The advent of artificial intelligence (AI) and machine learning (ML) has significantly transformed production management in industrial manufacturing by enabling data-driven decision-making. While human decision-making is valued for its adaptability and contextual understanding, AI-driven systems offer the advantage of faster, data-driven decisions. The concept of hybrid intelligence combines these two poles by utilizing the potential of AI without neglecting the necessary integration of human expertise. However, a structured method for determining the appropriate degree of automation - defining the division of tasks between humans and AI for each decision - is still lacking. Thus, this thesis develops a framework to determine the optimal level of Human-AI collaboration for production management use cases. This framework enables organizations to leverage AI effectively across various scenarios, complementing human expertise to enhance operational efficiency and decision-making processes. By offering a systematic method, the framework helps avoid suboptimal AI/ML deployments and supports organizations in harnessing hybrid intelligence for innovative and future-ready production management.

Autorentext

Carl René Sauer studied industrial engineering at the University of Siegen. During his doctoral research at the Chair of International Production Engineering and Management at the University of Siegen, he focused on Human-AI collaboration in production management.