DMaaST aims to enhance the manufacturing ecosystem's resiliency and capability of self-adaptation in response to external events. It is achieved through a Smart Manufacturing Platform comprising 4 layers: The data layer establishes a foundation for mapping manufacturing ecosystem information using ontologies and decentralized knowledge graphs, ensuring a trusted cross-organization real-time data integration. Next, a layer with a two-level cognitive digital twin is created, with the low-level DT modelling two use cases' manufacturing services production line; and the high-level DT modelling the main stages of use-cases’ sectors value chains. The resulting DTs will use human expertise-knowledge, data-driven algorithms and physical modelling to provide a reliable and robust DT of the manufacturing ecosystem. The next layer employs the data and modelling layer's information to present a multi-objective distributed decision support system algorithm combining multi-objective techniques and the latest trends in Federated Deep Learning. This makes DTs actionable models and provides the necessary information to make optimal production decisions. The fourth layer focuses on presenting the information in a user-friendly manner with timely scoreboards. Additionally, a dedicated module will assess the production's circularity and sustainability and considering products traceability through the EU-DPP. Therefore, the sustainability and remanufacturing opportunities of the production process will be improved. The project ensures scalability, providing information for replicating and trying new manufacturing processes thanks to the manufacturing services digital warehouse while assessing risks and opportunities for improvement. DMaaST innovations enable the manufacturing ecosystem to adopt the Manufacturing as a Service concept by smoothly evolving all the technologies from a TRL3 to a consolidated TRL6 in 2 use cases in key sectors, aerospace and electronics.

Reliable and efficient electricity supply to geographically distributed customers is the main task of the Distribution System Operators (DSOs). An increasing number of grid-related data sources is in principle accessible to DSOs, but this information is in most cases not yet utilized for grid operation. Leveraging measurements and ICT reachability of Smart Meters and grid connected systems (such as Intelligent Electronic Devices) for digital distribution grid operation is challenging as it requires resilient and secure data collection, and data-quality aware processing and distribution system control solutions. The Net2DG project will develop a proof-of-concept solution based on off-the-shelf computing hardware that uses existing communication technologies to leverage measurement capabilities of Smart Meters and DER inverters deployed in low-voltage (LV) grids. The solution will correlate this data with information from existing DSO subsystems in order to provide novel LV grid observability applications for voltage quality, grid operation efficiency and LV grid outage diagnosis. The resulting observability is subsequently used by specifically developed robust control and coordination approaches, which utilize existing actuation capabilities for voltage quality enhancement and loss minimization in the LV grid. The use of off-the shelf components, the system level resilience and security solution, and the offered customizability of the Net2DG approach specifically address the needs of small and medium-sized DSOs (less than 100.000 clients). Therefore, the Net2DG solution will make small and medium-sized DSOs early adopters of digital technologies for LV outage diagnosis, grid operation efficiency and voltage quality. The Net2DG solutions will be developed by a consortium including two small and medium-sized DSOs from two different European countries, academic partners, SME and startup technology companies, as well as Smart Meter and inverter vendors.

MAASiveTraditional value chains are facing challenges due to the fast-moving markets, customer demands, and unpredictable manufacturing and logistics. To address these challenges, Manufacturing as a Service (MaaS) is introduced as a concept that utilizes existing resources in a value network by connecting manufacturers to service providers on demand through a connected network. The MAASive project aims to develop models of value networks that enable companies to recover from unforeseen external events by connecting to new services and reconfiguring value networks utilizing internal and external manufacturing services. MAASive will provide a toolkit for industry, which will consist of a blend of existing methods and technology applied in the MaaS context, and new models and technology developed as part of the project. Four distinct aspects are addressed in the MAASive project to increase resilience in value networks: network building, impact assessment, reorchestration of networks, and value network operation. The overall aim of MAASive is to increase value network resilience by enabling manufacturers to rapidly respond to unforeseen external events or sudden changes in supply or demand, utilizing manufacturing as a service. MAASive uses an iterative approach to develop technical solutions and identify potential technology risks early on. The project is focused on creating a toolkit from a human-centered perspective and involving professionals and workers in requirement and scenario definition. The iterative approach follows three loops focusing on 1) model foundations, 2) impact simulation and scenarios, and 3) network orchestration and operation. The results of MAASive will be developed in two use case demonstrators. The results of MAASive will contribute to companies being more resilient towards external, unforeseen events, by being able to utilize services in a value network better and faster, while also increasing utilization of network resources.