#DigitalTwin | Center of Excellence Logistics and IT https://leistungszentrum-logistik-it.de/en/ Wir bringen Technologien in Ihr Unternehmen Thu, 04 Dec 2025 17:30:09 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.1 https://leistungszentrum-logistik-it.de/wp-content/uploads/2018/11/cropped-favicon_512-32x32.png #DigitalTwin | Center of Excellence Logistics and IT https://leistungszentrum-logistik-it.de/en/ 32 32 Development of cloud edge architecture for digital twins https://leistungszentrum-logistik-it.de/en/project/development-of-cloud-edge-architecture-for-digital-twins/ Wed, 03 Dec 2025 10:51:34 +0000 https://leistungszentrum-logistik-it.de/project/development-of-cloud-edge-architecture-for-digital-twins/ The Cloud-based Collaborative Condition Monitoring (CCM) ecosystem project has developed a cloud-edge architecture for distributed digital twins using the example of the CCM of the Industry 4.0 platform. In particular, data sovereignty was integrated, which enables the secure sharing of data between participants within a CCM ecosystem.

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Steffen Biehs
Fraunhofer ISST
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Philipp Hagenhoff
Fraunhofer ISST
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]]> Data-driven learning of control rules https://leistungszentrum-logistik-it.de/en/project/data-driven-learning-of-control-rules/ Wed, 03 Dec 2025 09:51:55 +0000 https://leistungszentrum-logistik-it.de/project/data-driven-learning-of-control-rules/ Digital twins create transparency in production and logistics systems and provide support, for example, in the production process. in layout, order and personnel deployment planning thanks to its simulation component. The DaCoR (Data-driven Learning of Control Rules) project focuses on the creation of digital material flow twins based on company data, as part of the open source OpenFactoryTwin (OFacT).

Although simulation-based planning has proven its added value in the past, its creation has so far been complex and time-consuming. This is where DaCoR comes in: Using a (partially) automated approach, not only can static elements such as process times and resource models be mapped, but also dynamic system aspects such as control rules (e.g. FIFO, SPT) can be learned automatically. This enables the efficient creation of realistic models that can be used in planning and control – with significantly reduced effort.

Photos: Fraunhofer IML/Vinzenz Neugebauer

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Jan Cirullies
Fraunhofer ISST
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Christian Schwede
Fraunhofer ISST
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