ENHANCING OPERATIONAL RESILIENCE THROUGH AI-DRIVEN MONITORING: A CASE STUDY OF MIDDLEWARE SYSTEM INTEGRATION AT UNITED AIRLINES
Keywords:
AI-Driven Monitoring Systems, Aviation Operations Management, System Reliability Engineering, Operational Resilience, Human-AI CollaborationAbstract
The aviation industry's operational complexity demands robust monitoring and rapid incident response capabilities to maintain service reliability. This article presents a comprehensive analysis of an AI-driven monitoring system implementation at United Airlines, focusing on middleware system integration and automated fault detection. Through the integration of Dynatrace's artificial intelligence monitoring solutions and Kubernetes' self-healing capabilities, the article demonstrates significant improvements in Mean Time to Resolution (MTTR) and operational resilience. The article examines the synergy between AI-powered analytics and human expertise in identifying and resolving system anomalies, presenting a framework for proactive issue detection and resolution. Results indicate substantial reductions in resolution times and enhanced operational efficiency, contributing to improved service reliability. This case study provides valuable insights into the practical application of AI-driven monitoring in large-scale aviation operations, offering implications for industry-wide adoption and future technological integration. The findings underscore the importance of combining advanced monitoring tools with human oversight to achieve optimal operational performance in complex airline systems.
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