A conceptual digital twin framework for supply chain recovery and resilience

Oluwagbenga Victor Ogunsoto , Jessica Olivares-Aguila , Waguih ElMaraghy
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Abstract

Amidst escalating global supply system risks and interruptions, the imperative for fortified supply networks is evident. Organizations striving for competitiveness and resilience must adeptly recognize, comprehend, and address disruptions. This study presents a three-phase digital supply chain twin framework, leveraging discrete event simulation and neural networks to anticipate floods—a typical natural catastrophe and disruptive event—and predict recovery indicators. This aids supply chain (SC) managers in making informed decisions. In the first phase, machine learning algorithms, including logistic regression and Long Short-Term Memory (LSTM), were trained on Kerala India's precipitation data to predict floods. LSTM outperforms logistic regression, achieving flood prediction with 73 % recall, 75 % accuracy, and 84 % Area Under Curve-Receiver Operating Characteristics score. In the second phase, simulations replicate value chain breakdowns. A process flow logic-driven discrete event simulation within a real-world SC network emulates operational disruptions. FlexSim is employed to model service-level failures, influencing SC model performance based on the distribution center service level. The third phase employs simulated case scenario data to train a multilayer neural perceptron network (MLPNN) for predicting production network recovery post-disruptions. The MLPNN monitors the mean squared error (MSE) and disruptive inputs throughout training and validation, revealing consistent MSE reduction over recovery periods. The number of epochs needed to achieve a minimum MSE is used as a recovery indicator to predict service restoration time. Consequently, this study introduces a conceptual digital twin framework for catastrophic operations chain breakdowns and recovery prediction. The framework's output assists SC planners in shaping robust strategies by foreseeing disruptions and facilitating recovery.
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供应链恢复和复原力数字孪生概念框架
在全球供应系统风险和中断不断升级的情况下,强化供应网络的必要性显而易见。努力提高竞争力和复原力的组织必须善于识别、理解和应对中断。本研究提出了一个三阶段数字供应链孪生框架,利用离散事件模拟和神经网络来预测洪水--一种典型的自然灾害和破坏性事件--并预测恢复指标。这有助于供应链(SC)管理者做出明智决策。在第一阶段,包括逻辑回归和长短期记忆(LSTM)在内的机器学习算法在印度喀拉拉邦的降水数据上进行了训练,以预测洪水。LSTM 的表现优于逻辑回归,其洪水预测的召回率为 73%,准确率为 75%,曲线下面积-接收器工作特性得分率为 84%。在第二阶段,模拟复制了价值链中断。在现实世界的 SC 网络中进行流程逻辑驱动的离散事件仿真,模拟运营中断。FlexSim 用于模拟服务级故障,根据配送中心的服务水平影响 SC 模型的性能。第三阶段采用模拟案例情景数据来训练多层神经感知器网络(MLPNN),以预测中断后生产网络的恢复情况。在整个训练和验证过程中,MLPNN 监测均方误差(MSE)和中断输入,发现在恢复期间,MSE 持续降低。达到最小 MSE 所需的历元数被用作预测服务恢复时间的恢复指标。因此,本研究为灾难性运营链断裂和恢复预测引入了一个概念性数字孪生框架。该框架的输出可帮助 SC 规划人员通过预测中断和促进恢复来制定稳健的战略。
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