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Service-oriented decisions on inventory levels in the case of incomplete demand information

机译：在需求信息不完整的情况下，针对库存水平的面向服务的决策

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摘要

New products without historical demand information or slow-moving items with little such information cause difficulties in defining inventory management policies facing demand uncertainty. The classical approach using the Normal distribution for describing the random demand during lead time might lead to a degraded level of customer service. But the choice for other types of distributions is also no option, so it is realistic that the full functional form of the distribution is unknown, but the decision-maker has some but not incomplete information on the demand distribution during lead time. As the distribution is only partially specified, several distributions satisfy the known information. Customer service measures therefore also take values in an interval between a lower and an upper bound. In this paper, upper and lower bounds are determined for two performance measures: the number of stock-out units and the stock-out probability per replenishment cycle, given incomplete information about the demand distribution, that is only the first two moments and the range, are known. Based on these results, the optimal inventory level given the desired maximum number of stock-out units or the desired maximum stock-out probability is calculated for the case where only the first two moments are known. The results of our approach are compared to the more traditional approach where a Normal distribution of demand during lead time is assumed. Comparisons with the Gamma, Uniform and symmetric triangular distribution are made. Furthermore, the robustness of our bounds to uncertainty in the parameters is tested.
机译：没有历史需求信息的新产品或很少有此类信息的滞销商品会导致难以定义面对需求不确定性的库存管理策略。使用正态分布来描述提前期的随机需求的经典方法可能会导致客户服务水平下降。但是选择其他类型的分布也是没有选择的，因此现实情况是分布的全部功能形式是未知的，但是决策者在提前期就需求分布掌握了一些但不是不完整的信息。由于仅部分指定了分布，因此多个分布可满足已知信息。因此，客户服务措施的取值范围也应介于上限和下限之间。在本文中，确定了两个绩效指标的上限和下限：缺货单位数和每个补货周期的缺货概率，前提是需求分配的信息不完整，仅是前两个时刻和范围，众所周知。基于这些结果，针对仅知道前两个时刻的情况，计算出给定所需最大缺货单位数或所需最大缺货概率的最优库存水平。我们的方法的结果与更传统的方法进行了比较，在传统方法中，提前期期间的需求呈正态分布。与Gamma，均匀和对称三角形分布进行比较。此外，测试了我们对参数不确定性范围的鲁棒性。

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