2019
DOI: 10.1007/s10479-019-03454-1
|View full text |Cite
|
Sign up to set email alerts
|

Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

3
53
0

Year Published

2020
2020
2021
2021

Publication Types

Select...
5
5

Relationship

2
8

Authors

Journals

citations
Cited by 84 publications
(56 citation statements)
references
References 63 publications
3
53
0
Order By: Relevance
“…Recent literature , Lücker et al, 2019, Schmitt et al, 2017Gupta and Ivanov, 2020) has recognized the risk mitigation inventory, lead-time and backup suppliers as crucial elements affecting the SC reactions to disruptions. Moreover, the ripple effect is usually accompanying the disruptions which are rarely to be localized and usually spread over many SC echelons (Ivanov et al, 2014, Garvey et al, 2015, Dolgui et al, 2018, Ivanov et al, 2019b, Pavlov et al, 2019b, Li and Zobel, 2020. Anparasan and Lejeune (2018) presented a data set of the cholera epidemic that occurred in the aftermath of the 2010 earthquake in Haiti.…”
Section: Methodsmentioning
confidence: 99%
“…Recent literature , Lücker et al, 2019, Schmitt et al, 2017Gupta and Ivanov, 2020) has recognized the risk mitigation inventory, lead-time and backup suppliers as crucial elements affecting the SC reactions to disruptions. Moreover, the ripple effect is usually accompanying the disruptions which are rarely to be localized and usually spread over many SC echelons (Ivanov et al, 2014, Garvey et al, 2015, Dolgui et al, 2018, Ivanov et al, 2019b, Pavlov et al, 2019b, Li and Zobel, 2020. Anparasan and Lejeune (2018) presented a data set of the cholera epidemic that occurred in the aftermath of the 2010 earthquake in Haiti.…”
Section: Methodsmentioning
confidence: 99%
“…Basole and Bellamy (2014) focused on the identification of 'healthy nodes' in the SC based on the level of risk diffusion. Chen, Xi, and Jing (2017) and Macdonald et al (2018) show that SC robustness and resilience should not merely be based on a straightforward disruption magnitude analysis, but rather seek trajectories of how different disruption scenarios influence the severity in network degradation and recovery (Pavlov et al 2019a).…”
Section: Viability Vs Stability Robustness and Resilience Of Scsmentioning
confidence: 99%
“…In this connection, research has considered supply-side disruption (Pal et al 2014;Gülpnar et al 2014;Ray & Jenamani 2013;Wang & Yu 2020), production disruption (Paul et al 2019b;Bao et al 2020), transportation and distribution disruption (Chaghooshi & Moein 2014;Wilson 2007;Hishamuddin et al 2015), demand-side disruption (Paul et al 2014a and b;Kirchoff et al 2011;Ray & Jenamani 2016), and the combination of two or more the previously listed types of disruption. This perspective focuses on how a disruption in a particular function of a supply chain can imbalance the entire supply chain network, due to the ripple effect it creates (Kim et al 2014;Dolgui et al 2020;Das et al 2019;Ivanov et al 2019;Pavlov et al 2019).…”
Section: Risk and Disruption Managementmentioning
confidence: 99%