Optimization Framework for Global Supply Chain of AI Hardware

The global supply chain for AI hardware has emerged as strategic infrastructure underpinning national competitiveness, economic growth, and security-sensitive technology deployment. Accelerated demand for GPUs, AI accelerators, high-bandwidth memory, and advanced packaging has exposed structural limitations in existing planning and allocation approaches, which remain largely reactive and qualitative. This paper introduces a novel optimization framework for global AI hardware supply chains that integrates probabilistic demand signals, explicit multi-tier capacity constraints, risk-adjusted multi-objective optimization, and real-time execution feedback. The framework enables early detection of systemic bottlenecks, defensible allocation decisions under sustained scarcity, and improved resilience to geopolitical and operational disruptions. A representative large-scale AI hardware program case study demonstrates measurable improvements in delivery reliability, lead-time stability, and executive decision confidence. The proposed framework provides a scalable decision architecture applicable to hyperscalers, enterprises, and public-sector AI programs.