## 7 Discussion  
The authors' findings align with the work of [PERSON] et al. [11] and [PERSON] and [PERSON] [12], who similarly concluded that predictive maintenance reduces AOG incidents by 20-30%. As in their studies, our work confirmed the application of Logistic Regression in identifying high-risk intervals of component failure, justifying the application of statistical models to operations. While earlier research such as those conducted by EASYN and Skywise [9] initiative were focusing primarily on system-level diagnostics or technical alarms, this research expands the application of forecasting models by directly linking them with spare parts logistics and AOG cost optimization. This expanded operational horizon distinguishes our approach and offers more integrated value to airline operations. The deviation from previous literature was noted in the performance of ARIMA models during unusual working periods. Unlike previous studies, our model achieved lower accuracy levels between 2021 and 2022 due to the unexpected impact of COVID-19 on flight operations and maintenance scheduling. This highlights the need for the application of hybrid models that are sensitive to outlier environments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Air Operators** & \begin{tabular}{c} **MTBF Goal** \\ **(Hours)** \\ \end{tabular} & **Avg. LT (Days)** & \begin{tabular}{c} **Demand Std. Dev.** \\ **(\(\sigma\)d)** \\ \end{tabular} & \begin{tabular}{c} **Safety Stock (S)** \\ **Range** \\ \end{tabular} & \begin{tabular}{c} **Reorder Point (R)** \\ **Range** \\ \end{tabular} \\ \hline \begin{tabular}{c} SmartLynx \\ (Baseline) \\ \end{tabular} & \(\sim\)1245 (Achieved) & 4–5 & Medium (5–8) & 7–10 units & 180–220 units \\ \hline \
\begin{tabular}{c} KLM \\ \end{tabular} & \(1190\to 1235\) & 5–7 &
\begin{tabular}{c} Medium-High \\ (6–9) \\ \end{tabular} & 8–12 units & 200–250 units \\ \hline Lufthansa & \(1220\to 1250\) & 6–8 & High (8–10) & 9–14 units & 240–300 units \\ \hline Delta & \(1215\to 1240\) & 4–6 & Medium (5–8) & 7–11 units & 190–240 units \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparative safety stock and reorder point under 90% service level. Unlike most of the literature, which separates maintenance forecasting from supply chain logistics, this study bridges the gap by combining real-time operational data, statistical modeling, and survey data. It provides a practical method for predictive spare part management that is both data-driven and operationally founded. 1. Based on the research findings, the authors can propose an action plan for AOG downtime. The proposed plan consists of five steps:  
The first one is to critically assess and strengthen a centralized maintenance and inventory system. Then, prepare and refine the bolt-winters model (Python implementation). In the third step, translate forecasts into inventory decisions. Then, distribute spare parts strategically. Finally, implementation steps and ongoing management. 2. Summary of Expert Roles  
Data Engineers and Analysts: Clean data, structure them for model input, and maintain a consistent data pipeline. Forecast Analysts: Implement, tune, and validate the Holt-Winters model in Python, ensuring minimal RMSE/MAPE. Supply Chain and Procurement Managers: Determine service levels, calculate inventory thresholds, and negotiate vendor deals. Maintenance Crews: Provide ground-level feedback on forecast accuracy and actual part usage trends. Finance and IT Specialists: Integrate forecasting outputs with cost analyses and maintain user-friendly alerts or dashboards. Project Managers and Steering Committees: Oversee timelines, resource allocation, and coordinate cross-department activities. 3. Leveraging Optimal MTBF for Financial and Supply Chain Gains  
Achieving optimal Mean Time Between Failures (MTBF) offers substantial financial and operational benefits, as shown in this research. Air operators can align safety stock levels with demand variability by integrating advanced forecasting models like Holt-Winters with accurate MTBF metrics.