The freight forwarding and logistics company, Duvenbeck, is taking part in a forward-looking research project related to using artificial intelligence (AI) in logistics and production. As a supplier company, Duvenbeck is providing two project partners in manufacturing industry with extensive, detailed data material from its corporate transport management system (TMS) via various interfaces. The first company is CLAAS, a market-leading agricultural machine manufacturer, and the other is the trailer producer, Schmitz Cargobull, both of which are long-standing partners of Duvenbeck.
Thanks to the data that has been processed at CLAAS and Schmitz Cargobull and then analysed using AI, Duvenbeck, the logistics partner, can make more precise predictions about consignment volumes, load factors on board vehicles and storage areas, the need for personnel and many other partial stages in supply chains. Ultimately, the processes on both sides become more efficient and costs are optimised, so that it is then possible to make better process decisions.
Nils Gerdemann, the Managing Director for Road Transport and Freight Forwarding at Duvenbeck, comments, “The transport operation itself won’t be the only crucial factor that makes the difference in transport logistics in future, but the data quality too, which is improving thanks to using AI technology. Our precise status data from our TMS is helping us to optimise supply chains, because this information provides a better basis for making decisions. We compare our existing data with the predictions generated by AI and can then adapt our processes to the expected volume of goods more accurately.”
Torben Süllwald, the Head of the “Data-Driven Logistics” Subproject, which is part of the Datenfabrik.NRW project, remarks on behalf of CLAAS, “Thanks to the practically oriented support from our long-standing partner, Duvenbeck, we can discuss data-driven topics and physical processes constructively and realistically within the research project. As a result, we’ve been able to jointly develop solutions based on data consistency, which are then ideal for both partners. The speed, with which we can handle our specific AI-supported application scenarios as a result of this, is impressively high.”
The research project is being supported by the Datenfabrik.NRW. As part of the ‘Data-Driven Logistics’ subproject, the partners are jointly developing application scenarios for using AI-supported processes in logistical value-added chains in conjunction with the Fraunhofer Institute for Material Flow and Logistics. The input provided by Duvenbeck mainly relates to the fields of inbound logistics and supply chains as well as internal transport operations and making the goods available at the recipient’s unloading point.