Quantifying supply chain disruption: a recovery time equivalent value at risk approach

Zhang, Allan N.; Wagner, Stephan M.; Goh, Mark

doi:10.1080/13675567.2021.1990872

Cited by 15 publications

(5 citation statements)

References 74 publications

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“…These changes on the other hand have knocked supply chain network off balance causing disruptions. Disruptions in supply processes pose significant threats to business operations 1 and can lead to increased operational cost, loss of profits, and damage the company's reputation 2 . Hedging against disruption is a call for concern in the supply chain community and there is evident that superior contingency planning can significantly mitigate the effects of disruptions.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…Works of literature have pointed out the vulnerability of today's supply chains to disruptions and the need for a systematic analysis of supply chain vulnerability, security, and resiliency. 1,6,13 Furthermore, strategies to manage disruptions can be categorized into three main groups: mitigation strategies, recovery strategies, and the passive acceptance approach. 14 The mitigation strategies are proactive measures and act in advance, irrespective of whether disruptions actually occur examples of such strategy include increasing amount of safety stock, multiple sourcing, capacity expansion, and multimodal transportation options, while recovery strategies generally take actions after the occurrence of a disruption some of these strategies are alternative sourcing, rerouting of products, alternative inventory locations, outsourcing productions, and cooperation among supply chain entities.…”

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confidence: 99%

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A mathematical modeling approach for supply chain management under disruption and operational uncertainty

Badejo

Ierapetritou

2023

AIChE Journal

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In this work, we proposed a two-stage stochastic programming model for a fourechelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two-stage stochastic model with a multi-period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.

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Section: Introduction and Literature Reviewmentioning

confidence: 99%

Section: Introduction and Literature Reviewmentioning

confidence: 99%

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confidence: 99%

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A mathematical modeling approach for supply chain management under disruption and operational uncertainty

Badejo

Ierapetritou

2023

AIChE Journal

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“…The research probes the influence of IoT-BDA in elevating SCFP, guided by an extensive review of pertinent literature. While some research addresses the adverse effects of disruptions on SC performance (Parast & Subramanian, 2021;Zhang et al, 2021), limited investigation exists into how performance can be achieved through the interplay of IoT-BDA, resilience, and flexibility, particularly in the context of the Covid-19 pandemic. To investigate these dimensions, a cross sectional survey involving 217 participants, including SC managers, HR managers, procurement managers, warehouse personnel, and inventory managers, was conducted.…”

Section: Introductionmentioning

confidence: 99%

Supply Chain Flexibility and Post-pandemic Resilience

Agrawal,

Sharma,

Raut

et al. 2023

Glob J Flex Syst Manag

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The COVID-19 outbreak in 2020–2021 caused unprecedented disruptions to global supply networks. Companies worldwide faced significant challenges as they dealt with the unexpected surge in demand for specific goods and services. This study delves into the importance of supply chain coordination (SCCO), supply chain resilience (SCRE), and supply chain robustness (SCRB), considering supply chain flexibility (SCFL) and Internet of Things and Big Data Analytics (IoT-BDA) integration. We explore how SCFL influences SCCO, SCRE, and SCRB, enhancing supply chain performance (SCFP). Using a cross sectional approach, we collected survey-based responses to ensure comprehensive representation from the supply chain domain. A total of 217 complete responses were collected and analyzed using AMOS 20. The findings suggest that SCCO, SCRE, and SCRB act as mediators between SCFL and IoT-BDA. However, statistical significance between SCCO and SCRB with SCRE was not established. The study emphasizes the robust predictive nature of SCFL, highlighting its pivotal role in fostering SCCO, SCRE, and SCBR through empirical evidence. Furthermore, it emphasizes the influence of SCFL on enhancing SCFP, particularly in the post-pandemic era.

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Section: Introduction and Literature Reviewmentioning

confidence: 99%

A Mathematical Modeling approach for Supply Chain Management under Disruption and Operational Uncertainty

Ierapetritou

Badejo

2022

Preprint

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In this work, we proposed a two-stage stochastic programming model for a four-echelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two-stage stochastic model with a multi-period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.

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Quantifying supply chain disruption: a recovery time equivalent value at risk approach

Cited by 15 publications

References 74 publications

A mathematical modeling approach for supply chain management under disruption and operational uncertainty

A mathematical modeling approach for supply chain management under disruption and operational uncertainty

Supply Chain Flexibility and Post-pandemic Resilience

A Mathematical Modeling approach for Supply Chain Management under Disruption and Operational Uncertainty

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