- Industry: Manufacturing (Indian industrial equipment manufacturer)
- Size: 3 plants, 500+ employees
- Challenge: Unplanned equipment downtime costing $3.5M annually
A mid-size manufacturer operating 3 production plants was losing an average of 120 hours per month to unplanned equipment downtime. Each hour of downtime cost approximately $2,400 in lost production, emergency repairs, and overtime labor. Their maintenance approach was purely reactive — fix it when it breaks — and scheduled preventive maintenance was either too early (wasting parts and labor) or too late (missing failures).
Artificial Intelligence involves creating computer systems capable of performing tasks that usually require human intelligence. This includes developing algorithms and models that allow machines to learn, reason, and perceive effectively.Adam Peterson
The manufacturer had thousands of IoT sensors generating data from their production equipment, but no way to turn that data into actionable maintenance predictions. They needed a system that could learn the normal operating patterns of each piece of equipment and predict failures days or weeks in advance.
QUYNT built a predictive maintenance platform that ingests real-time sensor data (vibration, temperature, pressure, power consumption) from 150+ machines across 3 plants. The system uses anomaly detection and time-series forecasting models to predict equipment failures 5–14 days in advance, allowing the maintenance team to schedule repairs during planned downtime windows.
- 60% reduction in unplanned downtime (from 120 hours/month to 48)
- $2.1M annual savings in avoided downtime, emergency repairs, and overtime
- Prediction accuracy of 88% for failures within 14-day window
- Preventive maintenance efficiency improved by 35% (fewer unnecessary part replacements)
- ROI achieved within 4 months of full deployment
