Charles Dumont

1
A Framework for Valuing, Optimizing and
Understanding Managerial Flexibility
Confidential

• Charles Dumont
• Gregory Vainberg

Real Options Conference Rio de Janeiro July 9,
2008
This report is solely for the use of client
personnel. No part of it may be circulated,
quoted, or reproduced for distribution outside
the client organization without prior written
approval from McKinsey Company. This material
was used by McKinsey Company during an oral
presentation it is not a complete record of the
discussion.
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Contents

• There is a need for a clearly defined real
  options valuation framework

• Description of the 3 step framework

• Client scenario demonstration Case example Oil
  sands development project

3
Typical real options available to managers
Scale up
Early entrants can scale up later through
cost-effective sequential investments as market
grows
Invest/grow
Scope up
Investments in proprietary assets in one industry
enables company to enter another industry
cost-effectively
Defer/lean
Study/start
Delay investment until more information or skill
is acquired
Scale down
Shrink or shut down a project part way through if
new information changes the expected payoffs
Disinvest/ shrink
Scope down
Limit the scope of (or abandon) operations when
there is no further potential in a business
opportunity
Switch up
Speedy commitment to first generation of product
or technology gives company preferential position
to switch to next generation
Switch
Switch down
Switch to more cost-effective and flexible assets
as new information is obtained
4
Illustration of value created by adding real
options
DISGUISED EXAMPLE
Fixed project plan
Curtail downside
Increase expected value
Increase up-side potential
Project plan with real options
5
The impact of real options on project valuation
is significant, especially with optimized results
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Value (Billions)
50
12
11
10
8
8
Real Option (Optimized threshold)
Real Option (Expert based)
Real options (Optimized representation)
Deterministic
Monte Carlo Simulation
n/a
1
3
3.5
4
P20
n/a
14
16
18
19
P80
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Contents

• There is a need for a clearly defined real
  options valuation framework

• Description of the 3 step framework

• Client scenario demonstration Case example Oil
  sands development project

7
A three step framework to leveraging managerial
flexibility
States
Options
Identify risk drivers and options
Identify options available at each state
Optimize

• Indentify and understand the impact of all of the
  key risk drivers needed to evaluate options
• Define possible project states

• Identify possible transition options in each
  state
• Define business constraints that dont allow
  certain options
• Estimate a threshold estimate

• Monte-Carlo simulation of key risk drivers
• Defined thresholds for option exercising
• Optimized real-option representation

Description
Removed options
Ramp-down
Illustration
Ramp-down
Ramp-down
Operate
Operate
Operate
Abandon
New-options
Abandon
Decommissioned
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Innovative framework combines discrete option
definition and Monte-Carlo distribution analysis
Monte-Carlo based valuation approach to
evaluation project risk-return relationship
Deterministic tree-based methods of evaluating
real-options
Future value
Genetic Programming

• Expands investment choices - Expands the world of
  investment opportunities
• Unlock business value Making choices to
  optimize the use of business flexibility
• Identify value of options Identify value
  creating options, previously not utilized by
  management, diverting attention from non-value
  creating ones

9
Contents

• There is a need for a clearly defined real
  options valuation framework

• Description of the 3 step framework

• Client scenario demonstration Case example Oil
  sands development project

10
Case example oil sands development project
OilsandCo, a large oil sands producer is
considering developing a new site and is well
advance in the financial analysis of the project.
Following an internal workshop on risk facing the
project they are considering delaying the
development. Even if oil price have been
sustainably high over the last 4 years their
economist are thinking that a change of
presidency in the US could put downward pressure
on oil price. Meanwhile the cost in the Athabasca
area will remain high there is a serious labor
shortage combine with an increase demand for oil
processing equipment. Moreover, the government of
Alberta has announced an increase of the
royalties from oil production. Due to our
recent work on real option we strongly believe
that OilsandCo is not seeing the full potential
of the project because they have not consider the
impact of managerial flexibility in the business
case. We are asked by the CEO to help them
structure the problem, quantify the hidden value
and guide them in the decision making process
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Monte-Carlo simulation of key risk drivers
Royalties ( of oil revenues)
Oil (USD/bbl)
2009
2010
2011
2012
2013
2014
2015
2009
2010
2011
2012
2013
2014
2015
Foreign exchange
Labor rates (CAD/hr)
CAD/EURO
CAD/USD
2009
2010
2011
2012
2013
2014
2015
2009
2010
2011
2012
2013
2014
2015
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Summary of oil sands expansion project states
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Summary of real options available
Option definitions
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Oil sands project influence diagram
States
Options
Real option representation
2
3
5
1
4
6
7
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Oil sands project influence diagram
States
Options
Real option representation
Operate
Develop
Scale-up
2
3
5
Operate
Idle
Operate
Develop
Ramp-down
Ramp- down
Idle
1
4
6
Delay
Ramp- down
Abandon
Abandon
7
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Description of the genetic optimization process
Description
Step 1 Encode and Initialize

• Encode the model logic into a genome a binary
  DNA like representation
• Randomly generate a population of genomes

• Test each genome of the population against the
  objective function (e.g. Maximize average NPV
  over 1000 stochastic paths) and tag the fitness
  score to each individual
• Identify the top performing individual out of the
  population

Step 2 - Evaluate

• Out of the top performing trench of the
  population, randomly select 2 genomes (the
  parents) and a crossover point
• Create a new individual from the genetic material
  of the parents
• Include mutations by randomly changing the DNA
  sequence

Step 3 - Reproduce

• Repeat for a selected number of generation step 2
  and 3 with a new population
• The population will converge towards individual
  which perform best against the selected objective
  function

Step 4 - Repeat
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Impact of the real options model on the results
of the Monte-Carlo simulation
Performance relative to the average
Performance with flexibility
In above average cases, flexibility adds value by
fully leveraging the market potential
In below average cases, flexibility adds value to
the project by curtailing losses
Performance without flexibility
Cases close to the average, flexibility adds
value by optimizing operations
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Case example impact of our work

• The mission is a success the team was able to
  help the OilsandCo management team in their
  decision making process by structuring the issues
  around options and flexibility, by valuing each
  one of the key options and by identifying sources
  of flexibility.
• Decision Execute the project in 3 phases instead
  of 2. Even if this option was eliminated in the
  preliminary stage of the analysis (before our
  involvement) the additional flexibility of the
  project more than fully compensate for the
  additional costs
• Insight Change the design of the mine to use
  trucks instead of a conveyor to move the sands to
  the processing facility. Even if more costly in
  the base case it will allow them to better
  ramp-up and down-down production according to oil
  price and processing cost
• Focus Demonstrate that the option to partner
  with an other producer for the processing
  facility was worthless and that management should
  not spend more time thinking about it

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Appendix