Ripple Effect Modeling of Supplier Disruption on the Distributor in the Three-stage Supply Chain

Document Type : Research Paper


1 Ph.D. Candidate, Department of Industrial Management, Faculty of Management, North Tehran Branch, Islamic Azad University, Tehran, Iran.

2 Assistant Prof., Department of Industrial Management, Faculty of Management, North Tehran Branch, Islamic Azad University, Tehran, Iran.

3 Prof., Department of Industrial Management, Faculty of Management and Economic, Tarbiat Modares University, Tehran, Iran.



Objective: The disruption caused by natural, economic and political crises, piracy, and disturbance in information systems and human agents (low probability factors with high impact intensity) travel like a wave from the upstream of the supply chain, i.e. from the suppliers to its downstream. The ripple effect is the phenomenon of disruption in the supply chain with serious impacts on the performance of the supply chain. It can influence sales, on-time delivery, and profits. It has more serious consequences in reducing short-term performance, which leads to market share reduction. This disruption affects the entire supply chain. It destroys the capacity or inventory of the chain, disrupts its facilities, and causes material loss and production reduction in the next stages of the supply chain. Since disruptions are inevitable in supply chains, it is necessary to address this issue and evaluate the impact of disruption risk and its distribution on the supply chain. The purpose of the present study is to provide a simulation model for the behavior of disruption propagation in a three-stage supply chain. It tries to take into account both the vulnerability and recovery capabilities of the disrupted supplier and to determine a criterion for quantitatively predicting the ripple effect of the supplier disruption on the distributor. As the disruption in suppliers prolongs deliveries to customers, and any increase in lead time will decrease sales, the current study seeks to present the quantitative estimation of the ripple effect of supplier disruption on the distributor in terms of lead time and lost sales.
Methods: First, using a discrete-time Markov chain, a recovery and vulnerability model was proposed for long-term disrupted suppliers with three states of operational, semi-operational, and fully disrupted. The model was integrated with a Bayesian network to show the way the supplier disruption spreads to the manufacturer and distributor by dynamic Bayesian modeling. Then, in order to quantitatively show the ripple effect of the supplier disruption on the distributor, a criterion was established in terms of the lead time for the delivery of goods and lost sales based on the decision tree and the data of the dynamic Bayesian simulation model.
Results: The model was presented through figures, definitions, and mathematical relations. Its capability was shown by an example based on the status of suppliers, manufacturers, and distributors in the production of a voltage-increasing electronic component. The obtained results showed that when a disturbance occurs in upstream of a supply chain, it spreads as a ripple to its downstream and affects its performance.
Conclusion: The proposed model by this study can quantitatively show the spread and impact of disruption along the supply chain. It can reveal the hidden risk paths and the role of each entity in the chain at the time of disruption. By quantitatively estimating the vulnerability caused by any disruption in the supply chain, the proposed model can help managers identify deviations or the risk of deviations in the chain in time. It can also help them with analyzing and performing control measures to restore the operation and process of the chain and prioritize possible policies and recovery. It is also useful for the selection of suppliers and inventory plans and making the right decisions to reduce the vulnerability of the supply chain sectors quickly with minimal cost.


Aghazadeh, H. & Maleki, H. (2020). Developing a Conceptual Framework of Buyer-Supplier Relationship Quality in the Supply Chain and Prioritizing its key Components: A Meta-Synthesis Method. Industrial Management Journal, 12(4), 578-608. (in Persian)
Behzadi, G., O'Sullivan, M.J., Olsen, T.L., Scrimgeour, F. & Zhang, A. (2017). Robust and resilient strategies for managing supply disruptions in an agribusiness supply chain. International Journal of Production Economics.doi: 10.1016/j.ijpe.2017.06.018.
Brusset, X., Davari, M., Kinra, A., & La Torre, D. (2022). Modelling ripple effect propagation and global supply chain workforce productivity impacts in pandemic disruptions. International Journal of Production Research, 1-20.
Chauhan, V. K., Perera, S., & Brintrup, A. (2021). The relationship between nested patterns and the ripple effect in complex supply networks. International Journal of Production Research, 59(1), 325-341.
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.
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, 58(5): 1285-1301.
Ekhtiari, M., Zandieh, M., Alem Tabriz, A., & Rabieh, M. (2019). Proposing a Bi-level Programming Model for Multi-echelon Supply Chain with an Emphasis on Reliability in Uncertainty. Industrial Management Journal, 11(2), 177-206. (in Persian)
Fenton, N. & Neil, M. (2013). Risk assessment and decision analysis with Bayesian networks. CRC Press, Taylor & Francis Group, Boca Raton, FL.
Ghadge, A., Er, M., Ivanov, D. & Chaudhuri, A. (2022). Visualisation of ripple effect in supply chains under long-term, simultaneous disruptions: a system dynamics approach, International Journal of Production Research, 60:20, 6173-6186, DOI: 10.1080/00207543.2021.1987547
Gholami-Zanjani, S. M., Jabalameli, M. S., Klibi, W., & Pishvaee, M. S. (2021). A robust location-inventory model for food supply chains operating under disruptions with ripple effects. International Journal of Production Research, 59(1), 301-324.
He, J., Alavifard, F., Ivanov, D. & Jahani, H. (2018). A Real-option Approach to Mitigate Disruption Risk in the Supply Chain. Omega. doi:10.1016/
Hosseini, S. & Barker, K. (2016a). A Bayesian network model for resilience-based supplier selection. International Journal of Production Economics, 180: 68-87.
Hosseini, S. & Barker, K. (2016b). Modeling Infrastructure Resilience Using Bayesian Networks: A Case Study of Inland Waterway Ports. Computers & Industrial Engineering, 93: 252–266.
Hosseini, S. & Ivanov, D. (2021). A multi-layer Bayesian network method for supply chain disruption modelling in the wake of the COVID-19 pandemic, International Journal of Production Research. doi:10.1080/00207543.2021.1953180
Hosseini, S., Al Khaled, A. & Sarder, M.D. (2016). A general framework for assessing system resilience using Bayesian networks: A case study of sulfuric acid manufacturer. Journal of Manufacturing Systems, 41: 211-227.
Hosseini, S., Barker, K.& Ramirez-Marquez, J.E. (2016). A Review of Definitions and Measures of System Resilience. Reliability Engineering & System Safety, 145: 47–61.
Hosseini, S., Ivanov, D. & Dolgui, A. (2020). Ripple effect modeling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach. International Journal of Production Research, 58(11), 3284-3303.
Ivanov, D. & Dolgui, A. (2021). OR-Methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications. International Journal of Production Economics, 232, 107921.
Ivanov, D. (2017). Simulation-based the ripple effect modeling in the supply chain. International Journal of Production Research, 55(7), 2083-2101.
Ivanov, D. (2018). OR/MS Methods for Structural Dynamics in Supply Chain Risk Management. In D. Ivanov (Ed.), Structural Dynamics and Resilience in Supply Chain Risk Management (pp. 115-159). Springer International Publishing.
Ivanov, D. (2020a). Predicting the impacts of epidemic outbreaks on global supply chains: A simulationbased analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, doi:10.1016/j.tre.2020.101922.
Ivanov, D. (2020b). Viable Supply Chain Model: Integrating agility, resilience and sustainability perspectives. Lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research, doi:10.1007/s10479-020-03640-6.
Ivanov, D., Dolgui, A., &Sokolov, B. (2019). Ripple effect in the supply chain: Definitions, frameworks and future research perspectives. In Handbook of ripple effects in the supply chain (pp. 1-33). Springer, Cham.
Ivanov, D., Dolgui, A., Sokolov, B., Ivanova, M. (2017). Literature review on disruption recovery in the supply chain. International Journal of Production Research, 55(20), 6158-6174.
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, 57(7), 2154-2172.
Ivanov, D., Tsipoulanidis, A. & Schönberger, J. (2019). Global Supply Chain and Operations Management: A Decision-oriented Introduction Into the Creation of Value. 2nd ed. Cham: Switzerland: Springer International Publishing.
Jingzhe, C., Hongfen, W. & Ray, Y.Z. (2022). A supply chain disruption recovery strategy considering product change under COVID-19. Journal of Manufacturing Systems, 60, 920-927.
Jones,M.T. (2005). Estimating Markov Transition Matrices Using Proportions Data: An Application to Credit Risk. International Monetary Fund: WP/05/219.
Khalili, S.M., Pooya, A., Kazemi, M. & Fakoor Saghih, A.M. (2022). Designing a sustainable and resilient gasoline supply chain network under uncertainty (Case study: gasoline supply chain network of Khorasan Razavi province). Industrial Management Journal, 14(1), 27- 79. (in Persian)
Levner, E. & Ptuskin, A. (2017). Entropy-based model for the ripple effect: managing environmental risks in supply chains. International Journal of Production Research. doi:10.1080/00207543.2017.1374575.
Li, Y., Chen, K., Collignon, S. & Ivanov, D. (2021). Ripple Effect in the Supply Chain Network: Forward and Backward Disruption Propagation, Network Health and Firm Vulnerability. European Journal of Operational Research. doi:10.1016/j.ejor.2020.09.053
LIaguno, A., Mula, J., Campuzano-B, F. (2022). State of the art, conceptual framework and simulation analysis of the ripple effect on supply chains. International Journal of Production Research. DOI: 10.1080/00207543.2021.1877842.
Lohmer, J., Bugert, N. & Lasch, R. (2020). Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study. International Journal of Production Economics.doi:10.1016/j.ijpe.2020.107882.
Lucker, F. (2019).Using inventory to mitigate the ripple effect, International Federation of Automatic Control. IFAC Papers OnLine, 52-13: 1272–1276.
Mazroui nasrabadi,E.,Habibi,A.&Shoul,A.(2022). Presenting a model of critical success factors to cope with the ripple effect in Iran's machine-made carpet supply chain: Corona pandemic effects.Industrial Management Perspective Journal. DOI: 10.52547/jimp.2022.228105.1383.(in Persian)
Mishra, D., Dwivedi, Y.K., Rana, N.P. & Hassini, E. (2021). Evolution of supply chain ripple effect: A bibliometric and metaanalytic view of the constructs. International Journal of Production Research, 119.
Moradi Masjedbari, A. & Makoei, A. (2017). Investigating the principles and strategies of supply chain resilience under disturbances. National Conference on Industrial Management and Engineering of Iran. 21 December 2018. Isfahan. (in Persian)
Park, Y. W., Blackhurst, J., Paul, C., & Scheibe, K. P. (2022). An analysis of the ripple effect for disruptions occurring in circular flows of a supply chain network. International Journal of Production Research, 60(15), 4693-4711.
Pournader, M., Kach, A. & Talluri, S. (2020). A Review of the Existing and Emerging Topics in Supply Chain Risk Management Literature. Decision Sciences. doi:10.1111/deci.12470.
Qazi, A., Dickson, A., Quigley, J. & Gaudenzi, B. (2018). Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks. International Journal of Production Economics, 196: 24-42.
Sawik, T. (2020). Selection of Supply and Demand Portfolios and Production and Inventory Scheduling. In: Supply Chain Disruption Management. International Series in Operations Research & Management Science, vol 291. Springer, Cham.
Sibevei, A., Azar, A. & Zandieh, M. (2021). Developing a Two-stage Robust Stochastic Model for Designing a Resilient Blood Supply Chain Considering Earthquake Disturbances and Infectious Diseases. Industrial Management Journal, 13(4), 664-703. (in Persian)
Sindhwani, R., Jayaram, J., & Saddikuti, V. (2022). Ripple effect mitigation capabilities of a hub and spoke distribution network: an empirical analysis of pharmaceutical supply chains in India. International Journal of Production Research, 1-33.
Singh, S., Kumar, R., Panchal, R., Kumar Tiwari, M. (2021). Impact of COVID-19 on logistics systems and disruptions in food supply chain. International Journal of Production Research, 59(7), 1993-2008.
Sokolov, B., Ivanov, D., Dolgui, A. & Pavlov, A. 2016. Structural Quantification of the Ripple Effect in the Supply Chain.International Journal of Production Research, 54 (1): 152–169.
Song, B., Lee, C. & Park Y. (2013). Assessing the Risks of Service Failures Based on Ripple Effects: A Bayesian Network Approach. International Journal of Production Economics 141 (2), 493–504.
Turkzadeh, N. & Boyer Hassani, A. (2020). Identifying and ranking resilience strategies in response to supply chain disruptions of Isfahan Atlas Flour Company in the face of corona conditions using the quality house approach, the first national conference on producing health knowledge in the face of corona and governance in the post-corona world, Najafabad. (in Persian)
Yılmaz, Ö.F., Özçelik, G. & Yeni, F.B. (2020). Ensuring sustainability in the reverse supply chain in case of the ripple effect: A two-stage stochastic optimization model. Journal of Cleaner Production. doi: 10.1016/j.jclepro.2020.124548.
Yoon, J., Talluri, S., Yildiz, H. & Ho, W. (2018). Models for Supplier Selection and Risk Mitigation: A Holistic Approach. International Journal of Production Research, 56(10), 3636-3661.
Zhiqing, D., Yongda, H., Hui, W. & Linhui, W. (2020). Is there a ripple effect in environmental regulation in China? Evidence from the local-neighborhood green technology innovation perspective. Ecological Indicators, 118(1), 106773.