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A method for analysing operational complexity in supply chains

Author

Listed:
  • Y R Wu

    (University of Westminster, London, UK)

  • L H Huatuco

    (University of Leeds, Leeds, UK)

  • G Frizelle

    (University of Cambridge, Cambridge, UK)

  • J Smart

    (University of Oxford, Oxford, UK)

Abstract

This paper proposes a method for analysing the operational complexity in supply chains by using an entropic measure based on information theory. The proposed approach estimates the operational complexity at each stage of the supply chain and analyses the changes between stages. In this paper a stage is identified by the exchange of data and/or material. Through analysis the method identifies the stages where the operational complexity is both generated and propagated (exported, imported, generated or absorbed). Central to the method is the identification of a reference point within the supply chain. This is where the operational complexity is at a local minimum along the data transfer stages. Such a point can be thought of as a ‘sink’ for turbulence generated in the supply chain. Where it exists, it has the merit of stabilising the supply chain by attenuating uncertainty. However, the location of the reference point is also a matter of choice. If the preferred location is other than the current one, this is a trigger for management action. The analysis can help decide appropriate remedial action. More generally, the approach can assist logistics management by highlighting problem areas. An industrial application is presented to demonstrate the applicability of the method.

Suggested Citation

  • Y R Wu & L H Huatuco & G Frizelle & J Smart, 2013. "A method for analysing operational complexity in supply chains," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(5), pages 654-667, May.
    • Handle: RePEc:pal:jorsoc:v:64:y:2013:i:5:p:654-667
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    Citations

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    Cited by:

    1. Germán Herrera Vidal & Jairo R. Coronado-Hernández & Claudia Minnaard, 2023. "Measuring manufacturing system complexity: a literature review," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2865-2888, October.
    2. H. Norouzi Nav & M. R. Jahed Motlagh & A. Makui, 2017. "Robust controlling of chaotic behavior in supply chain networks," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 711-724, June.
    3. Yoon, Jungsub & Lee, Jeong-Dong & Hwang, Seogwon, 2022. "Episodic change: A new approach to identifying industrial transition," Technovation, Elsevier, vol. 115(C).
    4. Menezes, Mozart B.C. & Ruiz-Hernández, Diego & Chen, Yen-Tsang, 2021. "On the validity and practical relevance of a measure for structural complexity," International Journal of Production Economics, Elsevier, vol. 240(C).
    5. Lin, Yun Hui & Wang, Yuan & Lee, Loo Hay & Chew, Ek Peng, 2021. "Consistency matters: Revisiting the structural complexity for supply chain networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).

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