A Unified Workload Metric for Goods Receiving Optimization: The CLP Model and Decision-Support Application

This paper focuses on the design and development of a decision support system (DSS) for thegovernance of intralogistics processes and, in particular, devoted to optimize capacity managementand the workload of activities characterizing freight entry. This research activity was carried out aspart of the Logistics Research and Development 4.0 Project launched by La Logistica Srl, a newplayer in the distribution of hydro-thermo sanitary products, with the support of the Puglia Region.The purpose of this study is to provide managers with a range of customized process information toimprove workforce efficiency and management in receiving, controlling, and storing goods enteringthe newly established logistics hub. Other researchers’ work had shortcomings, as did the lack of thisstudy’s practical approach. This study aims to overcome these constraints. To do so, the studyintroduces the Content Load Parameter (CLP) index. This index integrates three variables relevant todefining the workload, such as quantity, size and weights of the goods to be handled, using a singlestandard value. This makes it easier for managers to estimate the required capacity all along the workcycle. As part of the system development, a module analyzes and simulates operational scenarios.Simulation facilitates the management of analysis capacity by allowing alternatives to be comparedand evaluated to bridge the gap between demand and availability of production capacity in varioussituations. Scenario simulation, combined with tools for concise and effective visualization of results,therefore allows organizations to identify critical capacity points and take preventative measures tomanage overhead and minimize consequences. In summary, this study demonstrates how integratingthe Content Load Parameter index into a decision support system can significantly contribute tomaking intralogistics process management more effective. By addressing quantifiable workloadparameters and facilitating scenario-based operational analysis, the proposed system providesmanagers with useful information for capacity optimization. These advances not only overcome thelimitations of previous research but also help develop resilient and efficient logistics operations, thusstrengthening the critical role of empirically informed decision-making in contemporary logisticsgovernance.