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.
Last week Len Tashman, Udo Sglavo and I announced release of our new collection: Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning. Today, we share an excerpt from the book, one of the sixteen opinion/editorial "Afterwords" contributed by influential leaders in academics and industry, this one by
With my collaborators Len Tashman (Editor of Foresight) and Udo Sglavo (VP of Analytics R&D at SAS), we are happy to announce the release of our new collection, Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning (Wiley, 2021). Building upon our previous collection Business Forecasting: Practical Problems and
Following is Editor Len Tashman's preview of the new issue of Foresight: The International Journal of Applied Forecasting. Jonathon Karelse, author of the lead story, will be presenting in the Practitioner Track of the International Symposium on Forecasting (June 27-30). Preview of Foresight #61 (2021:Q2) For many years, we’ve identified
Please join us on Tuesday, March 16 for what should be an exciting discussion on the current challenges and emerging best practices in demand forecasting. I'll be moderating this very distinguished panel: Nicolas Vandeput - supply chain data scientist, specialized in demand forecasting and inventory optimization; author of the book
We learned this week of the passing of one of the giants in our field, Tom Wallace. Tom was as gracious and fine a gentleman as you'll meet. Through his writing, teaching, and consulting work -- in frequent collaboration with Bob Stahl -- tens of thousands of industry practitioners have
Through the M4 and M5 competitions, we've seen the promising performance of machine learning approaches in generating forecasts. The SAS whitepaper "Assisted Demand Planning Using Machine Learning for CPG and Retail" describes a role for ML in augmenting the demand planning by guiding the review and override of statistical forecasts.
We seem to be in the height of webinar season, so please add one more to your calendar, brought to you by SAS, CT Global Solutions, and the International Institute of Forecasters: I'll be delivering a short introduction, covering the disappointing state of real life business forecasting in contrast to
On Friday Nov 27, 2:00pm GMT (9:00am EST in the US), Robert Fildes is presenting his latest research in the webinar "What do we need to know about Forecast Value Added?" This is part of the Lancaster University Centre for Marketing Analytics and Forecasting's "CMAF Friday Forecasting Talks." Here is
Here is the final installment of the 3-part series by guest bloggers Jessica Curtis and Andrea Moore. (If you missed them, here are Part 1 and Part 2). HOW SAS MAKES IT HAPPEN There are two main steps for integrating open source forecasting models into SAS Visual Forecasting, each with increasing
Here is the second installment of the 3-part series by guest bloggers Jessica Curtis and Andrea Moore. (If you missed it, here is Part 1.) THE VALUE SAS DELIVERS TO OPEN SOURCE First and foremost, SAS distributes the input data for forecasting. SAS knows how to split up data intelligently
