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Mike Gilliland
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Product Marketing Manager

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.

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Guest blogger: Dr. Chaman Jain previews Winter issue of Journal of Business Forecasting

JBF Special Issue on Predictive Business Analytics Dr. Chaman Jain, professor at St. Johns University, and editor-in-chief of the Journal of Business Forecasting, provides his preview of the Winter 2014-15 issue: Predictive Business Analytics, the practice of extracting information from existing data to determine patterns, relationships and future outcomes, is

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Brilliant forecasting article from 1957!!! (Part 2)

Combining Statistical Analysis with Subjective Judgment (continued) After summarily dismissing regression analysis and correlation analysis as panaceas for the business forecasting problem, Lorie turns next to "salesmen's forecasts."* He first echoes the assumption that we still hear today: This technique of sales forecasting has much to commend it. It is based

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Gaming the forecast

Business forecasting is a highly politicized process, subject to the biases and personal agendas of all forecasting process participants. This is why many -- perhaps most -- human adjustments to the forecast fail to make it better. And this is why relative metrics, such as FVA, are so helpful in

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5 steps to setting forecasting performance objectives (Part 2)

And now for the five steps: 1. Ignore industry benchmarks, past performance, arbitrary objectives, and what management "needs" your accuracy to be. Published benchmarks of industry forecasting performance are not relevant. See this prior post The perils of forecasting benchmarks for explanation. Previous forecasting performance may be interesting to know, but

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