Ripple Effect in the Supply Chain: Definitions, Frameworks and Future Research Perspectives

This chapter aims at delineating major features of the ripple effect and methodologies to mitigate the supply chain disruptions and recover in case of severe disruptions. It observes the reasons and mitigation strategies for the ripple effect in the supply chain and presents the ripple effect control framework that is comprised of redundancy, flexibility and resilience. Even though a variety of valuable insights has been developed in the given area in recent years, new research avenues and ripple effect taxonomies are identified for the near future. Two special directions are highlighted. The first direction is the supply chain risk analytics for disruption risks and the data-driven ripple effect control in supply chains. The second direction is the concept of low-certainty-need (LCN) supply chains.

[1]  Dmitry A. Ivanov,et al.  Disruption tails and revival policies: A simulation analysis of supply chain design and production-ordering systems in the recovery and post-disruption periods , 2019, Comput. Ind. Eng..

[2]  Boris V. Sokolov,et al.  Optimal distribution (re)planning in a centralized multi-stage supply network under conditions of the ripple effect and structure dynamics , 2014, Eur. J. Oper. Res..

[3]  Alexandre Dolgui,et al.  Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience , 2018, Int. J. Prod. Res..

[4]  Eugene Levner,et al.  Entropy-based model for the ripple effect: managing environmental risks in supply chains , 2018, Int. J. Prod. Res..

[5]  Dmitry Ivanov,et al.  Coordination of production and ordering policies under capacity disruption and product write-off risk: an analytical study with real-data based simulations of a fast moving consumer goods company , 2017, Annals of Operations Research.

[6]  Alexandre Dolgui,et al.  Ripple effect in the supply chain: an analysis and recent literature , 2018, Int. J. Prod. Res..

[7]  D. Ivanov,et al.  Global Supply Chain and Operations Management: A Decision-Oriented Introduction to the Creation of Value , 2016 .

[8]  Dmitry Ivanov,et al.  Simulation-based ripple effect modelling in the supply chain , 2017, Int. J. Prod. Res..

[9]  Alexandre Dolgui,et al.  Hybrid Fuzzy-Probabilistic Approach to Supply Chain Resilience Assessment , 2018, IEEE Transactions on Engineering Management.

[10]  Alexandre Dolgui,et al.  Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain† , 2019, Int. J. Prod. Res..

[11]  Dmitry Ivanov,et al.  Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics , 2019, Annals of Operations Research.

[12]  D. Ivanov Structural Dynamics and Resilience in Supply Chain Risk Management , 2017 .

[13]  Iris Heckmann,et al.  Towards Supply Chain Risk Analytics , 2016 .

[14]  Alexandre Dolgui,et al.  Review of quantitative methods for supply chain resilience analysis , 2019, Transportation Research Part E: Logistics and Transportation Review.

[15]  Linda Hendry,et al.  Supply-chain uncertainty: a review and theoretical foundation for future research , 2012 .

[16]  Tadeusz Sawik Supply Chain Disruption Management Using Stochastic Mixed Integer Programming , 2017 .

[17]  D. Waters Supply Chain Risk Management: Vulnerability and Resilience in Logistics , 2007 .

[18]  David Simchi-Levi,et al.  Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain , 2015, Interfaces.

[19]  O. Tang,et al.  Identifying risk issues and research advancements in supply chain risk management , 2011 .

[20]  D. Ivanov Revealing interfaces of supply chain resilience and sustainability: a simulation study , 2018, Int. J. Prod. Res..

[21]  Amanda J. Schmitt,et al.  OR/MS models for supply chain disruptions: a review , 2014 .

[22]  Steven A. Melnyk,et al.  Supply chain risk and resilience: theory building through structured experiments and simulation , 2018, Int. J. Prod. Res..

[23]  Kamil J. Mizgier Global sensitivity analysis and aggregation of risk in multi-product supply chain networks , 2017, Int. J. Prod. Res..

[24]  Boris V. Sokolov,et al.  Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty , 2013, Eur. J. Oper. Res..

[25]  S. Chopra,et al.  Managing Risk To Avoid Supply-Chain Breakdown , 2004 .

[26]  Alain Martel,et al.  The design of robust value-creating supply chain networks , 2010, Eur. J. Oper. Res..

[27]  Alexandre Dolgui,et al.  Structural quantification of the ripple effect in the supply chain , 2016 .

[28]  デイビッド スミチレビ,et al.  From Superstorms to Factory Fires : Managing Unpredictable Supply-chain Disruptions , 2014 .

[29]  Alexandre Dolgui,et al.  Integration of aggregate distribution and dynamic transportation planning in a supply chain with capacity disruptions and the ripple effect consideration , 2015 .

[30]  Kevin P. Scheibe,et al.  Supply chain disruption propagation: a systemic risk and normal accident theory perspective , 2018, Int. J. Prod. Res..

[31]  Dmitry Ivanov,et al.  Integrated customer-oriented product design and process networking of supply chains in virtual environments , 2012, Int. J. Netw. Virtual Organisations.

[32]  Brian Tomlin,et al.  On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks , 2006, Manag. Sci..

[33]  Luk N. Van Wassenhove,et al.  Supply Chain Tsunamis: Research on Low‐Probability, High‐Impact Disruptions , 2018 .

[34]  KwangSup Shin,et al.  Evaluation mechanism for structural robustness of supply chain considering disruption propagation , 2016 .

[35]  Dmitry Ivanov,et al.  An adaptive framework for aligning (re)planning decisions on supply chain strategy, design, tactics, and operations , 2010 .

[36]  Fengpeng Zhang,et al.  Modeling and analysis of under-load-based cascading failures in supply chain networks , 2018 .

[37]  Saibal Ray,et al.  Supply chain disruptions : theory and practice of managing risk , 2012 .

[38]  Alexandre Dolgui,et al.  Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies , 2016 .

[39]  Kevin B. Hendricks,et al.  Association Between Supply Chain Glitches and Operating Performance , 2005, Manag. Sci..

[40]  Dmitry Ivanov,et al.  The inter‐disciplinary modelling of supply chains in the context of collaborative multi‐structural cyber‐physical networks , 2012 .

[41]  Panos Kouvelis,et al.  Handbook of Integrated Risk Management in Global Supply Chains , 2011 .

[42]  Luis M. Camarinha-Matos,et al.  A conceptual model of value systems in collaborative networks , 2010, J. Intell. Manuf..

[43]  Terje Aven,et al.  How some types of risk assessments can support resilience analysis and management , 2017, Reliab. Eng. Syst. Saf..

[44]  Kevin McCormack,et al.  Supply chain risk management : minimizing disruptions in global sourcing , 2007 .

[45]  William Ho,et al.  Supply chain risk management: a literature review , 2015 .

[46]  S. Chopra,et al.  Reducing the Risk of Supply Chain Disruptions , 2014 .

[47]  Alexandre Dolgui,et al.  The Ripple effect in supply chains: trade-off ‘efficiency-flexibility-resilience’ in disruption management , 2014 .

[48]  Hai Zhuge,et al.  Semantic linking through spaces for cyber-physical-socio intelligence: A methodology , 2011, Artif. Intell..

[49]  Yacob Khojasteh,et al.  Supply Chain Risk Management , 2018 .

[50]  Boris V. Sokolov,et al.  A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations , 2010, Eur. J. Oper. Res..

[51]  Dmitry Ivanov,et al.  Adaptive Supply Chain Management , 2009 .

[52]  Tadeusz Sawik,et al.  On the risk-averse optimization of service level in a supply chain under disruption risks , 2016 .

[53]  Dmitry Ivanov,et al.  Integrated dynamic scheduling of material flows and distributed information services in collaborative cyber-physical supply networks , 2014 .

[54]  S. Fawcett,et al.  Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management , 2013 .

[55]  Boris Sokolov,et al.  Minimization of disruption-related return flows in the supply chain , 2017 .

[56]  Alexandre Dolgui,et al.  Literature review on disruption recovery in the supply chain* , 2017, Int. J. Prod. Res..

[57]  Maria Paola Scaparra,et al.  Hedging against disruptions with ripple effects in location analysis , 2012 .

[58]  Saibal Ray,et al.  Supply Chain Disruptions , 2012 .

[59]  Kathryn E. Stecke,et al.  Mitigating disruptions in a multi-echelon supply chain using adaptive ordering , 2017 .

[60]  Huy Truong Quang,et al.  Risks and performance in supply chain: the push effect , 2018, Int. J. Prod. Res..

[61]  Boris V. Sokolov,et al.  Structure Dynamics Control-Based Service Scheduling in Collaborative Cyber-Physical Supply Networks , 2012, PRO-VE.

[62]  Joseph Sarkis,et al.  Quantitative models for managing supply chain risks: A review , 2015, Eur. J. Oper. Res..

[63]  Eric Ballot,et al.  Mitigating supply chain disruptions through interconnected logistics services in the Physical Internet , 2017, Int. J. Prod. Res..

[64]  Boris V. Sokolov,et al.  Simulation Vs. Optimization Approaches to Ripple Effect Modelling in the Supply Chain , 2018, LDIC.

[65]  Dmitry Ivanov,et al.  A real-option approach to mitigate disruption risk in the supply chain , 2019, Omega.

[66]  Alexandre Dolgui,et al.  The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics , 2018, Int. J. Prod. Res..

[67]  Boris V. Sokolov,et al.  Intelligent Supply Chain Planning in Virtual Enterprises , 2004, Virtual Enterprises and Collaborative Networks.