A Reinforcement Learning Powered Digital Twin to Support Supply Chain Decisions

Martin, Guillaume
Oger, Raphaël
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The complexity of making supply chain planning decisions is growing along with the Volatility, Uncertainty, Complexity and Ambiguity of supply chain environments. As a consequence, the complexity of designing adequate decision support systems is also increasing. New approaches emerged for supporting decisions, and digital twins is one of those. Concurrently, the artificial intelligence field is growing, including approaches such as reinforcement learning. This paper explores the potential of creating digital twins with reinforcement learning capabilities. It first proposes a framework for unifying digital twins and reinforcement learning into a single approach. It then illustrates how this framework is put into practice for making supply and delivery decisions within a drug supply chain use case. Finally, the results of the experiment are compared with results given by traditional approaches, showing the applicability of the proposed framework.
Digital and Hyperconnected Supply Chain Systems, deep learning, digital twin, reinforcement learning, supply chain management
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