How can Data-Driven Decision-Making support performance improvements in Production, Maintenance, and Sustainability in SMEs?

Data-driven production is a growing field. Big data technologies enable companies to become data-driven in their strategies and thereby more competitive. These technologies support improved efficiency and sustainability. Marcus Komponenter and BALTMA are two SMEs looking to digitalize operations and use data-driven technologies for improved productivity and environmental performance. This thesis is about finding ways for SMEs to adopt data-driven technologies aligned to SMEs goals and limitations. A literature review was done to understand the current field of research on this topic and the components of data-driven systems for improved decision-making. Understanding how the components interact in a data-driven system is key. Furthermore, understanding the synergies of data-driven systems and performance improvements in production, maintenance, and sustainability is central. The literature review was used alongside a literature analysis, explaining the results of other researchers, and their limitations. The knowledge of other research lays the foundation for recommendations made to the two SMEs of this study. This study suggests that SMEs have ambitions to move further with their digitalization efforts and discusses what data they need to monitor and analyze in their operations. A conceptual model of how SMEs can adopt data-driven technologies for performance improvements is presented with its components and interactions. The conceptual model was created to make suitable suggestions while considering financial limitations and the desires of the SMEs of this study