The core idea behind Industry 4.0 is the so-called Internet of Things and Services. IoT for short. In the next few years, this global network will connect not only computers and cell phones, but also everything else imaginable. From vacuum cleaner robots to cars, and even sensors and machines in factories. The Internet of Things is increasingly conquering production halls and supply networks. Due to the complexity of highly automated production, the quality, effectiveness and productivity of processes can hardly be monitored “manually”. Real-time data stream analytics can not only detect signs of machine wear, but also provide valuable information for future product and process optimization, capacity utilization improvement, and service development. And yet we are only at the beginning of exploiting these opportunities.
Industry 4.0 for individual products
The individualized production of Industry 4.0 creates products that are tailored to the individual wishes of customers. This is also the view of the German Federal Ministry for Economic Affairs and Energy. This requires means of production that are able to change their settings quickly. In addition, these require an infrastructure that enables automated and decentralized control of production orders.
Connection of man and machine
Through the connection of man and machine, Industry 4.0 is creating a constant and enormous flow of useful information, which is often referred to as Big Data. Big data refers to data volumes that are either too large, too fast-moving or too complex for conventional data analysis. In a factory, for example, this can consist of general information about the shape and condition of the material, but also possible production errors. With the help of a network, some of which is wireless, the machines can exchange and evaluate this information with each other independently.
Industry 4.0 leads to Big Data
For evaluation, corresponding services are needed that extract the relevant information from big data. For example, the data that a sensor collects on a machine is evaluated to find out when the machine needs to be serviced. This is called predictive maintenance. Before the machine breaks down, parts that are closing down can be replaced in this way. Especially in industrial production, such a detailed analysis of large amounts of data is very useful. A network of machines can then work much more autonomously and flexibly than before and adapt to customer requirements. These interconnected production machines already exist. Among other things, they use wireless transmission paths to communicate with each other.
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Dr. Florian Bross