Manager ex Machina – Investigation of the transferability of productionmanagement tasks by systems with artificial intelligence

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Due to the continuous development of artificial intelligence (AI), novel possibilities arise to control even complex decisions by a computer. From a scientific perspective, the question arises for which types of production management decisions an AI instance is superior to a human. Similarly, this question has been answered with great resonance and scientific applied for use cases in games such as Chess, Go, or Jeopardy. Advances in AI, especially in reinforcement learning (RL), show that artificial agents can keep up with or even outperform human players in complex scenarios. Therefore, this research project takes the original approach of transferring the knowledge gained from the duel between humans and "machines" (AI instances) to production management. To this end, production management decisions are modeled with a broad spectrum in a simulation environment and then executed by both humans and an AI using RL. After the AI instance completes learning to the simulation environment, the performance of the human compared to the performance of the machine in the simulation environment is determined individually. The comparison of the two performances provides information on which decisions are superior to AI-based approaches and thus can take over the decision that would otherwise be made by a human. Finally, to ensure that the simulation-derived results are applicable in a real-world environment, they are validated in a real production environment as an example. The expected gain in knowledge is the identification of production management decisions that can be adopted by AI-based approaches by applying RL to production management decisions. Second, and more significant for the inductive conclusion, the question is answered whether the human-machine duel is suitable as a method for evaluating the performance of AI-based approaches in production management.