Ten-tier and multi-scale supply chain network analysis of medical equipment: random failure & intelligent attack analysis

Abstract

Motivated by the COVID-19 pandemic, this paper explores the supply chain viability of medical equipment, an industry whose supply chain was put under a crucial test during the pandemic. This paper includes an empirical network-level analysis of supplier reachability under Random Failure Experiments (RFE) and Intelligent Attack Experiments (IAE). Specifically, this study investigates the effect of RFE and IAE across multiple tiers and scales. The global supply chain data was mined and analysed from about 45,000 firms with about 115,000 intertwined relationships spanning across 10 tiers of the backward supply chain of medical equipment. This complex supply chain network was analysed at four scales, namely: firm, country-industry, industry, and country. A notable contribution of this study is the application of a supply chain tier optimisation tool to identify the lowest tier of the supply chain that can provide adequate resolution for the study of the supply chain pattern. We also developed data-driven-tools to identify the thresholds for breakdown and fragmentation of the medical equipment supply chain when faced with random failures or different intelligent attack scenarios. The novel network analysis tools utilised in the study can be applied to the study of supply chain reachability and viability in other industries.

KEYWORDS:

• Supply chain network
• medical equipment
• multi-tier
• random failure
• intelligent attack
• supply chain disruption

Acknowledgments

We would like to acknowledge and appreciate the valuable and constructive feedback provided by the anonymous reviewers. We acknowledge Peter Mucha, David Grimsman, Sean Warnick, Tyler Burrows, Benjamin Webb and Mike Aguilar for helpful discussions. The first and second authors contributed equally.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The raw data was mined from proprietary databases and prepared by IEBE Lab housed at North Carolina Central University. Derived data supporting the findings of this study that includes notable results of the supply chain disruption analysis at each of the four scales of analysis is available on GitHub. Further derived data supporting the findings of this study can be made available from the corresponding author (K. L.) on request.

Notes

1 Zhou and Wang defined supply chain efficiency ‘as the average of the reciprocal of the shortest path lengths between each node pair in the network’.

Additional information

Funding

This project is part of the NCCU Advanced Center for COVID Related Disparities (ACCORD) supported by the North Carolina Policy Collaboratory at the University of North Carolina at Chapel Hill with funding from the North Carolina Coronavirus Relief Funds and University of North Carolina at Chapel Hill (US).

Notes on contributors

Kayvan Miri Lavassani

Kayvan Miri Lavassani is an Associate Professor with North Carolina Central University's School of Business. In addition to his academic activities, he has worked with several private, public, and third-sector organizations both in Canada and the United States. He founded/co-founded three businesses before joining academia and has worked in the areas of high-tech, manufacturing, international trade, and consulting. An award-winning educator and researcher, Dr. Lavassani has received the institutional Award for Teaching Excellence, as well as research awards from institutions in the US, Canada, and Europe.

Zachary M. Boyd

Zachary M. Boyd is an Assistant Professor of mathematics at Brigham Young University's mathematics department. Zachary completed a mathematics Ph.D. as an NDSEG Fellow at UCLA and prior to that was a Presidential Scholar at Brigham Young University. Before joining Brigham Young University, Zachary was a postdoctoral research associate at the University of North Carolina at Chapel Hill. He is an expert in network science, dynamics, and applied math, having worked with domain experts in a range of applications including brain networks, social drinking, supply chain, and family networks.

Bahar Movahedi

Bahar Movahedi is a graduate of Carleton University's Sprott School of Business. She is associated with the Department of Decision Sciences at North Carolina Central University's School of Business. She has over 50 publications in the area of operations and management, and is a recipient of several awards honoring the quality of her scholarly work.

Jason Vasquez

Jason Vasquez is a research associate at Brigham Young University's College of Physical and Mathematical Sciences. He is pursuing his degree in the area of applied and computational mathematics and his research interests include data mining and the study of complex networks.