## 5 Research Methodology and Data Analysis
### The Methodological Justification and Model Selection Criteria
Using quantitative data, it identifies patterns in AOG occurrences, fleet-specific vulnerabilities, and their impact on flight delays and disruptions. A key focus is the relationship between aircraft types and AOG trends. Each fleet's unique characteristics, such as common issues with landing gear or communication systems, provide insights for targeted maintenance planning. The analysis also compares newer, more reliable models like B737 with older fleets like A320 and A330, showing how newer aircraft contribute to fewer operational interruptions. By contextualizing the numerical trends within practical applications, this study aims to offer actionable recommendations for further reducing AOG incidents, streamlining flight schedules, and enhancing fleet performance. Through a data-centered approach, the analysis positions itself as a resource for identifying key areas of focus for future optimizations in the aviation sector. **Interpretation of AOG Trends**  
Decrease in AOG Incidents: There is a 57% reduction in AOG incidents from 2020 to 2024, indicating the effectiveness of predictive maintenance and operational planning. Fleet-Specific Patterns:  
* The A320 fleet experienced the highest AOG incidents in 2020 and 2021 due to air conditioning and communication issues. * The A330 fleet showed increased AOG in 2022 and 2023 due to APU faults and landing gear problems. * The B737 fleet exhibited lower incidents overall, reflecting better reliability with newer aircraft models. Impact on Flight Delays: Flight delays consistently correlate with AOG incidents. As AOG decreased, flight delays also dropped from 320 in 2020 to 210 in 2024. Operational Improvement: The mean downtime per AOG reduced from 4.3 h in 2020 to 2.9 h in 2024, indicating faster maintenance resolution. **Statistical Assessment of Flight Delays and Schedule Disruptions**  
SmartLynx conducted a detailed analysis of AOG delays and schedule disruptions over 2020-2024 to identify trends and address operational challenges; the analysis results are represented in Table 7. The study revealed that AOG events caused an average downtime of 3.5 h per incident, which, although moderate, had a cascading effect on flight schedules. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Total AOG** & **Fleet with** & **Total Flight** & **Schedule** \\  & **Incidents** & **Most AOG** & **Delays** & **Disruptions** \\ \hline
2020 & 147 & A320 & 320 & 230 \\ \hline
2021 & 131 & A320 & 310 & 210 \\ \hline
2022 & 106 & A330 & 270 & 190 \\ \hline
2023 & 90 & A330 & 240 & 170 \\ \hline
2024 & 63 & B737 & 210 & 140 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quantitative analysis of AOG incidents [38]. Seasonal peaks, particularly during high-demand ACMI periods in the second and third quarters, intensified the impact of these disruptions. The variability in delays, as indicated by a high standard deviation, underscored the need for advanced forecasting methods to adapt to shifting demands. A critical aspect identified during the preprocessing phase of this study was the pronounced class imbalance in the dataset, wherein non-AOG cases accounted for over 80% of all recorded maintenance events. Such skewed class distribution poses significant challenges for classification algorithms, particularly Logistic Regression, which tends to favor the majority class, often at the expense of the model's ability to detect the minority class--in this case, actual AOG occurrences. Given the operational implications of failing to predict AOG incidents accurately, we applied the Synthetic Minority Oversampling Technique (SMOTE) to enhance the model's sensitivity. SMOTE artificially generates new instances of the minority class by interpolating between existing examples, thereby reducing bias and allowing the classifier to better capture patterns associated with rare AOG events. This technique was implemented using Python's \"imblearn\" library as part of our Logistic Regression training pipeline.