Table 2.
Operational Research studies on the disruption propagations in the SCs.
| Authors and publication year | Title | Journal | Central Focus | Method(s) | Outcomes & Managerial Insight(s) | Analysis level |
|---|---|---|---|---|---|---|
| Basole, R.C. and Bellamy, M.A. (2014) | Supply Network Structure, Visibility, and Risk Diffusion: A Computational Approach | Decision Sciences | Network tendency toward disruption propagation | Graph theory; Complexity theory | Significant association between network structure and risk propagation; small-world supply network topologies consistently outperform supply networks with scale-free characteristics | N |
| Blackhurst, J., Rungtusanatham, M.J., Scheibe, K., Ambulkar, S. (2018) | Supply chain vulnerability assessment: A network based visualization and clustering analysis approach | Journal of Purchasing and Supply Management | Visualization and mapping of disruption propagation | Petri net and Triangularization Clustering Algorithm | Understand potential weaknesses in SC design while taking into account structure, connectivity, and dependence within the SC | N |
| Bueno-Solano, A., Cedillo-Campos, M.G. (2014). | Dynamic impact on global supply chains performance of disruptions propagation produced by terrorist acts | Transportation Research Part E: Logistics and Transportation Review | Understanding disruption propagation through the SC to ensure security and efficient movement of goods | System Dynamics simulation | Measures for disruption propagation can drastically increase inventory levels in the SC | P |
| Cao, S., Bryceson, K., Hine, D. (2019). | An Ontology-based Bayesian network modeling for supply chain risk propagation | Industrial Management and Data Systems | To quantitatively assess the impact of dynamic risk propagation in fresh product SCs | Ontology-based Bayesian network | Supply discontinuity, product inconsistency, and/or delivery delay originating from the ripple effect | N |
| Deng, X., Yang, X., Zhang, Y., Li, Y., Lu, Z. (2019). | Risk propagation mechanisms and risk management strategies for a sustainable perishable products supply chain. | Computers and Industrial Engineering | Identify dimensions of risk propagation SCs with perishable products | Tropos Goal-Risk framework | Three-dimension model to control the ripple effect (paths of risk propagation, dependencies between nodes, modes of risk propagation); sustainability issues connected to ripple effect | N |
| Dolgui A., Ivanov D., Rozhkov M. (2020). | Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain | International Journal of Production Research | To identify relations between the bullwhip effect and ripple effect | Discrete-event simulation | The ripple effect can be a bullwhip-effect driver, while the latter can be launched by a severe disruption even in downstream direction; backlog accumulation over disruption time is the major influencer of the ripple effect on SC performance; SC visibility and information coordination is the key capability to cope with the ripple effect. | C |
| Garvey, M.D., Carnovale, S. (2020) | The Rippled Newsvendor: A New Inventory Framework for Modeling Supply Chain Risk Severity In The Presence of Risk Propagation | International Journal of Production Economics | Inventory control policies with ripple effect considerations | Bayesian Network simulation | Reliability control of inventory policies; managers should focus more attention on control or mitigation of exogenous events that directly impact their own firm, while spending less effort and resources on mitigating the propagation of exogenous risk from a supplier to the exogenous risk of the firm itself. | P |
| Garvey, M.D., Carnovale, S., Yeniyurt, S. | An analytical framework for supply network risk propagation: A Bayesian network approach | European Journal of Operational Research | Inter-dependencies among different risks, as well as the idiosyncrasies of SC structures | Bayesian Network simulation | Measuring disruption propagation in the SC to analyze network vulnerability to ripple effect | N |
| Ghadge, A., Dani, S., Chester, M., & Kalawsky, R. (2013). | A systems thinking approach for modeling supply chain risk propagation | Supply Chain Management: An International Journal | Prediction of potential failure points in an SC and overall impact of failure risks on performance | System Dynamics simulation | Prediction of potential failure points in the SC along with overall impact of ripple effect on performance | P |
| Goldbeck, N., Angeloudis, P., Ochieng, W. (2020) | Optimal supply chain resilience with consideration of failure propagation and repair logistics | Transportation Research Part E: Logistics and Transportation Review | Resilient SC designs with considerations of trade-offs between redundancy costs and disruption-resistance | Scenario tree generation method for risk propagation modeling; Multi-stage stochastic programming model |
Joint optimization of SC capacities and recovery capabilities for new and existing SCs; trade-off between investments in increased recovery capability and redundant capacity provision; decision-making support on safety stock management, reconfiguration of production and inventory plans after disruption, and recovery scheduling | P |
| Han, J., Shin, K.S. (2016) | Evaluation mechanism for structural robustness of supply chain considering disruption propagation | International Journal of Production Research | Structural robustness evaluation | Reliability theory/Probabilistic analysis | To verify whether the SC design is robust to disruption propagation | N |
| Hosseini S., Ivanov D. (2019). | Resilience Assessment of Supply Networks with Disruption Propagation Considerations: A Bayesian Network Approach | Annals of Operations Research | Measuring of the ripple effect with consideration of both disruption and recovery stages | Bayesian Network simulation | To identify the resilience level of their most important suppliers; to identify disruption profiles in the supply base and associated SC performance degradation due to the ripple effect | N |
| Hosseini S., Ivanov D., Dolgui A. (2019). | Ripple effect modeling of supplier disruption: Integrated Markov Chain and Dynamic Bayesian Network Approach | International Journal of Production Research | Measuring of the ripple effect with consideration of state changes within individual SC nodes | Discrete-Time Markov Chain (DTMC) and a Dynamic Bayesian Network (DBN) | A metric that quantifies the ripple effect of supplier disruption on manufacturers in terms of total expected utility and service level; uncovering latent high-risk paths in the SC and prioritizing contingency and recovery policies | N |
| Ivanov D. (2019) | Disruption tails and revival policies: A simulation analysis of supply chain design and production-ordering systems in the recovery and post-disruption periods | Computers and Industrial Engineering | Production-ordering behavior in an FMCG SC with disruption risks during recovery and post-disruption periods | Discrete-event simulation | Non-coordinated ordering and production policies during the disruption period may result in backlog and delayed orders, the accumulation of which causes post-disruption SC instability, resulting in further delivery delays and non-recovery of SC performance; Specific policies must be developed for the transition from recovery to disruption-free operation mode to avoid “disruption tails” |
C |
| Ivanov D. (2020) | Predicting the impact of epidemic outbreaks on the global supply chains: A simulation-based analysis on the example of coronavirus (COVID-19/SARS-CoV-2) case | Transportation Research Part E: Logistics and Transportation Review | Predicting the impact of epidemic outbreaks on global SCs | Discrete-event simulation | Timing of the closing and opening of facilities at different echelons might become a major factor that determines the epidemic outbreak impact on SC performance. Lead-time, speed of epidemic propagation, and the upstream and downstream disruption duration in the SC are other important factors; results can be used to predict the operative and long-term impacts of epidemic outbreaks on SCs, to develop pandemic SC plans, and to identify the successful and problematic elements of risk mitigation/preparedness and recovery policies in case of epidemic outbreaks | C |
| Ivanov D., Sokolov B., Pavlov, A. (2013) | Dual problem formulation and its application to optimal re-design of an integrated production-distribution network with structure dynamics and ripple effect considerations | International Journal of Production Research | Identify an SC design structure that would satisfy some performance criteria under different disruptions | Optimization: linear Programming | Building robust distribution plans and interconnecting decisions on distribution network design, planning, and sourcing. | P |
| Ivanov, D. (2017) | Simulation-based the ripple effect modeling in the supply chain | International Journal of Production Research | Performance impact of disruption propagation in the SC | Discrete-event simulation | Advantages and costs of backup SC designs for mitigating ripple effect | C |
| Ivanov, D., Sokolov B., Kaeschel J. (2010) | A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations | European Journal of Operational Research | SC multi-structural design and dynamic control of macro states | Control theory | SC designs are not restricted to the network of firms; rather, they are multi-structural systems spanning organizational, informational, financial, technological, process-functional, and productive structures | N |
| Ivanov, D., Sokolov, B., & Dolgui, A. (2014b) | The ripple effect in supply chains: Trade-off ‘efficiency-flexibility-resilience’ in disruption management | International Journal of Production Research | Conceptualization of the ripple effect concept in SCs; Dynamic view on SC ripple effect |
Control theory | Disruption propagation represents a specific type of SC risks, i.e., the ripple effect | N |
| Ivanov, D., Sokolov, B., & Pavlov, A. (2014a) | Optimal distribution (re)planning in a centralized multi-stage network under conditions of the ripple effect and structure dynamics | European Journal of Operational Research | Reconfiguration of material flows in an SC subject to changes in network structures over many periods | Optimization: linear programming and optimal control | Considering different execution scenarios and developing suggestions on re-planning in the case of disruption propagation; scenario-based risk identification strategy and operational distribution planning | P |
| Ivanov, D., Sokolov, B., Hartl, R., Dolgui, A., Pavlov, A., Solovyeva, I. (2015) | Integration of aggregate distribution and dynamic transportation planning in a supply chain with capacity disruptions and ripple effect considerations | International Journal of Production Research | Distribution and transportation capacity disruptions and the ripple effect | Optimization: linear programming and optimal control | Dynamic, time-dependent issues of the ripple effect | P |
| Ivanov, D., Sokolov, B., Pavlov, A., Dolgui, A., & Pavlov, D. (2016) | Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies | Transportation Research Part E: Logistics and Transportation Review | Impact of disruption durations on the ripple effect and SC performance with consideration of recovery costs | Optimization: linear programming and optimal control | A model to analyze proactive SC structures, compute recovery policies, and to re-direct material flows to mitigate the ripple effect; a method to compare SC design resistance to the ripple effect; suggesting rules to recover and reallocate resources and flows after a disruption | P |
| Lei, Z., Lim, MK., Cui L. & Y. Wang (2020) | Modeling of risk transmission and control strategy in the transnational supply chain. | International Journal of Production Research. | Mechanisms of risk transmission in global SCs | Susceptible-infectious-susceptible (SIS) model; complexity theory | Global supplier diversification and risk control are crucial management activities to mitigate the ripple effect | N |
| Levner E., Ptuskin A. (2018) | Entropy-based model for the ripple effect: managing environmental risks in supply chains | International Journal of Production Research | Impact of environmental risks on the ripple effect | Complexity theory; entropy analysis | Assessing the economic loss caused by the ripple effect due to environmental risks | N |
| Li, Y., Zobel, C. W. (2020). | Exploring Supply Chain Network Resilience in the Presence of the Ripple Effect | International Journal of Production Economics | Impact of the ripple effect on SC resilience | Graph theory; simulation | Network type has more influence on resistance to the ripple effect from a short-term perspective; from a long-term perspective, it is more advantageous to enhance node risk capacity as adjusted to the structure; increasing robustness may lead to prolonged recovery time | N |
| Li, Y., Zobel, C. W., Seref, O., and Chatfield, D. C. (2019) | Network Characteristics and Supply Chain Resilience under Conditions of Risk Propagation | International Journal of Production Economics | Impact of network characteristics on SC resilience with disruption propagation considerations | Graph theory | Metrics to analyze impact of the ripple effect on SC resilience; recovery time is primarily determined by the disruption process, and significantly less so by the network structure | N |
| Liberatore F, Scaparra M.P., Daskin M.S. (2012). | Hedging against disruptions with ripple effects in location analysis | Omega | How to fortify SC facilities to hedge against the ripple effect | Optimization: mixed-integer programming | Identification of facilities to be fortified to mitigate the ripple effect | P |
| Lu, M., Ran, L., Shen, Z.-J.M. (2015) | Reliable facility location design under uncertain correlated disruptions | Manufacturing & Service Operations Management | Worst-case analysis of reliable facility location problems with consideration of correlated disruptions | Robust optimization | Reliable SC design with cost minimization for some given disruption probabilities of correlated events | P |
| Mizgier, KJ, SM Wagner, JA Holyst (2013) | Modeling defaults of companies in multi-stage supply chain networks | International Journal of Production Economics | Modeling defaults of companies caused by structural dynamics | Agent-based simulation | Should a company be unable to quickly adapt to the changing environment, it might be exposed to the risk of the collective defaults of suppliers, which can give rise to disruptions and delays in production. | N |
| Ojha, R., Ghadge, A., Tiwari M.K. & U. S. Bititci (2018) | Bayesian network modeling for supply chain risk propagation | International Journal of Production Research | Analysis of SC exposure to the ripple effect risk | Bayesian Network simulation | Ripple effect quantification by fragility, service level, inventory cost, and lost sales | N |
| Osadchiy, N., Gaur, V., Seshadri, S. (2016) | Systematic risk in supply chain networks | Management Science | Mapping supply networks of industries and firms to investigate how the SC structure mediates the effect of economy on industry or firm sales. | Statistical analysis | To identify mechanisms that can affect the correlation between sales levels and SC states; effects of risk propagation on production decisions, aggregation of orders from multiple customers in an SC, and aggregation of orders over time. | N |
| Özçelik, G., Ö. F. Yılmaz & F. B. Yeni (2020) | Robust optimization for ripple effect on reverse supply chain: an industrial case study | International Journal of Production Research | Ripple effect in reverse SC | Robust optimization | Method to proactively increase SC design robustness against the ripple effect with consideration of reverse network | P |
| Pariazar, M., Root, S., Sir, M.Y. (2017). | Supply chain design considering correlated failures and inspection in pharmaceutical and food supply chains | Computers and Industrial Engineering | Impact of correlated disruptions on SC design | Stochastic programming; Monte-Carlo simulation | Correlated supplier failures increase total cost and influence SC design | P |
| Pavlov A., Ivanov D., Pavlov D., Slinko A. (2019) | Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics | Annals of Operations Research | Search for an optimal SC design with intensities of processing policies at nodes and arcs subject to multi-period changes in network structures and budget restrictions | Optimization: linear programming | To identify balanced levels of capacity utilization and production rates at different firms in the SC to achieve maximum performance. | P |
| Pavlov A., Ivanov D., Werner F., Dolgui A., Sokolov B. (2020). | Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains | Annals of Operations Research | Identification of disruption scenarios of different severity and the resulting ripple effects | Reliability theory | A methodology to identify the most severe disruption scenarios, respective ripple effects, and optimal recovery paths | N |
| Sinha, P., Kumar, S., Prakash S. (2019) | Measuring and Mitigating the Effects of Cost Disturbance Propagation in Multi-Echelon Apparel Supply Chains | European Journal of Operational Research | Impact of demand variation propagation on SC performance | Graph theory | SC reconfiguration strategies to reduce the negative impact of disturbance propagation | P |
| Sokolov, B., Ivanov, D., Dolgui A., Pavlov A. (2016). | Structural quantification of the ripple effect in the supply chain | International Journal of Production Research | Analysis of different performance indicators in light of uncertainty for SCs with ripple effects | Graph theory, MCDM | Interrelations between network robustness, centralization, and flexibility | N |
| Tang, L., K. Jing, J. He, H.E. Stanley (2016) | Complex interdependent supply chain networks: Cascading failure and robustness | Physica A | Robustness of cyber-physical SC with disruption propagation considerations in material and information flows | Reliability theory | Helps to identify critical nodes, the removal of which would lead to network discontinuity, or even collapse | N |
| Zeng, Y., & Xiao, R. (2014). | Modeling of cluster supply network with cascading failure spread and its vulnerability analysis | International Journal of Production Research | Analysis and mitigation of SC vulnerability in the presence of disruption propagation | Complexity theory; entropy analysis | To analyze and predict dynamic SC behaviors caused by vulnerabilities during the process of failure spreading | N |
| Zhao M., Freeman, N.K. (2019) | Robust Sourcing from Suppliers under Ambiguously Correlated Major Disruption Risks | Production and Operations Management | Sourcing policies under conditions of ambiguously correlated disruptions | Distributionally robust model | Profit maximization for scenarios with worst-case disruption distribution. | P |