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A 30% reduction in unplanned downtime leads to 4% lift in gross profit for this manufacturer

AI / Reduce Downtime
AI / Challenge

Reduced Downtime and Higher Throughput

Before we deployed the Asset Health AI application, unplanned mill downtime was a costly issue for our customer, a multi-billion-dollar global steel producer.

Ch. 01

More than ten leading software vendors, including large enterprise software providers, specialty software shops and consulting firms competed for the best results.

Even while it's visualizing high-frequency multivariate signals across complex and varying asset hierarchies, the application makes challenging data easy to access and use. was selected as the AI solution partner with superior predictive capabilities and demonstrated results; an operating model, people, and approach that is transparent and educational; and deep metals industry expertise.
AI / Solution

Two AI Engines to Power Asset Health

The Asset Health AI implementation extends across the majority of this customer's manufacturing processes, addressing 140 unique failure-modes. From continuous casting, to hot rolling, to cold rolling, to the galvanizing lines, data pipelines fuel two important AI inference engines.

Ch. 02

Anomaly Detection

Detect complex anomalies from real-time sensor data. Show relevant, critical warnings to the operations/maintenance team based on abnormal multi-variate sensor patterns. Provide underlying sensor contributions to support inspection and diagnoses.


Predict equipment failure days in advance with associated probability. Show the time frame in which the equipment is predicted to fail. Provide a confidence interval with the prediction.

AI / Results

Increased Revenue. Reduced CAPEX & OPEX.

The goal was simple, the outcomes clear:

  • maximized revenue with increased uptime,
  • less CAPEX by extending the life of factory equipment, and
  • less OPEX by limiting unnecessary preventive maintenance.
Ch. 03
Reductions in downtime across plants and assets
Positive impact on gross profit

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