Hermes Logistics Technologies teams up with the IT University of Copenhagen and dnata for machine learning trials
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Hermes Logistics Technologies (HLT) is working with researchers at the IT University of Copenhagen (ITU), Denmark, and dnata Australia to explore new machine learning models aimed at delivering predictive business analytics.
The Artificial Intelligence (AI) algorithms will run data from dnata Australia’s new Hermes Digital Ecosystem, which has a full Datalake infrastructure that captures and stores all of dnata’s Hermes New Generation (NG) Business Intelligence events.
The machine learning models will enable dnata to make predictive business process decisions providing key insights on efficiencies, costs, and new services.
“Machine learning is part of HLT’s digital agenda and our datalakes are a fantastic source of events and data, which are always up to date and ready to inform and train AI models in the Hermes Cloud,” said Alex Labonne, Chief Technology Officer at HLT.
“Successfully trained models will form new predictive functionalities for dnata and help them refine an already competitive cargo handling offering.”
The ITU team, headed by Professor Philippe Bonnet and working with HLT, will create, test, and develop the predictive models over the coming months to explore the design of cloud-native enterprise machine learning solutions.
“This is the future of enterprise machine learning envisaged by cloud providers, where any enterprise can incorporate data-driven predictions into their business processes,” said Prof. Bonnet.
“Collaborating with HLT and dnata is a unique opportunity for us to explore the capabilities and limitations of cloud-based enterprise machine learning.”
dnata recently went live with HLT’s H5 Cargo Management System (CMS) at six airports across Australia in Melbourne, Sydney, Adelaide, Darwin, Perth, and Brisbane.
“dnata is looking forward to using predictive modelling to enhance our cargo planning and operational processes. This data science not only benefits our interaction with customer airlines, it enables us to anticipate the demand patterns in advance for more efficient operations,” said Terence Yong, Cargo Development Director, Asia Pacific, dnata.
The dnata machine learning prototype is part of HLT’s digital agenda to deliver value added services using Big Data analytics.
Questions & Answers
Q.Which specific systems or data sources will the machine learning models utilise for their analysis?
Which specific systems or data sources will the machine learning models utilise for their analysis?
The AI algorithms will run data from dnata Australia’s new Hermes Digital Ecosystem. This system includes a Datalake infrastructure that captures and stores all of dnata’s Hermes New Generation (NG) Business Intelligence events.
Q.What are the primary objectives for dnata in implementing these new predictive machine learning models?
What are the primary objectives for dnata in implementing these new predictive machine learning models?
Dnata aims to make predictive business process decisions to gain key insights on efficiencies, costs, and new services. They also seek to enhance cargo planning, operational processes, and anticipate demand patterns for more efficient operations.
Q.What is the role of the IT University of Copenhagen in this collaborative project?
What is the role of the IT University of Copenhagen in this collaborative project?
The ITU team, led by Professor Philippe Bonnet, will create, test, and develop the predictive models over the coming months. Their work will explore the design of cloud-native enterprise machine learning solutions.
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