Work-Based Learning in Smart Manufacturing: Current State and Future Perspectives

Abstract

The ongoing digital transformation in manufacturing is raising the demand for skilling, re-skilling and upskilling of workforces. At the same time, it largely affects skill mismatches in socio-technical work systems. Industry 4.0´s enabling technologies, especially AI and collaborative robotics offer new ways of learning, which introduce innovative work-based learning (WBL) approaches. In this context, Learning Factories confront a challenge on how to cope with the pace of technological enhancement and increasing skill mismatches and competence gaps on the job market. This paper is to conduct a systematic and community-specific literature survey to elicit and cluster the current state of the art in WBL represented in the International Association of Learning Factories (IALF) over the past years. Of particular importance are articles i) emphasizing new ways of technology-enhanced WBL, and ii) examining research gaps regarding (reciprocal) learning among human and intelligent machines in collaborative work systems. Finally, the future pathways of research in WBL within and beyond IALF are identified.