AI

LLM-Based Feature Extraction for Multi-Criteria Inventory Classification

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/11/2026, 05:03:24 UTC.

Briefings

A new study proposes using large language models to extract features from unstructured customer reviews, which are then combined with the Hadi-Vencheh model for multi-criteria inventory classification. The research demonstrates that including customer sentiment signals can improve inventory prioritization by transforming qualitative feedback into quantitative data.

Key points

• The study introduces an LLM-based feature extraction method that converts unstructured customer reviews into quantitative features using vector embeddings.
• These features are integrated into an MCIC framework based on the Hadi-Vencheh model to enhance inventory prioritization.

Context

E-commerce supply chains generate large volumes of customer feedback and operational data that remain underutilized in inventory management. Traditional methods like ABC analysis rely on annual dollar usage, whereas this approach leverages unstructured information to improve classification.

Limitations

Experimental results show measurable reclassification effects when customer sentiment signals are included, but the specific impact on overall inventory accuracy remains to be measured. The study relies on data from arXiv and does not provide a full-scale deployment or long-term validation.

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