An intelligent production optimization solution that analyzes production data in real time to derive optimal process conditions,
improving production efficiency and quality at the same time
On the manufacturing floor, productivity and quality shift with a wide range of equipment and complex process conditions, making stable production conditions and process optimization a core competitive advantage.
Conventional, experience-driven process management can lead to variations in production conditions depending on operator skill, equipment status, and raw material characteristics, making it difficult to consistently maintain optimal conditions.
AI-based production condition optimization integrates and analyzes equipment, process, and quality data to identify the key factors affecting productivity and quality, then derives data-driven optimal process conditions to improve production efficiency and quality stability.
We integrate and analyze process, equipment, and quality data collected from production equipment and manufacturing systems such as MES to identify the correlation between production conditions and quality. Using AI-based analytics, we derive the key process variables that affect productivity and quality and recommend optimal operating conditions, supporting stable production and continuous process improvement.
Integrates and analyzes equipment, process, and quality data to identify the key process factors affecting productivity and quality, and recommends optimal conditions.
Collects and visualizes equipment operating status and key process data in real time to detect process changes and early signs of abnormalities.
Analyzes the relationship between process conditions and quality outcomes to find conditions that reduce the likelihood of defects and raise production efficiency.
Refines AI analytics models and optimization criteria based on accumulated production data to keep pace with a changing manufacturing environment.
Collects and centrally manages equipment operating information and key process data from PLCs, sensors, MES, and more
Analyzes how process variables such as temperature, pressure, speed, and time relate to productivity and quality outcomes
Uses AI analytics models to explore and recommend process conditions that account for target quality and productivity
Visualizes key process variables, production results, and quality indicators in real time and analyzes trends
Compares actual operating conditions against defined control criteria to analyze process deviations and anomalies
Links process condition change history with production and quality results to analyze improvements and support continuous optimization
We provide a data-driven optimization process that runs from manufacturing data collection to AI analysis, optimal condition derivation, and improved production operations.
Collects equipment operating information, process variables, production results, and quality data from PLCs, sensors, MES, and more
Analyzes collected data to identify the key process variables and correlations that affect productivity and quality
Derives optimal process conditions that account for target quality and production efficiency based on AI analysis results
Applies reviewed and approved optimal conditions on the shop floor and analyzes changes in productivity and quality to confirm improvements
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