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Predictive Maintenance

Machine learning enables cognitive predictive maintenance

An ERP platform with real-time data from the factory halls plus predictive maintenance thanks to machine learning - this combination is currently helping Progress move forward among the IIoT providers.

30 Nov. 2017
Marie-Lucine Tapyuli
Data RPM
Machine learning enables cognitive predictive maintenance (picture: DataRPM, a Progress company)

Progress took over DataRPM and its cognitive predictive maintenance technology for the Industrial Internet of Things (IIoT) in 2017. The platform provider has now integrated the added power into its OpenEdge development platform to develop and deliver mission-critical business applications. The announced Progress OpenEdge PdM Integrator Kit will apparently enable industry users to set up a "proactive maintenance strategy": Predictive analytics from the vast amounts of Industry 4.0 data is meant to help prevent device failures and increase the likelihood that they will be rectified in advance. Cognitive predictive maintenance (CPdM) works - at least according to case studies - almost like automated throughput management in line with Goldratt’s Theory of Constraints (TOC) or bottleneck-oriented organization : finding where the process flow will get stuck - and preventing the bottleneck. The system also enables impact analysis that helps prioritize maintenance repairs and ascertain long-term equipment replacement strategies.

Progress has already received a lot of recognition for this solution, including the Technology Leadership Award for Cognitive Predictive Maintenance in Automotive Manufacturing , and was nominated " Cool Company in Cognitive Computing ".