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.
If you are looking to find a job in business forecasting, or trying to fill one, there are many online resources available: Professional Organizations Institute of Business Forecasting & Planning - Very active searchable list of currently available jobs in forecasting and planning, from entry level to executive. Employers post
Q: Is the MAPE of the naive forecast the basis for understanding the forecastability of the behavior? Or are there other more in depth ways to measure the forecastability of a behavior? MAPE of the naive forecast indicates the worst you should be able to forecast the behavior. You can
Q: Company always try to forecast 12 or 24m ahead. Whether we should track accuracy of 1m/3m/ 6m or x month forecast, does that depend on lead time? How to determine out of these 12/24 months, which month should we track accuracy? Correct, forecast performance is usually evaluated against the
Q: What is a legitimate goal to expect from your FVA...5%, 10%? Q: How do we set Target FVA which Forecasters can drive towards? The appropriate goal is to do no worse than a naive model, that is FVA ≥ 0. Sometimes, especially over short periods of time, you may
Please enjoy a much-needed break from FVA Q&A with editor Len Tashman's preview of the Summer 2013 issue of Foresight: Enlightenment has been our guiding principle through this, our 30th issue of Foresight. Since the journal’s inception in 2005, our mission has been to help the forecasting profession come to
With this Q&A Part 3, we are about halfway through the questions submitted during the FVA webinar. We did over 15 minutes of live Q&A at the end of the webinar, and covered many of the submitted questions at that time, however I always prefer to issue complete written responses to
Q: Could you send me the presentation? With audio if possible. If you'd like a pdf of the slides, email me directly: mike.gilliland@sas.com For the audio, the webinar recording is available for free on-demand review: FVA: A Reality Check on Forecasting Practices Q: Can we get the case study referred here
As promised in yesterday's Foresight-SAS sponsored webinar on "Forecast Value Added: A Reality Check on Forecasting Practices," here is Part 1 of my written response to the over 25 questions that were submitted during the event. (Note: It may take a week or so to get through all of them.)
If an organization is spending time and money to have a forecasting process, is it not reasonable to expect the process to make the forecast more accurate and less biased (or at least not make it any worse!)? But how would we ever know what the process is accomplishing? To
Last time we saw two situations where you wouldn't bother trying to improve your forecast: When forecast accuracy is "good enough" and is not constraining organizational performance. When the costs and consequences of a less-than-perfect forecast are low. (Another situation was brought to my attention by Sean Schubert of
