According to the "Internet of Things 2018" study by Computerwoche and CIO, 47% of respondents rate the relevance of IoT as very high or high. Some companies have already tested IoT concepts in a proof of concept, but have not yet put them into practice. They now face the challenge of putting their concepts into practice and scaling them up - without disrupting ongoing operations. To do this, old machines often first have to be made internet-enabled. With an IoT platform, this step - namely integrating existing systems - can be taken quickly. It offers simple integration into existing systems and processes and can be tailored to the respective requirements. More than half of German companies have already recognized the importance of IoT platforms: 57% of those surveyed consider them to be the most important technology in relation to the Internet of Things. However, only 22 percent have deployed such a platform to date. How platforms help to get IoT projects on the road quickly is shown by the areas of application of IoT in the manufacturing sector.

Potential and application scenarios for IoT in industry

The Industrial Internet of Things creates the conditions for an expanded form of networked production and therefore offers new potential for holistic process optimization. Although production plants are already largely networked within classic system architectures, IIoT goes beyond networking production machines: it connects the operational technology (OT) of the production line with the company's IT. If production, logistics, purchasing and sales have the same database, the entire value chain can be mapped digitally and new opportunities arise: By digitally recording their value-based goods, companies can monitor them and increase overall equipment effectiveness. If production lines at different sites are networked, a production manager can monitor the clones remotely from the main site at the same time. Quality checks and maintenance are no longer carried out manually as before, but automatically - with the help of condition monitoring, predictive maintenance and maintenance execution: with condition monitoring, institutions use data to monitor the condition of production machines in order to manually control maintenance intervals. Predictive maintenance enables predictive maintenance through the use of intelligent algorithms. Machine data can be used to determine when maintenance is required so that employees can carry it out in good time before problems occur. Maintenance execution then triggers the maintenance process automatically. The following use cases describe how some of these scenarios have been successfully implemented in practice.

Example 1: Quality assurance in electrical wire production

Schwering & Hasse Elektrodraht produces over 50,000 tons of wire every year, which is used in industry and other sectors. Quality assurance plays a major role for the wire manufacturer, as the electrical wire is coated with a fine insulating layer. If this deviates slightly from the specification, the entire wire becomes unusable. In order to optimize quality control, the wire manufacturer wanted to make its production transparent and introduce an early warning system that would promptly detect deviations from the standard. Software AG 's Apama Streaming Analytics platform makes it possible to analyze quality factors in real time and compare them with relevant, historical production data. For example, if a wire becomes warmer than intended when the insulating layer is applied, this can cause quality defects. The system sounds an alarm so that the company can rectify the fault immediately. With the Apama-based solution, Schwering & Hasse has been able to increase quality assurance and is now in a position to bring new products to market more quickly, as the quality of production is continuously monitored and the risk of defects has been reduced.