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Forecasting With Temporally Aggregated Demand Signals in a Retail Supply Chain
Author(s) -
Jin Yao “Henry”,
Williams Brent D.,
Tokar Travis,
Waller Matthew A.
Publication year - 2015
Publication title -
journal of business logistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.611
H-Index - 79
eISSN - 2158-1592
pISSN - 0735-3766
DOI - 10.1111/jbl.12091
Subject(s) - extant taxon , consolidation (business) , supply chain , business , fast moving consumer goods , order (exchange) , variance (accounting) , point of sale , aggregate (composite) , supply chain management , marketing , industrial organization , computer science , finance , materials science , accounting , evolutionary biology , world wide web , composite material , biology
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.