International Journal of Multidisciplinary Research and Growth Evaluation  |  ISSN (Online): 2582-7138  |  Double-Blind Peer Review  |  Open Access  |  CC BY 4.0

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International Journal of Multidisciplinary Research and Growth Evaluation

ISSN (Online): 2582-7138 | Open Access

Proposed data-driven facility operations model using predictive analytics and smart tools

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Published:

Volume: 1 | Issue: 5 | Pages: 168-177

Subject: Engineering

Country: Nigeria

DOI: https://doi.org/10.54660/IJMRGE.2020.1.5.168-177

License: CC BY 4.0

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Abstract

Facility management is increasingly challenged by rising operational costs, complex building systems, and growing expectations for sustainability and occupant satisfaction. Traditional reactive maintenance and manual monitoring approaches are often inefficient, leading to unplanned downtime, excessive energy consumption, and reduced service quality. This proposes a data-driven facility operations model that leverages predictive analytics and smart tools to optimize performance, improve decision-making, and enhance operational efficiency. The model integrates real-time data acquisition through IoT sensors, smart meters, and connected building systems, providing continuous monitoring of energy usage, HVAC performance, lighting, and critical equipment. Data is centralized and standardized through integration with Building Information Modeling (BIM) platforms and Computerized Maintenance Management Systems (CMMS), forming the foundation for advanced predictive analytics. Machine learning algorithms are employed for fault detection, anomaly identification, predictive maintenance scheduling, and performance optimization. Scenario simulations enable proactive planning, risk assessment, and resource prioritization. Smart operational tools, including AI-driven maintenance systems, automated energy management, and digital twins, support decision-making by providing actionable insights through intuitive dashboards and mobile interfaces. The framework incorporates multi-criteria decision-making to balance operational costs, risk, sustainability objectives, and service quality, while feedback loops ensure continuous refinement and learning. Expected outcomes of the model include reduced operational costs, optimized energy consumption, improved asset reliability, and decreased downtime. Additionally, the framework enhances service quality, occupant comfort, and stakeholder satisfaction, while aligning facility operations with sustainability goals and ESG compliance. By integrating predictive analytics and smart technologies, the proposed model transforms facility management from a reactive, labor-intensive function into a proactive, data-driven, and strategic organizational capability. Future work includes empirical validation across different facility types and scaling the model for broader industry adoption, ensuring both operational excellence and long-term resilience.

How to Cite This Article

Joshua Oluwaseun Lawoyin, Zamathula Sikhakhane Nwokediegwu, Ebimor Yinka Gbabo (2020). Proposed data-driven facility operations model using predictive analytics and smart tools . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 1(5), 168-177. DOI: https://doi.org/10.54660/IJMRGE.2020.1.5.168-177

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Publication Information

Journal: International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE)

Publisher: Anfo Publication House

ISSN: 2582-7138 (Online)

Frequency: Bimonthly

Language: English

Open Access: Yes - This article is distributed under the terms of the Creative Commons Attribution 4.0 International License

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