Singh, RK, Modgil, S and Shore, A (2023) Building artificial intelligence enabled resilient supply chain: a multi-method approach. Journal of Enterprise Information Management. ISSN 1741-0398

Preview

Text Building artificial intelligence enabled resilient supply chain a multi-method approach.PDF - Accepted Version Available under License Creative Commons Attribution Non-commercial. Download (376kB) | Preview

Abstract

Purpose: In the uncertain business environment, the supply chains are under pressure to balance routine operations and prepare for adverse events. Consequently, this research investigates how artificial intelligence is used to enable resilience among supply chains.
Design/methodology/approach: This study first analyzed the relationship among different characteristics of AI-enabled supply chain and how these elements take it towards resilience by collecting the responses from 27 supply chain professionals. Furthermore, to validate the results, an empirical analysis is conducted where the responses from 231 supply chain professionals are collected.
Findings: Findings indicate that the disruption impact of an event depends on the degree of transparency kept and provided to all supply chain partners. This is further validated through empirical study, where the impact of transparency facilitates the mass customization of the procurement strategy to Last Mile Delivery to reduce the impact of disruption. Hence, AI facilitates resilience in the supply chain.
Originality/value: This study adds to the domain of supply chain and information systems management by identifying the driving and dependent elements that AI facilitates and further validating the findings and structure of the elements through empirical analysis. The research also provides meaningful implications for theory and practice.

Item Type:	Article
Additional Information:	This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact permissions@emerald.com
Uncontrolled Keywords:	0806 Information Systems; 0807 Library and Information Studies; 1503 Business and Management; Business & Management
Subjects:	H Social Sciences > HF Commerce > HF5001 Business H Social Sciences > HF Commerce > HF5001 Business > HF5410 Marketing. Distribution of Products Q Science > QA Mathematics > QA76 Computer software
Divisions:	Business & Management (from Sep 19)
Publisher:	Emerald
SWORD Depositor:	A Symplectic
Date Deposited:	12 May 2023 11:36
Last Modified:	15 May 2023 09:41
DOI or ID number:	10.1108/jeim-09-2022-0326
URI:	https://researchonline.ljmu.ac.uk/id/eprint/19491

View Item