85% Of Firms Believe Predictive Analytics Are Critical For Operational Excellence In 2020
According to the Verdantix annual survey of 284 managers responsible for operational excellence, 85% mentioned predictive analytics for asset failure to be either ‘very significant’ or ‘significant’ for their firms’ operational excellence programme in the next two years. Analytical tools using machine learning that can identify issues before they occur or generate warnings regarding asset failures helps reduce downtime and cut costs as maintenance activities can be planned and prioritized. The majority of APM vendors have developed robust capabilities for predictive asset failure analytics.
AspenTech’s Aspen Mtell, a product built on the Mtell acquisition, uses autonomous agents to constantly monitor and capture failure patterns from operations and maintenance data to provide early warnings of impending failures. Uptake, headquartered in Chicago has built an expansive database of 800 asset types and 58,000 failure modes. It uses AI to analyse this vast amount of data to predict anomalies. The rising value of predictive analytics is also driving M&A activity. In June 2019, Baker Hughes entered into a joint venture with C3.ai, an enterprise AI software provider headquartered in California. They launched the BHC3 Reliability AI software targeted at the oil and gas sector in September 2019. BHC3 Reliability uses machine learning and natural language processing (NLP) to predict asset failures and recommend suitable actions. In October 2019, SKF, the SEK 86 billion ($9 billion) revenue firm acquired Presenso, an Israeli headquartered AI-based predictive maintenance software firm. Presenso’s customer base consists of firms mainly operating in the chemicals, oil and gas, power and energy and pulp and paper sectors.
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