## 2 Literature Review
In instances where irregular operations affect over 10% of the daily flights, enhanced AOM performance can lead to significant economic gains. Operations Research (OR) methods, such as mathematical programming, successfully assist AOMs in recovering from disturbances. These algorithms can create a solution that can address specific real-time challenges, focusing on such factors as aircraft utilization and minimizing downtime delays. Nevertheless, despite these optimization tools' potential, AOMs often do not have the necessary support systems to assess the algorithm outputs in their specific contexts. Creating those systems aims to impart precision capability to AOMs so they can make decisions that match the actual data. On top of that, malfunctions in some departments can create Aircraft On-Ground (AOG) anomalies. For instance, maintenance delivery of parts or a staffing issue can lead to the aircraft taking longer to be repaired. This, in turn, would complicate the recovery work when the problems are being addressed. AOG events can cause broader operational difficulties, such as flight delays and lower customer satisfaction. To conclude, designing efficient AOG management solutions is a top priority for the aviation industry. The past few years have seen the aviation industry experience a technological shift towards data-driven maintenance, with predictive maintenance being a highlight. Studies such as [8; 9] have explored the application of AI and data analytics in anomaly detection in aircraft systems. But these works generally have a tendency to concentrate on system-level diagnostics, typically without wider integration with supply chain activities and spare parts forecasting--a domain of enormous strategic significance for effective AOG (Aircraft On-Ground) management. Also, while such products as Skywise by Airbus and Prognos by AFI KLM [10] have demonstrated their effectiveness at applying real-time data for the monitoring of components, they tend to focus mostly on technical alarms rather than on predicting spare parts demand or on aligning maintenance strategy with procurement cycles. This disconnection is also experienced in the academic literature, where methods like ARIMA and LSTM have been applied to forecast equipment failure but rarely connected to real fleet-level logistics, cost savings, or spare part optimization. Works by [11; 12] have raised the logistical challenges of predictive maintenance but with a tendency to treat them in isolation from operations data like MTBF, frequency of AOG events, and delay times. The novelty of this study lies in the integrated combination of real-time airline data, state-of-the-art prediction models, and operational impact analyses. Through the combination of statistical techniques such as ARIMA, Holt-Winters, and Logistic Regression with real-time SmartLynx operational data, this research provides not only predictive data but also prescriptive recommendations for inventory and downtime management. This paper also addresses a critical literature need by analyzing the economic return on predictive maintenance and quantifying the effects of how accurate forecasting can eliminate AOG events, optimize part availability, and minimize fleet disruption. The methodology used goes beyond theoretical modeling--it includes real feedback from airline logisticians and engineers and compares findings over multiple aircraft types. Therefore,this research makes a distinct contribution by bringing together predictive modeling, airline maintenance planning, and inventory forecasting under one holistic AOG mitigation framework. It extends the current body of literature by introducing a scalable, data-tested roadmap that is implementable across various airline operating models.