Design for Postponement: A Comprehensive Characterization of Its Benefits Under Unknown Demand Distributions
Design for Postponement: A Comprehensive Characterization of Its Benefits Under Unknown Demand Distributions
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DOI：
10.1287/opre.49.4.578.11229
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发表时间：
2001-07
期刊：
影响因子：
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通讯作者：
Yossi Aviv;A. Federgruen
中科院分区：
文献类型：
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作者：
Yossi Aviv;A. Federgruen
最近的论文开发了分析模型来解释和量化延迟分化和快速反应计划的好处。这些模型假设虽然每个时期的需求是随机的，但它们在时间上是独立的，并且它们的分布是完全已知的，也就是说，销售预测不需要随着时间的推移而更新。在本文中，我们在更一般的设置中描述这些好处，其中需求分布的参数无法准确地知道，或者连续的需求是相关的。在这里，有必要根据观察到的需求数据修正需求分布参数的估计。我们在贝叶斯框架中分析这些系统，假设我们关于需求分布参数的初始信息是通过先验分布来表征的。我们还描述了这些系统中接近最优排序规则的结构，适用于各种类型的排序成本函数。
Recent papers have developed analytical models to explain and quantify the benefits of delayed differentiation and quick response programs. These models assume that while demands in each period are random, they are independent across time and their distribution is perfectly known, i.e., sales forecasts do not need to be updated as time progresses. In this paper, we characterize these benefits in more general settings, where parameters of the demand distributions fail to be known with accuracy or where consecutive demands are correlated. Here it is necessary to revise estimates of the parameters of the demand distributions on the basis of observed demand data. We analyze these systems in a Bayesian framework, assuming that our initial information about the parameters of the demand distributions is characterized via prior distributions. We also characterize the structure of close-to-optimal ordering rules in these systems, for a variety of types of order cost functions.