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The evolutionary complexity of complex adaptive supply networks: A simulation and case study

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  • Li, Gang
  • Yang, Hongjiao
  • Sun, Linyan
  • Ji, Ping
  • Feng, Lei

Abstract

A supply chain should be treated not just as a supply chain but also as a complex adaptive supply network (CASN). However, the literature on supply chain management has given little attention to the evolutionary complexity of the network structure and collaboration mechanism of CASNs. In this paper, we first model and simulate the evolution of CASNs based on complex adaptive system and fitness landscape theory. The simulation results indicate the evolutionary complexities such as emergence, quasi-equilibrium, chaos, and lock-in of CASNs. Then, a case study of the evolution of the LVEA (low voltage equipment apparatus) supply network in the emerging Chinese market has been explored to validate the findings from the simulation and develop a better understanding of the general principles influencing the emergence, adaptation and evolution of CASNs in the real world. Based on the simulation and the case study, we propose some propositions about the factors and principles influencing the evolutionary complexity of CASNs. The external environment factors and firm-internal mechanisms appear to be the dominant forces that shape the gradual evolution of CASNs. Factors in the external environment, such as government regulation, market demand and market structure appear to have a long-term impact on the evolution, while a firm's strategies, product structure, technology, and organization appear to be the internal factors that exert an immediate influence on the evolution of CASNs. Among these factors, cost and quality considerations appear to be the primary forces that influence the structure complexity, centralization and formalization of CASNs.

Suggested Citation

  • Li, Gang & Yang, Hongjiao & Sun, Linyan & Ji, Ping & Feng, Lei, 2010. "The evolutionary complexity of complex adaptive supply networks: A simulation and case study," International Journal of Production Economics, Elsevier, vol. 124(2), pages 310-330, April.
  • Handle: RePEc:eee:proeco:v:124:y:2010:i:2:p:310-330
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    1. Stephen Guisinger, 2001. "From OLI to OLMA: Incorporating Higher Levels of Environmental and Structural Complexity into the Eclectic Paradigm," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 8(2), pages 257-272.
    2. Wilkinson, Ian & Young, Louise, 2002. "On cooperating: firms, relations and networks," Journal of Business Research, Elsevier, vol. 55(2), pages 123-132, February.
    3. Akanle, O.M. & Zhang, D.Z., 2008. "Agent-based model for optimising supply-chain configurations," International Journal of Production Economics, Elsevier, vol. 115(2), pages 444-460, October.
    4. Beamon, Benita M., 1998. "Supply chain design and analysis:: Models and methods," International Journal of Production Economics, Elsevier, vol. 55(3), pages 281-294, August.
    5. Alfaro, Miguel D. & Sepulveda, Juan M., 2006. "Chaotic behavior in manufacturing systems," International Journal of Production Economics, Elsevier, vol. 101(1), pages 150-158, May.
    6. Holweg, Matthias & Bicheno, John, 2002. "Supply chain simulation - a tool for education, enhancement and endeavour," International Journal of Production Economics, Elsevier, vol. 78(2), pages 163-175, July.
    7. Potter, Andrew & Mason, Robert & Naim, Mohamed & Lalwani, Chandra, 2004. "The evolution towards an integrated steel supply chain: A case study from the UK," International Journal of Production Economics, Elsevier, vol. 89(2), pages 207-216, May.
    8. Yang, Jie & Wang, Jinjun & Wong, Christina W.Y. & Lai, Kee-Hung, 2008. "Relational stability and alliance performance in supply chain," Omega, Elsevier, vol. 36(4), pages 600-608, August.
    9. Wu, Y. & Zhang, D.Z., 2007. "Demand fluctuation and chaotic behaviour by interaction between customers and suppliers," International Journal of Production Economics, Elsevier, vol. 107(1), pages 250-259, May.
    10. Kinra, Aseem & Kotzab, Herbert, 2008. "A macro-institutional perspective on supply chain environmental complexity," International Journal of Production Economics, Elsevier, vol. 115(2), pages 283-295, October.
    11. Wikner, J. & Towill, D. R. & Naim, M., 1991. "Smoothing supply chain dynamics," International Journal of Production Economics, Elsevier, vol. 22(3), pages 231-248, December.
    12. Crespo Marquez, Adolfo & Blanchar, Carol, 2004. "The procurement of strategic parts. Analysis of a portfolio of contracts with suppliers using a system dynamics simulation model," International Journal of Production Economics, Elsevier, vol. 88(1), pages 29-49, March.
    13. Larsen, Erik R. & Morecroft, John D. W. & Thomsen, Jesper S., 1999. "Complex behaviour in a production-distribution model," European Journal of Operational Research, Elsevier, vol. 119(1), pages 61-74, November.
    14. Carbonara, Nunzia & Giannoccaro, Ilaria & Pontrandolfo, Pierpaolo, 2002. "Supply chains within industrial districts: A theoretical framework," International Journal of Production Economics, Elsevier, vol. 76(2), pages 159-176, March.
    15. Li, Suhong & Ragu-Nathan, Bhanu & Ragu-Nathan, T.S. & Subba Rao, S., 2006. "The impact of supply chain management practices on competitive advantage and organizational performance," Omega, Elsevier, vol. 34(2), pages 107-124, April.
    16. Fiala, P., 2005. "Information sharing in supply chains," Omega, Elsevier, vol. 33(5), pages 419-423, October.
    17. Nagatani, Takashi & Helbing, Dirk, 2004. "Stability analysis and stabilization strategies for linear supply chains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(3), pages 644-660.
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    Cited by:

    1. Li, Gang & Fan, Huan & Lee, Peter K.C. & Cheng, T.C.E., 2015. "Joint supply chain risk management: An agency and collaboration perspective," International Journal of Production Economics, Elsevier, vol. 164(C), pages 83-94.
    2. Kemp-Benedict, Eric, 2013. "Resource Return on Investment under Markup Pricing," MPRA Paper 49154, University Library of Munich, Germany.
    3. Fan, Huan & Li, Gang & Sun, Hongyi & Cheng, T.C.E., 2017. "An information processing perspective on supply chain risk management: Antecedents, mechanism, and consequences," International Journal of Production Economics, Elsevier, vol. 185(C), pages 63-75.
    4. Lorentz, Harri & Kittipanya-ngam, Pichawadee & Singh Srai, Jagjit, 2013. "Emerging market characteristics and supply network adjustments in internationalising food supply chains," International Journal of Production Economics, Elsevier, vol. 145(1), pages 220-232.
    5. Dass, Mayukh & Fox, Gavin L., 2011. "A holistic network model for supply chain analysis," International Journal of Production Economics, Elsevier, vol. 131(2), pages 587-594, June.
    6. He, Zhou & Wang, Shouyang & Cheng, T.C.E., 2013. "Competition and evolution in multi-product supply chains: An agent-based retailer model," International Journal of Production Economics, Elsevier, vol. 146(1), pages 325-336.
    7. Cecile Gerwel Proches & Shamim Bodhanya, 2015. "Exploring stakeholder interactions through the lens of complexity theory: lessons from the sugar industry," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2507-2525, November.
    8. Cannella, Salvatore & Dominguez, Roberto & Framinan, Jose M., 2017. "Inventory record inaccuracy – The impact of structural complexity and lead time variability," Omega, Elsevier, vol. 68(C), pages 123-138.

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