PepsiCo, Siemens, and Nvidia bring AI and digital twins to plant and supply chain operations
PepsiCo has kicked off a multi-year collaboration with Siemens and Nvidia to redesign how its plants and warehouses are planned, simulated, and run. Announced at CES 2026, the move centers on AI-driven digital twins that let teams test changes virtually before touching physical assets.
For operations leaders, this is a clear shift: simulate first, implement second. Fewer blind spots, faster cycles, and measurable gains in throughput and capital efficiency.
What PepsiCo is building
PepsiCo is rolling out a digital-first planning model. Facilities are reconstructed as physics-based digital twins with every machine, conveyor, operator path, and pallet flow modeled to fine detail. Layouts and logic are refined with AI agents before anything changes on the floor.
The stack combines Siemens' new Digital Twin Composer with Nvidia's Omniverse platform for real-time, physically accurate 3D simulation. That pairing enables high-fidelity testing of scenarios that span design, commissioning, and live operations.
Early pilots point to a 20% increase in throughput, near-complete design validation pre-build, and a 10%-15% reduction in capital spend for upgrades. Teams are also spotting up to 90% of operational issues in simulation-before they become real-world downtime.
Why this matters for plant and supply chain leaders
- Faster change cycles: Validate layouts, logic, buffers, and labor plans virtually, then roll precise work orders.
- Higher OEE and capacity: Remove bottlenecks, tune flow, and test run rules without risking live production.
- Lower capex and rework: Prove the case in the twin before placing orders, cutting redesigns and vendor change orders.
- Standardization at scale: Deploy proven configurations as templates across sites with local adjustments.
- Better cross-functional alignment: Engineering, ops, maintenance, safety, and finance work from the same model and KPIs.
How the tech fits together
Siemens' software brings a managed 3D environment that blends 2D/3D twin data with live signals from the floor and warehouse systems. Think of it as a single place to see a site's current state, test "what-ifs," and push changes with traceability across the life cycle.
Nvidia's Omniverse enables real-time simulation with physically accurate behavior-critical for material handling, transport timing, collision checks, and human-machine interactions. Learn more about Nvidia Omniverse and Siemens digital twin software.
Signal from leadership
Ramon Laguarta, PepsiCo's Chairman and CEO, said the scale from farm to shelf demands AI embedded throughout operations, and partnerships like this speed that shift.
Nvidia's Jensen Huang emphasized that physical industries are moving into AI at scale and that digital twins are the base layer for doing it right across real assets.
Siemens CEO Roland Busch highlighted the role of physics-based twins and AI from design through operations as a new standard for manufacturing and warehousing.
Athina Kanioura, CEO for Latin America and Global Chief Strategy & Transformation Officer at PepsiCo, framed the plan as a digital blueprint for a unified supply chain-plants and warehouses that anticipate demand and adjust automatically.
Results so far and what's next
Pilots in select US sites are live. PepsiCo reports improved throughput, fewer surprises during commissioning, and lower capex needs for upgrades. AI and computer vision are being applied to end-to-end flows, not just isolated lines.
The intent is global scale. As twins mature, they become a performance baseline for every site and a test bed for new layouts, logic, SKUs, and labor mixes-without disrupting live production.
Practical playbook to start in your network
- Pick a high-impact pilot: Choose a line, picking zone, or dock area with clear constraints (changeovers, dwell time, congestion).
- Inventory data and systems: PLC tags, SCADA/MES, WMS/TMS, maintenance logs, SKU master data, operator routes, and shift patterns.
- Build an MVP twin: Model flow, buffers, speeds, failure modes, staffing, and safety constraints. Keep scope tight; expand after wins.
- Run scenario packs: SKU mixes, order profiles, labor allocations, layout variants, control logic changes, and planned downtime windows.
- Validate with the floor: Involve supervisors, engineering, safety, and maintenance to check realism and standard work impacts.
- Tie scenarios to business KPIs: Throughput, OEE, changeover minutes, dock-to-stock, order cycle time, capex/opex, and energy per case.
- Create a rollout template: BOMs, layouts, SOPs, parameter sets, and training plans. Version control everything.
- Governance and security: Data ownership, model approvals, vendor access, and cybersecurity policies for connected assets.
KPIs to track (with target ranges)
- Throughput lift: 10%-25% from debottlenecking and run-rule optimization.
- Capex reduction: 10%-15% by validating layouts and right-sizing equipment.
- Commissioning time: 20%-40% faster by pre-testing logic and flows in the twin.
- Changeover time: 10%-30% reduction with standardized setups and sequencing.
- Issue detection pre-go-live: 70%-90% of faults caught in simulation.
- Energy per case: 5%-12% reduction via tuned speeds, idle rules, and batch logic.
Watch-outs and how to handle them
- Model fidelity: Start with the few variables that move the KPI. Add detail only when it changes decisions.
- Data drift: Instrument data quality checks; schedule model re-validation after product, layout, or control changes.
- Integration fatigue: Use open interfaces and a data model that maps to PLCs, MES, WMS, and CMMS without brittle custom code.
- Cybersecurity: Treat twins like systems of record-access control, network segmentation, and vendor policies.
- Workforce adoption: Train operators and engineers in the twin; show side-by-side "old vs. new" runs to build confidence.
What this signals for the industry
Simulate-first operations are becoming a baseline expectation. The combination of physics-based twins, AI agents, and live data is moving decisions from weekly reviews to real time-across design, scheduling, maintenance, and inventory flow.
For ops leaders, the play is clear: start small, pick measurable constraints, prove ROI in the twin, and template the wins across sites. The companies that do this well will adapt faster, spend less on rework, and deliver more consistent service levels.
If you're building team capability for AI in operations, explore curated learning paths by job role here: AI Courses by Job.
Your membership also unlocks:
