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Research Projects
Discover our innovative research projects, where we team up with collaborators to develop cutting-edge AI solutions. Focused on real-world challenges, these projects bring exciting new developments and shape the future of technology.
PrePAIR
AI-based error management for the automotive and commercial vehicle industry
PrePAIR is focused on moving beyond isolated solutions to create an AI-driven, cross-company error management system. The automotive and commercial vehicle industry faces increasing challenges, including greater product variety, higher quality standards, a stronger emphasis on sustainability, and stricter regulatory requirements. Additionally, supply chains are subject to high volatility, further complicating production processes. This complexity makes it difficult to implement effective error management, as issues often arise from different stages of the production process and can be hard to trace.
Despite the growing amount of data, error management is often siloed within individual departments, leading to fragmented solutions. PrePAIR aims to address this complexity by developing alternatives to these isolated approaches. The project focuses on four key use cases across the value chain, including production, sales, and usage phases, within the automotive and commercial vehicle industry. These use cases are presented by industry partners situated at different stages of the value chain.
The project seeks to improve error management across companies by applying AI-driven methods and enabling data exchange along the value chain. AI applications will enhance data quality, feature extraction, and predictive analysis, while the resulting services will be integrated into an interoperable and sovereign data ecosystem. The ultimate vision is to make error reports transparent across the entire value chain and allow other companies to benefit from these services.
Through comprehensive data analysis, PrePAIR aims to detect and prevent errors as early as possible, manage their impacts reactively, and even proactively control them. This approach is expected to reduce resource losses and make problem-solving more effective and efficient. The project also aims to contribute to the development of new standards in error management.
Scientific support for the PrePAIR project is provided by two research institutes, the Institute for Production Management, Technology and Machine Tools (PTW) at TU Darmstadt and the professorship of Information, Quality and Sensor Systems in Production (WZL | IQS) at RWTH Aachen University.