## 3 Methodology  
### Method  
The PICO methodology (Population, Intervention, Comparison, Outcome) has been widely used in scientific research due to its ability to provide a clear and well-defined framework for systematically analyzing research questions. When applied rigorously, this approach enables an objective and detailed evaluation of the relationships between key elements of the study, ensuring more accurate investigations free from the subjective biases often present in narrative reviews [18]. PICO has proven to be a valuable tool not only in medical and healthcare fields but also across various other areas of research, such as engineering, social sciences, and technology, as highlighted by recent studies (include updated references) [19, 20, 21]. This methodology facilitates the formulation of specific research questions and the selection of relevant studies, ensuring that data are handled consistently and comparably [19]. In this study, the use of the PICO methodology is based on the need to structure the analysis in a way that integrates various dimensions of the research problem, offering a comprehensive and objective view of the adoption of robotic process automation (RPA) and machine learning (ML) in predictive maintenance systems. Adapting this methodology to the specific context of automation and artificial intelligence technologies allows for a clear focus on the variables of interest and provides a precise assessment of the impact of implementing these technologies on organizations' operational performance [20]. In this study, the \"population\" refers to organizations that have adopted or are considering implementing RPA and ML solutions within the realm of predictive maintenance. Defining the population is crucial to ensuring that the results are relevant and applicable to sectors facing similar challenges related to maintenance and the integration of intelligent systems [22]. Focusing on companies that are applying advanced technologies to optimize processes and increase operational efficiency allows for an in-depth analysis of adoption practices and their impact on specific industrial environments. The \"intervention\" in this study refers to the adoption of RPA and ML technologies aimed at improving failure prediction models, thereby increasing the efficiency of maintenance operations. The choice of this intervention is supported by growing evidence that integrating RPA and ML can provide significant benefits, such as improved accuracy in failure predictions and reduced operational costs. Additionally, it is crucial that the implementation of these technologies is carried out in a sustainable manner, adhering to environmental, social, and economic principles, contributing to a more responsible business model aligned with sustainability goals (include updated references on RPA and ML applied to predictive maintenance) [23, 24, 25]. The \"comparison\" in this study is made between the state of organizations before and after the implementation of RPA and ML technologies. To develop the analytical model, a review of previous studies was conducted, covering relevant topics and approaches. This review helped identify gaps in the literature and best practices, which were incorporated into the proposed model. The comparison aims to assess the impacts of adopting these technologies on organizational operational efficiency, with a focus on improving failure predictions and optimizing maintenance processes. The study also seeks to determine how the implementation of these technologies can address common issues faced by companies, such as lack of maintenance control, and identify tangible benefits, such as reduced unexpected failures, lower operational costs, and increased reliability in maintenance processes. The \"outcome\" expected from this study is the identification of substantial improvements in the operational efficiency of organizations that adopt the proposed model.