MODELING SUPPLY CHAIN COMPLIANCE RESPONSE STRATEGIES BASED ON AI SYNTHETIC DATA WITH STRUCTURAL PATH REGRESSION:A SIMULATION STUDY OF EU 2027 MANDATORY LABOR REGULATIONS

Authors

DOI:

https://doi.org/10.18623/rvd.v22.n3.3510

Abstract

This study constructs a simulation framework integrating AI synthetic data with structural path regression to examine corporate response behaviours and performance outcomes under the EU's 2027 mandatory labour compliance policy. High-quality data is generated via Monte Carlo mechanisms and NIST standards, employing multiple regression, logistic regression, and analyses of mediating and moderating effects. Results indicate that compliance investments significantly enhance corporate survival rates via the mediating pathway of AI sophistication, with EU market dependency acting as a significant moderator of this effect. Findings demonstrate that AI synthetic data combined with structural path modelling provides quantitative foundations for simulating high-intensity regulatory environments and informing strategic decision-making.

References

Arthur, L., Costello, J., Hardy, J., O’Brien, W., Rea, J., Rees, G., & Ganev, G. (2023). On the challenges of deploying privacy-preserving synthetic data in the enterprise. arXiv. https://arxiv.org/abs/2307.04208

Belgodere, B., et al. (2023). Auditing and Generating Synthetic Data with Controllable Trust Trade-offs. arXiv. https://arxiv.org/abs/2304.10819

Belgodere, B., Dognin, P., Ivankay, A., Melnyk, I., Mroueh, Y., Mojsilović, A., Navratil, J., Nitsure, A., Padhi, I., Rigotti, M., Ross, J., Schiff, Y., Vedpathak, R., & Young, R. A. (2024). Auditing and generating synthetic data with controllable trust trade-offs. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 14(4), 773–788. https://doi.org/10.1109/JETCAS.2024.3477976

Culot, G., Podrecca, M., & Nassimbeni, G. (2024). Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. Computers in Industry, 154, 104132. https://doi.org/10.1016/j.compind.2024.104132

Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Luo, Z., Wamba, S. F., & Roubaud, D. (2022). Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view. International Journal of Production Economics, 250, 108618. https://doi.org/10.1016/j.ijpe.2022.108618

Godbole, A. (2025). Synthetic data for robust AI model development in regulated enterprises. arXiv. https://arxiv.org/abs/2503.12353

Li, L., Zhu, W., Chen, L., & Liu, Y. (2024). Generative AI usage and sustainable supply chain performance: A practice-based view. Transportation Research Part E: Logistics and Transportation Review, 192, 103761. https://doi.org/10.1016/j.tre.2024.103761

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Published

2025-11-05

How to Cite

Meng, W. (2025). MODELING SUPPLY CHAIN COMPLIANCE RESPONSE STRATEGIES BASED ON AI SYNTHETIC DATA WITH STRUCTURAL PATH REGRESSION:A SIMULATION STUDY OF EU 2027 MANDATORY LABOR REGULATIONS. Veredas Do Direito, 22, e223510. https://doi.org/10.18623/rvd.v22.n3.3510

Issue

Vol. 22 (2025): Veredas do Direito, v. 22, 2025

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Articles

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