Forecasting With Temporally Aggregated Demand Signals in a Retail Supply Chain
Forecasting With Temporally Aggregated Demand Signals in a Retail Supply Chain
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DOI：
10.1111/jbl.12091
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发表时间：
2015-06-01
影响因子：
10.3
通讯作者：
Waller, Matthew A.
中科院分区：
文献类型：
--
作者：
Jin, Yao Henry;Williams, Brent D.;Waller, Matthew A.
消费品供应商正面临着越来越具挑战性的形势，因为他们正在努力履行来自零售合作伙伴分销设施的订单。传统上，这些供应商会根据某家零售商过去的订单记录，对该零售商的订单进行预测。然而，零售公司收集和共享大量销售点(POS)数据正变得越来越常见，从而为供应商提供了一种替代数据信号，用于生成预测。然后就出现了一个问题，即哪些数据能产生最准确的预测。让这个问题雪上加霜的是，预测者经常临时汇总数据以进行整合，或者在更大的时间段内做出预测。现有文献规定了两种相互抵消的统计效应，即信息损失和方差减少，这两种效应在确定时间聚集对预测精度的影响方面发挥了重要作用。利用大量配对的订单和POS数据，本研究检验了这些关系。
Suppliers of consumer packaged goods are facing an increasingly challenging situation as they work to fulfill orders from their retail partners' distribution facilities. Traditionally these suppliers have generated forecasts of a given retailer's orders using records of that retailer's past orders. However, it is becoming increasingly common for retail firms to collect and share large volumes of point-of-sale (POS) data, thus presenting an alternative data signal for suppliers to use in generating forecasts. A question then arises as to which data produce the most accurate forecasts. Compounding this question is the fact that forecasters often temporally aggregate data for consolidation or to produce forecasts in larger time buckets. Extant literature prescribes two countervailing statistical effects, information loss and variance reduction, that could play significant roles in determining the impact of temporal aggregation on forecast accuracy. Utilizing a large set of paired order and POS data, this study examines these relationships.