Project Overview
Precision Manufacturing Co. is a 150-person engineering firm specializing in high-tolerance
metal components for aerospace and automotive sectors. Despite strong demand, the company
faced operational challenges: manual production tracking, frequent equipment downtime, and
limited visibility into factory-floor performance.
GridMatrix designed and deployed an end-to-end IoT platform with real-time dashboards,
predictive maintenance algorithms, and automated alerts — enabling data-driven decision-making
across the factory floor.
Challenges
- Manual production tracking via spreadsheets
- Unplanned equipment downtime (avg. 8 hours/week)
- No predictive maintenance strategy
- Legacy machinery without sensor integration
- Poor visibility into OEE (Overall Equipment Effectiveness)
- Inconsistent quality reporting across shifts
Strategy
- Deploy IoT sensors on critical machinery
- Build cloud-based real-time production dashboard
- Integrate predictive maintenance models
- Automate quality and downtime alerts
- Train floor supervisors on data-driven operations
- Phase 2: Extend to inventory and supply chain
Actions Taken
IoT Hardware & Connectivity
- Installed 42 industrial IoT sensors on CNC machines, presses, and assembly lines
- Set up secure edge computing gateway for low-latency data processing
- Implemented 5G and Ethernet connectivity for real-time data streaming
- Integrated legacy PLC systems via protocol adapters
Cloud Dashboard & Analytics
- Built custom web and mobile dashboards with live OEE metrics
- Created machine-level performance heatmaps and status indicators
- Implemented 60-second data refresh intervals for real-time visibility
- Added role-based access for supervisors, engineers, and management
Predictive Maintenance & Alerts
- Developed ML models to predict maintenance needs 7-10 days in advance
- Set up automated SMS/email alerts for anomalies and thresholds
- Created preventive maintenance schedules based on predictive signals
- Integrated with existing ERP for spare parts procurement automation
Results (8 Months)
35%
Increase in Production Output
-42%
Reduction in Unplanned Downtime
28%
Improvement in OEE (Overall Equipment Effectiveness)
-18%
Reduction in Maintenance Costs
Technical Implementation
The platform leverages industrial-grade IoT sensors (vibration, temperature, pressure)
connected via secure MQTT protocols to a cloud backend. Machine learning models trained
on 2+ years of historical data predict failure modes 7-10 days in advance. The dashboard
aggregates 500+ data points per second and visualizes KPIs for shift teams and management.
Integration with the ERP system enables automated work orders and parts procurement.
Final Outcome
Precision Manufacturing Co. successfully transitioned from manual operations to a data-driven
smart factory. By combining IoT sensors, predictive analytics, and real-time dashboards,
the company achieved 35% output growth, reduced downtime by 42%, and cut maintenance costs
by 18%. The platform now serves as the foundation for Industry 4.0 expansion into supply
chain visibility and automated quality control.
