Predictive analytics
Predict when equipment is likely to fail, reducing unplanned downtime, extending equipment
lifespan and optimizing maintenance costs
lifespan and optimizing maintenance costs
QualityLine AI manufacturing analytics provides advanced prediction and analytics algorithms for:
-
Machines and testing stations performance and prediction
-
Product performance and prediction
-
Automated data mapping
-
Machine learning
-
Anomaly detection
This integration leads to smarter, data-driven decision-making, enhanced efficiency, reduced risks, and overall improved operational effectiveness in manufacturing environments.
Automated data mapping
QualityLine automatically maps each manufacturing data structure (testing, sensing, manual, ERP/MES, any other shop floor systems).
This enables a quick integration of any data sources, including changes.
QualityLine runs a scanning process on each data structure sample and automatically identifies the mapping location of the data fields that are relevant for the analytics.
The scanning process is done automatically and it is based on AI pattern recognition technology that enables us to automatically map each structure.
Once mapping is done, we automatically create the data capture tool that memorizes the mapping of each data structure.
This capture software tool is installed in the local servers in the factory (or in the cloud) Then this data capture tool continuously captures the required data from each data structure and reorganizes it all in a unified global database.
Anomaly Detection
QualityLine automatically maps each manufacturing data structure (testing, sensing, manual, ERP/MES, any other shop floor systems).
This enables a quick integration of any data sources, including changes.
QualityLine runs a scanning process on each data structure sample and automatically identifies the mapping location of the data fields that are relevant for the analytics.
QualityLine uses automated anomaly detection by automatically scanning the collected data and identifying quality and yield problems of each product and each process according to the level of severity.
The findings of this anomaly detection are shown in a methodological way in the dashboards to enable a quick and accurate root cause analysis.
