The goal of the collaborative project is to explore methods for detecting even rare SCM events in text-based sources and in the context of supply chain due diligence. Our methods require little training data from the SME and can be operated via dashboards. An important goal is highly customizable learning methods with few training data for niche domains. These include methods of Few-Shot, Transfer Learning, Multi-Task Learning, Prototypical Learning, and Meta Learning. The latter, in particular, also enable explainability. The basis of the risk indicators for this project is compliant with the Supply Chain Due Diligence Act, which comes into effect on Jan. 1, 2023, and must also be implemented by SMEs. These indicators include the holistic view of the company, such as economic relationships between suppliers and partners.


  • Neofonie GmbH
  • Ubermetrics Technologies GmbH
Associated Partners:
  • BMW
  • Westaflexwerk GmbH
  • Höveler Holzmann GmbH
  • Claas
  • Düspohl GmbH
  • Divisio GmbH
Funded by:

BMBF - KI in Unternehmen

Contact at the BHT:

Prof. Dr. Alexander Löser

Prof. Dr. Felix Gers