# Operations and Supply Chain Management, 8th Edition

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Operations and Supply Chain Management, 8th Edition
Chapter 1 Supplement Decision Analysis Russell and Taylor Operations and Supply Chain Management, 8th Edition

Lecture Outline Decision Analysis – Slide 3
Decision Making without Probabilities – Slide 4 Decision Analysis with Excel – Slide 14 Decision Analysis with OM Tools – Slide 15 Decision Making with Probabilities – Slide 16 Expected Value of Perfect Information – Slide 20 Sequential Decision Tree – Slide 22 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Decision Analysis Quantitative methods Decision analysis
a set of tools for operations manager Decision analysis a set of quantitative decision-making techniques for decision situations in which uncertainty exists Example of an uncertain situation demand for a product may vary between 0 and 200 units, depending on the state of market © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Decision Making Without Probabilities
States of nature Events that may occur in the future Examples of states of nature: high or low demand for a product good or bad economic conditions Decision making under risk probabilities can be assigned to the occurrence of states of nature in the future Decision making under uncertainty probabilities can NOT be assigned to the occurrence of states of nature in the future © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Payoff Table Payoff table Payoff States Of Nature Decision a b
method for organizing and illustrating payoffs from different decisions given various states of nature Payoff outcome of a decision States Of Nature Decision a b 1 Payoff 1a Payoff 1b 2 Payoff 2a Payoff 2b © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Decision Making Criteria Under Uncertainty
Maximax choose decision with the maximum of the maximum payoffs Maximin choose decision with the maximum of the minimum payoffs Minimax regret choose decision with the minimum of the maximum regrets for each alternative © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Decision Making Criteria Under Uncertainty
Hurwicz choose decision in which decision payoffs are weighted by a coefficient of optimism, alpha coefficient of optimism is a measure of a decision maker’s optimism, from 0 (completely pessimistic) to 1 (completely optimistic) Equal likelihood (La Place) choose decision in which each state of nature is weighted equally © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Southern Textile Company
STATES OF NATURE Good Foreign Poor Foreign DECISION Competitive Conditions Competitive Conditions Expand \$ 800,000 \$ 500,000 Maintain status quo 1,300, ,000 Sell now 320, ,000 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Maximax Solution Expand: \$800,000 Status quo: 1,300,000  Maximum
STATES OF NATURE Good Foreign Poor Foreign DECISION Competitive Conditions Competitive Conditions Expand \$ 800,000 \$ 500,000 Maintain status quo 1,300, ,000 Sell now 320, ,000 Expand: \$800,000 Status quo: 1,300,000  Maximum Sell: 320,000 Decision: Maintain status quo © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Maximin Solution Expand: \$500,000  Maximum Status quo: -150,000
STATES OF NATURE Good Foreign Poor Foreign DECISION Competitive Conditions Competitive Conditions Expand \$ 800,000 \$ 500,000 Maintain status quo 1,300, ,000 Sell now 320, ,000 Expand: \$500,000  Maximum Status quo: -150,000 Sell: 320,000 Decision: Expand © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Minimax Regret Solution
States of Nature Good Foreign Poor Foreign Competitive Conditions Competitive Conditions \$1,300, ,000 = 500, \$500, ,000 = 0 1,300, ,300,000 = ,000 - (-150,000)= 650,000 1,300, ,000 = 980, , ,000= 180,000 Expand: \$500,000  Minimum Status quo: 650,000 Sell: 980,000 Decision: Expand © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Hurwicz Criteria  = 0.3 1 -  = 0.7 Decision: Expand STATES OF NATURE
Good Foreign Poor Foreign DECISION Competitive Conditions Competitive Conditions Expand \$ 800,000 \$ 500,000 Maintain status quo 1,300, ,000 Sell now 320, ,000  =  = 0.7 Expand: \$800,000(0.3) + 500,000(0.7) = \$590,000  Maximum Status quo: 1,300,000(0.3) -150,000(0.7) = 285,000 Sell: 320,000(0.3) + 320,000(0.7) = 320,000 Decision: Expand © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Equal Likelihood Criteria
STATES OF NATURE Good Foreign Poor Foreign DECISION Competitive Conditions Competitive Conditions Expand \$ 800,000 \$ 500,000 Maintain status quo 1,300, ,000 Sell now 320, ,000 Two states of nature each weighted 0.50 Expand: \$800,000(0.5) + 500,000(0.5) = \$650,000  Maximum Status quo: 1,300,000(0.5) -150,000(0.5) = 575,000 Sell: 320,000(0.5) + 320,000(0.5) = 320,000 Decision: Expand © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Decision Analysis with Excel
= MAX(C6:D6) = MAX(D6:D8)-F7 = MIN(C6:D8) = MAX(F6:F8) = MAX(G7:H7) = C19*E7+C20*F7) = .5*E8+.5*F8 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Decision Analysis with OM Tools
© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Decision Making with Probabilities
Risk involves assigning probabilities to states of nature Expected value a weighted average of decision outcomes in which each future state of nature is assigned a probability of occurrence © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

 Expected Value EV (x) = p(xi)xi n i =1 where xi = outcome i
p(xi) = probability of outcome i © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Decision Making with Probabilities
STATES OF NATURE Good Foreign Poor Foreign DECISION Competitive Conditions Competitive Conditions Expand \$ 800,000 \$ 500,000 Maintain status quo 1,300, ,000 Sell now 320, ,000 p(good) = p(poor) = 0.30 EV(expand): \$800,000(0.7) + 500,000(0.3) = \$710,000 EV(status quo): 1,300,000(0.7) -150,000(0.3) = 865,000  Maximum EV(sell): ,000(0.7) + 320,000(0.3) = 320,000 Decision: Status quo © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Decision Making with Probabilities: Excel
Expected value computed in cell D6 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Expected Value of Perfect Information
EVPI maximum value of perfect information to the decision maker maximum amount that would be paid to gain information that would result in a decision better than the one made without perfect information © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

EVPI Good conditions will exist 70% of the time
choose maintain status quo with payoff of \$1,300,000 Poor conditions will exist 30% of the time choose expand with payoff of \$500,000 Expected value given perfect information = \$1,300,000 (0.70) + 500,000 (0.30) = \$1,060,000 Recall that expected value without perfect information was \$865,000 (maintain status quo) EVPI = \$1,060, ,000 = \$195,000 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Sequential Decision Trees
A graphical method for analyzing decision situations that require a sequence of decisions over time Decision tree consists of Square nodes - indicating decision points Circles nodes - indicating states of nature Arcs - connecting nodes © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Evaluations at Nodes Compute EV at nodes 6 & 7
EV(node 6)= 0.80(\$3,000,000) (\$700,000) = \$2,540,000 EV(node 7)= 0.30(\$2,300,000) (\$1,000,000)= \$1,390,000 Decision at node 4 is between \$2,540,000 for Expand and \$450,000 for Sell land Choose Expand Repeat expected value calculations and decisions at remaining nodes © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e

Decision Tree Analysis
\$1,290,000 \$2,000,000 0.60 Market growth 2 0.40 No market growth \$225,000 Market growth \$3,000,000 Expand (-\$800,000) \$2,540,000 Expand (-\$800,000) 0.80 \$1,740,000 6 \$700,000 0.20 1 4 \$1,160,000 No market growth growth (3 years, Market \$0 payoff) Sell land \$450,000 Purchase Land (-\$200,000) 0.60 Market growth \$2,300,000 \$1,390,000 3 0.40 Warehouse (-\$600,000) 0.30 \$790,000 7 \$1,360,000 \$1,000,000 0.70 growth (3 years, No market \$0 payoff) 5 No market growth Sell land \$210,000 © 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e