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

A Unified Workload Metric for Goods Receiving Optimization: The CLP Model and Decision-Support Application | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Unified Workload Metric for Goods Receiving Optimization: The CLP Model and Decision-Support Application Nicola Magaletti, Giancarlo Caponio, Angelo Amodio, Valeria Notarnicola, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8223497/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. JEL Codes: C61; L91; M11; O33; R41. Software Engineering Management Decision-Support Systems (DSS) Warehouse Capacity Planning Data-Driven Logistics Simulation and Workload Optimization Artificial Intelligence in Supply Chain Management Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8223497","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551850987,"identity":"c7667cbf-924a-4c89-8891-6b2cc0a19c8c","order_by":0,"name":"Nicola Magaletti","email":"","orcid":"","institution":"LUM Enterprise s.r.l.","correspondingAuthor":false,"prefix":"","firstName":"Nicola","middleName":"","lastName":"Magaletti","suffix":""},{"id":551850988,"identity":"0d0d1a1f-4bf0-41a1-8dba-6a546360c64d","order_by":1,"name":"Giancarlo Caponio","email":"","orcid":"","institution":"La Logistica 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This research activity was carried out as\u003c/p\u003e\n\u003cp\u003epart of the Logistics Research and Development 4.0 Project launched by La Logistica Srl, a new\u003c/p\u003e\n\u003cp\u003eplayer in the distribution of hydro-thermo sanitary products, with the support of the Puglia Region.\u003c/p\u003e\n\u003cp\u003eThe purpose of this study is to provide managers with a range of customized process information to\u003c/p\u003e\n\u003cp\u003eimprove workforce efficiency and management in receiving, controlling, and storing goods entering\u003c/p\u003e\n\u003cp\u003ethe newly established logistics hub. Other researchers’ work had shortcomings, as did the lack of this\u003c/p\u003e\n\u003cp\u003estudy’s practical approach. This study aims to overcome these constraints. To do so, the study\u003c/p\u003e\n\u003cp\u003eintroduces the Content Load Parameter (CLP) index. This index integrates three variables relevant to\u003c/p\u003e\n\u003cp\u003edefining the workload, such as quantity, size and weights of the goods to be handled, using a single\u003c/p\u003e\n\u003cp\u003estandard value. This makes it easier for managers to estimate the required capacity all along the work\u003c/p\u003e\n\u003cp\u003ecycle. As part of the system development, a module analyzes and simulates operational scenarios.\u003c/p\u003e\n\u003cp\u003eSimulation facilitates the management of analysis capacity by allowing alternatives to be compared\u003c/p\u003e\n\u003cp\u003eand evaluated to bridge the gap between demand and availability of production capacity in various\u003c/p\u003e\n\u003cp\u003esituations. Scenario simulation, combined with tools for concise and effective visualization of results,\u003c/p\u003e\n\u003cp\u003etherefore allows organizations to identify critical capacity points and take preventative measures to\u003c/p\u003e\n\u003cp\u003emanage overhead and minimize consequences. In summary, this study demonstrates how integrating\u003c/p\u003e\n\u003cp\u003ethe Content Load Parameter index into a decision support system can significantly contribute to\u003c/p\u003e\n\u003cp\u003emaking intralogistics process management more effective. By addressing quantifiable workload\u003c/p\u003e\n\u003cp\u003eparameters and facilitating scenario-based operational analysis, the proposed system provides\u003c/p\u003e\n\u003cp\u003emanagers with useful information for capacity optimization. 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