Digital twins in production: what to automate and what to keep manual

Digital twins can provide strong operational guidance, but only if their limits are clear. Production use requires careful boundaries around what is automated and what remains manual.

Automate stable, low risk parts first. Data collection, state updates, and basic validation are good candidates. They are repetitive and they reduce human error. Keep manual review for unusual scenarios or high impact actions.

Approval boundaries

Require human approval for actions that affect safety, compliance, or large costs. Build these approvals into the platform so they are tracked, not handled in email or chat. This creates an audit trail and prevents unreviewed actions.

Sync with reality

Calibrate models regularly by comparing predictions to actual outcomes. If drift increases, pause automated actions until the model is corrected. A digital twin that is wrong is more dangerous than no twin at all.

Monitoring the model

Track accuracy, data freshness, and coverage. Use alerts when inputs are missing or stale. The model is part of production and should be monitored like any other service.

Add a feedback loop from operators. If operators cannot flag incorrect predictions quickly, the model will drift further. Build a fast path for feedback so the team can correct issues early.

A useful digital twin is a reliable decision aid with clear limits. That is what keeps it safe in production.

Define the data latency that the twin can tolerate. If the twin relies on delayed inputs, it should not be used for real time control. This is a common source of unsafe automation.

Separate model changes from operational changes. A new model version should be validated before it drives production actions. Use a staging or shadow mode to compare outputs before switching.

Document failure modes. If the twin loses data or produces unexpected output, operators should know how the system behaves. A clear fallback mode builds trust in the tool.