Optimizing Supplier Selection with Large Language Models: An AI-Driven Approach for Resilient Supply Chains

Optimizing Supplier Selection with Large Language Models: An AI-Driven Approach for Resilient Supply Chains | 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 Optimizing Supplier Selection with Large Language Models: An AI-Driven Approach for Resilient Supply Chains Demiral Akbar, Murat ŞİMŞEK This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4767072/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Artificial Intelligence (AI) is poised to play a transformative role in Industry 4.0 and beyond. The challenges amplified by global crises, such as pandemics, have further emphasized the need for digitalization and optimization to maintain a resilient and sustainable supply chain from both buyer and supplier perspectives. Buyers must build and sustain a robust pool of suppliers capable of withstanding disruptions, while suppliers must enhance their visibility and demonstrate their capabilities effectively. Achieving this balance increasingly relies on AI-powered systems, once the appropriate digital infrastructure and data are in place. Traditional supplier selection, often based solely on cost, is no longer sufficient. Instead, procurement strategies must focus on long-term risk mitigation, requiring multi-criteria decision-making approaches. Among the AI techniques employed, Genetic Algorithms (GA) offer optimal solutions by evaluating numerous factors simultaneously. Recently, Large Language Models (LLMs) have gained attention for their ability to process vast volumes of unstructured data, enabling more comprehensive and nuanced supplier evaluations. LLMs, such as DistilGPT-2, can be integrated into decision support systems to enhance planning, forecasting, and supplier selection by considering diverse criteria, including price, delivery time, customs, taxes, production capabilities, warranty policies, and quality assurance. In this study, an AI-driven, end-to-end supplier selection approach using DistilGPT-2 was implemented. The model generated and evaluated textual data to effectively identify the most suitable supplier, demonstrating both time efficiency and decision accuracy. These findings highlight the growing potential of LLMs in optimizing supply chain management under increasingly complex market dynamics. Artificial Intelligence and Machine Learning Industrial Engineering Management Supply Chain Management Genetic Algorithms Artificial Intelligence Supplier Relationship Management Generative AI Large Language Model Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. 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