# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
## 4 Discussion
### Current Challenges and Research Agenda
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Cluster 1** & **Cluster 2** & **Cluster 3** \\ \hline Focus & Deep learning for real-time actions and risk optimisation. & Maturity models. Prescriptive decision-making rules. & Ecosystem analysis for adoption of PsM. Surveys and interviews. \\ Methods & Deep RL. Neural networks. & Deep RL. Fuzzy logic. Monte Carlo simulation. & Reference modelling. Multicriteria analysis. \\ Granularity & Component and machine-level operational settings. & System/process-level under uncertainty. Integration with production planning and control. & Enterprise-level planning, control, and design integration. \\ PsM Contribution & Prescribes technical actions to prevent degradation. & Prescribes actions based on imperfect/limited observations. & Aligns prescriptions with people, processes, and assets. \\ Data & Operational data. Maintenance logs. Sensors. & Operational data. Maintenance logs. Sensors. & Questionnaires and surveys. \\ Human Factors & Minimal to moderate. Neglect of knowledge in the analysis. & High (technical variability, inspection quality). & Considering (e.g., workforce skill gap, learning, task allocation). \\ Strategic & Operational efficiency and automation. & Maintenance maturity. Risk–cost trade-off. & Strategic transformation. PsM’s role in advancing operational effectiveness. \\ Key Challenges & Data scarcity, generalisation, and explainability. & Partial observations, both simulations and limited case studies. Imperfection. & Knowledge feedback. Workforce scaling. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Description Matrix of clusters. Scalability and Complexity: Scalability issues persist when transferring frameworks from experimental to complex systems with many components and dynamic constraints. This is often observed in applications where studies demonstrate the PsM application on a single-unit system, which limits the scalability of the PsM model to multi-unit, interdependent systems, using a modular and interoperable design. To demonstrate a potential solution, we exemplify a scenario of a food manufacturing facility that operates a fully automated packaging line comprising interconnected subsystems (bottle filling station \(\rightarrow\) cap screwing unit \(\rightarrow\) label applicator \(\rightarrow\) quality inspection \(\rightarrow\) palletizer). Each subsystem has unique failure modes but is interdependent--a failure in one affects the following. Consider the proposed architecture of a packaging line that is modelled using a microservices-based PsM framework, such as those proposed in [2; 103]. However, unlike previous models, here each subsystem has its own containerised PsM module, such as Dockerised service (see [117; 118]), handling health monitoring (e.g., vibration, temperature), local fault prediction (e.g., ML trained per subsystem), and local prescriptive action (e.g., adjusting speed, triggering technician alert). A central orchestration layer (e.g., Kubernets) with machine-to-machine support (e.g., MQTT broker--Message Queue Telemetry Transport) coordinates cross-system dependencies, such that it synchronises downtime and reallocates line capacity. Exchanging data using ontology-based semantic models, such as OPC UA (Open Platform Communications Unified Architecture), facilitates interoperability, alongside standardised messaging protocols that ensure communication between subsystems and MES (Manufacturing Execution System). Here, the causal inference layer could identify how failure in one subsystem affects downstream performance (e.g., the capping unit has torque drift). In practical scenarios, this would allow PsM frameworks to be deployed and compared similarly to previous propositions by inspecting states with no PsM (baseline), isolated PsM (per machine), and integrated PsM (full orchestration).