## 7 Conclusions
Advanced technology integration: the combination of RPA and machine learning improves the system's effectiveness, providing a continuous data flow between sensors, analysis platforms, and operational teams, enhancing failure predictions and response times. 2. Intelligent automation: automation reduces human intervention, minimizing errors and increasing the efficiency and safety of processes. 3. Continuous learning: the machine learning system adapts over time, improving predictions and decision-making. 4. Flexibility and scalability: the model is modular and can be gradually implemented, allowing for expansion into other areas of the company. 5. Real-time monitoring: the detection of anomalies and the generation of real-time alerts facilitate quick responses to failures, reducing unplanned downtime. 6. Energy efficiency and sustainability: the model promotes resource optimization and waste reduction, aligning with environmental goals and corporate sustainability standards. These features not only make the SIRPM model more effective but also ensure its long-term viability as a solution for companies seeking innovation, cost reduction, and sustainability in the management of their industrial assets. The reduction in operational costs, improvement in energy efficiency, and positive environmental impact, alongside team development, make the model a key element for digital and sustainable transformation in small and medium-sized enterprises. The SIRPM model also stands out for addressing sustainability in the following three main dimensions:  
* Social: It promotes a better allocation of human resources by reducing repetitive and hazardous tasks while empowering teams with new digital skills. Additionally, it contributes to improved working conditions and enhanced operational safety. * Environmental: the model contributes to waste reduction, decreased CO\({}_{2}\) emissions, and lower energy consumption, aligning with global environmental preservation goals. * Economic: the reduction in operational costs and increased productivity result in greater competitiveness for companies while ensuring long-term financial returns. The central research question was comprehensively addressed in this study. The results showed that the integration of RPA and ML is an effective approach to optimizing predictive maintenance systems. This combination allowed for substantial improvements in fault prediction, as evidenced by the significant increase in the MTBF, as well as reductions in the MTTR and unplanned downtime events. These improvements translated into greater operational efficiency, with tangible benefits in terms of reduced operational costs and increased productivity. Additionally, the developed model incorporated sustainability principles. These findings demonstrate that the predictive maintenance model based on RPA and ML is effective in addressing the central research question, showcasing its potential to transform the management of industrial maintenance in small and medium-sized businesses. The SIRPM model not only promoted operational efficiency but also offered sustainable solutions aligned with contemporary business needs. The validity of the hypotheses reinforces the importance of integrating advanced technologies to foster more efficient and responsible industrial management. The investigation validated both hypotheses, showing the following:  
* H1: The combined application of RPA and ML significantly enhanced the ability to anticipate failures. This improvement was evident in the increase in the MTBF (+100%), the reduction in the MTTR (\(-67\%\)), and the decrease in unplanned downtime events (\(-66.67\%\)). These advances resulted in more efficient maintenance management and a \(37.5\%\) reduction in operational costs. * H2: The research revealed positive impacts in all sustainability dimensions. Environmentally, there was a \(25\%\) reduction in energy consumption and a \(40\%\) decrease in CO\({}_{2}\) emissions. Socially, the model promoted team empowerment, with greater safety and efficiency in operations.