Combining KPI Metrics and Expert Insights to Evaluate and Recommend Improvements for Warehouse Management Systems

Abstract

In the today's world of logistics,digitalization and complexity have become the key characteristics of modern operations,which means that being able to evaluate the warehouse performance,the use of warehouse management systems(WMS) has become a requirement for the discovery of gaps and potential improvements.The focus of this thesis lies on the performance and efficiency of Consafe Logistics WMS users across the four industries of Manufacturing, Retail, Wholesale, and Food & Beverage,analyzing the operational data received from warehouse sites,and conducting expert interviews.The research was conducted with the use a mixed method approach, combining both quantitative and qualitative data.For the quantitative analysis,the selection of four core Key Performance Indicators(KPIs) was decided,with items per hour ,lines per day ,inventory accuracy and pick accuracy to consist this group.The qualitative analysis,on the other hand,was based on five interviews,all of which were with users of the system,in an attempt to make the collected knowledge more accurate. The qualitative findings gave a strong performance when it comes to the quality and the productivity KPIs.Something unexpected was the existence of warehouses doing manual labour operations that achieved a higher productivity output than automated ones. The qualitative findings gave insights in relation to WMS and to which KPIs are considered most important based on companies perceptions.Furthermore, the WMS had made significant progress in modernizing the warehouses and making them more digital, contributing to the trend towards Industry 4.0. KPIs can be presented in WMS systems to measure the operational health of warehouses, but many factors contribute to the results. Therefore,it is important to improve the integration of WMS with automation and other Industry 4.0 technologies,while ensuring to remain aligned with the WMS users’ objectives,such as priorities,costs,and system complexity.