Data-driven decision support for airline operations

For airline passengers, customer satisfaction decreases with every flight delay. As a result, airlines are continuously seeking to better understand and address their passenger requirements and to improve the quality of service they provide. Accordingly, metrics for the quality of service to passengers in the airline industry have long been centered on managing flight delays.

Costs associated with flight delays include many elements. Passengers are inconvenienced when their flight arrives late, resulting in cancellations, missed connections, or creating additional expenses such as food and accommodation. Costs are also incurred directly by airlines themselves through increased operating expenses for crews, fuel and maintenance, among other factors.

Recently, the concept of predictability and optimisation of flight delays has received increased attention in airline industry passenger service quality assessments. As a result, IBM Research - Ireland is using big data analytics to optimise the decision-making process within the airline’s operations management service. The research aims to improve customer service, optimise flight operations systems while reducing costs to both passengers and the airline.

The research team is also working to develop techniques to calculate and predict flight delays and to better manage disruption when delays happen. For this project, the team is developing automated systems to learn and interact naturally with airline operators, helping them to make better decisions. The goal is a more proactive flight scheduling system to boost operational efficiency and customer service. It will also help avoid costly downtime as well as reduce maintenance and services costs for the airline.

This application has potential uses in other settings with multiple dependent tasks and predictable, time-dependent root causes of delay including shipping and logistics, hospitals, manufacturing and construction.


Martin Mevissen

Application areas

Smarter water
Smarter energy
Smarter healthcare
Smarter transportation
Smarter cities