Logistics & supply chain digital twins for predictable, high-flow operations

Model warehouses, yards, ports, fleets, and routes as living digital twins—so you can anticipate congestion, improve flow, and protect service levels across your network.

Supply chains are only as strong as their weakest node

Logistics and supply chain teams operate in environments where small disruptions quickly cascade. When flow spans many interconnected nodes, local optimization isn't enough.

What's breaking the flow

  • Limited end-to-end visibility across warehouses, yards, and transport
  • Congestion that appears suddenly and is hard to predict
  • Schedules that break down under real-world variability
  • Firefighting to protect OTIF and customer commitments
  • Decisions made locally that create downstream problems

The network advantage

  • Predict and prevent congestion before it impacts flow
  • Coordinate decisions across the entire network
  • Build resilient schedules that adapt to real conditions
  • Balance OTIF with operational efficiency
  • Understand and manage system-wide dependencies

From reactive control to predictive flow management

In logistics and supply chains, an intelligent digital twin provides a living model of how goods, vehicles, and resources move through the network.

It continuously represents:

  • Warehouse operations, yard movements, and terminal activity
  • Transport schedules, arrival patterns, and dwell times
  • Constraints such as labor availability, equipment, and space
  • How disruptions in one node affect downstream performance

With an intelligent digital twin, teams can:

  • Predict congestion before it materializes
  • Test alternative schedules, routing, or staffing scenarios
  • Coordinate decisions across sites instead of in silos
  • Protect service levels without constant manual intervention

Logistics use cases powered by intelligent digital twins

Supply Chain Visibility

See flow end-to-end across the network.

  • Unified view of warehouses, yards, ports, and routes
  • Early detection of emerging constraints
  • Faster, more informed intervention
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Flow & Congestion Optimization

Reduce dwell time and improve throughput.

  • Predict congestion at yards, docks, and terminals
  • Test slotting, appointment, and routing changes
  • Improve asset and labor utilization

(Often paired with Production / Flow Optimization)

Scenario Simulation & Planning

Test decisions before disrupting operations.

  • Simulate demand spikes, weather events, or labor shortages
  • Evaluate trade-offs between cost, speed, and reliability
  • Improve preparedness without risking live operations
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Energy & Emissions Optimization

Reduce cost and environmental impact.

  • Understand energy and fuel drivers across facilities and fleets
  • Test efficiency initiatives virtually
  • Support sustainability and reporting goals
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Value across logistics and supply chain roles

Operations & control tower teams

  • Monitor network health in near real time
  • Spot risks to service levels early
  • Coordinate actions across sites instead of reacting in isolation

Warehouse & yard managers

  • Understand where congestion is building
  • Adjust staffing, appointments, and sequencing proactively
  • Reduce last-minute disruptions

Network planners & analysts

  • Evaluate changes to routes, schedules, and capacity
  • Test "what-if" scenarios before implementation
  • Support data-driven network design and improvement

What logistics teams typically target

Outcomes depend on network complexity and starting point, but teams often aim for:

Improved
OTIF and service reliability
Reduced
dwell time at critical nodes
Better
network predictability
  • Better asset, labor, and space utilization
  • Fewer last-minute schedule changes and expediting

The biggest gains usually come from anticipating issues—rather than reacting once queues form.

Start with one node or flow. Prove value. Scale network-wide.

1. Start

Choose a high-impact area: a congested warehouse, busy yard, port terminal, or critical transport lane.

2. Prove

Use real operational data to validate predictions and improvements.

3. Scale

Extend the twin across additional sites, flows, and scenarios—reusing what works.

How the platform supports scale →

Common questions from logistics teams

Will this integrate with our WMS, TMS, and yard systems?
Yes. Intelligent digital twins typically integrate with existing logistics systems rather than replacing them.
Do we need perfect real-time data everywhere?
No. Many teams start with partial visibility and improve coverage as value is demonstrated.
Is this only for large global networks?
No. Digital twins are used for single warehouses or terminals as well as complex multi-node networks.
How quickly can we see impact?
Focused use cases often deliver early insight within weeks.
Does this replace control towers or dashboards?
Not necessarily. Digital twins complement existing tools by adding prediction, simulation, and system-level insight.

See how intelligent digital twins can improve supply chain flow

Start with one logistics challenge—visibility, congestion, or reliability—and build from there.