Beyond Accuracy: Evaluating Forecasting Models by Multi-Echelon Inventory Cost
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917350343704576 |
|---|---|
| author | Marik, Swata Saha, Swayamjit Chatterjee, Garga |
| author_facet | Marik, Swata Saha, Swayamjit Chatterjee, Garga |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16815 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Accuracy: Evaluating Forecasting Models by Multi-Echelon Inventory Cost Marik, Swata Saha, Swayamjit Chatterjee, Garga Artificial Intelligence 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. |
| title | Beyond Accuracy: Evaluating Forecasting Models by Multi-Echelon Inventory Cost |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.16815 |