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.
We ended last time having selected a cluster of surrogate products -- a subset of the original selection of like-items that had the same attributes as the new product. Judgment has been used throughout the process so far, in specification of the relevant attributes, filtering the original candidate pool of
Seems like we've been here before. It is January, so time again to announce the Fortune 100 Best Companies to Work For in the US. This year SAS is at #2, our fifth straight year in the top 3, over which our average rank has been 1.8. We've covered this topic
The Query step begins by selecting like-items based on the appropriate product attributes, then reviewing historical sales of past new product introductions. Continuing with the DVD example, suppose the new release is an R-rated horror movie. For like-items, we would query our database and pull the history of all prior
In the previous installment we were reminded of the potential abuses of forecasting by analogy. People are naturally reluctant to forecast that their new product idea is going to flop. Therefore, there is an inclination to ignore similar items that failed in the marketplace, or apply less weight to the failures than
The real estate market provides a good example of the use of analogies. To determine a reasonable listing price for a property (such as this dump on the right) that is new on the market, the sales agent will prepare a list of "comps" (comparable homes) that are currently on the market or
How do you build a forecast when you have no historical data? This is a recurring challenge for businesses that update their product offerings, and a recurring question in online forecasting discussion groups (e.g. this one on LinkedIn). The bad news is that you probably can't expect to achieve highly
While fancy new forecasting models will always be of interest to researchers, there is plenty of really interesting and practical new work being led by forecasting practitioners. Last month Steve Morlidge (who spent 30 years at Unilever, now with CatchBull), shared his promising new approach on the “Avoidability of Forecast
Forecasting Support Systems (FSS) – essentially, decision support systems for forecasters – are being given increasing scrutiny in forecasting circles, including our recent half-dozen articles in Foresight. Additionally this year, there has been a special issue of the International Journal of Forecasting focused on the topic. Keith Ord and
Live from the Institute of Business Forecasting conference in Orlando this week, where I'm excited to announce that my longtime SAS colleague Charlie Chase just received the 2013 Lifetime Achievement Award for his contributions to the IBF and the forecasting profession. More details and and pictures later this week... Reminder:
In this quarter's installment of the SAS/Foresight Webinar Series, Steve Morlidge will discuss his promising new approach to evaluating the "Avoidability of Forecast Error." Based on his article in the Summer 2013 issue of Foresight, and examined in a four-part series on The BFD blog (Part 1, Part 2, Part
