Impact Stories

Hazardous-materials factory, going unmanned and automated

From automation, informatization, and intelligence concept development to MES, logistics control, and inspection automation

• Some processes are automated, but the share of manual work is high, and simple repetitive tasks such as handling hazardous materials (while wearing gas masks) reduce production efficiency
• In-process and inter-process logistics run on carts and manned forklifts, and raw materials, semi-finished goods, and product containers vary in material and size
• Raw material weighing and input adjustment (valve operation) are done manually, causing quality variation between workers and slow adaptation for new staff
• Semi-finished and finished product quality relies mostly on visual inspection

• Unmanned automation concept and optimal layout: automation plans for hazardous-materials handling and inspection processes, throughput analysis, and optimal layout derived and verified through 3D modeling and simulation
• Automated material transport and storage: automates transfer between buildings and processes, and storage of semi-finished and finished products, through logistics control (MCS: Stocker and Conveyor control)
• Inspection automation: automatically judges visual, leak, and propellant hole uniformity inspections using image analysis, and links judgment results and inspection values to the system
• Paperless production operations: Factova MES automatically collects start/finish, equipment status, and alarm data, and RMS (recipe) and SPC manage process conditions
• Standard operating framework: master data reestablished (Globalization Item, equipment classification, and quality codes) and system integration based on EAI and MOM to establish a Global Operation standard

• Zero direct production workforce: analyzed 811 manual tasks across all processes and reviewed AI-based automation to identify process consolidation and unmanned areas, designing an unmanned process
• Foundation for maximum annual output: unmanned processes ensure work continuity, while bottleneck improvements and logistics automation optimize line balance between processes, and an AI/ML-based predictive maintenance concept was derived
• Optimized investment: minimizes construction costs through process consolidation and layout optimization, and calculates the optimal number of machines based on line balance
• Secured a Global One smart factory standard model for rolling out across domestic and overseas plants