Responsive pick face replenishment strategy for stock allocation to fulfil e-commerce order

Abstract

In the rapidly growing e-commerce industry, pallet picking is no longer feasible as the stock keeping units (SKUs) change from the pallet level to the carton or item level. Thus, an effective order-picking process focusing on fast and efficient retrieval of SKUs from shelves is required to fulfil numerous small lot-sized e-commerce orders within a short time. This study investigates a responsive pick face replenishment (RPFR) strategy that divides the high-bay racks in the distribution centres (DCs) into two parts: the upper-deck reserve areas and the pick-face forward areas to improve the operational efficiency in order picking. To address the fluctuating order demand and limited space in the pick-face forward areas, the proposed RPFR system integrates a predictive analytics algorithm with an adaptive network-based fuzzy inference system (ANFIS) and adaptive genetic algorithm-based stock allocation model to generate an optimal stock replenishment plan. By predicting the order demand of each SKU in the next time interval, the types of selected SKUs and their quantities to be loaded into the pick-face forward areas are determined. Numerical experiments are performed to validate the system performance, and comparative analyses are conducted to determine the best parameter settings for the models.