Supply chain glossary
Digital Twin
What a digital twin is, how it differs from a one‑off simulation model and why it has to replay a recorded week before its scenarios can be trusted.
10. november 2026
3 min
A digital twin is a dynamic model of a physical operation, such as a warehouse, production line or distribution network, that reproduces its behavior over time and is fed with data from the real operation. Planners use it to test changes to layout, staffing, equipment or order profiles before committing budget or disrupting the running operation.
The term is used in two senses. In the strict sense, a twin is connected to operational systems such as the warehouse management system (WMS) or ERP and is regularly resynchronized with current master data, orders and equipment states. In logistics projects, it also means a detailed discrete event simulation model built for one decision, such as a new warehouse design or an automation investment. Unlike a spreadsheet capacity calculation, both capture variability and interactions: overlapping order waves or a replenishment run that blocks a picking aisle disappear in averages and show up in a dynamic model.
Digital twin in practice
A twin earns trust only after it reproduces the past. The test is to replay a recorded week of orders from the existing operation and compare the model’s output with the WMS logs. Say the model handles 2,400 order lines per hour at the peak, while the logs show 2,150. The model overstates capacity by about 12%, and until the gap is explained, its answer to “will two more pickers cover the Christmas peak?” is not usable. Such a gap comes from something the model does not contain: picker congestion in narrow aisles, replenishment that interrupts picking, or pick times taken from a vendor datasheet instead of measured on the floor.
A greenfield site has no history to replay. Validation then moves to the parts: equipment performance against vendor test data, process times against measurements from a comparable operation, and order profiles from the company’s own sales history rather than from a design brief. A strict‑sense twin also has a running cost that a one‑off model does not: layouts, master data and rules have to be kept current, otherwise the model drifts away from the operation it mirrors.
Logio builds dynamic simulation models of warehouses, production and material flows in its dynamic simulation consulting, for decisions such as an automation investment or a layout change, and connects them with intralogistics and material flow design.
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