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

Autores

DOI:

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

Resumo

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.

Referências

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

Downloads

Publicado

2025-11-05

Como Citar

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(3), e223510. https://doi.org/10.18623/rvd.v22.n3.3510

Edição

v. 22 n. 3 (2025): Veredas do Direito, v. 22, n. 3 2025

Seção

Artigos

Licença

Submeto (emos) o presente trabalho, texto original e inédito, de minha (nossa) autoria, à  avaliação de Veredas do Direito - Revista de Direito, e concordo (amos) que os direitos autorais a ele referentes se tornem propriedade exclusiva  da Revista Veredas, sendo vedada qualquer  reprodução total ou parcial, em qualquer outra parte ou outro meio de divulgação impresso ou eletrônico, dissociado de Veredas do Direito, sem que a necessária e prévia autorização seja solicitada por escrito e obtida junto ao Editor-gerente. Declaro (amos) ainda que não existe conflito de interesse entre o tema abordado, o (s) autor (es) e empresas, instituições ou indivíduos.

Reconheço (Reconhecemos) ainda que Veredas está licenciada sob uma LICENÇA CREATIVE COMMONS:

Licença Creative Commons Attribution 3.0