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A resilience measure for supply chain systems considering the interruption with the cyber-physical systems

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  • Chen, Liwei
  • Dui, Hongyan
  • Zhang, Chi

Abstract

In supply chain systems, the entities supplier, manufacturer and distribution center make up the communication platform, which can obtain the data of production, stock, demand, etc. The cyber-physical system (CPS) typically involves multiple computational platforms interacting over communication networks. In the CPS, when the supply chain is disturbed and interruption occurs, supply chain resilience measurement is important to reduce order losses in the supply chain. In this paper, firstly, according to the operational characteristics of different resilience measures, strategy of the supply chain in the interrupted environment is described. Secondly, based on the cost composition of the supply chain operating in the interrupted environment, measurement model of supply chain resilience is established. Thirdly, by introducing the time that the customers can wait as an indicator to represent the external demand environment of supply chain, the customer expectation can be distinguished quantitatively, and the customer expectation factor can be integrated into the measurement model of supply chain resilience. Finally, based on the measurement model of supply chain resilience, quantitative results of the supply chain resilience measurement are obtained, and the relationship between the results and resilience measures is analyzed based on resilience analysis, which illustrates the relationship between the unit capital investment and its ability to reduce order losses.

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  • Chen, Liwei & Dui, Hongyan & Zhang, Chi, 2020. "A resilience measure for supply chain systems considering the interruption with the cyber-physical systems," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:reensy:v:199:y:2020:i:c:s095183201930907x
    DOI: 10.1016/j.ress.2020.106869
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    References listed on IDEAS

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    1. Xing, Liudong & Levitin, Gregory, 2017. "Balancing theft and corruption threats by data partition in cloud system with independent server protection," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 248-254.
    2. Cardoso, Sónia R. & Paula Barbosa-Póvoa, Ana & Relvas, Susana & Novais, Augusto Q., 2015. "Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty," Omega, Elsevier, vol. 56(C), pages 53-73.
    3. Hosseini, Seyedmohsen & Barker, Kash & Ramirez-Marquez, Jose E., 2016. "A review of definitions and measures of system resilience," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 47-61.
    4. Tang, Ou & Nurmaya Musa, S., 2011. "Identifying risk issues and research advancements in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 133(1), pages 25-34, September.
    5. Lücker, Florian & Seifert, Ralf W., 2017. "Building up Resilience in a Pharmaceutical Supply Chain through Inventory, Dual Sourcing and Agility Capacity," Omega, Elsevier, vol. 73(C), pages 114-124.
    6. Eleftherios Iakovou & Dimitrios Vlachos & Anastasios Xanthopoulos, 2007. "An analytical methodological framework for the optimal design of resilient supply chains," International Journal of Logistics Economics and Globalisation, Inderscience Enterprises Ltd, vol. 1(1), pages 1-20.
    7. Dui, Hongyan & Li, Shumin & Xing, Liudong & Liu, Hanlin, 2019. "System performance-based joint importance analysis guided maintenance for repairable systems," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 162-175.
    8. Kevin B. Hendricks & Vinod R. Singhal, 2005. "Association Between Supply Chain Glitches and Operating Performance," Management Science, INFORMS, vol. 51(5), pages 695-711, May.
    9. Cerqueti, Roy & Ferraro, Giovanna & Iovanella, Antonio, 2019. "Measuring network resilience through connection patterns," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 320-329.
    10. Barker, Kash & Ramirez-Marquez, Jose Emmanuel & Rocco, Claudio M., 2013. "Resilience-based network component importance measures," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 89-97.
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