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.
Chris Gray of Gray Research is a longtime contributor to the practice of Sales and Operations Planning. He is author of several books on S&OP, software selection, and other supply chain related areas, including Sales and Operations Planning Standard System (2007). In 2006 he co-authored Sales & Operations Planning –
Yesterday I recommended Steve Morlidge’s The Little Book of Beyond Budgeting, for helping to illuminate the troubling usage of business forecasts in the traditional management / budgeting process. Steve reached out to me overnight with some additional points that he shares in this guest blogger post. Steve Morlidge on Forecasts
Is There Something Beyond Budgeting? Forecasting is an integral part of the business planning and budgeting process. Presumably the forecast (which should be an "unbiased best guess" at what is really going to happen in the future) can provide a reasonable foundation upon which the annual budget and operating plans
IIF Tao Hong Award Dr. Tao Hong is a friend, former SAS colleague, former SAS basketball league opponent, a major contributor to SAS Energy Forecasting software, and now Associate Professor at UNC Charlotte. When he isn't raining three-pointers on me on the basketball court, Tao is Director of the Big
Editor-in-Chief Len Tashman's Preview The forecasting field is surely cross-disciplinary, as exemplified by the diverse membership of the International Institute of Forecasters (the publisher of this journal), but it is also multidimensional, as can be clearly seen in this Summer 2017 issue. The articles you’ll read here encompass sales and
For the fifteenth year, the International Institute of Forecasters, in collaboration with SAS®, is proud to announce research grants for how to improve forecasting methods and business forecasting practice. The award for the 2017-2018 year will be two $5,000 grants, in Business Applications and Methodology. Criteria for the award of
Registration is now open for the SAS Analytics Experience 2017, being held September 18-20, in Washington, DC. (The Analytics Experience moves to Amsterdam, October 16-18 -- details on that event to follow.) For anyone interested in FVA analysis, Chip Wells and I will be delivering a half-day pre-conference training session
My colleague Gerhard Svolba (Solutions Architect at SAS Austria) has authored his third book, Applying Data Science: Business Case Studies Using SAS®." While the book covers a broad range of data science topics, forecasters will be particularly interested in two lengthy case studies on "Explaining Forecast Errors and Deviations" and
The Foresight Practitioner Conference returns to Raleigh, NC (November 15-16), with a theme of "Recoupling Forecasting with Inventory Control and Supply Planning." This event is produced jointly by Foresight and the North Carolina State University Institute for Advanced Analytics, and deals with an important topic. Too often we consider forecasting
So you think you are smarter than the average forecaster, and can identify a trend in time series data? You now have a chance to put your trend detection skills (aka trendar) to the test, and help the cause of forecasting research in the process. Nikos Kourentzes, Associate Professor at
Preview of the Spring 2017 Issue of Foresight Since our first issue in 2005, Foresight has strived to serve up articles that unite the scholarship of our field’s academic researchers with the perspectives of experienced organizational practitioners, all the time emphasizing lessons learned—and sometimes those lessons are learned the hard
While The BFD normally favors a lighter approach, with a focus on forecasting process (the politics and personalities therein), forecasting news, and forecasting gossip, we sometimes have to get serious. In this guest blogger post from Koen Knapen, Principal Consultant at SAS Belgium, Koen takes us into the netherworlds of
Good Judgment® Open Ever wondered how good you are at forecasting? As a business forecaster, you can do the usual comparison against a naive model (and hopefully you are beating it!). You might also compare your forecast accuracy to published industry benchmarks -- although I would strongly recommend against this.
To make it easy to identify non-value adding areas, you can build a simple application using SAS® Visual Analytics software. Such an application lets you point and click your way through the organization’s forecasting hierarchy, and at each point view performance of the Naïve, Manual, Statistical, and Automated forecasts (or
To properly evaluate (and improve) forecasting performance, we recommend our customers use a methodology called Forecast Value Added (FVA) analysis. FVA lets you identify forecasting process waste (activities that are failing to improve the forecast, or are even making it worse). The objective is to help the organization generate forecasts
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
