OM2
SUPPLEMENTARY
CHAPTER E
DECISION ANALYSIS
DAVID A. COLLIER
AND
JAMES R. EVANS
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E Learning Outcomes
learning outcomes
LO1 Describe types of management decisions where
LO2
LO3
decision analysis techniques are useful and the
basic elements of a decision problem.
Explain how to evaluate risk in making decisions
and apply decision criteria to select an appropriate
decision alternative.
Describe how to construct simple decision trees
and use them to select optimal expected value
decisions.
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E. Decision Analysis
What do you think we should do? We’re down by 10
with 5 minutes left—plenty of time to get the ball back,”
pondered Ken Kendall, head coach of West High
in talking to offensive coach Craig Russell. West was
facing fourth down and short yardage for another first
down from their opponent’s 9-yard line. “Should we try
for the first down or go for the field goal?” Craig noted
that statistically a run is better than a field goal attempt
inside the 10-yard line. Ken wasn’t so sure, trying to
weigh the risk of not getting the first down or a
touchdown instead of an almost sure field goal.
OM2, Supp. Ch. E. Decision Analysis
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posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E. Decision Analysis
What do you think?
Describe a situation in your personal or work life
where you need to make an important decision.
What criteria will you use?
How will you make the decision?
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E. Decision Analysis
Decision analysis is the formal study of how
people
make decisions, particularly when faced with
uncertain
information, as well as a collection of techniques to
support the analysis of decision problems.
Applications:
• Product selection
• Facility capacity expansion and location
• Inventory analysis
• Technology and process selection
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E. Decision Analysis
Applying Decision Analysis Tools
• Decision analysis techniques apply when decisions
• Are important
• Are probably unique
• Allow some time for study
• Are complex
• Involve uncertainty and risk
Uncertainty refers to not knowing what will happen in
the future. Risk is the uncertainty associated with an
undesirable outcome, such as financial loss.
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E. Decision Analysis
Structuring Decision Problems
• Decision alternatives represent the choices that a
decision maker can make.
• Events represent the future outcomes that can occur
after a decision is made and that are not under the
control of the decision maker
• A numerical value associated with a decision coupled
with some event is called a payoff.
• For many situations, we can estimate probabilities of
events.
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Exhibit E.1
Supplemental Chapter E. Decision Analysis
Example
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E. Decision Analysis
Selecting Decision Alternatives
• For one-time decisions, managers must
take into account the risk associated with
making the wrong decision.
• For decisions that are repeated over and
over, managers can choose decisions
based on the expected payoffs that might
occur.
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E. Decision Analysis
One Time Decisions Without Event Probabilities
1. Maximax—choose the decision that will
maximize
the maximum possible profit among all events. This
is an aggressive, or risk-taking, approach.
2. Maximin—choose the decision that will
maximize the minimum possible profit among all
events. This is a conservative, or risk-averse,
approach.
3. Minimax regret—choose the decision that will
minimize the maximum opportunity loss associated
with the events.
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E. Decision Analysis
Example
Maximax criterion: choose to build new plant
Maximin criterion: choose to expand existing plant
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E. Decision Analysis
Example
Opportunity loss criterion: choose to build new plant
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E. Decision Analysis
Cost Payoffs
1. Minimin—choose the decision that will
minimize the minimum possible cost among all
events.
2. Minimax—choose the decision that will
minimize the maximum possible cost among all
events.
3. Minimax regret—choose the decision that
will minimize the maximum opportunity loss
associated with the events. When the output
measure is cost is that the “best” payoff is the
lowest cost, not the highest profit.
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E. Decision Analysis
Repeated Decisions With Event
Probabilities
The expected value approach is to select
the decision alternative with the best
expected payoff.
P(sj ) = probability of occurrence for event sj
N = number of events
EV (di ) = ΣjP (sj )V (di, sj )
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Exhibit E.3
Supplemental Chapter E. Decision Analysis
Example
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
15
Supplemental Chapter E. Decision Analysis
Expected Value of Perfect
Information
The expected value of perfect
information, or EVPI, is the difference
between the expected payoff under
perfect information and the expected
payoff of the optimal decision without
perfect information.
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E. Decision Analysis
Decision Trees
A decision tree is a graphical schematic of
the logical order with which decisions are
made and events occur. Nodes refer to the
intersections, or junction points, of the
tree. Arcs are the connectors between the
nodes. Arcs are sometimes called
branches.
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Exhibit E.4
Supplemental Chapter E. Decision Analysis
Example
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Exhibit E.7
Supplemental Chapter E. Decision Analysis
Optimal Decision Strategy
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Exhibit E.6
Supplemental Chapter E. Decision Analysis
New Product Introduction Decision Tree
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
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Supplemental Chapter E. Decision Analysis
Trendy’s Pies Case Study
1. Use these cost, revenue, and probability estimates
along with the decision tree to identify the best
decision strategy for Trendy’s Pies.
2. Suppose that Trendy is concerned about her probability
estimates of the consumer response to the regional test
market. Although her estimates are .7 for a high
response and .3 for a low response, she is not very
confident of these values. Determine how the decision
strategy would change if the probability of a high
response varies from .1 to .9 in increments of .1. How
sensitive is the best strategy in Question 1 to this
probability assumption?
OM2, Supp. Ch. E. Decision Analysis
©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or
posted to a publicly accessible website, in whole or in part.
21