Beyond Accuracy: Evaluating Forecasting Models by Multi-Echelon Inventory Cost
2026-03-17 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors created a system that combines different forecasting methods, including traditional, machine learning, and deep learning models, to help predict inventory needs. They tested their approach using Walmart sales data and compared seven forecasting techniques. Their results showed that the deep learning models (Temporal CNN and LSTM) helped reduce inventory costs and improved product availability. The system also works well in different supply chain setups, making it a useful tool for managing stock more efficiently.
forecasting modelsmachine learningdeep learningTemporal CNNLSTMinventory optimizationnewsvendor modelM5 datasetsupply chain managementmulti-echelon systems
Authors
Swata Marik, Swayamjit Saha, Garga Chatterjee
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.