If you want to make your production more efficient, you have to collect and use your data. For user companies, this means intelligently linking technical concepts such as Big Data and Industry 4.0 with each other. This is known as industry analytics.

What is Industry Analytics?

With Industry Analytics, i.e. the combination of the two subject areas Big Data and Industry 4.0, data can be evaluated using analytical methods. This promises deep insights into how production can be optimized, which errors are to be expected, and even when a machine breakdown threatens. Based on these results, appropriate preventive measures can be taken. Ideally, companies will save considerable costs and increase product quality at the same time. Many projects at leading companies have shown that Industry Analytics actually pays off.

If you want to control your production systematically and optimize it in the long term, you can’t avoid the strategic use of your data. And in machine maintenance, too, astonishing relationships can be identified and exploited by evaluating the sensor data. But where to start and what needs to be considered?

Creating the basics: Recording machine data

The basis of everything is the data. Ideally, this data is provided independently by the individual production machines. Usually MDE/PDE systems are used for this purpose. These are optimized to collect and evaluate local process parameters. In the age of industry 4.0, this approach has been somewhat short-lived. You want to use the full power of Industry-Analytics and not get lost in data acquisition. Therefore, a product that is able to capture large amounts of data flexibly and easily is the right choice. These can then be provided to higher-level systems such as BI (Business Intelligence) or Industry Analytics.

With our standard software PLCDataReader, even large amounts of data can be read from programmable logic controllers (PLCs). The data format can be xlsx or CSV. This allows you to easily export large amounts of machine data to your analysis software.

How to get started

All users gain insights from the available data – regardless of whether it is a medium-sized or large company. Depending on the degree of automation or digitization, one chooses to start an Industry Analytics project. If a central data repository is not yet available, the data that can be used to analyze a process must first be defined. Together with the business users, the goals of the process analysis are defined. The next step is to determine the parameters that could influence the result. The next step in the process is to read these out at the corresponding plants or transfer them to a central database.

If the required data is available in a central repository, it can be evaluated in detail in standard reports or analyzed for recurring patterns using statistical methods. Using sophisticated data mining algorithms, these patterns can be determined in the data mountains and thus gain completely new insights and correlations.

Conclusion

In order to meet the constantly increasing customer requirements for individuality and quality at the lowest price, state-of-the-art manufacturing processes are absolutely necessary. The digitization and networking of data brings real added value to companies and enables them to differentiate themselves from the competition. It is not just a matter of collecting, integrating and centralizing a multitude of heterogeneous production and machine data: The decisive advantage lies in concrete analyses that make it possible to efficiently control production, optimize processes and make predictions about machine bottlenecks or breakdowns.