This thesis explores the application, maintenance, and evaluation of laser cutting and bending technologies in the industrial sector. Through a combination of interviews, case studies, and machine performance data analysis, it identifies challenges like software integration issues, maintenance practices, and workforce training needs. Key Performance Indicators (KPIs) such as OEE, MTBF, and MTTR were analyzed to evaluate the impact of different maintenance strategies on machine performance. The findings highlight the superiority of predictive maintenance in reducing downtime and enhancing reliability compared to preventive approaches, particularly for SMEs facing integration and training challenges. Recommendations include adopting predictive maintenance, improving workforce training, and optimizing software systems.