Quantifying supply chain disruption: a recovery time equivalent value at risk approach

被引:9
作者
Zhang, Allan N. [1 ]
Wagner, Stephan M. [2 ]
Goh, Mark [3 ,4 ]
Asian, Sobhan [5 ]
机构
[1] Singapore Inst Mfg Technol, Singapore, Singapore
[2] Swiss Fed Inst Technol Zurich, Dept Management Technol & Econ, Zurich, Switzerland
[3] Natl Univ Singapore, TLI AP, Singapore, Singapore
[4] Natl Univ Singapore, NUS Business Sch, Singapore, Singapore
[5] La Trobe Univ, La Trobe Business Sch, Melbourne, Vic, Australia
关键词
Supply chain risk management; reglobalization; COVID-19; disruption risk quantification; recovery time equivalent; value at risk; MODEL; DESIGN; UNCERTAINTY; RESILIENCE; MANAGEMENT; DECISIONS; SELECTION; SCALES;
D O I
10.1080/13675567.2021.1990872
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
The global pandemic COVID-19 has disrupted supply chains in many industries all over the world. If not well contained and managed at an early stage, such unprecedented disruptions may lead to even more serious consequences in the era of supply chain reglobalization. The attendant challenge for research and practice is on how to readily measure and quantify the disruption risk. To address the plethora of concerns, this article presents a recovery time equivalent (RTE) disruption risk measurement model using Value at Risk (VaR). We consider a disruption recovery model comprising abrupt, normal, fast, and slow modes. To demonstrate the practical relevance of our study, we establish a case study with a multinational corporation from the IT sector. Decision makers and supply chain managers can use the model to conduct 'what-if' analyses on their supply chain vulnerabilities and risks for a more proactive business continuity planning and contingency management.
引用
收藏
页码:667 / 687
页数:21
相关论文
共 90 条
[31]  
Hendricks KB, 2005, PROD OPER MANAG, V14, P35, DOI 10.1111/j.1937-5956.2005.tb00008.x
[32]   The effect of operational slack, diversification, and vertical relatedness on the stock market reaction to supply chain disruptions [J].
Hendricks, Kevin B. ;
Singhal, Vinod R. ;
Zhang, Rongrong .
JOURNAL OF OPERATIONS MANAGEMENT, 2009, 27 (03) :233-246
[33]   Resilient supplier selection and optimal order allocation under disruption risks [J].
Hosseini, Seyedmohsen ;
Tajik, Nazanin ;
Ivanov, Dmitry ;
Sarder, M. D. ;
Barker, Kash ;
Al Khaled, Abdullah .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2019, 213 :124-137
[34]   Managing Risk of Supply Disruptions: Incentives for Capacity Restoration [J].
Hu, Xinxin ;
Gurnani, Haresh ;
Wang, Ling .
PRODUCTION AND OPERATIONS MANAGEMENT, 2013, 22 (01) :137-150
[35]   Risk Uncertainty and Supply Chain Decisions: A Real Options Perspective [J].
Hult, G. Tomas M. ;
Craighead, Christopher W. ;
Ketchen, David J., Jr. .
DECISION SCIENCES, 2010, 41 (03) :435-458
[36]   Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case [J].
Ivanov, Dmitry .
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2020, 136
[37]  
Jorion P., 2007, Value at risk: The new benchmark for managing financial risk
[38]  
Kafali C., 2005, ICOSSAR 05 P 9 INT C
[39]   Modeling approaches for the design of resilient supply networks under disruptions [J].
Klibi, Walid ;
Martel, Alain .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2012, 135 (02) :882-898
[40]   Proactive planning for catastrophic events in supply chains [J].
Knemeyer, A. Michael ;
Zinna, Walter ;
Eroglu, Cuneyt .
JOURNAL OF OPERATIONS MANAGEMENT, 2009, 27 (02) :141-153