## 6 AOG Downtime Reduction and Implementation Results
### Aircraft Type and AOG Incident Relationship  
Exploratory visualization: The count plot that compares AOG incidents between aircraft types in more detail shows that the extent to which the organizations faced these problems differs significantly. As one would expect, aircraft operating for lengthy periods, like A320, record higher incident levels, probably because their components are more deprecated or prone to flaws because of their design. On the other hand, newer versions, such as B737 MAX 8, file fewer accidents because of enhanced engineering, better materials, and maintenance (Figure 13).  
Figure 12: Trend of AOG incidents over time.  
Figure 13: AOG incidents by aircraft type.  
In this figure, the legend values represent the occurrence of Aircraft On-Ground (AOG) incidents.  
* 0: Represents days or events where no AOG incident occurred (normal operational conditions).
* 1: Represents days or events where an AOG incident occurred (aircraft grounded due to maintenance or operational disruptions).  
**Part Failure Rate Distribution**  
The frequency distribution of part failure rates is positively skewed, with most frequencies significantly close to the mean of 2.56 (Figure 14). Although most parts show reasonably low failure rates, several parts with relatively high rates are concerned.  
Key Observations and Results:  
Uniform Distribution:  
The histogram bars show an even distribution of part failure rates across the range of 0 to 5, with frequencies oscillating around 40 to 60 occurrences per bin.  
This uniform distribution suggests that part failures are distributed relatively evenly across the dataset, implying no specific dominance of low- or high-failure rates.  
Peaks and Troughs:  
The KDE curve highlights minor peaks around the mid-range (2.5 to 3.5) and at the higher end (5). There is also a visible dip near the 1.0 range.  
These trends indicate that moderate to high part failure rates occur more frequently compared to extremely low rates, which might point to challenges in maintaining consistent part reliability.  
Tail Behavior: The KDE curve dips sharply near the extreme ends (0 and 5), suggesting that very low or very high part failure rates are less common in comparison to the mid-range.  
**Maintenance Schedule Compliance vs. AOG Incidents**  
This boxplot (Figure 15) compares Maintenance Schedule Compliance (Hours) for two categories: days with no AOG incidents (labeled as 0) and days with AOG incidents (labeled as 1). The data provide valuable insights into how maintenance compliance correlates with AOG occurrences.  
Figure 14: Distribution of part failure rate.  
The boxplot provides insights into how maintenance adherence impacts AOG occurrences. The median compliance hours for both groups are similar, around 3000 h, indicating consistent maintenance efforts overall. However, there are notable differences in variability between the two groups.  
Key Observations:  
Median Maintenance Compliance: Slightly higher on non-AOG days, supporting the idea that better schedule adherence reduces the likelihood of AOG incidents.  
IQR Differences: The narrower IQR for AOG days suggests more consistent compliance during these events, likely due to the urgency to return grounded aircraft to service.  
Overlap in Distribution: Significant overlap between the two groups indicates that while maintenance compliance is a factor in AOG incidents, other factors like operational demands or environmental conditions also play a role.  
Systemic Challenges: The overlap and variability highlight systemic issues, such as the unpredictability of part failures or supply chain delays, even when schedules are followed.