value chAIn – AI-Based Failure Management in Value Chains

Key Info

Basic Information

01.08.2021 to 31.07.2024
Organizational Unit:
Chair of Production Metrology and Quality Management, Quality Intelligence
Federal Ministry for Economic Affairs and Energy (BMWi)

Research partner

    • Fraunhofer Institute for Production Technology IPT
    • i2solutions GmbH
    • DATAbility GmbH
    • IconPro GmbH
    • MAN Truck & Bus SE
    • KRONE Business Center GmbH & Co. KG



Robin Günther

Group Leader


+49 241 80 28221



The aim of the project value chAIn is to achieve production and usage optimization of utility vehicles across the value chain, using Artificial Intelligence. Within the scope of the research project, increasing transparency with regard to relevant dependencies between different instances along the value creation stages is seen as a key factor. Focus is the development and implementation of intelligent analytical methods for decision support in failure management. For example, this approach is intended to proactively eliminate failures in production processes, carry out predictive maintenance during the use phase of commercial vehicles, and optimize product development. These goals are achieved through horizontal and vertical integration and evaluation of digital state and failure data. Essential prerequisite is the cross-organizational provision of production and usage data.

By means of Artificial Intelligence, analyses regarding Predictive Maintenance (for prediction and optimization of utility vehicles and production units’ inspection), Predictive Quality (for prediction of product quality in production) and Process Optimization (for identification of optimal parameters) are implemented. Based on the results of the machine learning models as well as human expert knowledge, a decision support system is developed which provides demand-oriented information and actions for optimal decision making.
These innovations are being implemented through the collaboration of a project consortium consisting of commercial vehicle manufacturers, IT enablers and research institutions.