Concerning the basic underlying idea behind “Sensitivity Analysis”, I cannot find a single negative thing to say about it. In order to understand the problem that exists in the implementation, consider the following line of thought:
Forecasts + Calculation Method = Result
±A% ±B% ±(A+B)%
We begin with the creation of some forecasts, which we process thru some Calculation Method, in order to arrive at a result. Now, when we say that forecasted sales for May 2011 are going to be 10 mil USD, we know that it is highly unlikely that this is going to be the absolutely accurate actual number. Some level of inaccuracy is incorporated into that figure, depending on the quality of the forecaster’s work. Generally speaking, let’s say that it is ±A% inaccurate. If the calculation method we use has built-in inaccuracies, like we have already seen in the “Net Present Value” method (let’s say that it is ±B% inaccurate), the end result is going to be ±(A+B)% inaccurate. In other words, one level of inaccuracy gets piled up on top of the other, to create an even bigger combined inaccuracy level.
If anyone hopes that A and B in some scenarios might cancel each other out (for example A= +8.00% and B= -7.50%), then please note that:
- Murphy’s Law states otherwise
- hoping is not part of a Calculation Method
After the Financial Analysis work has finished, and you have established a result, any experienced professional will tell you that this is just the beginning of the real and the most meaningful part of the work, which is the “What if” scenario storm. What will the profitability look like:
- if we make only 92% of the sales target
- if the days of credit to the customers are 19 more than we expected
- if the Interest rates do this
- if the cost of TV advertisement does that
- if the cost of raw materials does the other thing
- if … if … if …
So, these numbers represent the variations of the ±A%. The purpose of the Sensitivity Analysis is to give us a heads-up for what is going to happen when the uncertain and the unexpected (the ±A%) occurs. However, now that we have covered the ±Α% inaccuracy, we are not really going to get an accurate result. What we are getting, is a result with an ±B% inaccuracy. So, what we need is a calculation method whose B=0, or in other words, one that you cannot put a single dime of its calculation into doubt.
Stick around, and we are going to see what’s inside every Financial Analyst’s wish list. We’ll see the calculation capabilities that before C2BII didn’t exist, and how they will revolutionize the work of almost every company department.
