Short answer
IoT decision intelligence connects sensor, machine and telemetry data — over OPC UA, MQTT, historians and edge gateways — detects anomalies and trends, combines them with business context such as schedules, spares and costs, and recommends or executes actions at the edge or in the cloud, with humans approving anything outside policy.
Decisions it handles
| Decision | Signal | Context it needs | Authority | Outcome measured |
|---|---|---|---|---|
| Alarm triage | Hundreds of alarms per shift | Asset hierarchy, history, criticality | Automated in bounds | Alarm floods cut |
| Maintenance timing | Vibration, temperature, current drift | Production plan, spares, windows | Augmented — engineer | Unplanned downtime |
| Energy optimization | Load peaks, tariff windows | Process constraints | Automated in bounds | kWh and cost |
| Edge safety stop | Out-of-envelope reading | Safety rules | Automated at edge | Incidents avoided |
How it works
- Connect devicesOPC UA, MQTT, historians and gateways — read-only first.
- Model the asset hierarchyWhich sensor belongs to which machine, line and order.
- Detect and predictAnomalies, trends and remaining useful life.
- Decide with contextCombine with schedules, spares and costs.
- Act at edge or cloudLocal rules for speed, cloud for coordination.
Where it fits
Digital twin3D assets with live condition heatmaps and fast-forward scenarios.Explore →ManufacturingPlant decisions on a live twin.Explore →Data integrationConnect where data lives.Explore →
Key takeaways
- Built on the same decision layer as every other solution.
- Humans keep authority; autonomy is earned.
- Every outcome improves the next decision.
Frequently asked questions
What is IoT decision intelligence?
Turning sensor and machine data into governed decisions and actions, rather than only dashboards and alarms.
Can decisions run at the edge?
Yes. Lightweight runners execute time-critical decisions locally and sync with the cloud when connected.