Optimized Standby or Reserve Crew Allocation Through AI-Based Prediction

Although airlines allocate large buffers of standby or reserve crews to meet unforeseen operational requirements, historically, their standby utilization levels have been low. But the COVID-19 pandemic has driven increases in direct and indirect crew-related expenditures. Airlines need smarter, analytics-driven approaches for standby or reserve crew allocation and artificial intelligence (AI) can help.

Rethinking Crew Allocation

  • Improvements to standby crew utilization levels without compromising service quality

  • Optimized deep learning models for airlines that identify hidden trends

  • Streamlined integration between historical data and inputs from planned roster period

  • Automated workflow from crew systems to facilitate up-to-date data in a data mart

Must-Have Technologies

  • Machine learning-based solution for standby count prediction

  • Historical-data availability for the AI model to learn necessary trends

  • A data mart for managing the information required for the crew allocation predictions

  • Core systems for generating all final and optimized crew rosters