AI-Enabled Customised Workflows for Smarter Supply Chain Optimisation: A Feasibility Study
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Big Data and Business Intelligence · Digital Transformation in Industry · Data Quality and Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Computing, Engineering, and the Built Environment, Birmingham City University, Birmingham B4 7RQ, UK
Data People Connected (DPC), Fraser House Hub, White Cross Business Park, South Road, Lancaster LA1 4XQ, UK
Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt
Funding
Innovate UK
References (48)
Show source-provided references
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