Skip to main content

Research Repository

Advanced Search

Artificial intelligence for supply chain resilience: learning from Covid-19.

Modgil, Sachin; Singh, Rohit Kumar; Hannibal, Claire


Sachin Modgil

Rohit Kumar Singh


Many supply chains have faced disruption during Covid-19. Artificial intelligence (AI) is one mechanism that can be used to improve supply chain resilience by developing business continuity capabilities. This study examines how firms employ AI and consider the opportunities for AI to enhance supply chain resilience by developing visibility, risk, sourcing and distribution capabilities. The authors have gathered rich data by conducting semistructured interviews with 35 experts from the e-commerce supply chain. The authors have adopted a systematic approach of coding using open, axial and selective methods to map and identify the themes that represent the critical elements of AI-enabled supply chain resilience. The results of the study highlight the emergence of five critical areas where AI can contribute to enhanced supply chain resilience; (1) transparency, (2) ensuring last-mile delivery, (3) offering personalized solutions to both upstream and downstream supply chain stakeholders, (4) minimizing the impact of disruption and (5) facilitating an agile procurement strategy. The study offers interesting implications for bridging the theory–practice gap by drawing on contemporary empirical data to demonstrate how enhancing dynamic capabilities via AI technologies further strengthens supply chain resilience. The study also offers suggestions for utilizing the findings and proposes a framework to strengthen supply chain resilience through AI. The study presents the dynamic capabilities for supply chain resilience through the employment of AI. AI can contribute to readying supply chains to reduce their risk of disruption through enhanced resilience.


MODGIL, S., SINGH, R.K. and HANNIBAL, C. 2022. Artificial intelligence for supply chain resilience: learning from Covid-19. International journal of logistics management [online], 33(4), pages 1246-1268. Available from:

Journal Article Type Article
Acceptance Date Jul 4, 2021
Online Publication Date Jul 27, 2021
Publication Date Oct 17, 2022
Deposit Date Nov 20, 2023
Publicly Available Date Dec 5, 2023
Journal International Journal of Logistics Management, The
Print ISSN 0957-4093
Electronic ISSN 1758-6550
Publisher Emerald
Peer Reviewed Peer Reviewed
Volume 33
Issue 4
Pages 1246-1268
Keywords Supply chain design; Covid-19; Dynamic capabilities; Supply chain resilience
Public URL


You might also like

Downloadable Citations