Coordination of production and ordering policies under capacity disruption and product write-off risk: an analytical study with real-data based simulations of a fast moving consumer goods company

Ivanov, Dmitry; Rozhkov, Maxim

doi:10.1007/s10479-017-2643-8

Cited by 109 publications

(54 citation statements)

References 56 publications

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“…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

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1,652

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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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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

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1,652

1,348

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“…Frequently mentioned more general attributes include flexibility, redundancy, collaboration, visibility, and agility ( Hohenstein et al, 2015 ; Johnson et al, 2013 ). More specific resilience strategies recognized in the literature include backup capacity and inventory, increased security, economical supply incentives, postponement, supplier relationship building, demand forecasting, as well as the development of IT infrastructure and information sharing ( Chopra and Sodhi, 2004 ; Ivanov and Rozhkov, 2017 ; Melnyk et al, 2014 ; Tang, 2006a ; Tomlin, 2006 ; Yilmaz et al, 2017 ).…”

Section: Supply Chain Resilience and Blockchain Technology In The Conmentioning

confidence: 99%

Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study

Lohmer

Bugert

Lasch

2020

International Journal of Production Economics

286

131

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Resilience enables supply chains to reduce their proneness to disruptions and recover faster. Many existing strategies to strengthen the resilience of supply chains are facilitated by the use of digital technology. Blockchain, as one of the promising innovative technologies, enables a transparent, secure, and timely data exchange and automation via smart contracts. In this paper, we discuss the impact of blockchain technology on supply chain risk management and, in particular, on supply chain resilience. We identify potential risk-related blockchain application scenarios and examine their impact on the existing resilience strategies. We explore the impact of the most promising applications with respect to resilience by using an agent-based simulation model of a complex supply network affected by disruptions. The theoretical analysis reveals a promotion of supply chain resilience strategies, especially if smart contracts are used for risk-related collaboration. The simulation study indicates an increase in resilience if the underlying collaboration is based on time-efficient processes: The propagation of disruptions, the network recovery time, and total costs can be substantially reduced. However, depending on the duration of the disruption, negative effects can occur if process efficiency is insufficient. From our investigations, we derive insights for managers who are interested in practical implementation.

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“…A discrete-event AnyLogic simulation model by Ivanov and Rozhkov (2017) considered capacity disruptions at a factory for a real-life example of a retail SC with product perishability considerations. Their results revealed that productionordering policies during the disruption period impact the post-disruption SC's operational and financial performance.…”

Section: Product and Process Flexibility: Reactive Stagementioning

confidence: 99%

“…SC resilience encompasses both proactive and reactive stages (Bhamra, Dani, and Burnard 2011;Jüttner and Maklan 2011;Spiegler, Naim, and Wikner 2012;Pettit, Croxton, and Fiksel 2013;Brandon-Jones et al 2014;Ambulkar, Blackhurst, and Grawe 2015;Tukamuhabwa et al 2015;Chowdhury and Quaddus 2017;Yu, Li, and Yang 2017). As such, an integration of pro-and reactive decisions is important for increasing SC resilience by utilising the synergetic effects between mitigation and contingency policies (Sheffi 2005;Tomlin 2006;Melnyk et al 2014;Ivanov et al 2016;Geng and Xiao 2017;Ivanov and Rozhkov 2017;Rezapour, Farahani, and Pourakbar 2017;Ivanov 2018a;Ivanov, Dolgui, and Sokolov 2018).…”

Section: Introductionmentioning

confidence: 99%

Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience

Ivanov

Dolgui

2018

International Journal of Production Research

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255

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This study suggests a new approach to supply chain (SC) disruption risk management where SC behaviour is less dependent on the certainty of our knowledge about the environment and its changes. The unpredictability of the occurrence of disruption and its magnitude suggests that designing SCs with a low need for 'certainty' may be as important, if not more so, than predetermined disruption control strategies. In this setting, this study calls for the development of a new perspective in SC disruption management, i.e. low-certainty-need (LCN) SCs. A number of principles and concepts is derived in recent, relevant literature to structure the characteristics of the LCN framework and its management. Structural variety, process flexibility, and parametrical redundancy are identified as key LCN SC characteristics that ensure efficient disruption resistance as well as recovery resource allocation. Two efficiency capabilities of the LCN SC are shown, i.e. low need for uncertainty consideration in planning decisions and low need for recovery coordination efforts based on a combination of lean and resilient elements. The results allow the identification of an LCN SC framework, concepts and technologies for its implementation as well as missing themes and new research questions which contribute to a better understanding of SC disruption risks. Special focus is directed on the digital technology usage in the LCN framework implementation.

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Coordination of production and ordering policies under capacity disruption and product write-off risk: an analytical study with real-data based simulations of a fast moving consumer goods company

Cited by 109 publications

References 56 publications

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

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

Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study

Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience

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