PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-1 Operations Management Decision-Making Tools Module A (page 686 in text)

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-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 Situation

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-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 Criteria  Decision making under risk

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-4 Models, and the Techniques of Scientific Management  Can Help Managers To  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 thatsurrounds business plans and actions

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-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

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-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

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-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 nature  tend to downplay the role and value of nonquantifiable information  often have assumptions that oversimplify the variables of the real world

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-8 The Decision-Making Process ProblemDecision Quantitative Analysis Logic Historical Data Marketing Research Scientific Analysis Modeling Qualitative Analysis Emotions Intuition Personal Experience and Motivation Rumors

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-9 Decision Problem Alternatives States of Nature Out- comes  Decision trees  Decision tables Ways of Displaying a Decision Problem

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-10 Fundamentals of Decision Theory The three types of decision models:  Decision making under certainty  Decision making under uncertainty  Decision making under risk

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-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

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-12 Decision Table States of Nature AlternativesState 1State 2 Alternative 1Outcome 1Outcome 2 Alternative 2Outcome 3Outcome 4

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-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.

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-14 Example A3- Decision Making Under Uncertainty (table A.2 Page 689) Maximax Maximin Equally likely

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-15  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 Decision Making Under Risk

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-16 Expected Monetary Value Equation Probability of payoff EMVAVPV VPVVPVVPV ii i i NN (() ()()() )= N =  * = * + * ++ * Number of states of nature Value of Payoff Alternative i...

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-17 Example - Decision Making Under Risk Now assume that we have arrived at a probability of 0.6 that a favorable market will occur Best choice

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-18 Expected Value of Perfect Information () Expected Value of Perfect Information ( EVPI )  EVPI  EVPI places an upper bound on what one would pay for additional information  EVPI  EVPI is the expected value with perfect information minus the maximum EMV (the difference between the expected payoff value with the uncertain information we have now, versus the expected payoff if we always knew what the outcome would be for certain [ie, if we had a perfect “crystal ball”])

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-19 Expected Value With (or, Given) Perfect Information () Expected Value With (or, Given) Perfect Information ( EV|PI ) )P(S* j    PI|EV n j  where j=1 to the number of states of nature, n ( Best outcome for the state of nature j)

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-20 Expected Value of Perfect Information EVPIEV|PIEMV EVPI = EV|PI - maximum EMV

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-21 Expected Value of Perfect Information Construct a large plant Construct a small plant Do nothing 200,000 -$180,000 $0 Favorable Market ($) Unfavorable Market ($) EMV $52,000 $100,000-$20,000 $0 $48,000

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-22 Expected Value of Perfect Information EVPI EMV EVPI = expected value with perfect information - max( EMV ) = $200,000* * $52,000 = $68,000

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-23 Expected Opportunity Loss  EOL  EOL is the cost of not picking the best solution  EOL  EOL = Expected Regret  Minimum Expected Regret = EVPI

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-24 Computing EOL - The Opportunity Loss Table

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-25 The Opportunity Loss Table - continued

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-26 The Opportunity Loss Table - continued

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-27 Sensitivity Analysis P1-P EMV(Large Plant) = $200,000 P - ( 1-P )$180,000 P1-P EMV(Small Plant) = $100,000 P - $20,000( 1-P ) P1-P EMV(Do Nothing) = $0 P + 0( 1-P )

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-28 Sensitivity Analysis - continued EMV (Small Plant) EMV(Large Plant)

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-29  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 Decision Trees

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-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

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-31 Decision Tree 1 2 State 1 State 2 State 1 State 2 Alternative 1 Alternative 2 Decision Node Outcome 1 Outcome 2 Outcome 3 Outcome 4 State of Nature Node