中國製造的供應鏈「暗戰」：被西方架構鎖死的神經中樞，與 N² 級複雜度的降維突圍

The search results do not contain specific technical specifications, model architecture, or implementation details about physics-based algorithms or dimensionality reduction approaches for non-steady-state manufacturing. The provided sources focus on supply chain management challenges and AI adoption trends rather than proprietary algorithmic solutions. To obtain technical details about the referenced physics-algorithm solution and its N²-level complexity approach, additional sources specifically addressing the author's patent portfolio and technical publications would be required.

The 2026 supply chain landscape reveals a critical gap between Western enterprise software capabilities and the operational realities of high-frequency, non-steady-state Chinese manufacturing. As companies pursue AI-driven supply chain solutions[2], those that can address transparency at every supply chain stage and handle synchronous disruptions will gain competitive advantage. The emphasis on scenario planning and early bottleneck identification[1] suggests that solutions offering real-time visibility and adaptive planning—particularly those designed for Chinese manufacturing dynamics—will become increasingly valuable. However, successful implementation will depend on data readiness and organizational capability to adapt AI systems to specific business environments[2].