Simulation vs. digital twin: A strategic lens on virtual manufacturing

As manufacturers increasingly turn to virtual tools, the goal extends beyond visualization to understanding, testing, and optimizing processes before they ever reach the shop floor. While simulation and digital twin technologies have become central to digital transformation strategies, their differences remain unclear for many manufacturers looking into implementing virtual technology in their processes. Clarifying these distinctions and understanding where each fits within the lifecycle of system design, planning, and operation is critical to making informed decisions that deliver real value.

At their best, both simulation and digital twin approaches make realistic virtual representations of physical scenarios. But the purpose, level of integration with real data, and scope of that representation differ. With clarity on these distinctions, manufacturers can better align technological choices with their own business objectives.

At its core, simulation is a controlled virtual environment that models the behavior of a specific scenario over time based on rules and assumptions. In manufacturing, the term typically refers to discrete event simulation, where components like machines, conveyors, robots , and tasks are represented symbolically and interact according to defined logic to show how this scenario might perform. While simulation and digital models can be static or predictive, digital twins represent a distinct class of virtual systems.

A digital twin is not just a digital representation; it is a dynamic, real-time counterpart of a physical system that continuously exchanges data with its real-world twin. This key emphasis on bidirectional data flow separates digital twins from both traditional digital models and what some call “digital shadows.” In a digital shadow, data may flow from the physical to the virtual system, providing up-to-date information. But without responsive feedback into the physical process, that model remains one-directional and limited in its scope.

Digital twins for manufacturing environments can take multiple forms, from twins of individual machines and cells to complete plant or process twins that represent entire factories. These virtual counterparts evolve with the physical system, reflecting current conditions and helping stakeholders understand not just what is happening, but why it is happening. Although simulation and digital twin technologies share similarities, especially in their use of virtual models, they serve different phases of the manufacturing lifecycle and have distinct roles.

Some of the contrasts can be subtle in practice, yet the underlying intent and integration are different: simulation is a deliberate experiment carried out in a controlled context; digital twins are living systems that evolve with their physical counterparts. Simulation plays a foundational role. It helps manufacturers explore possibilities, validate design alternatives, and build confidence before systems are integrated and connected to real data streams.