Ripple effect in the supply chain: an analysis and recent literature

By Alexandre Dolgui, Dmitry A. Ivanov, Boris V. Sokolov. International Journal of Production Research, 2018

749 citations recorded by the source corpus.

Subfields: Management Information Systems, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/63b44e872320291b21b07e918bb1547ae6bc32c8

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Citations in the corpus

Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience (2019, background): “Ponomarov and Holcomb (2009), argue that every activities of supply chain has inherent risk (Dolgui et al. 2018), that may cause unexpected disruption (Namdar et al.”
A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing (2019, methodology): “According to Tomlin [31] and Dolgui et al. [48], the risks in SCs can be divided into recurrent or disruptive.”
Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review (2022, background): “…on these approaches can enhance supplier selection when attempting to restore operations to the business-as-usual state, because managers can predict supplier performance following the disruption (Cav - alcante et al., 2019) and achieve stabilisation and supply continuity (Dolgui et al., 2018).”
Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic (2020, background): “, agile, lean, sustainable, resilient, and digital SC) (Altay et al. 2018; Bier et al. 2020; Blackhurst et al. 2011; Brandenburg and Rebs 2015; Choi et al. 2018; Das et al. 2006; Dolgui et al. 2018; Dubey et al. 2015; Govindan et al. 2016; DuHadway et al. 2019; Hosseini et al. 2019a; Ivanov 2018b; I”
Reconfigurable supply chain: the X-network (2020, neutral): “with static structures, there are intertwined supply networks which comprise multiple behaviors by changing the buyer-supplier roles in interconnected or even competing SCs and causing dependence asymmetry (Dong et al. 2015). For example, a competitor of a focal firm can also serve as the focal firm”
Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study (2020, neutral): “…2016; Papadopoulos et al., 2017; Stecke and Kumar, 2009) and the effects of disruptions no longer only affect individual members but tend to spread across the entire network, a phenomenon known as the ripple effect (Dolgui et al., 2018, Dolgui et al., 2020b; Ivanov, 2018a; Pavlov et al., 2019).”
Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience (2018, neutral): “The literature analysis shows that complex networks become more vulnerable to severe disruptions which change the SC structures and are involved with SC structural dynamics (Ivanov et al. 2010, Ivanov and Sokolov 2010, Mistree et al. 2017, Ivanov 2018).”
The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics (2018, methodology): “In this section, the ripple effect control framework is presented (Ivanov, Sokolov, and Dolgui 2014b; Ivanov, Sokolov, and Pavlov 2014c; Dolgui, Ivanov, and Sokolov 2018) which will be used in Section 4 to match the digital technologies impacts on different SC disruption risks.”
Impacts of COVID-19 on Global Supply Chains: Facts and Perspectives (2020, background): “A supply chain disruption in one segment frequently has a ripple effect, and it impacts the entire chain [64], [65].”
Resilient and sustainable supply chain design: sustainability analysis under disruption risks (2018, background): “…are able to concurrently incorporate the three dimensions of sustainability (i.e. economic, environmental and social) when designing resilient supply chains (Hassini, Surti, and Searcy 2012; Seuring 2013; Fahimnia, Sarkis, and Eshragh 2015; Dolgui, Ivanov, and Sokolov 2017; Fahimnia et al. 2017).”
A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0 (2020, methodology): “…method applications to SC disruption risks and resilience, the number of model-based studies has been increasing exponentially over the last decade (Ho et al. 2015; Ivanov et al. 2017; Dolgui, Ivanov, and Sokolov 2018; Dolgui et al. 2020a; Ivanov and Dolgui 2019; Hosseini, Ivanov, and Dolgui 2019).”