Hybrid Intelligence in Production Systems and Its Effects on Human Work: Insights from Four Use-Cases

Nikolas J. Schierhorst,Laura Johnen, Christian Fimmers, Vincent Lohrmann, Josefine Monnet, Hanwen Zhang,Thomas Bergs,Christian Brecher,Alexander Mertens,Verena Nitsch

Procedia Computer Science(2024)

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摘要
Industry 4.0 has initiated a data-driven transformation of production systems. With AI applications capitalizing on the surge of data availability, their introduction is reshaping workplaces and altering work tasks and profiles around the world. As AI-driven automation of work proliferates, so does the potential for the substitution of human labor. Rather than replacing human work, the concept of hybrid intelligence seeks to combine human and artificial intelligence with the effect of increasing productivity of the overall work system. As such, the concept may prove useful to support human-friendly automation of work, i.e., automation that supports human well-being and empowerment. This requires a deeper understanding of the projected effects of different automation solutions on human workers. In this context, this paper examines possible effects of AI applications in production systems based on four use-cases of coating and machining processes, thereby focussing on the changes from the perspective of workers and the resulting human-AI interactions. The potential challenges and opportunities of the workplace transformation are discussed, specifically highlighting possible implications for the workforce.
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hybrid intelligence,production systems,human-AI interaction,Operator 4.0
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