Currently, airlines have very little insight into the progress of their fleet’s turnarounds at an airport. Between the “in-block” (time the aircraft arrives at the gate) and the “off-block” (time the aircraft pushes back from the gate) times, an aircraft needs to be cleaned, fuelled, receive catering, unload/load cargo and passengers, and pass visual inspection. Whenever an airline wants to know if one of these services is being provided, or what the status of that service is, they have to make a series of phone
calls between ground operations centers, airline dispatch, pilots, ramp agents, ground servicing companies, and airport control offices.
Over a three months internship, Durham researchers used compute vision algorithms, aircraft sensors, and operational data to bring clarity to the turnaround process. The researchers combined machine learning with the power of Boeing AnalytX, providing airline customers with the insights necessary to proactively respond to airport disruptions. They worked as a self-managed team and experienced the fast-paced environment of one of Germany’s most innovative labs: Boeing’s Digital Solutions & Analytics Lab in Frankfurt, Germany.