Enhancing Sustainable Global Supply Chain Performance: A Multi-Criteria Decision-Making-Based Approach to Industry 4.0 and AI Integration

Dalia Štreimikienė (), Ahmad Bathaei and Justas Streimikis
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Dalia Štreimikienė: Lithuanian Centre for Social Sciences, Institute of Economics and Rural Development, 03220 Vilnius, Lithuania
Ahmad Bathaei: Lithuanian Centre for Social Sciences, Institute of Economics and Rural Development, 03220 Vilnius, Lithuania
Justas Streimikis: Lithuanian Centre for Social Sciences, Institute of Economics and Rural Development, 03220 Vilnius, Lithuania

Sustainability, 2025, vol. 17, issue 10, 1-19

Abstract: The integration of Industry 4.0 and Artificial Intelligence (AI) technologies has redefined global supply chain operations, with increasing emphasis on sustainability as a strategic priority. Despite this evolution, there remains a significant gap in the literature regarding the structured prioritization of sustainability-related indicators influenced by digital transformation. This study addresses that gap by identifying and ranking key sustainability enablers across environmental, operational, strategic, and social dimensions using the Best–Worst Method (BWM), a robust multi-criteria decision-making (MCDM) technique. Based on expert input from 37 professionals in the fields of supply chain management, sustainability, and digital technologies, twenty indicators were evaluated within four separate thematic groups. Results reveal that Emissions Monitoring and Reduction and Energy Efficiency are the most critical in the environmental dimension, while Supply Chain Traceability and Smart Inventory Management dominate the operational category. Supply Chain Resilience is identified as the top strategic factor, and Ethical Sourcing is deemed most vital from a social sustainability standpoint. These findings provide actionable insights for policymakers and practitioners, supporting data-driven decision-making and strategic alignment of digital investments with sustainability goals. This research contributes to both academic discourse and practical frameworks by offering a replicable approach to prioritizing sustainability indicators in the context of digital transformation. This study also identifies limitations and proposes future research directions to enhance the integration of digital and sustainable development in global supply chains.

Keywords: sustainable supply chain; Industry 4.0; artificial intelligence; multi-criteria decision making (MCDM); best–worst method (BWM) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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