Supply Chain Safety: A Diversification Model Based on Clustering

  • Andreas Brieden
  • Peter Gritzmann
  • Michael Öllinger
Part of the Lecture Notes in Logistics book series (LNLO)


The issue of supply chain safety has received broad attention which has led to a wide range of methodologically different approaches; for a survey see (Pfohl, Köhler & Thomas, 2010). The present paper introduces a novel quantitative algorithm that provides a multiple covering of the commodity graph via constrained clustering. In fact, we construct supply chain components in the overall supply network of a company, each being able to account for some percentage of the company’s overall production. They are all isomorphic to and can hence be viewed as different realizations of the commodity graph which are most independent with respect to known hazards. Consequently, suppliers (of each level) are assigned to supply chain components so as to minimize the probability for a total (or severe enough) breakdown. The basic new model is given in detail, complemented by an outline of more involved ramifications that are able to deal with realistic scenarios. Also, we give computer simulations that indicate the favorable behavior already of our basic model in terms of risk reduction.


Supply Chain Supply Network Supply Chain Network Supply Chain Performance Supply Chain Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Borgwardt, S., Brieden, A., Gritzmann, P.: Constrained minimum-k-star clustering and its application to the consolidation of farmland. Operational Research 11, 1–17 (2011)CrossRefGoogle Scholar
  2. Brieden, A., Gritzmann, P.: On clustering bodies: Geometry and polyhedral approximation. Discrete Comp. Geom. 44, 508–534 (2009)CrossRefGoogle Scholar
  3. Brieden, A., Gritzmann, P.: On optimal weighted balanced clusterings: gravity bodies and power diagrams. SIAM J. Discrete Math. (2012) (to appear)Google Scholar
  4. Brieden, A., Gritzmann, P., Öllinger, M.: A new model for supply chain safety (2011) (in preparation)Google Scholar
  5. Christopher, M., Peck, H.: Building the resilient supply chain. Intern. J. Logistics Manag. 15, 1–13 (2004)CrossRefGoogle Scholar
  6. Deane, J., Craighead, C., Ragsdale, C.: Mitigating environmental and density risk in global sourcing. Intern. J. Physical Distr. Logistics Manag. 39, 861–883 (2009)CrossRefGoogle Scholar
  7. Goh, M., Lim, J., Meng, F.: A stochastic model for risk management in global supply chain networks. European J. Operational Research 182(1), 164–173 (2007)CrossRefGoogle Scholar
  8. Hull, J.C.: Option, Futures, and other Derivatives, 8th edn. Pearson, New Jersey (2011)Google Scholar
  9. Jüttner, U., Peck, H., Christopher, M.: Supply chain risk management. Outlining an agenda for future research. Intern. J. Logistics: Research & Applications 6, 197–210 (2003)CrossRefGoogle Scholar
  10. Jüttner, U.: Supply chain risk management. Understanding the Business Requirements from a Practitioner Perspective, Intern. J. Logistics Manag. 16, 120–141 (2005)Google Scholar
  11. Klibi, W., Martel, A., Guitouni, A.: The design of robust value-creating supply chain networks: A critical review. Europ. J. Operational Research 203, 283–293 (2010)CrossRefGoogle Scholar
  12. Norrman, A., Jansson, U.: Ericsson’s proactive supply chain risk management approach after a serious sub-supplier accident. Intern. J. Physical Distr. Logistics Manag. 34, 434–456 (2004)CrossRefGoogle Scholar
  13. Peck, H.: Drivers of supply chain vulnerability: an integrated framework. Intern. J. Physical Distr. Logistics Manag. 35, 210–232 (2005)CrossRefGoogle Scholar
  14. Pfohl, H.-C., Köhler, H., Thomas, D.: State of the art in supply chain risk management research: Empirical and conceptual findings and a roadmap for the implementation in practice. Logistics Research 2, 33–44 (2010)CrossRefGoogle Scholar
  15. Ponomarov, S., Holcomb, M.: Understanding the concept of supply chain resilience. Intern. J. Logistics Manag. 20, 124–143 (2009)CrossRefGoogle Scholar
  16. Ravindran, A.R., Bilsel, R.U., Wadhwa, V., Yang, T.: Risk adjusted multicriteria supplier selection models with applications. Intern. J. Production Research 48, 405–424 (2010)CrossRefGoogle Scholar
  17. Santoso, T., et al.: A stochastic programming approach for supply chain network design under uncertainty. European J. Operational Research 167(1), 96–115 (2005)CrossRefGoogle Scholar
  18. Schütz, P., Tomasgard, A., Ahmed, S.: Supply chain design under uncertainty using sample average approximation and dual decomposition. European J. Operational Research 199(2), 409–419 (2009)CrossRefGoogle Scholar
  19. Sheffi, Y., Rice, J.: A supply chain view of the resilient enterprise. MIT Sloan Management Review 47, 41–48 (2005)Google Scholar
  20. Svensson, G.: A conceptual framework of vulnerability in firms’ inbound and outbound logistics flows. Intern. J. Physical Distr. Logistics Manag. 32, 110–134 (2002)CrossRefGoogle Scholar
  21. Tandler, S., Eßig, M.: Supply Chain Safety Management: Konzeption und Gestaltungsempfehlungen. In: Bogaschewsky, R., Eßig, M., Lasch, R., Stölzle, W. (eds.) Supply Management Research: Aktuelle Forschungsergebnisse 2011, Gabler, Wiesbaden, pp. 57–92 (2011)Google Scholar
  22. Tang, C.: Perspectives in supply chain risk management. Intern. J. Production Economics 103, 451–488 (2006a)CrossRefGoogle Scholar
  23. Tang, C.: Robust strategies for mitigating supply chain disruptions. Intern. J. Logistics: Research & Applications 9, 33–45 (2006b)CrossRefGoogle Scholar
  24. Teuteberg, F.: Supply chain risk management: A neural network approach. In: Ijioui, R., Emmerich, H., Ceyp, M. (eds.) Strategies and Tactics in Supply Chain Event Management, pp. 99–118. Springer, Berlin (2008)CrossRefGoogle Scholar
  25. Wagner, S., Bode, C.: An empirical examination of supply chain performance along several dimensions of risk. J. Business Logistics 29, 307–325 (2008)CrossRefGoogle Scholar
  26. Wagner, S., Neshat, N.: Assessing the vulnerability of supply chains using graph theory. Intern. J. Production Economics 126, 121–129 (2010)CrossRefGoogle Scholar
  27. Yang, Z.-L., Wang, J., Bonsall, S., Yang, J.-B., Fang, Q.-G.: A subjective risk analysis approach for container supply chains. Int. J. Automation Comput. 1, 85–92 (2005)CrossRefGoogle Scholar
  28. Zsidisin, G., Ellram, L.: An agency theory investigation of supply risk management. J. Supply Chain Management 39, 15–27 (2003)CrossRefGoogle Scholar
  29. Zsidisin, G., Wagner, S.: Do perceptions become reality? The moderating role of supply chain resiliency on disruption occurrence. J. Business Logistics 31, 1–20 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andreas Brieden
    • 1
  • Peter Gritzmann
    • 2
  • Michael Öllinger
    • 3
  1. 1.Inhaber der Professur für Statistik, insbesondere Risikomanagement (English: Statistics and Risk Management)Universität der Bundeswehr MünchenNeubibergGermany
  2. 2.Zentrum Mathematik, Lehrstuhl für Angewandte Geometrie und Diskrete Mathematik (English: Applied Geometry und Discrete Mathematics)Technische Universität MünchenGarchingGermany
  3. 3.Wissenschaftlicher Mitarbeiter an der Professur für Statistik, Insbesondere RisikomanagementUniversität der Bundeswehr MünchenNeubibergGermany

Personalised recommendations