Data ecosystems and AI
The guidelines identified data ecosystems, data collection, quality assurance, interoperability and cross-system traceability as foundations for circular value creation. Projects were also expected to reduce disparities in data availability between small and medium-sized enterprises.
Other possible approaches included Industry 4.0 technologies for monitoring and controlling value-creation chains; AI-based diagnostic and detection systems; process and system control; material and product design; assistance and expert systems; simulation; and predictive analytics.
Key questions
Only interoperable, trustworthy data ecosystems make value creation cycles transparent and reliable. Central to this are questions such as:
1
How can data ecosystems be made interoperable, quality-assured and proportionate to actual need?
2
How can AI and Industry 4.0 technologies support circular decisions and processes?
3
How can the value of data, energy demand, data availability and intellectual-property protection be balanced?
Project examples
Funded projects related to this topic.
TRANSFORM-R
Data-based decisions for a circular economy
Data-based decision support for a circular economy.
BatProPass
Digital battery production passport
Digital battery production passport for the circular economy.