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 the governance of intralogistics processes and, in particular, devoted to optimize capacity management and the workload of activities characterizing freight entry. This research activity was carried out as part of the Logistics Research and Development 4.0 Project launched by La Logistica Srl, a new player 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 to improve workforce efficiency and management in receiving, controlling, and storing goods entering the newly established logistics hub. Other researchers’ work had shortcomings, as did the lack of this study’s practical approach. This study aims to overcome these constraints. To do so, the study introduces the Content Load Parameter (CLP) index. This index integrates three variables relevant to defining the workload, such as quantity, size and weights of the goods to be handled, using a single standard value. This makes it easier for managers to estimate the required capacity all along the work cycle. 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 compared and evaluated to bridge the gap between demand and availability of production capacity in various situations. Scenario simulation, combined with tools for concise and effective visualization of results, therefore allows organizations to identify critical capacity points and take preventative measures to manage overhead and minimize consequences. In summary, this study demonstrates how integrating the Content Load Parameter index into a decision support system can significantly contribute to making intralogistics process management more effective. By addressing quantifiable workload parameters and facilitating scenario-based operational analysis, the proposed system provides managers with useful information for capacity optimization. These advances not only overcome the limitations of previous research but also help develop resilient and efficient logistics operations, thus strengthening the critical role of empirically informed decision-making in contemporary logistics governance.