AI-Enabled Customised Workflows for Smarter Supply Chain Optimisation: A Feasibility Study

AI-Enabled Customised Workflows for Smarter Supply Chain Optimisation: A Feasibility Study | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 May 2025 V1 Latest version Share on AI-Enabled Customised Workflows for Smarter Supply Chain Optimisation: A Feasibility Study Authors : Vahid Javidroozi 0000-0002-7249-4359 [email protected] , Abdel-Rahman Tawil , Atif Azad , Brian Bishop , and Nouh Sabri Elmitwally Authors Info & Affiliations https://doi.org/10.22541/au.174731428.89232281/v1 Published Applied Sciences Version of record Peer review timeline 181 views 102 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study investigates the integration of Large Language Models (LLMs) into supply chain workflow automation, with a focus on their technical, operational, financial, and socio-technical implications. Building on Dynamic Capabilities Theory and Socio-Technical Systems Theory, the research explores how LLMs can enhance logistics operations, increase workflow efficiency, and support strategic agility within supply chain systems. Using two developed prototypes—the Q inventory management assistant and the nodeStream© workflow editor, the paper demonstrates the practical potential of GenAI-driven automation in streamlining complex supply chain activities. A detailed analysis of system architecture and data governance highlights critical implementation considerations, including model reliability, data preparation, and infrastructure integration. The financial feasibility of LLM-based solutions is assessed through cost analyses related to training, deployment, and maintenance. Furthermore, the study evaluates the human and organisational impacts of AI integration, identifying key challenges around workforce adaptation and responsible AI use. The paper culminates in a practical roadmap for deploying LLM technologies in logistics settings and offers strategic recommendations for future research and industry adoption. Supplementary Material File (chainai_manuscript.docx) Download 2.51 MB Information & Authors Information Version history V1 Version 1 15 May 2025 Peer review timeline Published Applied Sciences Version of Record 27 Aug 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords artificial intelligence intelligent process design large language models process modelling and analysis supply chain management Authors Affiliations Vahid Javidroozi 0000-0002-7249-4359 [email protected] Birmingham City University Faculty of Computing Engineering and the Built Environment View all articles by this author Abdel-Rahman Tawil Birmingham City University Faculty of Computing Engineering and the Built Environment View all articles by this author Atif Azad Birmingham City University Faculty of Computing Engineering and the Built Environment View all articles by this author Brian Bishop Data People Connected (DPC View all articles by this author Nouh Sabri Elmitwally Birmingham City University Faculty of Computing Engineering and the Built Environment View all articles by this author Metrics & Citations Metrics Article Usage 181 views 102 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Vahid Javidroozi, Abdel-Rahman Tawil, Atif Azad, et al. AI-Enabled Customised Workflows for Smarter Supply Chain Optimisation: A Feasibility Study. Authorea . 15 May 2025. DOI: https://doi.org/10.22541/au.174731428.89232281/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Sorina Moica, Tripon Lucian, Vassilis Kostopoulos, Adrian Gligor, Noha A. Mostafa, GenAI Technology Approach for Sustainable Warehouse Management Operations: A Case Study from the Automative Sector, Sustainability, 17 , 20, (9081), (2025). https://doi.org/10.3390/su17209081 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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