Balancing picking and outbound loading efficiency in an SBS/RS through a digital twin

Abstract

Warehouses are essential elements of almost every Supply Chain and have a significant impact on its performance. However, existing research on warehouse operations mainly aims at maximizing operational performance, neglecting their effect on downstream nodes. In this paper, we propose the use of a digital twin (DT) to support warehouse managers to identify the picking policy that most effectively balances picking and outbound loading efficiencies in an SBS/RS, with the aim of providing both a cost-effective and timely delivery to the subsequent nodes. The problem is set referring to a real case study of the logistics hub of a tire distributor company. The DT was built and validated based on real data from plant sensors and information systems. Afterwards, the DT was used to define three picking strategies that differently impact on both picking and outbound loading efficiency. The DT was then employed on a daily basis and fed with real orders, machine and rack availability to replicate stocking and picking operations and to directly communicate the recommended picking strategy to the warehouse PLC. Several demand scenarios have been considered to extend managerial inferences. Results show that the DT is a valuable tool to support the balancing of picking and outbound loading performance.