NEXPOM plays the role of the main control office in the factory by providing a real-time monitoring function. NEXPOM’s system module is integrated into the current status of the factory based on manufacturing, quality, energy, and facility standards. It also provides an index of optimum factory operations by identifying correlations between data.
Integrated monitoring that can manage the factory operation status through an optimization index.
Total monitoring of manufacturing-related index using a TOP-DOWN method from line to process
Check the real-time quality data which affects production
Maintain optimized faciltiy status with main indicators
Manage cost-effective energy with the flow of the energy
Examples of monitoring utilization
STEP. 1 Data collection and OEE analysis & monitoring through controllers, and PLC I/F of processing facilities
STEP. 2 Prioritization of loss factors by analyzing types, frequencies, accumulated time, etc. of loss factors
STEP. 3 Achieving improvement of productivity and cost reduction effect by managing and improving higher priority factors
STEP. 4 Using KPI updates through application, analysis, and verification of new scenarios on undefined factors
Use case of monitoring
A company in the field of medical devices
Monitoring + MES
To establish a smart factory that prepares for managing the quality (UDI) of medical devices that gradually increases and maximizes production efficiency through precise stock management.
- Lot track management through MES from warehousing to shipment of materials
- Automation of manufacturing data collection through facility I/F
- Precise stock identification and monitoring of raw materials, half-finished products, and complete products
25% increase in output based on an organizational systemization of the manufacturing process
24% reduction in defect rate of complete products through real-time management of measurement data
4% reduction of stock fees by identifying accurate material quantity demand according to output
10% reduction of lead time through precise delivery period prediction based on process status management