Industrial Management Journal

Industrial Management Journal

Modeling the Key Enablers for Artificial Intelligence-Driven Transformation of the Green Hydrogen Supply Chain

Document Type : Original Research Article

Authors
seyed Kamal Tourang 1
Mostafa Hajiaghaei-Keshteli 2
1 Assistant Prof., Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran.
2 Prof., Department of Industrial Engineering, School of Engineering and Science, Tecnologico de Monterrey, Puebla, Mexico.
10.22059/imj.2026.419757.1008335
Abstract
Objective: The increasing complexity of green hydrogen supply chains requires the adoption of advanced digital technologies capable of enhancing operational efficiency, resilience, and sustainability. Among these technologies, Artificial Intelligence (AI) has emerged as a key enabler of intelligent decision-making, predictive analytics, and process optimization. Despite its significant potential, AI adoption in green hydrogen supply chains remains constrained by multiple technological, organizational, economic, institutional, and environmental factors. Therefore, this study aims to identify, evaluate, and analyze the causal relationships among the critical enablers influencing AI adoption in green hydrogen supply chains.
Methodology: Initially, an extensive literature review was conducted to identify the major AI adoption enablers. Subsequently, thirteen critical enablers were validated through expert consultation involving specialists in AI, hydrogen energy, renewable energy systems, and supply chain management. The DEMATEL method was then employed to examine the interdependencies, driving power, dependence relationships, and causal structure among the identified enablers based on expert judgments.
Results: The findings indicate that top management support, technological capability, digital infrastructure readiness, regulatory and policy support, and stakeholder collaboration and ecosystem integration are the primary driving enablers facilitating AI adoption. Conversely, investment and financial support, expected operational benefits, and sustainability and environmental commitment were identified as highly dependent enablers with considerable centrality, reflecting their strategic importance within the AI adoption ecosystem.
Conclusion: Unlike previous studies that primarily focused on identifying adoption factors, this research uncovers the causal relationships among the enablers and provides a structured understanding of their interactions. From a practical perspective, the findings offer valuable guidance for managers and policymakers seeking to accelerate AI implementation within GHSCs.
Keywords
Artificial intelligence
green hydrogen supply chain
AI adoption
dematel
digital transformation
sustainable supply chain
Subjects
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Industrial Management Journal
Volume 18, Issue 4
2026
Pages 235-262