Advaneo has been actively involved in national and international research projects for over ten years.
With our experience as both consortium leaders and project partners, we bring technological and organizational expertise to every project.
We take responsibility for project management and architecture, develop interoperable Data Spaces, create innovative solutions for data sovereignty, interoperability, and secure data exchange, and advance AI-driven data analysis and applications.
Regardless of our role, Advaneo stands for high-quality research outcomes, practical implementations, and the establishment of secure, sovereign data ecosystems.
Objective:
Develop a blueprint for cross-sector, cross-application, and cross-border data exchange as well as a transferable trustee model.
Outcome:
Establishment of an open ecosystem (“Green Deal Dataspace”); implementation via use cases such as carbon accounting (CO₂) in production networks and risk management in supply chains; development of legally compliant standard contracts, processes, and an operating model.
Benefits:
Trusted infrastructure for secure data exchange; strengthens transparency, resilience, and sustainability in the circular economy and supply chains, as well as cross-industry data innovation.
Objective:
Build a cross-domain, learning AI platform (Privacy-Aware, Intelligent and Resilient Crisis Management) for the early detection and assessment of crises – for example in manufacturing, energy, logistics, and healthcare.
Outcome:
Development of the Supply Chain Radar (SCR), an AI-powered early-warning system that identifies critical nodes in supply chains, assesses risks, and generates recommended actions, while preserving data protection and anonymity.
Benefits:
Enables economic and public-sector stakeholders to anticipate crisis scenarios at an early stage, take targeted countermeasures, strengthen resilience and security of supply, and promote the protection of critical infrastructures.
Objective:
Standardize the capture of shop-floor data using the Asset Administration Shell (Eclipse BaSyx) and securely transfer it into a data space via an IDS connector; connected to the Trusted Data Hub (TDH) for privacy-preserving analytics.
Outcome:
Digital twins (AAS) plus data-space mechanisms enable AI/ML across companies—without sharing raw data and with full data sovereignty.
Benefits:
Foundation for predictive maintenance and CO₂ accounting, including for SMEs.
Objective:
Structure CMS/SCADA data, define KPIs, and develop a data/hub concept for operations management.
Outcome:
Implemented Basic and Extended Data Hubs (BDS/EDS) with HMI, web interfaces, and automated KPI analysis including recommended actions; integration at the enterprise level; onshore remote operation.
Benefits:
Transparent KPIs, more efficient maintenance, faster decision-making.
Advaneo contributed expertise in data management, data governance, data sharing, and privacy-preserving analytics. The goal was to securely network production data, enable AI applications, and in particular give SMEs access to Industry-4.0 technologies – independent of large IT vendors.
Advaneo participated in developing a collaborative platform that digitally connects locomotives and wagons, captures location data in real time, and thereby enables process optimization, higher utilization, and new service offerings in freight transport.
As a partner, Advaneo contributed the Advaneo Data Marketplace – an IDS-based platform for secure data exchange and the development of new data-driven business models. In doing so, Advaneo helped build an open, fair, and sustainable data ecosystem.
