## 4 Model (SIRPM)
### Proposal for a Model (SIRPM)
* Representativeness of data: the degree to which the captured data reflects the performance and conditions of the machine under study. * Storage capacity: the available space in local devices (e.g., hard drives) or cloud solutions to accommodate the volume of collected data. Figure 2: Diagram of proposal for a sustainable model (SIRPM). Scalability: the flexibility of the infrastructure to adapt to variations in demand, including the potential need for future expansions. After assessing the adequacy of data collection and infrastructure, the organization must determine the appropriate storage solution--either local storage or cloud-based platforms. This decision is guided by the KPIs outlined above. Cloud storage is often preferred in scenarios requiring high integration and real-time data analysis, as it aligns with Industry 4.0 principles and enables enhanced connectivity and interoperability. * Phase 4: Integration of Results with RPA: Predictive results are integrated into operations through RPA bots, automating data analysis and anomaly detection (Table 7). The implementation of control automation processes in the proposed model requires the existence of an infrastructure that allows for automated interventions. These processes are more easily applicable in robust infrastructures, such as cloud-based solutions, which offer greater resilience and real-time responsiveness. For on-premise environments, the application of these processes may be limited due to resource constraints. However, when implemented, these mechanisms ensure the continuity of operations and the safe execution of machine learning algorithms, allowing the system to not only predict possible failures but also perform automatic corrections without compromising the integrity of business processes. In the specific case of this model, control automation processes can be applied given that there are platforms where automation intervention is required to collect data and subsequently organize structured data for the later application of machine learning algorithms. * Phase 5: Action and Feedback: predictions are transformed into corrective actions by sending automated alerts, executing maintenance, and gathering feedback to improve the models (Table 8). * Phase 6: Continuous Learning and Sustainability: continuous improvement is ensured through performance metrics, team training, and regular reviews, promoting sustainability and operational efficiency (Table 9). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Phase 3: Application of Machine Learning** \\ \hline \hline  & **Assessment Criteria** & **Description** \\ \hline
6. & Selection of the machine learning algorithm that best fits the data. &  • Choose an algorithm (e.g., regression, neural networks, decision trees) based on the data characteristics and prediction goals. \\ \hline
7. & Development of the machine learning script. &  • Implement and train the selected model, adjusting its hyperparameters to maximize accuracy and minimize errors. \\ \hline
8. & Forecast results generated by the machine learning script. &  • Produce predictions about failures, anomalies, or maintenance patterns based on historical and real-time data. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Assessment criteria and description for Phase 3: Application of Machine Learning. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Phase 4: Integration of Results with RPA** \\ \hline \hline  & **Assessment Criteria** & **Description** \\ \hline
9. & Forecast data collection through RPA. & • Deploy RPA bots to monitor and extract predictions directly from the machine learning model. \\ \hline
10. & Data analysis through RPA. & • Automatically cross-reference forecast data with the patterns stipulated by the company. \\ \hline
11. & Are there data points outside the company’s stipulated average values? & • Automatically identify deviations or anomalies that could indicate imminent failures or maintenance needs. \\ \hline \hline \end{tabular}
\end{table}