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Sustainability 2017, 9(1), 144;

A New Resilience Measure for Supply Chain Networks

1,2,* , 1
School of Reliability and Systems Engineering, Beihang University, No. 37, Xue Yuan Road, Beijing 100191, China
Science and Technology on Reliability and Environmental Engineering Laboratory, No. 37, Xue Yuan Road, Beijing 100191, China
Author to whom correspondence should be addressed.
Academic Editor: Yongrok Choi
Received: 29 July 2016 / Revised: 3 January 2017 / Accepted: 12 January 2017 / Published: 19 January 2017
(This article belongs to the Section Economic, Business and Management Aspects of Sustainability)
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Currently, supply chain networks can span the whole world, and any disruption of these networks may cause economic losses, decreases in sales and unsustainable supplies. Resilience, the ability of the system to withstand disruption and return to a normal state quickly, has become a new challenge during the supply chain network design. This paper defines a new resilience measure as the ratio of the integral of the normalized system performance within its maximum allowable recovery time after the disruption to the integral of the performance in the normal state. Using the maximum allowable recovery time of the system as the time interval under consideration, this measure allows the resilience of different systems to be compared on the same relative scale, and be used under both scenarios that the system can or cannot restore in the given time. Two specific resilience measures, the resilience based on the amount of product delivered and the resilience based on the average delivery distance, are provided for supply chain networks. To estimate the resilience of a given supply chain network, a resilience simulation method is proposed based on the Monte Carlo method. A four-layered hierarchial mobile phone supply chain network is used to illustrate the resilience quantification process and show how network structure affects the resilience of supply chain networks. View Full-Text
Keywords: resilience; supply chain networks; measure; Monte-Carlo; simulation resilience; supply chain networks; measure; Monte-Carlo; simulation

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Li, R.; Dong, Q.; Jin, C.; Kang, R. A New Resilience Measure for Supply Chain Networks. Sustainability 2017, 9, 144.

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