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ItemSupply Chain Threats and Countermeasures: From Elicitation through Optimization( 2022-01-04)There are many checklists for improving supply chain resilience under different threats, but a lack of concrete procedures to rigorously assess and select among countermeasures (CMs). We present a novel process and method to elicit the needed information to identify CMs and assess their ability to reduce risk. We report on the fine-grained analysis underlying an effective simulation developed to model both the impact of threats and the impact of alternative CMs in the information and communication technology supply chain subject to disruptions due to natural hazards. We also describe the coarse-grained descriptions needed to elicit risk reduction estimates from subject matter experts, and the problems of integrating these two approaches, bottom up, and top down, to support management decisions to choose an optimal set of CMs given a limited budget.
ItemA Reinforcement Learning Powered Digital Twin to Support Supply Chain Decisions( 2022-01-04)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.