# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
## 4 Discussion
### Prescriptive Maintenance Actions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PsM Activity** & **Characterisation** & **Example** & **I4.0 Tools** & **References** \\ \hline \multirow{4}{*}{Component-level Repair vs. Replace.} & Deciding whether to replace or repair a specific part based on degradation and resource constraints. & The compressor blade of a turbofan engine is evaluated using RUL; if degradation is within tolerance, the part is repaired instead of replaced. & AI/ML. & [62; 71; 73; 82; 86; 87; 89] \\  & Spare parts pre-ordering, resource allocation, technician dispatching, and alarm adjustment. & Automate work orders and spare part requests based on sensor anomalies, and dispatch a technician based on their skill and profile. & AI/ML. IoT. Big Data. & [39; 54; 62; 63; 64; 75; 78; 90; 91; 92; 93] \\ \multirow{4}{*}{Proiitisation and Planning.} & Workforce optimisation and intelligent scheduling to maximise OEE. Selection of alternative & Maintenance tasks are reprioritised dynamically due to unexpected failures. & AI/ML. H&VI. [66; 68] \\  & & & & Cloud. \\ \multirow{4}{*}{Decision Support for Strategic Management.} & \multirow{4}{*}{Synchronising operation and maintenance schedules (e.g., control sequencing, load balancing).} & \multirow{4}{*}{AII/ML. Edge. Optimising load to reduce failures.} & \multirow{4}{*}{[62; 66; 78]} \\  & & & & \\ \multirow{4}{*}{Automated Execution and Self-Regulation.} & \multirow{4}{*}{Self-regulating workflows, feedback loops, and minimal human intervention to prevent stoppages.} & \multirow{4}{*}{AII/ML. H&VI. Big Data. IoT. Edge. Optimisation. & [62; 64; 75; 78] \\  & & & & \\ \multirow{4}{*}{Fleet- and Mission-Level Prescriptions.} & \multirow{4}{*}{Mission planning for fleets, coordinated scheduling, and downtime optimisation.} & \multirow{4}{*}{Scheduling maintenance for an aircraft fleet considering mission, availability windows, and component fatigue.} & AI/ML. Cloud. & [66; 68; 81; 83; 91; 92; 94] \\  & & & & \\ \hline \hline \end{tabular} Note: AI/ML = Artificial Intelligence/Machine Learning; IoT = Internet of Things; H&VI = Horizontal and Vertical Integration; Edge = Edge Computing Cloud = Cloud Computing and Connectivity; Big Data = Big Data and Analytics.  
\end{table} 
Table 5: Characterisation and exemplification of PsM activities. from system-oriented to process-oriented reasoning replaces discrete (static) decisions with continuous (dynamic) prescriptive response actions. Moreover, the activities are highly dependent on the industrial domain. For instance, in manufacturing (Figure 7), actions are oriented to preventing disruptions in production flows by minimising downtime and aligning maintenance with operational schedules [22, 60, 98]. Consequently, many have begun integrating maintenance with planning systems that support decisions such as workforce assignment, spare parts forecasting, and production (re)configuration. Implicitly, using H&VI seems crucial for determining the quality of products (or services). Unlike manufacturing, the use of PsM in the energy sector exhibits a higher level of autonomy and intelligence, where self-optimising systems rely on real-time data inputs and ML algorithms that trigger early warnings for adapting operations. Prescriptive actions here are sensor-driven and embedded within broader strategic management goals, such as grid stability, load balancing and/or emission reduction. Therefore, sequencing maintenance and operation activities is critical for preventing disruptions. Here, PsM facilitates intelligent and real-time interventions to maintain safety, reliability, and sustainability in volatile operational environments. In aerospace, PsM actions are driven by mission-critical priorities with a focus on field service engineers [99], flight delay minimisation [100, 101], and safety-aware recommendations [91, 102]. Prescriptive activities include integrating data from onboard diagnostics, flight history, and sensor logs to offer real-time, adaptive decisions for field repair.