Digitalization is considered as the main driver that directly impacts on the advanced manufacturing and transversally affects all the others The emerging technologies are represented by sensors, big data, machine learning, artificial intelligence (AI), internet-of-things (IoT), internet-of- services, automation, cloud computing, cybersecurity, additive manufacturing and digital twins. The vision of the VMAP-analytics project is to bring sensors, measurement data, process modelling, process simulations and data science-tools together into a smart digital twin platform. This project consists of three use cases from Sweden. Three use cases in this project involve deeper understanding of manufacturing processes that include reheating furnaces, degassing of steel making and flat rolling. In the first use case, a better Furnace Optimization and Control System (FOCS) will be developed to avoid skid marks during reheating operations. The second use case deals with optimization of degassing time and better chemistry control of steel making process. The third use case deals with better profile control of aluminum sheets during hot rolling process. Within these use cases, smart digital twins representing the physical processes are being developed. A three-dimension finite element model for nonlinear reheating furnace coupled with process data analytics forms smart digital twin for furnace model. Data and image based smart digital twin is being developed for degassing problem. While three-dimensional finite element model, phenomenological model and data model forms smart digital twin for hot rolling problem. A necessary material model based on Johnson-Cook formulation is also developed for aluminum alloy to be employed in FEM model. All three use cases delas with physics-based modelling as well as data-based modelling to develop necessary process models for deeper understanding of the process. The present paper discusses various stages of development of smart digital twins for the use cases mentioned above. The challenges faced during data collection and data preparation are highlighted. The final goal of the project is the concept digital twin platform implementing all required analysis tools, methods, models and process data using the standardized interfaces. The industrial use cases will implement this platform, thereby highlighting potential gaps in sensors data and creating insights enabling improved product quality, improved production process robustness, condition monitoring of the mill, and visualization of the state of the process.