Michael Gilliland is a longtime business forecasting practitioner and formerly a Product Marketing Manager for SAS Forecasting. He is on the Board of Directors of the International Institute of Forecasters, and is Associate Editor of their practitioner journal Foresight: The International Journal of Applied Forecasting. Mike is author of The Business Forecasting Deal (Wiley, 2010) and former editor of the free e-book Forecasting with SAS: Special Collection (SAS Press, 2020). He is principal editor of Business Forecasting: Practical Problems and Solutions (Wiley, 2015) and Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning (Wiley, 2021). In 2017 Mike received the Institute of Business Forecasting's Lifetime Achievement Award. In 2021 his paper "FVA: A Reality Check on Forecasting Practices" was inducted into the Foresight Hall of Fame. Mike initiated The Business Forecasting Deal blog in 2009 to help expose the seamy underbelly of forecasting practice, and to provide practical solutions to its most vexing problems.
In a pair of articles published in Foresight, and in his SAS/Foresight webinar "Avoidability of Forecast Error" last November, Steve Morlidge of CatchBull laid out a compelling new approach on the subject of "forecastability." It is generally agreed that the naive model (i.e. random walk or "no change" model) provides
Engaging the sales force in forecasting sounds like a good idea, doesn't it? Compared to everyone else in the organization, don't sales people have the closest contact with our customers? Therefore, shouldn't they know better than anyone else our customers' future behavior? There are at least three problems with assuming
Whether to engage sales people in the forecasting process remains hotly debated on LinkedIn. While I have no objection in principle to sales people being involved in the process, I'm very skeptical of the value of doing so. Unless there is solid evidence that input from the sales force has improved
