Today we’re joined by Zainab Hussain, an e-commerce strategist and expert in operations management, to discuss a groundbreaking shift in industrial supply chains. PepsiCo is pioneering a new digital-first strategy, collaborating with tech giants to build intelligent, virtual models of its facilities before a single brick is laid. We’ll explore how this fusion of AI and digital twin technology is not just simulating but actively co-designing the supply chain of the future, optimizing everything from brand-new distribution centers to aging warehouses. This conversation will delve into the tangible results of this approach, the challenges of scaling it globally, and what it signals for the entire consumer goods industry.
Your partnership with Siemens and NVIDIA is central to this initiative. Can you describe how Siemens’ digital twin data and NVIDIA’s Omniverse libraries specifically integrate to simulate everything from operator paths to pallet routes, and what that process looks like in practice?
It’s a really powerful synergy. Think of it this way: Siemens provides the incredibly detailed 2D and 3D blueprints of our physical world—every machine, every conveyor, every square foot of the facility. But a blueprint is static. NVIDIA’s Omniverse libraries breathe life into it. It’s the physics-based engine that allows us to run hyper-realistic simulations within that digital space. We can literally recreate every single moving part—the exact pallet routes, the paths our operators take, the speed of a conveyor belt—and watch how they all interact. In practice, our teams can put on a headset, walk through a virtual warehouse, and see potential bottlenecks before they ever exist in reality.
The strategy is two-fold, targeting new infrastructure and optimizing existing facilities. Could you walk us through a specific example of how the AI “co-designer” helps retool an older warehouse to handle demand spikes, versus how it would lay out a brand-new distribution center?
That’s a crucial distinction. For our older warehouses, many of which are decades old, the challenge is adapting them to modern pressures without costly, disruptive overhauls. Here, the AI acts as an optimization expert. We can feed it data on a potential demand spike and it will simulate millions of scenarios to find the most efficient way to reconfigure the existing layout to handle that stress. It might suggest a new pallet route or a different staging area that we hadn’t considered. For a new distribution center, the AI is a true “co-designer” from day one. It helps us optimize the layout of the entire facility in a simulated environment—the space, the hardware, the software—ensuring the most efficient flow from the very beginning, before any capital is spent on construction.
The article cites impressive initial results, including a 20% throughput increase and up to 15% CAPEX reduction. Could you share an anecdote from a U.S. pilot where the simulation uncovered a specific design flaw or capacity gain that directly led to these concrete savings?
Absolutely. During one of the early U.S. pilots, we were modeling a proposed layout, and the simulation immediately flagged a critical flaw. It showed that a specific conveyor intersection would create a major chokepoint during peak hours, something that would have been a nightmare to discover after installation. By virtually testing different pallet routes and machine placements, the AI uncovered a way to not only solve the bottleneck but also increase the facility’s total capacity. That discovery directly led to that 20% jump in throughput. Better yet, because we found this hidden capacity, we realized we didn’t need some of the equipment we had originally budgeted for, which is exactly how we achieved a 10% to 15% reduction in capital expenditure.
It’s noted that this new technology reflects real-time inputs, unlike older digital twins. How does the system process millions of parameters to avoid “blind spots,” and can you give a recent example of a potential supply chain disruption it helped you simulate and solve before it happened?
The real-time aspect is the game-changer. Older digital twins were essentially static snapshots, useful but limited. This new system is a living model, constantly processing millions of parameters—everything from machine performance to inventory levels. This dynamic view prevents the “blind spots” that arise when you only look at isolated parts of the chain. For example, the system can simulate the cascading effect of a potential component shortage from a supplier. Instead of reacting when the part doesn’t show up, the AI can model the impact weeks in advance and test different solutions, like re-routing inventory from another facility, allowing us to solve the disruption before it even happens.
As you plan to scale this globally by 2026, what key challenges do you foresee in adapting this digital blueprint for diverse markets like Mexico or the U.K.? How will you account for regional differences in logistics, equipment, and labor within the virtual environment?
Scaling is never a simple copy-paste operation. The digital blueprint is a powerful foundation, but its strength lies in its adaptability. For a market like Mexico, we have to input regional variables, such as different trucking logistics, specific customs processes, or locally sourced equipment with unique performance specs. In the U.K. or Western Europe, the constraints might be different—perhaps stricter labor regulations affecting operator pathing or different physical footprints for warehouses. The beauty of the virtual environment is that we can plug all these regional parameters into the model and test them rigorously, ensuring the design is optimized for local realities before we commit to a physical build-out.
What is your forecast for how AI-powered digital twins will reshape the broader consumer goods industry’s supply chain over the next five years?
My forecast is that we are moving toward a future where the supply chain operates as a single, intelligent ecosystem. The idea of isolated, reactive facilities will become a thing of the past. Within five years, I believe the industry standard will be for facilities to not just respond to demand but to anticipate and adapt to it in real time, guided by these AI-powered digital twins. This represents a fundamental shift from simply fulfilling orders to proactively shaping supply flow. It will lead to a new era of resilience and efficiency, completely reimagining how consumer goods companies design, build, and scale their global operations.
