DenmarkTravel and aviation2015 – ongoingCopenhagen Airport
Machine-learning prediction of baggage timestamps
Copenhagen Airport put machine-learning baggage predictions into production in 2016
Problem
The airport reached its luggage-handling capacity during summer 2015. Ground handlers needed better timing estimates to use existing capacity more effectively.
Approach
Copenhagen Airport and Netcompany developed a proof of concept using baggage-handling data. The model predicted baggage timestamps to support handling decisions and entered production in 2016. The supplier said its predictions became more accurate than those made by experienced airport staff.
Results
No published figures
Takeaway
The initial project addressed a specific baggage-handling bottleneck before the partners expanded their airport-management work. No quantified capacity gain was disclosed for this model.
Categories
- Industry
- Travel and aviation
- Business function
- Production and quality
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and ML
- Adoption
- Partner co-development
- Evidence
- Vendor case study
Sources
Source summaries
The report traced the partners' baggage-prediction work from a 2015 capacity problem to production in 2016. The supplier described improved prediction accuracy, without giving a numerical comparison.
Full details are in the original sources.
