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The client, a leading manufacturing enterprise, was facing unexpected equipment failures and production downtime across multiple plants. Traditional maintenance approaches were reactive, resulting in increased operational costs, delayed deliveries, and inefficient resource utilization. The organization wanted to leverage Artificial Intelligence and IoT data to predict failures before they occurred and optimize maintenance cycles
Thinknyx designed and implemented an AI-driven Predictive Maintenance platform powered by Machine Learning and real-time sensor analytics. Data from industrial IoT devices was collected and processed using scalable cloud-native pipelines. Advanced anomaly detection and predictive models were trained to identify equipment degradation patterns and forecast failures in advance