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Analysing bullwhip and backlash effects in supply chains with phase space trajectories

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  • Florian Klug

Abstract

We will investigate demand and supply variations in supply chains commonly known as bullwhip and backlash effects. The main goal of this paper is to detect bullwhip and backlash effects and its interactions by measuring only a few supply chain variables. To do so, we consider a multistage supply chain and analyse it with the aid of phase space trajectories. An examination of the geometrical structures of trajectories can reveal the dynamics of the bullwhip and backlash behaviour in supply chains. Therefore, we use a physical analogy to wave theory and describe order and delivery oscillations as a process of wave reflection. The proposed methodology systematically provides the basis for analysing a number of dynamic and transient behaviours that supplement the traditional analysis. These behaviour patterns include supply chain stability, compactness and bullwhip/backlash trade-offs, which have important consequences for the design, operation and control of supply chains.

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  • Florian Klug, 2016. "Analysing bullwhip and backlash effects in supply chains with phase space trajectories," International Journal of Production Research, Taylor & Francis Journals, vol. 54(13), pages 3906-3926, July.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:13:p:3906-3926
    DOI: 10.1080/00207543.2016.1162342
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    References listed on IDEAS

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    1. Disney, Stephen M. & Lambrecht, Marc R., 2008. "On Replenishment Rules, Forecasting, and the Bullwhip Effect in Supply Chains," Foundations and Trends(R) in Technology, Information and Operations Management, now publishers, vol. 2(1), pages 1-80, April.
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    1. 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.
    2. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    3. Florian Klug, 2022. "Modelling oscillations in the supply chain: the case of a just-in-sequence supply process from the automotive industry," Journal of Business Economics, Springer, vol. 92(1), pages 85-113, January.

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