This thesis investigates how small and medium-sized enterprises (SMEs) can effectively integrate automation to enhance productivity and competitiveness. By using a SME as a case study, the research identifies key areas within the manufacturing process that would benefit most from automation. The Analytical Hierarchy Process (AHP) was employed to systematically evaluate and prioritize these areas based on criteria such as implementation complexity, expected efficiency gains, worker safety, and scalability. Following the AHP analysis, simulation tools were used to model and quantify the benefits of the proposed automation strategies, with a specific focus on human-robot collaboration. 

The study's findings offer practical insights for SMEs seeking to navigate the complexities of automation. It emphasizes the importance of aligning automation initiatives with both operational goals and workforce capabilities, highlighting the role of education and continuous training in ensuring the success of human-robot collaboration. The research also introduces the concept of "automation debt," which describes the challenges SMEs face when delayed automation leads to suboptimal processes and infrastructure. Overall, this thesis provides a strategic framework for SMEs to implement automation in a way that maximizes efficiency, safety, and long-term scalability, while also addressing the unique challenges faced by smaller enterprises in an increasingly digitalized industrial landscape.