The forecast is always wrong.
Okay, the weatherman is occasionally right. But my point is that no one can predict the future with perfect accuracy.
That is certainly the case when it comes to calculating a project's return on investment (ROI). At first glance, simply coming up with all the necessary data may appear to be the trickiest aspect of cost justification. I wouldn't want to wade through accounting ledgers myself.
But while it's true that such things as huge overhead categories and intangible benefits can make for some pretty murky numbers, the real challenge lies in managing the uncertainty that is an inherent part of life itself.
Because no matter how good one thinks his or her forecasting ability is, unanticipated engineering change orders can blow the budget. A vendor's inability to deliver prototypes when promised can cause unexpected delays. Defective parts can rack up huge costs. The list goes on and on.
Despite the uncertainty, I am amazed how often people obsess over getting the "right" answer—maybe it has something to do with all those tests in school! Some even go so far as to calculate a project's ROI out to three significant decimal places, when the data is shaky to begin with! And few of them expect anything to actually go wrong, even when they know how many ECOs they racked up on the last project.
Sure, engineers should hope for sunny weather in putting together the cost justification for a project. But what they really need to do is plan for the possibility of a little rain. Hedging against a 100-year flood or invasion of locusts, however, might be a little extreme.
Rather than obsessing over the right forecast, engineers can make far better use of their time by examining the expected financial performance of a proposed investment over a wide range of conditions. The interest or discount rate selected, the estimated annual savings and costs, and even the schedule can completely transform the economics of a proposed project. By doing this type of sensitivity analysis, engineers can gain insight into how "wrong" their data can be and still remain confident that the project is financially viable—even if Murphy's Law prevails.