The Digital Twin Playbook: How Manufacturers Are Unlocking Hidden Capacity and Reducing CAPEX in 2026

In January 2026, at the Consumer Electronics Show in Las Vegas, something remarkable happened. Siemens AG CEO Roland Busch stood on stage with PepsiCo's Global Chief Strategy & Transformation Officer Athina Kanioura and NVIDIA's Jensen Huang to announce an industry-first collaboration .

The news wasn't about a new snack flavor or a faster chip. It was about digital twins.

The partnership revealed that PepsiCo had deployed Siemens Digital Twin Composer—built on NVIDIA Omniverse—at a Gatorade manufacturing plant in the United States. Within three months, the facility achieved a 20% increase in throughput. Across operations, PepsiCo now estimates a 10-15% reduction in capital expenditures (CAPEX) by uncovering hidden capacity and validating designs virtually before committing capital .

This is not a pilot. This is production-scale digital transformation at one of the world's largest consumer packaged goods companies.

Welcome to the era of the industrial digital twin—where every machine, conveyor, pallet route, and operator path can be recreated with physics-level accuracy, simulated thousands of times, and optimized before a single physical change is made.

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Executive Summary: The State of Digital Twins in 2026

Key Findings

The message is clear: Digital twins have moved from "nice visualization" to "operational necessity." Companies that master this technology are unlocking capacity they didn't know they had—and doing it faster and cheaper than competitors still designing in the physical world.

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Section I: What Is a Digital Twin? (2026 Definition)

The term "digital twin" has been around for years, but its meaning has evolved. In 2026, a digital twin is not a static 3D model. It is a living, breathing virtual representation that:

• Connects to real-time data from sensors, PLCs, and enterprise systems

• Simulates physics-based behavior with high accuracy

• Enables predictive analytics using AI and machine learning

• Supports what-if scenarios for optimization

• Continuously updates as the physical asset changes

As Siemens explains, Digital Twin Composer enables companies to combine 2D and 3D digital twin data with physical real-time information in a secure, managed, photorealistic virtual environment accelerated by NVIDIA Omniverse .

Types of Digital Twins in Manufacturing

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Section II: The Business Case—Why Digital Twins Matter in 2026

The Hidden Capacity Problem

Most manufacturers face the same challenge: demand is growing, but building new capacity is expensive and slow. A new plant can take 2-3 years and hundreds of millions of dollars. Even expanding an existing facility requires months of planning, construction, and validation.

The alternative? Unlock hidden capacity in existing facilities.

This is precisely what PepsiCo discovered. By creating digital twins of their plants, they found efficiencies they couldn't see on the factory floor. Within three months at one Gatorade facility, they increased throughput by 20% without adding a single square foot of space or a single new machine .

The CAPEX Reduction Opportunity

When you can validate designs virtually, you avoid costly mistakes in the physical world. PepsiCo estimates a 10-15% reduction in capital expenditures by:

• Identifying design flaws before construction

• Optimizing layouts for maximum throughput

• Uncovering capacity in existing assets, avoiding new builds

• Testing multiple scenarios without physical prototyping 

For a company spending billions on capital projects annually, this represents hundreds of millions in savings.

The Risk Mitigation Factor

Physical changes to operating plants are risky. A poorly planned modification can shut down production for days or weeks. Digital twins enable companies to identify up to 90% of potential issues before any physical modifications occur .

As Jensen Huang, CEO of NVIDIA, stated at CES 2026: "Physical industries are entering the age of AI. For companies with real-world assets, digital twins are the foundation of their AI journey" .

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Section III: The PepsiCo Case Study—A Blueprint for the Industry

The Challenge

PepsiCo operates a "farm to shelf" supply chain at global scale, serving billions of consumers through billions of daily touchpoints. Some facilities are modern; others are decades old. Demand spikes, weather disruptions, and unexpected events constantly stress physical networks not designed for such volatility .

The core challenge wasn't simply capacity—it was unlocking inaccessible capacity within existing facilities.

The Solution

At CES 2026, PepsiCo announced a multi-year collaboration with Siemens and NVIDIA to transform plant and supply chain operations using AI-powered digital twins .

Central to this transformation is Siemens Digital Twin Composer, built on NVIDIA Omniverse libraries. This technology enables PepsiCo to:

• Create high-fidelity, photorealistic 3D representations of plants, warehouses, and logistics flows

• Connect digital twins to real-time operational data

• Simulate hundreds or thousands of potential facility layouts

• Recreate every machine, conveyor, pallet route, and operator path with physics-level accuracy 

The Results

What This Means

As Athina Kanioura, CEO of PepsiCo Latin America and Global Chief Strategy & Transformation Officer, explained: "We are deploying the first digital blueprint that reimagines how the supply chain is designed, built, and scaled, a first for the industry. With a unified, AI-powered digital foundation, PepsiCo is building toward a world where every plant and warehouse operates as part of a single, intelligent ecosystem. In this future, our facilities don't just respond to demand—they anticipate and then adapt to it" .

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Section IV: Digital Twins Across Industries

Automotive

Automakers have been digital twin pioneers. BMW's use of digital twins extends to entire factories, with virtual commissioning reducing ramp-up time for new models by months. The company's Spartanburg plant, where Figure AI's humanoid robots now operate, relies on digital twins to integrate automation seamlessly.

Aerospace

Boeing and Airbus use digital twins throughout the aircraft lifecycle—from design and manufacturing to maintenance. Digital twins of engines enable predictive maintenance that prevents in-flight failures and optimizes overhaul schedules.

Food and Beverage

Beyond PepsiCo, major food manufacturers are adopting digital twins to:

• Optimize changeover times between product runs

• Ensure food safety through traceability simulation

• Manage cold chain integrity

• Balance production across multiple facilities

Pharmaceuticals

In regulated industries, digital twins offer a unique advantage: the ability to validate processes virtually before submitting them to regulators. This "virtual validation" can shave months off time-to-market for new drugs.

Oil and Gas

Digital twins of refineries and pipelines enable operators to simulate emergency scenarios, train operators in safe environments, and optimize maintenance schedules across distributed assets.

Logistics and Warehousing

Amazon, GXO, and other logistics leaders use digital twins to optimize warehouse layouts, test automation implementations, and simulate peak season demand before it hits.

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Section V: The Technology Stack—What You Need to Build Digital Twins

Core Components

A modern digital twin implementation requires several technology layers:

1. Data Infrastructure

• Industrial IoT sensors to capture real-time data

• Edge computing for local processing

• Data lakes for historical storage

• Real-time streaming platforms (like HiveMQ) for continuous data flow 

2. Modeling and Simulation

• Physics-based modeling engines

• 3D visualization platforms (like NVIDIA Omniverse)

• Simulation software (like Siemens Digital Twin Composer)

• Digital twin authoring tools

3. AI and Analytics

• Machine learning for pattern recognition

• Predictive algorithms for forecasting

• Optimization engines for what-if scenarios

• Computer vision for reality capture

4. Integration Layer

• Connections to ERP, MES, and PLM systems

• APIs for data exchange

• Digital thread for lifecycle traceability

5. Visualization and Interaction

• Immersive 3D environments

• AR/VR for operator interaction

• Dashboards for management

• Mobile access for field teams

Key Vendors and Platforms

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Section VI: Implementation Roadmap—How to Start Your Digital Twin Journey

Based on industry best practices and the PepsiCo blueprint, here is a step-by-step approach to digital twin implementation.

Phase 1: Foundation (3-6 Months)

Objective: Establish data infrastructure and prove concept

Steps:

1. Identify a high-value pilot area—a single production line, a bottleneck process, or a facility with expansion plans

2. Audit existing data sources—what sensors exist? What data is already collected? What's missing?

3. Install necessary IIoT sensors to capture missing data points

4. Establish data connectivity—ensure real-time data flows to a central repository

5. Create a basic digital twin of the pilot area using available tools

6. Validate the twin against actual performance

Key Success Factors:

• Start small—don't try to twin the entire enterprise at once

• Ensure data quality from Day 1—garbage in, garbage out 

• Involve operators who know the process intimately

Phase 2: Optimization (6-12 Months)

Objective: Use the digital twin to drive improvements

Steps:

1. Run what-if scenarios—test layout changes, schedule adjustments, automation investments virtually

2. Identify bottlenecks using simulation data

3. Implement physical changes validated by the digital twin

4. Measure actual vs. predicted results to refine the twin's accuracy

5. Expand to additional lines or processes

Key Success Factors:

• Track ROI rigorously—document every improvement

• Share successes across the organization

• Build internal expertise in simulation and analysis

Phase 3: Integration (12-24 Months)

Objective: Connect twins across the enterprise

Steps:

1. Link production twins to supply chain twins—connect plant-level optimization to network-level planning

2. Integrate with ERP and MES for seamless data flow

3. Enable real-time decision-making—AI agents that can adjust operations based on twin insights

4. Create digital threads that trace products from design through manufacturing to end-of-life

Key Success Factors:

• Standardize data models and APIs across facilities

• Invest in change management—processes must adapt to new capabilities

• Consider center of excellence to share best practices

Phase 4: Autonomous Operation (24+ Months)

Objective: Enable self-optimizing systems

Steps:

1. Deploy AI agents that continuously monitor twins and recommend improvements

2. Implement closed-loop control—systems that can adjust operations without human intervention

3. Scale across global operations

4. Connect with supplier and customer twins for end-to-end visibility

Key Success Factors:

• Build trust in autonomous systems gradually

• Maintain human oversight for critical decisions

• Continuously update twins as physical assets change

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Section VII: The Data Challenge—Why Most AI Projects Fail to Scale

The 2026 Industrial AI Readiness Report, based on a survey of 272 industrial professionals, reveals a sobering reality: AI ambition is accelerating faster than AI readiness .

The Top Barriers

The Reality Check

While 64% of manufacturers are using or planning AI for predictive maintenance, and 55% for process optimization, only 7% say AI is embedded in most core processes today. However, in three years, 44% expect it to be .

The Path Forward

The report's conclusion is clear: AI progress depends on data progress. Before investing in sophisticated AI or digital twins, manufacturers must:

1. Fix data quality—ensure sensors are calibrated, data is clean, and histories are complete

2. Break down silos—connect OT and IT systems that have historically been separate

3. Establish real-time streaming—batch data is insufficient for real-time optimization

4. Build trust—ensure AI recommendations are explainable and validated

As HiveMQ, which sponsored the research, emphasizes: "The number one blocker isn't AI, it's data" .

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Section VIII: The Human Element—Digital Twins and the Workforce

Changing Roles, Not Eliminating Them

Like AI and robotics, digital twins transform jobs rather than eliminating them. The plant manager of 2026 doesn't just walk the floor—they also walk through virtual twins, analyzing data and testing scenarios.

New Skills Required

Training and Adoption

Companies succeeding with digital twins invest heavily in:

• Hands-on training where workers use the tools themselves

• Cross-functional collaboration—engineers, operators, and IT working together

• Celebrating wins—sharing stories of problems solved through digital twins

• Continuous learning—as twins evolve, so must workforce capabilities

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Section IX: The Mexico Opportunity

For Mexican manufacturers, digital twins represent a significant opportunity—particularly in the context of nearshoring.

Why Mexico Is Well-Positioned

1. Greenfield facilities—many nearshoring plants are new, with modern equipment and data infrastructure ready for digital twins

2. Strong automotive and aerospace sectors—industries where digital twins are already proven

3. Cost advantage—Mexican engineering talent can build and maintain digital twins at lower cost than U.S. equivalents

4. Export orientation—global customers increasingly expect digital twin capabilities from suppliers

Applications in Mexican Manufacturing

A Path Forward

Mexican manufacturers should:

• Start with pilot projects in areas with existing data

• Partner with technology providers (Siemens, NVIDIA, etc.)

• Leverage government programs supporting Industry 4.0 adoption

• Build university partnerships for talent development

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Section X: Future Trends—Digital Twins in 2027-2030

Trend 1: AI Agents as Co-Designers

PepsiCo's use of "AI agents as co-designers" is just the beginning . By 2028, expect AI agents that:

• Continuously monitor digital twins for optimization opportunities

• Automatically generate and test thousands of what-if scenarios

• Recommend (or implement) changes without human prompting

• Learn from every simulation to improve future recommendations

Trend 2: The Industrial Metaverse

Siemens AG CEO Roland Busch declared at CES 2026: "The industrial metaverse is no longer a vision—it is becoming operational reality" .

The industrial metaverse—persistent, shared digital environments where companies design, simulate, and operate—will become standard. NVIDIA Omniverse and similar platforms will host these environments, enabling:

• Global teams to collaborate in virtual factories

• Suppliers and customers to connect digital twins

• Training in immersive, risk-free environments

Trend 3: End-to-End Digital Threads

Digital twins will connect across the entire product lifecycle—from raw material sourcing through design, manufacturing, distribution, use, and end-of-life. This "digital thread" will enable:

• Full traceability for compliance and sustainability

• Closed-loop feedback from field performance to design improvements

• Predictive maintenance based on actual usage patterns

Trend 4: Democratization Through Low-Code Platforms

As tools like Siemens Digital Twin Composer become more accessible, smaller manufacturers will adopt digital twins. Low-code platforms will enable:

• Engineers to build twins without programming expertise

• Faster deployment at lower cost

• Wider adoption across the manufacturing base

Trend 5: Sustainability Optimization

Digital twins will become essential for sustainability reporting and optimization. Companies will:

• Simulate energy consumption under different scenarios

• Optimize logistics for minimum carbon footprint

• Validate circular economy designs before production

• Generate auditable data for regulatory compliance

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Section XI: Recommendations for Manufacturers

For Companies Just Starting

1. Start with a clear business problem—don't build a digital twin because it's trendy. Identify a specific challenge: a bottleneck line, high changeover times, or a planned expansion.

2. Fix your data first—digital twins require clean, real-time data. Invest in sensors and connectivity before simulation software .

3. Start small, prove value, then scale—twin one line or one process. Document ROI. Use that success to fund expansion.

4. Involve operators from Day 1—they know the process better than anyone. Their input ensures accuracy and adoption.

5. Partner with experts—vendors like Siemens, system integrators, and consultants can accelerate your journey.

For Companies Already Using Digital Twins

1. Connect isolated twins—link production twins to supply chain twins. The real value is in the network.

2. Add AI capabilities—move from descriptive (what happened) to predictive (what will happen) to prescriptive (what should we do).

3. Build internal capability—develop expertise so you're not dependent on external partners for every enhancement.

4. Share learnings across facilities—if one plant discovers an improvement, spread it. Create centers of excellence.

5. Prepare for the industrial metaverse—ensure your twins are built on platforms that will connect to broader ecosystems.

For Technology Providers

1. Focus on interoperability—manufacturers have diverse systems. Your tools must work with theirs.

2. Simplify deployment—complexity is the enemy of adoption. Make it easy to start.

3. Demonstrate ROI—case studies like PepsiCo's are powerful. Collect and share them.

4. Invest in training—the talent gap is real. Help customers build skills.

5. Embrace open ecosystems—walled gardens will lose to platforms that connect.

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Conclusion: The Twin Imperative

The message from CES 2026 is unequivocal: Digital twins have arrived.

PepsiCo's achievement—20% throughput gain in three months, 10-15% CAPEX reduction—is not an outlier. It's a preview of what's possible when manufacturers combine physics-based simulation with real-time data and artificial intelligence.

For companies not yet on the digital twin journey, the gap is widening. Competitors are unlocking hidden capacity, reducing capital costs, and de-risking changes—all while you're still designing in the physical world.

The question is no longer whether digital twins will transform manufacturing. It's whether you will lead or follow.

As Athina Kanioura of PepsiCo put it: "With a unified, AI-powered digital foundation, PepsiCo is building toward a world where every plant and warehouse operates as part of a single, intelligent ecosystem. In this future, our facilities don't just respond to demand—they anticipate and then adapt to it" .

That future is here. Are you ready?

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Methodology

This report is based on:

• Analysis of the PepsiCo/Siemens/NVIDIA collaboration announced at CES 2026

• The 2026 Industrial AI Readiness Report from IIoT World and HiveMQ (survey of 272 industrial professionals) 

• Industry interviews and secondary research

• Publicly available case studies and vendor documentation