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There are various often-asked
questions that I hear from prospective customers, fellow
practitioners, and other
business acquaintances. Most of these
questions relate to how we model
such complex managerial decision
domains. Some pertain to how
we package and deliver our
expertise and tools. Others deal
with the origins of how we came
to start PROXI Management
Decisions. I
hope my answers below give you
insight into some issues that
may not be explicitly addressed in our website.
Jeff Baum -
Managing Director |
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Q: How did you come up with the
idea for Project Executive? |
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A: While living product
development in various divisions
of a global semiconductor
company for 11 years, I became
intimately familiar with how
weak and unstructured business
case development and project
selection can be
for new product development
investments. I then studied
corporate finance, financial
valuation methods, and
management science analytics for
two years full-time. While
working for a leading high-tech
management consultancy, I
"wrestled" with this domain in
both global corporations and
start-up companies, and I
discovered that
"best practices" are
still structurally,
functionally, and analytically
flawed. Operations
that have even moderate amounts
of human resource management
complexity and more project opportunities than they can
simultaneously support need to
determine the optimal way to
allocate their time, people, and
financial resources. This is an
ongoing process that to a large
extent dictates the success of a
business. While bad execution on
a great set of project
investments is certain to fail,
great execution on a poor set of
project choices will also produce
dismal returns. A means for
selecting the fundamentally best
set of project investments and
for efficiently allocating
resources is crucial to healthy
business performance.
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Q: How do you model
risk and uncertainties? |
A: There are many
kinds of risks to consider when
evaluating a business case.
Inevitably, customers are
concerned about whether or not
the market demand and selling
prices they forecast will
materialize. There are also
risks associated with project schedule
delays and technical
feasibility. Schedule and
technical risks need to be
managed, whereas market forecast
uncertainties should be modeled.
- There are purely
judgmental ways of
qualifying risk, such as a
"high, med, low" or
"scale of 1 to 10"
ratings.
These methods have little
utility for quantifying
project-specific risk, but they can be used
to visually plot the mix or
balance of the portfolio on such
risk dimensions.
- Another approach is
to scale unit volumes,
prices, or revenues by
"probability-of-success"
factors to produce
"de-rated" versions of the
forecasted quantities. Most
forecasts already incorporate
biases about incumbent market
shares, industry and
economic conditions, and
relative attractiveness of
competing products.
Subsequent scaling results in
a subjective form of "double
counting" the
uncertainties of concern.
- Still other
techniques attempt to
modulate valuation parameters,
such as cost-of-capital
discount rates, but this
simply injects "noise" into
project valuations.
Project-specific risks can
be modeled in pro-forma
forecasts for each investment,
but the limitations of an
organization's forecasting
capabilities are often the
greater source of uncertainty.
We integrate effective risk
diversification functionality, to
limit financial performance
exposures to the
dominant uncertainties of
interest.
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Q: What good is portfolio
analysis, if our data inputs are
not accurate? |
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A: Data is often the
"scapegoat" to defer addressing
many business processes in
organizations. Organizations spend tremendous
time and effort to forecast,
collect, "scrub", and validate
project and operational data. Regardless of the
ultimate quality of the data, it
does represent the best
estimates of a given
organization. These data
forecasts are used to make
business decisions on a regular
basis. If there is a
long-term need to "beef up" the
forecasting, operational
modeling, and business analytics
skills of your team, that should
be addressed; however, that
should not hold up advancing important
business processes needed for
effective decision making today. In the
meantime, there is a need to
avoid layering poor analysis and
decision making on top of
whatever data quality problems
you think you may have.
I've seen companies that have
the best intentions of "fixing"
their data, before considering
any improvements in related business
processes.
Those companies often have the
same data quality and ad-hoc
methods at-large, even years
later. On the contrary, I've
seen companies aggressively
pursue business process
improvements that turned out to
be the vehicle by
which poor
data practices were identified
and eradicated.
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