An AI-based predictive maintenance analytics solution for a multinational automaker - StreamAnalytix

Case Study

An AI-based predictive maintenance analytics solution for a multinational automaker

Pre-emptive fault prediction can help manufacturers avoid business losses and is therefore gaining importance across all industries. Accurate and on-time maintenance requires predictive insights on the functioning of equipment, next breakdown forecast, primary faults and their causes, and reasons for downtime. These insights enable businesses to ensure fault-free production.

Business needs

A Fortune 500 American multinational automaker was looking for a solution that would predict faults in their auto parts to proactively ensure fault-free production, thereby saving maintenance time and improving the customer experience. They were looking for a scalable solution that would be able to:

  • Process data from disparate sources and in multiple formats
  • Predict in real-time, giving them enough time to replace the waned cutting tools

Solution

StreamAnalytix enabled the client to implement an end-to-end predictive maintenance solution leveraging out-of-the-box drag-and-drop operators. It helped them effortlessly design a complete solution with the following ready-to-use capabilities:

  • Reading data from various sources
  • Out-of-the-box data-wrangling transformations
  • Data quality management
  • Rule-based alerts
  • Scalable scoring of trained models
  • Data aggregation
  • Data profiling
  • Monitoring and reporting
5x improvement in delivery time

Results

  • Connected multiple discrete sources
  • End-to-end data quality and preparation
  • Model lifecycle management
  • Out-of-the-box connectors for upstream and downstream integration
  • Real-time notifications for tool replacement

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