OPERATIONS MANAGEMENT:
Creating Value Along the Supply Chain,
Canadian Edition
Robert S. Russell, Bernard W. Taylor III, Ignacio Castillo, Navneet Vidyarthi
CHAPTER 1 SUPPLEMENT
Decision Analysis
Supplement 1-1
Lecture Outline
 Decision Analysis
 Decision Making without Probabilities
 Decision Analysis with Excel
 Decision Analysis with OM Tools
 Decision Making with Probabilities
 Expected Value of Perfect Information
 Sequential Decision Tree
Supplement 1-2
Decision Analysis
 Quantitative methods
• 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
Supplement 1-3
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
Supplement 1-4
Payoff Table
 Payoff table
• method for organizing and illustrating payoffs from
different decisions given various states of nature
 Payoff
• outcome of a decision
Supplement 1-5
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
Supplement 1-6
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
Supplement 1-7
Southern Textile Company
Supplement 1-8
Maximax Solution
Decision: Maintain status quo
Supplement 1-9
Maximin Solution
Decision: Expand
Supplement 1-10
Minimax Regret Solution
Decision: Expand
Supplement 1-11
Hurwicz Criteria
Decision: Expand
Supplement 1-12
Equal Likelihood Criteria
Decision: Expand
Supplement 1-13
Decision Analysis with Excel
Supplement 1-14
Decision Analysis with OM Tools
Supplement 1-15
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
Supplement 1-16
Expected Value
n
EV (x) =
 p(xi)xi
i =1
where
xi = outcome i
p(xi) = probability of outcome i
Supplement 1-17
Decision Making with Probabilities
Supplement 1-18
Decision Making with Probabilities:
Excel
Supplement 1-19
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
Supplement 1-20
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 - 865,000 = $195,000
Supplement 1-21
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
Supplement 1-22
Evaluations at Nodes
 Compute EV at nodes 6 & 7
 EV(node 6)= 0.80($3,000,000) +
0.20($700,000) = $2,540,000
 EV(node 7)= 0.30($2,300,000) +
0.70($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
Supplement 1-23
Decision Tree Analysis
Supplement 1-24
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