Manufacturing Applications

Predictive Maintenance

Predictive maintenance models, designed to foresee equipment failures and suggest preemptive measures, are crucial for manufacturing success. Monitoring these models is vital due to their real-time impact, although troubleshooting them can be challenging because of industrial equipment complexity and large data volumes.

  • 24/7 monitoring to detect anomalies, unusual patterns, and reduce overall downtime before a failure becomes costly
  • Quickly root cause the precise features impacting predictive performance to inform where to retrain or rebuild
  • Efficiently maintain models without significant oversight by streaming high-volume production data for automated monitoring at scale
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Supply Chain Optimization

Supply chain models forecast real-world demand and influence manufacturing, logistics, and planning. Yet, they can cause prediction errors and quickly become obsolete due to sudden drift caused by volatile economic conditions, shifting trends, and unpredictable events.

  • Monitor distribution changes for all model inputs and outputs to catch when (and where) your model inferences begin to drift
  • Proactively find opportunities to improve your supply chain with native A/B comparison and cohort filtering on any model dimension
  • Track upstream data quality issues such as missing values and cardinality changes to inform areas to fix or adjust in your data pipeline
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Quality Control

Quality control models inspect products and ensure quality standards are met. These models help maintain the status quo for customer experience and have severe consequences if they perform poorly. Quality control models need to adapt to different products or production lines, yet they are often not generalizable and struggle to integrate with existing manufacturing systems.

  • Prevent unreliable predictions due to retraining cycles by monitoring cardinality to prevent inadvertent changes
  • A/B compare different versions based on retraining, new weights, or new features to identify the most performant dataset for a specific product line
  • Easily analyze unstructured data patterns and identify areas to improve with an embedding analyzer
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