IoT-Enabled Device - Finding the Right Software Solution

In this phase of the IIoT maturity model, the foundations of machine networking are laid in the existing IT infrastructure. The focus is now on application areas that emerge from the company's IT ecosystem. These include all current and future applications and services based on collected data that offer decisive added value for the company. These include monitoring, behavioral monitoring, and maintenance topics.

The data collected by IoT-enabled machines can and must be analyzed extensively. This data basis results in numerous use cases for which specific software is developed. The following is an example of the possibilities for transferring data into value-adding applications.

The first application is software for directly evaluating machine data to obtain an overview of production in real-time. Continuous data acquisition enables the software to analyze various current parameters, data, and production processes. This subsequently provides valuable insights, for example, into the machine's performance. For a machine manager, one task is finding the optimum operating parameters. A comparison between historical and current operating data is an essential approach and ultimately promotes production efficiency.

However, it is not only possible to find the optimum machine parameters with the help of software. The permanent connection between software and machine also makes it possible to identify potential bottlenecks and machine failures early. A key point here is the integration of notifications and warning messages into the application. This means that all response times to sources of error can be reduced cost-effectively.

Another critical factor that offers significant added value is predictive maintenance. The fundamental difference is that the software is based on simulation models and various approaches and algorithms, such as artificial intelligence, machine learning, and data mining. The collected data flows into the software as a basis from which corresponding maintenance strategies can be developed and derived. Among other things, this makes it possible to instruct maintenance work in good time and at an early stage and to minimize unplanned downtimes efficiently through preventive measures. As a result, machine downtimes are minimized, and service staff time is put to optimum use.

To make the above possible, visualization is crucial for decision-makers. This is because both the real-time data from the machine and the data processed in the cloud are primarily used to obtain information. However, information must be understood in a specific context depending on the use case. Ultimately, the information obtained from the data is used for business decision-making. It is precisely for this purpose to be converted into clear and understandable forms of presentation and visualization.

With the secure and stable connection of the machines, numerous applications are conceivable that go beyond the use cases described above. In combination with efficient interface management (APIs) and good architecture governance, software solutions from different manufacturers can be used according to a plug-and-play principle. Networking across specialist departments (end-to-end process), with partners, or with the company's competitors (e.g., Manufacturing X) enables high innovation speeds, new sources of growth, and reduced maintenance and administration costs for the IT infrastructure.

The transformation to an "IoT-enabled device" allows companies to make their machines smarter and more responsive and thus offer customers more significant benefits. This step requires the successful implementation of the previous steps and is crucial for realizing the full potential of IoT integration. This is where the partnership between Arvato Systems and TTTech Industrial with the Nerve software platform comes in. They help you with this all-encompassing implementation to lead your company to a new IT standard. Once this has been achieved and the value-added services have been implemented correctly, the final step is introducing the hyperautomation processes and enhancing the benefits using artificial intelligence. We will inform you about this in the next and final blog post in the series on the IIoT maturity model.