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World’s biggest manufacturers lose almost a trillion dollars a year to machine failures

Large industrial facilities lose more than a day’s worth of production each month and hundreds of millions of dollars a year to machine failures, according to a report published by Senseye. The report shares findings from 72 major multinational industrial and manufacturing companies. It reveals that, on average, large plants lose 323 production hours a year. The average cost of lost revenue, financial penalties, idle staff time and restarting lines is 532,000 dollars per hour, amounting to 172 million dollars per plant annually.

Cumulatively, Fortune Global 500 (FG500) manufacturing and industrial firms are estimated to lose 3.3 million hours a year to unplanned downtime. The financial cost of this downtime to those organisations is calculated at 864 billion dollars, the equivalent of eight percent of their annual revenues. Unplanned downtime levels were highest in the automotive sector, where plants lost 29 production hours a month, on average, at the cost of 1.3 million dollars per hour. As a result, automotive vehicle and parts manufacturers are estimated to lose 557 billion dollars and 414,800 hours a year.

“One of the biggest challenges manufacturers face is reducing the amount of unplanned downtime and the figures in Senseye’s report clearly show the huge cost impact of not doing this,” Jim Davison, region director at Make UK, commented. “What is clear, is that predictive maintenance can play a crucial role in not only reducing costs, but also boosting productivity at a time when manufacturers need to be using every tool at their disposal to meet the demands of an ever-changing industry.”

The study also showed that more than two-thirds of large industrial organisations have made predictive maintenance a strategic objective and that one in five have established in-house predictive maintenance teams to lead these initiatives. 51 per cent of organisations said they already performed some condition monitoring and 87 per cent said that they collected at least some of the data that can be used to support predictive maintenance.

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