## 6 AOG Downtime Reduction and Implementation Results
### Correlation Heatmap  
The correlation heatmap (Figure 11) provides a clear overview of how key operational variables such as part failure rates, maintenance compliance, total flight hours, downtime, and delay time interact. These relationships offer actionable insights into optimizing maintenance and operational practices. One significant finding is the positive correlation (0.46) between part failure rates and downtime hours. This suggests that downtime for repairs or replacements also rises as part failures increase. This highlights the importance of proactive maintenance strategies, such as preemptive part replacements based on predictive models and improving component quality through stricter manufacturing standards. Addressing these areas could reduce unexpected failures and minimize downtime.  
The heatmap also reveals a weak positive correlation (0.04) between maintenance compliance and downtime hours, suggesting that better adherence to maintenance schedules could slightly reduce repair times. Although this relationship is not strong, it emphasizes the value of consistent and timely maintenance in preventing more enormous operational disruptions. Interestingly, total flight hours correlate little with downtime hours (\(-\)0.01) or delay time hours (\(-\)0.04). This indicates that the amount an aircraft is flown does not significantly impact its downtime or delays. Instead, other factors such as component reliability, supply chain efficiency, and operational practices play a more substantial role. Finally, the weak negative correlations involving delay time hours, such as part failure rates (\(-\)0.09) and maintenance compliance (\(-\)0.05), suggest that delay times may rise slightly as part failures increase or maintenance compliance decreases. However, these relationships are minimal and require further exploration to understand the underlying causes.  
The analysis shows that maintenance compliance has a low but positive correlation with downtime hours (0.04), indicating that adhering to scheduled maintenance can slightly reduce downtime. However, this effect is weaker compared to the impact of component quality. Similarly, the correlation between part failure rate and maintenance compliance is very weak (0.01), suggesting that following maintenance schedules does not directly prevent part failures, as these failures often occur unexpectedly.  
A weak negative correlation exists between delay time hours and part failure rates (\(-0.09\)) and between delay time hours and maintenance compliance (\(-0.05\)). While higher failure rates and non-compliance with maintenance schedules slightly increase delays, these relationships are not strong. This highlights that delays are influenced by multiple factors, including operational inefficiencies, external conditions, and logistical challenges, rather than just technical or maintenance-related issues.  
Additionally, the minimal negative correlations among total flight hours and downtime (\(-0.01\)) and delay times (\(-0.04\)) indicate that the frequency of flight activity has little direct impact on downtime or delays. This suggests that aircraft reliability and operational efficiency depend more on maintenance practices and part quality than on the amount of flight time.