# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
## 4 Discussion
### Research Clusters  
Exploring latent thematic structures leveraging MCA-HCPC (Figure 6), we identified three distinguishable clusters. Each cluster encapsulates a coherent line of research focus and mainly reflects varying levels of technical maturity, organisational engagement, and systematic integration. The characterisation of each cluster and references are provided in Table 6. Technical integration and real-time diagnostics primarily characterise Cluster 1 and focus mainly on machine- and (multi)component-level analysis. The studies in this cluster primarily focus on the manufacturing and energy sectors, and the prescriptive part of PsM mainly relies on reinforcement learning models and IoT deployments. Applications typically demonstrate the use of AI's potential in operational settings for identifying and preventing failures, ultimately avoiding unnecessary downtime. Another characteristic is that PsM applications, such as frameworks and decision-support systems, thrive in sensor-rich operational settings. However, the enterprise-scale integration and generalisability remain limited, which is where H&VI I4.0 technology plays a crucial role in enabling a Cyber Physical Production System (CPPS). Cluster 2 mainly focuses on tactical and strategic decision-making. This includes enterprise-level decision-support systems, primarily relying on probabilistic modelling, fuzzy logic, Monte Carlo simulations, and optimisation frameworks. Unlike Cluster 1, the scope is dedicated to fleets and process-wide systems. This considers incorporating not only technical and organisational but also human factors. Here, studies extend beyond operational efficiency to address decision-making at the higher levels and planning under uncertainty, which usually builds upon prognosis models. This cluster fills the gap between operational-level and enterprise-wide decision frameworks by considering the complexities and uncertainty of operational settings. Typical examples are PriMa (Prescriptive Maintenance Model) models [2; 103], which comprise multiple functional dimensions and encompass multiple hierarchical decision-making levels. Cluster 3 primarily describes studies that focus on conceptual frameworks, organisational enablers, and strategic transformation for adopting PsM practices. The methods typically include conceptual models, case-based reasoning, and survey analysis. Primarily, the work here describes socio-technical endeavours and lifecycle thinking. Theoretically, the work discusses the role of human factors, such as workforce dynamics, upssfilling, and knowledge management, on organisational and PsM performance. While technically less engaged studies, the studies here mainly focus on broader industrial transformation and analysing the readiness of PsM. In summary, the clustering analysis reveals distinctive properties. Firstly, the cluster-based differentiation reveals a pathway from operational PsM (Cluster 1) towards enterprise-level integration (Cluster 2) and ecosystem-level consideration (Cluster 3). It is essential to note that clusters are not hierarchical but complementary. Advanced analytics, as observed in Cluster 1, are ecosystem-level and necessary but insufficient without organisational alignment (Cluster 2) and social-technical support structures (Cluster 3). Additionally, the integration of PsM models across verticals (from shop floor to management) and horizontals (across departments and ecosystems) highly depends on I4.0 horizontal and vertical integration technologies. Rhetorically, for an industrial organisation to manage these I4.0 technological solutions, achieving such a high level of success depends heavily on its organisational digital capacities. It has been reported that the adaptation of metrics outside of the purely technical sphere, like availability (e.g., Maintenance Sustainable Responsibility metric), highly depends on the digital capabilities of maintenance personnel [115].