## 3 Results
### Analytics of Aviation Health Management Framework  
The aviation health management framework represents a paradigm shift in aviation maintenance and operations, moving towards a more proactive, data-driven approach. In this case, the focus on the different types of analytics utilized within the HMGT framework is one of its core components. HMGT systems use a progression from descriptive to diagnostic, predictive, and prescriptive analytics, each adding layers of complexity and value to aircraft maintenance and operations (Figure 2). Descriptive analytics involves the examination of historical data to understand what has happened in the past within aircraft systems and operations. This foundational level of analytics focuses on data aggregation, summarization, and the creation of dashboards and reports. In the context of HMGT, descriptive analytics helps maintenance teams identify trends, patterns, and anomalies in aircraft performance data, offering insights into the regular operational status and highlighting areas that may require attention. It is the starting point for more advanced analytical processes, providing a clear picture of the aircraft's health status over time. Building on descriptive analytics, diagnostic analytics seeks to determine why something happened. It involves more sophisticated data processing techniques, including drilling down into data, data mining, and causal analysis. In HMGT, diagnostic analytics is used to pinpoint the root causes of observed anomalies or performance deviations. By analyzing correlations and patterns within the data, maintenance teams can identify the underlying factors leading to equipment failure or suboptimal performance. This type of analytics is crucial for effective troubleshooting and ensuring that maintenance actions address the actual causes of issues, not just their symptoms. Predictive analytics represents a more advanced stage, utilizing statistical models and machine learning algorithms to forecast future events based on historical data. Within HMGT, predictive analytics aims to anticipate potential failures and maintenance needs before they occur. By analyzing trends and patterns in the data, predictive models can identify the likelihood of component failures, enabling proactive maintenance planning. This capability significantly enhances operational efficiency and safety, as it allows for the scheduling of maintenance activities at optimal times, minimizing downtime and preventing disruptions. Figure 2: Analytics of aviation health management. Prescriptive analytics is the most sophisticated level of analytics, offering recommendations on what actions should be taken to achieve desired outcomes. It goes beyond predicting future events to suggesting specific maintenance actions that can prevent failures or optimize performance. In HMGT, prescriptive analytics uses optimization algorithms and simulation techniques to recommend the best courses of action under various scenarios. This not only helps in preventing potential issues but also in optimizing the overall maintenance strategy for efficiency, cost-effectiveness, and improved aircraft availability. The types of analytics depicted in Figure 2 of this study underscore the progressive depth and value they bring to aircraft HMGT. Starting from understanding what has happened and why, through predicting what could happen, to prescribing actions to mitigate or prevent issues, analytics transforms aircraft maintenance from a reactive to a proactive and strategic function. As HMGT systems evolve, the integration of these analytics types promises to significantly enhance the safety, reliability, and efficiency of aviation operations. The integration of the IoT and AI significantly enhances the capabilities of the different types of analytics in aircraft HMGT. Table 2 describes the role of the IoT and AI across the analytics spectrum. Table 2 highlights how the IoT serves as the foundation for collecting detailed and continuous data across the aircraft's systems and components.