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Categorizing Supply Chain Risks: Review, Integrated Typology and Future Research

  • Michalis LouisEmail author
  • Mark Pagell
Chapter
Part of the Springer Series in Supply Chain Management book series (SSSCM, volume 7)

Abstract

Nowadays, supply chains operate in a dynamic environment where a potential unwanted event that does not constitute a risk today may be a severe risk tomorrow. Recent events indicate that supply chain operations of a focal firm may be harmed, when risks that were not successfully identified, materialize. This underlines the importance of supply chain risk identification process in supply chain risk management (SCRM), which can affect a firm’s long-term survival. Firms that are aware of the types of risks that may adversely affect their supply chain operations can improve the management of these risks. Despite the merit of previous studies on supply chain risk identification, current literature is incomplete to offer an inclusive categorization of supply chain risks. This hinders the efforts of firms to develop meaningful risk categorizations, and subsequently their overall supply chain risk management effort. This chapter aims to alleviate this problem. First, it reviews the various typologies proposed in the SCRM literature since 2000 using the systematic network analysis method. By explaining how the categorization of supply chain risks has evolved, this chapter provides valuable insights, both to researchers and practitioners, regarding the historical development of the field. A key conclusion from this review is that none of the existing typologies is complete, though most overlap. Therefore, the second contribution of the chapter is to build on what is already known to offer a new typology of supply chain risks that is both inclusive and parsimonious. Finally, the chapter provides recommendations for future research.

Keywords

Supply chain risk typology Supply chain risk identification Citation network analysis Supply chain risk categorization 

Notes

Acknowledgements

We acknowledge the use of HistCite from Clarivate Analytics to identify studies of interest using GCS measure. We also acknowledge the use of Gephi to visually adjust citation networks that are free to use for commercial use (https://gephi.org/legal/faq/). Last, we confirm that the first author has been granted permission to use Pajek (De Nooy et al. 2011) for academic purposes by Dr. Mvar (Andrej.Mrvar@fdv.uni-lj.si). This study was conducted while the first author Michalis Louis was a Doctoral Student in UCD Michael Smurfit Graduate Business School. This author received a Doctoral Scholarship from the school from 2015 to 2019.

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Authors and Affiliations

  1. 1.Michael Smurfit Business SchoolUniversity College DublinDublinRepublic of Ireland

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