In 2015 Foresight: The International Journal of Applied Forecasting will celebrate 10 years of publication. From high in his aerie in the Colorado Rockies, here is Editor-in-Chief Len Tashman's preview of the current issue:
In this 35th issue of Foresight, we revisit a topic that always generates lively and entertaining discourse, one where business experience has been far more enlightening than academic research: the question of the proper Role of the Sales Force in Sales Forecasting. Our feature article by Mike Gilliland formulates the key aspects of the issue with three questions: Do salespeople have the ability to accurately predict their customers’ future buying behavior, as many assume they do? Will salespeople provide an honest forecast? And does improving customer-level forecasts improve company performance? Incisive commentaries follow Mike’s piece, contributed by forecast directors at three companies.
As Foresight Editor, I welcome continued discussion from our readers on your experiences and lessons learned at your own organizations.
Paul Goodwin’s Hot New Research column addresses a promising new method for properly representing the uncertainty behind a forecast. Called SPIES (Subjective Probability Interval Estimates), it offers a more intuitive way (than standard statistical approaches) for forecasters to determine and present the probability distribution of their forecast errors. I think you’ll find it provocative.
Our section on Forecasting Support Systems features the article Data-Cube Forecasting for the Forecasting Support System. Noted Russian consultant Igor Gusakov draws on his many years at CPG companies to show how we can achieve the best of what are now two distinct worlds, by synthesizing statistical forecasting capabilities with the OLAP (online analytical processing) tools now commonly used for business intelligence and reporting. Data cubes provide the requisite infrastructure.
Igor is also the subject of our Forecaster in the Field interview.
Our Summer 2014 issue included the first part of a feature section on Forecasting by Aggregation. Two articles there examined “temporal aggregation” opportunities, which deal with the choices of time dimension (daily, weekly, monthly, etc.) for forecasting demands.* Now we present Part Two on Forecasting by Cross-Section Aggregation within a product hierarchy. Giulio Zotteri, Matteo Kalchschmidt, and Nicola Saccani question the usual belief that the level of aggregation for forecasting is specified by the operational requirements of the company. Rather, they argue – quite convincingly – that the best level of aggregation for forecasting should be chosen by the forecasters in an attempt to balance the errors from forecasting with data at too granular a level with those at too aggregate a level.
Rob J. Hyndman and George Athanasopoulos extend the discussion by presenting a way for Optimally Reconciling Forecasts in a Hierarchy. Rarely will the sum of forecasts at a granular level equal the forecast at the group level; hence reconciliation is necessary. The authors argue that traditional reconciliation methods – bottom-up, top-down, and middle-out – fail to make the best use of available data. Their optimal reconciliation is based on a weighted average of forecasts made at all different levels of the hierarchy.
*Aris Syntetos will discuss "Forecasting by Temporal Aggregation" in the Foresight/SAS Webinar Series on October 30, 11:00am ET.