1. Operations Management
Module A – Decision-Making Tools
PowerPoint presentation for
Operations Management Class
Updated and extended by Prof. Dedeke
© 2006 Prentice Hall, Inc.

2. 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)

3. Introduction
Decision Making Approaches
Structured
Unstructured

4. Structured Decision Making Process
Clearly define the problem and the factors that influence it
Develop the specific goals to be achieved
Develop quantitative measures that relate the goals to the problem
Develop alternate solutions to problem
Compare the alternate solutions using a model or structured methodology and the quantitative measures from step 3
Select the best alternative
Implement the decision and set a timetable for completion
This slide provides some reasons that capacity is an issue. The following slides guide a discussion of capacity.

5. Decision Making Environment
Level of confidence about occurrence of outcomes and consequences
Decisions
under
risk
Decisions
under
certainty
Total
Decisions
under
uncertainty
Decisions
under
risk
This slide provides some reasons that capacity is an issue. The following slides guide a discussion of capacity.
Partial
Value or Size of outcomes and
consequences
Estimate-able
Known

6. 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

7. Decision Making Under Certainty
States of Nature
Favorable Unfavorable Alternatives Market 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
From Table A.3
EMV(A1) = (1)($200,000) + (0)(-$180,000) = $200,000
EMV(A2) = (1)($100,000) + (0)(-$90,000) = $100,000
The preferable option is A1

8. Decision Making Under Risk
States of Nature
Favorable Unfavorable Alternatives Market Market
Construct large plant (A1) $200,000 -$180,000
Construct small plant (A2) $100,000 -$90,000
Do nothing (A3) $0 $0
Probabilities
From Table A.3
EMV(A1) = (0.3)($200,000) + (0.7)(-$180,000) = -$66,000
EMV(A2) = (0.3)($100,000) + (0.7)(-$90,000) = -$33,000
EMV(A3) = (0.3)($0) + (0.7)($0) = $0
A3 is the option to choose.
If A3 is excluded, The preferable option is A2

9. 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 Alternatives Prob Market Prob Market
Sell 100 tickets now (A1) $200, $50,000
Sell 100 tickets later (A2) $150, $300,000
Do nothing (A3) $ $0
EMV(A1) = (0.7)($200,000) + (0.3)($50,000) = $155,000
EMV(A2) = (0.3)($150,000) + (0.7)($300,000) = $255,000
EMV(A3) = (0)($0) + (0)($0) = $0
The preferable option is A2

10. 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

11. 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)

12. Decision Making Under Uncertainty
States of Nature
Favorable Unfavorable Maximum Minimum Row
Alternatives Market Market in Row in Row Average
Construct large plant $200,000 -$180,000 $200,000 -$180,000 $10,000
Construct small plant $100, $20,000 $100, $20,000 $40,000
Do nothing $0 $0 $0 $0 $0
Maximax
Equally likely
Maximin
Maximax choice is to construct a large plant
Maximin choice is to do nothing
Equally likely choice is to construct a small plant

13. Using Decision Trees to Solve Decision Making Under Risk
Symbols used in a decision tree:
—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

14. Decision Tree Example A decision node A state of nature node
Favorable market
Unfavorable market
Construct large plant
Favorable market
Unfavorable market
Construct small plant
Do nothing
Figure A.1

15. Decision Table Example
State of Nature
Alternatives Favorable Market Unfavorable Market
Construct large plant $200,000 –$180,000
Construct small plant $100,000 –$ 20,000
Do nothing $ $
Table A.1

16. Decision Tree Example EMV for node 1
= (.5)($200,000) + (.5)(-$180,000)
EMV for node 1
= $10,000
Payoffs
$200,000
-$180,000
$100,000
-$20,000 $0
Favorable market (.5)
Unfavorable market (.5) 1
Construct large plant
Construct small plant
Do nothing
Favorable market (.5)
Unfavorable market (.5) 2
EMV for node 2
= $40,000
= (.5)($100,000) + (.5)(-$20,000)
Figure A.2

17. Complex Decision Tree Example
Figure A.3

18. Decision Trees in Ethical Decision Making
Maximize shareholder value and behave ethically
Technique can be applied to any action a company contemplates

19. Decision Trees in Ethical Decision Making
Yes No
Yes
Is it ethical? (Weigh the affect on employees, customers, suppliers, community against shareholder benefit)
Do it
Don’t do it
Do it,
but notify appropriate parties
Yes
Does action maximize company returns? No
Is it ethical not to take action? (Weigh the harm to shareholders vs. the benefits to other stakeholders)
Is action legal? No
Figure A.4

20. 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

21. Example: Priorities and Decision Making
Employee A
Employee B
Weight (W)
Score (S1 )
W x S1
Score (S2 )
W x S2
Language
0.1 10 6
Analytical
0.25 8
Technical
0.30 7 9
Salary Expect.
0.20 5
Degree
0.15
TOTAL

22. Example: Priorities and Decision Making
Employee A
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
1.5 8 2
Technical
0.30 7
2.1 9
2.7
Salary Expect.
0.20 5
1.2
Degree
0.15
0.75
TOTAL
1.0
7.1
7.25

23. Qualitative Decision Making: Location Strategies
Review Chapter 8, Example 1, page & Solved problem 8.1, page 263
See problem 8.1 in Excel spreadsheet