Integrated Solutions for Power and Cooling Asset Management through Oracle PLM

Main Article Content

Rafa Abdul
Imran Khan
Satish Vadlamani
Dr. Lalit Kumar
Prof. (Dr) Punit Goel
Md Abul Khair

Abstract

Integrated solutions for managing power and cooling assets are becoming essential in modern industries due to the growing need for optimized energy consumption, system reliability, and sustainability. Oracle Product Lifecycle Management (PLM) offers a comprehensive framework to streamline asset management by integrating data from various systems, enhancing operational efficiency, and improving asset performance. This study explores the role of Oracle PLM in unifying power and cooling asset management processes, from design and development to maintenance and upgrades.


By leveraging Oracle PLM, organizations can establish a single source of truth for managing product data, facilitating cross-functional collaboration and real-time visibility into asset performance. Predictive maintenance strategies can be integrated through PLM’s data analytics capabilities, minimizing downtime and extending the lifecycle of critical assets. Additionally, Oracle’s platform supports compliance with energy regulations and environmental standards, further driving sustainable practices in asset management.


The research highlights how Oracle PLM can align power and cooling management strategies with business goals by automating workflows, improving decision-making, and reducing operational costs. With integrated asset monitoring and preventive maintenance features, companies can proactively address potential issues, ensuring uninterrupted operations. Furthermore, Oracle PLM’s compatibility with IoT devices and smart sensors offers real-time insights into energy usage and environmental conditions, promoting efficient energy utilization

Article Details

How to Cite
Abdul, R., Khan, I., Vadlamani, S., Kumar, D. L., Goel, P. (Dr) P., & Khair, M. A. (2024). Integrated Solutions for Power and Cooling Asset Management through Oracle PLM. Journal of Quantum Science and Technology (JQST), 1(3), Aug(53–69). Retrieved from https://jqst.org/index.php/j/article/view/90
Section
Original Research Articles

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• Mahimkar, E. S., Agrawal, K. K., & Jain, S. (2024). "Extracting insights from TV viewership data with Spark and Scala," International Journal of New Trends in Informatics, 2(1), a44-a65. [IJNTI](rjpn ijnti/papers/IJNTI2401006.pdf)

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