Abstract: The concept of Digital Twins has emerged more than a decade ago and has been promoted still then as a potential solution for many industrial challenges. Still their industrial adoption is limited to high value use cases only. Scalable solutions and workflows to build real-time models as required for most Digital Twin applications are missing. Reduced Order Modelling approaches offer unique approaches to do so. In this talk, we review the-state-of-the art industrial Surrogate Modelling approaches focusing on POD-based methods as well as brute force Machine Learning based Surrogate Modelling approaches.  We highlight the most important application domains as well as concrete use cases. We conclude with a summary of major obstacles limiting broader industrial adoption today with the goal to spur further research to address the problem as well as to initiate new research collaborations.