## 4 Model (SIRPM)
### Proposal for a Model (SIRPM)

Table 7: Assessment criteria and description for Phase 4: Integration of Results with RPA. The model allows each phase to be tailored according to the available technological infrastructure and the needs of the organization. For instance, different ML algorithms can be selected in Phase 3 depending on the data types and failure patterns being addressed, and RPA tools can be integrated in various ways depending on the organization's existing platforms. The proposed model, SIRPM, was designed as an innovative solution to meet the predictive maintenance needs of organizations facing challenges in adopting Industry 4.0 technologies. We recognize that I4 principles prioritize the centralization and control of cloud-based processes, but many organizations are not yet fully prepared for this transition. In response, SIRPM was designed to function as a strategic transition stage, enabling a gradual evolution towards the full adoption of cloud-based I4 without compromising efficiency and sustainability objectives in the short term. Therefore, we detail how the model was designed to function as a transition stage as follows:  
* Data collection based on Industry 4.0 guidelines: Data collection in SIRPM is performed through smart sensors, aligned with I4 principles, such as connectivity and interoperability. These sensors generate data that can be stored locally, in the cloud or in hybrid infrastructures, depending on the organization's technological maturity. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Phase 5: Action and Feedback**} \\ \hline \multicolumn{3}{c}{**Assessment Criteria**} & **Description** \\ \hline
12. & Sending alerts to the responsible & \(\bullet\) & Automatically send notifications to the responsible team when deviations or failures are detected. \\ \hline
13. & Maintenance execution. & \(\bullet\) & Ensure corrective actions are taken based on forecasts and alerts issued. \\ \hline
14. & Feedback through evaluation of the & \(\bullet\) & Assess the generated alerts to verify if they were accurate, relevant, and helped avoid issues. \\ \hline
15. & Record this evaluation for machine & \(\bullet\) & Update the machine learning model with the feedback received, enabling continuous learning and improved accuracy. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Assessment criteria and description for Phase 5: Action and Feedback. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Assessment Criteria**} & **Description** \\ \hline
16. & Operational efficiency and & \(\bullet\) & Calculate metrics such as mean time to repair (MTTR), mean time between failures (MTBF), and sustainability indicators like reduced energy consumption and emissions. \\ \hline
17. & Training and adaptation of teams for & \(\bullet\) & Train staff to interpret the results and interact with the system, ensuring all teams align with the new approach. \\ \hline
18. & Periodic review of system performance. & \(\bullet\) & Establish regular reviews to assess the performance of RPA bots, the accuracy of machine learning models, and the overall impact on the production process. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Assessment criteria and description for Phase 6: Continuous Learning and Sustainability. * Cloud-based storage and control: The model prioritizes cloud solutions to ensure resilience, scalability, and access to advanced machine learning (ML) tools. However, it is adaptable, allowing for initial control in on-premise infrastructures and a progressive evolution to cloud solutions as organizations advance in the process of technological modernization. * Implementation of machine learning logic: The ML logic in SIRPM is designed to be implemented both in the cloud and on-premise, in a scalable and flexible way. In the context of I4, cloud technologies offer access to advanced ML libraries and robust computational capabilities, overcoming internal limitations that many companies still face.