LLM-Based Feature Extraction for Multi-Criteria Inventory Classification
Summary
- Researchers propose an LLM-based feature extraction approach to transform unstructured supply chain sources, such as customer reviews, into quantitative features.
- The method utilizes vector embeddings and LLM-generated evaluation criteria to enrich multi-criteria inventory classification (MCIC) frameworks.
- Experimental results demonstrate measurable reclassification effects and enhanced inventory prioritization when integrating customer sentiment signals.
Why it matters
This research highlights how large language models can bridge the gap between unstructured operational feedback and quantitative supply chain management, improving inventory prioritization.