Budget Optimization Model for Cost-Efficient Facility Management and Service Quality

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@article{1710507,
      author = {Joshua Oluwaseun Lawoyin, Zamathula Sikhakhane Nwokediegwu, Ebimor Yinka Gbabo},
      title = {Budget Optimization Model for Cost-Efficient Facility Management and Service Quality},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {12},
      pages = {360-375},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710507.pdf},
      abstract = {Facility management (FM) is increasingly challenged by rising operational costs, aging infrastructure, stringent regulatory requirements, and growing expectations for service quality and sustainability. Traditional budget allocation methods often rely on incremental adjustments or reactive spending, which can lead to inefficiencies, underfunded priorities, and compromised service delivery. To address these challenges, this study proposes a budget optimization model that integrates cost-efficiency with service quality objectives, providing a systematic approach for resource allocation in FM. The model is designed to minimize the total cost of ownership while ensuring compliance, risk management, and adherence to service-level agreements (SLAs). It incorporates decision variables such as preventive maintenance intensity, vendor selection, energy management strategies, and retrofit investment, with constraints reflecting budget ceilings, regulatory requirements, capacity limits, and sustainability targets. Service quality is quantified through measurable key performance indicators (KPIs), including uptime, mean time to repair, cleanliness scores, and occupant satisfaction, which are modeled as functions of budget allocation. Risk considerations, including asset reliability and contingency planning, are embedded to ensure resilience against disruptions. To enhance adaptability, the model integrates digital tools such as IoT sensors, predictive analytics, and energy management systems for real-time data collection and forecasting. Advanced optimization methods, including mixed-integer linear programming and robust or stochastic approaches, are employed to capture uncertainty in demand, costs, and operating conditions. The proposed framework enables FM teams to allocate budgets strategically, balancing short-term operational efficiency with long-term value creation. Expected outcomes include reduced downtime, optimized preventive maintenance, improved service quality, and enhanced transparency in decision-making. Ultimately, the model supports organizations in achieving cost-efficient facility management while safeguarding service quality, resilience, and stakeholder satisfaction in diverse and dynamic operational contexts.},
      keywords = {Budget Optimization, Cost Efficiency, Facility Management, Service Quality, Resource Allocation, Operational Cost Reduction, Predictive Maintenance, Performance Metrics},
      month = {June},
  }