Predict anomalies with context-aware
Anomaly Management

When you predict anomalies before they turn into deviations with Anomaly Management, you really start to see a difference in your OEE. Via an easy workflow plotter, you set rules, context and dependencies. And the rest happens automatically. A real-time overview enables you to monitor behavior and you receive push notifications about anomalies that might be a deviation.

anomaly Management

Self-learning technology

IoT is the foundation for Anomaly Management in 4Industry. Data is gathered from your assets via IoT, context-aware sensors and other smart devices. In turn, IoT is linked to the rule engine. Via machine learning, the platform learns when operation is normal, but also predicts if an abnormal situation or breakdown is about to occur. If a data point deviates, the rule engine enters into force. The anomaly is then converted to either a deviation, SHE issue or defect. Or it goes straight into the bin, in case it wasn’t applicable (e.g. because the line was stopped).

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anomaly Management

How does it work in practice?

You simply plot your entire factory in our ShopFloor Workflows application and draw up all rules, context and dependencies in the workflow. By combining rules with context, our platform automatically creates an anomaly to prevent a deviation.

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"Equipment Management and the IoT Hub supply the data that allow you to spot anomalies."
Luc Raeskin - CEO App4mation
anomaly managament


Push notifications
Workflow plotter
Add rules, thresholds, context and dependencies
Monitor behavior
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We would love to talk to you about how to increase your OEE
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