According to the study, 3% of all working days are lost annually in manufacturing due to faulty machinery, equating to 49 hours of work and £31,000 per company.
Figures from the Office for National Statistics (ONS) show that there are 133,000 manufacturers in the UK, who contribute £6.7tn to the global economy.
Three-quarters of the senior business leaders surveyed outsource their machine maintenance, at a cost of £120,000 annually, but nearly all (83%) said they replace machines at least once a year.
According to these UK manufacturers, 53% of machinery downtime is caused by hidden internal faults.
Oneserve said this has led to a rise in predictive models that help prevent the losses experienced by manufacturers.
These models use machine learning algorithms and data collected from machine sensors to monitor performance 24/7 to predict when machines will break before they do.
Chris Proctor, CEO of Oneserve said: “It is truly shocking to see the scale of losses businesses endure due to machine downtime. It is clear, that existing maintenance processes aren’t working and the time has come for predictive methods to become the norm – not the exception.”
Oneserve found that downtime was largely attributed to internal technical faults, the age of machinery, misuse by employees, external damage, and poor maintenance.
Proctor added: “The types of technical fault that cause machine downtime varies widely depending on the type, age and usage of the machine.
“However, one of the most common technical faults is the overheating of particular parts, especially where there is metal on metal, as these can short electrical circuits and cause the machines to stop running.
“Vibrations, usually the first sign a machine is breaking, are another major cause of internal technical fault – they cause a cascading effect which can have a devastating impact on the machine. General wear and tear, as well as operator misuse, can also be the cause of technical fault.”
He added that predictive maintenance software harnesses the data collected by sensors in the machines to predict these technical faults in advance, before they happen and cause the machine to break.
Source (The Manufacturer) https://www.themanufacturer.com/articles/machine-downtime-costs-uk-manufacturers-180bn-year/
Research conducted by Oneserve in partnership with British manufacturers found that downtime – i.e. broken machines and faulty parts – is hampering productivity.