## 4 Model (SIRPM)
### Characteristics and Benefits of the Model (SIRPM)
* [48] RPA in sustainability in supply chains: RPA for process automation in supply chains, aiming at sustainability and operational efficiency. * [49] The IoT and machine learning for predictive maintenance integrates the IoT and machine learning to optimize the maintenance of industrial systems and reduce failures. * [50] Smart predictive maintenance with AI and machine learning applies AI and machine learning to improve predictive maintenance in industrial environments. * [51] Predictive maintenance and environmental impact uses predictive maintenance to reduce the environmental impact of industrial processes and promote sustainable practices. * [52] Integration of AI and the IoT for predictive maintenance combines AI and the IoT to optimize maintenance and ensure sustainability in industrial systems. * [53] Sustainable RPA: the implementation of sustainable RPA in industrial processes, promoting efficiency and reducing environmental impact. * [54] Machine learning for predictive maintenance in Industry 4.0 uses machine learning to optimize maintenance and improve the efficiency of industrial systems. * [55] Deep learning for predictive maintenance applies deep learning to optimize maintenance and predict failures in industrial systems. * [56] The integration of AI and RPA proposes a sustainable model for integrating AI and RPA in industrial processes, optimizing operations and promoting sustainability. * [57] RPA and energy efficiency: RPA used to optimize industrial processes, improving energy efficiency and reducing consumption. * [58] Framework for RPA implementation: development of a framework for RPA project implementation and control, focusing on sustainability and operational efficiency. * [59] Decision models for sustainable RPA implementation: review of decision models for the sustainable implementation of RPA, aiming for optimization and reduction in environmental impacts. * [60] Sustainable RPA implementation in healthcare administrative services: analysis of RPA implementation in healthcare, focusing on sustainability and efficiency. * [61] Universal model for sustainable RPA implementation: proposal of a universal model for sustainable RPA implementation, promoting efficiency and sustainability. * [62] Sustainable RPA implementation: development of a mathematical model for the sustainable implementation of RPA across different industrial contexts. The IoT plays a crucial role in real-time data collection and transmission. Many of the solutions highlighted in Table 12 adopt wireless technologies due to their efficiency and mobility. However, in specific cases, wired networks may be employed depending on security and bandwidth requirements. Table 12 illustrates a combination of technologiessuch as RPA, machine learning, the IoT, and AI, with a focus on intelligent automation and energy efficiency, among other aspects. The interaction between the IoT and technologies such as RPA and machine learning is noteworthy, especially in the development of smart solutions for automation and monitoring. The IoT, by integrating connected devices, facilitates data collection and communication between systems, allowing for the application of machine learning algorithms for predictive analysis and process optimization. Regarding IoT solutions, the table reveals that it is often associated with machine learning technologies, especially in contexts involving techniques such as random forest, decision trees, and support vector machines (SVMs), applied to process data collected from IoT devices. These systems may include both wired and wireless solutions, depending on the context's needs. Wireless solutions (greater mobility and efficiency): These solutions are predominantly used in IoT technologies, being more common in environments that require greater flexibility and mobility. Examples include wireless sensor networks and IoT devices connected via Wi-Fi or 5G, offering freedom of movement and ease of implementation.