An analytical framework for supply network risk propagation: A Bayesian network approach

By Myles D. Garvey, Steven Carnovale, Sengun Yeniyurt. European Journal of Operational Research, 2015

310 citations recorded by the source corpus.

Subfields: Safety, Risk, Reliability and Quality, Statistics and Probability, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/e11c4094dd737be960e35da715b9b4105d7f1d9d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety, Risk, Reliability and Quality

1. Dmitry A. Ivanov

In Statistics and Probability

1. Dmitry A. Ivanov
2. Trent D. Penman
3. Bruce G. Marcot

In Strategy and Management

1. Dmitry A. Ivanov
2. Alexandre Dolgui
3. Boris V. Sokolov
4. Hakan Yilmazkuday
5. Hendrik Birkel
6. Alexander Spieske
7. Niels Bugert
8. Rainer Lasch

Citations in the corpus

• Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case (2020, unknown): “Adversely, the resulting material shortages and delivery delays propagate downstream the SC, causing the ripple effect and performance degradation in terms of revenue, service level and productivity decreases (Ivanov et al., 2014 , Garvey et al., 2015 , Dolgui et al., 2018 , Ivanov et al., 2019b , P”
• Advances in Bayesian network modelling: Integration of modelling technologies (2019, background): “Some aspects of this have appeared in the application of BNs in areas of finance (Giudici and Spelta, 2016; Garvey et al., 2015), and medical diagnosis and decision-support (Constantinuo et al., 2016).”
• Coronavirus disease 2019 and the global economy (2020, unknown): “For example, according to Ivanov (2020), 94% of the Fortune 1000 companies have experienced supply chain disruptions due to COVID-19. Similarly, Araz, Choi, Olson, and Salman (2020) underline that COVID-19 has broken many global supply chains simultaneously. Although these e¤ects are not unique to C”
• Supply chain risk management and artificial intelligence: state of the art and future research directions (2018, unknown): “To the best of the authors’ knowledge, the only recent studies that implement learning algorithms in the context of SCRM are Zage, Glass, and Colbaugh (2013), Garvey, Carnovale, and Yeniyurt (2015) and Ojha et al. (2018).”
• Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic (2020, background): “In addition, literature on the SC ripple effect allows deducing the SC viability antecedents and drivers (Ivanov et al. 2014a, b; Garvey et al. 2015; Dolgui et al. 2018; Levner and Ptuskin 2018; Scheibe and Blackhurst 2018; Hosseini and Ivanov 2019; Ivanov et al. 2019a; Kinra et al. 2020; Li et al. ”