A novel approach to the storage assignment in an order picking system

Purpose – In an order picking system, storage assignment deals with the determination of the best location of stock keeping units (SKU) and is one of the most effective strategy to control the efficiency and minimize costs. The aim of this work is to develop of an adaptive approach addressing storage assignment in order to minimize the total cost of travelling with a particular focus on picking activities. The proposed approach aims to efficiently handle seasonality in the products mix avoiding expensive periodic re-arrangements of the warehouse. Methodology – We implement a greedy-heuristics based on SKU popularity, dynamically calculated, able to handle seasonality and frequent inventory–mix changes as in the case of 3PL provider warehouses. The proposed adaptive approach is embedded within a properly designed decision support tool which uses a numerical simulation to perform multi-scenario analyses and to validate the approach with real warehousing instances. Findings – We apply the proposed approach with a 5,000 storage locations 3P warehouse dedicated to perishable biomedical products. The obtained results quantify consistent reduction of the travel time for the outbound activities. In particular, the expected travel time for the picking tours decreases of about 10% over the observed horizon. Value – The proposed adaptive assignment policy does not require a labor-intensive rearrangement of the storage layout, but exploit the daily incoming SKUs and the empty storage locations to reduce day-by-day the distance between the as-is and the desirable storage configuration. Practical implication – The proposed approach is particularly suitable for those warehouses where the rapid response to the changing inventory mix is a strategic lever for market positioning (e.g. 3P warehouses). Furthermore, the proposed approach might be quickly enriched by constraints (e.g., conservation temperature) regarding the quality and safety of the inventory along the storage cycle.