Machine downtime costs UK manufacturers £180bn a year

Advanced Manufacturing

The impact of machine downtime is costing Britain’s manufacturers in excess of £180bn every year, a new study has found.

Research conducted by Oneserve, an Exeter-based field service management company, in partnership with British manufacturers, found that broken machines and faulty parts are hampering productivity.

Consequently, three per cent 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 ONS show that there are 133,000 manufacturers in the UK who contribute £6.7 trillion 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 per cent) said they replace machines at least once a year.

According to these UK manufacturers, 53 per cent of machinery downtime is caused by hidden internal faults.

Chris Proctor, CEO of Oneserve said: “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.”

The situation can be reversed with predictive models that use machine learning algorithms and data collected from machine sensors to monitor performance 24/7.

“There are many different ways this can be done,” said Proctor. “At a simple level, the software can analyse the timings of certain actions against the PLC to monitor whether the machine is slower or quicker than usual; this can also be done in relation to heat and vibrations. Alternatively, a video camera can be installed inside the machine, which along with sophisticated neural networks can analyse to the nth degree the machine’s performance – notifying supervisors in advance when performance strays from the norm.”