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This paper introduces a Semantic Signal-Assisted Decision Support framework that transforms return notes into a condition factor and signal-quality score, optimizing inspection depth and recovery allocation in reverse logistics. Evaluated across three synthetic scenarios鈥攊nformation technology decommissioning, aircraft maintenance, and consumer-electronics returns鈥攖he approach significantly enhances net recovery value while reducing inspection costs. Notably, in the aircraft maintenance scenario, the method adds an average of $53.9k per batch compared to traditional inspection methods, demonstrating the potential of narrative evidence in decision-making processes.
Optimizing inspection strategies in reverse logistics can yield significant financial gains, with one method adding nearly $54k per batch in aircraft maintenance alone.
Reverse-logistics operators often decide how to inspect and route returned assets before their condition is fully observed, while full inspection consumes scarce labor. Semantic Signal-Assisted Decision Support converts return notes into a condition factor and a signal-quality score that guide inspection depth and recovery allocation under shared labor capacity. We evaluate the framework in three synthetic benchmark scenarios spanning information technology decommissioning, aircraft maintenance, and consumer-electronics returns. Across 30 paired simulation seeds, the keyword implementation improves net recovery value relative to a structured-feature comparator with noisy full inspection while reducing inspection cost in all three scenarios. A risk-blind comparator that skips inspection altogether still records higher value under the benchmark's purely economic objective. At matched inspection cost, score-guided targeting adds 53.9 thousand United States dollars per batch in the aircraft scenario but has little economic effect in the other two configurations; phrase and large language model extractors provide further gains in the aircraft scenario. These results show how narrative evidence can support inspection allocation before recovery decisions are made.
