Lecture Operations management: Creating value along the supply chain (Canadian edition) - Chapter 1S

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

EV (x) =
p(xi)xi

n
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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