# 基于LLM的多准则库存分类特征提取

> 全文为英语

**Published:** 2026年11月9日
**Author:** Humza Akhtar, Rami Pinto
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/zh/articles/llm-based-feature-extraction-for-multi-criteria-inventory-classification

arXiv:2611.08626v1 发布类型: new 摘要: 电子商务供应链产生大量客户反馈和运营数据，这些数据在库存管理中仍未被充分利用。传统的库存分类技术（如ABC分析）主要依赖于…

arXiv:2611.08626v1 Announce Type: new Abstract: E-commerce supply chains generate large volumes of customer feedback and operational data that remain underutilized in inventory management. Traditional inventory classification techniques such as ABC analysis rely primarily on annual dollar usage, while multi-criteria inventory classification (MCIC) methods extend this but still depend on structured quantitative data and do not utilize unstructured information available in modern supply chains. This paper proposes an LLM-based feature extraction approach that transforms unstructured sources such as customer reviews into quantitative features using vector embeddings and LLM-generated evaluation criteria. The enriched feature set is incorporated into an MCIC framework based on the Hadi-Vencheh model. Experimental results show measurable reclassification effects when customer sentiment signals are included, demonstrating that integrating unstructured data into MCIC can enhance inventory prioritization.

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*内容来自原始来源： [arXiv — cs.AI](https://arxiv.org/abs/2611.08626). 查看原文.*

**标签：** #arXiv

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