A systematic Approach to constructing a Chance-and-Risk Matrix for Semiconductor Supply Chains

2026-09-01

AI Summary

本文提出一种使用大型语言模型从公司公开信息中提取并结构化半导体供应链风险和机会的方法，形成知识图谱，并通过三层次机制进行排序。

Abstract

Semiconductor supply chains face escalating risks from geopolitical tensions, geographic concentration, and rapid technological shifts, yet no scalable system continuously extracts, structures, and prioritizes risk intelligence from public corporate disclosures. We present an end-to-end pipeline that retrieves corporate documents for semiconductor companies and uses large language models (LLMs) to extract the risks and opportunities they describe. It organizes these into a knowledge graph linking each item to its category, sources, and related events, then merges duplicates and ranks them with a three-layer mechanism combining an algorithmic formula, an LLM relevance adjustment, and expert validation. Applied to five companies across the value chain, the pipeline produces 76,207 scored items, of which an independent check finds 92.6% valid. The automated rankings match expert judgment at an average Spearman correlation of 0.55 for risks and 0.72 for opportunities, and the resulting matrices identify trade restrictions as the dominant cross-company risk.

Problem

• semiconductor supply chains
• risk intelligence
• public corporate disclosures

Innovation

• large language models
• end-to-end pipeline
• knowledge graph
• risk intelligence
• automated ranking