PGS SOFTWARE

324 see whether the trend is going in the right direction and , thanks to machine learning , you can run these distributed experiments across many devices , production lines or even factories .”

PGS Software ’ s approach also overcomes some of the problems inherent to digital twins . “ Normally , when you have a production line and digital twins you are sending the data from the machine to the cloud , but this means you have to deal with latency issues and is also a security risk . However , in the MTL concept we are using something called local , small machine learning models which can then be exported from devices and imported into the cloud , meaning you are only sharing a model of the data .”

Achieving such projects would be impossible without the use of the cloud , which serves as a great enabler for PGS Software ’ s endeavours ; as Panusz emphasises : “ About 98 % of our projects , not only in manufacturing but overall , are done with one of the cloud providers . In the context of MTL , let ’ s choose one example like Amazon Web Services . We use edge devices and AWS Greengrass

APRIL 2020