Skip to main content

Advertisement

SpringerLink
Log in

A measure of supply chain complexity incorporating virtual arcs

  • Published:
Journal of Systems Science and Systems Engineering Aims and scope Submit manuscript

Abstract

Increased globalization, as well as the ability to have virtual supply chain partners, has had numerous effects on supply chains. While some of these effects are positive, making more resilient supply chains, there are also the negative effects of scale and complexity, making these supply chains more challenging than ever to manage. Having a means to measure the complexity is crucial for today’s managers to make more informed decisions. This measure must not only account for the number of arcs, but the amount of information and material carried on it, as well as incorporate the benefit that virtual arcs add to the network by increasing efficiency and reducing information, product and financial transfer costs and time. This research utilizes newer models in network clustering and complexity theory to make them applicable to supply chains and creates a new, practical approach to measuring supply chain complexity which can be easily implemented by practitioners.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Allesinia, S., Azzi, A., Regattieri, A. & D. Battini (2010). Performance mesurement in supply chains: new network analysis and entropic indexes. International Journal of Production Research, 48 (8): 2297–2321.

    Article  Google Scholar 

  2. Bailey, S. & Grossman, R. L. (2013). A network complexity index for networks of networks. Accessed February 2015. http://www.flowforwarding.org/docs/Bailey%20-%20Grossman%20article%20on%20network%20complexity.pdf.

    Google Scholar 

  3. Barrat, A., Barthelemy, M., Pastor-Satorras R. & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the Natural Academy of Sciences, 101 (11): 3747–3752.

    Article  Google Scholar 

  4. Bonchev, D. & Buck, G. (2005). Quantitative measures of network complexity. Complexity in Chemistry, Biology and Ecology, 191–235. Springer US.

    Chapter  Google Scholar 

  5. Calinescu, A., Efstathious, J., Schim, J. & Bermejo, J. (1998). Applying and assessing two methods for measuring complexity in manufacturing. Journal of the Operational Research Society, 49 (7): 723–733.

    Article  MATH  Google Scholar 

  6. Caridi, M., Crippa, L., Perego, A., Sianesi, A. & Tumino, A. (2010). Do virtuality and complexity affect supply chain visibility? International Journal of Production Economics, 127: 372–383.

    Article  Google Scholar 

  7. Chandrashekar, A. & Schary, P.B. (1999). Toward the virtual supply chain: the convergence of IT and organization. The International Journal of Logistics Management, 10 (2): 27–40.

    Article  Google Scholar 

  8. Choi, T.Y. & Krause, D.R. (2006). The supply base and its complexity: implications for transaction costs, risk, responsiveness and innovation. Journal of Operations Management, 24 (5): 637–652.

    Article  Google Scholar 

  9. Choi, T.Y., Dooley, K.J. & Mungusanatham, M. (2001). Supply networks and complex adaptive systems: control versus emergence. Journal of Operations Management, 19 (3): 351–366.

    Article  Google Scholar 

  10. De Reyck, B. & Herroelen, W. (1996). On the use of the complexity index as a measure of complexity in activity networks. European Journal of Operational Research, 91: 347–366.

    Article  MATH  Google Scholar 

  11. Deshmukh, A.V., Talavage, J.J. & Barash, M.M. (1992). Characteristics of part mix complexity measure for manufacturing systems. IEEE International Conference on Systems, Man and Cybernetics. New York, NY. 1384–1389.

    Google Scholar 

  12. Deshmukh, A.V., Talvage, J.J. & Barash, M.M. (1998). Complexity in manufacturing systems, Part 1. IIE Transactions, 30 (7): 645–655.

    Google Scholar 

  13. Doncheva, N,T., Assenov, Y., Dominues, F.S. & Albrect, M. (2012). Topological Network Parameters. Nature, 7: 670–685.

    Google Scholar 

  14. Efstathiou, J., Calinescu, A. & Blackburn, G. (2002). A web-based expert system to assess the complexity of manufacturing organizations. Robotics and Computer Integrated Manufacturing, 18 (3-4): 305–311.

    Article  Google Scholar 

  15. Frizelle, G. & Woodcock, E. (1995). Measuring complexity as an aid to developing operational strategy. International Journal of Operations & Production Management, 15 (5): 26–39.

    Article  Google Scholar 

  16. Grandori, A. & Soda, G. (1995). Inter-Firm networks: antecedents, mechanisms and forms. Organization Studies, 16 (2): 183–214.

    Article  Google Scholar 

  17. Hall, N.R. & Preiser, S. (1984). Combined network complexity measures. IBM Journal of Research Development, 28 (1): 15–27.

    Article  Google Scholar 

  18. Harland, C., Brenchley, R. & Walker, H. (2003). Risk in supply networks. Journal of Purchasing and Supply Management, 9: 51–62.

    Article  Google Scholar 

  19. Harland, C., Lamming, R.C., Zheng, J. & Johnsen, T.E. (2001). A taxonomy of supply networks. The Journal of Supply Chain Management, 37 (4): 21–27.

    Article  Google Scholar 

  20. Henry, S.M. & Kafura, D.G. (1981). Software structure metrics based on information flow. IEEE Transactions on Software Engineering, 7 (5): 510–518.

    Article  Google Scholar 

  21. Holme, P., Park, S.M., Kim, B.J. & Edling, C.R. (2007). Korean university life in a network perspective: dynamics of a large affiliation network. Physica A: Statistical Mechanics and Its Applications, 373: 821–830.

    Article  Google Scholar 

  22. Isik, F. (2010). An entropy-based approach for measruing complexity in supply chains. International Journal of Production Research, 48 (12): 3681–3696.

    Article  MATH  Google Scholar 

  23. Kaimann, R.A. (1974). Coefficient of network complexity. Management Science, 21 (2): 172–177.

    Article  MATH  Google Scholar 

  24. Karp, A. & Ronen, B. (1992). Improving shop floor control: an entropy model approach. International Journal of Production Research, 30 (4): 923–938.

    Article  MATH  Google Scholar 

  25. Keating, M. (2000). Measuring design quality by measuring design complexity. Proceedings of the 1st International Symposium on Quality of Electronic Design (ISQED 2000). San Jose, CA. 103–108.

    Google Scholar 

  26. Kleindorfer, R. Paul R. & Saad, G.H. (2005). Managing disruption risks in supply chains. Productin and Operations Management, 14 (1): 53–68.

    Article  Google Scholar 

  27. Laue, R. & Gruhn, V. (2006). Complexity measures for business process models. Proceedings of the 9th International Conference on Business Information Systems. 1–12.

    Google Scholar 

  28. Luce, R.D. & Perry, A.D. (1949). A method of matrix analysis of group structure. Psychometrika, 14 (1): 95–116.

    Article  MathSciNet  Google Scholar 

  29. Mabert, V.A. & M.A. Venkatarmanan. (2009). Specail research focus on supply chain linkages: challenges for design and management in the 21st century. Decision Sciences, 29: 537–552.

    Article  Google Scholar 

  30. Madhavan, R., Gynwali, D.R. & He, J. (2004). Two's company, three's a crowd? triads in cooperative-competetive networks. Academy of Management Journal, 47: 918–927.

    Article  Google Scholar 

  31. McCabe, T.J. (1976). A complexity measure. IEEE Transactions on Software Engineering, SE-2, 4: 308–320.

    Article  MathSciNet  Google Scholar 

  32. Meepetchdee, Y. & Shah, N. (2007). Logisitical network design with robustness and complexity considerations. International Journal of Physical Distribution & Logistics Management, 37 (3): 201–222.

    Article  Google Scholar 

  33. Meyer, M.H. & Curley, K. F. (1995). The impact of knowledge and technology complexity on information systems development. Expert Systems with Applications, 8 (1): 111–134.

    Article  Google Scholar 

  34. Nagurney, A., Cruz, J., Dong, J. & Zhang, D. (2005). Supply chain networks, electronic commerce and supply side and demand side risk. European Journal of Operational Research, 164 (1): 120–142.

    Article  MATH  Google Scholar 

  35. Nellnore, R., Chanaron, J.J. & Soderquist, K.E. (2001). Lean supply and price based global sourcing- the interconnection. European Journal of Purchasing and Supply Management, 7 (2): 101–110.

    Article  Google Scholar 

  36. Onnela, J.P., Saramaki, J., Kertesz, J. & Kaski, K. (2005). Intensity and coherence of motifs in weighted complex networks. Physical Review, E71, 6: 065103.

    Google Scholar 

  37. Opsahi, T. & Panzarasa, P. (2009). Clustering in weighted networks. Social Networks, 31 (2): 155–163.

    Article  Google Scholar 

  38. Pascoe, T.L. (1966). Allocation of resources-CPM. Revue Francaise de Recherche Operationelle, 38: 31–38.

    Google Scholar 

  39. Pathak, S.D., Day, J.M., Nair, A., Sawya, W.J. & Kristal, N.M. (2007). Complexity and adaptivity in supply networks: building supply networktheory using a complex adaptive systems perspective. Decision Sciences, 38: 547–580.

    Article  Google Scholar 

  40. Perona, M. & Miragliotta, G. (2004). Complexity management and supply chain performance assessment. A field study and a conceptual framework. International Journal of Production Economics, 90: 103–115.

    Article  Google Scholar 

  41. Rogerson, S. & Fidler, C. (1994). Strategic information systems planning: its adoption and use. Information Management & Computer Security, 2 (1): 12–17.

    Article  Google Scholar 

  42. Shannon, C.E. (1948). The mathematical theory of communication. The Bell System Technical Journal, 27: 379–423.

    Article  MathSciNet  MATH  Google Scholar 

  43. Shih, B.Y. & Efstathious, J. (2002). An introduction of network complexity. Manufacturing Complexity Network Conference, Cambridge UK. 249–258.

    Google Scholar 

  44. Sivadasan, S. & Efstathiou, J. (2002). An information-theoretic methodology for measuring the operational complexity of supplier-customer system. International Journal of Operations and Production Management, 22 (1): 80–102.

    Article  Google Scholar 

  45. Skilton, P.F. & Robinson, J.L. (2009). Traceability and normal accident theory: how does supply network complexity influence the traceability of adverse events? Journal of Supply Chain Management, 45 (3): 40–53.

    Article  Google Scholar 

  46. Soffer, S.N. & A. Vazques. (2005). Network clustering coefficiient without degree-correlation biases. Physical Review, Series E, E71 (5).

    Google Scholar 

  47. Stuikys, V. & Damasevicius, R. (2009). Measuring complexity of doamin models represented by feature diagrams. Information Technology and Control, 38 (3): 179–187.

    Google Scholar 

  48. Sun, H. & Wu, J. (2005). Scale-free characteristics of supply chain distribution networks. Modern Physics Letters B, 19 (17): 841–848.

    Article  Google Scholar 

  49. Tang, O. & Musa, S.N. (2011). Identifying risk issues and research advancements in supply chain risk management. International Journal of Production Economics, 133: 25–34.

    Article  Google Scholar 

  50. Troy, D.A. & Zweben, S.H. (1981). Measuring the quality of structured designs. Journalof Systems and Software, 2: 113–120.

    Article  Google Scholar 

  51. U.S. Department of Commerce. (2015). Quarterly Retail E-Commerce Sales. Washington, D.C., May 15.

    Google Scholar 

  52. Vollman, T., Berry, W., Whybark, D.C. & Jacobs, R. (2005). Manufacturing Planning and Control for Supply Chain Management. New York: McGraw Hill/Irwin.

    Google Scholar 

  53. Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. New York NY: Cambridge University Press.

    Book  Google Scholar 

  54. Zhang, B. & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4 (1).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Engineering Management, Western New England University, London, England

    Julie Drzymalski

Authors
  1. Julie Drzymalski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Julie Drzymalski.

Additional information

Julie Drzymalski is an associate professor and Chair of Industrial Engineering and Engineering Management at Western New England University. She received her Ph.D. in industrial engineering from Lehigh University in 2008. Her research interests focus on supply chain optimization, risk analysis and management and service systems. She is a member of IIE, INFORMS, ASEM and ASEE.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Drzymalski, J. A measure of supply chain complexity incorporating virtual arcs. J. Syst. Sci. Syst. Eng. 24, 486–499 (2015). https://doi.org/10.1007/s11518-015-5290-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11518-015-5290-0

Keywords

Search

Navigation