Treffer: JD.com Improves Fulfillment Efficiency with Data-Driven Integrated Assortment Planning and Inventory Allocation.

This study develops a data-driven method integrating assortment planning and inventory allocation to boost fulfillment efficiency for JD.com. The novel approach significantly improves order fulfillment rates and stock availability. Implementation across JD.com's network demonstrates substantial operational benefits. 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 are 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. History: This paper has been accepted for the INFORMS Journal on Applied Analytics Special Issue—2024 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research. Funding: This research is partially supported by the ITC Mainland-Hong Kong Joint Funding Scheme [MHP/192/23] and the RGC Theme-based Research Scheme [T32-707/22-N]. [ABSTRACT FROM AUTHOR]

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