Supply Chain Viability and the COVID-19 pandemic: a conceptual and formal generalisation of four major adaptation strategies

Abstract

The COVID-19 pandemic has challenged supply chains (SC) on an unprecedented scale testing viability and adaptation under severe uncertainty. However, the literature on the adaptation strategies and quantification of their impacts is still scarce. Mixing literature analysis, case study approach, and quantitative techniques for performance assessment under disruptions, our study generalises four adaptations strategies – intertwining, scalability, substitution, and repurposing – to maintain SC viability when facing a pandemic, and offers a model to analyse and quantify deployment and impact of adaptation. First, we analyse the recent literature and identify some of the general characteristics of adaptation strategies during the COVID-19 pandemic. We then describe case studies to illustrate the practical context and supplement the literature analysis in order to derive relevant determinants for building of a conceptual framework and construction of a formal model. In the conceptual framework, we show how the adaptation strategies can be aligned with the SC viability, encompassing the levels of the ecosystem, network, and resources. In the generalised model, we formalise the impacts and efforts in deploying and assessing the adaptation strategies as both a process and an outcome. We close by proposing some open research questions and outline several future research directions.

KEYWORDS:

• Supply chain dynamics
• supply chain resilience
• pandemic
• COVID-19
• adaptation
• supply chain viability

Acknowledgement

The author is deeply thankful to four anonymous reviewers for their invaluable comments and guidance that helped to improve the manuscript immensely.

Disclosure statement

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

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

Dmitry Ivanov

Prof. Dr. Dr. habil. Dmitry Ivanov is professor of Supply Chain and Operations Management at Berlin School of Economics and Law (HWR Berlin), deputy director and executive board member of Institute for Logistics (IfL) at HWR Berlin, and faculty director of M.A. Global Supply Chain and Operations Management programme at HWR Berlin. His research coined several seminal academic and practical directions such as the ripple effect in supply chains and supply chain viability. His research record includes over 350 publications, with over 100 papers in prestigious academic journals, three editions of the leading textbooks ‘Global Supply Chain and Operations Management’ and ‘Introduction to Supply Chain Resilience’, and several research books „Structural Dynamics and Resilience in Supply Chain Risk Management’, ‘Scheduling in Industry 4.0 and Cloud Manufacturing’, and ‘Handbook of Ripple Effects in the Supply Chain’. He delivered invited plenary, keynote and panel talks at the conferences of INFORMS, IFPR, DSI, IFAC, and IFIP. He is leading working groups, tracks and sessions on the Digital Supply Chain, Supply Chain Risk Management and Resilience in global research communities. He is Editor of International Journal of Integrated Supply Management, Associate Editor in International Journal of Production Research, International Transactions in Operational Research, and International Journal of Systems Science, and guest-editor in Annals of Operations Research, Annual Reviews in Control, International Journal of Production Economics, International Journal of Production Research, International Journal of Information Management, International Journal of Physical Distribution and Logistics Management, and Omega. He is Chairman of IFAC TC 5.2 ‘Manufacturing Modelling for Management and Control’. He regularly presents his research results and has been Chairman, IPC and Advisory Board member of over 60 international conferences in supply chain and operations management, industrial engineering, control and information sciences.