Process, equipment, quality and energy data joined into one — predicting faults and waste, and pointing to the right action.
QFactory leaves your equipment as it is, gathers data from the processes that matter, and carries predictions through into action on the line.
Start with whichever fits the problem, then widen the proven scope step by step.
Adopting it in stages Nothing is ripped out. Add-on sensors and edge systems sit alongside what you have, and coverage grows from the process that needs it most.
Control, quality and production systems and sensor data, aligned by time, job and machine.
Heat generation and steam consumption read together, with settings that cut both oversupply and shortfall.
Changes in rotating and drying equipment are read to predict faults, risk and how much life is left.
From raw material and additive through to final quality, time and lot history are linked to find where the variance came from.
Video and equipment events read together to detect unsafe behaviour, entry into restricted areas, fire and other incidents.
Training, deployment and accuracy are managed, and recommend → approve → act → check runs as one flow.
Equipment, process, operations and performance data analysed and improved as one flow.
Vacuum pumps, heaters, vibration and temperature sensors are read to find risk and early warning signs before the run.
Product, loading and past results are analysed to propose the best operating profile, stage by stage.
A 3D view of the equipment, with temperature, current and vacuum trends, watches the process as it runs.
A finished run is replayed in order, checking when the AI called it and what the sensors were doing.
Equipment, process, yield and quality measures are analysed to show how close you are to target and what to fix first.
Accuracy and versions managed through an MLOps practice, with retraining as needed.
The small drifts, the causes and the right time to service — the things a threshold alarm alone tends to miss.
| Aspect | Threshold-based monitoring | QFactory manufacturing AI |
|---|---|---|
| Anomaly detection | Alarms once a fixed limit is passed — after the fact | Learns the normal pattern, then catches small departures early |
| Finding the cause | Someone traces the logs by hand | Generative AI answers questions with likely causes and remedies |
| Process settings | The recipe stays as it is | A profile recommended from condition and quality data |
| Maintenance | By schedule, or after it breaks | Predictive, based on remaining useful life |
| Operation | Tuned by hand, then left alone | Data shifts watched, models retrained |
The existing alarms stay. AI judgement is added on top to catch what they miss, and the change happens gradually.
For plants dealing with the kind of drift an alarm never catches.
Slow decline inside the normal range goes unnoticed, and the problem only shows up downstream
Heat treatment and assembly, where quality shifts when the operator changes
Round-the-clock plants that can only schedule maintenance if they know when it will fail
Where the savings only appear when equipment, process and energy are looked at together
