The AI in Production and Logistics Special Interest Group (SIG) brings together researchers and practitioners. It focuses on the development and application of artificial intelligence and machine learning for industrial value creation in manufacturing and logistics. The SIG addresses data- and model-driven decision support across multiple scales. These range from individual workplaces to complex value networks. It covers the full operational lifecycle, including product and process planning, scheduling, and shopfloor control. Additional areas include quality assurance, maintenance, intralogistics, warehousing, and supply chain operations.

A key focus is on industrial constraints that determine practical impact. These include safety, robustness, resilience, explainability, cyber-physical integration, and human-in-the-loop workflows.

Methodologically, the SIG covers a broad spectrum. This includes optimization and hybrid AI, reinforcement learning, and causal and probabilistic models. It also addresses process mining, knowledge graphs, and digital twins. In addition, foundation-model-based approaches for multimodal industrial data are considered.

Special emphasis is placed on transferability and scalability. This applies across sites, assets, and operating conditions. The SIG is designed as a cross-faculty initiative. It especially aims to strengthen collaboration between the Faculty of Informatics and the Faculty of Mechanical and Industrial Engineering. The goal is to bridge AI methods with domain expertise and experimental approaches in production systems. Activities include research exchanges such as seminars and focused working sessions. The SIG also promotes joint benchmarking and dataset-driven discussions. It engages in co-creation with industry partners on high-impact use cases. Furthermore, it supports collaborative outputs, including master’s theses, publications, and competitive project proposals. Overall, the SIG aims to translate scientific rigor into measurable improvements in productivity, resilience, and sustainability.

Chair

• Fazel Ansari, Head of the Research Unit Production and Maintenance Management and University Professor for Data-driven Maintenance Management, TU Wien

Co-Chair

• Co-Chair: Friedrich Bleicher, Head of the Research Department for Manufacturing Technology, IFT - Institute for Manufacturing Technology and Photonic Technologies, TU Wien
• Co-Chair: Sebastian Schlund, Head of the Industrial Engineering Research Unit and the Human-Machine Interaction Research Group, Institute of Management Sciences, TU Wien

Board Members

• Thomas Trautner, Research Group for Control Technology and Integrated Systems, TU Wien
• Sareh Aghaei, University Assistant Postdoc, TU Wien
• Taeho Kang , Post Doctoral Researcher, TU Wien