## 4 Model (SIRPM)  
### Proposal for a Model (SIRPM)  
This paper proposes an innovative model focused on the sustainable integration of robotic process automation (RPA) and machine learning (ML) in predictive maintenance systems. The objective is to enhance operational efficiency, improve failure forecasting, and reduce maintenance costs. Named SIRPM--sustainable integration of robotic process automation (RPA) and machine learning (ML) in predictive maintenance (PM)--this model provides a flexible yet structured approach that follows a set of well-defined criteria, ensuring the successful integration of these technologies into existing maintenance systems. While the model adheres to a structured set of criteria, it is adaptable in terms of the specific algorithms and technological solutions applied, allowing for alignment with the technological environment and needs of each organization. This flexibility is crucial, as it enables the model to accommodate a variety of industrial settings while still meeting sustainability goals and optimizing maintenance processes. The model aims to enhance the accuracy of failure predictions and operational efficiency by automating repetitive, rule-based tasks through RPA and leveraging ML algorithms to process large volumes of real-time data. By predicting failures before they occur, organizations can optimize resource allocation, minimize material and energy waste, and extend the lifespan of equipment. This approach is designed to support a responsible and sustainable integration of RPA and ML into maintenance systems, balancing technological advancements with environmental, social, and economic considerations. The criteria that guide the application of these technologies ensure that the integration aligns with the organization's overall objectives without compromising sustainability goals. Furthermore, the model is designed with interoperability in mind, ensuring seamless integration with existing systems such as CMMS, ERP, and IoT platforms without requiring major infrastructure overhauls. This guarantees that companies can apply predictive maintenance solutions without disrupting their existing operations. The model's implementation is structured in several phases, each with specific objectives that ensure both operational and sustainability goals are met. These phases are illustrated in Figure 2, which highlights the main benefits and improvements resulting from the model's adoption. Due to the flexibility inherent in the model, it can be customized to meet the unique needs and technological landscapes of various organizations. This ensures that the model evolves with the technological landscape of the company, providing a sustainable and adaptable predictive maintenance solution. The phases of the SIRPM model are presented below, outlining the structured steps required to implement a sustainable and efficient integration of RPA and ML in predictive maintenance systems. Each phase focuses on a specific objective, ensuring a comprehensive approach to achieving operational and sustainability goals. * Phase 1: Infrastructure Assessment: The organization's technological readiness is evaluated to ensure the presence of smart sensors and reliable connectivity for data collection (Table 4). A company's ability to obtain data from its machines depends on specific KPIs related to the infrastructure's capacity and flexibility. Initially, the company must verify if it has the means to gather relevant data from the respective machines. If such capabilities are lacking, the installation of sensors or equipment capable of measuring key variables becomes necessary. These variables can include metrics such as the number of completed items, temperature, pressure, or other factors specific to the machine and study objective. The decision regarding whether the infrastructure is sufficient for data collection also considers KPIs such as the following:  
* Frequency of data collection: the capability to capture and process data at the intervals required for meaningful analysis.