CASE STUDY
The collaboration with Qatar Airways Cargo is an example of how Wiremind Cargo ’ s advanced AI solutions can transform commercial operations .
Qatar Airways Cargo and Wiremind Cargo are continuing to work closely together in their joint effort to drive innovation and efficiency across all aspects of the air cargo processes .
Data training for CARGOSTACK
Wiremind Cargo ' s CARGOSTACK product suite is a cloud-based SaaS solution engineered specifically to help airlines to manage and optimise their cargo business . Leveraging Data Science , Data Engineering and Software Engineering capabilities , CARGOSTACK Optimiser gives flexibility to its users by offering three approaches to take decisions , which are captured in the MRI acronym .
M stands for Model . The AI model , trained on the customer ’ s historical data as well as on additional data sources such as public data , market data from third party providers , can take the decision automatically , with a very high degree of reactivity to real time events on a flight
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Qatar Airways Cargo , the world ’ s cargo carrier , has made a significant leap in revenue management innovation by launching CARGOSTACK Optimiser , the Revenue Management suite of Wiremind Cargo , a member of Cargo Tech . As the first airline worldwide to go live with this solution , Qatar Airways Cargo positions itself at the forefront of the industry , leveraging the most advanced AI-driven solutions .
In the wake of both parties partnering in early 2023 , a range of solutions for demand forecasting , inventory optimisation , and overbooking recommendations have been steadily rolled out , until the most recent implementation of a bid price Machine Learning model .
Multiple teams within Qatar Airways Cargo ’ s revenue management teams now benefit from CARGOSTACK ’ s improved AI-generated recommendations , its intuitive UI , UX , as well as various features which the users themselves provided input on during the implementation phase .
These include CARGOSTACK ’ s fully configurable business rules engine and the overbooking strategy recommendation algorithm . Both parties undertook extensive efforts to validate the Machine Learning models , including testing and iterating on multiple approaches to deliver significantly improved revenue results .
R stands for Rules . A highly flexible business rules engine allows it to predefine behaviours . While the decision is taken by the system just like in the above case , this gives further control to the users as both the inputs and outputs are known to the users , which leads to more control . This is typically useful for when there is not enough data for the AI Model to be properly trained .
I stands for Insights . The system provides real-time data and insights to users , which can be used to make a manual decision . When the decisions are made in a particular context and should not be automated , the users ’ needs to have immediate access to data that can be cross-checked with business and market knowledge .
Decision-making is at the heart of CARGOSTACK Optimiser , and it is designed around the principle of exception management . Users can easily spot flights that require attention , because they deviate from a predefined , expected behaviour .
CARGOSTACK Optimiser includes the following :
Revenue Management . The ability to set hurdle rates or entry conditions for incoming bookings , depending on market conditions , flight performance and a series of KPI that are closely monitored by flight analysts .
Overbooking optimisation . Based on forecasting customer behaviour , in particular the tendency for bookings to end up as a no-show or a low-show ,
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