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.
Life Lesson from a Black Eye I'm fond of arguing that Plato is the father of philosophy. Apparently that is the wrong argument to make, in a bar, with a stranger, when said stranger takes the opposing view. (And I thought politics, religion, and his mother were the only things never
Among the suitable-for-blog-publication-without-risking-my-job definitions of masochism is this: A willingness or tendency to subject oneself to unpleasant or trying experiences So to be a forecaster, must you also be a masochist? Few people enjoy the difficulties and degradation that go with being a forecaster, so few are willing to do it
A common mistake in bad or misused software is choosing a forecasting model based solely on the model’s “fit to history” (often referred to as “best fit” or “pick best” functionality). The software provides (or the forecaster builds) several competing models which are then evaluated against recent history. The model
Of course, forecasting the stock market is not perfectly analogous to forecasting demand for a product. The asking price for a stock is largely "anchored" by the price of its most recent trades. While market values may appear to randomly drift up and down, or in a general direction, we generally
In this second of three flash reports from last week's Analytics2011 conference, we hear about a favorite topic of mine -- the relationship between demand volatility and forecastability. Rob Miller of Avantor Performance Materials, on Forecastability and Demand Volatility The "comet chart," illustrating the relationship between demand volatility and forecast
We'll interrupt the series on Why Forecasts are Wrong, with a report from the inaugural Analytics 2011 conference, held last week in Orlando. A2011 drew over 1025 attendees (from 44 states and over 25 counties). The Analytics conference series features a wide range of topics (including forecasting, optimization, data mining, text
This week brought big news of one of the most cruel and heartless tyrants of the 21st century. This man is known for narcissistic behavior, surrounding himself with a cadre of beautiful women, sleeping in a different place every night, picking new favorites each week, and bringing tears and untold suffering
Two weeks ago I had the pleasure of reading Tom Wallace's new book, Sales & Operations Planning: Beyond the Basics. This is not an introductory "how to" book -- Tom and Bob Stahl have already written those -- but instead covers nine major companies that have implemented (and extended!) S&OP
Webinar October 4, 1:10 pm ET: What is Your Product Forecastability??? Thanks to Rich Gendon and the Chicago APICS chapter for hosting me last week at their professional development dinner meeting. I always enjoy evening speaking gigs, as they provide a chance to break out some of my nightclub-worthy material.
We continue my colleague Udo Sglavo's example with the SAS code for incorporating R models into SAS Forecast Server: Code for Including R Model Results in SAS As a first step I’m exporting a data set containing one time series from SAS to R (actually I will use the same
