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

ABSTRACT

Ripple effect is a specific area of SC disruptions and a strong stressor to SC resilience. Research on the ripple effect analyses how one or more disruptive events propagate through the SC and impact its resilience and performance. The phenomenon of the ripple effect, immensely existing in practice, has received great research interest in recent years. Ripple effect management, modelling and assessment became visible research avenues with a growing number and scope of contributions. This Special Issue presents recent developments on the ripple effect in SCs. The Special Issue focuses on studies that address the ripple effect and provide a comprehensive picture of the state of the art and future perspectives. The methodologies comprise of mathematical optimisation, simulation, game theory, control theoretic, data-driven analytics, network complexity, reliability theory research, and empirical research. Even though a variety of valuable insights have been developed in this area in recent years, new research avenues and ripple effect taxonomies are identified for further exploring the ripple effect in the settings of the COVID-19 pandemic, SC viability, viable SC model, and reconfigurable SCs.

KEYWORDS:

• Ripple effect
• supply chain resilience
• supply chain risk management
• supply chain dynamics
• supply chain engineering

Supply chain engineering is one of the main issues in production research (Dolgui and Proth (2010). Research on supply chain (SC) disruption management has focused on both empirical and analytical methodologies in understanding and appeasing risk (Ho et al. 2015; Ivanov 2018; Hosseini, Ivanov, and Dolgui 2019; Ivanov and Dolgui 2019; Yoon, Talluri, and Rosales 2020; Bier, Lange, and Glock 2020). Irrespective of the methodology utilised, majority of the work in this domain has concentrated on disruption impact on strategic/tactical/operational performance and stabilisation/recovery policies (Ivanov 2017, 2019; Li et al. 2019; Pournader, Kach, and Talluri 2020; Sawik 2020; Xu et al. 2020). A significant number of disruption studies have analysed how one or more events propagate through the SC and impact performance thus placing increased emphasis on the ripple effect in SC. Ripple effect is a specific area of SC disruptions and a strong stressor to SC resilience. The phenomenon of the ripple effect, immensely existing in practice, has received great research interest in recent years. The study by Ivanov and Dolgui (2020c) provides the most recent overview of the ripple effect research in SCs. Ripple effect management, modelling and assessment became visible research avenues with a growing number and scope of contributions (Figure 1).

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

We conducted a SCOPUS search based on the titles of the literature dealing with the ripple effect and in line with (Ivanov and Dolgui 2020c) as follows: (supply AND chain AND (ripple AND effect) OR (cascading AND failure) OR (risk AND transmission) OR (snowball AND effect) OR (risk AND propagation) OR (disruption AND propagation) OR (disturbance AND propagation) OR (structural AND dynamics). We limited the results to ‘Business and Management’, ‘Engineering’, and ‘Decision Science’ areas, and articles published in international journals. Figure 1 shows that the number of articles published on the ripple effect-related topics in SC has been growing in 2012–2020.

The ripple effect occurs when a disruption, rather than remaining localised or being contained to one part of the SC, cascades downstream and impacts the performance of the entire SC (Dolgui, Ivanov, and Sokolov 2018). Ripple effect is followed by SC structural dynamics (Dolgui and Ivanov 2020; Ivanov 2018) and its impact might include lower revenues, delivery delays, loss of market share and reputation, etc., with adverse effects on the profitability of SC (Hosseini and Ivanov 2019; Gupta, Ivanov, and Choi 2020; Li et al. 2020). Some examples of the ripple effect are as follows:

1. Earthquake and tsunami in Japan in 2011 have disrupted multiple suppliers in the automotive industry and lead to production breaks and material shortages worldwide (Ivanov 2018).

2. In May 2017, production at BMW was disrupted as a consequence of a supply shortage of steering gears. BMW’s 1st tier supplier, Bosch, was unable to deliver the steering gears since an Italian 2^nd tier supplier experienced production delays for certain steering parts due to internal machine breakdowns (Moetz, Stylos-Duesmann, and Ottoi 2019).

3. In Winter 2020, the COVID-19 virus outbreak resulted in production disruptions at many locations in China and missing deliveries from/to China impacting the global supply chains and even the stock values of many multi-national companies (Ghaffary and Molla 2020; Queiroz et al. 2020; Ivanov and Dolgui 2020a).

This Special Issue presents recent developments on the ripple effect in SCs. The Special Issue aims at delineating major features of the ripple effect and methodologies to mitigate the SC disruption propagations and recover. It focuses on studies that address the ripple effect and provide a comprehensive vision of the state of the art and future research perspectives. The methodologies comprise of mathematical optimisation, simulation, game theory, control theoretic, data-driven analytics, network complexity, reliability theory research, and empirical research. Even though a variety of valuable insights have been developed in this area in recent years, new research avenues and ripple effect taxonomies need to be identified for the near future.

Some recent studies on the ripple effect and the related SC resilience issues show the diversity of topics and methodologies applied to examination of this phenomenon, e.g. (Liberatore, Scaparra, and Daskin 2012; Mizgier, Jüttner, and Wagner 2013; Ghadge et al. 2013; Ivanov, Sokolov, and Dolgui 2014a, 2014b; Zeng and Xiao 2014; Swierczek 2014, 2016; Garvey, Carnovale, and Yeniyurt 2015; Han and Shin 2016; Sokolov et al. 2016; Tang et al. 2016; Levner and Ptuskin 2018; Scheibe and Blackhurst 2018; Ojha et al. 2018; Zhang, Chen, and Fang 2018; Pavlov et al. 2019; Sinha, Kumar, and Prakash 2019; Ivanov, Dolgui, and Sokolov 2019a; Dolgui, Ivanov, and Sokolov 2018, 2020a; Deng et al. 2019; Hosseini, Ivanov, and Dolgui 2020; Nguyen and Nof 2019; Garvey and Carnovale 2020; Kinra et al. 2020; Li and Zobel 2020). Recently, a Handbook on the Ripple Effects in the Supply Chain appeared showing a growing importance of this research field (Ivanov, Dolgui, and Sokolov 2019b). Besides, ripple effect has been seen as an important phenomenon in SC management evolution, e.g. in Industry 4.0 and digital SC systems (Ivanov and Dolgui 2020b; Ivanov et al. 2020; Ralston and Blackhurst 2020).

This Special Issue was established to furnish fresh and extend the existing insights in the ripple effect in SCs regarding the following questions:

• Under what circumstances does one failure cause other failures?

• Which structures of the SC are especially susceptible to the ripple effect?

• What are the typical ripple effect scenarios and what are the most efficient and effective ways to respond to them?

Table 1 provides an overview of the papers included in this special issue, their methodologies and major outcomes. In Table 2, the contributions shown in Table 1 are divided into a three-level structure following the classification proposed by Ivanov and Dolgui (2020c).

Given the reflections shown in Tables 1 and 2, multiple ways to apply quantitative analysis to ripple effect modelling arise, e.g. considering:

• disruption propagation in the SC;

• dynamic recovery policies;

• gradual capacity degradation and recovery;

• multiple performance impact dimensions including financial and operational performance.

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)

Finally, we point to some future research topics on the ripple effect in SCs which can be devoted to the following topics but are not limited to:

• Network structures, ripple effect and resilience: What are the SC design structures that are more prone to the ripple effect?

• Efficiency and resilience: contradictory or complementary goals in ripple effect mitigation?

• Impact of digitalisation, Big Data analytics, and additive manufacturing on SC ripple effect and resilience: How to make use of available digital technology to improve resilience and mitigate the ripple effect?

• Digital twins to manage the ripple effect: how to balance model-driven and data-driven approaches to mitigate and control the ripple effect?

• Sourcing strategies and ripple effect

• The interplay of flexibility and redundancy to ensure SC resilience and mitigate the ripple effect

• Quantification and measuring of the ripple effect

• Behavioural modelling of the ripple effect

• Dynamic analysis of the SC ripple effect (simulation, control theory)

• Interplay of financial and operational techniques in the ripple effect analysis

• Ripple effect visualisation and control

• Information coordination and ripple effect

• Organisational theory and ripple effect

• Competition and ripple effect 

• Sustainability and ripple effect: How to control the ripple effect with an efficient, sustainable resource utilisation?

• Use cases with real company data allowing medium-term and long-term empirical observations of the ripple effects across the industry sectors and considering time lags between the disruption, ripple effect dispersal, and recovery

• COVID-19 pandemic-driven ripple effects and SC viability.

The wider research community is invited to develop new studies in the above or closely related areas. Strong, new, and insightful conceptual and applications oriented studies on the ripple effect that add significantly to the existing body of knowledge will be particularly solicited in future IJPR publications. Especially, we encourage to explore the ripple effect in the setting of SC viability and viable SC model, reconfigurable SCs, and intertwined supply networks (Dolgui, Ivanov, and Sokolov 2020b; Hosseini, Ivanov, and Blackhurst 2020; Ivanov and Dolgui 2020a; Ivanov 2020).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Alexandre Dolgui

Alexandre Dolgui is an IISE Fellow, Distinguished Professor, and the Head of Automation, Production and Computer Sciences Department at the IMT Atlantique, France. His research focuses on manufacturing line design, production planning and supply chain optimization. His main results are based on the exact mathematical programming methods and their intelligent coupling with heuristics and metaheuristics algorithms. He is the co-author of 5 books, the co-editor of 20 books or conference proceedings, the author of 249 refereed journal papers, 4 prefaces of books, 29 editorials and 37 book chapters as well as over 400 papers in conference proceedings. He is the Editor-in-Chief of the International Journal of Production Research (IJPR).

Dmitry Ivanov

Dmitry Ivanov is a professor of supply chain and operations management at Berlin School of Economics and Law (HWR Berlin). His publication list includes around 350 publications, including more than papers in international academic journals and a leading textbook Global Supply Chain and Operations Management. His main research interests and results span the resilience and ripple effect in supply chains, risk analytics, and digital twins. He co-edits International Journal of Integrated Supply Management (IJISM) and is an associate editor of the International Journal of Production Research (IJPR).