Unlocking Tacit Knowledge in Industrial Production: Exploring Barriers, Practices, and LLM-Driven Potentials for Knowledge Management

Abstract

In aging societies across western industrialized nations, the loss of expertise due to retiring skilled workers presents a critical challenge for industry. That is especially true on the shop floor, where much of the knowledge is tacitly gained through years of hands-on experience rather than formal documentation. This study explores current knowledge management (KM) challenges and systematically identifies high-potential applications for large language models (LLMs) as part of a broader research initiative aiming to develop human-centered KM solutions supported by generative artificial intelligence (GenAI). We conducted two structured workshops with 23 participants from 14 German manufacturing companies. Three core barriers and two prioritized LLM use cases were identified, contributing specific design recommendations for LLM-supported KM systems for companies. The results advance the understanding of GenAI-assisted knowledge retention in industrial settings and provide a practical foundation for addressing the demographic shift through intelligent, technology-driven solutions.