IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v67y2019i3p831-852.html
   My bibliography  Save this article

Disruption Risk Mitigation in Supply Chains: The Risk Exposure Index Revisited

Author

Listed:
  • Sarah Yini Gao

    (Lee Kong Chian School of Business, Singapore Management University, Singapore 188065)

  • David Simchi-Levi

    (Institute for Data, Systems, and Society, Department of Civil and Environmental Engineering, and Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Chung-Piaw Teo

    (Institute of Operations Research and NUS Business School, National University of Singapore, Singapore 119077)

  • Zhenzhen Yan

    (School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 639798)

Abstract

In recent years, supply chains are more prone to disruptions. The impact on performance depends on the system's ability to discover and then recover after the disruption has occurred. In this paper, we proposed a new method to integrate probabilistic assessment of disruption risks into the Risk Exposure Index (REI) approach proposed previously by Simchi-Levi et al. and measure supply chain resiliency by analyzing the worst-case CVaR (WCVaR) of total lost sales under disruptions. We show that the optimal strategic inventory positioning strategy in this model can be fully characterized by a conic program. The optimal primal and dual solutions to the conic program can be used to shed light on comparative statics in the supply chain risk mitigation problem. This information can help supply chain risk managers focus their mitigation efforts on critical suppliers and/or installations that will have greater impact on the performance of the supply chain when disrupted.

Suggested Citation

  • Sarah Yini Gao & David Simchi-Levi & Chung-Piaw Teo & Zhenzhen Yan, 2019. "Disruption Risk Mitigation in Supply Chains: The Risk Exposure Index Revisited," Operations Research, INFORMS, vol. 67(3), pages 831-852, May.
    • Handle: RePEc:inm:oropre:v:67:y:2019:i:3:p:831-852
    DOI: 10.1287/opre.2018.1776
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/opre.2018.1776
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.2018.1776?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Robert R. Meyer & Michael H. Rothkopf & Stephen A. Smith, 1979. "Reliability and Inventory in a Production-Storage System," Management Science, INFORMS, vol. 25(8), pages 799-807, August.
    2. David Simchi‐Levi & He Wang & Yehua Wei, 2018. "Increasing Supply Chain Robustness through Process Flexibility and Inventory," Production and Operations Management, Production and Operations Management Society, vol. 27(8), pages 1476-1491, August.
    3. Antonio Arreola‐Risa & Gregory A. DeCroix, 1998. "Inventory management under random supply disruptions and partial backorders," Naval Research Logistics (NRL), John Wiley & Sons, vol. 45(7), pages 687-703, October.
    4. Tingting Yan & Thomas Y. Choi & Yusoon Kim & Yang Yang, 2015. "A Theory of the Nexus Supplier: A Critical Supplier From A Network Perspective," Journal of Supply Chain Management, Institute for Supply Management, vol. 51(1), pages 52-66, January.
    5. Jing-Sheng Song & Paul H. Zipkin, 1996. "Inventory Control with Information About Supply Conditions," Management Science, INFORMS, vol. 42(10), pages 1409-1419, October.
    6. Shushang Zhu & Masao Fukushima, 2009. "Worst-Case Conditional Value-at-Risk with Application to Robust Portfolio Management," Operations Research, INFORMS, vol. 57(5), pages 1155-1168, October.
    7. Karthik Natarajan & Chung Piaw Teo & Zhichao Zheng, 2011. "Mixed 0-1 Linear Programs Under Objective Uncertainty: A Completely Positive Representation," Operations Research, INFORMS, vol. 59(3), pages 713-728, June.
    8. Gurnani, Haresh, 1996. "Optimal ordering policies in inventory systems with random demand and random deal offerings," European Journal of Operational Research, Elsevier, vol. 95(2), pages 299-312, December.
    9. David Simchi-Levi & William Schmidt & Yehua Wei & Peter Yun Zhang & Keith Combs & Yao Ge & Oleg Gusikhin & Michael Sanders & Don Zhang, 2015. "Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain," Interfaces, INFORMS, vol. 45(5), pages 375-390, October.
    10. Anders Levermann, 2014. "Climate economics: Make supply chains climate-smart," Nature, Nature, vol. 506(7486), pages 27-29, February.
    11. Gregory A. DeCroix, 2013. "Inventory Management for an Assembly System Subject to Supply Disruptions," Management Science, INFORMS, vol. 59(9), pages 2079-2092, September.
    12. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhu, Xiaoyan & Cao, Yunzhi, 2021. "The optimal recovery-fund based strategy for uncertain supply chain disruptions: A risk-averse two-stage stochastic programming approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    2. Essuman, Dominic & Bruce, Patience Aku & Ataburo, Henry & Asiedu-Appiah, Felicity & Boso, Nathaniel, 2022. "Linking resource slack to operational resilience: Integration of resource-based and attention-based perspectives," International Journal of Production Economics, Elsevier, vol. 254(C).
    3. Maureen S. Golan & Laura H. Jernegan & Igor Linkov, 2020. "Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic," Environment Systems and Decisions, Springer, vol. 40(2), pages 222-243, June.
    4. Mengshi Lu & Zuo‐Jun Max Shen, 2021. "A Review of Robust Operations Management under Model Uncertainty," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1927-1943, June.
    5. Dixit, Vijaya & Verma, Priyanka & Tiwari, Manoj Kumar, 2020. "Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure," International Journal of Production Economics, Elsevier, vol. 227(C).
    6. Chen, Daqiang & Sun, Danzhi & Yin, Yunqiang & Dhamotharan, Lalitha & Kumar, Ajay & Guo, Yihan, 2022. "The resilience of logistics network against node failures," International Journal of Production Economics, Elsevier, vol. 244(C).
    7. Qingxia Kong & Shan Li & Nan Liu & Chung-Piaw Teo & Zhenzhen Yan, 2020. "Appointment Scheduling Under Time-Dependent Patient No-Show Behavior," Management Science, INFORMS, vol. 66(8), pages 3480-3500, August.
    8. David Eshun Yawson & Fred A. Yamoah, 2022. "Review of Strategic Agility: A Holistic Framework for Fresh Produce Supply Chain Disruptions," Sustainability, MDPI, vol. 14(22), pages 1-15, November.
    9. ur Rehman, Attique & Shakeel Sadiq Jajja, Muhammad & Farooq, Sami, 2022. "Manufacturing planning and control driven supply chain risk management: A dynamic capability perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    10. Cao, Yunzhi & Zhu, Xiaoyan & Yan, Houmin, 2022. "Data-driven Wasserstein distributionally robust mitigation and recovery against random supply chain disruption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    11. Mohammadi, Mehrdad & Dehghan, Milad & Pirayesh, Amir & Dolgui, Alexandre, 2022. "Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic," Omega, Elsevier, vol. 113(C).
    12. Zhang, Abraham & Zheng, Zhichao & Teo, Chung-Piaw, 2022. "Schedule reliability in liner shipping timetable design: A convex programming approach," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 499-525.
    13. Seyedmohsen Hosseini & Dmitry Ivanov, 2022. "A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach," Annals of Operations Research, Springer, vol. 319(1), pages 581-607, December.
    14. Youngsoo Kim & Yuqian Xu, 2024. "Operational Risk Management: Optimal Inspection Policy," Management Science, INFORMS, vol. 70(6), pages 4087-4104, June.
    15. Rahman, Md Mamunur & Nguyen, Ruby & Lu, Liang, 2022. "Multi-level impacts of climate change and supply disruption events on a potato supply chain: An agent-based modeling approach," Agricultural Systems, Elsevier, vol. 201(C).
    16. Qiang Wei & Xinyu Gou & Baiyang Zhang, 2024. "Urban Sharing Logistics Strategies against Epidemic Outbreaks: Its Feasibility and Sustainability," Sustainability, MDPI, vol. 16(17), pages 1-29, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gregory A. DeCroix, 2013. "Inventory Management for an Assembly System Subject to Supply Disruptions," Management Science, INFORMS, vol. 59(9), pages 2079-2092, September.
    2. Liu, Zhongyi & Li, Mengyu & Zhai, Xin, 2022. "Managing supply chain disruption threat via a strategy combining pricing and self-protection," International Journal of Production Economics, Elsevier, vol. 247(C).
    3. Süleyman Demirel & Roman Kapuscinski & Man Yu, 2018. "Strategic Behavior of Suppliers in the Face of Production Disruptions," Management Science, INFORMS, vol. 64(2), pages 533-551, February.
    4. Yimin Wang & Brian Tomlin, 2009. "To wait or not to wait: Optimal ordering under lead time uncertainty and forecast updating," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(8), pages 766-779, December.
    5. Zhu, Xiaoyan & Cao, Yunzhi, 2021. "The optimal recovery-fund based strategy for uncertain supply chain disruptions: A risk-averse two-stage stochastic programming approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    6. David Simchi-Levi & Nikolaos Trichakis & Peter Yun Zhang, 2019. "Designing Response Supply Chain Against Bioattacks," Operations Research, INFORMS, vol. 67(5), pages 1246-1268, September.
    7. Berger, Niklas & Schulze-Schwering, Stefan & Long, Elisa & Spinler, Stefan, 2023. "Risk management of supply chain disruptions: An epidemic modeling approach," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1036-1051.
    8. Yuanying Guan & Zhanyi Jiao & Ruodu Wang, 2022. "A reverse ES (CVaR) optimization formula," Papers 2203.02599, arXiv.org, revised May 2023.
    9. Benati, S. & Conde, E., 2022. "A relative robust approach on expected returns with bounded CVaR for portfolio selection," European Journal of Operational Research, Elsevier, vol. 296(1), pages 332-352.
    10. Jiang, Chun-Fu & Peng, Hong-Yi & Yang, Yu-Kuan, 2016. "Tail variance of portfolio under generalized Laplace distribution," Applied Mathematics and Computation, Elsevier, vol. 282(C), pages 187-203.
    11. Jingfu Huang & Gaoke Wu & Yiju Wang, 2021. "Retailer’s Emergency Ordering Policy When Facing an Impending Supply Disruption," Sustainability, MDPI, vol. 13(13), pages 1-21, June.
    12. Maria Scutellà & Raffaella Recchia, 2013. "Robust portfolio asset allocation and risk measures," Annals of Operations Research, Springer, vol. 204(1), pages 145-169, April.
    13. Huang, Jinbo & Ding, Ashley & Li, Yong & Lu, Dong, 2020. "Increasing the risk management effectiveness from higher accuracy: A novel non-parametric method," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    14. Schlütter, Sebastian & Fianu, Emmanuel Senyo & Gründl, Helmut, 2022. "Responsible investments in life insurers' optimal portfolios under solvency constraints," ICIR Working Paper Series 45/22, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).
    15. Kim, Sojung & Weber, Stefan, 2022. "Simulation methods for robust risk assessment and the distorted mix approach," European Journal of Operational Research, Elsevier, vol. 298(1), pages 380-398.
    16. Nishat Alam Choudhary & Shalabh Singh & Tobias Schoenherr & M. Ramkumar, 2023. "Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications," Annals of Operations Research, Springer, vol. 322(2), pages 565-607, March.
    17. Li Chen & Simai He & Shuzhong Zhang, 2011. "Tight Bounds for Some Risk Measures, with Applications to Robust Portfolio Selection," Operations Research, INFORMS, vol. 59(4), pages 847-865, August.
    18. Xu Leiyan & Zhiqing Meng, 2019. "Multi-Loss WCVaR Risk Decision Optimization Based On Weight for Centralized Supply Problem of Direct Chain Enterprises," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(02), pages 1-16, April.
    19. Yu, Jing-Rung & Paul Chiou, Wan-Jiun & Hsin, Yi-Ting & Sheu, Her-Jiun, 2022. "Omega portfolio models with floating return threshold," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 743-758.
    20. Martin Branda & Max Bucher & Michal Červinka & Alexandra Schwartz, 2018. "Convergence of a Scholtes-type regularization method for cardinality-constrained optimization problems with an application in sparse robust portfolio optimization," Computational Optimization and Applications, Springer, vol. 70(2), pages 503-530, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:oropre:v:67:y:2019:i:3:p:831-852. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.