## 6 Analysis of Results and Discussion
### Discussion  
The implementation of the SIRPM (smart integrated predictive maintenance) model at Alpha represents a significant advancement in both operational efficiency and sustainability. The results clearly demonstrate the effectiveness of the predictive maintenance system, which integrates RPA (robotic process automation) and machine learning, contributing to improvements across multiple dimensions, such as maintenance performance, cost efficiency, sustainability, and overall financial outcomes. These findings are not only relevant to Alpha but also offer valuable perceptions for broader applications across various industries. The analysis of the key performance indicators (KPIs) revealed substantial improvements in operational performance. Notably, the mean time between failures (MTBF) more than doubled, from 23.47 h per event to 70.4 h per event, representing a 100% improvement. This increase in the MTBF highlights the enhanced reliability of the machinery, allowing Alpha to reduce unplanned downtime and improve overall productivity. Furthermore, the mean time to repair (MTTR) saw a remarkable reduction of 67%, from 12 h to 4 h, reflecting more efficient and faster response times from the maintenance team. These improvements are directly attributable to the predictive capabilities of the SIRPM model, which allowed Alpha to anticipate and address failures before they occurred. The increased prediction accuracy, from 60% to 90%, demonstrates the effectiveness of machine learning in improving failure detection and proactive maintenance strategies. This reduced downtime and repair time, ultimately enhancing operational performance and reducing costs. The financial benefits of implementing the SIRPM model are equally impressive. The reduction in average repair costs by 52%, from EUR 2500 to EUR 1200 per incident, and the 37.5% reduction in total maintenance costs, from EUR 4000 to EUR 2500 per month, clearly demonstrate the cost-saving potential of predictive maintenance. The model enabled the company to reduce unplanned downtime costs by 71.4%, from EUR 3500 to EUR 1000 per month, leading to a substantial decrease in total operational costs, down by 16.67%, from EUR 30,000 to EUR 25,000 per month. These reductions were driven by the model's ability to identify potential failures early, allowing for less expensive and more planned repairs rather than emergency interventions. Additionally, the predictive maintenance model led to a more efficient allocation of resources. By reducing the need for emergency repairs and minimizing unplanned downtime, Alpha was able to better manage its workforce, repair materials, and energy resources, ultimately leading to reduced operational costs and improved profitability. The environmental impact of the SIRPM model was also substantial. The reduction in energy consumption by 25%, from 10,000 kWh to 7500 kWh per month, can be attributed to the more efficient use of machinery and reduced downtime. Similarly, CO\({}_{2}\) emissions decreased by 40%, from 5000 kg to 3000 kg per month, and waste production decreased by 30%, from 500 kg to 350 kg per month. The improvement in material usage, which dropped by 18.75%, from 8000 kg to 6500 kg per month, reflects better resource management practices. The recycling rate also saw an improvement, rising by 21.43%, from 70% to 85%, indicating a stronger commitment to sustainability and efficient resource use. These environmental improvements align with global sustainability standards, such as ISO 50001, which focuses on continuous energy performance improvement. The 25% reduction in energy consumption exceeds the standard's typical targets of 5-10% annual reductions, demonstrating Alpha's leadership in energy efficiency. The implementation of the SIRPM model had a positive effect on Alpha's workforce. Employees were provided with training to operate new technologies, which enhanced their technical skills and empowered them to make more data-driven decisions.