Digital Twin & Industrial AI Platforms for Supply Chain Optimization covers the convergence of high-fidelity simulation models, production-grade AI stacks, and autonomous logistics to improve visibility, resilience, and cost efficiency across supply networks. The topic focuses on digital twins that mirror assets, processes and flows; industrial AI that turns telemetry into forecasts and decisions; and the orchestration layer that ties models to real-world execution. This area is timely (as of 2026-01-18) because organizations are moving beyond pilots to operational deployments: multimodal and LLM capabilities are being embedded into decision workflows, edge and accelerator hardware is addressing latency and energy limits, and governance/privacy requirements are driving hybrid architectures. Key tooling reflects these shifts: Vertex AI and Google Gemini provide managed platforms and multimodal models for building, fine-tuning and operationalizing predictive and generative capabilities; Cohere and Mistral AI supply enterprise-tunable and efficient foundation models for private inference and domain adaptation; LlamaIndex and OpenPipe enable retrieval-augmented agents, dataset capture, and fine-tuning pipelines for document-driven forecasting and root-cause analysis. On the logistics execution side, Gatik exemplifies autonomous middle-mile solutions that can be integrated with digital twins to validate routing and load plans. Infrastructure vendors like Rebellions.ai highlight the growing importance of energy-efficient inference hardware for continuous simulation and on-site decisioning. Together these components form stacks that support scenario planning, predictive maintenance, dynamic routing, and market-intelligence-driven sourcing. Successful implementations balance real-time telemetry, model governance, and edge/cloud tradeoffs so organizations can run accurate what-if simulations, automate recurring decisions, and maintain compliance and cost control in complex global supply chains.