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.
Ten years ago I spent some time in women's undergarments*, as Director of Forecasting at Sara Lee Intimate Apparel (now Hanesbrands). Sure, it sounds glamorous -- product posters on our office walls, quarterly runway shows of new products, and partying with the full-figured Playtex models (some of whom were fuller than I figured).
Love can make a person do bad, dangerous, stupid, and irresponsible things. Love of country can make a politician stray from his wife. Love of Pepsi can make a pop musician lose his hair in a pyrotechnics-gone-bad commercial. Love of acting can make academy award winners accept starring roles in Ishtar. And for
The Winter 2012 issue of Foresight is now available. Here is Editor Len Tashman's preview: --------------------------- Our last two issues featured Steve Morlidge’s Guiding Principles for managing an organization’s forecasting process. You can see the summary table of these principles on page 31. With this issue, we continue their development
Have you taken the Myers-Briggs Type Indicator (MBTI) assessment? It is a psychological test wherefrom you are classified on Extraversion vs. Introversion, Sensing vs. Intuition, Thinking vs. Feeling, and Judging vs. Perceiving. I, along with roughly 15% of the population, come out an ISTJ or "Guardian Inspector" (the single largest
Is there anything you'd like to tell your management? Of course, in the spirit of the holiday season, I mean is there anything you'd like to tell your management that isn't anatomically impossible? If so, please join me and Ryan Rickard, Sr. Supply Chain Manager at Newell Rubbermaid, for our
Managing expectations for forecast accuracy is very important, as often those expectations are extreme after management invests in a new system. Software vendors have also been known to make overly (choose one: optimistic? sanguine? idyllic?) accuracy claims as part of their sales pitch. Of course, there is no arbitrary level of accuracy
My Google Alert on "forecastability" paid off with a gem this weekend, in the blog post "Forecastability and Over Fitting" by Shaun Snapp on SCM Focus. I was not previously familiar with Shaun or this site, but found a lot to like -- in content and attitude. In his post, Shaun kindly
Paul Goodwin's Hot New Research column is a must-read in each issue of Foresight. The current column, "High on Complexity, Low on Evidence: Are Advanced Forecasting Methods Always as Good as They Seem?" ends with this sage advice: If the name of a method contains more words than the number of observations that
Mean Absolute Percent Error (MAPE) is the most commonly used forecasting performance metric, and for good reason, the most disparaged. When we compute the absolute percent error the usual way, as APE = | Forecast - Actual | / Actual this is undefined when Actual = 0. It can also lead to
Sometimes you can't forecast worth a darn because something is just not forecastable. Being "unforecastable" doesn't mean you can't create a forecast, because you can always create a forecast. It just means there is so much instability or randomness in your demand patterns that even sophisticated forecasting methods don't help
