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

Swata Marik; Swayamjit Saha; Garga Chatterjee

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arXiv · 2603.16815

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

Swata Marik·Swayamjit Saha·Garga Chatterjee

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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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Swata Marik, Swayamjit Saha, Garga Chatterjee. 2026-03-17. Beyond Accuracy: Evaluating Forecasting Models by Multi-Echelon Inventory Cost. https://arxiv.org/abs/2603.16815

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