Data-driven retail inventory management with backroom effect

The backroom effect (BRE) constitutes the handling effort of a replenishment that does not fit on the shelf of a retailer. This effect needs to be included in the decision making of inventory policy parameters as it influences the handling effort, which constitutes a major part of the retailer’s operational cost. We propose a mixed integer linear program to calculate the parameters of a periodic review ( s , c , S , nq ) policy while considering the BRE. The ( s , c , S , nq ) policy triggers an order when inventory drops below the reorder point s . Also, an order is triggered whenever the inventory drops below the can-order point c , provided at least one other product’s inventory level is below s and thus ordered. The order then comprises the smallest integer number n of case packs with size q that brings the inventory level to or above S . As retailers face stochastic non-stationary demand, a data-driven approach based on historical data is applied to this joint replenishment problem. The numerical study shows that including the BRE into the optimization can lead to cost savings with a median of 0.96% compared to neglecting its effects. Considering the stochasticity in the decision making, cost improvements with a median of 53.23% have been realized against an approach that only considers average daily demands and a safety stock. The advantage of an ( s , c , S , nq ) order policy over an ( s , S , nq ) policy is shown by median savings of 17.99%.