Digital Twin
A digital twin is a virtual representation of a physical object, process, system, or supply chain that is continuously updated with real-time or near-real-time data. In inventory and material tracking, it is a live digital model of suppliers, raw materials, work-in-process, warehouses, transport lanes, and finished goods, fed by ERP, WMS, MES, IoT, and shipment data.
On a manufacturing shop floor, a digital twin links machine status, line rates, queue lengths, material availability, and routing logic to a simulation model. If a coil or lot goes missing, planners can immediately see how throughput will be affected and compare rescheduling, rerouting, or pulling safety stock from another node. In warehousing, the twin mirrors dock appointments, receiving queues, putaway capacity, pick-face inventory, and outbound staging, allowing managers to test labor shifts, slotting changes, or wave-picking rules before implementing them. For raw material tracking, the twin combines supplier commitments, transit status, lot genealogy, and consumption rates to predict shortages, identify which plants will be impacted first, and estimate the buffer needed to avoid production stops or expedite fees. It thus becomes a continuous decision-support tool for inventory positioning and material flow.
How is a digital twin different from a static simulation?
A digital twin is continuously or near-continuously refreshed with live operational data, while a static simulation is run on a fixed dataset and does not automatically reflect current plant or inventory conditions.
What data feeds are typically required for a digital twin?
ERP transactions, WMS inventory records, MES production events, supplier and shipment status, machine/IoT telemetry, and sometimes external risk or market signals.
What decisions does a digital twin support best?
Inventory buffering, bottleneck prediction, capacity planning, supplier risk response, routing changes, and scenario planning for disruptions or demand swings.