Warehouse management system customization and information availability in 3pl companies

Abstract

PurposeThe purpose of this paper is to illustrate an original decision-support tool (DST) that aids 3PL managers to decide on the proper warehouse management system (WMS) customization. The aim of this tool is to address to the three main issues affecting such decision: the cost of the information sharing, the scarce visibility of the client’s data and the uncertainty of quantifying the return from investing into a WMS feature.Design/methodology/approachThe tool behaves as a digital twin of a WMS. In addition, it incorporates a set of WMS’s features based both on heuristics and optimization techniques and uses simulation to perform what-if multi-scenario analyses of alternative management scenarios. In order to validate the effectiveness of the tool, its application to a real-world 3PL warehouse operating in the sector of biomedical products is illustrated.FindingsThe results of a simulation campaign along an observation horizon of ten months demonstrate how the tool supports the comparison of alternative scenarios with theas-is, thereby suggesting the most suitable WMS customization to adopt.Practical implicationsThe tool supports 3PL managers in enhancing the efficiency of the operations and the fulfilling of the required service level, which is increasingly challenging given the large inventory mix and the variable clients portfolio that 3PLs have to manage. Particularly, the choice of the WMS customization that better perform with each business can be problematic, given the scarce information visibility of the provider on the client’s processes.Originality/valueTo the author’s knowledge, this paper is among the first to address a still uncovered gap of the warehousing literature by illustrating a DST that exploits optimization and simulation techniques to quantify the impacts of the information availability on the warehousing operations performance. As a second novel contribution, this tool enables to create a digital twin of a WMS and foresee the evolution of the warehouse’s performance over time.