In traditional models of manufacturing, the information flow from product design, over production processes, to the manufactured product has been strictly unidirectional. The production equipment “blindly” executes tasks that have no direct relationship to the concepts that are present in the original design models and the product is used with little or no feedback concerning product use patterns. In order to enhance the manufacturability of products and at the same time the flexibility of both factory production systems and modern products, both effective configurability and feedback to design and production is required to assure their highest efficiency. The SAFIRE project will provide technology and infrastructure to enable Reconfiguration as a Service for dynamic smart factory systems and manufactured smart products that take advantage of cloud-based services and computing power to continually optimise the performance of manufacturing systems and products with respect to key performance characteristics including throughput, power consumption, utilisation, maintenance and other factors. A key objective of the project is to develop cloud-based analytics and reconfiguration capabilities that extend the operating systems of smart factories with: 1) both reactive and predictive reconfiguration for production systems; 2) flexible run-time reconfiguration decisions during production rather than pre-planned at production planning time; 3) real-time reconfiguration decisions for optimisation of performance and real-time production functions. The advanced analytics and reconfiguration capabilities will be based on innovations in shared situational awareness and mastering the big data challenges associated with sensor, smart objects and process data from manufacturing, logistics and enterprise systems.

The MAESTRI project aims to advance the sustainability of European manufacturing and process industries. This is done by providing a management system in the form of a flexible and scalable platform, and to guide and simplify the implementation of an innovative approach, the Total Efficiency Framework. The overall aim of this framework is to encourage a culture of improvement within process industries by assisting the decision-making process, supporting the development of improvement strategies and helping define the priorities to improve the company's environmental and economic performance. Its development and validation will be achieved through application in four real industrial settings across a variety of activity sectors. The Total Efficiency Framework will be based on four main pillars to overcome the current barriers and promote sustainable improvements: a) an effective management system targeted at process and continuous improvement; b) efficiency assessment tools to define improvement and optimisation strategies and support decision-making processes; c) integration with a toolkit for Industrial Symbiosis focusing on material and energy exchange; d) a software Platform, based on the Internet of Things (IoT), to simplify the concept implementation and ensure an integrated control of improvement process. Over a period of 4 years, the project will deliver exploitable resultsclustered into technological outputs (including eco-innovative products, processes and services tailored to industrial end-users) and structured solutions (involving technical, economical, legislative and policy solutions synergistically combined).

AI4Work will investigate practical methods and tools for optimal sharing of work between humans and AI/robots. AI and robotics are likely to be most powerful means for radical improvement of working conditions in diverse domains, as they can support human operators in diverse tasks starting from difficult and tedious manual labor tasks up to complex decision-making tasks. The vision of the AI4Work project is to improve communication and collaboration between humans, AI and robots, allowing for an improvement of the working conditions within different processes in organisations in several domains in terms of increased efficiency of work, reduction in stress upon employees, increased confidence in decision-making process etc. Due to the high level of uncertainty in modern organisations an appropriate balance between human and machine activities must be found. The key assumption is that to cope with the required flexibility and dynamics, Sliding Work Sharing (SWS), where this balance varies during the operation depending on the situational context, machine-based confidence levels and human interactions, is likely to be the most appropriate for modern organisations. The key challenge of the project is to develop a set of common methods and tools (methodology framework, digital twin service platform, SW building blocks for SWS) that can be applied in diverse sectors and with different AI/robotics services, allowing for an effective experience exchange. The project will make use of living digital twins of working systems as a mean to increase efficiency and trustworthiness of AI/robotics solutions. By this, the project, aiming at improved quality of jobs and creating more decent work for human operators, will contribute to the acceptance of the AI/robots support of work in diverse domains. The project will be driven by six pilots in different sectors: logistics, manufacturing industry, construction, healthcare, education and agriculture.