How AI-Native Returns Routing Is Reshaping Reverse Logistics: Luxome Uses LiquiDonate to Reduce Warehouse and Distribution Return Costs

Introduction

Against the backdrop of continuously expanding return volumes, AI is turning “reverse logistics” from a cost center into an optimizable part of the supply chain. Bedding brand Luxome uses the AI-native platform LiquiDonate to directly match some usable returns with nonprofit organizations, rather than sending everything back to the warehouse for processing. For retailers, this approach not only reduces the burden of warehousing, sorting, and repackaging, but also cuts transportation mileage and last-mile disposal costs.

Key Developments

Luxome founder Hyaat Chaudhary said that when the brand was first established in 2018, it did not fully consider the returns issue, and as a result returns kept piling up in the warehouse. The company later tested a variety of handling methods, including repair and cleaning services, but the resale quality for textiles was unsatisfactory. Luxome then began using LiquiDonate, which determines item condition through the system and routes them to the appropriate destination.

LiquiDonate’s model uses AI and computer vision to classify returned items and, by taking into account product condition, transportation costs, retailer preferences, nonprofit needs, distance, and fairness, selects the most suitable handling path, including restocking, resale, donation, or recycling. The platform currently connects with about 4,300 nonprofit organizations.

Chaudhary said that for Luxome, the transportation cost of the donation route is about 25% lower than sending returns back to the warehouse, because the matched nonprofit organizations are usually closer to consumers. The company also said that the cost of warehouse returns in third-party handling, storage, and landfill disposal is about $4 to $5 per order, while donation labels cost less.

Supply Chain Impact

The core value of this kind of AI routing system is not “automation” itself, but reducing inefficient handling in the reverse logistics chain. Traditional returns processes usually require products to first go back to a centralized warehouse, where they are inspected, graded, cleaned, repackaged, and then either restocked or otherwise handled. For bulky, low-margin, or hard-to-resell items, this chain often means additional warehouse occupancy and transportation costs.

For supply chain managers, platforms like LiquiDonate have three key implications:

1. Shorten transportation distances: The system prioritizes nonprofit organizations closer to consumers, reducing the mileage of returns flowing back. 2. Reduce warehousing and distribution pressure: Usable returns no longer all flow into core warehouses, helping ease the load on storage and sorting nodes. 3. Improve disposal efficiency: AI automatically matches the processing path based on item condition, reducing manual judgment and cross-department approval time.

This model is especially suitable for large items that are difficult to refurbish efficiently, such as furniture, bedding, and some apparel. For these categories, reverse logistics costs often exceed residual value; if handled improperly, returns can directly erode gross margins.

Regional ImplicationsAlthough the case took place in the U.S. retail supply chain, its logic has reference value for global logistics networks. In North America, where consumers are widely dispersed and regional warehouse networks are complex, if returned goods are routed back in bulk to just a few warehouses, localized congestion can easily occur. AI-based diversion can help companies organize reverse transportation more flexibly at the regional level.

For European and Asia-Pacific markets, this model is equally aligned with e-commerce returns, the circular economy, and carbon-reduction goals. In cross-border e-commerce and multinational distribution networks in particular, if returns are sent back to the original shipping warehouse, transportation distances and customs clearance costs will rise further. By localizing donation, reuse, or recycling, companies can reduce the pressure of cross-border return shipments.

From a global supply chain perspective, more and more companies are beginning to include “return destination management” in logistics technology discussions, rather than treating it as merely an inventory or customer service issue. As sustainability compliance requirements intensify, reverse logistics is also becoming part of international trade and supply chain resilience.

Industry Perspective

The industry has long treated returns as a post-retail “ancillary process,” but the case shows that return handling is already affecting warehouse network design, transportation cost control, and inventory turnover efficiency. Huseyn Abdullah noted that returns are not a new problem, but their scale has expanded significantly with e-commerce growth. Business Insider, citing an Optoro report, said that in 2022 about 9.5 billion pounds of returns were sent to landfills; NRF estimated that U.S. retail returns in 2025 would reach about $850 billion.

These figures show that returns management is shifting from an operational detail to a key variable in supply chain management. For third-party logistics providers, warehouse operators, and retailers, AI-driven returns diversion affects not only cost structures but may also change the layout of reverse logistics nodes.

Future Outlook

LiquiDonate’s model shows that AI’s role in the supply chain is expanding from demand forecasting and route optimization to handling “exception flows” — including returns, excess inventory, and donation diversion. In the future, more retailers may integrate AI into existing ERP, WMS, and returns management systems, allowing goods to be initially classified and assigned a destination before they even enter the warehouse.

Key areas to watch in the next stage include:

• whether AI-based returns diversion will expand from apparel and home goods to more categories;
• whether nonprofit organizations and recycling networks can handle larger volumes of goods;
• whether return labels, tax documentation, and compliance reviews can be further automated;
• whether companies will evaluate reverse logistics alongside sustainability metrics and cost metrics.

Conclusion

The partnership between Luxome and LiquiDonate shows that AI’s value in global logistics and supply chains is not only about improving transportation efficiency, but also about reducing unnecessary backflow, optimizing warehouse utilization, and restructuring return pathways. For retailers, such tools are turning reverse logistics from a “burden that must be handled” into a supply chain function that is manageable, quantifiable, and optimizable.