Modern industrial systems are characterized by increasing technological complexity, high capital intensity, and severe penalties associated with unexpected downtime. In such environments, maintenance is no longer a supporting function but a strategic determinant of system availability, safety, and long-term economic performance. The classical maintenance literature was built around age-based and time-based replacement models that assumed perfect or minimal repairs at failure, as exemplified by early foundational work on optimal replacement policies. Over the last four decades, however, the scope of maintenance theory has expanded toward condition-based, predictive, and digitally enabled strategies that incorporate imperfect repairs, multiple failure modes, stochastic degradation, and organizational practices such as Total Productive Maintenance. Despite this evolution, there remains a conceptual and methodological gap between classical replacement theory and modern predictive maintenance frameworks. The former offers mathematically rigorous optimization logic but often lacks realism regarding system degradation and maintenance imperfection, while the latter provides rich operational insight but is frequently fragmented and difficult to integrate into a unified decision structure.

This article develops a comprehensive, theoretically grounded, and operationally relevant synthesis of preventive and condition-based maintenance policies for degrading systems. Drawing strictly and exclusively on the provided scholarly references, the study constructs an integrative framework that unifies age-replacement, block replacement, minimal repair, imperfect preventive maintenance, condition-based maintenance, and organizationally embedded approaches such as Total Productive Maintenance. The analysis begins by revisiting the seminal models of continuous and discrete replacement with minimal repair, and extended age-replacement with imperfect restoration, showing how they provide the economic and probabilistic foundations for all subsequent maintenance optimization. It then incorporates later advances in age-dependent replacement costs, availability modeling under imperfect maintenance, and block replacement policies, which collectively demonstrate that maintenance is a dynamic economic decision rather than a fixed technical routine.

Building on these foundations, the article extends the theoretical discussion to condition-based maintenance for degrading systems with multiple failure modes, cumulative damage, environmental shocks, and age- and state-dependent costs. The proportional hazards perspective and multi-state degradation frameworks are interpreted not as separate methodologies but as logical continuations of age-based thinking, where time is replaced by a richer description of system health. The integration of predictive and adaptive maintenance, including sequential decision models and non-periodic inspection policies, is shown to provide a bridge between stochastic degradation modeling and real-time operational control.

In parallel, the article incorporates organizational and digital dimensions of maintenance. Total Productive Maintenance and macroergonomic fatigue management are analyzed as essential complements to technical optimization, ensuring that preventive and predictive policies are executed reliably by human-machine systems. Industry 4.0, predictive maintenance 4.0, and product-service system–based maintenance strategies are interpreted as enabling infrastructures that transform theoretical maintenance policies into actionable industrial practices. Advanced scheduling and optimization models that integrate preventive maintenance with production planning, stochastic energy contracts, and reinforcement learning further demonstrate how maintenance decisions have become embedded in enterprise-level decision systems.

The result is a unified theoretical and practical framework in which maintenance is understood as a continuous process of managing degradation, risk, and economic trade-offs through a combination of age-based logic, condition-based information, imperfect repair modeling, and organizational alignment. The findings show that no single policy dominates across all contexts; instead, optimal maintenance emerges from the coherent integration of classical replacement theory, modern condition monitoring, and digitally enabled decision support. This synthesis provides both a rigorous academic contribution and a strategic guide for industrial organizations seeking to design robust, efficient, and sustainable maintenance systems.