Rebalancing and Clearance Pricing of Near-Expiry Inventory in Online Grocery Retail

Perishable inventory management remains a critical challenge for online grocery retailers, where mismatches between demand and shelf life can lead to substantial financial losses and food waste. This article describes the development and deployment of an integrated, data-driven system for managing near-expiry (“red-line”) inventory across a nationwide network of microfulfillment centers operated by Meituan’s Xiaoxiang Supermarket. The system jointly optimizes nighttime inventory rebalancing and daytime clearance pricing using a closed-loop learning-and-optimization framework. It combines continuous causal forest to estimate price elasticities, a linearized model for inventory rebalancing, and a Markov decision process for dynamic pricing. Across the full operational network, spoilage rates are about 20% lower and revenue from red-line inventory about 14% higher since deployment, with an annual financial benefit and an annual reduction in the value of food waste each estimated on the order of tens of millions of CNY. This case shows how analytics can be embedded into large-scale retail operations to improve both economic and sustainability outcomes.

History: This paper has been accepted for the INFORMS Journal on Applied Analytics Special Issue—2025 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research.