Knowledge Management in Production: Human-Centered and AI-Supported Approaches for the Shop Floor

Abstract Knowledge management is a critical enabler for competitiveness in industrial production, especially under demographic change, workforce fluctuation, and digital transformation. Small- and medium-sized enterprises face particular risks, as experiential knowledge is often bound to individuals and easily lost when employees leave. This paper emphasizes the need for systematic approaches to knowledge retention and transfer. While established frameworks such as the knowledge ladder, SECI model, and knowledge balance provide foundations, they remain limited in capturing implicit, action-related expertise essential for production. To address this gap, a model-based approach is proposed that combines cognitive task analysis, methods-time measurement, and AI-assisted tracking technologies, including motion sensors and video analysis, to externalize implicit knowledge. The transformation of human actions and decision-making into structured knowledge objects enables integration into digital assistance and learning systems. The study outlines a research agenda for scalable, technology-enhanced knowledge management systems to safeguard experiential knowledge, reduce training times, and strengthen innovation.