On September 2 (3pm UTC / 11am EDT), I'll be joining Jonathon Karelse, CEO of NorthFind Management, for an interactive "fireside chat" on the application of Behavioral Economics in demand planning. This is part of the Foresight Webinar Series, and registration is free. Since we first met at an Institute
I am gratified to see the continuing adoption of Forecast Value Added by organizations worldwide. FVA is an easy to understand and easy to apply approach for identifying bad practices in your forecasting process. And I'm particularly gratified to see coverage of FVA in two new books, which the authors
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.
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
In recent posts (March 26, April 21) we've looked at forecasting in the face of chaos and disruption. We've seen that traditional time series forecasting methods (used during "normal" times) can be creatively augmented with additional methods like clustering, similarity analysis, epidemiologic models, and simulation. While it is unreasonable to
Two weeks ago we looked at the first two steps in effecting forecasting process change: Justify your suspicions with data Communicate your findings That was the easy part. So why is it that so many organization realize they have a forecasting problem, yet are unable to do anything about it?
Applying machine learning approaches to forecasting is an area of great research interest. Progress is being made on multiple fronts, for example: In the M4 Forecasting Competition, completed earlier this year, the top two performers utilized machine learning with traditional time series forecasting methods. At the link you'll find full
If you think machine learning will replace demand planners, then don’t read this post. If you think machine learning will automate and unleash the power of insights allowing demand planners to drive more value and growth, then this article is a must read.
What is Forecast Value Added? Please enhance your Valentine's Day with this treat offered up by the Journal of Business Forecasting. Eric Wilson's very nice discussion of Forecast Value Added, originally published in the Spring 2016 issue of JBF, is now available online: "What is Forecast Value Added?" Eric also
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
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
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
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
Journal of Business Forecasting columnist Larry Lapide is a longtime favorite of mine. As an industry analyst at AMR, and more recently as an MIT Research Affiliate, Larry's quarterly column is a perpetual source of guidance for the practicing business forecaster. No wonder he received IBF's 2012 Lifetime Achievement in
Last week I had the pleasure of attending (with six of my SAS colleagues) the IBF's Best Practices Forecasting Conference in Orlando. Some of the highlights: Charlie Chase and I were interviewed by Russell Goodman of SupplyChainBrain.com. The videos will be posted on SCB's website later this year. Meantime, enjoy
The SAS Business Knowledge Series now offers an online version of the "Forecast Value Added Analysis" course, taught via live web in two afternoon sessions, May 7-8. The instructor is my colleague Chip Wells, who expanded our original 1/2 day FVA workshop with new material, examples, and exercises based on his
The Institute of Business Forecasting's FVA blog series continued on March 2, with my interview of Steve Morlidge of CatchBull. Steve's research (and his articles in Foresight) have been a frequent subject of BFD blog posts over the last couple of years (e.g. The "Avoidability of Forecast Error (4 parts),
My colleague Charlie Chase, Advisory Industry Consultant and author of the book Demand-Driven Forecasting, has developed a new course for the SAS Business Knowledge Series (BKS): Best Practices in Demand-Driven Forecasting. The 2-day course will be offered for the first time April 20-21 in Atlanta (and then again September 24-25 in Chicago). From the
Sports provide us with many familiar clichés about playing defense, such as: Defense wins championships. The best defense is a good offense. Or my favorite: The best defense is the one that ranks first statistically in overall defensive performance, after controlling for the quality of the offenses it has faced. Perhaps not
The Institute of Business Forecasting's FVA blog series continued in January, with my interview of Shaun Snapp, founder and editor of SCM Focus. Some of Shaun's answers surprised me, for example, that he doesn't compare performance to a naïve model (which I see as the most fundamental FVA comparison). But he went
This isn't such a brilliant article because we learn something new from it -- we really don't. But it is amazing to find, from someone in 1957, such a clear discussion of forecasting issues that still plague us today. If you can get past some of the Mad Men era words and
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
In December the Institute of Business Forecasting published the first of a new blog series on Forecast Value Added. Each month I will be interviewing an industry forecasting practitioner (or consultant/vendor) about their use of FVA analysis. The December interview featured Jonathon Karelse, co-founder of NorthFind Partners. Among his key
There are some things every company should know about the nature of its business. Yet many organizations don't know these fundamentals -- either because they are short on resources, or their resources don't have the analytical skills to do the work. The summer research projects offered by the Lancaster Centre for Forecasting,
Calling All Forecasters Have you tried Forecast Value Added analysis? What did you find out? Are you willing to share your learnings (at least those that can be revealed publicly)?Would you like to be featured in a new blog series on FVA, published by the Institute of Business Forecasting? The IBF was
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
