A data-supported description of objects and processes is necessary for the consistent implementation of digital technologies. The perfect digital twin is a digital representation of the physical component, system and process dynamics across the value chain and behaves like the real system. Strictly speaking, there is no one digital twin in the sense of a single perfect image, but rather a collection of requirement-optimized data models within the digital twin - the appropriate model can be selected or generated for each task.

In terms of the AI production network, digital twins are more than just a simple collection of process data: Digital twins derive correlations from this data (data models) and are even able to make predictions for their real-life counterparts. Digital twins don't just see the process - they understand the process. This makes it possible to derive future decisions - such as the adaptation of further process steps - from past data.

A large number of projects are now working on the utilization of digital twins, although these are usually still highly isolated concepts in today's industrial applications. In order to design the digital twin of tomorrow, which can also exist beyond the boundaries of individual companies, common interfaces and forms of representation based on proven industrial standards are required.

Digital twins are not limited to the product level alone, but also exist for entire production processes and even across the boundaries of individual production sites and companies. At production network level, the individual processes should be brought together in order to create a comprehensive digital image of the manufactured product and thus its quality. However, in order to implement self-organization approaches and smart optimization of the entire plant, a digital image of the production network itself is also necessary to enable virtual production planning. This seamless digital traceability of all processes involved in the product and the ability to use this data in a meaningful way leads to shorter innovation cycles and increased cost-effectiveness combined with higher quality.