## 2 State of the Art
The integration of RPA with predictive maintenance systems allows operations to be more efficient and less prone to human errors, which is essential in high-demand production environments. The use of ML in manufacturing, in turn, is one of the greatest advances in predictive maintenance. ML systems can monitor a range of operational parameters of equipment in real time, such as temperature, vibration, pressure, and wear, identifying failure patterns that may indicate imminent failures. These systems enable more effective interventions, reducing corrective maintenance costs and extending equipment lifespan while also minimizing production costs by avoiding unplanned downtime [11]. Furthermore, the combination of RPA and ML creates a closed-loop cycle of monitoring and action, where failures can be predicted and interventions can be carried out autonomously without the need for constant manual interaction. This not only improves operational efficiency but also contributes to sustainability by reducing resource waste and extending asset lifespans. This cycle optimizes equipment usage and minimizes the environmental impact of industrial operations, aligning with the principles of a circular economy [11]. The application of predictive maintenance results in significant improvements in operational efficiency, as it enables the identification of failures before they occur, preventing unexpected downtime and the associated costs of emergency repairs. Maintenance is carried out only when necessary, reducing operational costs and improving resource utilization [11]. Sustainability is also supported, as predictive maintenance helps reduce material and energy waste by ensuring that equipment operates optimally and preventing catastrophic failures that could lead to large amounts of waste. Additionally, the use of RPA and ML, by automating processes and optimizing data analysis, contributes to the efficiency of maintenance and operational processes, reducing human errors and improving decision-making accuracy. These technologies are fundamental to the digital transformation of industrial maintenance, creating intelligent and autonomous systems that ensure greater reliability and efficiency in operations [11]. Sustainability, a critical aspect of modern industrial practices, is built on three foundational pillars, namely economic, social, and environmental pillars. These pillars emphasize efficient resource utilization, equitable societal impact, and environmental preservation, respectively. Predictive maintenance aligns with these principles by enabling operations that are both cost-effective and environmentally responsible. Through its focus on efficiency and proactive management, predictive maintenance contributes to sustainable practices, ensuring that industries meet present needs without compromising future resources [11]. The integration of predictive maintenance technologies, such as robotic process automation (RPA) and machine learning (ML), further enhances sustainability outcomes. Economically, these technologies reduce costs by optimizing equipment lifecycles and minimizing unplanned downtime. Socially, they improve workplace safety by identifying and mitigating potential hazards before they lead to failures. Environmentally, predictive maintenance lowers energy consumption and material waste, reinforcing a commitment to reducing industrial footprints. This approach is particularly relevant in the context of a circular economy, where waste is minimized and resource value is preserved across operational processes [12; 13; 14]. By seamlessly embedding sustainability into predictive maintenance strategies, industries can achieve a balanced approach that maximizes operational efficiency while addressing ecological and societal responsibilities [15, 16]. The proactive nature of predictive maintenance, supported by advanced analytical tools, allows organizations to extend asset lifespans, reduce waste, and operate more sustainably.