Manufacturing and logistics companies are subject to unforeseen events that disrupt the supply chain, causing production slowdowns, reduced output, and increased costs, making it difficult to meet customer demand. To mitigate these risks, manufacturers must build resilience across entire value chains. NARRATE will develop a sophisticated tool using AI, Digital Twin, and IoT technologies allowing end-to-end visibility and control over supply chain operations to monitor and predict potential disruptions, enabling supply chains to achieve improved resilience. The Intelligent Manufacturing Custodian (IMC) will leverage data from various production sources to enable proactive decision-making and act as a nerve centre for a supply-chain network, providing real-time monitoring and coordination of intelligent production processes and logistics. Integrating an IMC into a supply-chain will evolve its operations into Smart Manufacturing Network (SMN): a connected and self-orchestrated ecosystem linked end-to-end with programmable Manufacturing-as-a-Service capabilities that can withstand disruptions. A Digital Twin will provide a reliable model to represent production and operational data of an SMN to unlock deeper IMC intelligence. Collected data will train machine learning models to predict potential disruptions, such as natural disasters or delayed shipments. AI algorithms will analyse the data and provide real-time reporting and visualization on a dashboard. The IMC and Digital Twin interaction will generate powerful insights and self-adapting abilities that support an SMN to evolve under human supervision by switching services between multiple external partners to respond to risks and disruptions and improve energy efficiency, product circularity and environmental sustainability across the entire production process. The effectiveness of NARRATE will be evaluated by testing the IMC in real production environments in quite diverse industry sectors.

There is an urgent need for novel approaches assessing functional decline in early AD. The main goal of the RADAR-AD project is to develop a digital platform to detect subtle functional deficits in early Alzheimer’s disease (AD) individuals by integrating a meaningful combination of smartphone, wearable and/or home sensor based parameters. The system developed will be suitable for future longitudinal studies, including trials. The objectives are to 1) Identify the most relevant functional domains and the most promising remote measurement tools (RMTs) for these domains based on reviewing the literature and piloting of RMTs in small studies; 2) Optimise the RADAR-CNS platform for use in AD studies; 3) Test the platform and selected RMTs in a real world environment clinical study with 240 participants across the AD spectrum ranging from the preclinical AD to the dementia stage; 4) Perform statistical modelling to estimate longitudinal predictions; 5) Discuss results with regulatory agencies in order to obtain guidance about how to develop a path for formal qualification as outcome measurements in future therapeutic interventions. Considering the limited budget and project duration, we will rely upon already available technology platforms and on available longitudinal datasets where possible. The consortium includes experts in clinical dementia studies, computer science, bioinformatics, regulatory policies, ethics, and patient and public involvement (PPI). Additional strengths of the consortium are the deep and broad interface with RADAR-CNS and related IMI projects, and the access to large amount of patient-level data from key European cohort studies for modelling purposes.