Field Visibility
Real-time integrated control — equipment, process and quality status on one screen
Smart Process Integration Solution
A lightweight smart-factory platform for small and mid-sized manufacturing sites.
It unifies PLC control data, IoT sensors and CCTV video, processes them at the edge in real time, and connects everything through a monitoring app and an sLLM (small large-language-model) chatbot.
Operators see field status in real time, receive instant alerts on anomalies, and diagnose equipment intuitively through natural-language chat.
Data Reliability
A basis for accurate decisions — analyzable data secured through edge collection and cleansing
Operational Response
Fast detection and action — anomaly alerts and history shorten response time
AX Scalability
AI service integration — extends to predictive maintenance, quality prediction and chatbot
Key Features
Data Collection & Integration
Synchronizes multimodal data — PLC control data, IoT sensors (vibration, temperature/humidity, current) and CCTV video.
Edge AI Diagnostics
Detects equipment anomalies in real time on site with time-series anomaly-detection models.
sLLM Chatbot
Explains equipment status and risk intuitively through natural-language Q&A.
Monitoring App
Brings production metrics, process status, CCTV video, alerts and the LLM chatbot into one app.
sLLM chatbot examples
Equipment temperature is trending upward.
Frequent stop alerts — risk level is high.
How It Works
STEP 01
On-Site Data Collection
- PLC & equipment data
- Inspection equipment data
- Camera data
- IoT sensors (vibration, current, temperature)
→↓
STEP 02
On-Site Edge Server
- Industrial-protocol collection (Modbus, OPC UA, S7, FEnet, MC Protocol)
- On-site data storage
- ETL, data cleansing and processing
- Network-failure resilience
→↓
STEP 03
Web / Mobile
- Production status (KPI) dashboard
- Process capability analysis (CPK, CP)
- Real-time alerts
- AX service integration
Mobile App
AX Analysis Services
Anomaly Detection & Predictive Maintenance
Automatic anomaly-window detection via Matrix Profile
Yield Optimization
Per-jig pressure distribution comparison
Quality Improvement & Defect Tracing
Process-capability (Cpk) distribution analysis
Case Study
Company J
Installed IoT sensors and M.AX gateways on the production line to automatically collect metrics such as utilization, equipment alerts and downtime, and analyzed per-process vibration and temperature trends to detect equipment anomalies and quality issues in advance.