## 6 Analysis of Results and Discussion  
### Analysis of Results  
In this chapter, the results obtained after implementing the model will be presented and analyzed. To this end, we will present data from one month of implementation, which was during month 8 of operation. During this period, the factory was in operation for 22 business days, and on each of these days, 16 daily predictions were generated for each sensor installed on the monitored machines. These predictions were made based on data from the last 24 h of operation using a machine learning model, specifically decision trees, which was executed every hour. The model considered critical variables such as the vibration, pressure, and temperature of each machine. For each sensor, the execution of the model generated a single prediction for the next hour of operation, based on the most recent historical data. In practical terms, the prediction generated for the \"next hour\" was based on information from the last 24 h and the model's learning history. Over the 22 business days of operation, the model generated a total of 352 predictions, distributed among the three types of sensors, namely vibration, pressure, and temperature sensors. To facilitate analysis, the results are presented through three distinct graphs, each corresponding to a type of sensor as follows: vibration (Figure 4), pressure (Figure 5), and temperature (Figure 6), allowing for a clear visualization of the fluctuations in the predictions and the comparison of the predicted failures throughout the month. When the predicted values for the vibration, pressure, and temperature sensors exceeded the established upper or lower limits, an automatic alert was generated for the maintenance team. These limits were defined as follows: for vibration, the maximum and minimum values were 1.5 mm/s and 1 mm/s, respectively; for pressure, the limits were 155 bar (maximum) and 140 bar (minimum); and for temperature, the established limits were 85 \({}^{\circ}\)C (maximum) and 70 \({}^{\circ}\)C (minimum). Figure 4: Prediction—vibration (mm/s). Whenever the predicted values for any of these parameters exceeded the limits, an alert was triggered, allowing the maintenance team to be immediately notified. This approach was crucial in preventing unexpected failures, as it allowed the team to monitor the machine proactively, identifying potential problems and carrying out the necessary maintenance before they could result in unforeseen downtime or serious damage to the machines. The implementation of the SIRPM model at Alpha resulted in significant improvements in both operational efficiency and sustainability. The analysis presents the key performance indicators (KPIs) before and after the adoption of the model during the 6-month operating period between months 7 and 12. This analysis highlights the effectiveness of the predictive maintenance system and its impact on cost reduction, resource optimization, and environmental sustainability. The effects of the SIRPM model on maintenance performance are evident when comparing the key indicators before and after its implementation. These indicators clearly demonstrate the operational benefits achieved with the predictive maintenance system, which combines RPA and machine learning. For the analysis, the values were compared with the historical maintenance data from the previous year, which were provided by the company. Figure 5: Prediction—pressure (bar). Figure 6: Prediction—temperature (°C). The impact of the SIRPM model on maintenance performance can be clearly seen in the comparison of key metrics before and after its adoption. These metrics show the operational benefits achieved through the predictive maintenance system, which integrates RPA and machine learning (Table 14). The formula for calculating MTBF is  
MTBF = (Total Operating Time)/(Number of Failures);  
Before the implementation of improvements, the company operated with an average of 15 failures per month.