# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
## 4 Discussion
### Current Challenges and Research Agenda
This would demonstrate scalability across subsystems, show that causal and semantic integration prevents local optima, which is often observed in isolated PsM models, and would ultimately validate how distributed PsM logic can adapt to system-wide scenarios. Prediction-Optimisation Integration: A critical barrier is the seamless integration of the fault prediction (PdM) and decision-support (PsM) modules, which often encompasses the PsM practice. Nevertheless, the existing body of work usually addresses fault prediction (e.g., RUL estimation) or optimisation separately. To put it in context, we exemplify the case of an end-to-end PsM framework, similar to one proposed by [68], but for the HVAC (Heating, Ventilation, and Air Conditioning) system in a pharmaceutical cleanroom. A central HVAC system, in this case, ensures compliance with air purity, humidity, and temperature. Failures in such environments could potentially lead to production stoppages, batch rejections, regulatory non-compliance, and other issues. A challenging situation arises when standard HVAC systems operate as separate models. One model predicts RUL (PdM model) for estimating the lifespan of HVAC filters and fans. At the same time, another is a static rule-based system for scheduling preventive replacements every \(t\) hours of operation (PsM model). The models operating in isolation would lead to suboptimal outcomes (e.g., premature filter replacements, unexpected breakdowns during production peaks). A potential solution space would include an infrastructure of sensors (e.g., monitoring air pressure differentials and airflow velocity), historical data logs (e.g., fault logs), and an operational knowledge base (e.g., technician availability). Integrated architecture in this case would comprise a predictive module, such as an LSTM, for RUL estimation and uncertainty bounds to inform risk-aware decisions. The prescriptive optimisation module could consider MILP (Mixed-Integer Linear Programming) that co-optimises filter replacement schedules, technician availability, and other factors, with various constraints (e.g., regulatory limits, cost of downtime). The last layer, the coordination layer, connectsthe management system (e.g., MES, CMMS) to ensure the real-time synchronisation of prescriptions with operational constraints, allowing decisions to be made on whether to delay or expedite filter replacement, switch to a backup fan, or resequence batch production. Ultimately, linking asset health with production and maintenance scheduling offers direct feedback to planners/operators with explainable recommendations. **Data Quality and Infrastructure Readiness**: As often encountered in transitioning from conventional to advanced I4.0 technologies, data-related barriers frequently inhibit PsM implementation. This suggests that sensors generate incomplete, noisy, or unlabelled data; incompatibility exists between legacy systems and outdated infrastructure; and, most notably, the need for edge processing and robust data fusion across maintenance, logistics, and operations. One possible research direction for addressing these issues is the recent development of plug-and-play IoT (see [119]), edge AI tools, and data standardisation protocols across value chains, eliminating the need for manual configurations. Leveraging edge-enabled anomaly detection and semantic interoperability frameworks offers a pathway to PsM adoption, particularly in smaller manufacturing enterprises with limited digital infrastructure. **Human-Centric and Sociotechnical Issues**: Human expertise and sociotechnical factors remain under-addressed in the literature. Specifically, the lack of operator trust and the skills required for the successful adoption of PsM are the reasons why many have initiated discussions on XAI and bypassing the need for human-machine understanding, thereby potentially circumventing the need for upskilling by relying on DRL algorithms.