Cognitive Predictive Maintenance
for Industrial IoT

The Challenge of Harnessing Machine Data

The Industrial Internet of Things (IIoT) generates enormous potential business value for companies, but harnessing the volumes of data produced is beyond human management—even with armies of data scientists. Predictive analytics and machine learning can provide the answer, but these technologies have been available only to the largest, most well-resourced organizations—until now.

The Solution

DataRPM Democratizes Machine Learning

The cognitive predictive maintenance solution (CPdM) from DataRPM automates data science enabling asset intensive organizations to gain exceptional control over the torrent of sensor data coming from every machine. The automated approach uses a patented meta-learning solution to reduce the time required to develop and operationalize models, making data scientists more effective.

Identify Unknown Errors

Identify Unknown Errors

Unlike traditional approaches, DataRPM identifies previously unknown errors, accounting for 80% of machine failures.

Analyze Every Asset

Analyze Every Asset

DataRPM’s digital twin model analyzes each and every asset to accommodate environmental, operational and manufacturing factors.

Leverage the Right Technology

Leverage the Right Technology

DataRPM’s open-box approach leverages and strengthens open-source technologies, while also providing visibility into complex predictive analytics, key to the success of data scientists.


Automate Decisions

An integral component of AI, DataRPM’s meta-learning provides APIs that automate decisions by running experiments automatically, learning from them by building an experience-based repository from multiple datasets for accurate predictions, maximum machine uptime and higher efficiency.

The Business Benefit

Cognitive Machine Learning for Cost Savings and Yield Maximization

DataRPM enables you not only to predict and respond to machine failure, but also to perform maintenance only when necessary based on analytics, not routinely—maximizing up-time and decreasing maintenance costs.


Harness machine data to identify failures to minimize downtime.

Maintenance Costs

Use your own IoT data to determine when maintenance is required, vs. performing routine maintenance that may not be necessary.

Minimize Warranty Claims

Minimizing finished product defects eliminates the cost and customer issues relating to warranty claims.

Optimize Inventory and Personnel

Leverage operational data to optimize inventory and personnel resources.

What Are The Results?

Enhanced Asset Failure Management

90% Reduce unplanned downtime by 90%
20% Optimize inventory cost by 20%
35% Boost Overall Equipment Effectiveness (OEE) by 35%
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Guaranteed Yield Maximization

35% Enhance asset life by 35%
25% Improve output quality by 25%
50% Increase asset’s operational efficiency by 50%
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Cognitive Analytics Powers Cognitive-First Applications

Organizations that can harness big data for business advantage will gain a massive competitive advantage. DataRPM provides the Machine Learning capabilities necessary for cognitive applications, making it possible for you to deliver business apps driven by predictive results.

Progress’ Cognitive Apps Offering

The Business Benefit

How IIoT helped Transform a Gas Pipeline

DataRPM’s CPdM Platform let the client identify gas pipeline stations with the highest contribution to pipeline performance and the reasons for varying performance between individual stations.


Progress DataRPM

Predict asset failures accurately by automating machine learning to achieve significant cost savings and business value.