CREATING ORDER FORECASTS: POINT‐OF‐SALE OR ORDER HISTORY?

Abstract

Point‐of‐sale (POS) data, shared by retailers, is often touted as the solution to suppliers' ongoing challenge of accurate order forecasting. However, we find neither empirical evidence of increased order forecast accuracy from the literature, nor consistent use of POS data in suppliers' order forecasting processes. Using a sample containing weekly POS and order data for 10 ready‐to‐eat (RTE) cereal stock‐keeping‐units (SKU's), 7 yogurt SKU's, and 7 canned soup SKU's from 18 retailer distribution centers (DC's) throughout the U.S, our research compares historical POS and order data as order forecasting inputs and finds that POS data does not always outperform order data in terms of order forecast accuracy. While we did find that POS data is a better forecast input in a majority of the forecasts and that on average POS data produces a lower order forecast error, we find that there remain a large number of forecasts where order data is a better predictor than is POS data. Hence, we operationalize this comparison in terms of the frequency and magnitude of order forecast improvement based on POS data. We then hypothesize affecting factors and empirically test these relationships.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 45 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.