Integrating Quantitative Parameters for Automating Medical Equipment Maintenance Using Industry 4.0 and FMEA

In the progression of Industry 4.0, the incorporation of cutting-edge technologies such as big data analytics, machine learning, and automation has revolutionized conventional maintenance methods across various industries, including healthcare. This study examines the impact of linking quantitative metrics with the advancement of Industry 4.0 applications, utilizing Failure Mode and Effects Analysis (FMEA) as a foundational methodology. The research aims to establish a comprehensive maintenance framework for medical equipment, emphasizing how FMEA's systematic approach to identifying and prioritizing potential failures can be enhanced through Industry 4.0 tools. By using quantitative data—such as failure rates (FR), mean time to repair (MTTR), and Risk Priority Numbers (RPN)—the study illustrates how predictive maintenance strategies can be refined, resulting in significant improvements in equipment reliability and operational efficiency. The incorporation of real-time monitoring and automated decision-making within the proposed framework not only reduces equipment downtime but also ensures the continuity of essential healthcare services, especially in critical environments. This study highlights the importance of quantitative analysis in developing and validating Industry 4.0 applications, offering insights into how these technologies can optimize maintenance practices and ultimately enhance patient outcomes in the healthcare field.