Beyond Accuracy: Evaluating Forecasting Models by Multi-Echelon Inventory Cost

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.

Source: arXiv:2603.16815v1 - http://arxiv.org/abs/2603.16815v1 PDF: https://arxiv.org/pdf/2603.16815v1 Original Link: http://arxiv.org/abs/2603.16815v1