Mixed-methods analysis shows GenAI enhances decision-making and demand forecasting in supply chains.
Purpose – The use of generative artificial intelligence (GenAI) can potentially revolutionize logistics and supply chain management (SCM) practices. This research provides an analysis of how GenAI, huge language models (LLMs), are redefining SCM's operational and strategic aspects. Design/methodology/approach – A mixed-methods approach, incorporating case studies, industry surveys, and performance data analysis, is employed to investigate the inclusion of LLMs in logistics and SCM operations across various industries, including retailing, manufacturing, and healthcare. Findings – The preliminary findings suggest that incorporating GenAI and LLMs into SCM systems enhances the speed of decision-making by 35%, accuracy of demand forecasting by 25%, and lowers logistics costs by 18%. Major innovations are dynamic route optimization, real-time inventory management, and customized customer service through AI-powered interfaces. Originality/value – The research makes an original contribution towards the digital evolution of SCM and logistics by presenting a systematic approach to using LLMs. It highlights strategic recommendations for managers who wish to future-proof supply chains.
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Kiranmayi et al. (2025) studied this question.
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