Trade-offs in Performance Objectives and Automation in Production; A case study for SKF Industrial Gothenburg

Abstract

As manufacturing companies face increasing pressure to balance cost efficiency, flexibility, and technological advancement, decisions regarding production setup and automation level have become central to long-term competitiveness. This thesis investigates how different levels of automation and production setup influence operational performance in a large-scale industrial setting, using SKF's D-factory in Gothenburg as a case study. The study is conducted through a combination of qualitative and quantitative methods, including semi-structured interviews with operators, production managers, and automation specialists, direct factory observations, and analysis of internal documentation and literature. The theoretical framework includes Hill's Manufacturing Strategy Framework, process design theory, levels of automation, manufacturing planning and control systems, and key performance indicators. The findings reveal a gap between the maturity of physical automation on the factory floor and the cognitive and systems-level automation required to support it. Disruptions in system integration, incomplete data flows, and varying knowledge distribution among operators are identified as key operational challenges. Furthermore, the study shows that strategic decisions regarding automation must be derived from a clear alignment between corporate objectives, market strategy, and manufacturing strategy, approached from both a top-down and bottom-up perspective. The thesis concludes that standardisation of processes, data structures, and automation frameworks is a prerequisite for SKF to achieve its 2030 strategic ambitions. Flexibility is identified as the primary performance objective, and the ability to adapt production strategy to evolving market requirements, including a shift toward smaller batch sizes, shorter lead times, and a postponed customer order decoupling point, is central to sustaining competitive advantage. The findings are intended to serve as a foundation for data-driven and context-aware decision-making in future automation projects at SKF Industrial in Gothenburg.