# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
### Related Reviews  
Before performing SLR, the overview of related PsM reviews is provided. Although literature reviews on PdM are extensive, comprehensive reviews focusing solely on PsM are sparse and fragmented. For searching related systematic reviews, we performed a search on Scholar using the search strings (\"_prescriptive maintenance_\" AND (\"_systematic literature review_\" or \"_systematic review_\"). However, although not mainly dealing with PsM, we extract several review studies that partially introduce the PsM aspect.  
A study by [PERSON] et al. [1] was the first foundational review of PsA; however, it was not in the business context. While they contributed to understanding PsA with AI-based prescriptions, their review did not cover industrial applications. Even so, the primary constructs and functional descriptions seem to serve as groundwork for introducing the analogy into the industrial domain. Some of the early reviews, like [24], examined post-prognostic decision-making models, identifying early-stage prescriptive functions; however, they lack in-depth discussion on the relevance of the prescriptive aspect of PsM practice. [PERSON] et al. [25] reviewed the evolution of maintenance in the power industry, recognising that PsM integration with Digital Twin significantly improves performance; however, PsM is minimally addressed and without action-based taxonomies. [PERSON] et al. [26] reviewed PsM in the biopharmaceutical industry, highlighting challenges of data silos, algorithmic opacity, and low deployment readiness. Although extensive, their contribution is domain-specific and with limited generalisability.  
[PERSON] s et al. [27] systematically mapped PdM under the Industry 4.0 (I4.0) domain, but only acknowledged PsM as a natural extension. Similarly, [PERSON] et al. [28] tackled the role of Explainable AI (XAI) in energy systems maintenance and included PsM only as a component. Although their work mapped PsM models in the energy context, the focus was primarily on the interpretability rather than the functionality of PsM practice. [PERSON] et al. [29] reviewed intelligent maintenance and remanufacturing, highlighting that PsM research is scarce and typically focuses on failure prevention rather than performance assurance.  
Recently, [PERSON] et al. [30] examined the association of 15.0 and PsM, suggesting that key challenges companies face are related to human-machine collaboration, Operator 4.0 integration, and sociotechnical transformation. [PERSON] et al. [31] proposed a framework to integrate PsM with production planning and control; however, although their review emphasised planning models and decision variables, the scope was narrow regarding sectors and real-world deployments. [PERSON] et al. [32] and [PERSON] et al. [33] reviewed data-driven maintenance models in Wind Turbines. While PsM was discussed, their retrieved studies mainly cover renewable energy systems and health prognosis. Lastly, a study by [PERSON] et al. [34] is the first review to perform a comprehensive PsM-specific review to date. Their work developed a taxonomy of prescriptive outputs, methods, and architectures while identifying enablers and limitations. However, the review focused heavily on methodological classification, while not sufficiently examining implementation maturity or industry-specific applications.  
Collectively, these reviews offer valuable contributions to specific aspects of PsM, including analytics, explainability, planning, and decision-making. However, none have offered a fully integrated synthesis of prescriptive actions and enabling technologies, nor have they clustered PsM knowledge into thematic research areas grounded in practical deployment. This review addresses the gap by providing an application-oriented and decision-centric mapping of the PsM body of expertise with industrial relevance. Hence, infrastructure maintenance, i.e., railway tracks [35], pipeline corrosion [36; 37], concrete bridges [38], etc., are described elsewhere [39].