NIMBLE: collaboration Network for Industry, Manufacturing, Business and Logistics in Europe will develop the infrastructure for a cloud-based, Industrie 4.0, Internet-of-things-enabled B2B platform on which European manufacturing firms can register, publish machine-readable catalogs for products and services, search for suitable supply chain partners, negotiate contracts and supply logistics, and develop private and secure B2B and M2M information exchange channels to optimise business work flows. The infrastructure will be developed as open source software under an Apache-type, permissive license. The governance model is a federation of platforms for multi-sided trade, with mandatory interoperation functions and optional added-value business functions that can be provided by third parties. This will foster the growth of a net-centric business ecosystem for sustainable innovation and fair competition as envisaged by the Digital Agenda 2020. Prospective NIMBLE providers can take the open source infrastructure and bundle it with sectoral, regional or functional added value services and launch a new platform in the federation. Internet platforms need fast adoption rates and the work plan reflects this: we start attracting early adopters from day one and develop the initial, working platform in year one. Added-value business functions follow in year two and final validation at large scale, involving hundreds of external firms, will happen in year three. Our adoption plan is designed to enable two or more platform providers at the end of the project, and to have 1000 to 2000 enterprises connected to the overall ecosystem at that point. NIMBLE has 17 partners grouped around 3 main activities: developing the infrastructure, running a platform adoption programme, and validating the platform with 4 supply chains (white goods, wooden houses, fashion fabrics, and child care furniture). NIMBLE will give manufacturing SMEs in Europe a stable and sustainable digital ecosystem.

The overall aim of MOBISTYLE is to raise consumer awareness and awareness of ownership, thus empowering consumers and providing confidence of choosing the right thing, by providing attractive tailor-made combined knowledge services on energy use, indoor environment, health and lifestyle, by ICT-based solutions. This awareness will support and motivate end-users to well informed pro-active behavior towards energy use, energy efficiency and health. The objectives are: 1. To make energy use and energy efficiency understandable and easy to handle in an attractive way by unlocking and translating large data sets using data science from energy monitoring for consumers. a. To transform ‘big data’ into ‘smart data’, i.e. giving meanings to data, making data understandable and findable. b. To develop easy to use, desirable ICT-based tools which will make energy monitoring a well-accepted and attractive ‘daily activity’ for end-users as well as for professionals (building managers). 2. To provide understandable information to consumers on health and life style in relation to energy use by combining energy monitoring with monitoring indoor environmental and behavior parameters a. To combine the several low-cost, non-intrusive devices and monitoring with the energy monitoring. b. To offer the end user transparency on energy use/efficiency, indoor environment, health and lifestyle. 3. To motivate behavioral change of consumers/energy end-users by combined modular information on energy use, health and lifestyle: To transform this information into knowledge for raising awareness on energy use and behavior, thus motivating and supporting to come to a behavioral consciousness and change of lifestyle concerning energy and health. 4. To foster new business models and applications 5. To deploy and validate the developed solutions and services in different building types and user types, demonstrating a significant reduction of final energy use, prompted by these solutions.

The adoption of robots in lower volume, diverse environment is heavily constrained by the high integration and deployment complexity that overshadows the performance benefits of this technology. If robots are to become well accepted across the whole spectra of production industries, real evidence that they can operate in an open, modular and scalable way is needed. ODIN aspires to fill this gap by bringing technology from the latest ground breaking research in the fields of a) collaborating robots and human robot collaborative workplaces b) autonomous robotics and AI based task planning c) mobile robots and reconfigurable tooling, d) Digital Twins and Virtual Commissioning and e) Service Oriented Robotics Integration and Communication Architectures. To strengthen the EU production companies’ trust in utilizing advanced robotics, the vision of ODIN is: “to demonstrate that novel robot-based production systems are not only technically feasible, but also efficient and sustainable for immediate introduction at the shopfloor”. ODIN will achieve this vision through the implementation of Large Scale Pilots consisting of the following components: - Open Component (OC): A small footprint, small scale pilot instance allowing the development, integration and testing of cutting-edge technologies. - Digital Component (DC): A virtual instance of the pilot implementing an accurate Digital Twin representation that allows the commissioning, validation and control of the actual pilot - Industrial Component (IC): A full-scale instance of the pilot, integrating hardware (HW) and software (SW) modules from the Open and Digital components and operating under an actual production environment. - Networked Component (IC): An integration architecture with open interfaces allowing the communication of all robotics HW and control systems through safe and secure means. ODIN will demonstrate its result in 3 Large Scale Pilots in the automotive, white goods and aeronautic sectors.

The success of 5G technologies depends closely on their ability to attract vertical stakeholders, seeking the move of their services from cloud to the edge to meet unique KPIs. 5G-INDUCE project is based on the belief that such attractiveness requires vertical stakeholders and Network Application (nApp) developers to be able to smoothly deploy and manage applications in distributed 5G network environments, in a secure fashion and with strict KPI requirements. 5G-INDUCE relies on the deployment of an open ETSI NFV compatible 5G orchestration platform for the deployment of advanced 5G nApps. The platform’s unique features provide the capability to the nApp developers to define and modify the application requirements while the underlay intelligent OSS can expose the network capabilities to the end users on the application level without revealing any infrastructure related information. This process enables an application-oriented network management and optimization approach that is in line with the operator’s role as manager of its own facilities, while it offers the operational environment to any developers and service providers through which tailored made applications can be designed and deployed, for the benefit of vertical industries and without any indirect dependency through a cloud provider. The project focuses on the Industry 4.0 vertical sector, as one of the fastest growing and most impactful sectors in European economy with high potentials for service development SMEs and with the capability to tackle all diverse cases of service requirements. The platform is integrated over 3 5G Experimentation Facilities in Spain, Greece, and Italy, and extended with links towards specific Industries, for the showcasing of nApps in real 5G environment. The consortium includes all the required stakeholders (MNOs, Industries, System integrators and SMEs) from the benefited business sectors evaluated in the project, while significant part of the work (>50%) is conducted by innovative SMEs.

Despite the indisputable benefits of AI, humans typically have little visibility and knowledge on how AI systems make any decisions or predictions due to the so-called “black-box effect” in which many of the machine learning/deep learning algorithms are not able to be examined after their execution to understand specifically how and why a decision has been made. The inner workings of machine learning and deep learning are not exactly transparent, and as algorithms become more complicated, fears of undetected bias, mistakes, and miscomprehensions creeping into decision making, naturally grow among manufacturers and practically any stakeholder In this context, Explainable AI (XAI) is today an emerging field that aims to address how black box decisions of AI systems are made, inspecting and attempting to understand the steps and models involved in decision making to increase human trust. XMANAI aims at placing the indisputable power of Explainable AI at the service of manufacturing and human progress, carving out a “human-centric”, trustful approach that is respectful of European values and principles, and adopting the mentality that “our AI is only as good as we are”. XMANAI, demonstrated in 4 real-life manufacturing cases, will help the manufacturing value chain to shift towards the amplifying AI era by coupling (hybrid and graph) AI "glass box" models that are explainable to a "human-in-the-loop" and produce value-based explanations, with complex AI assets (data and models) management-sharing-security technologies to multiply the latent data value in a trusted manner, and targeted manufacturing apps to solve concrete manufacturing problems with high impact.