全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Network Analysis for Systemic Risk Assessment in Supply Chains: A Cross-Disciplinary Framework Integrating Financial Contagion Models

DOI: 10.4236/jdaip.2025.133022, PP. 347-369

Keywords: Supply Chain Resilience, Systemic Risk Assessment, Network Vulnerability Analysis, Financial Contagion Modeling

Full-Text   Cite this paper   Add to My Lib

Abstract:

This research develops a novel cross-disciplinary framework that bridges financial systemic risk modeling with supply chain network analysis to advance resilience assessment and policy guidance. The approach integrates established financial contagion frameworks with the topology of the supply chain network, introducing the concept of “too central to fail” suppliers through systematic importance scoring methodologies. The framework reveals striking asymmetries in supply chain vulnerability patterns. While the majority of suppliers demonstrate systemic importance within network structures, financial fragility analysis indicates remarkable overall network robustness, with minimal nodes exhibiting high vulnerability thresholds. Most significantly, comprehensive stress testing exposes a critical paradox: networks demonstrate moderate resilience to random disruptions yet remain substantially vulnerable to strategic targeting of central nodes. Cascade failure analysis through multiple simulation approaches unveils the dual nature of supply chain risk propagation. Random shock scenarios generate manageable failure rates, while targeted attacks on high-centrality suppliers achieve disproportionate network impact. Most alarmingly, liquidity crisis simulations demonstrate how financial contagion mechanisms can affect nearly half of all network participants, highlighting the interconnected nature of operational and financial vulnerabilities. These findings establish quantitative foundations for the assessment of systemic risk in supply chains, with immediate implications for regulatory frameworks, early warning systems, and resilience enhancement strategies. The integrated financial-operational risk framework advances the theoretical understanding of the propagation of cross-sector vulnerability while providing systematic methodologies for identifying critical suppliers whose failure could trigger systemic collapse.

References

[1]  Choi, T. (2021) Risk Analysis in Logistics Systems: A Research Agenda during and after the COVID-19 Pandemic. Transportation Research Part E: Logistics and Transportation Review, 145, Article ID: 102190.
https://doi.org/10.1016/j.tre.2020.102190
[2]  Verschuur, J., Koks, E.E. and Hall, J.W. (2021) Global Economic Impacts of COVID-19 Lockdown Measures Stand Out in High-Frequency Shipping Data. PLOS ONE, 16, e0248818.
https://doi.org/10.1371/journal.pone.0248818
[3]  Tang, C.S. (2006) Perspectives in Supply Chain Risk Management. International Journal of Production Economics, 103, 451-488.
https://doi.org/10.1016/j.ijpe.2005.12.006
[4]  Kleindorfer, P.R. and Saad, G.H. (2005) Managing Disruption Risks in Supply Chains. Production and Operations Management, 14, 53-68.
https://doi.org/10.1111/j.1937-5956.2005.tb00009.x
[5]  Eisenberg, L. and Noe, T.H. (2001) Systemic Risk in Financial Systems. Management Science, 47, 236-249.
https://doi.org/10.1287/mnsc.47.2.236.9835
[6]  Battiston, S., Puliga, M., Kaushik, R., Tasca, P. and Caldarelli, G. (2012) DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk. Scientific Reports, 2, Article No. 541.
https://doi.org/10.1038/srep00541
[7]  Basel Committee on Banking Supervision (2011) Global Systemically Important Banks: Assessment Methodology and the Additional Loss Absorbency Requirement. Bank for International Settlements.
https://www.bis.org/publ/bcbs201.htm
[8]  Acemoglu, D., Carvalho, V.M., Ozdaglar, A.E. and Tahbaz-Salehi, A. (2011) The Network Origins of Aggregate Fluctuations. Econometrica, 80, 1977-2016.
[9]  Elliott, M., Golub, B. and Jackson, M.O. (2014) Financial Networks and Contagion. American Economic Review, 104, 3115-3153.
https://doi.org/10.1257/aer.104.10.3115
[10]  Drehmann, M. and Tarashev, N. (2013) Measuring the Systemic Importance of Interconnected Banks. Journal of Financial Intermediation, 22, 586-607.
https://doi.org/10.1016/j.jfi.2013.08.001
[11]  Battiston, S., Caldarelli, G., May, R.M., Roukny, T. and Stiglitz, J.E. (2016) The Price of Complexity in Financial Networks. Proceedings of the National Academy of Sciences of the United States of America, 113, 10031-10036.
https://doi.org/10.1073/pnas.1521573113
[12]  Greenwood, R., Landier, A. and Thesmar, D. (2015) Vulnerable Banks. Journal of Financial Economics, 115, 471-485.
https://doi.org/10.1016/j.jfineco.2014.11.006
[13]  Billio, M., Getmansky, M., Lo, A.W. and Pelizzon, L. (2012) Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors. Journal of Financial Economics, 104, 535-559.
https://doi.org/10.1016/j.jfineco.2011.12.010
[14]  Bisias, D., Flood, M., Lo, A.W. and Valavanis, S. (2012) A Survey of Systemic Risk Analytics. Annual Review of Financial Economics, 4, 255-296.
https://doi.org/10.1146/annurev-financial-110311-101754
[15]  Hanson, S.G., Kashyap, A.K. and Stein, J.C. (2011) A Macroprudential Approach to Financial Regulation. Journal of Economic Perspectives, 25, 3-28.
https://doi.org/10.1257/jep.25.1.3
[16]  Bode, C. and Wagner, S.M. (2015) Structural Drivers of Upstream Supply Chain Complexity and the Frequency of Supply Chain Disruptions. Journal of Operations Management, 36, 215-228.
https://doi.org/10.1016/j.jom.2014.12.004
[17]  Garvey, M.D., Carnovale, S. and Yeniyurt, S. (2015) An Analytical Framework for Supply Network Risk Propagation: A Bayesian Network Approach. European Journal of Operational Research, 243, 618-627.
https://doi.org/10.1016/j.ejor.2014.10.034
[18]  Ambulkar, S., Blackhurst, J. and Grawe, S. (2014) Firm’s Resilience to Supply Chain Disruptions: Scale Development and Empirical Examination. Journal of Operations Management, 33, 111-122.
https://doi.org/10.1016/j.jom.2014.11.002
[19]  Wagner, S.M. and Bode, C. (2008) An Empirical Examination of Supply Chain Performance along Several Dimensions of Risk. Journal of Business Logistics, 29, 307-325.
https://doi.org/10.1002/j.2158-1592.2008.tb00081.x
[20]  Iyer, A.V., Seshadri, S. and Vasher, R. (2009) Toyota Supply Chain Management: A Strategic Approach to Toyota’s Renowned System. McGraw-Hill.
[21]  Choi, T.Y. and Krause, D.R. (2005) The Supply Base and Its Complexity: Implications for Transaction Costs, Risks, Responsiveness, and Innovation. Journal of Operations Management, 24, 637-652.
https://doi.org/10.1016/j.jom.2005.07.002
[22]  Shan, J. and Zhu, K. (2013) Inventory Management in Supply Chains with Consideration of Logistics, Green Investment and Different Carbon Emissions Policies. Computers & Industrial Engineering, 64, 194-203.
[23]  S&P Capital IQ (2023) Industry Financial Data and Analytics Platform. S&P Global Market Intelligence.
[24]  Damodaran, A. (2023) Applied Corporate Finance. 5th Edition, Wiley.
[25]  Sodhi, M.S. and Tang, C.S. (2012) Managing Supply Chain Risk. Springer.
https://doi.org/10.1007/978-1-4614-3238-8
[26]  Campbell, J.Y., Hilscher, J. and Szilagyi, J. (2008) In Search of Distress Risk. The Journal of Finance, 63, 2899-2939.
https://doi.org/10.1111/j.1540-6261.2008.01416.x
[27]  Altman, E.I., Iwanicz‐Drozdowska, M., Laitinen, E.K. and Suvas, A. (2016) Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman’s z‐Score Model. Journal of International Financial Management & Accounting, 28, 131-171.
https://doi.org/10.1111/jifm.12053
[28]  Jenks, G.F. (1967) The Data Model Concept in Statistical Mapping. International Yearbook of Cartography, 7, 186-190.
[29]  Freeman, L.C. (1978) Centrality in Social Networks Conceptual Clarification. Social Networks, 1, 215-239.
https://doi.org/10.1016/0378-8733(78)90021-7
[30]  Bonacich, P. (1987) Power and Centrality: A Family of Measures. American Journal of Sociology, 92, 1170-1182.
https://doi.org/10.1086/228631
[31]  Brin, S. and Page, L. (1998) The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems, 30, 107-117.
https://doi.org/10.1016/s0169-7552(98)00110-x
[32]  McNeil, A.J., Frey, R. and Embrechts, P. (2015) Quantitative Risk Management: Concepts, Techniques and Tools. Princeton University Press.
[33]  Gai, P. and Kapadia, S. (2010) Contagion in Financial Networks. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 466, 2401-2423.
https://doi.org/10.1098/rspa.2009.0410
[34]  Albert, R. and Barabási, A. (2002) Statistical Mechanics of Complex Networks. Reviews of Modern Physics, 74, 47-97.
https://doi.org/10.1103/revmodphys.74.47
[35]  Sandve, G.K., Nekrutenko, A., Taylor, J. and Hovig, E. (2013) Ten Simple Rules for Reproducible Computational Research. PLOS Computational Biology, 9, e1003285.
https://doi.org/10.1371/journal.pcbi.1003285
[36]  Efron, B. and Tibshirani, R. (1986) Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Statistical Science, 1, 54-75.
https://doi.org/10.1214/ss/1177013815

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133