Original Equipment Manufacturers (OEMs) face increasing pressure to deliver cost-effective, reliable maintenance services amid labor shortages, rising customer expectations, and regulatory demands. This thesis proposes a structured, scalable framework for optimizing maintenance strategies across complex, multi-component systems. Moving beyond reactive corrective maintenance, it emphasizes preventive approaches—age-based, condition-based, inspection-based, and failure-based—tailored to component characteristics and system-level dependencies. The research introduces a bi-objective optimization model that schedules preventive maintenance during regular business hours, reducing urgent interventions and improving resource efficiency. A two-threshold policy further refines condition-based maintenance by balancing scheduled and semi-urgent replacements. Later chapters explore flexible strategies, including opportunistic maintenance, where multiple components are serviced during triggered visits, and scalable heuristics to manage computational complexity.nnAdvanced heuristics distinguish between critical and semi-critical components, enabling dynamic scheduling that minimizes downtime and cost. These strategies enhance sustainability by reducing emergency interventions, conserving resources, and easing workforce pressure. Validated through case studies and simulations, the framework equips OEMs with practical tools for future-ready maintenance planning.nnUltimately, the thesis offers OEMs a roadmap for integrating intelligent, adaptive maintenance policies that support operational stability, cost-efficiency, and long-term competitiveness in high-tech, volatile markets.