Large companies, most notably in aerospace & defense, engineering & construction, or economic development, are particularly susceptible to poor forecasts as they try to extend their home market success beyond their borders with big projects that help them “make their numbers.” Our analysis shows that compared to forecasts in their home markets, these “adjacent” market predictions are wildly off. Experienced companies tend to compensate for this uncertainty by overriding their revenue models with ad hoc rules and executive judgment. Unfortunately, this approach leads to systemic bias and unpredictable forecasting accuracy.

Fortunately, there is a simple fix that business development teams and planners can apply to standard forecasting techniques. Our analysis shows that integrating a timing probability matrix into the forecasting process can dramatically improve the accuracy of the results. In our new white paper, Forecasting Revenues In Ancillary Markets, Ajay Patel, SMA President & CEO, and Eddie Solares, SMA Analyst, use simulation analysis to show how this technique dramatically lessens the systematic errors of predicted revenues.

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