with largest expected monetary value (EMV)EMV = Average return for alternative if decision were repeated many timesDecision Making Under Risk17Expected Monetary Value EquationProbability of payoffEMVAXPXXPXXPXXPXjiiiNN(()()()())==*=*+*++*11122Number of states of natureValue of PayoffAlternative i...N18Example - Decision Making Under UncertaintyStates of NatureAlternativesFavorableMarketP(0.5)UnfavorableMarket P(0.5)ExpectedvalueConstruct$200,000-$180,000$10,000Constructsmall plant$100,000-$20,000$40,000Do nothing$0$0$0Best choicelarge plant19Expected Value of Perfect Information (EVPI)EVPI places an upper bound on what one would pay for additional informationEVPI is the expected value with certainty minus the maximum EMV 20Expected Value With Perfect Information (EV|PI)21Expected Value of Perfect InformationEVPI = Expected value under Certainty - maximum EMV22Expected Value of Perfect InformationConstruct alarge plantConstruct a small plantDo nothing200,000-$180,000$0Favorable Market ($)Unfavorable Market ($)0.500.50EMV$40,000$100,000-$20,000$0$0$20,00023Expected Value of Perfect InformationEVPI = expected value with perfect information - max(EMV) = $200,000*0.50 + 0*0.50 - $40,000 = $60,00024Graphical display of decision processUsed for solving problems With one set of alternatives and states of nature, decision tables can be used alsoWith several sets of alternatives and states of nature (sequential decisions), decision tables cannot be usedEMV is criterion most often usedDecision Trees25Analyzing Problems with Decision TreesDefine the problemStructure or draw the decision treeAssign probabilities to the states of natureEstimate payoffs for each possible combination of alternatives and states of natureSolve the problem by computing expected monetary values for each state-of-nature node26Decision Tree12State 1State 2State 1State 2Alternative 1Alternative 2Decision NodeOutcome 1Outcome 2Outcome 3Outcome 4State of Nature Node27Getz Products Decision TreeCompleted and SolvedPayoffs$200,000-$180,000$100,000-20,000012Unfavorable market (0.5)Unfavorable market (0.5)Favorable market (0.5)Favorable market (0.5)Construct small plantConstruct large plantDo nothingEMV for node 2 = $40,000EMV for node 1 = $10,00028Getz Products Decision Tree with Probabilities and EMVs Shown147$49,200$106,400$40,000$2,4002356$190,000-$190,000$90,000$30,000$10,000$190,000-$190,000$90,000$30,000$10,000$200,000-$180,000$100,000$20,000$0SurveyNo surveyLarge plantSmall plantNo plantLarge plantSmall plantNo plantLarge plantSmall plantNo plantFav. Mkt (0.78)Fav. Mkt (0.78)Fav. Mkt (0.27)Fav. Mkt (0.27)Fav. Mkt (0.5)Fav. Mkt (0.5)Unfav. Mkt (0.22)Unfav. Mkt (0.22)Unfav. Mkt (0.73)Unfav. Mkt (0.73)Unfav. Mkt (0.5)Unfav. Mkt (0.5)$106,000$63,600-$87,400$2,400$10,000$40,000Sur. Res. Neg. (.55)Sur. Res. Pos. (.45)1st decision point2nd decision point29