Advertisement

Fuzzy Entropy Method for Quantifying Supply Chain Networks Complexity

  • Jihui Zhang
  • Junqin Xu
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 5)

Abstract

Supply chain is a special kind of complex network. Its complexity and uncertainty makes it very difficult to control and manage. Supply chains are faced with a rising complexity of products, structures, and processes. Because of the strong link between a supply chain’s complexity and its efficiency the supply chain complexity management becomes a major challenge of today’s business management. The aim of this paper is to quantify the complexity and organization level of an industrial network working towards the development of a ‘Supply Chain Network Analysis’ (SCNA). By measuring flows of goods and interaction costs between different sectors of activity within the supply chain borders, a network of flows is built and successively investigated by network analysis. The result of this study shows that our approach can provide an interesting conceptual perspective in which the modern supply network can be framed, and that network analysis can handle these issues in practice.

Keywords

supply network complexity complex network network analysis entropy fuzzy variable 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Christopher, M.: Logistics and Supply Chain Management. Richard D. Irwin, Inc., Financial Times, New York (1994)Google Scholar
  2. 2.
    Gunasekaran, A., Yusuf, Y.Y.: Agile manufacturing: a taxonomy of strategic and technological imperatives. International Journal of Production Research 40(6), 1357–1385 (2002)CrossRefGoogle Scholar
  3. 3.
    Yusuf, Y.Y., Sarhadi, M., Gunasekaran, A.: Agile manufacturing: the drivers, concepts and attributes. International Journal Production Economics 62(1), 33–43 (1999)CrossRefGoogle Scholar
  4. 4.
    Barábasi, A.L.: Linked. Plume Books (2003)Google Scholar
  5. 5.
    Can You Reduce Your Supply Chain Complexity? PRTM report (2006)Google Scholar
  6. 6.
    Huan, S., Sheoran, S., Wang, G.: A review and analysis of supply chain operations reference (SCOR) model. International Journal of Supply Chain Management 9(1), 23–29 (2004)CrossRefGoogle Scholar
  7. 7.
    Perona, M., Miragliotta, G.: Complexity management and supply chain performance assessment. A field study and a conceptual framework. International Journal of Production Economics 90(1), 103–115 (2004)CrossRefGoogle Scholar
  8. 8.
    Milgate, M.: Supply chain complexity and delivery performance: an international exploratory study. International Journal of Supply Chain Management 6(3), 106–118 (2001)CrossRefGoogle Scholar
  9. 9.
    Battini, D., Persona, A., Allesina, S.: Towards a use of network analysis: quantifying the complexity of Supply Chain Networks. International Journal of Electronic Customer Relationship Management 1(1), 75–90 (2007)CrossRefGoogle Scholar
  10. 10.
    Karp, A., Ronen, B.: Improving shop floor control: an entropy model approach. International Journal of Production Research 30(4), 923–938 (1992)CrossRefzbMATHGoogle Scholar
  11. 11.
    Frizelle, G., Woodcock, E.: Measuring complexity as an aid to developing operational strategy. International Journal of Operations & Production Management 15(1), 26–39 (1994)Google Scholar
  12. 12.
    Frizelle, G.: Measuring complexity in manufacturing. In: Proceedings Advances in Manufacturing Conference, Bath (1996)Google Scholar
  13. 13.
    Sivadasan, S., Efstathiou, J., Frizelle, G., Shirazi, R., Calinescu, A.: An information-theoretic methodology for measuring the operational complexity of supplier-customer systems. International Journal of Operation and Production Management 22(1), 80–102 (2002)CrossRefGoogle Scholar
  14. 14.
    Choi, T.Y., Dooley, K.J., Manus, R.: Supply networks and complex adaptive systems: control versus emergence. Journal of Operations Management 19(3), 351–366 (2001)CrossRefGoogle Scholar
  15. 15.
    Sharon, N., Eppinger, S.D.: Sourcing By Design: Product Complexity and the Supply Chain. Management Science 47(1), 189–204 (2001)CrossRefGoogle Scholar
  16. 16.
    Richard, W.: The supply chain complexity triangle: uncertainty genersation in the supply chain. International Journal of Physical Distribution and Logistics Management 28(8), 599–616 (1998)CrossRefGoogle Scholar
  17. 17.
    Bozarth, C.C., Warsing, D.P., Flynn, B.B., et al.: The impact of supply chain complexity on manufacturing plant performance. Journal of Operations Management (forthcoming) (2008), doi:10.1016/j.jom.2008.07.003Google Scholar
  18. 18.
    Carlsson, C., Fuller, R.: On possibilistic mean value and variance of fuzzy numbers. Fuzzy Sets and Systems 122(2), 315–326 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Graves, S.C., Willems, S.P.: Optimizing the supply chain configuration for new products. Management Science 51(8), 1165–1180 (2005)CrossRefzbMATHGoogle Scholar
  20. 20.
    Geoffrion, A.M., Powers, R.F.: Twenty years of strategic distribution system design: An evolutionary perspective. Interfaces 25(5), 105–127 (1995)CrossRefGoogle Scholar
  21. 21.
    Geoffrion, A.M., Graves, G.W.: Multicommodity distribution system design by Benders decomposition. Management Science 20(5), 822–844 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Arntzen, B.C., Brown, G.G., Harrison, T.P., Trafton, L.L.: Global supply chain management at Digital Equipment Corporation. Interfaces 25(1), 69–93 (1995)CrossRefGoogle Scholar
  23. 23.
    Li, H., Womer, K.: Modeling the supply chain configuration problem with resource constraints. International Journal of Project Management 26(6), 646–654 (2008)CrossRefGoogle Scholar
  24. 24.
    Wang, J., Shu, Y.: A possibilistic decision model for new product supply chain design. European Journal of Operational Research 177(2), 1044–1061 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Wang, H.S., Che, Z.H.: An integrated model for supplier selection decisions in configuration changes. Expert Systems with Applications 32(4), 1132–1140 (2007)CrossRefGoogle Scholar
  26. 26.
    Wang, H.S.: Configuration Change Assessment: Genetic Optimization Approach with Fuzzy Multiple Criteria for Part Supplier Selection Decisions. Expert Systems with Applications 34(2), 1541–1555 (2008)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2009

Authors and Affiliations

  • Jihui Zhang
    • 1
  • Junqin Xu
    • 2
  1. 1.Institute of Complexity ScienceQingdao UniversityQingdaoChina
  2. 2.School of Mathematics ScienceQingdao UniversityQingdaoChina

Personalised recommendations