Tools of decision and risk analysis applied to product commercialization projects. This presentation was originally published on the Project Management Institute Rochester Chapter website.

1. Risk Analysis
Some Useful Tools
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Eastman Kodak Company, 2002
Risk Analysis for
Strategic Decisions
Some Useful Tools
Jerry E. Boger
Eastman Kodak Company
October 16, 2002
PMI – Rochester Chapter

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Some Useful Tools
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Risks in Projects
Technological /
Market Scenario
Competition
Analysis
Rough
Project
Profitability
Objectives &
Constraints
Planning
Project
Requirements
Milestone
Zero
Organizing
The Team
Kickoff
Session
Structuring
Project
Activities
Objectives &
Constraints
Analysis
Activity Path
Sequencing
Activity
Planning
Milestones
Plan
Project
Costing
Resource
Availability
Milestone
One
Feasibility
Preparation
“Strategic” Risk Analysis
“Operational” Risk Analysis
Source: Report of EIRMA Working Group 53

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Some Useful Tools
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What is an Influence Diagram?
• A decomposition of a “big uncertainty,” like, “How much
money will this project make the company,” into its
fundamental uncertainty components
• The outcome of the “influencing” element “Revenue”
changes the probability distribution of the “influenced”
uncertainty “Earnings” in some significant way
Revenue
Earnings
Costs
Unit Sales
Price

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Some Useful Tools
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Example of a Media Technology
Decision Influence Diagram
NPV
Earnings
Capital
R&D Expenses
Paper
UMC
Industry
Volume
Paper &
Chem Margin
% Kodak
Owned
Kodak P/F
Earnings
P/F
UMC
Market
Potential
Market
Share
Price to
Consumer
Performance
Indep. P/F
Accept.
Competitive
Offerings
P/F
Capital Req.
Price
to P/F
Manuf'g
Capital
P/F Capital
Media
Earnings
Advertising
Time to
Market
P/F Labor
Process
Control
New Steps
Coating
Speed
# of
Passes
License /
Royalty

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Some Useful Tools
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Process Steps for Building an
Influence Diagram
• Start with the “big uncertainty” on the right side of the page
• Work on one uncertainty element at a time; find all the
direct influences for that uncertainty before moving on
• Do not show influences that are not uncertainties (knowns)
• Define each uncertainty as a question that can be answered
by a number (specify the units), yes or no, or a well-defined
multiple choice
• Like peeling an onion, complete one “shell” or level of
influencing elements before moving deeper
• Stop when you have enough detail to directly assess the
uncertainty elements

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Some Useful Tools
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Process Questions
Use the “clairvoyant questions”
• To discover the first influence into a target uncertainty,
ask, “What element (uncertainty or decision) would you
most want to know to reduce (or eliminate) the uncertainty
in the target in question?”
• To discover subsequent influences on that target
uncertainty, ask, “If I told you the answer to all the
influences identified so far, what other element (if any)
would you most want to know to reduce the uncertainty in
the target uncertainty?”

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Some Useful Tools
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Example from a Technology Decision
• The full tree has a branch for every
decision-outcome combination
• Each branch has a probability and
an outcome value
This Skeleton Tree
Option Technique Degree of Success
Results in This Full Tree
(continued for two more pages)
.
.
.

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Some Useful Tools
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Influence Diagram for “Don’t Delay”
Revenue
Cost
Price/Unit
Unit Volume
Feature Set
at Launch
Key Component
Price Escalation
UMC
C
U
U
NPV
Market Share
Market Size
U
COGS

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Some Useful Tools
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Example Conceptual Tree for “Commercialization Example”
Payoff is 6 year NPV
Features
at launch
Price and
share
On Time
Component
price
escalation
SG&A
Market
size
Don’t Delay
Add technical
resources
Delay
launch
1 year
Abandon
program
De-featured
Full
Price and
share
1 Year
Late
Component
price
escalation
SG&A
Market
size

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Some Useful Tools
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F10 F50 F90
• We can characterize uncertainty by estimating
three points on the probability distribution:
f10, f90, and f50
• The range from f10 to f90 captures 80% of
the probability
• By using these points in a sensitivity analysis,
the analysis is calibrated for an equal range of
probability for each uncertainty
• This is in contrast to a traditional “what-if”
analysis using arbitrary ranges of variation
• We can see which uncertainties have the most
impact on the uncertainty of the “big uncertainty”

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Some Useful Tools
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<== Type Name of 1st Alternative Here
Variable
Name
F10
Variable
Value
F50
Variable
Value
F90
Variable
Value F10 Payoff F50 Payoff F90 Payoff Range
1 Features Defeat. Full -6.560951075 1.637795057 1.6378 8.198751075
2 Price/Share Poor Anticip Favor -8.264185535 1.637795057 9.895658888 18.15984442
3 Market Size 80% 100% 130% -0.501100967 1.637795057 4.846139094 5.347240062
4 UMC Escal 100% 120% 150% 6.02725387 1.637795057 -4.946393162 10.97364703
5 SADA 80% 100% 130% 2.165471629 1.637795057 0.846280199 1.31919143
6
Don't Delay
<== Type Name of 2nd Alternative Here
Variable
Name
F10
Variable
Value
F50
Variable
Value
F90
Variable
Value F10 Payoff F50 Payoff F90 Payoff Range
1 Price/Share Poor Anticip Favor -9.082205772 -2.374529692 3.614403533 12.69660931
2 Market Size 80% 100% 130% -3.704241368 -2.374529692 -0.379962177 3.324279191
3 UMC Escal 100% 120% 150% 1.069878092 -2.374529692 -7.541141367 8.611019459
4 SADA 80% 100% 130% -1.736758359 -2.374529692 -3.331186691 1.594428332
5
6
Delay 1 Year
Example of data input to a sensitivity analysis spreadsheet
• A generic sensitivity analysis workbook can be used
• Use a custom model to calculate payoffs then insert the data into this spreadsheet

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Some Useful Tools
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Poor FavorPrice/Share-8.3 9.9
1.5 1UMC Escal-4.9 6.0
Defeat. Features-6.6 1.6
0.8 1.3Market Size-0.5 4.8
1.3 0.8SADA0.8 2.2
Poor FavorPrice/Share-9.1 3.6
1.5 1UMC Escal-7.5 1.1
0.8 1.3Market Size-3.7 -0.4
1.3 0.8SADA-3.3 -1.7
Commercialization Quandry
1.6377950
5733088f50 of Don't Delay
Red indicates Payoff Value
Blue indicates Uncertainty Outcome
-2.37
f50 of Delay 1 Year
Red indicates Payoff Value
Blue indicates Uncertainty Outcome
Don't Delay
Delay 1 Year
After entering the data and running the macros, a “Tornado Diagram” is plotted

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Some Useful Tools
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For problems with up to five uncertainties, Gary Brauer at Kodak has written Microsoft Excel
software macros for analysis
• Each decision alternative must be treated as a separate tree
• You choose the appropriate tree structure, input your data, and link a payoff calculation
• For problems that don’t fit the structure choices, special software such as DPL may be used,
but a large number of cases do fit

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Some Useful Tools
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Don't Delay
Scenario
IndexUMC Escalation (% of base) Market Size (% of base) Features At Launch Price / Share Branch Branch
# Outcome Prob Outcome Prob Outcome Prob Outcome Prob Prob Payoff
1 1 0.3 0.8 0.3 Full 0.7 FF-Poor 0.30 0.0189 -7.018058621
2 1 0.3 0.8 0.3 Full 0.7 FF-Anticipated 0.40 0.0252 3.010466083
3 1 0.3 0.8 0.3 Full 0.7 FF-Favorable 0.30 0.0189 9.967913852
4 1 0.3 0.8 0.3 1.00 #N/A
5 1 0.3 0.8 0.3 #N/A
6 1 0.3 0.8 0.3 #N/A
7 1 0.3 0.8 0.3 Defeatured 0.3 De-Poor 0.30 0.0081 -8.573302988
8 1 0.3 0.8 0.3 Defeatured 0.3 De-Anticipated 0.40 0.0108 -4.953157643
9 1 0.3 0.8 0.3 Defeatured 0.3 De-Favorable 0.30 0.0081 1.148078203
10 1 0.3 1 0.4 Full 0.7 FF-Poor 0.3 0.0252 -6.50840201
11 1 0.3 1 0.4 Full 0.7 FF-Anticipated 0.4 0.0336 6.02725387
12 1 0.3 1 0.4 Full 0.7 FF-Favorable 0.3 0.0252 14.72406358
13 1 0.3 1 0.4 1 #N/A
14 1 0.3 1 0.4 #N/A
15 1 0.3 1 0.4 #N/A
16 1 0.3 1 0.4 Defeatured 0.3 De-Poor 0.3 0.0108 -8.452457468
17 1 0.3 1 0.4 Defeatured 0.3 De-Anticipated 0.4 0.0144 -3.927275787
18 1 0.3 1 0.4 Defeatured 0.3 De-Favorable 0.3 0.0108 3.699269021
19 1 0.3 1.3 0.3 Full 0.7 FF-Poor 0.3 0.0189 -5.743917093
20 1 0.3 1.3 0.3 Full 0.7 FF-Anticipated 0.4 0.0252 10.55243555
21 1 0.3 1.3 0.3 Full 0.7 FF-Favorable 0.3 0.0189 21.85828818
22 1 0.3 1.3 0.3 1 #N/A
23 1 0.3 1.3 0.3 #N/A
24 1 0.3 1.3 0.3 #N/A
25 1 0.3 1.3 0.3 Defeatured 0.3 De-Poor 0.3 0.0081 -8.271189188
26 1 0.3 1.3 0.3 Defeatured 0.3 De-Anticipated 0.4 0.0108 -2.388453003
27 1 0.3 1.3 0.3 Defeatured 0.3 De-Favorable 0.3 0.0081 7.526055247
28 1.2 0.4 0.8 0.3 Full 0.7 FF-Poor 0.3 0.0252 -8.422685441
29 1.2 0.4 0.8 0.3 Full 0.7 FF-Anticipated 0.4 0.0336 -0.501100967
30 1.2 0.4 0.8 0.3 Full 0.7 FF-Favorable 0.3 0.0252 6.105190097
The Excel software macro creates the full tree behind the problem distribution

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Some Useful Tools
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Scenario Inputs and Payoff
Input Area
Scenario Scenario Payoff = -6.809
UMC Escalation (% of base) Outcome 1.5 1 1.2 1.5
Market Size (% of base) Outcome 1.3 0.8 1 1.3
Features At Launch Outcome Defeatured Full 0 Defeatured
Price / Share Outcome De-Favorable
Candidate Values
You have to develop a model that can be linked to this spreadsheet and provide a payoff
value for each scenario, i.e., for each branch of the tree
Cumulative Probability Plot
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-15 -10 -5 0 5 10 15 20 25
Payoff Axis
CumulativeProbability
Next, a macro runs the model, fills out the tree, and graphs the payoff