We are experiencing technical difficulties. Your form submission has not been successful. Please accept our apologies and try again later. Details: [details]
We are experiencing technical difficulties. Your form submission has not been successful. Please accept our apologies and try again later. Details: [details]
Fill in your details below and get direct access to view content
We are experiencing technical difficulties. Your form submission has not been successful. Please accept our apologies and try again later. Details: [details]
We are experiencing technical difficulties. Your form submission has not been successful. Please accept our apologies and try again later. Details: [details]
Get the full value from your factory floor data with data sciences
Published on 19 June 2020 in AI
Industry 4.0 and IIoT have been buzz words for several years and these concepts are actually implemented on more and more machines. A huge amount of data becomes available: machine data, data of the production process and data regarding the manufactured product. Big Data has entered the factory floor.
Drowning in a flood of data?
Data is nowadays easily collected and stored, but in most cases the ‘data pipeline’ stops here and there is hardly any value extracted from the data. The ‘data pipeline’ is often not completed in a proper way so that the right person(s) can easily exploit the value inside the data. It is a challenge to extract the value from the huge stream of data and not to drown in the flood. Only collecting and storing of data is not enough to monetize the investments in the Industry 4.0 and IIoT infrastructure.
Getting the maximum value out of the data and keeping an overview of data streams nowadays goes beyond standard statistical methods and tooling. Manual analysis and creation of dashboards and reports is not sufficient. The dashboards become too complicated and are not showing the right information at the right time, in the right way, to be able to see at a glance what is going on and to be able to act. The routines implemented in a normal machine controller to observe the production process and to detect errors, are able to detect present deviations and problems, but are not suitable to predict future problems. Machine controllers are not suitable to combine all available information and to perform advanced analytics on it.
Transforming data into information
The valuable information needs to be extracted from the data and presented to the right audience, at the right time and in the right way. The key is to put enough effort in the transformation process of the data into useful information. This has to be done in close collaboration between data scientists, who know how to tame the data and domain experts of the manufacturing process, who know the story behind the data. Only then a solution can be developed that not only looks interesting but is also used and brings value in the long run.
Would you like to exploit the full value of your industrial data or do you have a problem for which you believe the solution is hidden in your data?