# A Machine Learning-Based Study on the Synergistic Optimization of Supply Chain Management and Financial Supply Chains from an Economic Perspective

arXiv: https://arxiv.org/abs/2509.03673 · HTML: https://mlsanity.com/paper/2509.03673/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Builds on (most cited first, 11 of 11)

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## Built on by (most cited first, 1 of 1)

- [Research on Metro Transportation Flow Prediction Based on the STL-GRU Combined Model](https://mlsanity.com/paper/2509.18130/) (2025)

## Abstract (arXiv)

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.