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Automated Root-Cause Analyses

Using deep-learning, steel defect classification accuracy improved from 30% to 86% compared to prior classification methods used at Tata Steel. The automated root-cause analyses performed 1000 times faster than steel experts, while having expert-like accuracy.

In return for zero € extra investment in infrastructure, Tata Steel now generates insight into more than 2 mn. € in defects annually at two plants in the Netherlands and Belgium, forming the groundwork for Predictive Maintenance to reduce the losses caused by the installations.

Predictive Part Rejection Optimization

Using predictive analyses on additional IoT-driven data collection, the part rejection decreased by 10% on the Finished Good (FG) part level. 

 

The connector system was insert molded and latched with a destructive lock. Any loss at the FG level resulted in extensive and expensive manual part sorting. After executing a root-cause analysis, a  3 sensor layer was implemented into the system, which remained isolated from the workflow and collected data. Using this data, failures were simulated and identified predictively.

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100% compatibility
100% reliability

All our solutions are compatible and built on top of your system, and do not interfere with critical operations.

This makes our solutions fully compatible with your software stack, with all of the advantages of data analysis without the risks