A New Resilience Measure for Supply Chain Networks
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 dur...
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doaj-4e14f315e5dd438aab84f78e578af0862020-11-24T22:17:14ZengMDPI AGSustainability2071-10502017-01-019114410.3390/su9010144su9010144A New Resilience Measure for Supply Chain NetworksRuiying Li0Qiang Dong1Chong Jin2Rui Kang3School of Reliability and Systems Engineering, Beihang University, No. 37, Xue Yuan Road, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, No. 37, Xue Yuan Road, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, No. 37, Xue Yuan Road, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, No. 37, Xue Yuan Road, Beijing 100191, ChinaCurrently, 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.http://www.mdpi.com/2071-1050/9/1/144resiliencesupply chain networksmeasureMonte-Carlosimulation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ruiying Li Qiang Dong Chong Jin Rui Kang |
spellingShingle |
Ruiying Li Qiang Dong Chong Jin Rui Kang A New Resilience Measure for Supply Chain Networks Sustainability resilience supply chain networks measure Monte-Carlo simulation |
author_facet |
Ruiying Li Qiang Dong Chong Jin Rui Kang |
author_sort |
Ruiying Li |
title |
A New Resilience Measure for Supply Chain Networks |
title_short |
A New Resilience Measure for Supply Chain Networks |
title_full |
A New Resilience Measure for Supply Chain Networks |
title_fullStr |
A New Resilience Measure for Supply Chain Networks |
title_full_unstemmed |
A New Resilience Measure for Supply Chain Networks |
title_sort |
new resilience measure for supply chain networks |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2017-01-01 |
description |
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. |
topic |
resilience supply chain networks measure Monte-Carlo simulation |
url |
http://www.mdpi.com/2071-1050/9/1/144 |
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