Full Description
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.
