Supply Chain Digital Twin

The foundational capability that distinguishes a digital twin from a static model. It ingests streaming data from IoT sensors, ERP systems, transportation management systems (TMS) , and warehouse management systems (WMS) to maintain a current-state mirror of the physical network. This includes inventory levels, shipment GPS coordinates, production line throughput, and supplier status. Without real-time connectivity, the twin becomes a historical snapshot rather than an operational tool.

Represents the entire supply chain as a connected graph of nodes and links, not isolated silos. The twin models the interdependencies between tier-n suppliers, manufacturing plants, distribution centers, cross-docks, and last-mile delivery routes. This holistic view captures the ripple effects of disruptions—showing how a supplier delay in one region cascades into a stockout at a specific retail location—enabling true end-to-end visibility.

Moves beyond deterministic planning by incorporating probability distributions for key variables like lead time, demand, and yield. The engine runs thousands of Monte Carlo simulations to model a range of possible futures. This allows planners to quantify risk and answer questions like 'What is the probability of a stockout if Supplier A is 3 days late?' rather than relying on a single, fragile point estimate.

A controlled environment for stress-testing the supply chain without real-world consequences. Planners can inject hypothetical disruptions—such as a port closure, a 20% demand surge, or a factory fire—and observe the simulated impact on service levels, cost, and inventory. This capability supports proactive contingency planning and capital investment decisions, such as evaluating the resilience benefit of adding a new distribution center.

Integrates machine learning models to not just predict outcomes but recommend optimal actions. The twin uses reinforcement learning and mathematical optimization to evaluate millions of potential decisions and surface the best course of action. For example, it can prescribe the lowest-cost expedited shipping route that meets a 98% service level target or dynamically reallocate inventory across a network to preempt a predicted regional shortage.

Provides an intuitive, geospatial 3D interface that overlays real-time alerts and KPIs onto a visual model of the supply chain. Users drill down from a global heat map of On-Time In-Full (OTIF) performance to a specific delayed shipment. The layer surfaces exceptions—not noise—highlighting nodes that are drifting out of tolerance and require human intervention, effectively serving as the user interface for the complex simulation running underneath.