## 6 Analysis of Results and Discussion
### Discussion
This upskilling not only increased employees' engagement with the system but also fostered a culture of continuous learning and improvement. As employees became more adept at using the predictive maintenance system, they were able to contribute more effectively to proactive maintenance efforts, resulting in greater operational ownership and improved team collaboration. The workforce's enhanced technical capabilities also contributed to greater job satisfaction, as employees were more involved in the decision-making process and better equipped to handle emerging challenges. The shift towards a more data-driven and proactive maintenance culture strengthened the overall performance of the maintenance team, ensuring a more responsive and efficient operation. One of the key strengths of the SIRPM model lies in its broad applicability across various industries. While the results presented here are specific to Alpha, the core principles of the model--predictive maintenance, RPA integration, and machine learning--can be adapted to suit the needs of other sectors. For example, industries such as manufacturing, energy, transportation, and healthcare can benefit from implementing similar predictive maintenance strategies. In manufacturing, the model could enhance machinery uptime and reduce production delays. In the energy sector, it could help optimize the performance of power plants and reduce the risk of equipment failures that lead to costly outages. Additionally, in the transportation industry, predictive maintenance could improve fleet management by identifying potential issues in vehicles or aircraft before they lead to operational disruptions. In healthcare, the model could be used to predict equipment failures in critical systems, such as medical imaging devices or patient monitoring systems, ensuring that equipment is always available and operational when needed. The scalability and flexibility of the SIRPM model make it a highly practical solution for businesses across different sectors. The ability to customize the model based on the unique needs of each industry ensures that the system can deliver optimal results regardless of the context. The results from the implementation of the SIRPM model at Alpha demonstrate the profound impact that predictive maintenance can have on operational efficiency, cost savings, environmental sustainability, and workforce development. By integrating RPA and machine learning, Alpha was able to achieve significant improvements in machine reliability, downtime reduction, and resource optimization, all of which contributed to enhanced financial performance and sustainability goals. The model's applicability to other industries further emphasizes its potential to drive similar improvements in diverse sectors, offering a cost-effective and scalable solution for companies looking to enhance operational efficiency and reduce their environmental footprint. As industries continue to embrace the digital transformation, predictive maintenance systems like the SIRPM model will play a pivotal role in shaping the future of operations management, sustainability practices, and overall business performance. While the results presented in this study are specific to Alpha, the SIRPM model demonstrates broad applicability across various industries, given its modular and adapt able nature. The integration of RPA and machine learning allows for a flexible solution that can be customized to the specific needs of diverse sectors. Industries such as manufacturing, energy, transportation, and healthcare are prime candidates for adopting similar predictive maintenance strategies. For example, in manufacturing, the model can enhance machinery uptime, reduce production delays, and lower maintenance costs. In the energy sector, it can optimize the performance of power plants and prevent costly outages caused by equipment failure. In transportation, predictive maintenance can improve fleet management by identifying potential issues in vehicles or aircraft before they result in operational disruptions.