## 5 Case Study
### Application of the Model to the Case Study
Phase 4: Integration of Results with RPA  
The ML model outputs are integrated into Alpha's operations through automation. Integration Details:  
Use RPA bots to extract ML predictions in real time. Automate the cross-referencing of predictions with Alpha's operational norms, identifying deviations. Highlight anomalies, enabling the identification of potential failures before they disrupt production. Phase 5: Action and Feedback  
Proactive measures are taken based on predictions, and feedback loops are created to enhance system performance. Implemented Measures:  
Automatically send alerts to the responsible department through RPA upon detecting deviations or failures. Execute maintenance, ensuring corrective actions are taken based on forecasts and alerts issued. Collect feedback on the usefulness of the received alerts, assessing whether they were accurate, relevant, and helped prevent issues. Record this evaluation for machine learning algorithm learning, updating the model with the feedback received, enabling continuous learning and improved accuracy. Phase 6: Continuous Learning and Sustainability  
Alpha ensures sustained efficiency through ongoing improvement and team adaptation. Sustainability Measures:  
16. Operational efficiency and sustainability through the calculation of key metrics, such as mean time to repair (MTTR), mean time between failures (MTBF), and sustainability indicators, such as reduced energy consumption and emissions. 17. Training and adaptation of teams for the predictive maintenance system, ensuring all teams are trained to interpret results and interact with the system. 18. Periodic review of system performance, conducting regular assessments of RPA bots, the accuracy of machine learning models, and the overall impact on the production process. The application of the SIRPM model at Alpha demonstrated the feasibility and effectiveness of the sustainable integration of RPA and ML into predictive maintenance systems, addressing the challenges faced by the company in a structured way. In the implementation of the SIRPM model, IoT sensors were strategically positioned on machines to monitor critical variables such as temperature, vibration, and pressure. These sensors were intended to identify operational anomalies and prevent failures by sending the collected data via LTE and Wi-Fi telecommunications networks to IoT gateways. The gateways aggregated the information and transmitted it to the central operations system, ensuring a real-time data flow with low latency and high reliability. An RPA robot, installed on a virtual machine configured in a hybrid cloud environment, was periodically executed to access the collected data. This robot extracted the information from the central system and stored it in a SQL database. Data processing and next-value predictions were performed using machine learning algorithms implemented in a secure virtualized environment. This environment, developed with Python, integrated libraries such as Pandas, NumPy, and Scikit-learn for data manipulation, mathematical operations, and predictive modeling. A second RPA robot was automatically triggered, analyzed the value, and if the predicted value was outside the defined reference range, the robot generated an alert. This alert was sent via email to the maintenance team, allowing for a proactive response and mitigation of potential failures before they compromised operations. This integrated architecture, using IoT sensors, RPA, and machine learning, was designed to optimize the collection, processing, and analysis of operational data, ensuring greater efficiency and reliability in machine monitoring. Data processing and forecast generation were performed by a computational service configured to integrate data from the SQL database and execute machine learning algorithms, using an architecture based on Python libraries. This service is designed to interact directly with the maintenance management system, offering real-time forecasts. Additional Process Description:  
1.