## 6 Analysis of Results and Discussion
### Analysis of Results
Considering that the monthly operation is 352 h (22 working days \(\times\) 16 working hours per day), the MTBF calculation before implementation was  
MTBF (before) = (352 h)/(15 events/month) = 23.47 h/events  
This means that on average, failures occurred every 23.47 h of operation. After implementing the improvements, the number of failures was reduced to five failures per month. The MTBF calculation after implementation was  
MTBF (after) = (352 h)/(5 events/month) = 70.4 h/events  
In other words, with the reduction in the number of failures, the system began to operate for an average of 70.4 h between failures, representing a +100% improvement in the MTBF. The MTTR is  
MTTR = (Total Downtime)/(Number of Failures);  
Before the improvements were implemented, the average time to repair a failure was 12 h. With 15 events per month, the total monthly downtime was  
Total Idle Time (before) = 12 h/events \(\times\) 15 h/events = 180 h/month  
After implementation, the average repair time was reduced to 4 h per failure, with five failures per month. The total monthly downtime was  
Total Idle Time (after) = 4 h/events \(\times\) 5 h/events = 20 h/month  
This results in a significant improvement in repair time, with a \(-\)67% reduction in the MTTR. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Metric**} & **Before** & **After** & **Improvement** \\  & **Implementation** & **Implementation** & **Improvement** \\ \hline Mean Time & 23.47 h/events & 70.4 h/events & +100\% \\ Between Failures (MTBF) & 23.47 h/events & 70.4 h/events & +100\% \\ \hline Mean Time to & & & \\ Repair (MTTR) & 12 h & 4 h & \(-\)67\% \\ \hline Unplanned & 15 events/month & 5 events/month & \(-\)66.67\% \\ Downtime Events & & & \\ \hline Failure Prediction & & & \\ Accuracy & +60\% & +90\% & +50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Maintenance performance improvements. Failure prediction accuracy was provided directly as a business performance metric, with no additional calculation required. Prior to the implementation of the improvements, failure prediction accuracy was 60%, meaning that 60% of failures were correctly anticipated. After the implementation of the improvements, this accuracy increased to 90%, representing a significant increase in the ability to predict failures before they occur. The +50% improvement is a value provided by the business, reflecting improvements in failure monitoring and prediction practices and technologies, allowing for a more effective anticipation of failure events and, consequently, more reliable operation. The predictive capabilities of the SIRPM model not only improved maintenance performance but also resulted in considerable cost savings (Table 15). By preventing catastrophic failures and reducing the need for emergency repairs, the model led to substantial reductions in repair and operational costs. As illustrated, the average repair costs decreased by 52%, from EUR 2500 to EUR 1200 per incident. This reduction reflects the ability of the predictive maintenance system to identify potential issues early, allowing for less costly repairs. The total maintenance costs also saw a 37.5% reduction, from EUR 4000 to EUR 2500 per month. Furthermore, unplanned downtime costs were significantly lowered by 71.4%, from EUR 3500 to EUR 1000 per month. This resulted in an overall decrease in total operational costs of 16.67%, from EUR 30,000 to EUR 25,000 per month. The cost reduction figures presented in Table 15 were provided by the company and represent average costs for the period before and after the implementation of the predictive maintenance model based on a study conducted over a period of 6 months. These costs reflect the average expenditure on repairs and maintenance during this period, both before and after the implementation of the new system. With the ability to predict failures before they occur, interventions could be planned in advance, resulting in a significant reduction in unexpected failures and the costs associated with these failures.