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A technique to develop simplified and linearised models of complex dynamic supply chain systems

Author

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  • Spiegler, Virginia L.M.
  • Naim, Mohamed M.
  • Towill, Denis R.
  • Wikner, Joakim

Abstract

There is a need to identify and categorise different types of nonlinearities that commonly appear in supply chain dynamics models, as well as establishing suitable methods for linearising and analysing each type of nonlinearity. In this paper simplification methods to reduce model complexity and to assist in gaining system dynamics insights are suggested. Hence, an outcome is the development of more accurate simplified linear representations of complex nonlinear supply chain models.

Suggested Citation

  • Spiegler, Virginia L.M. & Naim, Mohamed M. & Towill, Denis R. & Wikner, Joakim, 2016. "A technique to develop simplified and linearised models of complex dynamic supply chain systems," European Journal of Operational Research, Elsevier, vol. 251(3), pages 888-903.
    • Handle: RePEc:eee:ejores:v:251:y:2016:i:3:p:888-903
    DOI: 10.1016/j.ejor.2015.12.004
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    Cited by:

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    2. Paul-Eric Dossou & Esther Alvarez-de-los-Mozos & Pawel Pawlewski, 2024. "A Conceptual Framework for Optimizing Performance in Sustainable Supply Chain Management and Digital Transformation towards Industry 5.0," Mathematics, MDPI, vol. 12(17), pages 1-31, September.
    3. Roberto Dominguez & Salvatore Cannella & Borja Ponte & Jose M. Framinan, 2022. "Information sharing in decentralised supply chains with partial collaboration," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 263-292, June.
    4. Lin, Junyi & Huang, Hongfu & Li, Shanshan & Naim, Mohamed M., 2023. "On the dynamics of order pipeline inventory in a nonlinear order-up-to system," International Journal of Production Economics, Elsevier, vol. 266(C).
    5. Dmitry Ivanov & Maxim Rozhkov, 2020. "Coordination of production and ordering policies under capacity disruption and product write-off risk: an analytical study with real-data based simulations of a fast moving consumer goods company," Annals of Operations Research, Springer, vol. 291(1), pages 387-407, August.
    6. Spiegler, Virginia L.M. & Naim, Mohamed M., 2017. "Investigating sustained oscillations in nonlinear production and inventory control models," European Journal of Operational Research, Elsevier, vol. 261(2), pages 572-583.
    7. Ponte, Borja & Naim, Mohamed M. & Syntetos, Aris A., 2019. "The value of regulating returns for enhancing the dynamic behaviour of hybrid manufacturing-remanufacturing systems," European Journal of Operational Research, Elsevier, vol. 278(2), pages 629-645.
    8. Lin, Junyi & Naim, Mohamed M. & Spiegler, Virginia L.M., 2020. "Delivery time dynamics in an assemble-to-order inventory and order based production control system," International Journal of Production Economics, Elsevier, vol. 223(C).
    9. Lin, Junyi & Zhou, Li & Spiegler, Virginia L.M. & Naim, Mohamed M. & Syntetos, Aris, 2022. "Push or Pull? The impact of ordering policy choice on the dynamics of a hybrid closed-loop supply chain," European Journal of Operational Research, Elsevier, vol. 300(1), pages 282-295.
    10. Sagawa, Juliana Keiko & Mušič, Gašper, 2019. "Towards the use of bond graphs for manufacturing control: Design of controllers," International Journal of Production Economics, Elsevier, vol. 214(C), pages 53-72.

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