The wide adoption of digital technologies in factories yields a large amount of data, but the collection and analyses of these data ask for transversal competencies (mathematics, informatics, engineering) together with approaches and tools to design and operate a complex digital infrastructure. The addressed topic is based on the concept of Digital Twin (DT) coupled with a physical object to support analyses and decisions. In the manufacturing context, a DT can be linked to a product, a specific machine tool or process, a production system, or a whole factory.

Virtual models composing a DT can be employed to verify, validate, and evaluate the performance of the physical counterpart along its lifecycle phases. An effective DT for manufacturing needs:

• A reference framework to support interoperability and communications;
• Multi-scale data models for the representation of products, processes, systems, and their evolution;
• Synchronization between real and digital factory;
• Integration of advanced methodologies that can benefit from the DT potential.

DFT aims at aggregating a community of researchers together with technology providers and users to foster the development and adoption of DT-based analysis, elaboration, and decision support tools in academic and industrial contexts. In particular:

• The development of Factory DT to model the characteristics of products, processes, and systems for the productions of mechanical components;
• The integration of DT in the factory digital infrastructure by defining the required level of details and coping with the information and decision flows;
• The integration with methodologies and tools for the management of production systems;
• The development of transversal competencies for engineers as new professionals that will address the digitising of manufacturing companies;
• The use of advanced interfaces for training and evaluations in a virtual environment.