Ordering Behavior in Retail Stores and Implications for Automated Replenishment

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 and investigate their potential drivers. Using orders, shipments, and POS data for 19, 417 item-store combinations over 5 stores, we find that store managers systematically modify automated order advices by advancing orders from peak to non-peak days. We show that order advancement is explained significantly by hypothesized product characteristics, such as case-pack size relative to average demand per item, net shelf space, product variety, demand uncertainty, and seasonality error. Our results suggest that store managers add value. They improve upon the automated replenishment system by incorporating two ignored factors: in-store handling costs and sales improvement potential through better in-stock. We test a heuristic procedure, based on our regression results, to modify order advices to mimic the behavior of store managers. Our method performs better than the store managers by achieving a more balanced handling workload with similar average days of inventory.