Executive Summary
Determining which countries are critical to international supply chains for microelectronics precursors is a key strategic challenge. Identifying states that would be difficult to replace is even more important because such knowledge would enable policymakers to anticipate and mitigate vulnerabilities. Generating a scalable estimate of criticality is a complex task. Trade data are inherently retrospective, reflecting past investments and capacities. Moreover, measures of criticality based on current trade relationships risk conflating path dependence and preferences with actual constraints or obligations.
This study combines network analysis, machine learning, and qualitative analysis to identify important exporters in global value flows for microelectronics precursor materials. We employ this technique to identify countries that have an outsized impact on the global value flow for microelectronics precursors and which exhibit unusual export patterns, a combination that may make them difficult to replace if they are removed from the trade system. For the top-ranked countries identified by our algorithm, we conduct a brief futures analysis to assess the uniqueness of their position, highlighting potential substitutes and opportunities for infrastructure consolidation. Our method is flexible and data-agnostic, allowing analysis to be enriched as more data becomes available.
Key Takeaways
- We present a hybrid qualitative and machine learning method that is extensible to incorporate additional and multifaceted data.
- Our method identifies the Democratic Republic of the Congo, Turkey, the United States, and Japan as countries that are both uniquely positioned toward and dominant over specific materials. At the same time, our initial results underweight the structural importance of the People’s Republic of China (PRC), indicating the need for additional validation and benchmarking.
- Machine learning and artificial intelligence are reshaping the landscape of supply chain leverage. Machine learning is being applied to accelerate existing research into substitute materials, which historically depended on a combination of researcher intuition and intensive laboratory processes.