AI Improves Reverse Logistics Inspection and Recovery Allocation.

Reverse logistics operations often face the challenge of inspecting and routing returned assets efficiently before their full condition is known, with manual inspection being a significant labor cost. This paper proposes a Semantic Signal-Assisted Decision Support framework designed to optimize these processes. The framework leverages natural language processing to convert unstructured return notes into quantifiable "condition factors" and "signal-quality scores." These scores then inform decisions on how deeply to inspect an item and how to allocate it for recovery, all while managing shared labor capacity.The framework was evaluated across three synthetic benchmark scenarios: IT decommissioning, aircraft maintenance, and consumer electronics returns. In these simulations, the keyword-based implementation consistently improved net recovery value compared to traditional structured-feature methods, while also reducing inspection costs. Notably, in the aircraft scenario, score-guided targeting added substantial economic value per batch.The findings suggest that narrative evidence from return notes can be a powerful tool for making smarter inspection and recovery decisions. By integrating semantic signals, businesses can achieve better economic outcomes and more efficient resource utilization in their reverse logistics operations, even outperforming risk-blind approaches in some economic objectives.