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This research proposes a framework using semantic signals and large language models to optimize inspection depth and recovery allocation in reverse logistics, specifically targeting scenarios like aircraft maintenance and IT decommissioning. By converting unstructured return notes into actionable condition factors, the method reduces labor costs while improving net recovery value compared to noisy full-inspection baselines. The study demonstrates that narrative evidence can effectively guide decision-making before final recovery actions are taken, offering a practical application for AI in supply chain efficiency.
Semantic Signal-Assisted Inspection and Recovery Allocation in Reverse Logistics
arXiv:2609.02116v1 Announce Type: new Abstract: 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.
Abstract: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.
Accepted at the IEEE 4th International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings 2026). 7 pages, 1 figure, 3 tables. Code and benchmark: this https URL
Artificial Intelligence (cs.AI)
arXiv:2609.02116 [cs.AI]
(or arXiv:2609.02116v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.02116 arXiv-issued DOI via DataCite (pending registration)
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