Forecasting for business planning: A case study of IBM product sales

Abstract

This is a case study of a closely managed product. Its purpose is to determine whether time‐series methods can be appropriate for business planning. By appropriate, we mean two things: whether these methods can model and estimate the special events or features that are often present in sales data; and whether they can forecast accurately enough one, two and four quarters ahead to be useful for business planning. We use two time‐series methods, Box‐Jenkins modeling and Holt‐Winters adaptive forecasting, to obtain forecasts of shipments of a closely managed product. We show how Box‐Jenkins transfer‐function models can account for the special events in the data. We develop criteria for choosing a final model which differ from the usual methods and are specifically directed towards maximizing the accuracy of next‐quarter, next‐half‐year and next‐full‐year forecasts. We find that the best Box‐Jenkins models give forecasts which are clearly better than those obtained from Holt‐Winters forecast functions, and are also better than the judgmental forecasts of IBM's own planners. In conclusion, we judge that Box‐Jenkins models can be appropriate for business planning, in particular for determining at the end of the year baseline business‐as‐usual annual and monthly forecasts for the next year, and in mid‐year for resetting the remaining monthly forecasts. Copyright © 1991 John Wiley & Sons, Ltd.