An Analytical Study on Integrating Artificial Intelligence and Industry 4.0 Technologies for Supply Chain Resilience and Sustainability

Haldar, DK, Saha, P, Belal, HM ORCID: 0000-0001-6737-1445 and Kayas, OG ORCID: 0000-0003-4541-8171 (2026) An Analytical Study on Integrating Artificial Intelligence and Industry 4.0 Technologies for Supply Chain Resilience and Sustainability. Supply Chain Analytics. ISSN 2949-8635

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Abstract

This study examines how the adoption of AI-driven predictive analytics and Industry 4.0 (I4.0) technologies enhances supply chain resilience (SCR) and sustainable supply chain performance (SSCP) in the food manufacturing industry, with SCR serving as a mediating factor. Grounded in the Dynamic Capabilities perspective, the research employs a quantitative approach using data collected from 194 professionals working in the food manufacturing sector. Structural Equation Modeling (SEM) with SmartPLS 4.0 was employed to test the hypothesized relationships and assess both direct and mediating effects. The results reveal that AI-driven predictive analytics and I4.0 integration have a strong positive influence on both SCR and SSCP. Moreover, SCR plays a critical mediating role in linking technology adoption to sustainable supply chain performance, underscoring its strategic importance in driving sustainability transitions. The study contributes to the growing body of knowledge on the intersection of digital technologies, resilience, and sustainability, offering practical insights for organizations in developing economies seeking to leverage technological capabilities to achieve adaptability, competitiveness, and sustainable growth through the lens of dynamic capabilities.

Item Type:	Article
Subjects:	H Social Sciences > HF Commerce > HF5001 Business Q Science > QA Mathematics > QA76 Computer software
Divisions:	Liverpool Business School
Publisher:	Elsevier
Date of acceptance:	31 March 2026
Date of first compliant Open Access:	7 April 2026
Date Deposited:	07 Apr 2026 15:04
Last Modified:	07 Apr 2026 15:04
DOI or ID number:	10.1016/j.sca.2026.100209
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28343

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