用真实供应链数据评估预测模型，发现深度模型更省钱
Beyond Accuracy: Evaluating Forecasting Models by Multi-Echelon Inventory Cost
- 构建统一仿真框架，对比7种预测模型在库存系统中的表现
- 时序CNN和LSTM使库存成本更低、缺货率更少，优于传统方法
- 适用于需要优化多级供应链的电商与零售企业
本研究开发了一个数字化的预测-库存优化流程，将传统预测模型、机器学习回归器和深度序列模型整合到统一的库存仿真框架中。基于M5 Walmart数据集，评估了七种预测方法在单级与两级新货商系统下的运营影响。结果表明，时序CNN和LSTM模型相比统计基线显著降低库存成本并提升补货满足率。敏感性分析与多级分析验证了其鲁棒性和可扩展性，为现代供应链提供数据驱动的决策支持工具。
原文摘要 · Abstract (English)
This study develops a digitalized forecasting-inventory optimization pipeline integrating traditional forecasting models, machine learning regressors, and deep sequence models within a unified inventory simulation framework. Using the M5 Walmart dataset, we evaluate seven forecasting approaches and assess their operational impact under single- and two-echelon newsvendor systems. Results indicate that Temporal CNN and LSTM models significantly reduce inventory costs and improve fill rates compared to statistical baselines. Sensitivity and multi-echelon analyses demonstrate robustness and scalability, offering a data-driven decision-support tool for modern supply chains.
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