Research on real-time data display and production management in a digitalized management factory

Traditional Manufacturing Execution Systems (MES) face critical limitations in addressing Industry 4.0 demands for real-time processing, flexible scheduling, and adaptive decision-making, with less than 1% of manufacturing data effectively utilized. This research develops an Artificial Intelligence (AI)-assisted flexible MES framework integrating real-time data visualization, digital twin technology, and distributed intelligence to enable proactive manufacturing management. The system employs Design Science Research (DSR) methodology and implements a microservices architecture using Apache Kafka for message streaming, Flink for real-time processing, and TensorFlow for AI inference, deployed across five production lines with 2,350 sensors and 45 Programmable Logic Controllers (PLCs). Results demonstrate exceptional performance with system throughput reaching 12,500 messages per second, the design target by 25%, average data collection latency below 10 milliseconds, and 99.9% availability over 72-hour continuous operation. Production efficiency improved significantly with 25% increased output, 65.7% reduction in defect rates (from 35,000 to 12,000 Parts Per Million), and 87.5% decrease in changeover time (from 120 to 15 minutes). Overall Equipment Effectiveness (OEE) increased from 60% to 82%, approaching world-class benchmarks (>85%). This research validates distributed intelligence architectures for achieving simultaneous improvements in manufacturing flexibility and efficiency, challenging traditional theoretical trade-offs while providing a practical implementation roadmap for digital transformation in manufacturing enterprises.