- Collected and normalized data previously trapped in 20+ types of legacy assets
- Performed pre-built Litmus Edge analytics for production and quality improvement
- Integrated data with enterprise systems including MES and Historian
- Performed offline machine learning on historical data
- Rapid deployment to 20 plants in the first year
Saint Gobain Sekurit, a division of Saint-Gobain, has been a leader in automotive glazing for more than 80 years. Sekurit has 16,000 employees and 39 highly automated plants worldwide. Their Industry 4.0 strategy is to “transform its shop floors into a digital workplace and generalize a data-driven approach,” according to Sebastien Thuillier, digital transformation program manager at Saint-Gobain Sekurit.
Previously Sekurit had data trapped in a variety of brownfield assets and up to 20 different machines on each production line. Their primary goal was basic visibility into these varied assets to help plant managers, maintenance engineers and others make production and quality improvements based on real-time analytics. As a result, they chose Litmus Edge for its ability to connect to their full breadth of OT and IT assets, provide key performance indicators and analytics at the edge, and then “provide the right data to the right person for the right purpose” according to Sebastien.
Sekurit started with a small deployment and then added data points and use cases as the project showed ROI. As the partnership got rolling, Sekurit deployed Litmus Edge rapidly to 20 plants in the first year. Litmus Edge collects data, provides key KPIs that can be accessed by anyone in the plant, and integrates the data with MES and Historian systems. Sekurit set up alerts so when KPIs deviate to a certain threshold they can make adjustments on the shop floor.
Sekurit is now expanding the Litmus use case by performing offline machine learning on historical data. The resulting models help them improve product quality at their 20 plants, with plans to roll out Litmus Edge to additional sites this year.