From Return to Revenue: AI Optimizes Every Step of Reverse Logistics
The Client:
A reverse logistics company managing high volumes of returned products from e-commerce and traditional retail channels. The client was losing recoverable revenue through manual, inconsistent triage decisions on returned goods.
The Engagement:
SSI developed an AI-powered disposition and valuation system that automatically classifies returned products and prices them by channel replacing manual research with ML models trained on historical sales and market data.
The Challenge:
When a product is returned, the question of what to do with it; resell, refurbish, or scrap, was being answered manually by operations staff with limited data and significant time pressure. Pricing decisions for resale channels (retail vs. wholesale vs. liquidation) were inconsistent and left significant margin on the table. There was no systematic way to match product condition to market value.
The volume of returns was growing faster than the team’s capacity to triage them. Manual research was the bottleneck and the longer a decision took, the more the product’s market value eroded.
The Solution:
SSI built a classification model using XGBoost and Random Forest trained on the client’s historical return data. The model predicts the optimal disposition for each returned item: resell, refurbish, or scrap, based on product specs, condition data, and return reason.
A regression model then estimates fair market value for each disposition channel, drawing on historical sales data, comparable market pricing, and current demand signals. The price recommendation updates in near real time as market conditions shift.
The two models work in sequence: classify first, then price per channel giving the operations team a clear, data-backed decision for every return that arrives.
The Impact:
Research time per return dropped by 40% as manual market lookups were replaced by automated model outputs.
Revenue recovered from returned goods improved as pricing decisions aligned more accurately with actual market value across resale channels.
The system paid for itself within the first operational quarter and established a scalable infrastructure for handling growing return volumes.
