Lowering Inventory Systems Costs by Using Regression‐Derived Estimators of Demand Variability*
研究证明，用回归拟合方差-均值关系来估计需求方差，比传统统计方差估计更能降低多品种库存系统的成本，即使存在异质性和模型设定错误也能节省成本。
ABSTRACT Scientific techniques for inventory management typically are applied to systems containing many items. Such techniques require an estimation of the demand variance (and mean) of each item from historical data. This research demonstrates a significant potential for improvement in system cost performance from using least‐squares regression fits of a variance‐to‐mean functional relation instead of the standard statistical variance estimate. Even when there is a moderate degree of heterogeneity among items and when the form of the variance‐to‐mean relation is misspecified, substantial cost savings may be realized. The cost of statistical uncertainty may be reduced by half. The research also provides evidence that system cost is fairly insensitive to the number of items used to fit the regression. This paper provides the underlying reason why a regression‐derived variance estimator yields lower cost: it is less variable than the usual individual item variance estimator.