用机器学习优化供应链与金融链协同，降本增效防风险。
A Machine Learning-Based Study on the Synergistic Optimization of Supply Chain Management and Financial Supply Chains from an Economic Perspective
- 融合经济理论与随机森林等算法，构建成本-效率-风险三维分析框架。
- 库存周转率提升30%，中小企业融资成本下降18%-22%，预测误差低于8%。
- 适合关注供应链金融、企业数字化转型的研究者与从业者。
基于经济理论并结合机器学习技术，本文提出一种协同供应链管理与金融供应链管理（SCM-FSCM）模型，解决效率损失、融资约束和风险传导问题。融合交易成本与信息不对称理论，采用随机森林等算法处理多维数据，构建数据驱动的三维（成本-效率-风险）分析框架。设计以核心企业信用赋能加动态质押融资的FSCM模式，利用LSTM进行需求预测，聚类/回归算法分配收益。结合博弈论与强化学习优化采购库存机制，采用XGBoost进行信用评估，实现库存快速变现。在20家核心企业和100家支持企业中验证，结果表明库存周转率提升30%，中小企业融资成本下降18%-22%，订单履约率稳定高于95%，需求预测误差不超过8%，信用评估准确率不低于90%。该模型有效降低运营成本，缓解融资难题，助力高质量供应链发展。
原文摘要 · Abstract (English)
Based on economic theories and integrated with machine learning technology, this study explores a collaborative Supply Chain Management and Financial Supply Chain Management (SCM - FSCM) model to solve issues like efficiency loss, financing constraints, and risk transmission. We combine Transaction Cost and Information Asymmetry theories and use algorithms such as random forests to process multi-dimensional data and build a data-driven, three-dimensional (cost-efficiency-risk) analysis framework. We then apply an FSCM model of "core enterprise credit empowerment plus dynamic pledge financing." We use Long Short-Term Memory (LSTM) networks for demand forecasting and clustering/regression algorithms for benefit allocation. The study also combines Game Theory and reinforcement learning to optimize the inventory-procurement mechanism and uses eXtreme Gradient Boosting (XGBoost) for credit assessment to enable rapid monetization of inventory. Verified with 20 core and 100 supporting enterprises, the results show a 30\% increase in inventory turnover, an 18\%-22\% decrease in SME financing costs, a stable order fulfillment rate above 95\%, and excellent model performance (demand forecasting error <= 8\%, credit assessment accuracy >= 90\%). This SCM-FSCM model effectively reduces operating costs, alleviates financing constraints, and supports high-quality supply chain development.
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