Using a guide, the teams went through a multi-stage development process to systematically develop digital twin solutions. In addition to implementing sensors for data collection and transmission, they programmed, among other things, dashboards with user-friendly interfaces for control and monitoring. Depending on the group’s focus, additional components were developed:

• the development of a fleet management concept,
• the development of a stress test for the systematic collection of machine data,
• the integration of analysis algorithms for performance monitoring and error prevention.

Finally, the teams conducted a maturity assessment, adapted the evaluation model to their own solution, and derived a roadmap for further development. Throughout the project, the groups received close guidance, including through seminars, workshops, and project reviews.

By the end of the session, participants were able to identify the key elements of digital twin technologies, as well as their development and use cases. Working in small groups, they applied a provided development guide to a real-world application scenario, developed a prototype, and reflected on their own approach. In addition, they specifically applied a maturity assessment model and derived a roadmap from it.

What set this module apart was the clarity of the data layer. Process variables that remain abstract in industrial applications became immediately tangible here, because the effect of an adjustment was immediately visible in the results.