Real Time Locating System for a Learning Cross-Docking Warehouse

Real time locating systems (RTLS) represent today an established and reliable technology to identify, track and monitor, in real time with any human commitment, the dynamic evolution of the spatial location of tagged entities inside factory layout. This results in more effective and continuous monitoring of products, stock-keeping units (SKU), and vehicles of production plants or logistic facilities. This paper targets the adoption of such RTLS in warehousing systems focusing on the related learning opportunities to enhance the efficacy and the productivity of the storage process which have consistent externalities during the order picking. For this purpose, a cross-docking warehouse is equipped with an ultrawideband (UWB)-based RTLS to track any forklifts traveling activities and SKU dynamic locations. A set of relevant data is automatically generated by the interactions between the transmitters, which are installed on board of the forklifts and connected to the SKU barcode readers, and the receivers, which are displaced in fixed positions all over the warehouse layout for optimizing the data transmissions. Furthermore, to yield further insights into the performed storage and retrieval operations, the daily incoming and outcoming shipping orders are also considered at the detail level of every single SKU. All this relevant information is merged into a unique database that evolves in real time representing the dynamic evolution of the warehouse operations over time. A digital representation of the physical storage system is developed to leverage such relevant datasets through adequate data analysis algorithms. A set of quantitative key performance indicators is evaluated, and suggestions are automatically offered to the warehouse manager to improve the storage system efficiency. The implementation of such modification, through a feedback loop, is to offer the unique opportunity to such warehousing system to analytically learn by its process’s underperformances, as well as to evaluate the efficacy and efficiency of the corrective actions suggested by the developed algorithms.