Reducing Inventory System Costs by Using Robust Demand Estimators
研究在(s, S)库存补货策略中，使用稳健统计估计量（如指数平滑均值和修正的指数平滑平均绝对偏差）替代样本均值和标准差，能否显著降低因需求分布参数估计不精确导致的系统总成本。通过计算机模拟和美国空军实际需求数据验证，发现稳健估计量在多数情况下能降低成本。
Applications of inventory theory typically use historical data to estimate demand distribution parameters. Imprecise knowledge of the demand distribution adds to the usual replenishment costs associated with stochastic demands. Only limited research has been directed at the problem of choosing cost effective statistical procedures for estimating these parameters. Available theoretical findings on estimating the demand parameters for (s, S) inventory replenishment policies are limited by their restrictive assumptions. The impact on total system cost of using the sample mean and standard deviation as compared to robust parameter estimators has not been tested. This paper explores the circumstances under which the cost due to statistical estimation can be substantially reduced by a better choice of estimators. Specifically, an exponentially smoothed average and a modified exponentially smoothed mean absolute deviation are shown to outperform the sample mean and standard deviation for a wide range of computer simulated and U.S. Air Force empirical demands when the (s, S) policies are calculated using Ehrhardt's Power Approximation. Those situations in which the method of demand parameter estimation has negligible impact on total system cost are also indicated.