Intelligent Supply Chain Governance: Integrating AI for Sustainable, Resilient, and ESG-Compliant Procurement
DOI:
https://doi.org/10.58425/jpscm.v4i2.431Keywords:
Artificial intelligence (AI), ESG risks assessment, supply chain management, machine learning, sustainable development, regulatory complianceAbstract
Aim: This study aims to evaluate how artificial intelligence (AI) can enhance ESG-oriented supplier risk assessment and strengthen sustainability compliance in global supply chains.
Methods: Theoretical foundations of ESG monitoring were analyzed, the main risks were categorized into Environmental, Social, and Governance domains, and the role of AI technologies in enhancing the adaptability, transparency, and efficiency of supply chains was substantiated. The study employed a case study approach, utilizing secondary data from the Prewave platform, and applied comparative analysis to evaluate improvements in ESG risk detection efficiency. Particular attention was given to the algorithmic structure of modern intelligent platforms capable of real-time cognitive analysis of textual and numerical data.
Results: The advantages of employing AI models in reducing ESG incident detection time, automating counterparty evaluation, and ensuring compliance with international regulatory frameworks (CSRD, LkSG) were empirically demonstrated. Furthermore, a conceptual structural model was proposed for implementing an AI-oriented ESG supplier assessment system, covering all stages - from data source formation to managerial decision-making.
Conclusion: The study concludes that AI-based ESG monitoring systems significantly enhance transparency, operational resilience, and sustainable procurement practices, ultimately marking a paradigm shift in global supply chain governance.
Recommendations: Key implementation barriers in emerging market contexts included fragmented data infrastructure, low digital literacy within public sectors, and inconsistent regulatory compatibility with international ESG standards. Future research should focus on developing and localizing AI models for ESG monitoring, specifically addressing the unique data, infrastructure, and policy environments of developing economies.
References
Chen, R., Li, Q., & Zhang, W. (2023). The temporal inconsistency of corporate ESG disclosures: Implications for supply chain risk management. Journal of Business Ethics, 187(2), 345–362.
Chen, Y., Li, X., & Zhang, H. (2021). Time series forecasting in supply chain risk management. Journal of Supply Chain Management, 57(3), 214–228.
Gunasekaran, A., Subramanian, N., & Papadopoulos, T. (2017). Information technology for competitive advantage within logistics and supply chains: A review. Transportation Research Part E: Logistics and Transportation Review, 99, 14–33.
Höfer, D., & Artmeier, J. (2024). How Prewave is helping to secure deep supply chains worldwide with AI on Google Cloud. Google Cloud Blog. https://cloud.google.com/blog/topics/customers/prewave-helps-secure-deep-supply-chains-with-ai-on-google-cloud.
Kraus, S., Moog, F., Gutzeit, E., & Fösel, T. (2022). Artificial intelligence in sustainability: Predictive modeling and real-time monitoring of corporate environmental, social, and governance (ESG) performance. Journal of Business Research, 145, 670–685.
Li, H., & Xu, L. D. (2019). Big data analytics for supply chain management: A literature review. Journal of Business Research, 70, 282–299.
Mirzaee, H., & Ashtab, S. (2024). Sustainability, resiliency, and artificial intelligence in supplier selection: A triple-themed review. Sustainability, 16(19), 8325. https://doi.org/10.3390/su16198325.
Mo, X. (2024). AI-based supply chain risk management. Applied and Computational Engineering, 106, 125–130. https://doi.org/10.54254/2755–2721/106/20241297.
Prewave Knowledgebase. (n.d.). Phase 1: Preparation and regular risk analysis. https://knowledgebase.prewave.com/en/knowledge/2.–phase–1-preparation-and-regular-risk-analysis.
Prewave White Paper. (2024). Navigating compliance: How Prewave supports stakeholders in meeting the EU batteries regulation. https://8402685.fs1.hubspotusercontent-na1.net/hubfs/8402685/Case%20Studies%20LP%20New/Battery%20Regulation%20WP.Pdf.
Sharma, V., & Singh, J. (2022). Artificial intelligence and responsible sourcing: Essential tools for real-time detection of environmental and social violations. Journal of Cleaner Production, 345(2), 130–148.
Smith, A. L., & Liu, B. (2021). Greenwashing and data opacity: Why self-disclosed ESG metrics fail for proactive risk detection. Strategic Management Journal, 42(8), 1675–1690.
Song, J., Jiang, L., Liu, Z., et al. (2022). Selection of a third-party reverse logistics service provider based on intuitionistic fuzzy multi-criteria decision making. Systems, 10(6), 188. https://doi.org/10.3390/systems10060188.
You, X., & Lou, S. (2025). Research on the evaluation and selection of IoT suppliers from an ESG perspective. Systems, 13(6), 422. https://doi.org/10.3390/systems13060422.
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