Enhanced accuracy
Fewer delays
Higher adoption
Streamlined operations
A major U.S. airline struggled with inaccurate carry-on baggage estimates, leading to boarding inefficiencies and delays. Their legacy model lacked real-time insights, limiting accuracy and adoption. To resolve this, they sought an advanced solution to optimize predictions, enhance gate agent decisions, and streamline the boarding process.
Key challenges
No real-time check-in or cabin space tracking
Rigid model hindered updates and fine-tuning
Agents avoided unclear, unreliable predictions
Errors reduced trust among gate agents
The solution
Enhanced predictive modeling
Improved forecasting with feature engineering
Built a CatBoost model for accuracy
Fixed under-prediction issues
Real-time insights
Integrated live data for dynamic updates
Shifted from batch to real-time processing
Provided range-based estimates for clarity
1
Data and model development
Aggregated real-time and historical data
Enhanced prediction accuracy
Continuous model refinements
2
AWS-based deployment
Scalable, real-time processing
Hosted on AWS for efficiency
Optimized system for speed
3
User adoption
Boosted trust with precise insights
Intuitive front-end for agents
Reduced flight delays
Immediate benefits
Accurate bag predictions
Higher agent adoption
Fewer flight delays
Long-term benefits
Real-time insights
Efficient baggage handling
Better passenger experience
KPIs
Strong agent engagement
90% prediction accuracy
Smoother boarding
Enhanced AI models
Continuous improvements for even higher accuracy
Expanded adoption
Broader rollout across more airports
Optimized operations
Further streamlining of boarding and baggage handling
