Industrial Innovation
The Factory's Virtual Mirror —
with Real-time Simulation
and Prediction.
Digital Twin technology creates an exact digital replica of the physical production line — fed by IoT data, analyzed with machine learning, updated in real time. Optimize without stopping production.
Capabilities
More Than Visualization — Working Prediction
The digital twin doesn't just display the factory — it analyzes, forecasts, and optimizes.
Virtual Factory Model
A digital replica of the physical production line or plant — with real-time state mirroring, 3D visualization, and simulation engine. Machines, conveyors, warehouses, and process parameters in a single model.
Real-time Synchronization
Continuous data feed from IoT sensors and PLCs into the digital model — via OPC-UA and MQTT, with millisecond accuracy. The model always reflects reality.
Predictive Maintenance
The digital twin model analyzes machine condition with machine learning — forecasting failures from vibration patterns, temperature trends, and energy consumption, enabling preventive action.
Process Optimization
Simulating new production sequences, parameter changes, and capacity expansions without stopping the real system. What-if analysis, bottleneck identification, and throughput time reduction.
Production Analytics
Comprehensive production KPIs, OEE analysis, bottleneck identification, and capacity utilization monitoring on a single platform — with Power BI dashboard, accessible on mobile.
Virtual Training & Testing
Operator training and process change testing on the digital twin model — risk-free, without production downtime. Especially valuable in hazardous or complex manufacturing environments.
Implementation
From Physical Factory to Digital Twin
Four phases — typically 8–16 weeks.
1
Process & Machine Mapping
Mapping production processes, machines, sensors, and data sources — with model boundary and resolution definition.
2
Model Build & Calibration
Building the digital model on Azure Digital Twins — calibrated and validated against historical data for accuracy.
3
Real-time Data Connection
Live connection of IoT sensors, PLCs, and other data sources to the model — OPC-UA, MQTT, and REST API integration.
4
Analysis & Optimization
Training ML models for predictive maintenance, running optimization algorithms, and developing dashboards for the operational team.
Technology Stack
Microsoft Azure and Industry Standards
OPC-UAMQTTPython / TensorFlowInfluxDBPower BIUnity 3DREST APIAzure IoT Hub
Let's Start a Digital Twin Pilot Project
We'll prove the concept in 8–12 weeks — on a selected machine or process, with measured results.