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Systematic Risk in Supply Chain Networks

Author

Listed:
  • Nikolay Osadchiy

    (Goizueta Business School, Emory University, Atlanta, Georgia 30322)

  • Vishal Gaur

    (Johnson Graduate School of Management, Cornell University, Ithaca, New York 14850)

  • Sridhar Seshadri

    (Indian School of Business, Hyderabad 500032, India)

Abstract

Industrial production output is generally correlated with the state of the economy. Nonetheless, during times of economic downturn, some industries take the biggest hit, whereas at times of economic boom they reap most benefits. To provide insight into this phenomenon, we map supply networks of industries and firms and investigate how the supply network structure mediates the effect of economy on industry or firm sales. Previous research has shown that retail sales are correlated with the state of the economy. Since retailers source their products from other industries, the sales of their suppliers can also be correlated with the state of the economy. This correlation represents the source of systematic risk for an industry that propagates through a supply chain network. Specifically, we identify the following mechanisms that can affect the correlation between sales and the state of the economy in a supply chain network: propagation of systematic risk into production decisions, aggregation of orders from multiple customers in a supply chain network, and aggregation of orders over time. We find that the first effect does not amplify the correlation; however, the latter two intensify correlation and result in the amplification of correlation upstream in supply networks. We demonstrate three managerial implications of this phenomenon: implications for the cost of capital, for the risk-adjusted valuation of supply chain improvement projects, and for supplier selection and risk.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2187 . This paper was accepted by Serguei Netessine, operations management .

Suggested Citation

  • Nikolay Osadchiy & Vishal Gaur & Sridhar Seshadri, 2016. "Systematic Risk in Supply Chain Networks," Management Science, INFORMS, vol. 62(6), pages 1755-1777, June.
  • Handle: RePEc:inm:ormnsc:v:62:y:2016:i:6:p:1755-1777
    DOI: 10.1287/mnsc.2015.2187
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    References listed on IDEAS

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    Cited by:

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    3. Yukiko Saito & Makoto Nirei & Vasco Carvalho, 2014. "Supply Chain Disruptions: Evidence from Great East Japan Earthquake," 2014 Meeting Papers 595, Society for Economic Dynamics.
    4. Li Zhu & Weiyong Yu & Jinxiu Yang, 2022. "Game analysis on the quality and safety control of pork supply chain - The case study of China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(11), pages 434-443.
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    9. Kostas Bimpikis & Ozan Candogan & Shayan Ehsani, 2019. "Supply Disruptions and Optimal Network Structures," Management Science, INFORMS, vol. 65(12), pages 5504-5517, December.
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    11. Wang, Jiepeng & Zhou, Hong & Zhao, Yujie, 2022. "Behavior evolution of supply chain networks under disruption risk — From aspects of time dynamic and spatial feature," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    12. Robert L. Bray & Juan Camilo Serpa & Ahmet Colak, 2019. "Supply Chain Proximity and Product Quality," Management Science, INFORMS, vol. 65(9), pages 4079-4099, September.
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    15. Bai, Xuelian & Fang, Ruirui & Henry, Elaine & Hu, Nan, 2020. "Supply chain hierarchical position and firms’ information quality," Journal of Financial Stability, Elsevier, vol. 51(C).
    16. Mu, Dong & Ren, Huanyu & Wang, Chao & Yue, Xiongping & Du, Jianbang & Ghadimi, Pezhman, 2023. "Structural characteristics and disruption ripple effect in a meso-level electric vehicle Lithium-ion battery supply chain network," Resources Policy, Elsevier, vol. 80(C).
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    19. Hu, Nan & Liang, Peng & Liu, Ling & Zhu, Lu, 2022. "The bullwhip effect and credit default swap market: A study based on firm-specific bullwhip effect measure," International Review of Financial Analysis, Elsevier, vol. 84(C).

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