## 5 Case Study
### Detailed Description of the SIRPM Model
On the other hand, if the vibration fell below 1.0 mm/s, this could indicate that the machine's moving components were having little interaction or contact, which could also cause operational problems. These values were used as a basis for triggering automatic alerts so that the maintenance team could investigate potential failures before they occurred. * Pressure sensor (hydraulic press): The pressure of the hydraulic fluid in the hydraulic press is essential to ensure that the pressing process occurs correctly and with quality. The ideal pressure range was established between 145 and 150 bar, with values outside this range indicating potential problems. If the pressure rose above 155 bar, this could signal failures in the pressure control system or even blockages in the hydraulic line, which would affect the quality of the packaging formation. On the other hand, pressures below 140 bar indicated that the hydraulic pressure was below what was necessary, which would impair the efficiency of the process and could cause failures in the pressing process. These alert values helped to anticipate the necessary maintenance, avoiding failures during operation. * Temperature sensor (flexographic printer): The temperature of the printheads is crucial to ensuring print quality and the efficiency of the flexographic printer's operation. The ideal temperature range was defined as between 75 and 80 \({}^{\circ}\)C, with upper and lower limits to alert for failures in the cooling or heating system. If the temperature rose above 85 \({}^{\circ}\)C, this could indicate failures in the cooling system, which would result in overheating and possible damage to the printheads. Temperatures below 70 \({}^{\circ}\)C, on the other hand, indicated failures in the heating system, compromising print quality. These alerts were essential to avoid damage to the equipment and ensure the continuity of the production process. These reference values and alerts were fundamental to the predictive maintenance system implemented by Alpha. Using the data collected by the sensors, the system was able to quickly identify when values were outside the ideal range, generating automatic alerts for the maintenance team. This allowed preventative maintenance to be carried out before serious failures occurred, improving operational efficiency and reducing machine downtime. * Phase 5: Proactive Action and Feedback  
When the model predicted an imminent failure, RPA automatically alerted the responsible teams, prompting corrective actions such as adjustments or repairs. Implemented Actions:  
Automated email alerts to the maintenance team. Execution of corrective maintenance and preventive adjustments. Feedback collection on alert effectiveness and prediction accuracy. The proactive measures' outcomes were monitored to refine the model using intervention feedback, thereby improving machine learning predictions. Phase 6: Sustainability and Continuous Improvement  
To ensure the model's sustainability and continued efficiency, key performance indicators such as the MTTR (mean time to repair) and MTBF (mean time between failures) were calculated, alongside sustainability metrics related to energy consumption and emissions reduction. Continuous Monitoring:  
The machine learning model's performance was periodically reviewed. The RPA system was adjusted as needed for accuracy and efficient integration. Continuous staff training ensured adaptation to the new predictive maintenance methodology. The machine learning model implemented in this study is a post-processing approach. The decision tree (DT) model was executed periodically at hourly intervals to process the data collected by the RPA system. This process was essential for generating failure predictions in the monitored systems based on the selected critical variables, such as T (temperature), V (velocity), and P (pressure). These variables were chosen for their significance to machinery performance, enabling precise analysis and the anticipation of potential failures.