Operations
Management
Decision-Making Tools
Module A
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Management, 6E (Heizer & Render)
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Outline
The 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
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Management, 6E (Heizer & Render)
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
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
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Management, 6E (Heizer & Render)
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Models, and the Techniques of
Scientific Management
 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 that surrounds business plans and
actions
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Management, 6E (Heizer & Render)
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
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
PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
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
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Management, 6E (Heizer & Render)
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
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
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Management, 6E (Heizer & Render)
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
The Decision-Making Process
Quantitative Analysis
Problem
Logic
Historical Data
Marketing Research
Scientific Analysis
Modeling
Decision
Qualitative Analysis
Emotions
Intuition
Personal Experience
and Motivation
Rumors
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A-8
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Ways of Displaying
a Decision Problem
 Decision trees
 Decision tables
Outcomes
States of Nature
Alternatives
Decision Problem
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Management, 6E (Heizer & Render)
A-9
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Fundamentals of
Decision Theory
The three types of decision models:
 Decision making under uncertainty
 Decision making under risk
 Decision making under certainty
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Management, 6E (Heizer & Render)
A-10
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
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
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Decision Table
States of Nature
Alternatives
State 1
State 2
Alternative 1
Outcome 1
Outcome 2
Alternative 2
Outcome 3
Outcome 4
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
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.
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Example - Decision Making Under
Uncertainty
States of Nature
Alternatives Favorable Unfavorable Maximum Minimum
Construct
large plant
Construct
small plant
Market
$200,000
$100,000
$0
Row
Market
in Row
in Row Average
-$180,000 $200,000 -$180,000 $10,000
-$20,000 $100,000
$0
Maximax
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-$20,000 $40,000
$0
Maximin
$0
$0
Equally
likely
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Decision Making Under Risk
 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
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Expected Monetary Value
Equation
Number of states of nature
N
EMV ( A i ) =
Value of Payoff
 V i * P (V i )
Probability of payoff
i =1
= V 1 * P (V 1 ) + V 2 * P (V 2 ) + ... +V N * P (V N )
Alternative i
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Management, 6E (Heizer & Render)
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Example - Decision Making Under
Uncertainty
States of Nature
Alternatives
Construct
large plant
Construct
small plant
Favorable
Unfavorable
Market
Market P(0.5)
P(0.5)
$200,000
-$180,000
$100,000
-$20,000
$0
$0
Do nothing
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Expected
value
$10,000
$40,000 Best choice
$0
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Expected Value of Perfect
Information (EVPI)
EVPI places an upper bound on what one
would pay for additional information
EVPI is the expected value with perfect
information minus the maximum EMV
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Expected Value With Perfect
Information (EV|PI)
n
EV | PI =  (Best outcome for the state of nature j) * P(S j )
j =1
where j=1 to the number of states of nature, n
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Expected Value of Perfect
Information
EVPI = EV|PI - maximum EMV
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Expected Value of Perfect
Information
Alternative
State of Nature
Favorable Unfavorable
Market ($) Market ($)
EMV
Construct a
large plant
Construct a
small plant
200,000
-$180,000
$20,000
$100,000
$20,000
$40,000
Do nothing
$0
$0
$0
Probabilities
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0.50
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0.50
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Expected Value of Perfect
Information
EVPI = expected value with perfect information
- max(EMV)
= $200,000*0.50 + 0*0.50 - $40,000
= $60,000
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Management, 6E (Heizer & Render)
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Expected Opportunity Loss
EOL is the cost of not picking the best
solution
EOL = Expected Regret
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Computing EOL - The Opportunity
Loss Table
Alternative
Large Plant
Small Plant
Do Nothing
Probabilities
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Management, 6E (Heizer & Render)
State of Nature
Favorable Market
Unfavorable
($)
Market ($)
200,000 - 200,000
0 - (-180,000)
200,000 - 100,000
0 -(-20,000)
200,000 - 0
0-0
0.50
0.50
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
The Opportunity Loss Table continued
Alternative
Large Plant
Small Plant
Do Nothing
Probabilities
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State of Nature
Favorable Market Unfavorable
($)
Market ($)
0
180,000
100,000
20,000
200,000
0
0.50
0.50
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
The Opportunity Loss Table continued
Alternative
Large Plant
Small Plant
Do Nothing
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(0.50)*$0 +
(0.50)*($180,000)
(0.50)*($100,000)
+ (0.50)(*$20,000)
(0.50)*($200,000)
+ (0.50)*($0)
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EOL
$90,000
$60,000
$100,000
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Sensitivity Analysis
EMV(Large Plant) = $200,000P - (1-P)$180,000
EMV(Small Plant) = $100,000P - $20,000(1-P)
EMV(Do Nothing) = $0P + 0(1-P)
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Sensitivity Analysis - continued
250000
200000
Point 1
Point 2
EMV Values
150000
100000
50000
0
-50000 0
0.2
0.4
0.6
0.8
1
-100000
-150000
-200000
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Values of P
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Decision Trees
 Graphical display of decision process
 Used for solving problems


With 1 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
PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
A-29
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
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
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Management, 6E (Heizer & Render)
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Decision Tree
State 1
1
State 2
State 1
2
Decision
Node
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State 2
Outcome 1
Outcome 2
Outcome 3
Outcome 4
State of Nature Node
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458