Source
Identification
SCOPUS = 385
WOS = 149
TOTAL= 534
3. Critical Analysis
1.Introduction
534 189 112 65
Book chapters
Conference papers
Other languages
Duplicated
papers removed,
using EndNote
Based on
abstracts, 47
papers were
excluded
Based on
content of full-
text, 22 papers
were excluded
43
First Exclusion process Second Exclusion process
Removing Duplicates Final Exclusion process
2. Methodology
Simulation methods can be applied in
many industries’ supply chains with
different parameters and scenarios as a
case study in order to decrease the
ripple effect during the containment and
recovery stage of a pandemic.
•Supply chain disruption
and its related studies can
be categorized into three
levels, network,process,
and control.[2]
4. Key Findings
Aim
To investigate the ripple
effect in SCM and methods
for coping with locally and
globally SCs disruptions
Objective
To review the literature of
SC disruption risks during
unforeseen situations to
understanding concepts and
identifying methods for
mitigating SC disruption
risks
11
3222
Authors
Ivanov, D. Pavlov, A Choi Li, Y. H Schmitt
A systematic literature →
review has been carried out
International
Journal of
Production
Research
International
Journal of
Production
Economics
Transportation
Research
Journals
Network
•Bayesian
Network
•Graph
theory
Process
•Monte-
Carlo
•Bayesian
Network
Control
•Discrete-Event
Simulation
7. References
[1] IVANOV, D. & DOLGUI, A. 2021. OR-methods
for coping with the ripple effect in supply chains
during COVID-19 pandemic: Managerial insights
and research implications. International Journal of
Production Economics, 232.
[2] IVANOV, D. 2020. Predicting the impacts of
epidemic outbreaks on global supply chains: A
simulation-based analysis on the coronavirus
outbreak (COVID-19/SARS-CoV-2) case.
Transportation Research Part E-Logistics and
Transportation Review, 136.
[3] Pariazar, M., Root, S., Sir, M.Y., 2017. Supply
chain design considering correlated failures and
inspection in pharmaceutical and food supply
chains. Comput. Ind. Eng. 111, 123–138. Paul, S.,
Rahman, S.
[4] Garvey, M.D., Carnovale, S., 2020. The rippled
newsvendor: a new inventory framework for
modelling supply chain risk severity in the
presence of risk propagation. Int. J. Prod. Econ.
[5] Hosseini, S., Ivanov, D., Dolgui, A., 2019. Ripple
effect modeling of supplier disruption:
integrated Markov chain and dynamic
Bayesian network approach. Int. J. Prod. Res.
(in press).
[6] Ojha, R., Ghadge, A., Tiwari, M.K., Bititci,
U.S., 2018. Bayesian network modelling for
supply chain risk propagation. Int. J. Prod.
Res. 56 (17), 5795–5819.
[7] Li, Y., Zobel, C.W., 2020. Exploring supply
chain network resilience in the presence of
the ripple effect. Int. J. Prod. Econ.
[8] Dolgui, A., Ivanov, D., Rozhkov, M., 2020.
Does the ripple effect influence the bullwhip
effect? An integrated analysis of structural
and operational dynamics in the supply
chain. Int. J. Prod. Res. 58 (5), 1285–1301.
[9] Ivanov, D., 2019. Disruption tails and
revival policies: a simulation analysis of
supply chain design and production-ordering
systems in the recovery and post-disruption
periods. Comput. Ind. Eng. 127, 558–570.
[10] Ivanov, D., 2017. Simulation-based the
ripple effect modelling in the supply chain.
Int. J. Prod. Res. 55 (7), 2083–2101.
Supply Chain Risks:
•Operational →Demand
fluctuation and lead-time
•Disruption risks →Natural
disasters (Earthquakes,
Tsunamis, Pandemics) and
Man-made catastrophes
(strikes, Legal disputes)[1]
Key features of disruption
risks: Unpredictable scaling,
Long-term, Propagation [2]
Scrutinizing Papers
An investigation into minimizing supply chain disruption propagation effect (ripple effect) during
COVID-19 pandemic using Simulation Techniques
In the containment and mitigation stage,
simulation techniques can be applied to:
•examine the impact of the ripple effect
on SCs, considering the inventory,
production, and ordering policies
•analyze reallocating demand and
supply during a pandemic
•propose recovery plans to mitigate
backlog accumulations over the
disruption period
•Supply chain risks were
identified
•The Ripple effect concept
was clarified
•OR methods for coping with
SCs risks during pandemics
were investigated
6. Recommendation
5. Conclusion
Jafar Amininik -21103465
ENG 7142 –Research Methods
M
Level
Author
Central Focus and Outcome
Advantages
Disadvantages
Monte
Carlo
Process
Pariazar et
al., (2017)
Correlated supplier failures increase
total cost and influence SC design
• Has a positive response to all parameters
• Can identify the most critical suppliers [3]
• Time consuming to
build simulation [3]
• Requires several runs [3]
Bayesian Network
simulation
Garvey,
M.D.,
Carnovale, S.
(2020)
Managers should focus more
attention on control or mitigation of
exogenous events
• Adapted with different situations [4]
• Risk-oriented suppliers can be classified [5]
• Suppliers with a low degree of resilience
and high importance can be identified [5]
• Can be applied to the design of resilient
supply networks which have large
numbers of suppliers [6]
• Highly complex [4]
• Reliant on probabilistic
inference [5]
• NP-Hard when the
network grows [6]
Network
Hosseini S.,
Ivanov D.
(2019).
Measuring of the ripple effect
considering both disruption and
recovery stages
Ojha, R et
al., (2018)
Analysis of SC exposure to the ripple
effect risk
Graph
theory
Li, Y., Zobel,
C. W. (2020).
Impact of the ripple effect on SC
resilience
• Assess the short-term and long-term
behavior of a network after a disruption [7]
• only consider one-time
disruptions[7]
Discrete-event simulation
Control
Dolgui A. et
al., (2020).
To identify relations between the
bullwhip effect and ripple effect
• Possibility of predicting both short-term
and long-term impacts of epidemic
outbreaks on the SCs [8]
• A set of sensitivity experiments allows to
illustrate the model’s behavior [8]
• Consider details and specific traits of the SC
elements [9]
• Visualizing network operations and tracing
every process inside [10]
• Based on a contextual
case-study simulation
analysis, restricting
insight generalization
[8]
• Complex structure and
the need for additional
controlling equations
[10]
Ivanov D.
(2019)
SC instability, resulting in further
delivery delays and non-recovery of
SC performance
Ivanov D.
(2020)
Predicting the impact of epidemic
outbreaks on global SCs
Ivanov, D.
(2017)
Advantages and costs of backup SC
designs for mitigating ripple effect
