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.

翻译：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于统一的库存仿真框架中。基于M5沃尔玛数据集，我们评估了七种预测方法，并考察其在单级与两级报童系统下的运营影响。结果表明，与统计基线模型相比，时序CNN和LSTM模型能显著降低库存成本并提升满足率。敏感性分析与多级库存仿真验证了该框架的鲁棒性与可扩展性，为现代供应链提供了数据驱动的决策支持工具。