Solution Briefs


Cognitive Approach to Unsupervised Anomaly Detection

Preventative maintenance is only as good as your anomaly detection. With many enterprises only capable of accurately predicting 20% of asset failures, they leave themselves exposed to a wide array of risks that could derail production. Progress® DataRPM® Cognitive Anomaly Detection and Prediction provides a framework that utilizes machine learning to automate and streamline the way enterprises approach preventative maintenance, promoting greater operational efficiency and productivity. 
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Creating Optimum Business Value for IIoT with Cognitive Anomaly Detection & Prediction

Financial setbacks stemming from existing asset failures can be significant, even fatalistic, to growing industrial organizations. Fortunately, the power of machine learning combined with the Industrial Internet of Things has provided leading industrial enterprises with pivotal data insights. This enables them to maximize asset performance and create new avenues to increase business value.
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Cognitive Anomaly Detection and Prediction Framework

Today’s industrial organizations are being challenged to improve their operational efficiency as a means of driving profit margins and meeting market demands. However, asset performance is a key limiting factor in this area. Progress DataRPM Cognitive Anomaly Detection and Prediction can help enterprises gain a deeper understanding of their assets and unlock greater operational efficiency. Download this solution brief to learn more.
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DataRPM Cognitive Predictive Maintenance Platform for Manufacturing

For global industrial enterprises, critical asset failure continues to be the leading operational risk. Cognitive predictive maintenance helps in accurately diagnosing your asset health, before impending issues can negatively impact asset performance. Download this whitepaper to learn more about the value of cognitive predictive maintenance.
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