buad306 chapter 5s – decision theory. why dm is important the act of selecting a preferred course...
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DM Applications Some Decision Making techniques can be specific: Capacity planning Location planning Lease/Buy But in general, we can improve Decision Making by using logical approachesTRANSCRIPT
BUAD306
Chapter 5S – Decision Theory
Why DM is Important
The act of selecting a preferred course of action among alternatives
A KEY responsibility of Operations Managers
DM Applications
Some Decision Making techniques can be specific:Capacity planningLocation planningLease/Buy
But in general, we can improve Decision Making by using logical approaches
How do WE make decisions?
Alternatives? Likelihoods? Outcomes?
Reasons for Poor DM
There may be better choices that have not been considered
Information about options may be imperfect Knowledge of existing circumstances may
be imperfect Past experience may be irrelevant Prediction of the future may be wrong Chains of cause and effect are subject to
high probability of error Too much Information Peer Pressure
DM Steps
1. Identify possible future conditions (states of nature)
2. Develop a list of alternatives3. Determine the estimated payoff for each
alternative for every condition4. Estimate the likelihood of each possible
condition5. Evaluate alternatives according to some
criterion and select best alternative
States of Nature
Possible outcomes that your business may experience
Examples:Demand: High, Medium, LowContracts: Awarded, Not AwardedWeather: Rainy, Mixed, Dry
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
Payoff Table
*Present value in $ millions(Page 180 in text)
Alternatives
Possible Future Demand
Low Moderate High
Small 100 100 100
Medium 70 120 120
Large (40) 20 160
Likelihoods of Conditions
Estimates of likelihood 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%)
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!
DM Under Certainty
When you know for sure which of the future conditions will occur, choose the alternative with the highest payoff!
DM Under Certainty Example
*Present value in $ millions(Page 180 in text)
We know for sure demand will be a) low, b) moderate, c) high
Alternatives
Possible Future Demand
Low Moderate High
Small 100 100 100
Medium 70 120 120
Large (40) 20 160
DM Under Uncertainty
Maximin Maximax Laplace
You don’t need to know Minimax Regret or Opportunity Loss Tables.
Maximin “The best of the worst”
Determine the worst possible payoff for each alternative, then
Choose the alternative that is the “best worst”.
AltsPossible Future Demand
MaximinLow Moderate HighSmall 100 100 100
Medium 70 120 120Large (40) 20 160
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”.
AltsPossible Future Demand
MaximaxLow Moderate HighSmall 100 100 100
Medium 70 120 120Large (40) 20 160
Laplace “The best average”
Determine the average payoff for each alternative, then
Choose the alternative that is the “best average”.
AltsPossible Future Demand
LaplaceLow Moderate HighSmall 100 100 100
Medium 70 120 120Large (40) 20 160
Alt Low Mod High
Droid 200 400 600
iPhone 100 500 800
Both -200 100 1000
Decision Under Uncertainty Example:
A Product Manager for a handheld software company is trying to decide whether to create an application for the Droid, iPhone or both devices. The revenue associated with each alternative depends on the demand for the product as noted below.
•What is the Maximin choice?
•What is the Maximax choice?
•What is the LaPlace choice?
Decision Under Uncertainty Example:
A Product Manager for a handheld software company is trying to decide whether to create an application for the Droid, iPhone or both devices. The revenue associated with each alternative depends on the demand for the product as noted below.
Alt Low Mod HighMaximin Maximax Laplace
Droid 200 400 600
iPhone 100 500 800
Both -200 100 1000
X Y ZA 150 70 130B 50 200 110C 160 60 100
Example: COST
Part A: Maximin, Maximax, Laplace
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
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
We have established likelihoods of future demand as follows: Low: .40, Medium, .50, High, .10
EMV Example:
Alternatives
Possible Future DemandLow Moderate High
Small 100 100 100
Medium 70 120 120
Large (40) 20 160
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?
EMV Example:
EMVDroid
EMViPhone
EMVCombo
Alt Low Mod High
Droid 200 400 600
iPhone 100 500 800
Both -200 100 1000
X Y ZA 150 70 130B 50 200 110C 160 60 100
Example: COST
Part B: Assume the following likelihoods: X= .5, Y = .2, Z = .3
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
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 PayoffUnder Certainty Under Risk (EMV)
EMV Example:
EVPI = Expected Payoff __ Expected Payoff
Under Certainty Under Risk
Expected Payoff Under Certainty:Expected Payoff Under Risk:EVPI =
Alternatives
Possible Future DemandLow Moderate High
Small 100 100 100
Medium 70 120 120
Large (40) 20 160
Low: .40, Medium, .50, High, .10
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?
EMV Example:
Expected Payoff Under Certainty =
Expected Payoff Under Risk =
Expected Value of Perfect Information =
Alt Low Mod High
Droid 200 400 600
iPhone 100 500 800
Both -200 100 1000
X Y ZA 150 70 130B 50 200 110C 160 60 100
Example: COST
Part C: EVPI
Decision Trees
Schematic representation Helpful in analyzing sequential
decisions Can see all the options in front of you
and compare easily
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
Should we run the light?
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.