1. Chapter 5S – Decision Theory
BUAD306
Chapter 5S – Decision Theory

2. Why DM is Important
The act of selecting a preferred course of action among alternatives
A KEY responsibility of Operations Managers
Some Decision Making techniques can be specific
Day-to-day decisions can be improved and made consistently by using logical approaches

3. How do WE make decisions?
Alternatives? Likelihoods? Outcomes?

4. Terms: States of Nature
Possible outcomes that your business may experience
Examples:
Demand: High, Medium, Low
Contracts: Awarded, Not Awarded
Weather: Rainy, Mixed, Dry

5. Terms: Alternatives
Choices the business can make, given the state of nature or other information
Examples:
Demand: Purchase new machinery, Don’t purchase machinery
Contracts: Hire Additional Staff, Don’t Hire
Weather: Invest in Irrigation System, Don’t Invest
Do Nothing

6. Possible Future Demand Present value in $ millions
Terms: Payoff Table
Alternatives
Possible Future Demand
Low
Moderate
High
Small
100
Medium 70
120
Large
(40) 20
160
Present value in $ millions
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7. Terms: Likelihoods Probability of state of nature
Typically stated in percentages, must total to 1.0
Based on historical data or subjective
Examples:
Demand: High (50%), Medium (30%), Low (20%)
Weather: Rainy (30%), Mixed (40%), Dry (30%)

8. Decision Environments
Certainty - Environment in which future events will definitely occur
Uncertainty - Environment in which it is impossible to assess the likelihood of various future events
Risk - Environment in which certain future events have probable outcomes
Different environments require
different analysis techniques!

9. DM Under Certainty
When you know for sure which of the future conditions will occur, choose the alternative with the highest payoff!

10. DM Under Certainty Example
We know for sure demand will be a) low, b) moderate, c) high
Alternatives
Possible Future Demand
Low
Moderate
High
Small
200
Medium
140
240
Large
(80) 40
320
Present value in $ millions

11. DM Under Uncertainty Maximin  Maximax  Laplace 
You don’t need to know Minimax Regret or Opportunity Loss Tables.

12. Possible Future Demand
Maximin 
“The best of the worst”
Determine the worst possible payoff for each alternative, then
Choose the alternative that is the “best worst”.
Alts
Possible Future Demand
Low
Moderate
High
Small
200
Medium
140
240
Large
(80) 40
320

13. Possible Future Demand
Maximax 
“The best of the best”
Determine the best possible payoff for each alternative, then
Choose the alternative that is the “best of the best”.
Alts
Possible Future Demand
Low
Moderate
High
Small
200
Medium
140
240
Large
(80) 40
320

14. Possible Future Demand
Laplace 
“The best average”
Determine the average payoff for each alternative, then
Choose the alternative that is the “best average”.
Alts
Possible Future Demand
Low
Moderate
High
Small
200
Medium
140
240
Large
(80) 40
320

15. Decision Under Uncertainty Example:
A Product Manager for a phone software company is trying to decide whether to create an app for the Droid, iPhone or both platforms. The revenue associated with each alternative depends on the demand for the app as noted below.
Alt
Low
Mod
High
Droid
300
600
1000
iPhone
400
500
900
Both
-300
1500
What is the Maximin choice?
What is the Maximax choice?
What is the LaPlace choice?

16. Example: COST X Y Z A
150 70
130 B 50
200
110 C
160 60
100
Part A: Maximin, Maximax, Laplace

17. DM Under Risk Most typical in business
Incorporates likelihoods into the process
Allows you to weight payoffs by the probability that the state of nature will occur

18. Expected Monetary Value
The best expected value among the alternatives
Steps:
For each cell in the Payoff Table, multiple the value by the likelihood of that state of nature
Sum up weighted values and selects the best payoff

19. Alternatives Possible Future Demand
EMV Example:
We have established likelihoods of future demand as follows:
Low: .60, Medium, .30, High, .10
Alternatives
Possible Future Demand
Low
Moderate
High
Small
200
Medium
140
240
Large
(80) 40
320

20. EMV Example:
Going back to our handheld application example, we now have the following likelihoods of future demand:
Low: 30%, Moderate: 50% and High: 20%
What are the EMVs for each alternative?
Alt
Low
Mod
High
Droid
300
600
1000
iPhone
400
500
900
Both
-300
1500

21. Example: COST X Y Z A
150 70
130 B 50
200
110 C
160 60
100
Part B: Assume the following likelihoods: X= .5, Y = .2, Z = .3

22. Expected Value of Perfect Information (EVPI)
What if you could delay your decision until you had more data? Would you??
How much would you be willing to pay for that extra time?
EVPI allows you to determine that figure

23. Calculating EVPI
Want to know if the cost of obtaining the perfect information will be less than the expected gain due to delaying your decision. Therefore:
EVPI =
Expected Payoff Expected Payoff
Under Certainty Under Risk (EMV)

24. Possible Future Demand
EMV Example:
Low: .60, Medium, .30, High, .10
Alternatives
Possible Future Demand
Low
Moderate
High
Small
200
Medium
140
240
Large
(80) 40
320
EVPI = Expected Payoff __ Expected Payoff
Under Certainty Under Risk

25. EMV Example:
Going back to our handheld application example, we now have the following likelihoods of future demand:
Low: 30%, Moderate: 50% and High: 20%
What is the EVPI for this scenario?
Alt
Low
Mod
High
Droid
300
600
1000
iPhone
400
500
900
Both
-300
1500

26. Example: COST X Y Z A
150 70
130 B 50
200
110 C
160 60
100
Part C: EVPI
.5, .2, .3

27. Zero EVPI? What does it mean? Example (revenue) Lo Mod Hi X 150 100
130 Y 50 80 90 Z
110 75
Likelihoods: Lo: .5, Mod: .2, Hi: .3

28. Decision Trees Schematic representation
Helpful in analyzing sequential decisions
Can see all the options in front of you and compare easily

29. Decision Tree Lingo Nodes –
Square Nodes - Make a decision
Round Nodes – Probabilities of events
Branches – contain information re: that decision or state of nature
Right to Left Analysis
Tree Pruning

30. Should we run the light?

31. HW #9
Firm must decide to build: Small, Medium or Large facility. Demand for all sizes could be low (.2) or high (.8).
If build small and demand is low, NPV = $42. If demand is high, can subcontract (NPV = $42) or expand greatly (NPV = $48)
If build medium and demand is low, NPV = $22. If demand is high, can do nothing (NPV = $46) or expand greatly (NPV = $50)
If build large and demand is low, NPV = -$20. If demand is high, NPV = $72.

32. Analysis with Decision Trees
Certainty?
Maximin, Maximax, LaPlace?
EMV
EVPI?