Improving New-Product Forecasting at Intel Corporation
Improving New-Product Forecasting at Intel Corporation
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DOI：
10.1287/inte.1100.0504
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发表时间：
2010-09
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影响因子：
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通讯作者：
S. David Wu;Karl G. Kempf;Mehmet O. Atan;Berrin Aytac;S. A. Shirodkar;A. Mishra
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文献类型：
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作者：
S. David Wu;Karl G. Kempf;Mehmet O. Atan;Berrin Aytac;S. A. Shirodkar;A. Mishra
随着技术的不断发展，产品的生命周期越来越短，预测新产品的需求变得越来越困难。对于电子产品来说，这一任务更具挑战性；这些产品的生命周期以季度为单位，制造过程以月为单位，市场波动以日为单位。我们提出了一个模型，随着时间的推移，随着新的市场信息的获取，预测方差会不断减少。我们的模型扩展了巴斯最初的产品扩散思想[Bass， f.m. 1969]。新产品增长为耐用消费品模型。管理科学，15(5)215—227]，以更全面的理论设置。我们首先描述了当结合来自多个扩散模型的预测信息时如何减少预测方差。然后，我们在贝叶斯框架中引入需求领先指标的概念，该框架通过结合产品生命周期中出现的各种信息来减少预测方差。我们描述了这个模型在英特尔的成功实现，我们测试了三分之一的微处理器产品。与当前的预测方法相比，我们的模型将预测时间从三天减少到两个小时，并将预测误差减少了33%，从而在四个月的需求实现中节省了1180万美元的成本。
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 [Bass, F. M. 1969. A new product growth for model consumer durables. Management Sci. 15(5) 215--227] 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.