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Why Most Sales Forecasts Suck…and How Monte Carlo Simulations Can Make Them Better

January 18, 2010 By Leave a Comment

By: Kevin Ertell, VP Retail Strategy, ForeSee Results
Sales forecasts don’t suck because they’re wrong. They suck because they try to be too right. They create an impossible illusion of precision that ultimately does a disservice to managers who need accurate forecasts to assist with our planning. Even meteorologists — who are scientists with tons of historical data, incredibly high powered computers and highly sophisticated statistical models — can’t forecast with the precision we retailers attempt to forecast. And we don’t have nearly the data, the tools or the models meteorologists have.

Luckily, there’s a better way. Monte Carlo simulations run in Excel can transform our limited data sets into statistically valid probability models that give us a much more accurate view into the future. And I’ve created a model you can download and use for yourself.


There are literally millions of variables involved in our weekly sales, and we clearly can’t manage them all. We focus on the few significant variables we can affect as if they are 100% responsible for sales, but they’re not and they are also not 100% reliable.

Monte Carlo simulations can help us emulate real world combinations of variables, and they can give us reliable probabilities of the results of combinations.


For some background on our current processes, a simplified explanation of how most retailers develop sales forecasts and to view the full blog post, visit: http://bit.ly/4EJ33t

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