Improving Payload Efficiency in Open-Pit Mining: An Integrated Model Using Six Sigma and Artificial Intelligence

Improving Payload Efficiency in Open-Pit Mining: An Integrated Model Using Six Sigma and Artificial Intelligence | 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 Article Improving Payload Efficiency in Open-Pit Mining: An Integrated Model Using Six Sigma and Artificial Intelligence Jherson Luis Valencia-Vargas, Sebastián Alonso Paucar-La-Rosa, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7530791/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 The mining sector is a very important part of Peru's economy, but open-pit operations are still losing productivity because of problems with the way materials are loaded. This study suggests a mixed model that combines Six Sigma methods with Artificial Intelligence (AI) to make loading the payload more efficient in a copper mining company. The research seeks to diminish operational variability, enhance payload compliance across truck models, and elevate equipment reliability through a systematic DMAIC and PDCA methodology. The methodology consists of calibration protocols, maintenance cycles, operator training, and AI-driven capacity analysis to assess performance through Pp and Ppk indicators. A case study conducted in a Peruvian mining company demonstrated substantial enhancements: loading efficiency achieved 100% across all truck models, null reading rates diminished by up to 13.82%, and equipment availability rose by 7%. Also, Six Sigma capacity indices showed that the Ppk for the CAT 797F model went up by 94% and that it went up by more than 50% for other models. These results show that the proposed model works to improve operational efficiency and can be used on a larger scale in the mining industry. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Artificial Intelligence Six Sigma Productivity Mining Efficiency Payload Optimization DMAIC PDCA Operational Excellence Full Text Additional Declarations No competing interests reported. Supplementary Files Appendix.docx 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. 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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-7530791","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":516697282,"identity":"21499e1c-8c9f-421f-8aff-598209151cfc","order_by":0,"name":"Jherson Luis Valencia-Vargas","email":"","orcid":"","institution":"Peruvian University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jherson","middleName":"Luis","lastName":"Valencia-Vargas","suffix":""},{"id":516697283,"identity":"9cbd1874-189d-41c2-9467-1350311c3119","order_by":1,"name":"Sebastián Alonso Paucar-La-Rosa","email":"","orcid":"","institution":"Peruvian University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sebastián","middleName":"Alonso","lastName":"Paucar-La-Rosa","suffix":""},{"id":516697286,"identity":"25d2eb81-791e-46bf-b4bd-1391ac3765aa","order_by":2,"name":"Jose Antonio Rojas-Garcia","email":"","orcid":"","institution":"Peruvian University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"Antonio","lastName":"Rojas-Garcia","suffix":""},{"id":516697287,"identity":"9661e9ef-4e3a-4d81-9820-ce61cbdf9067","order_by":3,"name":"S. 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