© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-1 Operations Management Decision-Making Tools Module A

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-2 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

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-3 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

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-4 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 non-quantifiable information  often have assumptions that oversimplify the variables of the real world

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-5 The Decision-Making Process ProblemDecision Quantitative Analysis Logic Historical Data Marketing Research Scientific Analysis Modeling Qualitative Analysis Emotions Intuition Personal Experience and Motivation Rumors

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-6 Decision-Making Tools  Decision Trees  Decision Tables

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-7 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

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-8 Getz Products Decision Table States of Nature Alternatives Favorable market Unfavorable market Large plant $200,000 – $180,000 Small plant $100,000 – $20,000 Do nothing $0

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-9 Three Types of Decision Models  Decision making under uncertainty  Decision making under risk  Decision making under certainty

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-10 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.

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-11 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 Maximax Maximin Equally likely Do nothing

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-12  Probabilistic decision situation  States of nature have probabilities of occurrence  Select alternative with largest Expected Monetary Value – EMV is the average return for the alternative if the decision were repeated many times Decision Making Under Risk

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-13 Decision Making Under Risk States of Nature AlternativesFavorable Market P(0.6) Unfavorable Construct small plant Do nothing $0 large plant $200,000 $100,000 – $180,000 – $20,000 $48,000 $52,000 Expected value Market P(0.4) Best choice

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-14 Decision Making Under Certainty  Expected Value of Perfect Information places an upper bound on what one would pay for additional information  Expected Value of Perfect Information is the difference between the payoff under certainty and the payoff under risk Payoff under Certainty = ($200,000 x 0.6) + ($0 x 0.4) Payoff under Risk = $52,000 EVPI = $120,000 – $52,000 = $68,000

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-15  Graphical display of decision process  Used for solving problems with sequential decisions and states of nature  Expected Monetary Value (EMV) is most often used Decision Trees State 1 State 2 State 1 State 2 Alternative 1 Alternative 2 Outcome 1 Outcome 2 Outcome 3 Outcome 4

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-16 Getz Products Decision Tree Payoffs $200,000 – $180,000 $100,000 – $20, Unfavorable market (0.4) Favorable market (0.6) Construct small plant Construct large plant Do nothing EMV for node 2 = $52,000 EMV for node 1 = $48,000 1

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-17 Payoffs $200,000 – $180,000 $100,000 – $20, Unfavorable market (0.4) Favorable market (0.6) small plant large plant Do nothing EMV for node 2 = $52,000 EMV for node 1 = $48,000 1

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A-18 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

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J A Decision alternatives 5. Define payoffs for each end point 2. States of Nature & probabilities 3. Subsequent alternatives 4. States of Nature & probabilities 9. Choose best alternative 6. Calculate (prob X payoff ) 7. Choose best alternative 8. Calculate (prob X payoff ) N  N N  N Decision Tree Worksheet