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S/04 · Intelligent logistics and supply chains

Simulate decisions before they hit the real world.

Network digital twins, forecasting and optimisation for logistics teams.

Challenge, solution and outcomes

01 — The challenge

Supply chains face volatile demand, disruption and capacity constraints. Decisions about routes, inventory and suppliers are made with limited ability to test them first.

02 — Our solution

Digital twins of your network, fed by operational data, let teams model scenarios, forecast demand and optimise plans before committing.

03 — Key capabilities

  • Network digital twins
  • Demand forecasting
  • Scenario simulation
  • Route and capacity optimisation

04 — Expected outcomes

  • Decisions tested before execution
  • Earlier warning of disruption
  • Better use of capacity
  • A shared operational picture

Outcomes describe what this approach is designed to achieve; results depend on each organisation's context.

02How it works

From problem to production.

  1. 01

    Model the network

    Sites, lanes, inventory policies and constraints represented and validated against history.

  2. 02

    Connect operational data

    Orders, shipments and capacity fed in so the model reflects current reality.

  3. 03

    Run scenarios

    Test demand shifts, disruptions and network changes side by side.

  4. 04

    Optimise and act

    Recommendations for routes, allocation and capacity with clear trade-offs.

03What we plan for

Key considerations.

  • Data availability across partners
  • Model validation
  • Planner-friendly interfaces
  • Integration with TMS and WMS

04Explore further

The services and products behind this solution.

Your next chapter starts with a conversation

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