Siemens Built a Factory in Software First. PepsiCo Went First.

By NeuralWired Staff · Updated July 13, 2026

Siemens just told manufacturers something they’ve heard before: build it virtually before you build it for real. What’s different this time is that PepsiCo already did it, and the company is putting a number on the payoff. At Siemens’ CES 2026 unveiling of Digital Twin Composer, the pitch moved from simulation slideware to a live production tool wired directly into plant floor data. If you run manufacturing operations or sign off on capital projects, this is the digital twin story worth actually reading this quarter.

What Siemens Actually Launched at CES 2026

Digital Twin Composer connects Siemens’ photorealistic 3D digital twins, built on NVIDIA Omniverse libraries, to the systems that actually run a factory floor: manufacturing execution systems, quality management systems, PLC code, and IIoT sensor feeds. That’s the real shift here. Older digital twins were design-phase artifacts, built once and largely frozen. This one updates continuously against live plant data, which means engineers can test a process change in the twin and watch how it behaves under real conditions before touching a single machine.

Siemens AG President and CEO Roland Busch framed the launch in sweeping terms at the announcement:

“Industrial AI is no longer a feature, it’s a force.” Roland Busch, President and CEO, Siemens AG, at the Siemens CES 2026 press release

The product is currently in early access with select customers. General availability on the Siemens Xcelerator Marketplace is scheduled for mid-2026, so if you’re evaluating this for a 2026 budget cycle, you’re looking at a young product, not a mature platform with years of deployment history behind it.

Siemens paired the launch with a second tool, Intelligence Center X, unveiled at Realize LIVE Americas 2026 in Detroit. It bundles Mendix, Graph Studio, and AI Studio (pulled from the RapidMiner portfolio Siemens acquired) into a governed workflow layer for AI models running on engineering and manufacturing data. Tony Hemmelgarn, President and CEO of Siemens Digital Industries Software, presented it as the governance layer that keeps AI agents from running loose on plant data, though full technical detail on that governance model wasn’t disclosed at the event.

The PepsiCo Case: What the Numbers Really Say

PepsiCo is the flagship customer, and it’s a genuinely useful case study precisely because the company already had deep Siemens infrastructure in place: Teamcenter for product data, Plant Simulation for process modeling, now layered with Digital Twin Composer and NVIDIA Omniverse. PepsiCo is converting select U.S. manufacturing and warehouse facilities into high-fidelity digital replicas before committing capital to physical changes.

Steve Hoinka, PepsiCo’s Global VP of Manufacturing Strategy and Transformation, laid out the operating principle at Realize LIVE:

“We will do nothing, make no capital investment unless we prove it digitally first.” Steve Hoinka, Global VP, Manufacturing Strategy and Transformation, PepsiCo, via TechHQ reporting on Realize LIVE Americas 2026

Hoinka reported two concrete figures on stage: a 20% throughput improvement across PepsiCo’s end-to-end value chain, and the avoidance of more than 90% of potential operational issues before they ever reached the physical plant. Industry analyst firm Verdantix separately reported PepsiCo capex reductions in the 10 to 15% range, though treat that as a secondary analyst estimate layered on top of PepsiCo’s own numbers, not a replacement for them.

Here’s the caveat that matters more than the stat itself. These are self-reported figures from one company, presented at a vendor-hosted conference, without independent audit. PepsiCo is also not a typical manufacturer. It’s a Fortune 50 company with years of prior Siemens ecosystem investment already sunk into the ground. A mid-market plant starting from zero won’t replicate PepsiCo’s numbers by buying the same software.

How Big Is the Digital Twin Market, Really

Market-sizing research on digital twins varies enough between firms that any single figure deserves a raised eyebrow. Here’s how three separate research houses currently size it:

Manufacturing holds roughly 35% of the total digital twin market by Mordor’s accounting, the single largest industry slice. Whichever number you trust, the direction is unambiguous: this is a market growing fast, and Siemens timed its launch to sit at the front of that curve.

Siemens’ own digital business posted €9 billion in revenue for its most recent full fiscal year on record, up 22% year over year, per Roland Busch’s fiscal year press conference. That’s the commercial scale Siemens is operating at in this category, and it’s worth checking whether a more current figure has since been published before you cite it elsewhere.

The Gartner Reality Check Nobody’s Marketing

Here’s the number that should sit next to every digital twin press release you read this year: according to Gartner’s 2024 IoT survey data, roughly one in three companies that began digital twin pilots in 2022 actually scaled past proof-of-concept. Two out of three didn’t.

Michael Grieves, the researcher widely credited with coining the term “digital twin” back in 2002, has pointed to a specific failure mode behind that dropout rate: organizations and the institutions training their engineers still default to siloed thinking rather than starting from the capability and working backward to the tool, per his comments to Manufacturing Engineering & Technology. Coming from the person who invented the concept, that’s not a vendor talking down its own category. It’s a founder flagging that the industry’s execution muscle hasn’t caught up to its ambition.

World Wide Technology, a systems integrator that implements these projects rather than sells the software, published its own hype-versus-reality breakdown identifying scope creep as a recurring killer: teams start with one clear intention and get pulled into an expanding set of alternate possibilities before the project ever ships. Their conclusion, echoed across the WWT analysis, is that digital twin success depends as much on project and stakeholder management as it does on modeling talent, a bottleneck that rarely makes it into a vendor’s demo reel.

What Manufacturing Leaders Should Do Now

If you’re a manufacturing CTO or VP of Operations evaluating this, three things change the moment you connect a digital twin to live MES and PLC data instead of using it as a design-phase sketchpad.

• IT/OT convergence stops being optional. A twin fed by live operational data means plant IT and operational technology teams need a joint data governance plan in place before the pilot starts, not after something breaks.
• This is a multi-year cost commitment, not a one-time purchase. Between Gartner’s projected 40% cost rise by 2029 and new machine-user licensing models, model the full budget curve before you sign.
• Benchmark against the one-in-three number, not the PepsiCo number. PepsiCo’s results came from a company with years of existing Siemens infrastructure and Fortune 50 resources. Gartner’s pilot-to-scale success rate is the more statistically honest baseline for what your own rollout is likely to look like.

Our read: Digital Twin Composer is a real product solving a real integration gap between design-phase and operational digital twins. But the industry’s execution track record, one in three pilots scaling successfully, is the more useful number for planning your own timeline than any single customer’s conference-stage stat, verified or not.

Frequently Asked Questions

What is a digital twin in manufacturing?

A digital twin in manufacturing is a real-time virtual replica of a machine, production line, or entire factory, continuously updated with live data from sensors, MES, and control systems. It lets engineers simulate and test changes virtually before applying them to physical equipment, cutting risk and downtime.

How much does a digital twin cost to implement?

Costs vary widely by scope. A single-machine or single-cell pilot can start in the tens of thousands of dollars, while full-plant platforms like Siemens Digital Twin Composer involve enterprise licensing plus integration, sensor, and simulation-engineering costs that scale with facility complexity.

What is the difference between a digital twin and a simulation?

A simulation models a system at a single point in time to answer one specific question. A digital twin is a continuously updated, bidirectional model synchronized with live data from its physical counterpart, meaning it evolves in real time instead of representing one static scenario.

How long does it take to see ROI from a digital twin?

Timelines vary by use case. Simulation-focused twins used to validate process changes before implementation can show payback in as little as four to six months, while predictive-maintenance-focused twins typically take longer, with full-deployment ROI often cited in the 12 to 36 month range.

Which industries use digital twins the most?

Manufacturing holds the largest share of digital twin deployments, followed by automotive and transportation, energy and power, aerospace, and healthcare. Manufacturing’s lead comes from mature IIoT infrastructure and established use cases like predictive maintenance and virtual commissioning.

Where This Goes Next

What’s genuinely new in 2026 isn’t the digital twin concept, it’s the convergence of digital twins with agentic AI. Gartner projects that by 2030, semi-autonomous AI agents will orchestrate roughly 10% of production, quality, and maintenance use cases, up from about 2% today. Siemens’ own €200 million “smart factory” build at its Amberg, Germany site, using Digital Twin Composer to virtually commission the plant before construction, with completion targeted for 2030, is one early bet on that trajectory.

Three things to watch over the next 6 to 18 months:

1. General availability in mid-2026. Watch whether early-access results from PepsiCo hold up once Digital Twin Composer reaches customers without PepsiCo’s existing Siemens footprint.
2. The Gartner one-in-three number, tracked forward. If Siemens and its competitors move that success rate, it’ll show up in Gartner’s next IoT survey cycle.
3. Machine-user pricing rollout. Watch how Siemens and rivals like Dassault Systèmes, Rockwell Automation, PTC, and ANSYS structure pricing for AI agents operating inside these platforms, since that’s where the real cost exposure sits.

The honest version of this story sits between the CES keynote and the Gartner survey data. Siemens shipped something real. PepsiCo’s results are real, too, as far as one company’s self-reported numbers go. Whether that translates to your plant floor depends far more on your organization’s execution discipline than on which vendor’s logo is on the software.