Aerospace systems provider improves real-time situational awareness using machine learning models
Aerospace systems provider engages Baker Tilly to create interactive discovery dashboards using machine learning (ML) models to improve their product support engineering team’s real-time situational awareness. Read this case study to learn more.
Client background
This company is one of the world’s largest providers of aerospace systems, avionics, interior systems and information management services for commercial, business and military aircraft.
The business challenge
The client needed to improve their real-time situational awareness. Their product support engineering team was manually collecting a significant amount of data because legacy systems had delayed reporting (typically 90 days). Real-time aircraft flight deck effect messages needed to be evaluated after a pattern of failures.
Strategy and solution
Baker Tilly worked with the client to create interactive discovery dashboards that prioritized maintained actions using machine learning (ML) models that would allow the product support engineering team to remediate support cases. This decreased the amount of time spent collecting and integrating data from weeks to minutes. Single source of truth data provided insights on the events and conditions of the aircraft leading up to a service interruption. Service engineers were able to cover 10-15 times more issues once they did not have to spend time assembling the data.
This approach allowed the client to achieve the following:
- Integrated real-time aircraft messages
- Removal data sources and service interruptions
- Waiver narratives with external flight tracking and weather data using natural language processing (NLP) for term extraction and matching