Operations
Management
Module A –
Decision-Making Tools
PowerPoint presentation for
Operations Management Class
Updated and extended by Prof. Dedeke
© 2006
Prentice
Hall, Inc. Hall, Inc.
©
2006
Prentice
A–1
Outline
 Fundamentals of Decision Making
 Decision Tables
 Types of Decision-Making
Environments
 Decision Making Under Uncertainty
 Decision Making Under Risk
 Decision Making Under Certainty
 Expected Value of Perfect
Information (EVPI)
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Introduction
 Decision Making Approaches
 Structured
 Unstructured
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Structured Decision Making
Process
1. Clearly define the problem and the factors that
influence it
2. Develop the specific goals to be achieved
3. Develop quantitative measures that relate the
goals to the problem
4. Develop alternate solutions to problem
5. Compare the alternate solutions using a model or
structured methodology and the quantitative
measures from step 3
6. Select the best alternative
7. Implement the decision and set a timetable for
completion
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Decision Making Environment
Level of confidence
about occurrence
of outcomes and consequences
Total
Partial
Decisions
under
risk
Decisions
under
certainty
Decisions
under
uncertainty
Decisions
under
risk
Estimate-able
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Known
Value or Size of
outcomes and
consequences
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Decision-Making
Environments
 Decision making under uncertainty
 Complete uncertainty as to which
state of nature may occur
 Decision making under risk
 Several states of nature may occur
 Each has a probability of occurring
 Decision making under certainty
 State of nature is known
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Decision Making Under Certainty
States of Nature
From Table A.3
Favorable
Market
Unfavorable
Market
Construct large plant (A1)
$200,000
-$180,000
Construct small plant (A2)
$100,000
-$90,000
Do nothing (A3)
$0
$0
Probabilities
1
0
Alternatives
1. EMV(A1) = (1)($200,000) + (0)(-$180,000) = $200,000
2. EMV(A2) = (1)($100,000) + (0)(-$90,000) = $100,000
The preferable option is A1
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Decision Making Under Risk
States of Nature
From Table A.3
Favorable
Market
Unfavorable
Market
Construct large plant (A1)
$200,000
-$180,000
Construct small plant (A2)
$100,000
-$90,000
Do nothing (A3)
$0
$0
Probabilities
0.3
0.7
Alternatives
1. EMV(A1) = (0.3)($200,000) + (0.7)(-$180,000) = -$66,000
2. EMV(A2) = (0.3)($100,000) + (0.7)(-$90,000) = -$33,000
3. EMV(A3) = (0.3)($0) + (0.7)($0) = $0
A3 is the option to choose.
If A3 is excluded, The preferable option is A2
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Decision Making Under Risk (2)
In some cases the states of nature expected are certain, however the
values of each states are uncertain.
States of Demand
Seasonal Ticket
Occasional
Prob.
Market
Prob.
Market
Alternatives
Sell 100 tickets now (A1)
0.7
$200,000
0.3
$50,000
Sell 100 tickets later (A2)
0.3
$150,000
0.7
$300,000
Do nothing (A3)
$0
$0
1. EMV(A1) = (0.7)($200,000) + (0.3)($50,000) = $155,000
2. EMV(A2) = (0.3)($150,000) + (0.7)($300,000) = $255,000
3. EMV(A3) = (0)($0) + (0)($0) = $0
The preferable option is A2
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Risk
 Each possible state of nature has an
assumed probability
 States of nature are mutually exclusive
 Probabilities must sum to 1
 Determine the expected monetary value
(EMV) for each alternative
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Expected Monetary Value
EMV (Alternative i) = (Payoff of 1st state of nature) x
(Probability of 1st state of nature)
+ (Payoff of 2nd state of nature) x
(Probability of 2nd state of
nature)
+…+ (Payoff of last state of nature) x
(Probability of last state of
nature)
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Decision Making Under
Uncertainty
States of Nature
Alternatives
Favorable
Market
Unfavorable
Market
Construct
large plant
$200,000
-$180,000
Construct
small plant
$100,000
$0
Do nothing
Maximum
in Row
Minimum
in Row
Row
Average
$200,000 -$180,000
$10,000
-$20,000
$100,000
-$20,000
$40,000
$0
$0
$0
$0
Maximax
Maximin
Equally
likely
1. Maximax choice is to construct a large plant
2. Maximin choice is to do nothing
3. Equally likely choice is to construct a small plant
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Using Decision Trees to Solve
Decision Making Under Risk
2. Symbols used in a decision tree:
a. —decision node from which one
of several alternatives may be
selected
b. —a state-of-nature node out of
which one state of nature will occur
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Decision Tree Example
A decision node
A state of nature node
Favorable market
Unfavorable market
Construct
small plant
Favorable market
Unfavorable market
Figure A.1
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Decision Table Example
Alternatives
Construct large plant
Construct small plant
Do nothing
State of Nature
Favorable Market
Unfavorable Market
$200,000
–$180,000
$100,000
–$ 20,000
$
0
$
0
Table A.1
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Decision Tree Example
EMV for node 1
= $10,000
= (.5)($200,000) + (.5)(-$180,000)
Payoffs
Favorable market (.5)
1
Unfavorable market (.5)
Favorable market (.5)
Construct
small plant
2
EMV for node 2
= $40,000
Figure A.2
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Unfavorable market (.5)
$200,000
-$180,000
$100,000
-$20,000
= (.5)($100,000) + (.5)(-$20,000)
$0
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Complex
Decision
Tree
Example
Figure A.3
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Decision Trees in Ethical
Decision Making
 Maximize shareholder value and
behave ethically
 Technique can be applied to any
action a company contemplates
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Decision Trees in Ethical
Decision Making
Yes
Does action
maximize
company
returns?
Yes
Is
action
legal?
No
No
Figure A.4
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Is it ethical? (Weigh the
affect on employees,
customers, suppliers,
community against
shareholder benefit)
Is it ethical not to take
action? (Weigh the
harm to shareholders
vs. the benefits to other
stakeholders)
Yes
Do it
No
Don’t
do it
Yes
Don’t
do it
No
Do it,
but notify
appropriate
parties
Don’t
do it
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Qualitative Decision Making:
Factoring Priorities
• Operations decisions often involve selection of
suppliers, vendors, markets, products and so on.
In most of these cases, one has to define
priorities.
• Steps
– Identify attributes to rank or compare, skill, age
– Have a scale to use for ranking, e.g. 1, 2, 3, 4, 5
– Choose a scale for priorities, e.g. 0.1, 0.3, 0.9…
– Use system to compare alternatives
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Example: Priorities and
Decision Making
Employee
A
Employee
A
Employee
B
Employee
B
Weight
(W)
Score
(S1 )
W x S1
Score
(S2 )
W x S2
Language
0.1
10
6
Analytical
0.25
6
8
Technical
0.30
7
9
Salary
Expect.
0.20
5
6
Degree
0.15
10
5
TOTAL
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Example: Priorities and
Decision Making
Employee
A
Employee
A
Employee
B
Employee
B
Weight
(W)
Score
(S1 )
W x S1
Score
(S2 )
W x S2
Language
0.1
10
1
6
0.6
Analytical
0.25
6
1.5
8
2
Technical
0.30
7
2.1
9
2.7
Salary
Expect.
0.20
5
1
6
1.2
Degree
0.15
10
1.5
5
0.75
TOTAL
1.0
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7.1
7.25
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Qualitative Decision Making:
Location Strategies
• Review Chapter 8, Example 1, page
253-254 & 258
Solved problem 8.1, page 263
See problem 8.1 in Excel
spreadsheet
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