Why do factories need to streamline their data to tap into artificial intelligence?

Maryse Colson
Sep 7, 2018 · 2 min read

Predictive maintenance is now a must-have for production lines. As machines produce a huge amount of data about their performance and own features, it has never been easier to predict the next failure or which defective part needs to be replaced. By planning ahead the problems, predictive maintenance prevents factories from interrupting their production lines and thus, save huge amount of money by reducing maintenance costs with automatic detection of the roots of defective processes.

However, putting predictive models into production can be costly when not rationalised. For a lot of factories that have not streamlined their data, each new model is a new cost.

digazu is a platform that works with you to implement the right and effective background to streamline your data in real time and implement predictive models in your systems.

In the data source registration interface, you ask digazu to collect and register data sources once and for all. Then, via the data user registration interface, data scientists create data sets to build and train their predictive models. When the model is validated, it is put in production and fed with real-time events thanks to the “models in prod” usage of digazu.

Some factories do not need predictive models yet. A dashboard with real-time data is just what they need to anticipate the failures of their production lines. digazu has a real-time reporting tool that allows anyone at any time to select data and visualise it in a user-friendly dashboard. The dashboard is fed by real-time data and is refreshed at any time to update data.

With these usages, digazu enables data scientists to create data sets easily and quickly. It allows predictive models to be fed by events and it gives a user-friendly, real-time reporting tool. As such, digazu is the next easy step to tap into predictive maintenance for 4.0 industries.

Check out the digazu website or read other digazu stories

data-science.be

Histoires belges sur la data science. Inspirées par la vie, écrites par EURA NOVA.

Maryse Colson

Written by

Inspired by real life and people. Tell stories about data science changing the world. Work @ EURA NOVA

data-science.be

Histoires belges sur la data science. Inspirées par la vie, écrites par EURA NOVA.

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