AI-powered Supply Chain & Digital Twin Platforms bring together autonomous logistics, real-time data orchestration, edge AI vision, and 3D model-driven digital twins to create continuous, actionable supply-chain simulations. By 2026 these systems respond to persistent labor shortages, stricter Scope 3 reporting requirements, and demand for real-time visibility across middle-mile, warehousing, and last-mile operations. Key building blocks include autonomous logistics providers (e.g., Gatik for repeatable middle-mile trucking), sustainability data platforms (e.g., Mavarick AI for automated Scope 1–3 measurement and decarbonization workflows), and commerce/intelligence data layers (e.g., Dema.ai for unified inventory, sales and logistics signals). Enterprise orchestration and conversational automation come from assistant and agent frameworks—IBM watsonx Assistant for no-code and developer-driven virtual agents and LangChain/AutoGPT toolchains for building stateful, agentic automation that can coordinate simulation, planning and exception handling. Complementary technologies include 3D model generation for virtual commissioning and scenario testing, and edge AI vision platforms that supply low-latency sensor inputs to on-site controllers and autonomous vehicles. Together these components enable digital twins that are not just visualization tools but operational systems: they ingest heterogeneous telemetry, run AI-driven what-if analyses, trigger automated actions, and capture emissions and cost impacts for compliance and optimization. The result is tighter feedback loops between simulation and execution, faster response to disruptions, and clearer pathways for emissions reduction. For procurement and engineering teams, evaluating solutions now means assessing integration with existing data stacks, edge compute capabilities, agent orchestration APIs, and the vendor’s approach to verifiable sustainability reporting.