Hermes Logistics Technologies (HLT) is working with researchers from the IT University of Copenhagen and dnata Australia to analyse the ground handler’s cargo activity to develop predictive business analytics models. 

The research team will use AI algorithms to develop machine learning models that will enable dnata to make business process decisions with key insights on efficiencies, costs, and new services. 

Also read: dnata deploys Hermes 5 software at six Australian airports 

The ITU team together with HLT will create, test, and develop the predictive models over the coming months to explore the design, which would serve as an opportunity to explore the capabilities and limitations of cloud-based enterprise machine learning.  

“Successfully trained models will form new predictive functionalities for dnata and help them refine an already competitive cargo handling offering,” said Alex Labonne, CTO at HLT. 

Hermes Logistics Technologies said the dnata machine learning prototype is part of its digital agenda to deliver value–added services using big data analytics.