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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.12183 |
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| _version_ | 1866914040872173568 |
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| author | Shen, Zuo-Jun Max Sun, Shuo Qi, Yongzhi Hu, Hao Kang, Ningxuan Zhang, Jianshen Wang, Xin Lin, Xiaoming |
| author_facet | Shen, Zuo-Jun Max Sun, Shuo Qi, Yongzhi Hu, Hao Kang, Ningxuan Zhang, Jianshen Wang, Xin Lin, Xiaoming |
| contents | This paper presents data-driven approaches for integrated assortment planning and inventory allocation that significantly improve fulfillment efficiency at JD\,.com, a leading E-commerce company. JD\,.com uses a two-level distribution network that includes regional distribution centers (RDCs) and front distribution centers (FDCs). Selecting products to stock at FDCs and then optimizing daily inventory allocation from RDCs to FDCs is critical to improving fulfillment efficiency, which is crucial for enhancing customer experiences. For assortment planning, we propose efficient algorithms to maximize the number of orders that can be fulfilled by FDCs (local fulfillment). For inventory allocation, we develop a novel end-to-end algorithm that integrates forecasting, optimization, and simulation to minimize lost sales and inventory transfer costs. Numerical experiments demonstrate that our methods outperform existing approaches, increasing local order fulfillment rates by 0.54% and our inventory allocation algorithm increases FDC demand satisfaction rates by 1.05%. Considering the high-volume operations of JD\,.com, with millions of weekly orders per region, these improvements yield substantial benefits beyond the company's established supply chain system. Implementation across JD\,.com's network has reduced costs, improved stock availability, and increased local order fulfillment rates for millions of orders annually. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12183 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | JD.com Improves Fulfillment Efficiency with Data-driven Integrated Assortment Planning and Inventory Allocation Shen, Zuo-Jun Max Sun, Shuo Qi, Yongzhi Hu, Hao Kang, Ningxuan Zhang, Jianshen Wang, Xin Lin, Xiaoming Optimization and Control This paper presents data-driven approaches for integrated assortment planning and inventory allocation that significantly improve fulfillment efficiency at JD\,.com, a leading E-commerce company. JD\,.com uses a two-level distribution network that includes regional distribution centers (RDCs) and front distribution centers (FDCs). Selecting products to stock at FDCs and then optimizing daily inventory allocation from RDCs to FDCs is critical to improving fulfillment efficiency, which is crucial for enhancing customer experiences. For assortment planning, we propose efficient algorithms to maximize the number of orders that can be fulfilled by FDCs (local fulfillment). For inventory allocation, we develop a novel end-to-end algorithm that integrates forecasting, optimization, and simulation to minimize lost sales and inventory transfer costs. Numerical experiments demonstrate that our methods outperform existing approaches, increasing local order fulfillment rates by 0.54% and our inventory allocation algorithm increases FDC demand satisfaction rates by 1.05%. Considering the high-volume operations of JD\,.com, with millions of weekly orders per region, these improvements yield substantial benefits beyond the company's established supply chain system. Implementation across JD\,.com's network has reduced costs, improved stock availability, and increased local order fulfillment rates for millions of orders annually. |
| title | JD.com Improves Fulfillment Efficiency with Data-driven Integrated Assortment Planning and Inventory Allocation |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2509.12183 |