A New Resilience Measure for Supply Chain Networks

Ruiying Li, Qiang Dong, Chong Jin and Rui Kang
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Ruiying Li: School of Reliability and Systems Engineering, Beihang University, No. 37, Xue Yuan Road, Beijing 100191, China
Qiang Dong: School of Reliability and Systems Engineering, Beihang University, No. 37, Xue Yuan Road, Beijing 100191, China
Chong Jin: School of Reliability and Systems Engineering, Beihang University, No. 37, Xue Yuan Road, Beijing 100191, China
Rui Kang: School of Reliability and Systems Engineering, Beihang University, No. 37, Xue Yuan Road, Beijing 100191, China

Sustainability, 2017, vol. 9, issue 1, 1-19

Abstract: 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.

Keywords: resilience; supply chain networks; measure; Monte-Carlo; simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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