Improving New-Product Forecasting at Intel Corporation

Forecasting demand for new products is becoming increasingly difficult as the technology treadmill continually drives product life cycles shorter. The task is even more challenging for electronic goods; these products have life cycles measured in quarters, manufacturing processes measured in months, and market volatility that takes place on a day-to-day basis. We present a model that perpetually reduces forecast variance as new market information is acquired over time. Our model extends Bass' original idea of product diffusion [1969] to a more comprehensive theoretical setting. We first describe how forecast variances can be reduced when combining predictive information from multiple diffusion models. We then introduce the notion of demand-leading indicators in a Bayesian framework that reduces forecast variance by incorporating a wide variety of information emerging during the product life cycle. We describe a successful implementation of this model at Intel, where we tested one-third of the microprocessor products. When compared with the current forecasting method, our model reduced forecasting time from three days to two hours and decreased forecasting errors by 33 percent, leading to $11.8 million in cost savings over four months of demand realization.