Digital Twin Technology for Predictive Maintenance in Aviation: An Engineering Management Perspective on Cost Reduction, Safety Improvement, and Operational Efficiency

Modern commercial aviation faces high pressure to reduce operational expenditures, satisfy strict international safety standards, and maximize commercial aircraft utilization. Traditional maintenance strategies, including run-to-failure frameworks and fixed chronological-schedule intervals, often introduce unpredictable equipment downtime or cause excessive material waste. This research paper evaluates the application of Digital Twin (DT) technology as a strategic predictive maintenance solution from a dedicated engineering management perspective. By establishing a fully synchronized virtual representation of a physical aircraft component powered by real-time Internet of Things (IoT) sensor arrays, engineering managers can accurately forecast technical asset degradation curves prior to structural component failure. Utilizing a qualitative managerial optimization framework, this study tracks how the structural integration of Digital Twin platforms impacts three core business performance indices: cost reduction, safety system upscaling, and logistical operational efficiency. Historical case analysis, including data from General Electric (GE) aviation analytics divisions, proves that deploying unified digital environments can significantly reduce unscheduled maintenance groundings. Finally, this paper outlines critical engineering management implementation barriers, such as complex multi-vendor data governance, large initial capital investments, and standard regulatory certification gaps, providing a comprehensive, phased deployment roadmap for aviation leaders pursuing structured enterprise digital transformation projects.