Transitioning Facility Management to Proactive Models Using AI-Driven Predictive Maintenance

Authors -Dayanand Jamkhandikar

Abstract- – As of December 2022, the transition from reactive to proactive facility management models has become a critical objective for organizations seeking operational efficiency and cost savings. This paper explores the integration of AI-driven predictive maintenance strategies to revolutionize traditional facility management practices. By leveraging machine learning (ML) and deep learning (DL) algorithms, organizations can predict equipment failures, optimize resource allocation, and enhance overall performance. The discussion aligns with frameworks introduced by Ramakrishna Manchana’s works on event-driven architectures and machine learning applications in real estate and facility management. The study also delves into the role of cloud-native solutions and data lake architectures in supporting predictive maintenance systems. Case studies and real-world applications demonstrate how AI technologies can reduce downtime, minimize maintenance costs, and foster sustainable facility operations.

DOI: /10.61463/ijset.vol.10.issue6.327