Multi-layer, multi-tier analysis of global supply chain in medical equipment sector: network science application

Abstract

The COVID-19 pandemic has highlighted the fragility of the globally intertwined supply chains. Our primary motivation is to demonstrate a new networked-based multi-tier and multi-level analysis of the global supply chain of medical equipment, which is enabled by a novel approach in data mining of financial records of corporations at a global scale. This work may be a harbinger for future research focussed on select tiers and layers of networked supply chain data. The current research uncovers several previously unknown patterns of the global supply chain, including the role of tax haven nations, computer software/hardware industry, semiconductor industry, and motor vehicle and auto bodies in the medical equipment industry. Additionally, we conducted a deep supply chain network analysis and multiple simulation analyses of the supply chain network. The results reveal some new and important insights into the previously uncovered global supply chain network patterns as it relates to medical equipment.

Keywords:

• Data science
• network analysis
• global supply chain
• COVID-19
• multi-tier
• multi-layer

Clustering analysis data

Data repository for the supply chain clustering data: https://github.com/businessecosystem

Acknowledgments

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). We would like to convey our appreciation to the editors and anonymous reviewers for their valuable feedback and recommendations.

Disclosure statement

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

Notes

1 https://meticulousblog.org/top-10-companies-in-global-ventilators-market/

2 https://www.nytimes.com/2020/03/13/business/masks-china-coronavirus.html

3 Except for layers 1 & 2, the other networks represent a single industry. Node sizes represent eigenvector centrality. Node colors represent cluster membership. Layer 3 in Figure 3 is the single-tier value-chain network, which includes –but is not limited to– the supply chain.

4 GitHub URL: https://github.com/businessecosystem .

5 The node colors identify clusters in the supply chain network. The node sizes illustrate eigenvector centrality.

6 Colors show the clustering algorithm result, and node sizes indicate eigenvector centrality. The color assignments in each networks in independent.

Additional information

Funding

This work was 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. This project is part of the NCCU Advanced Center for COVID Related Disparities (ACCORD).

Notes on contributors

Kayvan Miri Lavassani

Dr. 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 organisations 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.

Bahar Movahedi

Dr. 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 honouring the quality of her scholarly work.

Raghavan J. Iyengar

Dr. Raghavan J. Iyengar, obtained his Ph.D. in Business (Major: Accounting) from the University of Maryland, College Park. His research is directed at topics in accounting that are of current interest to accounting practitioners, accounting policy-makers and academicians in the accounting, finance and management disciplines. Some of the major themes of his research include investigating the value relevance of accounting earnings, analysing the economic incentive effect of executive compensation methods, and examining the relationship between corporate performance and board governance. He is currently associated with North Carolina Central University where he is a full Professor of Accounting and Chair of the Department of Accounting. His research papers have appeared in top-tier accounting and management journals including Strategic Management Journal, Journal of Business Research, Journal of Corporate Finance, Contemporary Accounting Research, Journal of Accounting and Public Policy, Accounting and Finance, and Accounting Research Journal.