Innovators and transformers: Enhancing supply chain employee training with an innovative application of a large language model

Abstract

Purpose The application of generative artificial intelligence (GenAI) has the potential to transform supply chain management (SCM) practice. This study focuses on the role of GenAI, specifically large language models (LLMs), in enhancing the training efficiency and outcomes for supply chain employees. Design/methodology/approach An intervention-based research approach is used to implement a novel LLM-based methodology for improving both the training process for new employees and the continuous knowledge acquisition experience for existing staff in the supply chain function of an eyewear company. Findings The preliminary findings show that incorporating an LLM significantly improved the efficiency of the training process and reduced the training cost for employees by 25%. New employees could access relevant information swiftly, reducing training time and enhancing the quality of training. Notable outcomes included faster knowledge acquisition, personalized learning pathways and continuous improvement through user feedback. Originality/value This study contributes to the literature by establishing a foundational framework for leveraging LLMs for knowledge management and process automation within SCM. It offers actionable insights for SCM practitioners, highlighting opportunities to adopt LLM-powered methodologies for optimizing training processes, improving decision-making and automate SCM tasks.

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