Case Study

Machine Learning improves cargo revenue optimisation for leading international airline

Advanced Analytics

Harmonic developed an international-flight cargo space estimation tool to maximise revenue in collaboration with a leading international airline.

The Challenge

The leading airline had wasted capacity due to high no-show rates for several international routes. As cargo customers would not be charged for unused space or weight on a flight, this high no-show rate resulted in flights operating below capacity and therefore, lost revenue. To address this loss the airline started working on a proof of concept model to accurately forecast the booking no-show rate for each international route, and enlisted Harmonic’s help to refine, enhance, and productionalise it.

The Solution

Harmonic developed a bespoke prediction model to forecast the booking no-show rate for each international route. The machine learning model used the historical booking show-rate, flight information, and cargo capacity information to forecast the no-show probability for the next 30 days. The statistical approach used was a combination of tree-based approaches, which proved to strike the best balance between model accuracy and interpretability. The model is automated, running daily in a production system and presented through a dashboard to support team decision making.

Additionally, the collaborative team created and deployed an attribution model to complement the no-show model. This second model tracked the revenue gained from using the no-show model and helped improve capacity planning.

The Result


  • A scalable model that can accommodate any number of flight routes.
  • Improved capacity utilisation leading to revenue optimisation.
  • Possibility to enhance the model to integrate pricing optimisation.
  • Use of the model generated additional annual revenue of ~$6 million per annum.
  • Model outputs are visualised in a user-friendly manner which supports decision making.

Get in touch

Please provide your first name.
Please provide your last name.
Please provide the company you are currently working at.
Please provide your current position.
Please provide your work email address.
Please provide a valid phone number.
Please provide a message.
Invalid Captcha. Please try again.