Structural quantification of the ripple effect in the supply chain

Abstract

International audienceIn recent years, remarkable advancements have been achieved in quantitative analysis methods for supply chain design (SCD). Typically, cost or service level optimisation has been included in the objective functions. At the same time, supply chain managers face the ripple effect that arises from vulnerability, instability and disruptions in supply chains. This research aimed to quantify the ripple effect in the supply chain from the structural perspective. The research agenda of this study includes issues of integrating operability objectives as new key performance indicators, e.g. resilience, stability, robustness into SCD decisions. The research is based on a simultaneous consideration of both static structural properties of SCD and execution dynamics subject to uncertainty and disruptions. Due to high dimensionality of real SCD problems, such integration can hardly be implemented in only one model. In this study, an original two-model multi-criteria approach is proposed in order to assess the potential ability of an SCD to remain stable and resilient. This modelling approach is based on a combined application of a static and a dynamic model. A multi-criteria approach relies on the analytic hierarchy process method. The results of this research can be used as an additional quantitative analysis tool in order to select an SCD. An additional application of the developed method is that it can be used at the control stage in order to adapt supply chain execution subject to the achievement of desired economic performance

Hybrid fuzzy-probabilistic approach to supply chain resilience assessment

“…Brandon-Jones et al (2015) find out the impacts of supply base complexity on disruptions and performance. Sokolov et al (2016) quantify ripple effect in the SC with the help of selected indicators from graph theory and develop a hybrid static-model model for performance impact assessment of disruption propagation in a distribution network. Han and Shin (2016) assess the SC structural robustness considering disruption propagation in a connected graph.…”

Supply chain disruption propagation: a systemic risk and normal accident theory perspective

“…We define supply chain disruption propagation as the spread of the disruption effects beyond the initial disruption location. Other terminology for supply chain disruption propagation in the academic literature include risk diffusion (Basole and Bellamy 2014), cascading failures (Hearnshaw and Wilson 2013) and the Ripple Effect (Ivanov et al 2014a;2014b;Solokov et al 2016).…”

Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience

“…Research focus Disruptions and the resulting ripple effect cause SC structural changes, also referred to as SC structural dynamics (Ivanov, Sokolov, and Kaeschel 2010;Ivanov 2018a). Structural SC properties have been recognised to have a crucial impact on the ripple effect and SC robustness and resilience (Xia et al 2004;Tomlin 2006;Nair and Vidal 2011;Hu, Gurnani, and Wang 2013;Basole and Bellamy 2014;Ivanov, Sokolov, and Dolgui 2014;Ambulkar, Blackhurst, and Grawe 2015;Bode and Wagner 2015;Kim, Chen, and Linderman 2015;Gunasekaran, Subramanian, and Rahman 2015;Kamalahmadi and Mellat-Parast 2016;Han and Shin 2016;Sokolov et al 2016;Tang et al 2016;Chen, Xi, and Jing 2017;Jain et al 2017;Scheibe and Blackhurst 2018;Pavlov et al 2018;Ojha et al 2018).…”

“…The literature analysis shows that complex networks become more vulnerable to severe disruptions which change the SC structures and are involved with SC structural dynamics (Ivanov, Sokolov, and Kaeschel 2010;Monostori 2018;Ivanov 2018a). Moreover, the ripple effect in the SC depends on structural network composition and complexity (Han and Shin 2016;Sokolov et al 2016;Levner and Ptuskin 2017;Ivanov 2017aIvanov , 2018bPavlov et al 2018). Chopra and Sodhi (2014) emphasised the impact of SC centralisation and decentralisation on SC resilience and disruption risk management.…”

“…Using the event of failure as the model input and the recovery time as the model output, they proposed to measure SC resilience using recovery time. Sokolov et al (2016) applied the AHP method to analyze the dependencies of different SC structures in terms of their recoverability. They estimated network connectivity, reachability, complexity, and centralisation, and used these indicators to analyze the performance impact of the ripple effect in a distribution network.…”