A Bayesian Model for Sales Forecasting at Sun Microsystems

An accurate short-term forecast of product sales is vital for the smooth operation of modern supply chains, especially where the manufacture of complex products is outsourced internationally. As a vendor of enterprise computing products whose business model has long emphasized extensive outsourcing, Sun Microsystems Inc. has a keen interest in the accuracy of its product sales forecasts. Historically, the company has relied on a judgment-based forecasting process, involving its direct sales force, marketing management, channel partners, and so on. Management recognized, however, the need to address the many heuristic and organizational distortions to which judgment-based forecasting procedures are prey. Simply replacing the judgmental forecasts by statistical methods with no judgmental input was unrealistic; short product life cycles and volatile demand demonstrably confounded purely statistical approaches. This article documents a forecasting system developed in Sun’s research laboratory and currently deployed by the company that uses Bayesian methods to combine both judgmental and statistical information. We discuss the development and architecture of the system, including steps that were taken to ease its incorporation into the company’s existing forecasting and planning processes. We also present an evaluation of the system’s forecasting performance, as well as possible directions for future devel-