## 3 Framework of Forecasting AOG Models in Aviation
### Forecasting Models  
Forecasting models are essential in supply chain management across the aviation industry [23], especially for the availability of spare parts and scheduled maintenance to avoid flight disruption In this way, forecasting helps to manage spare parts inventory so there is less chance of an Aircraft On-Ground (AOG) event, which can significantly harm airlines' dynamics and revenue. These models help the airlines predict part failures and maintenance schedules, all of which avoid costly downtime. Through the assembly of linear regression, econometrics, and bootstrap methods, airlines gain insight into the wear and fail patterns of equipment so that inventory control can be effectively carried out [24]. Applying real-time data in combination with predictive maintenance models allows airlines to optimize their supply chain processes.  
Effective forecasting reduces the procurement of excess spare parts inventory, cuts operating expenses, and enables quicker response to crises. Robust forecasting models are prerequisites to operational performance for SmartLynx Airlines and other airlines, in general, to avoid the occurrences of AOG and increase fleet availability.  
While this study focuses primarily on traditional statistical forecasting methods, we recognize the growing relevance and potential of more advanced machine learning models, specifically Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and logistic growth models such as S-curve formulations. These methods havedemonstrated notable success in domains involving complex, non-linear patterns, including predictive maintenance, inventory management, and anomaly detection.  
However, their application in the context of this study was constrained by both practical and regulatory factors. Most critically, the datasets available to us were highly sensitive and confidential, derived from SmartLynx Airlines' operational logs. These data sources, while valuable, were inherently limited in granularity, with restricted temporal resolution, inconsistent labeling, and capped historical depth due to compliance with EASA data privacy policies. Furthermore, the volume of data, particularly the number of clearly labeled AOG incidents, was insufficient to reliably train deep learning architectures without introducing bias or overfitting.  
Another consideration was the computational and infrastructural requirements associated with deploying such models. LSTM- and CNN-based models require not only substantial preprocessing and parameter tuning but also integration into cloud-based or GPU-accelerated environments. SmartLynx's current digital ecosystem, while capable of supporting classic statistical pipelines, is not yet optimized for high-volume, real-time neural network deployment.  
For these reasons, our study prioritized interpretable and operationally deployable models like Logistic Regression, Holt-Winters exponential smoothing, and ARIMA. These techniques aligned better with the available data structure and the technical framework of existing airline tools such as RAIDO and eCentral8, where real-time forecasting outputs must be immediately actionable and compliant with regulatory audit standards.