Michael Gilliland is author of The Business Forecasting Deal (the book), and editor of Business Forecasting: Practical Problems and Solutions. He is a longtime business forecasting practitioner, and currently Product Marketing Manager for SAS Forecasting software. Mike serves on the Board of Directors for the International Institute of Forecasters, and received the 2017 Lifetime Achievement in Business Forecast award from the Institute of Business Forecasting. He initiated The Business Forecasting Deal (the blog) to help expose the seamy underbelly of forecasting practice, and to provide practical solutions to its most vexing problems.
Preview of the Winter 2017 issue of Foresight Foresight begins the new year with our 44th issue since the journal began publishing in 2005, and in this Winter 2017 collection we’re showcasing a broad range of incisive and entertaining pieces. We’re looking at new research on the effectiveness of collaboration
Aphorism 6: The Surest Way to Get a Better Forecast is to Make the Demand Forecastable Forecast accuracy is largely dependent on volatility of demand, and demand variation is affected by our own organizational policies and practices. So an underused yet highly effective solution to the forecasting problem can be
Aphorism 3: Organizational Policies and Politics Can Have a Significant Impact on Forecasting Effectiveness We just saw how demand volatility reduces forecastability. Yet our sales, marketing, and financial incentives are usually designed to add volatility. We reward sales spikes and record weeks, rather than smooth, stable, predictable growth. The forecast
The Aphorisms of the New Defensive Paradigm I want to finish this blog series with a set of 7 aphorisms – concise statements of principle – that characterize the new Defensive paradigm for business forecasting. The first is that: Aphorism 1: Forecasting is a Huge Waste of Management Time This
Academic Research In an approach akin to FVA analysis, Paul Goodwin and Robert Fildes published a frequently cited study of four supply chain companies and 60,000 actual forecasts.* They found that 75% of the time an analyst adjusted the statistical forecast. They were trying to figure out, like FVA does,
Typical Business Forecasting Process Let’s look at a typical business forecasting process. Historical data is fed into forecasting software which generates the "statistical" forecast. An analyst can review and override the forecast, which then goes into a more elaborate collaborative or consensus process for further adjustment. Many organizations also have
The Means of the Defensive Paradigm The Defensive paradigm pursues its objective by identifying and eliminating forecasting process waste. (Waste is defined as efforts that are failing to make the forecast more accurate and less biased, or are even making the forecast worse.) In this context, it may seem ridiculous
Why the Attraction for the Offensive Paradigm? In addition to the reasons provided by Green and Armstrong, I'd like to add one more reason for the lure of complexity: You can always add complexity to a model to better fit the history. In fact, you can always create a model
Fresh from chairing the Foresight Practitioner Conference on “Worst Practices in Business Forecasting,” hosted two weeks ago at the Institute for Advanced Analytics at North Carolina State University, Foresight editor-in-chief Len Tashman previews the Fall 2016 issue. Preview of the Fall 2016 issue of Foresight In the provocative article, “The
Implications for the Offensive Paradigm The worldview promulgated by the Offensive paradigm is that if we only had MORE – more data, more computational power, more complex models, more elaborate processes – we could eventually solve the business forecasting problem. But this just doesn’t seem to be the case. Operating
Is Complexity Bad? It’s necessary to point out that Goodwin’s article is not arguing against complexity per se, and I’m not either. When you have a high value forecast, where it is critical to be as accurate as possible, of course you are going to want to try every technique
Anomalies: The Beginning of a Crisis While even trained scientists can fail to see things that fall outside what they are looking for, anomalies eventually start to get noticed. But still, for a long time, anomalies within an existing paradigm are seen as mere “violations of expectation.” The response within
The Current Paradigm for Business Forecasting So what is the current paradigm that we, the community of business forecasting practitioners and researchers, are operating under? I’d argue that for at least the last 60 years, since 1956 when Robert G. Brown published his short monograph Exponential Smoothing for Predicting Demand,
In a February 2015 post Offensive vs. Defensive Forecasting, I sought to distinguish two very different approaches to the business forecasting problem: Offensive: The "offensive" forecaster is focused on forecast accuracy -- on extracting every last fraction of a percent of accuracy we can hope to achieve. The approach is
NIJ's Real-Time Forecasting Challenge If you want to show off your forecasting chops, and maybe even make a little money, the National Institute of Justice has just the challenge for you. The NIJ's Real-Time Crime Forecasting Challenge: ...seeks to harness the advances in data science to address the challenges of
My friend and colleague Charlie Chase, author of the new book Next Generation Demand Management, has developed a 2-day course to go along with the book. The course is part of the SAS Business Knowledge Series, and is being offered in Chicago, October 19-20. Here are the details: Next Generation
Book Review in Journal of Business Forecasting The Summer 2016 issue of Journal of Business Forecasting includes a book review of Business Forecasting: Practical Problems and Solutions. The review is by Simon Clarke, Group Director of Forecasting at The Coca-Cola Company. You may be familiar with Clarke's many previous contributions
Companies launch initiatives to upgrade or improve their sales & operations planning and demand planning processes all the time, but many fail to deliver the results they should. Has your forecasting operation fallen short of expectations? Do you struggle with "best practices" that seem incapable of producing accurate, useful results?
Announcing New Book by Charlie Chase: Next-Generation Demand Management My colleague Charlie Chase has just published his latest book, Next-Generation Demand Management. It is available August 29, and can be pre-ordered now on amazon.com. It will also be available for purchase at the SAS Bookstore, along with Charlie's other books:
For the fourteenth year, the International Institute of Forecasters, in collaboration with SAS®, is proud to announce financial support for research on how to improve forecasting methods and business forecasting practice. The award for the 2016-2017 year will be two $5,000 grants, in Business Applications and Methodology. Criteria for the
The online SAS Support Communities are a vibrant source of information and interaction for over 90,000 registered participants. Here you can ask (and answer) questions, grow (and share) your SAS expertise, and explore these collection points for other resources (like hot tips, articles, blogs, and events) relating to the community
Editor Len Tashman's Preview of the Summer Issue of Foresight Clarity and effectiveness of communication are key to success – so says a recent survey by the National Association of Business Economists (NABE), which reported that industry leaders and hiring managers considered communication skills to be the single most important
Paul Goodwin is Professor Emeritus of Management Science at University of Bath, and one of the speakers at this fall's Foresight Practitioner Conference (October 5-6 in Raleigh, NC). His topic will be "Use and Abuse of Judgmental Overrides to Statistical Forecasts"-- an area in which he has contributed much of
Let's continue now to Nikolaos Kourentzes' blog post on How to choose a forecast for your time series. Using a Validation Sample Nikos first discusses the fairly common approach of using a validation or "hold out" sample. The idea is to build your model based on a subset of the
Nikolaos Kourentzes is Associate Professor at Lancaster University, and a member of the Lancaster Centre for Forecasting. In addition to having a great head of hair for a forecasting professor, Nikos has a great head for explaining fundamental forecasting concepts. In his recent blog post on How to choose a
SAS® Insights is a section of the sas.com website devoted to being "your top source for analytics news and views." It contains articles, interviews, research reports, and other content from both SAS and non-SAS contributors. In a new article posted this week, we added three short videos containing practical advice
Sometimes one's job gets in the way of one's blogging. My last three months have been occupied with the launch of SAS® Viya™, our next-generation high-performance and visualization architecture. Please take the time to find more information on the SAS Viya website, and apply for a free preview. Rob Hyndman
Editor Len Tashman's Preview of the Spring Issue of Foresight Misbehaving, the feature section of this 41st issue of Foresight, was prompted by the publication of Richard Thaler’s eye-opening book of the same title, a work that explains the often surprising gap between (a) the models we use and organizational
The new book Business Forecasting: Practical Problems and Solutions contains a large section of recent articles on forecasting performance evaluation and reporting. Among the contributing authors is Rob Hyndman, Professor of Statistics at Monash University in Australia. To anyone needing an introduction, Hyndman's credentials include: Editor-in-chief of International Journal of
"The Role of Model Interpretability in Data Science" is a recent post on Medium.com by Carl Anderson, Director of Data Science at the fashion eyeware company Warby Parker. Anderson argues that data scientists should be willing to make small sacrifices in model quality in order to deliver a model that
