The Forecasting "Bake-Off"


Just as we all eagerly awaited announcement of the $1 million prize winner of the Pillsbury Bake-Off(R), every forecasting software vendor has endured the "bake-off" hosted by organizations in the market for new forecasting software.

Software selection teams utilize a bake-off to help evaluate competing vendors. Vendors are given a sample of the organization’s historical data and asked to generate forecasts. Sometimes the most recent data is withheld, and the forecasts are made over the withheld data. A better (although more time-consuming approach) is to have the vendors forecast future periods, and then patiently wait-and-see the results as actuals roll in. The purpose of the bake-off is to gain insight into the expected forecasting performance from each vendor.

Rob Stevens, Analytical Consultant in SAS Global Professional Services, has suggested guidelines for running a fair and informative bake-off, emulating real-world circumstances. (It is easy for a bake-off to be “fixed” in favor of a preferred vendor, so these guidelines should be adhered to.):

• Provide all necessary information (including historical demand data and events).
• Provide sufficient history for vendors to be able to utilize holdout samples for evaluating their models.
• Give vendors sufficient time to analyze your data—don’t force them to take shortcuts that you would not take in real-life forecasting.
• Assist vendors with the domain experience they may lack regarding your business—perhaps by having a project team member assigned to support the vendors during the bake-off.
• Do not “fix” the bake-off with arbitrary rules to put favored vendors at an advantage. (Not only is this completely unethical but, it prevents you from getting a fair assessment of each vendor’s performance.)
• Be aware that a bake-off is a one-shot event, and the results may be somewhat due to chance. Better forecasting systems evolve over time, incorporating learnings from each forecasting cycle.
• Utilize appropriate evaluation metrics—not just focusing on error (MAPE) or accuracy. Also consider the bias in the competing forecasts, and the “value added” compared to using a naïve model.
• Be aware that simple models are often better at forecasting the future, even though they may not fit the past as well as more complex models. Access to a wide range of model choices is good, but fancier models will not guarantee better forecasts. Focus on the quality of the future forecasts, not on the model fit to history.
• Beware of organization politics that may contaminate the bake-off evaluation.

Note that a bake-off is no substitute for a full-scale Proof-of-Concept/Proof-of-Value that demonstrates the software’s performance with the organization’s full data. A proper POC/POV can require a significant commitment of time and resources from the vendor, and the selection team should budget to pay for the service provided.


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.

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