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Siemens unveiled Digital Twin Composer at CES 2026, uniting IoT data, industrial AI, and photorealistic digital twins in one platform.
PepsiCo's pilot deployment identified up to 90 percent of design issues in advance and lifted throughput by 20 percent before construction began.
The tool runs on Siemens Xcelerator with NVIDIA Omniverse, signaling a shift from static factory planning to continuous virtual simulation.
The cost of a bad manufacturing decision has never been higher. A single design change, delayed shipment, or equipment failure can affect the entire factory floor. Manufacturers do not just need more automation. They need confidence before work even starts.
Siemens laid out its answer at CES 2026 with Digital Twin Composer, connecting Industrial IoT, AI, and digital twins so manufacturers can predict outcomes, cut risk, and refine production before a single physical change gets made.
Siemens leans on three technologies here, and honestly, none of them do much on their own. Start with industrial IoT. It's the unglamorous part: sensors and connected machines scattered across the floor, quietly logging what's actually happening in real time. This raw data then feeds into the AI layer, which is where things get useful.
It identifies bottlenecks, flags equipment likely to fail soon, and runs through various production scenarios before anyone commits to one. Then comes the digital twin, essentially a working copy of the factory that engineers can poke and prod without touching anything real. Siemens has bundled all three into a single platform called Digital Twin Composer.
Digital twins are not a new concept for Siemens. What changed at CES 2026 is scale and integration. Digital Twin Composer merges 2D and 3D digital twin data with live operational information from manufacturing execution systems, plus physics-based simulation built on NVIDIA Omniverse libraries.
The output is a photorealistic virtual environment mapping an entire plant, not a single machine. This marks the point at which an industrial digital twin stops being a design tool and begins functioning as a live operational model.
Digital Twin Composer sits within Siemens Xcelerator, the company's broader software portfolio, and will be available on the Xcelerator Marketplace in mid-2026. One Xcelerator tool, Tecnomatix, handles the manufacturing side directly.
It optimizes material flow and equipment use and supports virtual commissioning, validating automation logic and production workflows inside the digital environment before physical deployment.
This cuts commissioning time and engineering risk well before a line goes live. The depth of integration shows Siemens is not offering a standalone app but an interconnected engineering ecosystem built over decades, purpose-built for digital twin factory optimization.
PepsiCo offers the clearest evidence available so far. The company is using Digital Twin Composer to transform select manufacturing and warehouse facilities in the United States, with plans to scale the approach globally.
Working alongside Siemens, NVIDIA, and computer vision technology, PepsiCo recreated machines, conveyors, pallet routes, and operator paths with physics-level accuracy inside a virtual environment.
PepsiCo Chairman and CEO Ramon Laguarta described the partnership as a way to convert intelligence into action at a scale matched to the company's rising demand.
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Siemens ties all of this to what it calls the industrial metaverse, a term that sounds abstract until placed against what it replaces: engineering, operations, and AI teams working in separate systems with disconnected data. Digital Twin Composer closes that gap by giving every team access to the same live, contextualized model of the factory.
Siemens and NVIDIA are extending their partnership further, working toward what they describe as an Industrial AI Operating System, a shared foundation meant to link design, engineering, and daily operations across a plant's full lifecycle.
None of this scales on its own. Analysts at Verdantix note that large-scale digital twin adoption has moved slowly, and the barriers sit outside the technology itself. Manufacturers still need IT and OT data convergence, stronger cybersecurity, proper data governance, and core systems such as MES and MOM in place before a digital twin can generate real value. A photorealistic simulation only helps if the data feeding it stays accurate and up to date.
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The competitive edge in manufacturing is shifting away from who collects the most data toward who can act on it fastest and with the least risk. Siemens has built a platform designed for that shift, and PepsiCo's early numbers give other manufacturers a concrete benchmark rather than a promotional claim to take on faith. The next test will be whether that performance holds once Digital Twin Composer moves from early access into wide industrial use.
Siemens' smart factory strategy combines Industrial IoT, artificial intelligence (AI), and digital twins to create connected manufacturing environments. This approach enables real-time monitoring, predictive maintenance, virtual simulation, and continuous process optimization.
Digital twins create virtual replicas of machines, production lines, or entire factories. They allow manufacturers to simulate processes, test changes before implementation, reduce downtime, and improve production efficiency without disrupting physical operations.
AI analyzes data collected from connected equipment to detect patterns, predict equipment failures, optimize production schedules, improve quality control, and support faster, data-driven decision-making across manufacturing operations.
Industrial IoT connects machines, sensors, and production systems to collect real-time operational data. This data provides the foundation for AI analytics and digital twins, helping manufacturers improve visibility, efficiency, and asset performance.
Siemens is advancing smart manufacturing by integrating Industrial IoT, AI, digital twins, and automation into a unified digital ecosystem. This enables manufacturers to design, simulate, optimize, and continuously improve factory operations with greater speed, flexibility, and sustainability.
