Körber is partnering with NVIDIA to embed AI warehouse simulations into daily planning, using virtual replicas of facilities to rehearse changes before they reach the floor. Built on NVIDIA’s Omniverse platform, these digital twins aim to compress design cycles, limit disruption risk, and keep throughput stable under volatile demand.
Simulation as a New Planning Layer In The Warehouse
The core of the collaboration is a different way of deciding how warehouses run. Rather than adjusting slotting rules, labour plans, or automation settings in live facilities, operational teams can trial those moves inside a detailed digital copy of the building. Körber is configuring Omniverse to mirror layouts, equipment behaviour, and control logic so that congestion, travel time, and inventory flow can be assessed upfront.
This matters most when operations are under pressure. Peak seasons, new product introductions, and major promotions often expose fragile processes and misaligned automation. Körber positions the joint offer as a way to stress-test staffing profiles, dock schedules, and routing strategies, with AI-driven simulations estimating how each scenario will affect service, cost, and utilisation. Helena Garriga, Executive Board Member and President of Körber’s supply chain business, calls the NVIDIA partnership a pivotal step in the push toward more adaptive logistics, combining Omniverse computation with Körber’s execution know-how.
Digital twins also give engineers a safer route into advanced automation. New robotics, high-density storage, or shuttle systems can be modelled in the virtual warehouse so that handoffs, queuing, and exception flows are understood before equipment is installed. Stephan Seifert, CEO of Körber, frames the work as a way to shorten the gap between concept and go-live, with simulation guiding layout, conveyor routing, and integration between robots, software, and human workstations.
Industry research shows that twin technology in logistics often stops at network design or static dashboards. Körber and NVIDIA are pushing into a more granular, event-driven use case inside the warehouse itself. The intent is to feed real operational data into the twin so that tests reflect actual constraints such as carrier cut-offs, shift calendars, and space limits, rather than relying on coarse planning averages.
From Control Towers To Virtual Testbeds
The partnership also signals a shift in how digital control infrastructure is used. Many organisations have invested in visibility and control towers that surface inventory and shipment status but leave experimentation to local trial and error. Körber’s use of Omniverse adds a structured test environment above that visibility layer, turning the warehouse into a place where scenarios can be run in software and only the strongest options are promoted into production.
Simulations can draw on AI models trained on order profiles and equipment behaviour. Teams can evaluate how a surge in single-line orders, a new packaging standard, or a carrier shift will alter picking strategies, packing capacity, and dock utilisation. This creates a more dynamic operating model in which parameters are tuned regularly rather than fixed for extended periods. Körber plans to use these capabilities to support continuous optimisation, treating digital twins as assets that evolve alongside physical networks.
Finance and technology teams gain clearer evidence when assessing capital projects. Business cases for automation, racking changes, or new shift patterns can be tested against scenarios that mirror live operations, rather than resting on static spreadsheets. Recent trade data shows growing interest in pre-implementation testing to contain costs linked to mis-specified automation and extended commissioning.
Peak season rehearsals are a direct application. Inside the twin, teams can experiment with labour mixes, overtime thresholds, and carrier allocations to reduce the risk of cascading backlogs when order volumes spike. For organisations managing nearshored and global flows, similar models can be extended across multiple buildings, supporting network-level decisions on overflow handling and inventory positioning.
Körber links the initiative to its LIFE 2035 programme, casting AI-based twins as a foundation for long-horizon logistics planning. NVIDIA, in turn, gains a high-profile operational use case for Omniverse that sits beyond product design and entertainment, reinforcing its role as an industrial simulation engine.
Why Digital Twins Demand a Different Kind Of Discipline
The next phase of this shift is less about graphics horsepower and more about operating discipline. Twins only stay useful when they track reality closely, which depends on accurate master data, stable process definitions, and clear ownership for model maintenance. Organisations that invest in those fundamentals can turn AI warehouse simulations into a routine decision step, not a special project, and are better placed to use each seasonal review, footprint change, or automation upgrade as fresh training data for the next round of design choices.
