## 3 Methodology
### Method
The application of the PICO methodology will allow for the formulation of robust hypotheses and conducting an analysis based on concrete data, facilitating the objective measurement of results. This will provide a clearer understanding of how the implementation of RPA and ML can optimize predictive maintenance while promoting sustainability in industrial operations across environmental, social, and economic dimensions. The methodology used in this study involved a detailed analysis of a rigorously selected set of relevant data sources. The PICO approach was applied to select the articles, with well-defined inclusion and exclusion criteria. Initially, the articles were identified through a bibliographic search, and the screening process involved reviewing titles and abstracts, prioritizing studies that directly addressed the integration of RPA, ML, predictive maintenance, and sustainability. Articles that did not meet the relevance criteria or were not directly related to the topic were discarded, resulting in a final selection of studies that significantly contribute to the research [26]. The information extracted was based on contributions from experts in the fields of RPA, ML, predictive maintenance, and sustainability. The collection of articles was carried out from renowned scientific databases, such as the \"B-on\" platform, [[https://www.b-on.pt/](https://www.b-on.pt/)]([https://www.b-on.pt/](https://www.b-on.pt/)) (accessed on 10 November 2024), which provides access to peer-reviewed scientific publications. \"B-on\" is a widely accessible platform, including articles indexed in databases such as ISI WOS and Scopus, ensuring that the selected sources are comprehensive and relevant to the subject matter. Thus, the use of the PICO methodology offers a clear framework for the analysis and interpretation of the collected data, facilitating the connection between the formulated hypotheses and the evidence found in the literature. The inclusion criteria were as follows:  
* Articles directly addressing the implementation or evaluation of RPA and ML in predictive maintenance. * Publications discussing the relationship between these technologies and sustainability in its various aspects (environmental, social, and economic). * Studies conducted between 2010 and 2024. * Articles published in peer-reviewed journals and available in full text. The following types of publications were excluded:  
* Articles that do not directly address the implementation of RPA and ML or that cover areas unrelated to predictive maintenance. * Works that do not significantly discuss sustainability aspects. * Publications that are not available in full text or are difficult to access. * Studies that are not based on empirical methodologies or that do not provide clear quantitative and qualitative data on the impacts of the technologies. This rigorous selection process ensured that the analysis was based on high-quality sources directly relevant to the study of the effects of adopting RPA and ML in predictive maintenance and sustainability. The central research question and the hypotheses that guided this study were appropriately formulated. Central Research Question (CRQ)  
CRQ: how can the integration of robotic process automation (RPA) and machine learning (ML) improve failure prediction and operational efficiency in predictive maintenance systems, considering sustainability in its environmental, social, and economic dimensions? Hypotheses (H)  
**H1. **: _The integration of RPA and ML improves accuracy in predicting failures in industrial systems, reducing downtime and operational costs._  
**H2.