DrivingIndustrial Automation with IoT and Real-Time Monitoring
How we helped a manufacturing company modernize its operations through IoT-enabled automation, predictive maintenance, and real-time production monitoring — resulting in reduced downtime and higher efficiency.
Overview
A mid-sized manufacturing plant was relying on manual monitoring and outdated machinery control systems. Inefficient maintenance schedules led to frequent downtime, while the absence of real-time data made it hard for managers to optimize production.
We delivered an IoT-powered industrial automation system that provided real-time visibility into machine performance, automated key workflows, and enabled predictive maintenance to avoid costly breakdowns.
The result was improved efficiency, reduced downtime, and stronger decision-making through accurate data insights.
The Challenge
What we needed to solve
Frequent Machine Downtime
Maintenance was reactive, leading to unexpected equipment failures and halted production lines.
Lack of Real-Time Monitoring
Managers had no live visibility into machine health or production performance.
High Operational Costs
Manual reporting, delayed alerts, and inefficient workflows increased resource costs.
The Solution
Installed IoT sensors across critical equipment to capture real-time data on performance, temperature, and vibration.
Set up a system that analyzed equipment data to predict failures in advance, reducing downtime and repair costs.
Deployed a Supervisory Control and Data Acquisition (SCADA) dashboard for managers to monitor production lines in real time.
Configured automated alerts for anomalies, with direct integration into maintenance management workflows.
Built data visualization dashboards to track efficiency, downtime, and overall equipment effectiveness (OEE).
Tech Stack
We used cutting-edge technologies to build a scalable, performant, and maintainable solution.
IoT Sensors (Modbus / OPC-UA Protocols)
Captured real-time machine data.
Edge Devices (Raspberry Pi / PLC Controllers)
Processed and transmitted data securely from shop floor machines.
Backend (Node.js + MQTT Broker)
Managed communication between sensors, controllers, and cloud.
Database (PostgreSQL / TimescaleDB)
Stored high-frequency machine and production data.
Visualization (Grafana / Power BI)
Real-time dashboards for monitoring and analytics.
Cloud Hosting (AWS IoT Core)
Scalable platform for device connectivity and data management.
Outcome
Key business results achieved through the solution implementation
- 200% increase in telehealth adoption.
- 90% reduction in scheduling delays.
- 70% boost in patient satisfaction scores.
- Full HIPAA & GDPR compliance achieved.
- Platform scaled to support 1M+ patients.
- 35% reduction in unplanned downtime with predictive maintenance.
- 50% faster response to anomalies due to real-time monitoring and alerts.
- 25% improvement in overall equipment efficiency (OEE).
- Lowered operational costs through workflow automation.
- Scalable system ready for deployment across multiple plants.