Bayesian Forecasting of Parts Demand

As supply chains for high technology products increase in complexity, and as the performance expected of those supply chains also increases, forecasts of parts demand have become indispensable to effective operations management in these markets. Unfortunately, rapid technological change and an abundance of product configurations mean that demand for parts in high-tech is frequently volatile and hard to forecast. The paper describes a Bayesian statistical model developed to forecast parts demand for Sun Microsystems, Inc., a major vendor of network computer products. The model embodies a parametric description of the part 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, and compares its forecast performance with that of alternatives.