# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
## 4 Discussion
### Current Challenges and Research Agenda  
Several challenges affect the full-scale adoption of PsM. Although economic considerations dominate optimisation efforts, a notable issue is the underrepresentation of sustainability aspects, namely, environmental and social dimensions. Many studies focus on specific machines or processes without addressing broader organisational or cross-industry applications. Another critical challenge is the low maturity of PsM applications, particularly in manufacturing, where exploratory research and propositional frameworks \begin{table}
\begin{tabular}{c c c} \hline \hline
**Cluster** & **Cluster Characterisation** & **References** \\ \hline \multirow{5}{*}{Cluster 1} & Component-level prescriptive decision-making. Use of Deep Reinforcement Learning. Real-time (partially) edge/IoT & \\  & integration. Multicomponent systems modelling. Hybrid physical-data modelling. Partial consideration of sustainability objectives. & [55; 64; 73; 78; 79; 84; 104; 105] \\  & Focus on prescriptive decision-making at tactical and strategic levels. Incorporates human and organisational factors. & \\  & Decision-making is considered under uncertainty using data-driven and probabilistic modelling. Strong emphasis on economic (cost) optimisation. Introduction of reference models and maturity frameworks (e.g., PriMa). Scalability through simulation and optimisation for real industrial applications under resource constraints. & [3; 7; 9; 23; 49; 54; 59; 60; 61; 62; 63; 65; 66; 67; 68; 69; 71; 72; 74; 75; 80; 83; 85; 87; 89; 91; 95; 99; 101; 103; 106; 112] \\  & Prescriptive maintenance is considered a strategic enabler, focusing on ecosystem and organisational readiness. It integrates with design (e.g., Design for Maintenance) and planning (e.g., Production Planning and Control). The importance of human and workforce dynamics is emphasised. & [22; 39; 76; 88; 90; 96; 97; 113; 114] \\  & Stresses PsM as a strategic business enabler. & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cluster characterisation. predominate. Enhancing the human-AI interface through trust-building mechanisms and workforce training will facilitate the adoption of PsM. To bridge the gap between theoretical models and industrial deployment, we translate challenges into targeted research opportunities by offering practical scenarios and solutions. **Industrial Validation**: Many studies suffer from limited real-world deployment, heavily relying on synthetic datasets and parameters (e.g., HackTheMachine [68], LMS Amesim [54]). Thus, existing work suggests that frameworks remain conceptual and theoretical. As a response, a solution space can be a practical deployment and validation of the PsM model, for instance, on a simple CNC machine, given that it is one of the most utilised industrial assets [116]. Validation setup could include remote sensor condition monitoring (e.g., vibration, current), which is input into the prediction layer (PdM). This model forecasts RUL of the cutting tool using time-series ML (e.g., LSTM). The prescription layer (PsM), relying on DRL, recommends _when_ to change the tool, _how_ to adjust cutting parameters (e.g., speed), and _whether_ to continue to operate or interrupt the batch based on tool performance. Validation would include comparison of two scenarios: (a) with proposed PsM (model) actions and (b) without PsM (e.g., standard preventive maintenance). Relying on tool life extension (%), number of unplanned stoppages, production throughput, and maintenance cost savings as key performance metrics, the proposed PsM model or framework is quantitatively established as valid. As a final step, the outcome can be determined by measurable improvements and by operators' feedback on the interpretability and trust in prescriptions, which can serve as an optional XAI layer. Rhetorically, by combining predictive and prescriptive logic, this approach will demonstrate decision quality, provide quantitative metrics for comparison, and support human-in-the-loop feedback.