## 8 Conclusions  
This research underscores the pivotal role of advanced forecasting models, specifically the Holt-Winters method, in revolutionizing AOG management strategies within the aviation industry. The analysis demonstrated how predictive maintenance and inventory optimization directly impact operational efficiency, cost savings, and service reliability. The results of the tests conducted at the research base are both promising and transformative. By leveraging the Holt-Winters model to forecast spare part demand, the research base airline achieved a EUR 1.5 million reduction in annual costs, increased fleet utilization by 12%, and reduced AOG incidents by 18%. These improvements are attributed to precise demand forecasting, which enabled the air operator to balance inventory costs with availability, particularly for high-value, frequently used components. One of the most significant findings was the improvement in Mean Time Between Failures, which increased to 1245 h, aligning with benchmarks achieved by leading air. By integrating MTBF metrics with the Holt-Winters model, air operators predicted part failures more accurately and adjusted safety stock and reorder points dynamically, preventing overstocking and minimizing emergency procurement costs. For instance, a clear correlation between demand variability and lead times revealed the need for robust safety stock adjustments, particularly for parts with high variability or extended lead times. The broader industry implications of these results are equally compelling. Similar gains were observed when applied to other air operators like Lufthansa Technik and KLM Engineering. Lufthansa's adoption of the model reduced AOG incidents by 20%, saved EUR 2.2 million annually, and increased fleet utilization by 15%. These parallel improvements validate the scalability and adaptability of the Holt-Winters approach across diverse operational contexts. Central to this success was the implementation of a centralized Maintenance and Inventory Program (S-MIP) adopted by air operators, which can provide real-time insights into part usage, lead times, and demand variability. This system facilitated the seamless integration of forecasting outputs into inventory decisions and fostered data-driven collaboration with partners and suppliers, ensuring smoother logistics and shorter downtimes. In conclusion, the research demonstrates the financial and operational benefits of forecasting models and highlights their potential to reshape industry norms. The aviation sector can collectively move toward a more resilient, efficient, and customer-focused future by extending these strategies to other operators. And finally, this study demonstrated that predictive forecasting models like Holt-Winters and Logistic Regression are capable of predicting AOG events and optimizing spare parts logistics effectively. Subsequently, the research base experienced a remarkable 57% reduction in operational efficiency in AOG events within five years. Despite these positive findings, the study is limited in that it draws conclusions based on a single airline case study that may not reflect the broader industry trends. Future research can validate these models on other airlines with diverse operational profiles. Also, the inclusion of more advanced machine learning models such as LSTM can improve forecast accuracy. Real-time cloud-based inter-department data sharing needs to be implemented in practice to reduce inter-department delays and optimize response time further. Conceptualization, I.A. ; Methodology, I.A. and D.N. ; Validation, D.N. ; Data curation, D.N. ; Writing--original draft, I.A. and D.N. ; Writing--review & editing, I.A. ; Supervision, I.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Institutional Review Board Statement**: Not applicable. **Informed Consent Statement**: Not applicable.