The Flex4fact project aims to develop an end-to-end ecosystem that takes a modular approach to enable flexible manufacturing in industry and create the conditions for the necessary energy transition in which all stakeholders can participate and benefit. Currently, complex process industries and the segment of mixed clusters and distributed renewable energy flows are not covered. Indeed, at least three key elements of the Flex4Fact approach differ from current offerings: 1) enabling industrial flexibility through digital process twinning and machine learning techniques to address complex operational scenarios and exploit the full potential of the selected industrial cases; 2) the end-to-end approach of the Flex4Fact platform, covering the entire technology stack required to achieve the results; 3) a modular and multi-level approach that leverages flexible, hybrid architectures, open protocols, and standard interfaces to enable greater flexibility in the system itself and associated service offerings, remove barriers to entry for vendors in the value chain, and ensure data communication with existing IT legacy systems. As a digital enabler for flexible energy consumption, the Flex4Fact platform will meet the needs of companies of all sizes, including SMEs that are affected by rising energy prices and have less "financial muscle" to deploy a combination of IT, manufacturing, and energy solutions (93% of European companies have fewer than 10 employees and only 1 in 5 SMEs are highly digitized). The Flex4fact platform will also reduce the digital divide between North and Southern Europe, another crucial aspect for European competitiveness: in fact, it will be developed by a balanced European consortium in which critical technologies will be developed and implemented by several partners in five industrial contexts, from North to South in Europe.

Facts: i) Zero-defect manufacturing and flexibility of production processes are some of the main challenges for European manufacturing; ii) One of the engineering tools with higher potential is the linking of simulation tools with measurement devices for real-time control of applications. The huge potential of this synergistic loop remains untapped for manufacturing processes and could be used for reducing product variability, increase line flexibility and achieve zero defect production. These objectives could be reached by integrating in the production line multi-physics simulation models, able to predict the product quality indicators in response to the values of critical input parameters (components dimensions, material properties, etc), which are unavoidably subject to variability: different batches, different suppliers,... The models will be fed with actual data from online measurements and, based on the model prediction, the critical steps of the line will be controlled to adjust the product to the exact design specifications or to quickly change specifications for producing customised batches. However, doing this in real time is not possible due to the computational cost of models. Reduced Order Modelling is a new generation of techniques which allow us to obtain parametric solutions of complex models that can be particularized in real time for any value of the parameters. The models run so fast that they can be executed on tablets or smart phones. ROM will be used to transform complex models into the real-time capable models that can be integrated in the production line. Moreover, the online deployment of ROMs and data gathering systems will generate big data which will be exploited through data analysis techniques for further improving the process. The project will show proof-of-concept demonstrations in three real process chains of the automotive sector, covering different types of production methods, products, materials and manufacturing processes.