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新冠疫情:通过整合解释结构模型和贝叶斯信念网络进行供应链风险管理

COVID-19 pandemic: Supply chain risk management by integrating Interpretive Structural Modeling and Bayesian belief network.

作者信息

Pellegrino Roberta, Gaudenzi Barbara, Qazi Abroon

机构信息

Department of Mechanics, Mathematics and Management, Politecnico di Bari (Italy).

Department of Business Administration, University of Verona (Italy).

出版信息

IFAC Pap OnLine. 2022;55(10):667-672. doi: 10.1016/j.ifacol.2022.09.481. Epub 2022 Oct 26.

Abstract

The paper proposes a theoretical framework, based on a literature review, that analyzes the links between COVID-19 impacts and supply chain risk mitigation strategies, investigating the role of digitalization as a potential key resource to improve the effectiveness of supply chain resilience. Then, the paper empirically tests the framework through a hybrid causal mapping technique using the frameworks of Interpretive Structural Modelling and Bayesian Belief Networks methods to support supply chain decision making approaches. The findings of this paper can support managers in developing simple and traciable models for assessing interdependences among supply chain disruption sources and to invest effectively in resilience strategies.

摘要

本文基于文献综述提出了一个理论框架,该框架分析了新冠疫情影响与供应链风险缓解策略之间的联系,研究了数字化作为提高供应链弹性有效性的潜在关键资源的作用。然后,本文通过一种混合因果映射技术,利用解释性结构建模和贝叶斯信念网络方法的框架对该框架进行实证检验,以支持供应链决策方法。本文的研究结果可以帮助管理者开发简单且可追踪的模型,以评估供应链中断源之间的相互依存关系,并有效地投资于弹性策略。

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本文引用的文献

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Pandemics and Supply Chain Management Research: Toward a Theoretical Toolbox.
Decis Sci. 2020 Aug;51(4):838-866. doi: 10.1111/deci.12468. Epub 2020 Jun 25.
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COVID-19 pandemic related supply chain studies: A systematic review.
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