The client is a major U.S. airline operating more than 4,500 flights daily across 350+ destinations in over 50 countries, with a fleet of more than 800 aircraft. In a highly competitive aviation industry, timely profitability insights are critical for optimizing routes, managing costs, and making informed operational and strategic decisions.
Since the airline relied on a fragmented and largely manual flight profitability analysis system it limited decision-making speed and accuracy. Financial workflows were slow and resource-intensive, delaying planning and operational adjustments. The existing architecture also lacked flexibility in cost allocation and could not easily support scenario modeling or forecasting for proactive planning.
The organization needed a modernized profitability analytics system to accelerate insights and improve strategic decision-making.
Ascendion partnered with the airline to redesign the flight profitability system architecture and implement a scalable analytics framework capable of supporting faster financial analysis and forecasting.
Optimized Architecture and Workflow Automation:
We implemented a high-performance architecture designed to improve data processing speeds and system reliability. Financial workflows were streamlined to simplify cost allocation processes, enhance transparency, and improve analyst productivity.
Advanced Analytics and Scenario Modeling:
AI-powered analytics capabilities were introduced to support cost allocation, anomaly detection, and profitability forecasting. Sandbox environments were also created to allow teams to test different cost allocation strategies and modeling scenarios quickly. A proof-of-concept solution built on Databricks demonstrated the potential for integrating advanced analytics with modern UI capabilities and a redesigned cost and revenue calculation engine.
Tech Stack:
Databricks
The modernized profitability system enabled faster financial insights, streamlined workflows, and improved operational decision-making.
At BMS, we wanted our consultants to focus on the possible, freeing up time lost to reading endless CV’s that could instead be used to really engage with people who are looking for that first step in their sales career. The solution we adopted found the right balance between machine efficiency and human empathy.
– Graham Halewood,
Chief Operations Officer
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