## 5 Research Methodology and Data Analysis
### Why Is MTBF Critical in Forecasting Failures?  
Mean Time Between Failures (MTBF) is the most appropriate measure for evaluating the reliability of aircraft parts and forecasting AOG occurrences. It calculates the mean time between two failures of the system or its constituent components, providing significant input to determining when to perform preventive maintenance [39]. The higher the MTBF, the more reliable the aircraft is. With MTBF data integrated into ARIMA and ETS analytical tools, it will facilitate enhancing the maintenance schedule, unloading the potential of an unexpected number of failures, and implementing ways to optimize fleet availability. To provide context, the authors compare research-based airline operational efficiency with other airlines within the same business model, focusing on flight delays, AOG incidents, and schedule disruptions. The analysis includes industry benchmarks for AOG rates, MTBF calculations, and maintenance effectiveness, allowing us to assess how SmartLynx ranks regarding operational reliability. The analysis results are presented in Table 8.  
**Role of Python in AOG Forecasting Models**  
This project used Python to analyze extensive aviation datasets, including flight schedules, AOG occurrences, and spare parts inventory records. Core libraries like pandas and NumPy were used for efficient data cleaning, manipulation, and aggregation, addressing missing data through imputation techniques to ensure accurate trend analysis. Visualization tools like Matplotlib and Seaborn played a vital role in identifying trends and correlations. For instance, Seaborn's heatmaps helped pinpoint clusters of frequent part failures, providing actionable insights for engineering and logistics teams during collaborative discussions. The study initially relied on ARIMA for forecasting AOG events. ARIMA was chosen for its ability to handle non-stationary time series data and predict seasonal trends effectively. By incorporating historical AOG data, maintenance schedules, and operational records, the model was used to predict peak periods of AOG incidents. This enabled SmartLynx to proactively allocate resources and optimize spare parts logistics, reducing downtime. The model's results were validated using SciPy to ensure accuracy and reliability. However, the study identified a key opportunity for improvement by transitioning to Holt-Winters (exponential smoothing) models. While ARIMA is adequate for general time series forecasting, Holt-Winters is better suited for aviation data because it can simultaneously account for seasonality, trends, and level changes. This method allows for smoother and more precise predictions, especially when dealing with fluctuating AOG trends and resource demands. Sentiment analysis further enriched the research, with tools like nltk and TextBlob processing staff feedback from interviews and surveys. Insights highlighted engineers' dissatisfaction with the lack of real-time data on part health, which was cross-referenced with quantitative findings to propose actionable solutions. Python's scalability allowed the integration of additional tools like Prophet and Scikit-learn to test alternative forecasting approaches. Automation scripts were developed to streamline repetitive tasks, such as data ingestion and preprocessing, ensuring efficiency and consistency.