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System dynamics modeling and robustness analysis for capital-constrained supply chain under disruption
Industrial Management & Data Systems ; 123(2):492-514, 2023.
Article in English | ProQuest Central | ID: covidwho-2288670
ABSTRACT
PurposeThe purpose of this study is to explore mitigation measures for cash flow interruption during the epidemic and provide decision support to ensure the regular operation and robustness of the supply chain (SC).Design/methodology/approachConsidering the scenarios of production capacity and demand disruption during the epidemic, the authors adopt system dynamics (SD) to construct a three-echelon SC financial system consisting of a core manufacturer, a capital-constrained retailer and the customer. In different interruption scenarios, through the decision adjustments of stakeholders, the differences in performance are compared to explore solutions for SC robust optimization.FindingsThe results show that partial credit guarantee (PCG) could solve cash flow interruption and maintain the regular operation of the SC. During epidemic, with the product price increases, the revenue of stakeholders and the robustness are generally negatively correlated. But when the manufacturer's production capacity is fully interrupted, increasing product price is the right decision for the retailer and could simultaneously promote performance and robustness.Originality/valueThis paper primarily focuses on the PCG under the cash flow interruption caused by epidemics. The authors adopt the supply chain finance (SCF) theory and SD method to supplement and expand existing research on interruption management of SC. It is a pioneering study to explore the robustness of the SC financial system under disruptions.
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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: Industrial Management & Data Systems Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: Industrial Management & Data Systems Year: 2023 Document Type: Article