## 7 Conclusions  
The primary aim of this study was to investigate how the implementation of a predictive maintenance model, based on robotic process automation (RPA) and machine learning (ML), could enhance efficiency and reduce operational costs within a company like Alpha, which faces recurrent equipment failure issues. The research demonstrated that by adopting this model, the company was able to anticipate failures before they led to unexpected downtime, thereby increasing equipment reliability and optimizing production. The PICO methodology (Population, Intervention, Comparison, and Outcome) played a crucial role in the development of the predictive maintenance model. The target population was Alpha, while the intervention involved the implementation of an integrated predictive maintenance system using RPA and ML. A comparison was made between pre- and post-implementation scenarios to identify improvements in the company's operational and financial processes. This methodology provided a clear structure for the study, enabling an accurate assessment of the intervention's effects and offering valuable visions into the applicability of the model for other small businesses. The PICO methodology provided a clear structure for the study, allowing for the identification of significant outcomes related to the implementation of the SIRPM model. However, there were limitations in the scope of the data available for analysis. Specifically, the dataset used in the study was limited to a single company, Alpha, and did not account for potential variations in industry-specific factors that may influence the model's effectiveness. Future studies should aim to incorporate larger, more diverse datasets to improve the generalizability of the findings and allow for a more comprehensive understanding of the model's applicability across different sectors. The synthesis of results had a significant impact on the development of the integrated RPA and ML predictive maintenance system (SIRPM), highlighting key trends and gaps that guided the proposal for a sustainable integration of RPA and ML technologies in predictive maintenance systems. The analysis revealed a strong focus on predictive maintenance and machine learning, with an increasing emphasis on sustainable contributions, suggesting that the model should align these technologies to optimize industrial processes while promoting environmental, social, and economic sustainability. Despite the promising results observed in this study, several limitations must be acknowledged. The scope of the study was confined to a single company, which means that the results cannot be generalized across all sectors or industries without further validation. In addition, while the study demonstrated improvements in predictive maintenance, the long-term effects of implementing the SIRPM model need further investigation to assess sustainability and continued performance. As a result, the SIRPM model was developed to combine these technologies in a modular and scalable way, fostering operational efficiency and offering a structured pathway for continuous implementation and adaptation. The results showed substantial improvements as follows:  
* A 100% increase in the mean time between failures (MTBF), from 23.47 h per event to 70.4 h per event. * A 67% reduction in the mean time to repair (MTTR), from 12 h to 4 h.
* A 66.67% decrease in the number of unplanned downtime events, from fifteen to five events per month. * A reduction in operational costs, with a 37.5% decrease in maintenance costs and a 71.4% reduction in costs related to unplanned downtime. * Environmental gains, including a 25% reduction in energy consumption and a 40% decrease in CO\({}_{2}\) emissions. These results demonstrate that the application of RPA and ML in the predictive maintenance system was effective in reducing failures and improving operational efficiency. Among the main advantages of the SIRPM model are the following:  
1.