Applications

Mobile machines and vehicles such as forklifts, dump trucks, and transport containers can also be integrated into toii^® via the IIoT network. They can therefore be utilized more efficiently.

The data collected here can be used to optimize transport routes or localize material precisely when moving it in and out of the warehouse. Order data can be transferred directly to the driver in their cockpit and used to optimize scheduling in real time.

Network and IoT-capable machines as well as older legacy machines from different manufacturers and generations can be digitally networked via the toii^® platform. Analog machines can be connected through a separate box that transmits status reports via light signals. They can also be connected by using additional, directly addressable sensors or by having the production worker enter the production data manually at a special terminal. The extended scope of the networking ranges from total integration of the programmable logic controllers (PLC) through to production automation.

Efficient production management aided by a manufacturing execution system (MES) can only be achieved if current production data, such as utilization, set-up times, and downtimes are integrated. These data can be collected through the toii^® platform and transferred to the MES for further processing. Once these data are combined, the entire potential of the production facility can be boosted sustainably.

By integrating an enterprise resource planning system (ERP), order data for manufacturing control can also be transferred to the production chain and measuring results from production can be imported into the enterprise resource planning system for order documentation, quality assurance, or other purposes.

Measurement data, including those from manual measurements taken with instruments such as micrometers or calipers, are transferred to the toii^® platform via WLAN. They are then automatically compared to the target values.

The operator is notified immediately of any deviations, which are automatically escalated if necessary. Integrated data collection and digital archiving facilitate documentation. As a result, quality defects can be corrected more rapidly and precisely.

In order to automate a production chain and optimize it for efficiency, production data must be collected and exchanged. Machine data provide information about utilization and maintenance requirements; measuring results are automatically imported and transferred in order to optimize the production chain. toii® is capable of reading machine data from the PLC and also writing data into the machine control system.

External data sources can also be integrated through the platform. This allows production relevant information and data to be incorporated into the production chain.

Once machine and production data have been collected, a deeper analysis can be performed in order to improve overall equipment effectiveness (OEE). Machine data collected over the long term (big data) are analyzed in order to detect anomalies, for example.

By applying machine learning methods, processes can be continually optimized and machine and equipment failure can be predicted in advance (predictive maintenance). The methods developed from the big data analysis can then be applied directly in the production using real-time data to optimize production (edge analytics).

The first step in increasing the efficiency of production processes is to consolidate the current performance parameters and display them in a transparent way. This step requires not only the collection, but also the visualization of data, so that the performance parameters can be analyzed. The dashboarding feature of the toii^®.Fusion software module provides a clear solution.

toii^® also enables other production-relevant data to be linked and displayed with the accumulating production data. For instance, order data from the enterprise resource planning system (ERP) can be linked with the actual utilization and capacity data of the machines, making it possible to define order times and perform efficiency calculations. Productivity and capacity curves combined with data from third party systems are visualized on a desktop. The results can be used to optimize production planning.