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

Sammanfattning

The automotive industry is undergoing rapid technological change driven by increasingautomation and digitalization. This thesis investigates how these developmentswill impact competence requirements in automotive final assembly within a 5–10year period and how suitable learning approaches can be designed for the timeandresource constrained conditions of a high-volume assembly plant. The studywas conducted as an exploratory qualitative case study at Volvo Cars’ final assemblyplant in Torslanda, Sweden. The research method included a literature review,semi-structured interviews with key roles, shop-floor observations, and internal documentanalysis.The findings indicate that final assembly is unlikely to be fully automated within5–10 years due to product variety and the need for human adaptability. Manualand procedural assembly skills will remain essential, but will increasingly be complementedby digital literacy, system understanding, cross-domain problem-solvingand AI literacy as automation, data-driven monitoring and AI/vision-based qualitysystems expand. Social and collaborative skills will also remain important for stableoperations in the resulting human-technology collaborative environment.The current learning environment is constrained by high information load, inconsistentstructure, limited verification of learning outcomes, and highly dependent onmentors and production conditions. To address these challenges, the thesis proposesa context adapted learning management system using microlearning, embedded assessments,distributed practice and mechanisms for engagement, complemented byQR-based contextual learning. XR-based scenario training is discussed as a futuredevelopment possibility. A prototype was developed as a proof of concept and evaluatedthrough stakeholder feedback