Ripple effect and supply chain disruption management: new trends and research directions

Figures & data

Figure 1. Journal articles on the ripple effect-related topics in supply chains in 2012–2020.

Table 1. Overview of the papers included in this special issue, their methodologies and major outcomes.

Title	Author(s)	Focus	Methodology	Major Outcomes and Insights
Empirical investigation of data analytics capability and organisational flexibility as complements to supply chain resilience	Rameshwar Dubey, Angappa Gunasekaran, Stephen J. Childe, Samuel Fosso Wamba, David Roubaud, and Cyril Foropon	Supply chain resilience and data analytics	Organisational information processing theory; variance-based structural equation modelling	Theoretical model that reconciles two different streams of literature: data analytics capability as a means to improve information-processing capacity and SC resilience as a means to reduce the ripple effect in SCs or quickly recover after disruptions.
Evolution of supply chain ripple effect: a bibliometric and meta-analytic view of the constructs	Deepa Mishra, Yogesh K. Dwivedi, Nripendra P. Rana, and Elkafi Hassini	Supply chain risk management with consideration of the ripple effect	Structured literature review	Structured summary of the challenges of current research, identification of main research clusters and future research directions.
Modelling of risk transmission and control strategy in the transnational supply chain	Zhimei Lei, Ming K. Lim, Li Cui, and Yanzhang Wang	Modelling of the ripple effect in complex, transnational SCs and selection of risk control strategy	An improved susceptible-infectious-susceptible (SIS) model combined with a complex network model and an optimised approach to the selection of risk control strategies	Can be applied to explore (i) dynamic change and trend of risk transmission; (ii) the relationships and impacts of SC structures on risk transmission; and (iii) selection of a risk control strategy
A Ripple effect in prehospital stroke patient care	Brandon W. Lee, Jiho Yoon, Seung Jun Lee	Ripple effect modeling in service industry on the example of emergency medical services	Empirically-grounded analytics	The model demonstrates the impact of a disruptive event at the dispatcher stage and the unavailability of an Advanced Life Support (ALS) ambulance on the severity of the stroke patient’s prehospital condition; a centralised prehospital system between the dispatch centre and the field providers could be a practical way to reduce the negative impact of the ripple effect
Dynamic generic and brand advertising decisions under supply disruption	Shigui Ma, Yong He, and Ran Gu	Optimal advertising strategies in the SC if a manufacturer is disrupted	Differential game theory	When supply/manufacturing is disrupted, the optimal advertising strategies and advertising subsidy scheme designed for the initially estimated product sales need to be revised; the model can be used to re-design the advertising strategies and advertising subsidy schemes under supply disruption
Designing profitable and responsive supply chains under uncertainty	Amir Azaron, Uday Venkatadri, and Alireza Farhang Doost	Supply network design with consideration of disruptions	Multi-objective two-stage stochastic programming	The model allows to maximise the SC total profit over multiple periods and to minimise the total travel times for unsatisfied customers, whose demands must be met by retailers to maximise the SC responsiveness
Disruption risk management in service level agreements	Hamed Jahani, Babak Abbasi, Zahra Hosseinifard, Masih Fadaki, and James P. Minas	Impacts of capacity/inventory disruption on a supplier's cost, when the supplier has heterogeneous service level agreements	Mathematical optimisation	The model can assist suppliers in determining their capacity levels and location, allocating capacity to customers and negotiating service level terms.
Robust optimisation for ripple effect on reverse supply chain: An industrial case study	Gökhan Özçelik, Ömer Faruk Yılmaz, and Fatma Betül Yeni	Analysis of the ripple effect impacts on the system performance of the reverse SC	Robust optimisation	Inclusion of the reverse logistics into the ripple effect analysis; the outcomes can be used in green logistics and closed-loop SCs to mitigate the ripple effect
A new robust dynamic Bayesian network approach for disruption risk assessment under the supply chain Ripple effect	Ming Liu, Zhongzheng Liu, Feng Chu, Feifeng Zheng, Chengbin Chu	Assessment of the robust performance under the ripple effect, measured by the worst-case probability in a disrupted state for a manufacturer	Bayesian Network	Estimation of the ripple effect risks when data about disruption probabilities is scarce
Facing market disruptions: values of elastic logistics in service supply chains	Tsan-Ming Choi	Impact of market disruptions and their upstream propagations on planning decisions of logistics service providers	Game theory	The model helps to decide the quantity of capacity to reserve to satisfy future demand in order to mitigate the Ripple effect
A robust location-inventory model for food supply chains operating under disruptions with ripple effects	Seyed Mohammad Gholami-Zanjani, Walid Klibi, Mohammad Saeed Jabalameli, Mir Saman Pishvaee	Ripple effect analysis for resilient supply chain design and inventory decisions with consideration of food product–specific characteristics and potential disruptions	Stochastic programming / Robust optimisation	The model allows to analyse three resilience strategies, i.e. readiness, flexibility, and responsiveness to hedge against ripple effects caused by food-specific disruptions
The relationship between nested patterns and the ripple effect in complex supply networks	Alexandra Brintrup, Vinod Chauhan, and Supun Perera	Impact of “nestedness” (i.e. a topological pattern of an organisation where specialist suppliers deliver subsets of products) on the ripple effect	Network theory	Nested organisations are significantly more robust to random disruptions but vulnerable to hub disruptions under the ripple effect. However, nested structures are not as resilient as they do not benefit from a response strategy where buyers seek alternative suppliers because alternative suppliers do not exist. On the other hand, randomly connected supply networks are vulnerable to the ripple effect but can allow for network reconfiguration.

Table 2. Taxonomy of the existing studies on the ripple effect in SCs.

Decision making levels	Spectrum of the ripple effect studies	Papers in this Special Issue
Network design level	Impact of the ripple effect on SC resilience and robustness; Sensitivity of different SC designs to the ripple effect; Criticality of some nodes/arcs in the SC for the ripple effect dispersal	Lei et al. (2020), Dubey et al. (2020), Mishra et al. (2020), Brintrup, Chauhan, and Perera (2020), Liu et al. (2020)
Process planning level	Proactive policies to mitigate the ripple effect (i.e. inventory and capacity buffers, backup sourcing, product substitution)	Ma, He, and Gu (2020), Özçelik, Yılmaz, and Yeni (2020), Azaron, Venkatadri, and Doost (2020), Jahani et al. (2020), Gholami-Zanjani et al. (2020)
Operative control level	Reactive policy deployments for ripple effect control and recovery	Choi (2020), Lee, Yoon, and Lee (2020)

Lei, Z., M. K. Lim, L. Cui, and Y. Wang. 2020. “Modelling of Risk Transmission and Control Strategy in the Transnational Supply Chain.” International Journal of Production Research, 1–20. doi:10.1080/00207543.2019.1698782.

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Dubey, R., A. Gunasekaran, S. J. Childe, S. F. Wamba, D. Roubaud, and C. Foropon. 2020. “Empirical Investigation of Data Analytics Capability and Organizational Flexibility as Complements to Supply Chain Resilience.” International Journal of Production Research, doi:10.1080/00207543.2019.1582820.

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Mishra, D., Y. Dwivedi, N. Rana, and E. Hassini. 2020. “Evolution of Supply Chain Ripple Effect: A Bibliometric and Meta-Analytic View of the Constructs.” International Journal of Production Research. doi:10.1080/00207543.2019.1668073.

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Brintrup, A., V. Chauhan, and S. Perera. 2020. “The Relationship Between Nested Patterns and the Ripple Effect in Complex Supply Networks.” International Journal of Production Research.

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Liu, M., Z. Liu, F. Chu, F. Zheng, and C. Chu. 2020. “A New Robust Dynamic Bayesian Network Approach for Disruption Risk Assessment Under the Supply Chain Ripple Effect.” International Journal of Production Research. forthcoming

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Ma, S., Y. He, and R. Gu. 2020. “Dynamic Generic and Brand Advertising Decisions Under Supply Disruption.” International Journal of Production Research. doi:10.1080/00207543.2020.1812751.

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Özçelik, G., ÖF Yılmaz, and F. B. Yeni. 2020. “Robust Optimisation for Ripple Effect on Reverse Supply Chain: An Industrial Case Study.” International Journal of Production Research. doi:10.1080/00207543.2020.1740348.

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Azaron, A., U. Venkatadri, and A. F. Doost. 2020. “Designing Profitable and Responsive Supply Chains Under Uncertainty.” International Journal of Production Research. doi:10.1080/00207543.2020.1785036.

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Jahani, H., B. Abbasi, Z. Hosseinifard, M. Fadaki, and J. P. Minas. 2020. “Disruption Risk Management in Service-level Agreements.” International Journal of Production Research. doi:10.1080/00207543.2020.1748248.

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Gholami-Zanjani, S. M., W. Klibi, M. S. Jabalameli, and M. S. Pishvaee. 2020. “A Robust Location-Inventory Model for Food Supply Chains Operating Under Disruptions with Ripple Effects.” International Journal of Production Research. in press

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Choi, T.-M. 2020. “Facing Market Disruptions: Values of Elastic Logistics in Service Supply Chains.” International Journal of Production Research. doi:10.1080/00207543.2020.1722861.

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Lee, B. W., J. Yoon, and S. J. Lee. 2020. “A Ripple Effect in Prehospital Stroke Patient Care.” International Journal of Production Research. doi:10.1080/00207543.2020.1825862.

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