Resource Allocation Model for Efficiency in Complex Facility Management Systems

Efficient resource allocation is a critical challenge in modern facility management systems, where the complexity of operations, diversity of assets, and varying service requirements demand strategic planning and optimization. Ineffective allocation of financial, human, and technological resources can lead to operational inefficiencies, increased costs, unplanned downtime, and reduced service quality. This proposes a comprehensive resource allocation model designed to optimize the deployment of resources in complex facility management environments, ensuring cost-effectiveness, operational continuity, and high service performance. The model integrates systematic cost analysis, performance metrics, and predictive planning to inform decision-making across multiple facility domains, including maintenance, energy management, cleaning, security, and infrastructure operations. It leverages real-time data from IoT-enabled sensors, Building Information Modeling (BIM), and computerized maintenance management systems (CMMS) to assess resource utilization, identify bottlenecks, and prioritize interventions. Optimization techniques, including linear programming, dynamic allocation, and scenario simulation, are applied to allocate resources efficiently while maintaining service quality and compliance with regulatory and sustainability standards. Key components of the model include strategic workforce deployment, energy and material optimization, preventive maintenance scheduling, and the integration of digital tools for monitoring, reporting, and predictive analytics. Performance indicators, such as response times, asset uptime, energy consumption, and service-level compliance, guide allocation decisions and support continuous improvement. Expected outcomes of implementing the model include improved operational efficiency, reduced costs and resource waste, enhanced service quality, and increased resilience against unplanned disruptions. Additionally, the framework aligns facility operations with organizational sustainability goals and environmental standards, promoting long-term operational and financial sustainability. Future research directions involve empirical validation across diverse facility types, integration with advanced AI-driven predictive analytics, and evaluation of the model’s impact on organizational performance, client satisfaction, and strategic decision-making.