Mitigating disruptions in a multi-echelon supply chain using adaptive ordering

Schmitt, Thomas; Kumar, Sanjay; Stecke, Kathryn E.; Glover, Fred; Ehlen, Mark Andrew

doi:10.1016/j.omega.2016.07.004

Cited by 115 publications

(58 citation statements)

References 56 publications

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“…However, when we turn our attention to Figure 3d, Figure 3b, we observe that as the number of firms increase, ceteris paribus, the severity decreases. This result is congruent with the extant literature which suggests that the complexity of the supply network structure is related to the level of severity (Schmitt et al, 2017). We further emphasize the reasoning for this with two primary explanations with respect to our study.…”

Section: Discussionsupporting

confidence: 92%

“…As one may expect, when firms are higher in the supply network, their decision's impact to severity appears more sensitive than firms lower in the supply chain. This is in congruence with extant literature that have addressed disruptions and service levels for multi-echelon systems (Schmitt et al, 2017). We also notice that severity is lower when firms produce more than what is ordered, but not too much so as to lead to a disruption in production.…”

Section: Discussionsupporting

confidence: 88%

“…severity) and risk. With risk propagation, we have confirmed the findings of other studies that suggest simpler structures lead to decreased risk propagation (Craighead et al, 2007;Schmitt et al, 2017). On the other hand, some in the traditional risk literature have suggested that supply chain risk (not propagation) is related to the complexity of the network in a positive curvilinear manner (Choi and Krause, 2006).…”

Section: Discussionsupporting

confidence: 88%

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The rippled newsvendor: A new inventory framework for modeling supply chain risk severity in the presence of risk propagation

Garvey

Carnovale

2020

International Journal of Production Economics

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How should managers take into account the propagation of supply chain disruptions and risks (i.e. the ripple effect) when they design their inventory policies? For over 60 years, various extensions and applications to the popular newsvendor model have been suggested, where cost/profit are often the focal objective. Accordingly, we propose a new version of the traditional single-period newsvendor model -the "Rippled Newsvendor" -with supply chain severity (i.e. risk propagation) as the primary objective while taking into account network structure. Our model considers exogenous and endogenous risk(s) of disruption while exploring the tension between under-supply and "wear-and-tear" (i.e system breakdown).To model the intricacies of this trade-off whilst minimizing the potential spread of risk, we leverage a Bayesian Network whereby the conditional probability distributions are functions of the inventory ordering decisions. We use a simulation study to understand the nature of our objective function as well as to gain insight into the potential optimal ordering policies of this new model. Furthermore, the simulation seeks to understand how the various factors in our system impact total risk severity, and if they do so in different ways. Our simulations indicate that local exogenous risk is of greater importance than non-local exogenous risk.Furthermore, we show that the type of risk, as well as the structural characteristics of the supply chain and inventory system, impact risk severity differently. As far as we are aware, this is the first framework to incorporate supply network structure, risk propagation, and inventory decision making.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 88%

Section: Discussionsupporting

confidence: 88%

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The rippled newsvendor: A new inventory framework for modeling supply chain risk severity in the presence of risk propagation

Garvey

Carnovale

2020

International Journal of Production Economics

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“…We now justify some assumptions and parameters used for simulation experiments. 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.…”

Section: Research Methodsologymentioning

confidence: 99%

“…Simulation studies allow adding additional, dynamic features to the optimization techniques which are widely used in SC risk analysis , Sadghiani et al, 2015, Cui et al, 2016, Ivanov et al, 2016 along with heuristic approaches (Meena and Sarmah, 2013, Zhang et al, 2015, Hasani and Khosrojerdi, 2016. Most of the existing studies utilize discrete-event simulation approach (Schmitt and Singh, 2012, Ivanov, 2017a, 2017b, Schmitt et al, 2017, Ivanov and Rozhkov, 2017, Macdonald et al, 2018, Ivanov, 2019, Tan et al, 2020 while some studies use agent-based (Li and Chan, 2013, Hou et al, 2018 and system dynamics (Wilson, 2007, Aboah et al, 2019 methods, too. A very few studies (e.g., Hackl and Dubernet, 2019) have incorporated the simulation and transportation disruptions during the epidemic crises.…”

Section: Simulation-based Sc Risk Modelingmentioning

confidence: 99%

Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case

Ivanov

2020

Transportation Research Part E: Logistics and Transportation Re

1,652

1,348

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Epidemic outbreaks are a special case of supply chain (SC) risks which is distinctively characterized by a long-term disruption existence, disruption propagations (i.e., the ripple effect), and high uncertainty. We present the results of a simulation study that opens some new research tensions on the impact of COVID-19 (SARS-CoV-2) on the global SCs. First, we articulate the specific features that frame epidemic outbreaks as a unique type of SC disruption risks. Second, we demonstrate how simulation-based methodology can be used to examine and predict the impacts of epidemic outbreaks on the SC performance using the example of coronavirus COVID-19 and anyLogistix simulation and optimization software. We offer an analysis for observing and predicting both short-term and long-term impacts of epidemic outbreaks on the SCs along with managerial insights. A set of sensitivity experiments for different scenarios allows illustrating the model's behavior and its value for decision-makers. The major observation from the simulation experiments is that the timing of the closing and opening of the facilities at different echelons might become a major factor that determines the epidemic outbreak impact on the SC performance rather than an upstream disruption duration or the speed of epidemic propagation. Other important factors are lead-time, speed of epidemic propagation, and the upstream and downstream disruption durations in the SC. The outcomes of this research can be used by decisionmakers to predict the operative and long-term impacts of epidemic outbreaks on the SCs and develop pandemic SC plans. Our approach can also help to identify the successful and wrong elements of risk mitigation/preparedness and recovery policies in case of epidemic outbreaks. The paper is concluded by summarizing the most important insights and outlining future research agenda.

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Circular business models: Babbling initial exploratory

Sehnem

2019

Environmental Quality Mgmt

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Mitigating disruptions in a multi-echelon supply chain using adaptive ordering

Cited by 115 publications

References 56 publications

The rippled newsvendor: A new inventory framework for modeling supply chain risk severity in the presence of risk propagation

The rippled newsvendor: A new inventory framework for modeling supply chain risk severity in the presence of risk propagation

Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case

Circular business models: Babbling initial exploratory

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