IBM recently made new technology available to help organizations relying on large pieces of equipment predict and work to prevent equipment downtime. The system leverages big data to identify any irregularities in machinery, according to ZDNet. When used in conjunction with equipment monitoring systems that can collect the necessary data, the systems can effectively help operators optimize the service lives of key machines.
"If there's a slowdown (or outright stoppage) in the supply chain, the losses pile up immediately," ZDNet contributor Andrew Nusca wrote. "That's the downside to being extremely fast and efficient: that speed can come back to bite you when the operation goes off the rails."
When companies gather information about the functionality of their machinery with an equipment monitoring system and analyze this data through a program such as IBM's, downtime events can be avoided.
VentureBeat reported that the Predictive Asset Optimization software has the potential to save billions of dollars in annual operating expenses for businesses. IBM's solution is being used to analyze data from equipment sensors on mining equipment at Thiess. The company collects information from several sources pertaining to environmental conditions, fuel usage and past repairs, and delivers it to IBM for analyzing.
"We plug that into the analytics and we optimize the whole business operation," said Matt Denesuk, IBM Research manager of Smarter Planet Modeling and Analytics. "We can look for patterns and come up with risk assessments and costs."
The company predicts that insights gained for its big data analytics platform could save mining companies like Thiess as much as $30 billion via productivity gains from performing repairs before issues cause downtime.
However, in order for such an analytics system to be effective, businesses must have the required data. Technology like equipment monitoring systems, which can include load sensors, strain gauges and sensors, plus mining and tension monitors, can enable operators to gather this information and analyze it for optimal processes.
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