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

Sammanfattning

As manufacturing companies face increasing pressure to balance cost efficiency,flexibility, and technological advancement, decisions regarding production setup andautomation level have become central to long-term competitiveness. This thesisinvestigates how different levels of automation and production setup influenceoperational performance in a large-scale industrial setting, using SKF's D-factory inGothenburg as a case study.The study is conducted through a combination of qualitative and quantitativemethods, including semi-structured interviews with operators, production managers,and automation specialists, direct factory observations, and analysis of internaldocumentation and literature. The theoretical framework includes Hill'sManufacturing 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 factoryfloor and the cognitive and systems-level automation required to support it.Disruptions in system integration, incomplete data flows, and varying knowledgedistribution among operators are identified as key operational challenges.Furthermore, the study shows that strategic decisions regarding automation must bederived from a clear alignment between corporate objectives, market strategy, andmanufacturing strategy, approached from both a top-down and bottom-upperspective.The thesis concludes that standardisation of processes, data structures, andautomation frameworks is a prerequisite for SKF to achieve its 2030 strategicambitions. Flexibility is identified as the primary performance objective, and theability to adapt production strategy to evolving market requirements, including a shifttoward smaller batch sizes, shorter lead times, and a postponed customer orderdecoupling point, is central to sustaining competitive advantage. The findings areintended to serve as a foundation for data-driven and context-aware decision-makingin future automation projects at SKF Industrial in Gothenburg.