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

Javidroozi, Vahid and Tawil, Abdel-Rahman and Azad, R. Muhammad Atif and Bishop, Brian and Elmitwally, Nouh (2025) AI-Enabled Customised Workflows for Smarter Supply Chain Optimisation: A Feasibility Study. Applied Sciences, 15 (17). p. 9402. ISSN 2076-3417

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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.

Item Type:	Article
Identification Number:	10.3390/app15179402
Dates:	Date Event 14 August 2025 Accepted 27 August 2025 Published Online
Uncontrolled Keywords:	supply chain management; artificial intelligence; large language models; inventory management automation; intelligent process design; logistical systems optimisation; process modelling and analysis
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	23 Sep 2025 14:25
Last Modified:	23 Sep 2025 14:25
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16653

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