Predictive Reliability-Driven Optimization of Spare Parts Management in Aircraft Fleets Using AI, IoT, and Digital Twin Technologies

Abstract

Inefficiencies in traditional spare parts management for aircraft maintenance—including excessive inventory costs, supply chain delays, and operational disruptions—have long hindered fleet readiness and increased maintenance expenditure. To address these challenges, an integrated, reliability-driven inventory optimization framework has been developed by leveraging predictive analytics, real-time sensor data, and emerging digital technologies. The proposed model is grounded in Reliability-Centered Maintenance (RCM) principles and enhanced by Artificial Intelligence (AI), the Internet of Things (IoT), and digital twin technologies. Through the deployment of advanced sensor networks, real-time performance data are continuously collected and analyzed to forecast component degradation and predict imminent failures. This enables the transition from time-based to condition-based maintenance scheduling. Predictive models, including Long Short-Term Memory (LSTM) neural networks and Random Forest classifiers, are employed to enhance the accuracy of failure prognostics and spare parts demand forecasting. The dynamic alignment of spare parts provisioning with actual equipment reliability has been shown to reduce overstocking and prevent critical shortages. A case study conducted within a commercial airline fleet demonstrated a 20% reduction in inventory-related costs and a 15% decrease in aircraft downtime. Furthermore, operational efficiency and safety were significantly improved by minimizing unscheduled maintenance events. The proposed framework not only supports predictive and prescriptive maintenance strategies but also establishes a replicable model for digital transformation in aviation logistics. By integrating real-time analytics with digital twin simulations, a data-centric paradigm is introduced for proactive maintenance decision-making. This advancement paves the way towards more sustainable, cost-effective, and resilient aviation operations, aligning with broader industry goals of environmental responsibility and performance optimization.