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The Rippling Effect of Non-linearities

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Handbook of Ripple Effects in the Supply Chain

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 276))

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Abstract

Non-linearities can lead to unexpected dynamic behaviours in supply chain systems that could then either trigger disruptions or make the response and recovery process more difficult. In this chapter, we take a control-theoretic perspective to discuss the impact of non-linearities on the ripple effect. This chapter is particularly relevant for researchers wanting to learn more about the different types of non-linearities that can be found in supply chain systems, the existing analytical methods to deal with each type of non-linearity and future scope for research based on the current knowledge in this field.

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References

  • Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1–14.

    Article  Google Scholar 

  • Colicchia, C., Dallari, F., & Melacini, M. (2010). Increasing supply chain resilience in a global sourcing context. Production Planning & Control: The Management of Operations, 21(7), 680–694.

    Article  Google Scholar 

  • Cook, P. A. (1986). Nonlinear Dynamical Systems. Exeter, UK: Prentice.

    Google Scholar 

  • Cross, R., Grinfeld, M., & Lamba, H. (2009). Hysteresis and economics. IEEE Control Systems, 29(1), 30–43.

    Article  Google Scholar 

  • Dolgui, A., Ben-Ammar, O., Hnaien, F., & Louly, M. A. (2013). A state of the art on supply planning and inventory control under lead time uncertainty. Studies in Informatics and Control, 22(3), 255–268.

    Article  Google Scholar 

  • Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: an analysis and recent literature. International Journal of Production Research, 56(1–2), 414–430.

    Article  Google Scholar 

  • Evans, G. N., & Naim, M. M. (1994). The dynamics of capacity constrained supply chain. In Proceedings of International System Dynamics Conference (pp. 28–39). Stirling: Scotland.

    Google Scholar 

  • Gelb, A., & Velde, W. V. (1968). Multiple-input describing functions and nonlinear system design. McGraw-Hill.

    Google Scholar 

  • Göcke, M. (2002). Various concepts of hysteresis applied in economics. Journal of Economic Surveys, 16(2), 167–188.

    Article  Google Scholar 

  • Gray, J. V., Skowronski, K., Esenduran, G., & Rungtusanatham, M. J. (2013). The reshoring phenomenon: What supply chain academics ought to know and should do. Journal of Supply Chain management, 49(2), 27–33.

    Article  Google Scholar 

  • Größler, A., & Schieritz, N. (2005). Of stocks, flows, agents and rules-“strategic” simulations in supply chain research. In H. Kotzab, S. Seuring, M. Müller, & G. Reiner (Eds.), Research methodologies in supply chain management. Germany: Physica-Verlag Heiedlberg.

    Google Scholar 

  • Grübbstrom, R., & Wang, Z. (2000). Introducing capacity limitations into multi-level, multi-stage production-inventory systems applying the input-output: Laplace transform approach. International Journal of Production Research, 38(17), 4227–4234.

    Article  Google Scholar 

  • Ivanov, D., & Sokolov, B. (2013). Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis, and adaptation of performance under uncertainty. European Journal of Operational Research, 224(2), 313–323.

    Article  Google Scholar 

  • Ivanov, D., Sokolov, B., & Dolgui, A. (2014). The ripple effect in supply chains: Trade-off ‘efficiency-flexibility- resilience’ in disruption management. International Journal of Production Research, 52(7), 2154–2172.

    Article  Google Scholar 

  • Jeong, S., Oh, Y., & Kim, S. (2000). Robust control of multi-echelon production-distribution systems with limited decision policy. KSME International Journal, 14(4), 380–392.

    Article  Google Scholar 

  • Johansson, M. (2003). Piecewise Linear Control Systems. Berlin, Germany: Springer.

    Book  Google Scholar 

  • Kearney, A. T. (2003). Supply chains in a vulnerable, volatile world. Executive Agenda, 6(3), 5–16.

    Google Scholar 

  • Kogut, B., & Kulatilaka, N. (1994). Operating flexibility, global manufacturing, and the option value of a multinational network. Management Science, 10, 123–139.

    Article  Google Scholar 

  • Kouvelis, P. (1998). Global sourcing strategies under exchange rate uncertainty. In S. Tayur, R. Ganeshan, & M. Magazine (Eds.), Quantitative models for supply chain management. Springer.

    Google Scholar 

  • Lang, D., & de Peretti, C. (2009). A strong hysteretic model of Okun’s law: theory and a preliminary investigation. International Review of Applied Economics, 23(4), 445–462.

    Article  Google Scholar 

  • Lin, J., Naim, M., Spiegler, V. (2018a). Delivery time dynamics in an assemble-to-order inventory and order based production control system. In Presented at: 20th International Working Seminar on Production Economics., Innsbruck, Austria, 19–23 February, 2018.

    Google Scholar 

  • Lin, J., Spiegler, V., & Naim, M. (2018). Dynamic analysis and design of a semiconductor supply chain: A control engineering approach. International Journal of Production Research, 56, 4585–4611.

    Article  Google Scholar 

  • Manners-Bell, J. (2018). Supply Chain Risk Management: Understanding Emerging Threats to Global Supply Chains. London: Kogan Page.

    Google Scholar 

  • Mohapatra, P. K. J. (1980). Nonlinearity in system dynamics models. Dynamica, 6(1), 36–52.

    Google Scholar 

  • Naim, M. M., Spiegler, V. L., Wikner, J., & Towill, D. R. (2017). Identifying the causes of the bullwhip effect by exploiting control block diagram manipulation with analogical reasoning. European Journal of Operational Research, 263, 240–246.

    Article  Google Scholar 

  • Odame, K., & Hasler, P. E. (2010). Nonlinear circuit analysis via perturbation methods and hardware prototyping. VLSI Design, 2010, 1–10.

    Article  Google Scholar 

  • Rugh, W. J. (2002). Nonlinear system theory: The Volterra/Wiener approach. The Johns Hopkins University Press.

    Google Scholar 

  • Spiegler, V., & Naim, M. (2017). Investigating sustained oscillations in nonlinear production and inventory control models. European Journal of Operational Research, 261, 572–583.

    Article  Google Scholar 

  • Spiegler, V. L. M., Naim, M. M., & Wikner, J. (2012). A control engineering approach to the assessment of supply chain resilience. International Journal of Production Research, 50(21), 6162–6187.

    Article  Google Scholar 

  • Spiegler, V. L. M., Potter, A. T., Naim, M. M., & Towill, D. R. (2016a). The value of nonlinear control theory in investigating the underlying dynamics and resilience of a grocery supply chain. International Journal of Production Research, 54(1), 265–286.

    Google Scholar 

  • Spiegler, V. L., Naim, M. M., Towill, D. R., & Wikner, J. (2016b). A technique to develop simplified and linearised models of complex dynamic supply chain systems. European Journal of Operational Research, 251(3), 888–903.

    Google Scholar 

  • Spiegler, V., Naim, M., Syntetos, A., & Zhou, L. (2018). Understanding the effects of lead-time disturbance in supply chains: on the design of an adaptive model to cope with lead-time changes. In The 60th Conference of Operational Research (OR60), 11–13 September. UK: Lancaster.

    Google Scholar 

  • Vukic, Z., Kuljaca, L., Donlagic, D., & Tesaknjak, S. (2003). Nonlinear Control Systems. New York: Marcel Dekker.

    Book  Google Scholar 

  • Wang, X., & Disney, S. M. (2012). Stability analysis of constrained inventory systems. European Journal of Operational Research, 223, 86–95.

    Article  Google Scholar 

  • Wang, G., & Gunasekaran, A. (2017). Modeling and analysis of sustainable supply chain dynamics. Annals of Operations Research, 250, 521–536.

    Article  Google Scholar 

  • Wang, X., Disney, S., & Wang, J. (2014). Exploring the oscilatory dynamics of a forbidden returns inventory system. International Journal of Production Economics, 147(A), 3–12.

    Google Scholar 

  • Wang, Z., Wang, X., & Ouyang, Y. (2015). Bounded growth of the bullwhip effect under a class of nonlinear ordering policies. European Journal of Operational Research, 247(1), 72–82.

    Article  Google Scholar 

  • Wikner, J., Naim, M. M., & Towill, D. R. (1992). The system simplification approach in understanding the dynamic behaviour of a manufacturing supply chain. Journal of Systems Engineering, 2, 164–178.

    Google Scholar 

  • Wikner, J., Naim, M. M., & Rudberg, M. (2007). Exploiting the order book for mass customized manufacturing control systems with capacity limitations. IEEE Transactions on Engineering Management, 54(1), 145–155.

    Article  Google Scholar 

  • Zhao, Y., Zhao, C., He, M., & Yang, C. (2016). A state-feedback approach to inventory control: Analytical and empirical studies. Production and Operations Management Society, 25(3), 535–547.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Kent Business School, University of Kent, Canterbury, CT2 7PE, UK

    Virginia L. M. Spiegler

  2. Cardiff Business School, Cardiff University, Aberconway Building, Colum Drive, Cardiff, CF10 3EU, UK

    Mohamed M. Naim & Junyi Lin

Authors
  1. Virginia L. M. Spiegler
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  2. Mohamed M. Naim
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  3. Junyi Lin
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Corresponding author

Correspondence to Virginia L. M. Spiegler .

Editor information

Editors and Affiliations

  1. Department of Business and Economics, Berlin School of Economics and Law, Berlin, Germany

    Dmitry Ivanov

  2. Department of Automation, Production and Computer Sciences, IMT Atlantique, LS2N - CNRS UMR, Nantes, France

    Alexandre Dolgui

  3. SPIIRAS, St. Petersburg, Russia

    Boris Sokolov

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Spiegler, V.L.M., Naim, M.M., Lin, J. (2019). The Rippling Effect of Non-linearities. In: Ivanov, D., Dolgui, A., Sokolov, B. (eds) Handbook of Ripple Effects in the Supply Chain. International Series in Operations Research & Management Science, vol 276. Springer, Cham. https://doi.org/10.1007/978-3-030-14302-2_3

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