## 2 Literature Review  
The airline industry is pivotal in today's global economy, driving economic growth and development. Expanding the travel and tourism sectors underscores air transport's significance as a cornerstone of this industry [1]. Additionally, aviation contributes approximately 3.5% of global GDP and supports over three billion air passengers annually, positioning air transport as one of the most influential industries [2]. Nevertheless, numerous challenges face the substantial growth of the aviation business. One of the critical challenges airlines faces is forecasting the spare parts needed for unexpected maintenance. Accurate forecasting methods are vital to minimize Aircraft On-Ground (AOG) events [3]. Ensuring the timely availability of required parts prevents delays and operational disruptions, ultimately preserving efficiency and reliability in airline services. In aviation, the term Aircraft On-Ground (AOG) refers to situations where technical issues prevent an aircraft from completing its flight [4]. The International Air Transport Association (IATA) categorizes airline spare parts into three primary types: notable, repairable, and expendable. Notable parts typically have a low scrap rate, while repairable parts require careful planning to avoid excessive waste [5]. Expendable parts, in contrast, are used only once before being discarded. Life-limited parts are designed for a specific operational cycle, while consumable parts are entirely disposed of after use. Technical failures often necessitate the replacement of critical components, causing grounded aircraft and operational disruptions. Frequent AOG situations impose substantial costs and damage an airline's reputation. The diverse aircraft types in a company's fleet sourced from multiple manufacturers further complicate inventory management and forecasting processes. The high costs associated with stockouts underscore the need for precise forecasting methods to optimize material management and ordering procedures. Numerous forecasting methods address the challenge of predicting spare part demand in aviation, yet their effectiveness varies across different parts and demand patterns [6]. Among these, time series analysis, time series modeling, and binary classification methods return to service are reliable tools for forecasting airline spare parts. These methods can significantly reduce operational costs, benefiting airlines and their customers [7]. Despite the well-documented importance of forecasting AOG events, the academic literature rarely explores advanced or real-time strategies tailored to these scenarios. This study focuses on innovative solutions like real-time sensor analytics, IoT-enabled data collection, and rapid anomaly detection algorithms. For example, live data feeds from aircraft systems can generate near-instant alerts for emerging technical issues, while machine learning algorithms can analyze failure patterns to expedite maintenance decisions. When tested in select airline operations, these methods have demonstrated their potential to detect anomalies early and facilitate proactive repairs. By integrating these advanced tools, this research contributes to a more robust understanding of AOG mitigation strategies and offers practical approaches for improving operational efficiency in the aviation industry. Airline operations management is a complex affair that involves coordination with various actors. The most critical link in a chain is the Airline Operations Manager (AOM), who is responsible for the daily operations of large fleets and often checks 40-50 flights an hour. AOMs are dealing with irregular operations caused by adverse weather, Air Traffic Control (ATC) delays, and unexpected maintenance, which is why they exist in the first place. Their job is not only to take the plane but also to make crucial decisions against delays, cancelations, vehicle switches, and dispatching of reserved crews; all of these will minimize disturbances and ensure the successful running of the operations.