IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/57949.html
   My bibliography  Save this paper

Supply Chain Disruption Management: Review of Issues and Research Directions

Author

Listed:
  • Brown, Adam
  • Badurdeen, Fazleena

Abstract

Supply Chain Risk Management (SCRM) is an increasingly popular subject of research which emphasizes the goals of achieving improved supply chain robustness through development of design and operational strategies. Disruption management is one aspect of SCRM which examines the ability of the supply chain to maintain a high level of performance under the effects of major disruptions. Specifically, disruptions refer to events characterized by a low likelihood of occurrence and a large impact. Because of their limited rate of occurrence, disruptions are associated with a high uncertainty with respect to their expected impact. Improved modeling of the disruption impact is one key issue in this field. Other issues include the design of methods for supply chain performance measurement, disruption monitoring and detection, evaluation of recovery strategies, and methods of optimal supply chain design. Design features to be considered include flexibility, redundancy, and operating efficiency. The relevant literature is presented in the context of these major issues and future directions suggested by researchers in the field are discussed.

Suggested Citation

  • Brown, Adam & Badurdeen, Fazleena, 2014. "Supply Chain Disruption Management: Review of Issues and Research Directions," MPRA Paper 57949, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:57949
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/57949/9/MPRA_paper_57949.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Klibi, Walid & Martel, Alain & Guitouni, Adel, 2010. "The design of robust value-creating supply chain networks: A critical review," European Journal of Operational Research, Elsevier, vol. 203(2), pages 283-293, June.
    2. Tang, Christopher S., 2006. "Perspectives in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 103(2), pages 451-488, October.
    3. Yusen Xia & Karthik Ramachandran & Haresh Gurnani, 2011. "Sharing demand and supply risk in a supply chain," IISE Transactions, Taylor & Francis Journals, vol. 43(6), pages 451-469.
    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. Araceli Zavala & David Nowicki & Jose Emmanuel Ramirez-Marquez, 2019. "Quantitative metrics to analyze supply chain resilience and associated costs," Journal of Risk and Reliability, , vol. 233(2), pages 186-199, April.

    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. Mazur, Christoph & Hoegerle, Yannick & Brucoli, Maria & van Dam, Koen & Guo, Miao & Markides, Christos N. & Shah, Nilay, 2019. "A holistic resilience framework development for rural power systems in emerging economies," Applied Energy, Elsevier, vol. 235(C), pages 219-232.
    2. Jabbarzadeh, Armin & Fahimnia, Behnam & Sheu, Jiuh-Biing & Moghadam, Hani Shahmoradi, 2016. "Designing a supply chain resilient to major disruptions and supply/demand interruptions," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 121-149.
    3. Roba W. Salem & Mohamed Haouari, 2017. "A simulation-optimisation approach for supply chain network design under supply and demand uncertainties," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1845-1861, April.
    4. Nathalie Fabbe-Costes & Yasmina Ziad, 2021. "Improving Supply Chain Robustness & Resilience? Lessons from a case study in the automotive industry during the first wave of Covid-19," Post-Print hal-03306223, HAL.
    5. Gholami-Zanjani, Seyed Mohammad & Klibi, Walid & Jabalameli, Mohammad Saeed & Pishvaee, Mir Saman, 2021. "The design of resilient food supply chain networks prone to epidemic disruptions," International Journal of Production Economics, Elsevier, vol. 233(C).
    6. Lydia Novoszel & Tina Wakolbinger, 2022. "Meta-analysis of Supply Chain Disruption Research," SN Operations Research Forum, Springer, vol. 3(1), pages 1-25, March.
    7. Snoeck, André & Udenio, Maximiliano & Fransoo, Jan C., 2019. "A stochastic program to evaluate disruption mitigation investments in the supply chain," European Journal of Operational Research, Elsevier, vol. 274(2), pages 516-530.
    8. 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).
    9. Wagner, Stephan M. & Mizgier, Kamil J. & Papageorgiou, Stylianos, 2017. "Operational disruptions and business cycles," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 66-78.
    10. Tianjian Yang & Weiguo Fan, 2016. "Information management strategies and supply chain performance under demand disruptions," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 8-27, January.
    11. Kelei Xue & Ya Xu & Lipan Feng, 2018. "Managing Procurement for a Firm with Two Ordering Opportunities under Supply Disruption Risk," Sustainability, MDPI, vol. 10(9), pages 1-32, September.
    12. Nader Azad & Elkafi Hassini, 2019. "A Benders Decomposition Method for Designing Reliable Supply Chain Networks Accounting for Multimitigation Strategies and Demand Losses," Transportation Science, INFORMS, vol. 53(5), pages 1287-1312, September.
    13. Sarkar, Sourish & Kumar, Sanjay, 2015. "A behavioral experiment on inventory management with supply chain disruption," International Journal of Production Economics, Elsevier, vol. 169(C), pages 169-178.
    14. Shiva Zokaee & Armin Jabbarzadeh & Behnam Fahimnia & Seyed Jafar Sadjadi, 2017. "Robust supply chain network design: an optimization model with real world application," Annals of Operations Research, Springer, vol. 257(1), pages 15-44, October.
    15. Brandenburg, Marcus, 2017. "A hybrid approach to configure eco-efficient supply chains under consideration of performance and risk aspects," Omega, Elsevier, vol. 70(C), pages 58-76.
    16. Nader Azad & Georgios Saharidis & Hamid Davoudpour & Hooman Malekly & Seyed Yektamaram, 2013. "Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach," Annals of Operations Research, Springer, vol. 210(1), pages 125-163, November.
    17. Tang, Christopher S. & Davarzani, Hoda & Sarkis, Joseph, 2015. "Quantitative models for managing supply chain risks: A reviewAuthor-Name: Fahimnia, Behnam," European Journal of Operational Research, Elsevier, vol. 247(1), pages 1-15.
    18. Torabi, S.A. & Baghersad, M. & Mansouri, S.A., 2015. "Resilient supplier selection and order allocation under operational and disruption risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 79(C), pages 22-48.
    19. Afraz, Muhammad Fawad & Bhatti, Sabeen Hussain & Ferraris, Alberto & Couturier, Jerome, 2021. "The impact of supply chain innovation on competitive advantage in the construction industry: Evidence from a moderated multi-mediation model," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    20. Sahebjamnia, Navid & Torabi, S. Ali & Mansouri, S. Afshin, 2018. "Building organizational resilience in the face of multiple disruptions," International Journal of Production Economics, Elsevier, vol. 197(C), pages 63-83.

    More about this item

    Keywords

    Supply Chain; Disruptions; Risk Management; Gray Swan;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • M20 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:pra:mprapa:57949. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.