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Predictive Maintenance Platform

Industrial Manufacturer

A global manufacturer with 200+ production facilities needed to reduce equipment downtime and maintenance costs through predictive maintenance.

The Challenge

Equipment failures were unpredictable, causing $50k+ losses per hour of downtime. Maintenance was reactive, not proactive.

Our Solution

  • Deployed 10k+ IoT sensors across all facilities
  • Built real-time data pipeline collecting 100M+ sensor readings daily
  • Developed LSTM neural network predicting failures 5-14 days in advance
  • Implemented predictive maintenance scheduling system
  • Created mobile alert system for maintenance teams

Key Outcomes

  • 35% reduction in equipment downtime ($8M annual savings)
  • 92% accuracy in failure prediction
  • 50% reduction in maintenance costs
  • 20% increase in overall equipment effectiveness (OEE)
  • 45-day ROI on IoT deployment

Technologies Used

PythonTensorFlowKafkaInfluxDBKubernetes

Results

The predictive maintenance platform delivered 35% reduction in downtime, equivalent to $8M annual savings, with ROI achieved in just 45 days.

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