Transcript modA - Pearsoncmg

Operations
Management
Decision-Making Tools
Module A
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Principles of Operations Management, 5e, and Operations
Management, 7e
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© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
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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Principles of Operations Management, 5e, and Operations
Management, 7e
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© 2004 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
Decision making under uncertainty
Decision making under risk
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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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Steps to Good Decisions
 Define problem and influencing factors
 Establish decision criteria
 Select decision-making tool (model)
 Identify and evaluate alternatives using decisionmaking tool (model)
 Select best alternative
 Implement decision
 Evaluate the outcome
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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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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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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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Ways of Displaying
a Decision Problem
 Decision trees
 Decision tables
Outcomes
States of Nature
Alternatives
Decision Problem
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© 2004 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, 7e
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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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Principles of Operations Management, 5e, and Operations
Management, 7e
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© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Getz Products Decision Tree
Favorable market
A state of nature node
1
Unfavorable market
Favorable market
A decision node
Construct
small plant 2
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Unfavorable market
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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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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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Example - Decision Making Under
Uncertainty
States of Nature
Alternatives Favorable Unfavorable Maximum Minimum
Construct
large plant
Construct
small plant
Do nothing
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
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
The Decisions
1.
The maximax choice is to construct a large
plant. This is the maximum of the maximum
number within each row or alternative.
2. The maximin choice is to do nothing. This is the
maximum of the minimum number within each
row or alternative.
3. The equally likely choice is to construct a small
plant. This is the maximum of the average
outcomes of each alternative. This approach
assumes that all outcomes for any alternative
are equally likely.
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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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Expected Monetary Value Equation
Number of states of nature
N
EMV ( A j ) =
Value of Payoff
 X i * P (X i )
Probability of payoff
i =1
= X 1 * P (X 1 ) + X 2 * P (X 2 ) + ... +X N * P (X N )
Alternative i
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Example - Decision Making Under
Uncertainty
States of Nature
Alternatives
Construct
large plant
Construct
small plant
Do nothing
Favorable
Unfavorable
Market
Market P(0.5)
P(0.5)
$200,000
-$180,000
$100,000
-$20,000
$0
$0
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Expected
value
$10,000
$40,000 Best choice
$0
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Expected Value of Perfect
Information (EVPI)
 EVPI places an upper bound on what one would
pay for additional information
 EVPI is the expected value with certainty minus
the maximum EMV
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Expected Value With Perfect
Information (EV|PI)
Ex pected v alue under certainty
i n
  Best outcome for the ith state of nature *P( S i )
i1
w here P(Si )  Probability of the ith state of nature
and i  1 to n, the number of states of nature
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Expected Value of Perfect
Information
EVPI = Expected value under Certainty - maximum EMV
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Expected Value of Perfect
Information
Alternative
State of Nature
Favorable Unfavorable
Market ($) 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
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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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© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Decision Trees
 Graphical display of decision process
 Used for solving problems


With one 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
Transparency Masters to accompany Heizer/Render –
Principles of Operations Management, 5e, and Operations
Management, 7e
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© 2004 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
Transparency Masters to accompany Heizer/Render –
Principles of Operations Management, 5e, and Operations
Management, 7e
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© 2004 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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© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Getz Products Decision Tree
Completed and Solved
EMV for node 1 = $10,000
1
Payoffs
Favorable market (0.5)
Unfavorable market (0.5) -$180,000
Favorable market (0.5)
Construct
small plant 2
$200,000
Unfavorable market (0.5)
$100,000
-20,000
EMV for node 2 = $40,000
0
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Management, 7e
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Getz Products Decision Tree with
Probabilities and EMVs Shown
2nd decision point
$106,000
2
$106,400
1st decision
point
$63,600
3
-$87,400
$2,400
4
$2,400
5
$10,000
6
$40,000
$49,200
1
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$40,000
7
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Fav. Mkt (0.78)
Unfav. Mkt (0.22)
Fav. Mkt (0.78)
Unfav. Mkt (0.22)
Fav. Mkt (0.27)
Unfav. Mkt (0.73)
Fav. Mkt (0.27)
Unfav. Mkt (0.73)
Fav. Mkt (0.5)
Unfav. Mkt (0.5)
Fav. Mkt (0.5)
Unfav. Mkt (0.5)
$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
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458