Aircraft Manufacturer Unlocks Flight Data for Huge Savings

An American aircraft manufacturer.

The organization was facing several challenges that impacted its financial efficiency and operational reliability. First, engines were mandated for overhaul regardless of their actual condition—at a significant cost of £500,000 each.

Compounding this issue was the urgent need to mitigate unplanned engine-related Aircraft-On-Ground (AOG) events that caused costly disruptions. Therefore, a critical objective was to extend the service life of engine overhauls, aligning maintenance schedules with the true state of engine health, to realize both cost savings and reliability improvements in their fleet operations.

Ensono embarked on a three-phase approach:

Phase one focused on transforming inaccessible data into actionable insights. By unlocking a wealth of data that had been trapped in its proprietary format on their existing data lake, it was efficiently converted into semi-structured files for advanced analysis, revealing vital engine metrics and flight patterns.

Phase two focused on improved data quality through strict controls and validation measures, expanding the data set for a comprehensive understanding of engine failure modes. This enabled the prediction and mitigation of potential issues, extending engine life and ensuring regulatory compliance.

Phase three consisted of a well architected, scalable AWS infrastructure with a focus on comprehensive data governance and streamlined DevOps processes. This collaborative approach, underpinned by rigorous documentation, peer review, and scalability for future growth, guaranteed platform integrity and effectiveness of the solution.

• Enhanced data utilization: Unlocked and maximized the potential of flight data, enabling strategic decision-making.
• Operational excellence in data management: Instituted industry-leading practices in DevOps and data engineering, adeptly managing large-scale data.
• Extended engine lifespan: Proved the feasibility of extending engine overhaul intervals, leveraging sophisticated statistical and machine learning techniques for predictive maintenance.
• Cultural shift towards data-driven decision making: Cultivated a data-centric organizational culture, exploring new applications for flight data.
• Skilled data team development: Built a proficient data team, capable of sustaining and evolving the data platform.