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.
First: A Report from the 67th Pine Tree Festival and Southeast Timber Expo Back in March The BFD investigated the topic of Google-ing yourself (aka egosurfing). I reported on finding a namesake in show business, a self-described "Magic Mike Gilliland" and his sidekick Lollipop the Clown. I attempted to disparage
Pet lovers do the most incredible things. My friend Grace told me she trained her pet rabbit to walk on a leash. I said, "That's amazing Grace -- I never heard of such a thing!!! I thought rabbits could only hop." Rest assured there are no dogs (cats or rabbits
Have you seen this week's news item on "tanning mom" Patricia Krentcil, the New Jersey mother accused of sunburning her young daughter in a tanning booth? Now I'm as big a fan of diversity as the next guy, and lovingly embrace people of every visible color (although I do find House Speaker John Boehner's orange a
Can you explain the "random error" in your forecasts? This question was posed two weeks ago by Sam Iosevich, Managing Principal at Prognos, during his presentation at the INFORMS Conference on Business Analytics and Operations Research. Sam stated that if your planners have knowledge that helps explain the "random error" in
Last week's INFORMS Conference on Business Analytics and Operations Research drew over 700 attendees to Huntington Beach, CA. I had the pleasure of serving on the conference selection committee, and wanted to share this content from one of our invited speakers, Kean Chew of HAVI Global Solutions. The Numbers Behind Burgers
The April 2012 issue of ORMS Today contains a piece on "How analytics enhance the guest experience at Walt Disney World," by Pete Buczkowski and Hai Chu. While many of us are used to forecasting just one or two things (such as unit sales or revenue), Pete and Hai illustrate
Are you a prefectionist when it comes to forecasting, or any kind of data analysis? If so, perhaps my SAS colleague Gary Cokins can cure you. Gary is a prolific writer and contributor in the performance management field, and describes himself as a "ready-fire-aim" kind of guy. By this he means
Being a Hollywood celebrity means plenty of perks in addition to willing groupies. For example, the 2012 Oscars Nominee Gift Bag (valued at over $62,000) included a 5-day elephant safari in Botswana ($15,580), Eminence organic body scrub (with virgin coconut oil and raw sugar cane, $48), Naughty Bits Brownies ($50), and a
The INFORMS Conference on Business Analytics and Operations Research kicks off April 15 in Huntington Beach, CA. I had a chance to preview a presentation by Glenn Bailey, Sr. Director of Operations Research at Manheim (the $3B wholesaler auto auctioneer). Glenn's talk is on "The Need for Speed: Responsive Predictive Analytics,"
Let's end 2012-Q1 with a graphic editorial comment: Forecast Accuracy vs. Effort Using a naïve model will achieve a certain level of forecast accuracy. That accuracy may be high if the demand is smooth and stable, or low if the demand is erratic. But you achieve this level of accuracy with virtually no
