How PepsiCo Is Using AI and Digital Twins to Upgrade Supply Chain Operations
PepsiCo is partnering with Siemens and Nvidia to rebuild its supply chain with AI and digital twins. Announced on 7 January 2026 at CES in Las Vegas, the program targets faster decisions, higher throughput and lower capital risk across plants and warehouses.
The focus is practical: model the warehouse, test the change, then implement with confidence. For operations leaders, that means fewer surprises on the floor and clearer ROI before any rack is moved.
Simulating layouts before touching the floor
Using Siemens' Digital Twin Composer, built on Nvidia Omniverse, PepsiCo is recreating warehouse layouts, conveyors, operator movements and pallet flows in a virtual environment. Teams can pressure-test configurations, validate equipment placement and evaluate multiple scenarios at once.
Early trials point to a step up in performance: +20% throughput, 10%-15% lower capex, and near-full design validation prior to rollout. The company says it can detect up to 90% of operational issues in simulation before changes hit the floor, which cuts downtime and rework during implementation.
Jensen Huang, Founder and CEO at Nvidia, said: "Physical industries are entering the age of AI. For companies with real-world assets, digital twins are the foundation of their AI journey. Working with Nvidia and Siemens, PepsiCo is re-architecting its operations - using physically-accurate digital twins and AI to reinvent how it designs, optimises and runs its global operations."
Connecting facilities with a shared digital backbone
Siemens' software layers digital twin data with real-time signals from operations to create industrial metaverse environments. The result is visibility across warehouse processes, inventory flows and logistics networks over the lifecycle of each facility.
Roland Busch, CEO at Siemens, said: "We are proud to partner with PepsiCo and Nvidia to digitally transform their manufacturing facilities using physics-based digital twins and AI from design to engineering to operations. This collaboration sets a new standard for all industries. Customers can turn ideas into real-world impact with greater speed, quality and efficiency."
Pilots are underway in select US locations with plans to scale across PepsiCo's global manufacturing and distribution network.
From reactive operations to adaptive networks
Ramon Laguarta, Chairman and CEO at PepsiCo, summed up the mandate: "The scale and complexity of PepsiCo's business, from farm to shelf, is massive - and we are embedding AI throughout our operations to better meet the increasing demands of our consumers and customers."
Athina Kanioura, CEO for Latin America and Global Chief Strategy & Transformation Officer at PepsiCo, added: "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."
What this means for operations leaders
- De-risk change: validate layouts, labor plans and slotting strategies virtually before capex and downtime hit.
- Make decisions faster: run parallel scenarios and choose based on throughput, dwell time and cost impact.
- Tighten ROI: use simulation data to justify spend, phase upgrades and avoid overbuilding.
- Shift to dynamic policies: update pick paths, dock schedules and replenishment rules based on model outputs.
- Stand up the data layer: connect WMS, MES, PLCs, IoT and safety systems to the twin for live feedback.
- Establish model governance: version control, validation against floor data and a clear roll-back plan.
- Start where it hurts: target bottlenecks (receiving, case picking, staging, yard) with the highest variance.
KPIs to track in a twin-driven operation
- Throughput per hour and per labor hour
- Dock-to-stock and order cycle time
- Pick rate, travel time and congestion hotspots
- Equipment utilization and unplanned downtime
- Capex avoided and payback on implemented changes
Implementation quick-start
- Baseline current state: time studies, flow maps and SKU velocity profiles.
- Build a minimal viable twin for one process (e.g., receiving to putaway) and validate it against real data.
- Integrate live feeds for constraints that move daily: labor availability, carrier arrivals, maintenance windows.
- Run A/B scenarios, lock decisions, then implement in tight waves with floor feedback loops.
- Monitor drift and recalibrate the model weekly; publish playbooks as standard work.
To learn more about the platforms behind this approach, see Nvidia Omniverse and Siemens' digital twin overview.
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