Adding a predictive maintenance element to an already efficient automotive assembly line can add unnecessary costs. Jendamark has found a better way to predict machine downtime, which will soon be added as the fourth module to its Odin software platform.
“Typically, a well-maintained machine runs at the industry standard of 95% uptime,” says Yanesh Naidoo.
“It’s already very efficient and the potential 5% improvement doesn’t justify the cost of adding a smart machine or artificial intelligence element that has to analyse data from various machines from scratch, look for abnormal trends, determine the reason for these trends and then take corrective action.”
Naidoo says when it comes to predicting what could go wrong with a machine, the smart solution would be to mine the rich quality data that Jendamark has already been gathering for the past 20 years.
“We’re confident that there is a relationship between the machine downtime and the quality data. When a machine does go down, it can generally be narrowed down to a handful of possible causes.
“Because we already know the outcome, the reverse analysis is much simpler, making downtime easier to predict – without huge cost implications.”