datarpm-automotive

Optimize Across the Entire Automotive Enterprise

Predict asset failures, reduce maintenance costs, optimize inventory and resources.

Automotive data monetization can potentially generate $750 billion by 2030. What share of that will be yours?

Cognitive Anomaly Detection and Prediction puts your data to use for everything from enhancing product output to reducing warranty claims.

Smart Automotive Manufacturing Powered by Cognitive Intelligence

Our Cognitive Anomaly Detection and Prediction solution enables you to rationalize all the data coming from your Industrial Internet and put it to use—from enhancing product output and quality to reducing warranty claims.

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Anomaly Detection

The key is anomaly detection—the process of identifying outliers in a normal range of performance. Historical data, current data and external influencing factors are analyzed simultaneously to establish baselines for asset performance on an ongoing basis.

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Predictive Maintenance

When implemented through unsupervised machine-learning, anomaly detection can help predict future anomalies before they happen so you can take appropriate action. The result is assets that last longer and perform at their best—helping you avoid costly repairs and downtime.

Benefits

Automated Failure Prediction
75% decreased breakdown
55% reduced malfunctions
25% lower maintenance costs
Enhanced Productivity
20% inventory cost savings
16% increase in uptime
10% improvement in delivery time
Minimized Warranty Claims and Risk
55% reduced malfunctions post-delivery
20% fewer warranty claims
15% lower product recalls
Talk to an Expert
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Success Story

UK Car Manufacturer Predicts Factors for Increasing Manufacturing Efficiency

Learn how Progress DataRPM’s Cognitive Anomaly Detection and Prediction identified segments with High OEE and low power consumption to reveal prescriptions for achieving higher machine efficiency.

Read the Story
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Progress Cognitive Anomaly Detection and Prediction

Detect and predict anomalies by automating machine learning to achieve higher asset uptime and yield maximization.