# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
## 3 Results
### Content-Based Data Descriptive and Exploratory Analysis  
The descriptive statistics suggest that most of the PsM studies (Figure 5A) are dedicated to manufacturing (32%), energy (23%), and aerospace (13%), followed by other domains. Accordingly, this suggests that most of the research (69%) is situated around these domains. Next, the research design indicates that 57% (\(n=35\)) of studies rely on a single case study, followed by simulation (\(n=33\%\)) and the simultaneous application of PsM frameworks (\(n=33\%\)). Looking at specific domains, the evidence shows that 30% of studies in manufacturing (Figure 5A\({}_{1}\)) rely on theoretical frameworks with validation through a simulation or a case study. In the energy sector, most of the work is performed similarly to that in manufacturing; however, there are more surveys and observational  
Figure 4: Co-occurrence mapping of titles and abstracts (threshold \(>\) 10-word co-occurrence). studies (7%). In contrast, the proposed frameworks in the aerospace domain are predominantly based on numerical simulations with case studies (31%). This suggests that scalable PsM applications are still in an infant phase, and more practical study designs are yet to be explored. Furthermore, the optimisation metrics (Figure 5B), particularly decision variables used for optimisation of maintenance activities and scheduling, primarily rely on time (85%), maintenance costs (84%), and operational costs (80%), followed by degradation metrics (62%) and resource efficiency (61%) metrics. It becomes apparent that social and environmental aspects are still neglected. For that reason, we performed an analysis of Systemic and Sustainability Aspects (Figure 5C), such as economic (ECO), environmental (ENV), social (SOC), Technical/Technological (TEC), and TBL (Triple Bottom Line--studies that consider economic, ecological, and social aspects). The evidence suggests that, before 2020, there was a lack of TBL and environmental considerations in PsM's decision-making, while a limited number of studies incorporated social aspects (e.g., safety). Post-2020 studies show that environmental and social elements are being considered in decision-making steps, while TBL aspects are being integrated into optimisation frameworks. This suggests that green initiatives imposed by, for instance, the EU (e.g., the Green Deal) may indicate the presence of this effect. Rhetorically, a rise in sustainable consideration and a drop in technical performance indicators (e.g., availability metrics) are significant. This presumably marks the shift from purely technical performance metrics (e.g., reliability,  
Figure 5: Descriptive statistics considering (**A**) the industrial sector, including (**A1**) the research design of manufacturing studies; (**A2**) energy studies; and (**A3**) the research design of aerospace studies. (**B**) Overall key performance metrics considered in the optimisation and decision-making. (**C**) Systematic and sustainability aspects considered for optimising of maintenance activities. (**D**) Condition Monitoring Parameters are functional energy (green) and waste energy (red). availability) to product- and user-centric design (e.g., product quality, customer satisfaction, user experience). Lastly, inspired by the _p-f_ curve proposition in the Energy-Based Maintenance (EBM) domain regarding primary (functional) and secondary (waste) energy indicators (see [50]), we devise functional condition monitoring indicators of power variables (e.g., hydraulic, electrical, mechanical) and secondary (waste) emission energy monitoring (e.g., temperature, vibration, sound) parameters (Figure 5D). This distinction aligns with growing interest in leveraging energy signals for condition assessment, as demonstrated in studies dealing with hydraulic power (i.e., pressure and flow) [51] and electrical power (i.e., current and voltage) [52] for estimating the state of the machine or at least comparing input and output signals, such as energy consumption profiles [53].