Case overview
Any leading industrial equipment manufacturer specializing in heavy machinery. Facing challenges with optimizing their production process, they sought to enhance efficiency, minimize downtime, and reduce maintenance costs.
Challenge: Companies are grappling with inefficiencies in their production line. Manual monitoring of equipment led to reactive maintenance, frequent breakdowns, and production halts. They aim to streamline operations, predict maintenance needs, and improve overall equipment effectiveness (OEE).
The Brief
Sensors were strategically installed across critical machinery to collect real-time data on temperature, vibration, pressure, and other operational parameters. These sensors continuously streamed data to a centralized platform.
The IoT platform collected and aggregated the sensor data in real-time. This data was then processed and analyzed using AI algorithms to identify patterns, anomalies, and predict potential failures.
Our Approach
Machine learning models were trained on historical and real-time data to predict equipment failure probabilities. The system generated proactive maintenance alerts, indicating when machinery required attention before critical failures occurred.
A user-friendly dashboard was developed to visualize machine health status, predictive maintenance alerts, and performance metrics. Automated alerts were sent to maintenance teams when maintenance thresholds were reached or anomalies detected.
The Results
Predictive maintenance reduced unplanned downtime by 40%, as maintenance teams could proactively address issues before they escalated.
Predictive maintenance reduced unplanned downtime by 40%, as maintenance teams could proactively address issues before they escalated.
The shift from reactive to predictive maintenance reduced maintenance costs by 30% and minimized the need for costly emergency repairs.
Real-time data insights empowered management to make informed decisions about resource allocation, equipment upgrades, and process improvements.
