Vendor-reported figures — source: blogs.sw.siemens.com
PepsiCo's 'farm to shelf' supply chain spans hundreds of manufacturing plants, warehouses, and distribution centers globally, serving billions of consumers across brands including Pepsi, Gatorade, Lay's, and Quaker. Many facilities are decades old, built for predictable demand patterns that no longer exist. Demand spikes, weather disruptions, and geopolitical shocks routinely stressed a physical network that had little flexibility built in. The deeper problem was invisible: significant capacity likely existed inside aging facilities, but identifying and unlocking it required months of traditional engineering analysis — analysis that was often obsolete by the time it was complete. Every cycle of manual modeling and physical validation represented delayed investment decisions and foregone throughput.
PepsiCo deployed Siemens Digital Twin Composer — built on NVIDIA Omniverse libraries — to construct physics-accurate, photorealistic 3D digital replicas of its manufacturing and logistics environments. Each digital twin integrates engineering specifications, real-time operational metrics, and time-series machine data, enabling engineers to simulate layout changes, throughput scenarios, and disruption responses before touching the physical facility. The Gatorade U.S. plant served as the initial deployment site, with findings intended to scale across the broader network. By connecting live sensor and operational data streams to the simulation layer, teams can run continuous 'what-if' analyses — testing conveyor configurations, pallet routing, and operator workflows in the virtual model and validating designs digitally rather than through costly physical pilots.
At the U.S. Gatorade manufacturing plant, PepsiCo achieved a 20% increase in throughput within three months of deploying Digital Twin Composer — without adding new physical infrastructure. Across the broader operation, PepsiCo estimates a 10–15% reduction in capital expenditures by identifying hidden capacity in existing assets and validating facility investments virtually before committing funds.
Have a similar implementation?
Share your customer's AI results and link it to your vendor profile.
Submit a case study →