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A control engineering approach to the assessment of supply chain resilience

Open access
Spiegler, Virginia L. M., Naim, Mohamed M., Wikner, Joakim·2012·International Journal of Production Research

Abstract

There is no consensus on the supply chain management definition of resilience. To aid in evaluating the dynamic behaviour of such systems we need to establish clearly elucidated performance criteria that encapsulate the attributes of resilience. A literature review establishes the latter as readiness, responsiveness and recovery. We also identify robustness as a necessary condition that would complement resilience. We find that the Integral of the Time Absolute Error (ITAE) is an appropriate control engineering measure of resilience when it is applied to inventory levels and shipment rates. We use the ITAE to evaluate an often used benchmark model of make-to-stock supply chains consisting of three decision parameters. We use both linear and nonlinear forms of the model in our evaluation. Our findings suggest that optimum solutions for resilience do not yield a system that is robust to uncertainties in lead-time. Hence supply chains will experience drastic changes in their resilience performance when lead-time changes.

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Metadata

Authors
Spiegler, V. L. M., Naim, M. M., & Wikner, J.
Venue
International Journal of Production Research
Published
2012-11
Volume
50
Issue
21
Pages
6162-6187
ISSN
  • 0020-7543 (ISSN-L)
  • 1366-588X
OpenAlex
W2004328626
Cited by
338
References
1
FWCI
29.65
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A control engineering approach to the assessment of supply chain resilience. https://app.cochat.ai/papers/10.1080/00207543.2012.710764
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Spiegler, V. L. M., Naim, M. M., & Wikner, J. (2012). A control engineering approach to the assessment of supply chain resilience. International Journal of Production Research. https://doi.org/10.1080/00207543.2012.710764

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.