Future Operator Competence and Learning Design in Automotive Final Assembly: A Human-Technology Perspective

Abstract

The automotive industry is undergoing rapid technological change driven by increasing automation and digitalization. This thesis investigates how these developments will impact competence requirements in automotive final assembly within a 5–10 year period and how suitable learning approaches can be designed for the timeand resource constrained conditions of a high-volume assembly plant. The study was conducted as an exploratory qualitative case study at Volvo Cars’ final assembly plant in Torslanda, Sweden. The research method included a literature review, semi-structured interviews with key roles, shop-floor observations, and internal document analysis. The findings indicate that final assembly is unlikely to be fully automated within 5–10 years due to product variety and the need for human adaptability. Manual and procedural assembly skills will remain essential, but will increasingly be complemented by digital literacy, system understanding, cross-domain problem-solving and AI literacy as automation, data-driven monitoring and AI/vision-based quality systems expand. Social and collaborative skills will also remain important for stable operations in the resulting human-technology collaborative environment. The current learning environment is constrained by high information load, inconsistent structure, limited verification of learning outcomes, and highly dependent on mentors and production conditions. To address these challenges, the thesis proposes a context adapted learning management system using microlearning, embedded assessments, distributed practice and mechanisms for engagement, complemented by QR-based contextual learning. XR-based scenario training is discussed as a future development possibility. A prototype was developed as a proof of concept and evaluated through stakeholder feedback