IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v180y2016icp148-157.html
   My bibliography  Save this article

Data-driven risk measurement of firm-to-firm relationships in a supply chain

Author

Listed:
  • Lee, Byung Kwon
  • Zhou, Rong
  • de Souza, Robert
  • Park, Jaehun

Abstract

Business entities are always exposed to potential risks as they are interconnected in a supply chain. The performance of a business entity would be disturbed by the realization of risks, and substantial effort would be required to bring its performance back to the previous level. This study proposes an approach to measure the degree of risk caused by a supplier to the manufacturer by considering the interaction between them in a supply chain. A supply chain simulation is developed based on a real business case for an assemble-to-order industry, and the operational dataset is used to measure the degree of risk. A binary response model with a latent variable is employed to estimate the degree of risk under different conditions. Sensitivity analyses are conducted using a numerical experiment. The results show that decremental demand outperforms incremental demand when the lead time of supply is the performance measure. In terms of the degree of risk, the converse is found to be true when the fulfillment rate is the performance measure. The proposed approach could be used to quantify the risk level, identify the bottleneck supplier, and provide a guide for updating the operational settings.

Suggested Citation

  • Lee, Byung Kwon & Zhou, Rong & de Souza, Robert & Park, Jaehun, 2016. "Data-driven risk measurement of firm-to-firm relationships in a supply chain," International Journal of Production Economics, Elsevier, vol. 180(C), pages 148-157.
    • Handle: RePEc:eee:proeco:v:180:y:2016:i:c:p:148-157
    DOI: 10.1016/j.ijpe.2016.07.025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527316301773
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2016.07.025?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Jo~ao P. da Cruz & Pedro G. Lind, 2010. "Self-organized criticality in a network of economic agents with finite consumption," Papers 1009.5830, arXiv.org, revised Dec 2012.
    3. Silvia Figini & Paolo Giudici, 2013. "Measuring risk with ordinal variables," DEM Working Papers Series 032, University of Pavia, Department of Economics and Management.
    4. Jo~ao P. da Cruz & Pedro G. Lind, 2011. "The bounds of heavy-tailed return distributions in evolving complex networks," Papers 1109.2803, arXiv.org, revised Jan 2013.
    5. J. Cruz & P. Lind, 2012. "The dynamics of financial stability in complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 85(8), pages 1-9, August.
    6. da Cruz, João P. & Lind, Pedro G., 2012. "Self-organized criticality in a network of economic agents with finite consumption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1445-1452.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nguyen, Son & Shu-Ling Chen, Peggy & Du, Yuquan, 2022. "Risk assessment of maritime container shipping blockchain-integrated systems: An analysis of multi-event scenarios," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    2. 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.
    3. Gerda Žigienė & Egidijus Rybakovas & Rimgailė Vaitkienė & Vaidas Gaidelys, 2022. "Setting the Grounds for the Transition from Business Analytics to Artificial Intelligence in Solving Supply Chain Risk," Sustainability, MDPI, vol. 14(19), pages 1-23, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Boon Kin Teh & Siew Ann Cheong, 2016. "The Asian Correction Can Be Quantitatively Forecasted Using a Statistical Model of Fusion-Fission Processes," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-13, October.
    2. Chong, You Quan & Wang, Bin & Yue Tan, Gladys Li & Cheong, Siew Ann, 2014. "Diversified firms on dynamical supply chain cope with financial crisis better," International Journal of Production Economics, Elsevier, vol. 150(C), pages 239-245.
    3. Jiang, Nan, 2014. "WDEM: Weighted dynamics and evolution models for energy-constrained wireless sensor networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 323-331.
    4. Wuttke, David A. & Blome, Constantin & Henke, Michael, 2013. "Focusing the financial flow of supply chains: An empirical investigation of financial supply chain management," International Journal of Production Economics, Elsevier, vol. 145(2), pages 773-789.
    5. Charles D. Brummitt & Kenan Huremović & Paolo Pin & Matthew H. Bonds & Fernando Vega-Redondo, 2017. "Contagious disruptions and complexity traps in economic development," Nature Human Behaviour, Nature, vol. 1(9), pages 665-672, September.
    6. 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.
    7. Silvia Facchinetti & Paolo Giudici & Silvia Angela Osmetti, 2020. "Cyber risk measurement with ordinal data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 173-185, March.
    8. Hou, Yunzhang & Wang, Xiaoling & Wu, Yenchun Jim & He, Peixu, 2018. "How does the trust affect the topology of supply chain network and its resilience? An agent-based approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 229-241.
    9. Chowdhury, Md. Maruf Hossan & Quaddus, Mohammed A., 2015. "A multiple objective optimization based QFD approach for efficient resilient strategies to mitigate supply chain vulnerabilities: The case of garment industry of Bangladesh☆,☆☆☆This manuscript was pro," Omega, Elsevier, vol. 57(PA), pages 5-21.
    10. Silva, Walmir & Kimura, Herbert & Sobreiro, Vinicius Amorim, 2017. "An analysis of the literature on systemic financial risk: A survey," Journal of Financial Stability, Elsevier, vol. 28(C), pages 91-114.
    11. Spiros Bougheas & Alan Kirman, 2015. "Complex Financial Networks and Systemic Risk: A Review," Dynamic Modeling and Econometrics in Economics and Finance, in: Pasquale Commendatore & Saime Kayam & Ingrid Kubin (ed.), Complexity and Geographical Economics, edition 127, pages 115-139, Springer.
    12. Mohammad Reza Seddigh & Sajjad Shokouhyar & Fatemeh Loghmani, 2023. "Approaching towards sustainable supply chain under the spotlight of business intelligence," Annals of Operations Research, Springer, vol. 324(1), pages 937-970, May.
    13. Wagner, Stephan M. & Mizgier, Kamil J. & Papageorgiou, Stylianos, 2017. "Operational disruptions and business cycles," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 66-78.
    14. Sandip Dutta & Vignesh Prabhu, 2019. "Effect of Franchised Business models on Fast Food Company Stock Prices in Recession and Recovery with Weibull Analysis," Papers 1912.12940, arXiv.org.
    15. 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.
    16. Nishihara, Michi & Shibata, Takashi, 2021. "Optimal capital structure and simultaneous bankruptcy of firms in corporate networks," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
    17. Dominguez, Roberto & Cannella, Salvatore & Framinan, Jose M., 2015. "On returns and network configuration in supply chain dynamics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 73(C), pages 152-167.
    18. Clive Hunt & Ross Taplin, 2019. "Aggregation of Incidence and Intensity Risk Variables to Achieve Reconciliation," Risks, MDPI, vol. 7(4), pages 1-14, October.
    19. Natalia Markovich & Marijus Vaičiulis, 2023. "Extreme Value Statistics for Evolving Random Networks," Mathematics, MDPI, vol. 11(9), pages 1-35, May.
    20. Schwab, Leila & Gold, Stefan & Reiner, Gerald, 2019. "Exploring financial sustainability of SMEs during periods of production growth: A simulation study," International Journal of Production Economics, Elsevier, vol. 212(C), pages 8-18.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:180:y:2016:i:c:p:148-157. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.