# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
###### Abstract  
Prescriptive Maintenance (PsM) transforms industrial asset management by enabling autonomous decisions through simultaneous failure anticipation and optimal maintenance recommendations. Yet, despite increasing research interest, the conceptual clarity, technological maturity, and practical deployment of PsM remains fragmented. Here, we conduct a comprehensive and application-oriented Systematic Literature Review of studies published between 2013-2024. We identify key enablers--artificial intelligence and machine learning, horizontal and vertical integration, and deep reinforcement learning--that map the functional space of PsM across industrial sectors. The results from our multivariate meta-synthesis uncover three main thematic research clusters, ranging from decision-automation of technical (multi)component-level systems to strategic and organisational-support strategies. Notably, while predictive models are widely adopted, the translation of these capabilities to PsM remains limited. Primary reasons include semantic interoperability, real-time optimisation, and deployment scalability. As a response, a structured research agenda is proposed to emphasise hybrid architectures, context-aware prescription mechanisms, and alignment with Industry 5.0 principles of human-centricity, resilience, and sustainability. The review establishes a critical foundation for future advances in intelligent, explainable, and action-oriented maintenance systems. p _Applied sciences_  
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and energy [3]. Using descriptive and diagnostic logic to detect anomalies, identify root causes, and anticipate failures, PsA enables organisations to move beyond forecasting to recommend concrete, contextual, and automation-oriented actions with minimal human intervention. Consequently, PsA formed analytical groundwork for Prescriptive Maintenance (PsM), translating data-driven insights that directly support operational excellence [4]. As industrial systems grow more complex and interconnected, maintenance strategies, such as Condition-Based Maintenance (CBM) [5] and Predictive Maintenance (PdM) [6], evolved to keep pace with growing operational demands. However, although PdM is beneficial for forecasting failures, it falls short in determining _what_ actions should be taken, _when_ to take them, and _how_ to optimise decisions under limited resources and environmental uncertainty [7]. In response, PsM emerged as the next evolutionary step, offering targeted and optimised interventions that align with business objectives [8; 9; 10; 11]. The strategic relevance of PsM is amplified by its alignment with Industry 4.0 (I4.0) technologies (e.g., Internet of Things (IoT) [12], Cyber Physical Systems (CPS) [13], Digital Twins [14]), providing the technical infrastructure for implementing intelligent, distributed, and real-time operational decision-making. Simultaneously, the emergence of Industry 5.0 (I5.0) [15] reinforces human-centricity, resilience, and sustainability as core industrial values [16] and encourages the development of decision-support systems that prioritise collaborative intelligence, environmental awareness, and social responsibility [17]. Understanding that these transformations do not occur in a policy vacuum, several strategic policy frameworks directly shape the scope and deployment of PsM technologies. Namely, the Green Deal [18] places explicit emphasis on energy efficiency and resource optimisation, indirectly promoting the PsM approach, stressing the importance of minimising waste and energy in asset management. The Digital Europe Programme [19] incentivises research and innovation in AI-driven maintenance, which is essential for scalable PsM deployment. Rhetorically, the Artificial Intelligence Act [20] establishes a legal framework to assure trustworthy use of AI in high-risk environments, including industrial systems, guiding PsM development towards transparency and explainability. Despite the convergence of technology and policy, the industrial implementation of PsM remains fragmented.