Forecasting With Temporally Aggregated Demand Signals in a Retail Supply Chain

Abstract

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.

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