## 6 AOG Downtime Reduction and Implementation Results
### Seasonal Trends of AOG Incidents  
The bar chart (Figure 17) illustrates the number of AOG incidents across the four quarters (Q1 to Q4). The data reveal a notable peak in Q2, with over 50 incidents, while the other quarters maintain a relatively stable count of approximately 45 incidents each. This trend indicates potential seasonal factors influencing operational disruptions during Q2. Figure 16: Pair plot of continuous variables by AOG incident. **Downtime Hours vs. Total Flight Hours**  
The scatterplot (Figure 18) examines the relationship between total flight hours and downtime hours, differentiating between AOG incidents (1 = Yes, 0 = No). Key Observations:  
General Trend: The majority of data points cluster along the \(x\)-axis at downtime hours close to 0, indicating that most operations have minimal downtime regardless of total flight hours. However, some data points show substantial downtime, reaching up to 50 h.  
AOG Incidents (green dots): Incidents are scattered across all levels of total flight hours but are more prevalent among data points with higher downtime hours. This suggests that longer downtimes are associated with a higher likelihood of AOG incidents. Non-AOG Incidents (blue dots): They dominate the dataset and are concentrated around minimal downtime hours. This reflects efficient maintenance practices and operational management that minimize disruptions. Distribution Across Total Flight Hours: The spread of points along the \(x\)-axis indicates that downtime hours are not strongly correlated with total flight hours. This suggests  
Figure 17: Seasonal trends of AOG incidents. Figure 18: Downtime hours vs. total flight hours. that downtime is influenced by factors beyond total utilization, such as maintenance schedules, part availability, or operational demands. Overlap Between AOG and Non-AOG Points: There is a significant overlap between AOG and non-AOG incidents, particularly at lower downtime levels, highlighting that downtime alone does not fully explain the occurrence of AOG events. Forecasting Analysis of AOG Incidents: ARIMA, Holt-Winters, and Logistic Regression This analysis evaluates the performance of three forecasting methods, ARIMA, Holt-Winters Exponential Smoothing, and Logistic Regression, applied to predicting AOG incidents in aviation operations. Metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) are used to measure accuracy and assess their implications. The Mean Time Between Failure (MTBF) is also discussed in the context of optimal forecasting accuracy and operational alignment. ARIMA Model The ARIMA model forecasts that the AOG incidents will slightly reduce over the next four quarters. Measures of predictive accuracy present moderate accuracy, with MAPE at 54.9% and RMSE at 3.19. The actual and fitted values corresponding to the time series plot eliminate the possibility of trend analysis using the model. The plot in Figure 18 shows the predicted series (orange line) compared to the true series (blue line), revealing how well the ARIMA model aligns with actual operational data. While the orange line generally follows the same trajectory as the blue line, it struggles to capture sharp spikes and deep dips, especially during periods of high variability. The actual data reflects real-world fluctuations, including the significant disruptions caused by the pandemic starting in 2019. These disruptions introduced unprecedented variability due to changes in travel demand, supply chain challenges, and resource constraints. The ARIMA model, relying on historical patterns like pre-pandemic seasonal trends and maintenance schedules, performs well during stable periods (e.g., 2019-2020 and 2023 onward). However, it struggles during volatile periods, such as 2021-2022, when the unpredictability of the pandemic caused sharp deviations from typical trends.