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1. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-1
Operations Operations
ManagementManagement
Decision-Making ToolsDecision-Making Tools
Module AModule A

2. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-2
OutlineOutline
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

3. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-3
Learning ObjectivesLearning 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

4. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-4
Models, and the Techniques of Models, and the Techniques of
Scientific ManagementScientific Management
Can Help Managers ToCan 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 thatsurrounds business plans
and actions

5. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-5
Steps to Good DecisionsSteps 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

6. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-6
ModelsModels
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

7. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-7
Limitations of ModelsLimitations 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

8. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-8
The Decision-Making ProcessThe Decision-Making Process
Problem Decision
Quantitative Analysis
Logic
Historical Data
Marketing Research
Scientific Analysis
Modeling
Qualitative Analysis
Emotions
Intuition
Personal Experience
and Motivation
Rumors

9. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-9
Decision Problem
Alternatives
States of Nature
Out-
comes
Decision trees
Decision tables
Ways of DisplayingWays of Displaying
a Decision Problem a Decision Problem

10. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-10
Fundamentals ofFundamentals of
Decision Theory Decision Theory
The three types of decision models:
Decision making under uncertainty
Decision making under risk
Decision making under certainty

11. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-11
Fundamentals ofFundamentals of
Decision Theory - continued 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

12. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-12
Decision TableDecision Table
States of Nature
AlternativesState 1 State 2
Alternative 1Outcome 1Outcome 2
Alternative 2Outcome 3Outcome 4

13. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-13
Decision Making Under Decision Making Under
UncertaintyUncertainty
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.

14. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-14
Example - Decision Making Under Example - Decision Making Under
UncertaintyUncertainty
States of Nature
AlternativesFavorable
Market
Unfavorable
Market
Maximum
in Row
Minimum
in Row
Row
Average
Construct
large plant
$200,000-$180,000$200,000-$180,000$10,000
Construct
small plant
$100,000-$20,000$100,000-$20,000$40,000
$0 $0 $0 $0 $0
MaximaxMaximinEqually
likely

15. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-15
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
Decision Making Under RiskDecision Making Under Risk

16. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-16
Expected Monetary Value Expected Monetary Value
EquationEquation
Probability of payoffEMVA VPV
VPVVPV VPV
i i
i
i
N N
( ()
() () ()
)=
N
=
*
=* +* ++*
1
1 1 2 2
Number of states of natureNumber of states of nature
Value of Payoff
Alternative i
...

17. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-17
Example - Decision Making Under Example - Decision Making Under
UncertaintyUncertainty
States of Nature
AlternativesFavorable
Market
P(0.5)
Unfavorable
Market P(0.5)
Expected
value
Construct
large plant
$200,000-$180,000$10,000
Construct
small plant
$100,000-$20,000$40,000
Do nothing $0 $0 $0
Best choice

18. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-18
Expected Value of Perfect Expected Value of Perfect
Information (Information (EVPI))
EVPIEVPI places an upper bound on what one
would pay for additional information
EVPIEVPI is the expected value with perfect
information minus the maximum EMV

19. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-19
Expected Value With Perfect Expected Value With Perfect
Information (Information (EV|PI))
)P(S*
j


PI|EV
n
j
where j=1 to the number of states of nature, n
(Best outcome for the state of nature j)

20. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-20
Expected Value of Perfect Expected Value of Perfect
InformationInformation
EVPIEVPI = EV|PIEV|PI - maximum EMVEMV

21. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-21
Expected Value of Perfect Expected Value of Perfect
InformationInformation
State of Nature
Alternative
Probabilities
Construct a
large plant
Construct a
small plant
Do nothing
200,000-$180,000
$0
Favorable
Market ($)
Unfavorable
Market ($)
0.50 0.50
EMV
$40,000$100,000 $20,000
$0 $0
$20,000

22. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-22
Expected Value of Perfect Expected Value of Perfect
InformationInformation
EVPIEVPI = expected value with perfect information
- max(EMVEMV)
= $200,000*0.50 + 0*0.50 - $40,000
= $60,000

23. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-23
Expected Opportunity LossExpected Opportunity Loss
EOLEOL is the cost of not picking the best
solution
EOLEOL = Expected Regret

24. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-24
Computing EOL - The Opportunity Computing EOL - The Opportunity
Loss TableLoss Table
State of Nature
Alternative Favorable Market
($)
Unfavorable
Market ($)
Large Plant 200,000 - 200,0000 - (-180,000)
Small Plant 200,000 - 100,0000 -(-20,000)
Do Nothing 200,000 - 0 0-0
Probabilities 0.50 0.50

25. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-25
The Opportunity Loss Table - The Opportunity Loss Table -
continuedcontinued
State of Nature
Alternative Favorable Market
($)
Unfavorable
Market ($)
Large Plant 0 180,000
Small Plant 100,000 20,000
Do Nothing 200,000 0
Probabilities 0.50 0.50

26. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-26
The Opportunity Loss Table - The Opportunity Loss Table -
continuedcontinued
Alternative EOL
Large Plant (0.50)*$0 +
(0.50)*($180,000)
$90,000
Small Plant (0.50)*($100,000)
+ (0.50)(*$20,000)
$60,000
Do Nothing (0.50)*($200,000)
+ (0.50)*($0)
$100,000

27. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-27
Sensitivity AnalysisSensitivity Analysis
EMV(Large Plant) = $200,000PP - (1-P1-P)$180,000
EMV(Small Plant) = $100,000PP - $20,000(1-P1-P)
EMV(Do Nothing) = $0PP + 0(1-P1-P)

28. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-28
Sensitivity Analysis - continuedSensitivity Analysis - continued
-200000
-150000
-100000
-50000
0
50000
100000
150000
200000
250000
0 0.2 0.4 0.6 0.8 1
Values of P
E
M
V
V
a
lu
e
s
Point 1
Point 2
EMV (Small Plant)
EM
V(Large Plant)

29. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-29
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
Decision TreesDecision Trees

30. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-30
Analyzing Problems with Decision Analyzing Problems with Decision
TreesTrees
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

31. PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
A-31
Decision TreeDecision Tree
1
2
State 1
State 2
State 1
State 2
Alternative 1
A
lte
r
n
a
tiv
e
2
Decision Decision
NodeNode
Outcome 1Outcome 1
Outcome 2Outcome 2
Outcome 3Outcome 3
Outcome 4Outcome 4
State of Nature NodeState of Nature Node