CFO Roundtable
Breakfast Meeting
Strategic Gains from
Uncertainty and Risk
Copyright © May 1, 1998
Thesis: The traditional planning process deals poorly with
uncertainty.

Point-in-time.

Can't tell whether the “Base
Case” is the mean, mode,
median or—more likely—an
arbitrary product of negotiation.

Can't tell whether the “Worst
Case” represents a 0.01%
probability or a 25%
probability.

Provides no guidance six
months out about how to get
back on plan if off.
Thesis: The traditional planning process deals poorly with
uncertainty.
Pro Forma New OSB Mill
Production Calculations
Mill Capacity (MSF)
Demand/Capacity Ratio
Units Produced and Sold (MSF)
Sheathing % of Total Volume
Sheathing Volume
Non-Sheathing % of Total Volume
Non-Sheating Volume
...
1994
1995
1996
2010
0
108.5%
0
100.0%
0
0.0%
0
78,750
91.3%
71,890
100.0%
71,890
0.0%
0
353,500
91.6%
323,773
100.0%
323,773
0.0%
0
406,339
82.4%
335,005
100.0%
335,005
0.0%
0
Sales Calculations
Avg Sheathing Selling Price ($/MSF)
$228.85
Sheathing Sales
0
Avg Selling Price Non-Sheathing ($/MSF) $228.85
Non-Sheathing Sales
0
$216.33
15,552
$216.33
0
$207.43
67,161
$207.43
0
$295.67
99,051
$295.67
0
Gross Sales (000's)
Less: Discounts (000's)
Claims (000's)
Net Sales (000's)
$15,552
0
0
15,552
$67,161
0
0
67,161
$99,051
0
0
99,051
16,490
25,117
Free Cash Flow (000's)
$0
0
0
0
* 0* *
PV Factor
PV Free Cash Flow
Sum of Forecast Free Cash Flow
Perpetuity Value
Total Value (000's)

Point-in-time.

Inflexible.
Forecast
4,301
0.000
0
93,870
38,626
132,495
0.942
15,526
0.174
4,380

Chart-of-accounts based.

Not adaptive.
Thesis: The traditional planning process deals poorly with
uncertainty.
Example:
Valuing Incentive
Stock Options
$40

Point-in-time.

Inflexible.

Mathematically abstruse.
Option Value
Exercise Value
$30
Option Value at Grant
Perceived
Value at Grant
$20
Value of
Option
Black-Scholes (with dividends)
Option Value Today
c  Pˆo N (d1 )  Xe
$10
$0
Perceived
Value Today
($10)
$40
$50
Slope: 1.0
$60
$70
Stock Price
$80
$90
$100
 rf N
N (d 2 )
 Pˆ 
 2N
log  o   rf N 
2
X
Where : d1 
 N
 Pˆ 
 2N
log  o   rf N 
2
X
d2 
 N
N (d )  cumulative normal probabilit y density function
Pˆ  P  E PV 
o
o
div
 N (1  coe   ) n 1 
E PVdiv   Po 

(1  rf ) n
 n 1

 (1  rf ) N  (1  coe   ) N 
 Po 
N 
 (rf  coe   )(1  rf ) 
Where : E PVdiv   present va lue of expected dividends before exercise
 = expected dividend yield
Thesis: The traditional planning process deals poorly with
uncertainty.

Point-in-time.

Inflexible.

Mathematically abstruse.

Reactive.

Does not anticipate or plan for
contingencies.

Results in renegotiation and
sand-bagging.
Impact: Dysfunctional strategic planning.

Bottom-line orientation.

Management of variances rather
than achievements.

Disconnect between value drivers
and performance measures.

Short-term perspective.

Tacit reward of negotiating skills.

Sluggish response to uncertainties.

Tension between line managers and
the corporate office.
A command-and-control approach to financial reporting and planning.
The three paradoxes of value-based management
Vision

Uniform cost of capital for each
business unit.

Projects selected on basis of rank IRR
or EVA.
The three paradoxes of value-based management
Vision
Reality

Uniform cost of capital for each
business unit.

Capital costs differ wildly between
projects—even within business units.

Projects selected on basis of rank IRR
or EVA.

Line managers forced to forgo projects
which, on paper, promise profitable EVA.
The three paradoxes of value-based management
Vision

Decentralization and empowerment
lead to improved responsiveness,
coordination, feedback and accuracy.
The three paradoxes of value-based management
Vision

Decentralization and empowerment
lead to improved responsiveness,
coordination, feedback and accuracy.
Reality

Decentralization and empowerment lead
to inconsistent assumptions, benchmarks
and objectives.
The three paradoxes of value-based management
Vision
1996
1997
1998

Managers encouraged to pursue all
value-enhancing opportunities, whether
from efficiency improvements,
downsizing or growth.
The three paradoxes of value-based management
!
Vision
Reality
1996
1997
1998

Managers encouraged to pursue all
value-enhancing opportunities, whether
from efficiency improvements,
downsizing or growth.

Managers pursue marginal product line
extensions and efficiency gains, instead of
identifying new opportunities.
The central issue is risk.
The central issue is risk.

Differing risk perceptions impede
successful project selection and
financing.
The central issue is risk.

Differing risk perceptions impede
successful project selection and
financing.

Capital budgeting distorted by
ignoring asymmetries in the
distribution of value drivers.
The central issue is risk.

Differing risk perceptions impede
successful project selection and
financing.

Capital budgeting distorted by
ignoring asymmetries in the
distribution of value drivers.

Valuation efforts compromised by
confusing goals with expectations,
modes with means.
The central issue is risk.

Differing risk perceptions impede
successful project selection and
financing.

Capital budgeting distorted by
ignoring asymmetries in the
distribution of value drivers.

Valuation efforts compromised by
confusing goals with expectations,
modes with means.

Financing decisions distorted by
not gauging downside risk
accurately, and by not evaluating
the fatness of “tails.”
The central issue is risk.

Differing risk perceptions impede
successful project selection and
financing.

Volatility in value drivers beyond
management’s control frustrates
decentralized decision-making.
The central issue is risk.

Differing risk perceptions impede
successful project selection and
financing.

Volatility in value drivers beyond
management’s control frustrates
decentralized decision-making.

Communication disrupted between
corporate office and the field.
The relationship
between weather and
performance means...
The central issue is risk.

Differing risk perceptions impede
successful project selection and
financing.

Volatility in value drivers beyond
management’s control frustrates
decentralized decision-making.

Communication disrupted between
corporate office and the field.
It’s easy to confuse
bad luck with bad
management.
The central issue is risk.

Differing risk perceptions impede
successful project selection and
financing.

Volatility in value drivers beyond
management’s control frustrates
decentralized decision-making.

Because growth-oriented strategies are
comparative long shots, managers held
accountable to “objective” metrics will
instead cut costs—regardless of the
opportunity foregone.
The central issue is risk.

Differing risk perceptions impede
successful project selection and
financing.

Volatility in value drivers beyond
management’s control frustrates
decentralized decision-making.

Because growth-oriented strategies are
comparative long shots, managers held
accountable to “objective” metrics will
instead cut costs—regardless of the
opportunity foregone.

Incentive payments rendered
arbitrary by not reflecting difficulty
of attainment.
The modern approach
to applied finance
Build models, not chart-of-account
forecasts, which explain business
behavior.
The modern approach
to applied finance
Build models, not chart-of-account
forecasts, which explain business
behavior.
Features:

Well-understood rules.

Conceptually intuitive.

Explicit articulation of uncertainty.

Conclusions determined by visible,
verifiable results, not abstract formulas.
The modern approach
to applied finance
Build models, not chart-of-account
forecasts, which explain business
behavior.
Features:

Well-understood rules.

Conceptually intuitive.

Explicit articulation of uncertainty.

Conclusions determined by visible,
verifiable results, not abstract formulas.
Characteristics:

Probabilistic.

Multi-period.

Adaptive.

Proactive.
The modern approach
to applied finance
Build models, not chart-of-account
forecasts, which explain business
behavior.
Features:

Well-understood rules.

Conceptually intuitive.

Explicit articulation of uncertainty.

Conclusions determined by visible,
verifiable results, not abstract formulas.
Characteristics:

Probabilistic.

Multi-period.

Adaptive.

Proactive.
The modern approach
to applied finance
Build models, not chart-of-account
forecasts, which explain business
behavior.
Features:

Well-understood rules.

Conceptually intuitive.

Explicit articulation of uncertainty.

Conclusions determined by visible,
verifiable results, not abstract formulas.
Characteristics:

Probabilistic.

Multi-period.

Adaptive.

Proactive.
Case Example 1: Evaluating the Yield Curve.
Issue:
 In 1994, at least one investment
bank claimed the yield curve was
too steep for a stable inflationary
environment—and thus offered
arbitrage opportunities to the
savvy corporate finance
department.
8%
7%
6%
5%
Expected
YTM
4%
3%
2%
1%
0%
0
100
200
300
Months til Maturity
400
Case Example 1: Evaluating the Yield Curve.
Means of testing hypothesis:
 Macro-driven simulation of
Treasury bond returns.
Go to Spreadsheet
Case Example 1: Evaluating the Yield Curve.
8%
Conclusions:
7%
6%

The yield curve was reasonably
consistent in 1994 with stationary
inflation expectations.

There did not appear, given
actual prices and historical
volatility, to be a sound basis for
betting long-term government
instruments against short ones.
5%
Expected
YTM
4%
3%
2%
1%
0%
0
100
200
300
Months til Maturity
400
Case Example 2: Evaluating Integrated or Concentric Risk
Insurance Programs.
Contention:
 Combining all lines of coverage
under a single, multi-year companywide program should reduce
insurance costs by eliminating
administrative costs and better
utilizing the company’s consolidated
ability to retain risk.
250%
PL
Variability
in Amount 125%
per Claim
(Severity)
EEOC
D&O
Env
PBM
GL
AL
WC
AP
0%
0%
Marine
20%
40%
Variability in Claim Count (Frequency)
Wor st Case
7 5th Percentile
Median
Challenge:
 Quantifying capacity to retain risk,
given the highly uncertain nature of
casualty and property losses.
 Structuring an integrated program
which actually saves money for the
company.
Case Example 2: Evaluating Integrated or Concentric Risk
Insurance Programs.
Simplified
Flowchart:
Simulation ...
Simulation 2
Frequency
+
Severity
Simulation 1
Incurred
Loss
+
Insurance
Allocation
of Loss
+
Payment
Pattern
PV Factor
+
Impact on
Cash Flow
Impact
on
Value
x
Multiple
Simulations
Confidence Map of
Each Risk Parameter
User-defined parameters
Computer-generated output
Go to Model
Case Example 2: Evaluating Integrated or Concentric Risk
Insurance Programs.
Conclusion:
 It is possible to quantify, with
reasonable precision, a company’s
exposure to various sources of risk,
and to assess how those risks
interact and affect cash flow.
250%
PL
Variability
in Amount 125%
per Claim
(Severity)
EEOC
D&O
Env
PBM
GL
AL
WC
AP
0%
0%
Marine
20%
40%
Variability in Claim Count (Frequency)
Wor st Case
7 5th Percentile
Median
Case Example 2: Evaluating Integrated or Concentric Risk
Insurance Programs.
Projected Benefits:
250%
PL
Variability
in Amount 125%
per Claim
(Severity)
EEOC

Improved awareness and understanding
of risk. Improved risk containment.

Better identification of areas requiring
insurance and/or hedging.

Better levels of self-insurance and
excess retained risk.

Less duplication of analysis and
administration.

Change in the way managers think about
(and plan around) uncertainty.
D&O
Env
PBM
GL
AL
WC
AP
0%
0%
Marine
20%
40%
Variability in Claim Count (Frequency)
Wor st Case
7 5th Percentile
Median
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
Average Selling Price
Because of tolerance for smaller
logs, OSB costs were $25 to $50 per
MSF cheaper than Southern
plywood...
Context:
 In 1994, the industry could
do no wrong. Price far
exceeded cost for most
producers and almost
everyone was betting on
growth through OSB—an
engineered substitute for
plywood.
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
Total OSB Capacity
(BSF)
25
20
Canada
15
West
South
NC
10
NE
5
0
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Source: RISI (7/95)
Nearly 10 billion square feet additional
capacity projected on a combined base
of 32 billion square feet.
Context:
 In 1994, the industry could
do no wrong. Price far
exceeded cost for most
producers and almost
everyone was betting on
growth through OSB—an
engineered substitute for
plywood.
Question:
 Was the industry overextending itself, or poised
for still further value-adding
growth?
 What should the client’s
policy be on OSB and
plywood investments?
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
Impact of OSB Expansion on
Demand/Capacity Ratios
Assuming No Reductions in Plywood Production
(BSF)
45
110%
40
35
OSB Capacity
100%
30
25
20
90%
15
10
80%
Plywood Capacity
(assuming no mill
closures)
Total Demand
Demand Capacity
Ratio
5
0
70%
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Source: RISI (7/95)
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
5 Demand Regions (plus exports)
6 Demand Types
Approach:
 Convert a complex body of financial
data and line expertise into a userfriendly model of industry and business
unit performance.
2 Major Products
10 Supply Regions
Plywood
OSB
2 Log Classes
3 Log Species
3 Owner Types
More than 160 Distinct Mills
3 Competing Uses of Fiber
80-120 Quarters
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.


Approach:
Convert a complex body of financial
data and line expertise into a userfriendly model of industry and business
unit performance.
Deliverables:

A pricing model for finished product
and raw material for each mill.

A model for identifying and weeding
out under-performing mills, taking into
account each owner’s willingness to
endure pain.

A model for weighing different
strategies’ prospects of creating value.
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
$300
Supply and
Demand (1994)
$250
$200
$150
Supply and
Demand (2003)
$100
$50
$0
5
10
15
20
25
30
35
40
45
50
Billion Square Feet (3/8" Basis)
Conclusions:
 In simulation after simulation, the supply curve flattened as plywood mills cut
costs and OSB mills entered production. But demand increased only marginally,
causing wholesale erosion in prices.
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
$300
Supply and
Demand (1994)
$250
$200
$150
Supply and
Demand (2003)
$100
$50
Existing OSB
Capacity
Projected
OSB
Expansion
(Aggressive
Case)
Remaining Plywood
Capacity
(Aggressive Growth Case)
$0
5
10
15
20
25
30
35
Billion Square Feet (3/8" Basis)
40
45
50
Conclusions:
 At the same time, competitors’ willingness to endure pain meant protracted excess
capacity, and further flattening of the supply curve. The days of justifying
unproductive mills as an option against volatile product prices were over.
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
Conclusions:
 Because the model simulated performance on a mill-by-mill basis, we were able
to predict who would suffer losses, who would shutter, and who would succeed
long-term.
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
Conclusions:
 We were also able to simulate whether hypothetical new mills could create
value—and the confidence intervals around success or failure .
Case Example 3: Evaluating Growth Opportunities in the
Structural Panels Industry.
Conclusions:
 Although only partially responsible, the model helped formulate an investment
strategy which disavowed further green-field expansion. This was a significant
departure from previous policy.
Summary: The key steps to strategically exploiting
uncertainty.

Build a dynamic and, where
appropriate, behavioral model of
the business.

Differentiate controllable and
uncontrollable uncertainties.

Build goals, performance measures,
investor expectations and strategy
around controllable measures, or
drivers.

Identify and plan for contingencies.
Narrow the tolerances in advance to
minimize cost and expedite
responsiveness.