Measuring the Resilience of Supply Chain Systems Using a Survival Model

Raj, R.; Wang, Junwei; Nayak, Ashish Kumar; Tiwari, Manoj Kumar; Bin, Han; Liu, C. L.; Zhang, W. J.

doi:10.1109/jsyst.2014.2339552

Cited by 68 publications

(21 citation statements)

References 22 publications

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“…Ivanov, Sokolov, and Kaeschel (2010) developed a SC structural dynamics control model that has been frequently used for multi-structural SC design analysis under uncertain conditions. Raj et al (2015) applied a survival model to simulate the recovery time in the SC. Using the event of failure as the model input and the recovery time as the model output, they proposed to measure SC resilience using recovery time.…”

Section: Structural Properties: Proactive Stagementioning

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

256

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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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Section: Structural Properties: Proactive Stagementioning

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

256

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“…Raj et al [26] recognized recovery, rather than failure cause as central to resilience, and provided a resilience measure relying solely on recovery time. Nan and Sansavini [27] consider a power transmission network providing a resilience measure in the single dimension of actual supply as a proportion of design capacity.…”

Section: A Unidimensional Measures Of Performance or Capabilitymentioning

confidence: 99%

A Resilience Measure to Guide System Design and Management

Ferris

2019

IEEE Systems Journal

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This paper presents a measure of resilience which can guide system design and management. Systems design must incorporate resilience to provide stakeholders with the most appropriate solution for their life-cycle needs. Design of resilient systems demands a measure of the resilience afforded by a system proposal which can be used to compare design proposals. The measurement method should balance the interest in resilience with all other proposal evaluation criteria, and incorporate the effect of the sequence of unknown future events affecting the system. Ideally, the resilience measure should also be useful to guide management decisions re maintenance or upgrade during the system life. This paper presents a method to measure system resilience which can be applied to engineered systems in general, not just a specific class of systems, is threat type agnostic, and does not presuppose any 'desirable' outcome allowing a system specific determination of 'desirable' outcomes.

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“…Roncoli et al (2013) developed a model for risk minimization for transport of dangerous goods by road [32]. Raj et. al.…”

Section: Related Workmentioning

confidence: 99%

“…al. (2015) developed a model measuring resilience using Cox-PH model [33]. These approaches are usually NP-hard [34], and become computationally expensive as extended supply networks can reach to thousands of nodes and tens of thousands of links [24].…”

Section: Related Workmentioning

confidence: 99%

Systemic Risk Assessment in Complex Supply Networks

Ledwoch

Brintrup

Mehnen

et al. 2018

IEEE Systems Journal

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The growth in size and complexity of supply chains has led to compounded risk exposure, which is hard to measure with existing risk management approaches. In this study, we apply the concept of systemic risk to show that centrality metrics can be used for complex supply network risk assessment. We review and select metrics, and set up an exemplary case applied to the material flow and contractual networks of Honda Acura. In the exemplary case study, geographical risk information is incorporated to selected systemic risk assessment metrics and results are compared to assessment without risk indicators in order to draw conclusions on how additional information can enhance systemic risk assessment in supply networks. Katz centrality is used to measure the node's risk spread using the World Risk Index. Authority and hub centralities are applied to measure the link risk spread using distances between geographical locations. Closeness is used to measure speed of disruption spread. Betweenness centrality is used to identify high risk middlemen. Our results indicate that these metrics are successful in identifying vulnerabilities in network structure even in simplified cases, which risk practitioners can use to extend with historical data to gain more accurate insights into systemic risk exposure.

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Measuring the Resilience of Supply Chain Systems Using a Survival Model

Cited by 68 publications

References 22 publications

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

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

A Resilience Measure to Guide System Design and Management

Systemic Risk Assessment in Complex Supply Networks

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