用AI生成数据模拟欧盟2027劳工新规，预测企业合规策略与生存概率。
Modeling supply chain compliance response strategies based on AI synthetic data with structural path regression: A Simulation Study of EU 2027 Mandatory Labor Regulations
- 结合AI合成数据与路径回归模型，模拟企业应对新规的策略路径。
- 合规投入显著提升企业存活率，智能水平是关键中介变量。
- 适合政策制定者、企业战略部门及合规技术研究者参考。
在欧盟将于2027年实施的新强制性劳工合规背景下，供应链企业面临严格的工作时间管理要求与合规风险。为科学预测企业在政策影响下的应对行为与绩效结果，本文构建了融合人工智能合成数据生成机制与结构化路径回归建模的方法框架，用于模拟企业应对新规的战略转型路径。研究采用基于蒙特卡洛机制和NIST合成数据标准生成的高质量仿真数据，构建包含多元线性回归、逻辑回归、中介效应与调节效应的结构路径分析模型，变量体系涵盖企业工作时长、合规投入、响应速度、自动化水平、政策依赖度等14项指标。通过探索性数据分析（EDA）与方差膨胀因子（VIF）多重共线性剔除筛选出具有解释力的变量集。研究发现，合规投入对企业生存具有显著正向影响，该效应通过智能化水平的中介路径传递；同时，企业对欧盟市场的依赖度显著调节该中介效应的强度。结论表明，人工智能合成数据结合结构路径建模可有效支撑高强度监管情景的模拟，为企业的战略应对、政策设计及预判阶段的AI辅助决策提供量化依据。
原文摘要 · Abstract (English)
In the context of the new mandatory labor compliance in the European Union (EU), which will be implemented in 2027, supply chain enterprises face stringent working hour management requirements and compliance risks. In order to scientifically predict the enterprises' coping behaviors and performance outcomes under the policy impact, this paper constructs a methodological framework that integrates the AI synthetic data generation mechanism and structural path regression modeling to simulate the enterprises' strategic transition paths under the new regulations. In terms of research methodology, this paper adopts high-quality simulation data generated based on Monte Carlo mechanism and NIST synthetic data standards to construct a structural path analysis model that includes multiple linear regression, logistic regression, mediation effect and moderating effect. The variable system covers 14 indicators such as enterprise working hours, compliance investment, response speed, automation level, policy dependence, etc. The variable set with explanatory power is screened out through exploratory data analysis (EDA) and VIF multicollinearity elimination. The findings show that compliance investment has a significant positive impact on firm survival and its effect is transmitted through the mediating path of the level of intelligence; meanwhile, firms' dependence on the EU market significantly moderates the strength of this mediating effect. It is concluded that AI synthetic data combined with structural path modeling provides an effective tool for high-intensity regulatory simulation, which can provide a quantitative basis for corporate strategic response, policy design and AI-assisted decision-making in the pre-prediction stage lacking real scenario data. Keywords: AI synthetic data, structural path regression modeling, compliance response strategy, EU 2027 mandatory labor regulation
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