These collaborators are CLAAS, a leading agricultural machinery manufacturer, and Schmitz Cargobull, a renowned trailer manufacturer, both of whom have longstanding partnerships with Duvenbeck.
By leveraging the data provided by CLAAS and Schmitz Cargobull, and employing AI for analysis, Duvenbeck says it has gained the ability to make more accurate forecasts regarding shipment volumes, vehicle and storage space utilisation, personnel deployment, and various other aspects of the supply chain.
According to Duvenbeck, this enhanced data-driven approach ultimately leads to increased efficiency and cost optimization on both ends, enabling better-informed decisions throughout the processes.
Nils Gerdemann, Managing Director of Duvenbeck Kraftverkehr und Spedition, emphasises the evolving significance of data quality in transport logistics.
Commenting on the initiative, he stated:
“In the future, the critical factor in transport logistics will not solely be the transport itself, but rather the quality of data, which is enhanced through AI technology. Our precise status data from the TMS serves as a foundation for optimising the supply chain, facilitating more informed decision-making. By comparing our actual data with AI-generated predictions, we can fine-tune our processes to align with the expected volume of goods.”
The research project has received support from Datenfabrik, NRW and the Fraunhofer Institute for Material Flow and Logistics, and has a strong focus on devising use cases for AI-supported processes within the logistics value chain.
Duvenbeck’s contributions predominantly pertain to inbound logistics, supply chain management, as well as internal transport and provisioning, the company states in a recent press release.