Driving Operational Excellence with Digital Twins in Manufacturing

This IDC Perspective examines how digital twins are evolving from visualization tools into the simulation and orchestration backbone of physical AI on the shop floor. It distinguishes between the simulation role, which models processes and assets to support engineering and AI training, and the orchestration role, which coordinates physical systems, AI agents, and workers in real time. It examines divergent adoption patterns across process and discrete manufacturing and profiles the vendor landscape. It provides guidance for operations and plant leaders on sequencing investments, building data foundations, and pairing twin programs with workforce enablement."Digital twins are evolving beyond visualization to become the operational backbone of AI-enabled manufacturing. As manufacturers move toward autonomous operations, digital twins provide the simulation environment to train AI and the orchestration layer to coordinate assets, systems, and people in real time," says Sarah Lee, senior research director, Manufacturing IT Strategies at IDC."A digital twin may be technically secure yet still become operationally unreliable if its data, models, and assumptions drift from reality. Trust requires both protection and continuous validation," says Gunjan Bassi, research manager, Worldwide Industrial Internet of Things (IIoT) and Intelligence Strategies at IDC."A digital twin is only as good as the data beneath it. Without a unified industrial data layer that mirrors real-world plant conditions, even the most sophisticated twin serves only as a virtual mirror. For real digital twins to evolve in the world of physical AI, real-time data analysis and automation must be in sync with digital twin simulation," says Carlos Gonzalez, research manager, Event-Driven Automation and Analytics at IDC.