Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-1 Operations Management Decision-Making Tools Module A

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-2 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 )  Decision Trees  A More Complex Decision Tree

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 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 making under risk  Decision making under uncertainty  Decision making under risk

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 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

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 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

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 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

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 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 complexity  tend to downplay the role and value of nonquantifiable information  often have assumptions that oversimplify the variables of the real world

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 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

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-9 Decision Problem Alternatives States of Nature Outcomes  Decision trees  Decision tables Ways of Displaying a Decision Problem

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-10 Fundamentals of Decision Theory The three types of decision models:  Decision making under uncertainty  Decision making under risk  Decision making under certainty

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 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

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-12 Getz Products Decision Tree 1 2 Unfavorable market Favorable market Construct small plant Construct large plant Do nothing A decision node A state of nature node

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-13 Decision Table States of Nature Alternatives State 1State 2 Alternative 1 Outcome 1Outcome 2 Alternative 2 Outcome 3Outcome 4

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-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.

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-15 Example - 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 MaximaxMaximin Equally likely Do nothing

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-16 The Decisions 1. The maximax choice is to construct a large plant. This is the maximum of the max imum number within each row or alternative. 2.The maximin choice is to do nothing. This is the maxi mum of the min imum number within each row or alternative. 3.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.

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-17  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

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-18 Expected Monetary Value Equation Probability of payoff EMVAXPX XPXXPXXPX ji i i NN (() ()()() )= =  * = * + * ++ * Number of states of nature Value of Payoff Alternative i... N

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-19 Example - Decision Making Under Uncertainty

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-20 Expected Value of Perfect Information () Expected Value of Perfect Information ( EVPI )  EVPI places an upper bound on what one would pay for additional information  EVPI is the expected value with certainty minus the maximum EMV

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-21 Expected Value With Perfect Information () Expected Value With Perfect Information ( EV|PI )

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-22 Expected Value of Perfect Information EVPIExpected value under CertaintyEMV EVPI = Expected value under Certainty - maximum EMV

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-23 Expected Value of Perfect Information Construct a large plant Construct a small plant Do nothing 200,000 -$180,000 $0 Favorable Market ($) Unfavorable Market ($) 0.50 EMV $40,000 $100,000-$20,000 $0 $20,000

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-24 Expected Value of Perfect Information EVPI EVPI = expected value with perfect information - max( EMV ) = $200,000* * $40,000 = $60,000

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-25  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 Decision Trees

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-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

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-27 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

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-28 Getz Products Decision Tree Completed and Solved Payoffs $200,000 -$180,000 $100, , Unfavorable market (0.5) Favorable market (0.5) Construct small plant Construct large plant Do nothing EMV for node 2 = $40,000 EMV for node 1 = $10,000

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-29 Getz Products Decision Tree with Probabilities and EMVs Shown $49,200 $106,400 $40,000 $2, $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 $0 Survey No survey Large plant Small plant No plant Large plant Small plant No plant Large plant Small plant No plant Fav. Mkt (0.78) Fav. Mkt (0.27) Fav. Mkt (0.5) Unfav. Mkt (0.22) Unfav. Mkt (0.73) Unfav. Mkt (0.5) $106,000 $63,600 -$87,400 $2,400 $10,000 $40,000 Sur. Res. Neg. (.55) Sur. Res. Pos. (.45) 1 st decision point 2 nd decision point