Unplanned equipment downtime costs Indian manufacturers an estimated ₹70,000 per hour on average. Digital twin technology builds a live virtual model of physical assets — so maintenance teams can see failures before they happen, not after.
What We Build
Six practice areas covering the full stack — from sensor integration and data pipelines through to 3D asset models, analytics, and ERP connectivity.
Build 3D-connected models of machines, production lines, or entire facilities that sync with sensor data every second. The virtual asset mirrors the physical one — temperature, vibration, pressure, and cycle state all reflected in real time.
Machine learning models trained on your equipment's historical failure data to forecast breakdowns before they occur. Alerts are generated 48–72 hours before a projected failure — giving maintenance teams time to act without stopping production.
Run what-if scenarios on a virtual copy of your production line before making changes to the physical one. Test new throughput configurations, line speeds, or shift patterns without any production risk.
Connect PLCs, SCADA systems, and industrial sensors to the digital twin layer without replacing existing hardware. Standard industrial protocols — OPC-UA, Modbus, and MQTT — are supported out of the box.
Dashboards that show real-time KPIs, anomaly alerts, and trend forecasting in one view. Operations managers can see the health of an entire facility at a glance — and drill into any asset in seconds.
Extract data from older industrial systems using OPC-UA, Modbus, and MQTT adapters. Facilities running 10-year-old control systems don't need a hardware refresh to get the benefits of real-time monitoring.
The Process
A structured four-phase approach that moves from sensor audit to production-ready twin — with operations teams trained and ready at handoff.
Identify which assets will benefit most from twinning. Define data sources, sensor availability, and failure modes worth monitoring. Output: a prioritised asset list and data gap analysis.
Set up IoT data pipelines, historians, and real-time streaming infrastructure. Data from PLCs, sensors, and SCADA feeds into a unified time-series layer that the twin reads from.
Build the virtual model, connect to live data, and configure alerts and simulation modules. Anomaly thresholds are calibrated against historical data so alert fatigue doesn't undermine adoption.
Operations teams trained on the dashboard. Full documentation and a defined support SLA provided at handoff — not weeks later.
Where We Work
Digital twin applications differ significantly by industry. Each sector below has distinct data sources, failure modes, and compliance requirements — the approach is adapted accordingly.
Production line twinning, OEE monitoring, and machine health tracking across discrete and process manufacturing environments.
Pipeline integrity monitoring, compressor health twins, and corrosion prediction models built for upstream and midstream operations.
Grid simulation, substation monitoring, and renewable asset performance twins for wind and solar installations.
Bridge and structural health monitoring, smart building systems, and facilities management twins for large-scale civil assets.
Ground support equipment twins, MRO predictive scheduling, and airport facility monitoring — aligned with DGCA data handling requirements.
Digital twins work wherever physical assets generate data. If your sector isn't listed, the underlying approach still applies.
Talk to the teamCommon Questions
Most implementations use existing industrial sensors and PLCs. Where new sensors are needed, Win Infosoft recommends cost-effective options that connect to standard industrial protocols — OPC-UA, MQTT, and Modbus. A full hardware replacement is rarely required.
The twin collects real-time operational data and runs it through ML models trained on historical failure patterns. When sensor readings approach conditions associated with past breakdowns, the system alerts maintenance teams — typically 48–72 hours before the failure would occur, giving teams time to schedule the repair without stopping production.
Win Infosoft's implementations connect to SAP, Oracle, and most major SCADA platforms. Data flows both ways — the twin can trigger work orders in the ERP when it detects anomalies, closing the loop between monitoring and maintenance execution.
Most clients see measurable ROI within 6–9 months through reduced maintenance costs and fewer production stoppages. The exact timeline depends on asset complexity and data quality going into the ML models. Assets with richer historical data tend to show results faster.
Get Started
Request a 30-minute demo showing how digital twins work with actual industrial equipment data. No slides — a working system connected to live sensor feeds.
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