From a business supplying compressor blades in the 1950’s ELE has evolved to manufacture products for blue chip customers in the power generation, aerospace and automotive sectors.
In recent years, digitalisation has been high on ELE’s agenda, and it has been investing in its future, spending £7m on new machinery and new digital tools during the pandemic. However, currently, most machines are operated manually and not managed by a digital system.
This means when the process varies due to machine issues, the operator can easily miss the moment when the parameter tolerance levels are exceeded.
This also means little is known of machine performance and product quality until the cycle is finished and end of shift bookings are made.
While the machines are serviced regularly, performance issues and mini breakages happen. However, the lack of data means root-cause analysis relies on people and their account of events which is not always reliable and not sustainable with growing production demand.
Dave explained: “When a machine breaks down, we diagnose the problem and fix it. But that reactive approach to maintenance has its problems. Some replacement parts take a long time to source or can even be obsolete. This leaves us in a challenging position as it can take down an entire cell for an unacceptable period or means we have to carry a large inventory of critical spares.”
“The reality is that with unplanned maintenance, more often than not there is never enough time for identifying the root cause. As a result, a sticking plaster may be applied to have the machine operational quickly.”
“What we needed to develop was an early warning signal to highlight when machines are no longer performing at their optimum level, so we could investigate any issues while the machine was still running.”