As supply chains for high technology products increase in complexity, and as the performance expectations of these supply chains also increase, forecasts of parts demands have become indispensable to effective operations management in these markets. Unfortunately, rapid technological change and an abundance of product configurations mean that the demand for parts in high-tech products is frequently volatile and hard to forecast. The paper describes a Bayesian statistical model which was developed to forecast the parts demand for Sun Microsystems, Inc., a major vendor of enterprise computer products. The model embodies a parametric description of the part's life cycle, allowing it to anticipate changes in demand over time. Furthermore, using hierarchical priors, the model is able to pool demand patterns for a collection of parts, producing calibrated forecasts for new parts with little or no demand history. The paper discusses the problem addressed by the model, the model itself, and a procedure for calibrating it, then compares its forecast performance with those of alternatives.
