A digital twin is useful only when it feeds one governed capacity truth
Simulation adds value when approved assumptions become part of the same capacity model used for operational planning.
A digital twin can generate a better scenario and still create a worse planning process if it becomes a second source of capacity truth.
Observation
Manufacturers are scaling AI-enabled digital twins across production networks.
Operational implication
Simulation can improve maintenance, sequencing and capacity decisions, but conflicting calendars and assumptions can create late plan failures.
Decision architecture
The digital twin should publish approved constraints, effective dates and scenarios into the governed capacity model.
Data requirements
Asset status, downtime, rates, yields, maintenance windows and recovery assumptions must be current and traceable.
What should remain lightweight
Exploratory simulations and alternative scenarios can remain in the twin environment.
What should be integrated
Approved capacity constraints and decision outcomes should flow into APS, ERP and BI.
The twin is not the system of record. It is the scenario engine for the system of decision.