Digital twins in the warehouse

Siemens and Kion are making supply chains and warehouses fit for the future with AI, automation and simulation technologies. The aim is to digitalize complex intralogistics processes in order to make them more predictable and flexible - and thus improve productivity and resilience. This is to be made possible by intelligent warehouses in which the digital and real worlds merge. Cameras and sensors collect huge amounts of data on site, which artificial intelligence evaluates. This results in digital twins of individual machines, entire systems and warehouses, on the basis of which complete processes can be simulated.

Real-time simulation

For example, logistics companies can virtually test how changes in the warehouse layout affect the quantity of goods handled. Kion will be the first company in Europe to use the new Digital Twin Composer simulation software from Siemens for this purpose. The solution can simulate the various processes in parallel and in real time.

From warehouse to digital nerve center

Rob Smith, CEO of the Kion Group © KION GROUP AG

Cedrik Neike, Member of the Managing Board of Siemens AG and CEO of Siemens Digital Industries, explains: "Together with Kion, we are using digital twins and industrial AI to transform the warehouse from a physical hub to a digital nerve center of the supply chain - laying the foundation for greater resilience and flexibility."

Rob Smith, CEO of the Kion Group, adds: "As the supply chain solutions company, Kion orchestrates end-to-end solutions for our customers - within warehouses and factories and, in perspective, along the entire supply chain. With Siemens as a strategic partner, Kion is strengthening its ability to connect the digital and physical worlds on a large scale. We are thus taking our solutions based on digital twins to a new level."

Data cooperation planned

The two companies are also planning to cooperate on data: Kion will contribute operational data from warehouse environments, while Siemens will use this data to further develop AI-supported applications. The aim is to establish data-based solutions in logistics more quickly and make industrial AI more widely usable.