The Business Forecasting Deal
Exposing bad practices and offering practical solutions in business forecasting
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
Forecasting is a daunting task during normal conditions, and even more so during a disruption. But in times of greatest stress our smartest and most creative people stand out, and our true leaders emerge. You'll find these kinds of leaders among my colleagues at SAS -- smart and creative people
Following is editor-in-chief Len Tashman's preview of the Spring 2020 issue of Foresight: The International Journal of Applied Forecasting. Preview of Foresight (Spring 2020) This Spring 2020 issue of Foresight—number 57 since the journal began in 2005— leads off with Associate Editor Mike Gilliland’s discussion of The M4 Forecasting Competition:
Forecasting During Chaos The Institute of Business Forecasting has produced an 80-minute virtual town hall on "Forecasting & Planning During the Chaos of a Global Pandemic." The on-demand video recording is available now and well worth a look. There is much solid practical guidance from an experienced panel: Eric Wilson,
Fildes and Goodwin (F&G) observed the subject (the regional subsidiary of a pharmaceutical company) was using a statistical forecasting system, but not fully trusting its output. Forecasters were making overrides to the system generated forecast to make it look like what they believed it should (e.g., following a life-cycle curve
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?