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Virgin Atlantic Airways ('VAA')
VAA suspected it was burning fuel unnecessarily and was not leveraging its large volumes of available data to address this. Fuel represents 20%-35% of an airline's operating costs, and VAA has sustainability targets (15% net CO2 reduction by 2030, 10% SAF). Of thirteen fuel categories, "arrival delay" fuel was added manually based on human intuition, despite being influenced by weather, no-fly-zones and fuel tankering — data VAA could access.
An end-to-end proof of concept on VAA's Databricks-based Enterprise Data Platform (EDP) using a medallion (Bronze/Silver/Gold) architecture. We built a pipeline and model dataset combining flight fuel/leg tables, the planner "statistical arrival delay", and weather data (historic via Meteostat and Open-Meteo; forecast via the Weather Aviation Centre). Multiple ML model types were explored, including a hurdle model (classifier for "delayed or not" plus regression for delay size). Deliverables included reproducible code, a prediction API, a Power BI dashboard for scenario and decision support, and documentation, with the pipeline being prepared for productionisation on AWS and Azure Databricks.
The final model reliably flags no-delay flights and predicts small delays to within ~±5 minutes on average, outperforming both human estimates and the existing "statistical arrival delay". Used as decision support, integrating the model into planning is projected to save in excess of $0.6m per year based on historical comparisons, with associated CO2 reduction supporting VAA's sustainability targets.
Another Client Ltd
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