Supply chain disruption propagation: a systemic risk and normal accident theory perspective

By Kevin P. Scheibe, Jennifer Blackhurst. International Journal of Production Research, 2018

366 citations recorded by the source corpus.

Subfields: Management Information Systems, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/3d8f5d0736301ebae7d4f6861142b06585c2d4e4

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In Management Information Systems

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Citations in the corpus

Resilient and sustainable supply chain design: sustainability analysis under disruption risks (2018, background): “As supply chains grow more complex, they become more vulnerable to disruptions caused by various means such as natural disasters, political unrest, strikes, unexpected regulatory issues, port problems and terrorist activities (Snyder et al. 2016; Scheibe and Blackhurst 2017).”
Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience (2018, background): “that complexity negatively affects SC performance, with a performance decrease depending on the scope of control. Based on these findings, different control strategies to mitigate the negative influence of complexity are formulated. AdensoDíaz, Mar-Ortiz, and Lozano (2018) showed that flow complexit”
A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0 (2020, background): “This propagation can increase the severity of an event ’ s impact (Blackhurst et al. 2005; Mizgier, Wagner, and Holyst 2012; Ivanov, Sokolov, and Dolgui 2014; Yildiz et al. 2016; Scheibe and Blackhurst 2018; Dolgui, Ivanov, and Sokolov 2018; Zhang, Chen, and Fang 2018).”
The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics (2018, background): “…to the ripple effect control framework (Dolgui, Ivanov, and Sokolov 2018) and other literature on the disruption propagation in the SC (e.g. Scheibe and Blackhurst 2018), disruption risks and their propagation in the SC are mainly caused by single sourcing, low risk mitigation inventory,…”
Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic (2020, background): “…dominate this research stream (Basole and Belami 2014; Kim et al. 2015; Brintrup et al. 2015; Sawik 2017; Macdonald et al. 2018; Yoon et al. 2018; Scheibe and Blackhurst 2018; Pavlov et al. 2018; Ojha et al. 2018; Ivanov 2018a; Ivanov et al. 2019a; Dolgui et al. 2018; Ivanov and Dolgui 2019; Li…”
A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing (2019, background): “Considering the SC as a whole, the dynamic nature of the supplier-198 customer relationship inﬂuences disruption propagation, and therefore the SC structure and dependence [67].”
Supply chain resilience for single and multiple sourcing in the presence of disruption risks (2018, background): “…an increased probability of disruption risks (Blackhurst et al. 2005), because they are longer, larger and more complex (Kilgore 2003; Chopra and Sodhi 2004; Blackhurst et al. 2005; Tang 2006a, 2006b; Craighead et al. 2007; Tang and Tomlin 2008; Merzifonluoglu 2015; Scheibe and Blackhurst 2017).”
Supply chain disruptions and resilience: a major review and future research agenda (2021, background): “Other important mitigation strategies for disruptive events focus on better demand forecasting (Scheibe and Blackhurst 2018), better coordination amongst the SC echelons before and after the disruption with the use of information-sharing (Dubey et al.”