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Disentangling diversification in supply chain networks

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  • Mizgier, Kamil J.
  • Wagner, Stephan M.
  • Jüttner, Matthias P.

Abstract

We propose a model for the quantification of business disruption risk in a global supply chain network. To calculate the loss distribution induced by supply chain disruptions for a focal firm, we apply a bottom-up modeling approach. On the firm level, we model production disruptions of various hazard events in reduced form. We incorporate the network structure explicitly and define the loss propagation between the firms. Via Monte Carlo simulation, we analyze the effects of different model specifications and network structures on the loss distribution of the focal firm. We show that diversification effects can lead to counterintuitive results when we consider the network structure and the correlations of hazard events. Our methodology and findings enable more informed and transparent decisions for supply chain design.

Suggested Citation

  • Mizgier, Kamil J. & Wagner, Stephan M. & Jüttner, Matthias P., 2015. "Disentangling diversification in supply chain networks," International Journal of Production Economics, Elsevier, vol. 162(C), pages 115-124.
    • Handle: RePEc:eee:proeco:v:162:y:2015:i:c:p:115-124
    DOI: 10.1016/j.ijpe.2015.01.007
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    References listed on IDEAS

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    1. Mizgier, Kamil J. & Wagner, Stephan M. & Holyst, Janusz A., 2012. "Modeling defaults of companies in multi-stage supply chain networks," International Journal of Production Economics, Elsevier, vol. 135(1), pages 14-23.
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    Cited by:

    1. Kamil J. Mizgier & Otto Kocsis & Stephan M. Wagner, 2018. "Zurich Insurance Uses Data Analytics to Leverage the BI Insurance Proposition," Interfaces, INFORMS, vol. 48(2), pages 94-107, April.
    2. Kelei Xue & Ya Xu & Lipan Feng, 2018. "Managing Procurement for a Firm with Two Ordering Opportunities under Supply Disruption Risk," Sustainability, MDPI, vol. 10(9), pages 1-32, September.
    3. Pourya Pourhejazy & Oh Kyoung Kwon, 2016. "The New Generation of Operations Research Methods in Supply Chain Optimization: A Review," Sustainability, MDPI, vol. 8(10), pages 1-23, October.
    4. Tong Shu & Xiaoqin Gao & Shou Chen & Shouyang Wang & Kin Keung Lai & Lu Gan, 2016. "Weighing Efficiency-Robustness in Supply Chain Disruption by Multi-Objective Firefly Algorithm," Sustainability, MDPI, vol. 8(3), pages 1-27, March.
    5. Dmitry Ivanov & Boris Sokolov, 2019. "Simultaneous structural–operational control of supply chain dynamics and resilience," Annals of Operations Research, Springer, vol. 283(1), pages 1191-1210, December.
    6. Sahebjamnia, Navid & Torabi, S. Ali & Mansouri, S. Afshin, 2018. "Building organizational resilience in the face of multiple disruptions," International Journal of Production Economics, Elsevier, vol. 197(C), pages 63-83.
    7. Niknejad, Ali & Petrovic, Dobrila, 2016. "A fuzzy dynamic Inoperability Input–output Model for strategic risk management in Global Production Networks," International Journal of Production Economics, Elsevier, vol. 179(C), pages 44-58.
    8. Sungchul Cho & Up Lim, 2016. "The Sustainability of Global Chain Governance: Network Structures and Local Supplier Upgrading in Thailand," Sustainability, MDPI, vol. 8(9), pages 1-13, September.

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