Glass, mirror and ceramic manufacturers face immense challenges in keeping the production costs down in a hyper competitive market. By improving process efficiencies and maintaining quality of the finished products, manufacturers can unlock huge potential in value creation and gain competitive advantage.
Time Series AI enables smarter operations across the glass manufacturing processes starting from batching and melting all the way to finishing and packaging lines. By ingesting the real time operational data, time series AI detects and classifies various operational deviations and alerts the operations team about any impending failure conditions. By directly interacting with the AI platform, the operations team is able to quickly identify, prioritize and resolve the critical issues in the plant.
Avoid lost production from downtime
Minimize quality rejects
Enable smart manufacturing
Unexpected failure of blower and heat exchange results in work stoppage & suboptimal quality of glass product.
Monitor equipment and sensor data to predict imminent component failures
Prevent time lost due to unplanned downtimes. Avoid production losses cascading to downstream processes.
Nonsynchronous and stuck rollers scratch the glass sheet and introduce surface defects
Analyze machine and production data to identify motor-roller anomalies and provide alerts before quality is compromised.
Convert reactive/preventive to predictive maintenance of rollers, minimize quality defects
Malfunctioning stacking robots and transporting lines cause damage to finished products.
Early detection of anomalies in finishing operations enables predictive maintenance.
Reduced incidence of damage to finished products.
Find out how a leading steelmaker leveraged Falkonry to analyze data from multiple sources, identify failure patterns and provide alerts 7 days before a pinch roll fails, thus maximizing the asset uptime.
Chief Information Officer at Ternium
