The project focused on developing a scalable data and AI architecture that consolidates relevant production data from various sources, analyzes it, and makes it available in real time. This creates a comprehensive picture of the assembly process – with the aim of identifying deviations at an early stage and supporting continuous improvements based on data.

‍

The challenge was to efficiently integrate existing IT systems, machine and sensor data, and manual processes. Our team supported John Deere from the analysis of data streams to the development and implementation of AI models – in close coordination with the teams on site. The result is a smart, highly adaptable system that acts as a digital twin of the assembly process. A recent article on produktion.de highlights the project in detail and shows how artificial intelligence, domain knowledge, and practical implementation can work together.

‍

The project with John Deere underscores the importance of a well-designed Digital Thread for the manufacturing of the future – and how technological innovations can be integrated into existing production environments in a meaningful and sustainable way. You can find out more in the full article at produktion.de.