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

• URL: http://arxiv.org/abs/2603.16815v1
• Date: Tue, 17 Mar 2026 17:19:34 GMT
• Title: Beyond Accuracy: Evaluating Forecasting Models by Multi-Echelon Inventory Cost
• Abstract summary: This study develops a digitalized forecasting-inventory optimization pipeline integrating traditional forecasting models, machine learning regressors, and deep sequence models.<n>Using the M5 Walmart dataset, we evaluate seven forecasting approaches and assess their operational impact under single- and two-echelon news systems.<n>Results indicate that Temporal CNN and LSTM models significantly reduce inventory costs and improve fill rates compared to statistical baselines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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