So far, digital twins in Germany have mainly been used for urban development, mobility, and energy and environmental planning. They are only being used to a limited extent for the policy management of regional structural change processes. In the Genesis project, we show which functions dominate in practice and what is needed for digital twins to better support strategic decisions. Over the course of the project, statistical twins will be made available using a web application that offers decision-makers overviews and options for action. Data-driven assessments and models will be created for this using data analysis and machine learning methods (clustering, indicators, pattern recognition in structural change indicators, etc.).
This project aims at providing scientific support to establish sovereign innovation and value chains in the quantum computing ecosystem in Germany. Operational knowledge and strategies are developed to establish a quantum computing ecosystem in the medium to long term, which is technologically sovereign and internationally competitive. AI methods are applied for a data-based documentation of the ecosystem based on the AIR model (actors, interactions, framework conditions) Actor networks are mapped and knowledge bases and key international indicators for comparison are made available.
To enable innovation with recycled fiber-based plastics, it is first necessary to obtain information about which companies recycle which materials and to what extent they make them available. An important step in establishing a functioning circular economy for fiber-based plastics is therefore to first identify companies and characterize their range of activities.
In the FiberConnect project, Fraunhofer ISI used AI technologies to develop a continuously updated overview of players along the value chain, thereby enabling better networking within the industry and closing material cycles. To this end, an AI-supported, largely automated solution was developed: a web crawler continuously searched the internet for relevant companies, while machine learning models used natural language processing to automatically classify the companies found into predefined categories, such as manufacturers, processors, and recyclers.
The research results were made publicly available on the platform: https://wir-recyceln-fasern.de/akteure
New technologies, materials, and raw materials, as well as new process and logistics knowledge, are among the most important drivers of innovation for companies. But which developments are relevant, and what impact will they have on tomorrow's value creation?
The project “Value Creation Radar: AI-supported forecasting for identifying value creation-relevant signals” developed methods for data-supported and expert-led forecasting. The AI-supported “radar tool” helped to perform semi-automated scanning of data sources to find signals relevant to value creation. The insights gained in the project help companies to set research priorities, adapt product strategies, and better secure long-term decisions.
In the project “CoCo – Connect & Collect”, we are researching and supporting the networking of actors in the field of labor research to pool ideas and open up perspectives for innovative work design. These actors have joined forces in the “Regional Competence Centers for Labor Research”. Their nationwide networking is supported by a digital knowledge platform.
The CoCo platform includes a publication database. To use this database in a targeted manner for the project, Fraunhofer ISI developed a classifier that recognizes publications on ergonomics and structures them in a taxonomy. In addition, similarities between publications and subject areas were visually represented and particularly dynamically growing research areas were identified.
Fraunhofer Institute for Systems and Innovation Research ISI