Modeling Company Risk and Importance in Supply Graphs

  • Lucas Carstens
  • Jochen L. Leidner
  • Krzysztof Szymanski
  • Blake Howald
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10250)

Abstract

Managing one’s supply chain is a key task in the operational risk management for any business. Human procurement officers can manage only a limited number of key suppliers directly, yet global companies often have thousands of suppliers part of a wider ecosystem, which makes overall risk exposure hard to track. To this end, we present an industrial graph database application to account for direct and indirect (transitive) supplier risk and importance, based on a weighted set of measures: criticality, replaceability, centrality and distance. We describe an implementation of our graph-based model as an interactive and visual supply chain risk and importance explorer. Using a supply network (comprised of approximately 98, 000 companies and 220, 000 relations) induced from textual data by applying text mining techniques to news stories, we investigate whether our scores may function as a proxy for actual supplier importance, which is generally not known, as supply chain relationships are typically closely guarded trade secrets. To our knowledge, this is the largest-scale graph database and analysis on real supply relations reported to date.

Keywords

Supply chain analysis Graph analysis Risk analysis Vulnerability analysis Linked data Procurement 

Notes

Acknowledgments

We would like to thank Khalid Al-Kofahi and the CTO office for supporting this work and thank Giuseppe Saltini, Shai Hertz, Yoni Mataraso and Geoffrey Horrell for discussions and data.

References

  1. 1.
    Aggarwal, C.C.: An introduction to social network data analytics. In: Aggarwal, C.C. (ed.) Social Network Data Analytics, pp. 1–15. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Alhomidi, M., Reed, M.: Attack graph-based risk assessment and optimisation approach. Int. J. Netw. Secur. Appl. 6(3), 31 (2014)Google Scholar
  3. 3.
    Aqlan, F., Lam, S.S.: A fuzzy-based integrated framework for supply chain risk assessment. Int. J. Prod. Econ. 161, 54–63 (2015)CrossRefGoogle Scholar
  4. 4.
    Bisias, D., Flood, M.D., Lo, A.W., Valavanis, S.: A survey of systemic risk analytics. US Department of Treasury, Office of Financial Research 0001 (2012)Google Scholar
  5. 5.
    Blome, C., Schoenherr, T.: Supply chain risk management in financial crises - a multiple case-study approach. Int. J. Prod. Econ. 134(1), 43–57 (2011)CrossRefGoogle Scholar
  6. 6.
    Borgatti, S.P., Li, X.: On social network analysis in a supply chain context. J. Supply Chain Manage. 45(2), 5–22 (2009)CrossRefGoogle Scholar
  7. 7.
    Ghadge, A., Dani, S., Chester, M., Kalawsky, R.: A systems approach for modelling supply chain risks. Supply Chain Manage. Int. J. 18(5), 523–538 (2013)CrossRefGoogle Scholar
  8. 8.
    Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V.-M., Tuominen, M.: Risk management processes in supplier networks. Int. J. Prod. Econ. 90(1), 47–58 (2004)CrossRefGoogle Scholar
  9. 9.
    Harland, C., Brenchley, R., Walker, H.: Risk in supply networks. J. Purch. Supply Manage. 9(2), 51–62 (2003)CrossRefGoogle Scholar
  10. 10.
    Huang, X., Vodenska, I., Havlin, S., Stanley, H.E.: Cascading failures in bi-partite graphs: model for systemic risk propagation. Sci. Rep. 3, Article no: 1219 (2013). doi:10.1038/srep01219
  11. 11.
    Jüttner, U.: Supply chain risk management: understanding the business requirements from a practitioner perspective. Int. J. Logist. Manage. 16(1), 120–141 (2005)CrossRefGoogle Scholar
  12. 12.
    Kim, Y., Choi, T.Y., Yan, T., Dooley, K.: Structural investigation of supply networks: a social network analysis approach. J. Oper. Manage. 29(3), 194–211 (2011)CrossRefGoogle Scholar
  13. 13.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)MATHGoogle Scholar
  14. 14.
    Nugent, T., Leidner, J.L.: Risk mining: company-risk identification from unstructured sources. In: IEEE International Conference on Data Mining, ICDM, pp. 1308–1311 (2016)Google Scholar
  15. 15.
    Phillips, C.A., Swiler, L.P.: A graph-based system for network-vulnerability analysis. In: Proceedings of the 1998 Workshop on New Security Paradigms, Charlottsville, VA, USA, September 22–25, 1998, pp. 71–79 (1998)Google Scholar
  16. 16.
    Poolsappasit, N., Dewri, R., Ray, I.: Dynamic security risk management using Bayesian attack graphs. IEEE Trans. Dependable Sec. Comp. 9(1), 61–74 (2012)CrossRefGoogle Scholar
  17. 17.
    Simchi-Levi, D., Schmidt, W., Wei, Y.: From superstroms to factory fires: managing unpredictable supply chain disruptions. Harv. Bus. Rev. 92(1), 96–100 (2014)Google Scholar
  18. 18.
    Stergiopoulos, G., Kotzanikolaou, P., Theocharidou, M., Gritzalis, D.: Risk mitigation strategies for critical infrastructures based on graph centrality analysis. IJCIP 10, 34–44 (2015)Google Scholar
  19. 19.
    Tan, K.H., Zhan, Y., Ji, G., Ye, F., Chang, C.: Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph. Int. J. Prod. Econ. 165, 223–233 (2015)CrossRefGoogle Scholar
  20. 20.
    Tayur, S., Ganeshan, R., Magazine, M.: Quantitative Models for Supply Chain Management, vol. 17. Springer, Heidelberg (2012)MATHGoogle Scholar
  21. 21.
    Timmer, M.P., Dietzenbacher, E., Los, B., Stehrer, R., Vries, G.J.: An illustrated user guide to the world input-output database: the case of global automotive production. Rev. Int. Econ. 23(3), 575–605 (2015)CrossRefGoogle Scholar
  22. 22.
    Wagner, S.M., Neshat, N.: Assessing the vulnerability of supply chains using graph theory. Int. J. Prod. Econ. 126(1), 121–129 (2010)CrossRefGoogle Scholar
  23. 23.
    Xu, N.-R., Liu, J.-B., Li, D.-X., and Wang, J.: Research on evolutionary mechanism of agile supply chain network via complex network theory. In: Mathematical Problems in Engineering 2016 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lucas Carstens
    • 1
  • Jochen L. Leidner
    • 1
  • Krzysztof Szymanski
    • 2
  • Blake Howald
    • 3
  1. 1.Thomson Reuters, Corporate Research and DevelopmentEnglandUK
  2. 2.Thomson Reuters, Platform GroupGdyniaPoland
  3. 3.Thomson Reuters, Platform GroupEaganUSA

Personalised recommendations