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)


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.


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



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.


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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

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