“They can generate more revenue with this solution by optimizing production; they will also generate more revenue because they will be able to generate more quality product.”
- Michael G. Simms, Practice Director,
Data & Analytics, Columbus
A glass fiber manufacturer needed to improve their quality control process and shorten the time it would take to make corrections.
Previously, they were using Excel to analyze data from IoT sensors on temperature, material and other factors that impact breakage rates on a seven-day rolling basis.
This not only took time – up to eight weeks to identify and fix the problem while the same production issues continued – it was also not the most accurate.
And because they weren’t comparing readings with production best practices over time, process improvements were not sustainable.
In an Azure AI environment, Columbus Global significantly improved the quality control process with a model created with data from IoT devices and multiple systems. The result was shortened quality control response time from 6-8 weeks to 2-4 days.