Ordering Behavior in Retail Stores and Implications for Automated Replenishment

TLDR

Retail store managers may not follow automated inventory replenishment advice when their incentives differ from the system’s cost‑minimization goal or when they perceive the advice as suboptimal. The study aims to characterize deviations in store managers’ ordering behavior, identify their drivers, and devise a method to enhance automated replenishment systems. Based on regression analysis of 19,417 item–store combinations, the authors construct a learning‑based method that modifies automated order advices by incorporating store managers’ behavior. We find that managers routinely shift automated order advices from peak to nonpeak days, a behavior largely driven by product characteristics such as.

Abstract

Retail store managers may not follow order advices generated by an automated inventory replenishment system if their incentives differ from the cost-minimization objective of the system or if they perceive the system to be suboptimal. We study the ordering behavior of retail store managers in a supermarket chain to characterize such deviations in ordering behavior, investigate their potential drivers, and thereby devise a method to improve automated replenishment systems. Using orders, shipments, and point-of-sale data for 19,417 item–store combinations over five stores, we show that (i) store managers consistently modify automated order advices by advancing orders from peak to nonpeak days, and (ii) this behavior is explained significantly by product characteristics such as case pack size relative to average demand per item, net shelf space, product variety, demand uncertainty, and seasonality error. Our regression results suggest that store managers improve upon the automated replenishment system by incorporating two ignored factors: in-store handling costs and sales improvement potential through better in-stock. Based on these results, we construct a method to modify automated order advices by learning from the behavior of store managers. Motivated by the management coefficients theory, our method is efficient to implement and outperforms store managers by achieving a more balanced handling workload with similar average days of inventory.