Andrea Matta is Full Professor of Manufacturing and Production Systems at Department of Mechanical Engineering of Politecnico di Milano and Guest Professor at Shanghai Jiao Tong University. He graduated in Industrial Engineering at Politecnico di Milano where he develops his teaching and research activities since 1998. He was Distinguished Professor at the School of Mechanical Engineering of Shanghai Jiao Tong University from 2014 to 2016. He has been visiting professor at Ecole Centrale Paris (France), University of California at Berkeley (USA), and Tongji University (China). He is scientific responsible of the Research Area Design and Management of Manufacturing Systems at MUSP (Laboratory for Machine Tools and Production Systems). His research area includes analysis, design and management of manufacturing and health care systems. He has published 130+ scientific papers on international and national journals/conference proceedings. With the coming of the Industry 4.0 wave, digital representations of production systems have been promoted from marginal to central. Digital twins are not simply conceived as simulation models of their physical counterparts for offline what-if analysis, differently they are developed as self-adaptable and empowered decision-makers timely aligned with the dynamics of the real system. Enriched by these new features, digital twins are widely recognized as the key enablers for the implementation of optimal control of smart manufacturing systems. Graphs are well recognized as the mathematical structures representing the common denominator of engineering activities in manufacturing. They are the unifying language in industrial engineering, underpinning, for example, toolpaths, process plans, workflows, and system topologies. This talk will present methods for automatically generating graphs from real process gathered data to represent physical entities in a digital twin scenario. Further, generative approaches based on discrete diffusion will be discussed in relation to model tuning, control, and optimization.