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

arXiv cs.AI / 3/18/2026

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Key Points

  • The paper develops a digitalized forecasting-inventory optimization pipeline that unifies traditional forecasting models, machine learning regressors, and deep sequence models within a single inventory simulation framework.
  • Using the M5 Walmart dataset, it evaluates seven forecasting approaches and analyzes their operational impact in both single- and two-echelon newsvendor systems.
  • Temporal CNN and LSTM models significantly reduce inventory costs and improve fill rates compared with statistical baselines.
  • Sensitivity and multi-echelon analyses demonstrate robustness and scalability, highlighting the method as a practical data-driven decision-support tool for modern supply chains.
  • The study offers a replicable methodology for practitioners to integrate forecasting with inventory policy to optimize costs in real-world settings.

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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