July 26, 2017—Frost & Sullivan has recognized DataRPM with the 2017 North American Frost & Sullivan Award for Technology Leadership in automotive manufacturing. The DataRPM approach enables repair shops to replace machines and parts before they fail. The results are new levels of efficiency and dramatic cost savings.
DataRPM's patented Meta-Learning framework runs multiple automated machine learning experiments on datasets, extracts data from every experiment, trains an ensemble of models, applies models to predict the best algorithms and finally builds machine-generated and human verified machine learning models for predictive maintenance.
DataRPM's Meta-Learning capabilities for self-learning and continuous optimization, accuracy and speed in predicting failures are features unique in the market, and critical to success as manufacturers embrace the Industrial Internet of Things (IIoT). Following adoption of the DataRPM platform, customers have reported achieving average annual cost savings of more than 30 percent, with three times higher accuracy than manual inspections and traditional approaches to data science and machine learning.
DataRPM's automated machine learning capabilities deliver actionable insights, predictions and maintenance recommendations for equipment, so that it can be repaired before it fails. The on-premise or cloud-based software monitors total uptime and other vital parameters to accurately forecast the next likelihood of failure for every piece of equipment throughout its lifecycle. The solution minimizes unplanned downtime and delivers higher yield at lower cost.
DataRPM will host a webinar titled "Future-Proofing Asset Failures Using Cognitive Predictive Maintenance" on Aug. 2, from 9.30-10.30 a.m. PST, featuring Vishwas Shankar, research manager for mobility at Frost & Sullivan, and Sundeep Sanghavi, DataRPM co-founder and general manager, to discuss how shops can experience the power of cognitive predictive maintenance (CPdM) to avoid unplanned downtimes, unscheduled maintenance and drive greater efficiencies and cost savings.
