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Webinar: What do we need to know about FVA?

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By Topics | Analytics

Robert Fildes photoOn 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 the link to register.

Robert is Distinguished Professor of Management Science at Lancaster, a Fellow of the International Institute of Forecasters, and Director of the CMAF. His long list of accomplishments include co-founding (with Scott Armstrong and Spyros Makridakis) the Journal of Forecasting in 1981, and the International Journal of Forecasting in 1985, serving as IJF's editor-in-chief for 10 years. Either alone or in collaboration with Paul Goodwin and others, Robert has contributed fundamental research in many areas, including judgmental forecasting, comparative evaluation of forecasting methods, and design of forecasting systems.

Webinar Abstract:

Forecast value added is a phrase now very much part of the organizational understanding needed to improve forecasting practice and to select between alternative forecasting methods. One of its more important applications is to understand the improved accuracy achieved by the common practice in demand planning of adjusting a statistical forecasting method based on information gathered through the sales and operations planning process. Various past studies have analysed the results of this adjustment process. This presentation considers a range of data sources to provide insight into the circumstance where gains have been achieved. We identify the key questions facing any organization where FVA of expert judgmental adjustment is part of the process. But be warned, the conclusions will not be unequivocal; FVA is a complex area and there remains a lot to be learnt and much to be done if organizations are going to develop effective demand planning processes.

I'll be setting the stage for Robert with a very brief "Introduction to FVA Analysis," and then we'll hear the latest updates from his FVA research.

Please join us Friday, and at future events in this valuable webinar series.

Tags CMAF FVA Robert Fildes
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About Author

Mike Gilliland

Product Marketing Manager

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.

2 Comments

  1. Andrey on

    The presentation was largely based on (Davydenko and Fildes, 2013), later published in (Davydenko and Fildes, 2016), see the references below.
    Importantly, some references were missing or wrong in this presentation.

    On the slide shown at 17:45:

    1) It must be (Davydenko and Fildes, 2013), the year is 2013, not 2011, The 'FVA' metric used on the slides was defined in this paper:
    Davydenko, A., & Fildes, R. (2013). Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts. International Journal of Forecasting, 29(3), 510–522

    An adapted version of (Davydenko and Fildes, 2013) was later published in Mike Gilliland's book:
    Davydenko, A., & Fildes, R. (2016). Forecast Error Measures: Critical Review and Practical Recommendations. In Business Forecasting: Practical Problems and Solutions. John Wiley & Sons

    2) The 'Bias' metric defined on the same slide is exactly the AvgRelAME proposed in the followшng Ph.D. thesis (page 64):
    Davydenko, A. (2012). Integration of judgmental and statistical approaches for demand forecasting: Models and methods (doctoral dissertation). Lancaster University, UK, https://doi.org/10.13140/RG.2.2.31788.62083

    I really would appreciate it if you could cite (Davydenko, 2012, p.64) when you use the AvgRelAME for measuring and reporting bias. This metric was proposed in (Davydenko, 2012). One of the authors of the slides is obviously familiar with the work where the metric was proposed. So a proper citation is really required here in accordance with all the rules for academic ethics.

    Btw,
    Here's a good citation from (Goodwin, 2018, p. 54):
    "If you do want to perform FVA across products, then researchers recommend a measure called the average relative MAE (see the Davydenko and Fildes reference at the end of the chapter). This has not yet been implemented in commercial software." The reference is (Davydenko and Fildes, 2016).
    Source:
    Goodwin, P. (2018). Profit from your forecasting software: A best practice guide for sales forecasters. Wiley and SAS Business Series. John Wiley & Sons, Inc., Hoboken, New Jersey.

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