Probabilistic techniques typically found
their practical application in the fields of science and engineering.
Certainly, they have demonstrated a great usefulness in engineering and design
for quality as well as quality in general. However, given the uncertainty that
is inherent in business, it is likely that even greater benefits can be had by
applying probabilistic techniques to a business’ strategy. However, I have
noticed that this is not often done. Why is this so; does it mean that there
actually isn’t much benefit because if there were someone would have done it by
now; are people in the business fields really that ignorant of the
probabilistic techniques used in technical fields? These questions remind me of
the time I first applied advanced probabilistic techniques to a business case.
My first exposure to the application of
probability theory to business was during a Master of Entrepreneurship and
Innovation (MEI) at the Australian Graduate School of Entrepreneurship (AGSE). I
had almost finished my PhD in probabilistic design, and I was interest in
applying them to business in some way. For a final semester research assignment
I applied Monte Carlo and error propagation to the Clarion case to see what insight such an approach
could offer. This case was part of the assessment for the course, and it was
familiar to others in the course. Therefore, when the findings of the
investigation were presented to them they would be able see which insights came
from the probabilistic approach. I would then be able to gauge if they could
see the value in this approach or not.
Few doing the course (or the academic staff
of the school) were familiar with the various probabilistic techniques that
were applied. Definitely none had heard of robustification. What’s more, they typically
didn’t know that it was possible to predict and quantify the risks associated
with a business strategy. And the notion of optimising a business strategy against
those risks almost seemed like magic to them. However, it became clear to
pretty much everyone on present how beneficial probability theory could be to
the management of business risk once the case had been presented.
As alluded to before, one of the key
benefits was the ability to predict the probability of outcomes that the
traditional approach could only identify as possible problems. Everyone who had
done the case realised that there was potential for a problem in the case
example, but it was not possible to say whether it was around 1, 10 or 100
percent likely to happen. For the Clarion case a key concern was the exhaustion
of the cash resources during an R&D project. With the probabilistic
approach, not only was it possible to predict the probability of this event,
but it was also possible to find the optimum investment strategy to minimise the
probability. This was not possible with regular techniques or intuition, and it
was the ability to make this quantification that clearly demonstrated the
advantage of the probabilistic approach to everyone else. While others were
impressed with the results there was still some concern about the application.
Our understanding of the risks associated
with a business venture can indeed be increased by an order of magnitude by
applying probabilistic methods to a model of the business plan; however, the
complexity of the model increases by a comparable amount. Therefore, the
application might seem daunting. It is worth starting with a typical model (a
multi sheet Excel spreadsheet file with P&L, balance sheet, cash flow,
asset acquisition and disposal, and other pertinent sheets as dictated by the nature
of the business). However, many of the key aspects (sales, operating costs,
project timelines etc.) need to be based on outside phenomena. For example, the
sales might be predicted with the Bass model, which might be affected by the
advertising budget, which might be limited by manufacturing costs, which might
be dependent upon an R&D project, which might be dependent upon the
availability of key employees that have not yet been employed. In short, you
need to determine the important phenomena that affect the business strategy’s
success and your model needs to be set up to predict the effects of random
fluctuations in the parameters of these phenomena; they parameters will
typically be your random inputs. This requires significantly more effort.
There is one last thing that you must keep
in mind when applying probabilistic techniques to a business plan’s strategy.
Some argue that there is a difference between risk and uncertainty. Frank
Knight (a fairly famous economist in the context of entrepreneurship) argued that risk is
that which can be predicted (even if it’s only a probability) and uncertainty
is that which remains unknown. The point is that there will always be the
possibility of a completely unforseen event that can have a serious affect upon
a business (good or bad, but usually bad). Often these events aren’t even
thought of until they happen, and this will always put a limit on the certainty
that you can expect from a probabilistic analysis. Still, by using a
probabilistic approach you can limit much of the risk that will be faced.
In summary, you need to work harder to make
your model of the business plan suitable for a probability analysis. However,
the benefits that come from the extra insight and optimisation are
considerable. Nevertheless, as considerable as the benefits are, they will
always be limited by the fact that we can’t think of everything.
Finally, take a look
here under ‘New venture financing’ to see a case example showing how you can do
this.
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