ZircNetics
ZircNetics offers a Software as a Service platform to monitor and control machine data from multiple sources. Zircoo creates a map of machines and components using digital twin technology, including information on manufacturing date and location, along with images, drawings, servicing instructions and other documents.
Furthermore, the ZircNetics IIoT Platform offers customization tailored to the company’s unique needs. This customization encompasses options to streamline information for enhanced presentation. Moreover, live operational data—such as running characteristics, power consumption, temperature, and wear and tear—can be presented, analyzed, and utilized to initiate specific actions through a configurable rule engine, facilitating the generation of alerts, among other functionalities.
ZircNetics IIoT Platform for Every Purpose
Digital Factory
ZircNetics provides comprehensive solutions that enable companies to supervise their plant and machinery from one centralized location, resulting in peak production efficiency and enhanced profitability.
New business and revenue models
Implementing IIoT solutions enables manufacturers to offer customers new ways to generate revenue and increase loyalty. With real-time data analytics, customers can optimize operations for better performance and profitability. IIoT benefits manufacturers and customers in a competitive market.
New service models
Use ZircNetics to easily and efficiently send data to your trusted tech support and service providers. With ZircNetics, you can preview your machine's digital twin prior to on-site service and ensure that you bring along all necessary spare parts.
Closed-loop scenarios
By seamlessly integrating with SAP, ZircNetics streamlines business processes by automating the detection and reporting of potential machine defects, ensuring efficient and effective operations.
Benefits of the ZircNetics IIoT Platform
The Zircoo IIoT Portal helps industrial enterprises exploit the industrial IoT for themselves, and strengthen their position in the market through digitalization.
FAQ: ZircNetics IIoT Platform
The planning required before connecting production machinery is as intricate as the machinery landscape itself. It demands a discerning eye for selecting suitable components and tools, including gateway infrastructure, sensors, and the right middleware for edge application development. There are four primary connection methods:
- Modern machines equipped with MQTT or OPC UA interfaces, connecting via cloud providers’ protocols.
- Machines with programmable logic controllers (PLCs), utilizing proprietary protocols that often require middleware with compatible drivers.
- Older machines lacking PLCs, necessitating the attachment of external sensors. These sensors can also be installed for machine types in categories 1 and 2.
- Customer-specific installations may require the development of bespoke local connection systems if no standard solution exists.
The timeframe for deriving meaningful findings typically spans between two and twelve months, contingent upon various factors:
Degree of Machine Connectivity: This factor ranges from machines being “almost fully networked” to “not connected at all.” Enhanced connectivity allows machines to furnish more data, enabling more accurate forecasts. The extent of connectivity in the plant influences the richness of data available for analysis.
Type of Data Collected: The nature of collected data varies, encompassing operational information and additional details like process and machine data. The relevance of data for forecasting varies case by case and is determined collaboratively by process experts and data scientists.
Quality and Quantity of Data: The adequacy and quality of data are paramount. Meaningful insights hinge upon collecting and utilizing a sufficient quantity of data of high quality. Reliable forecasts necessitate a robust dataset that reflects the intricacies of the processes under consideration.
Creating appropriate machine learning models often does not necessitate an immediate connection to the cloud. Initially, data collection from the shop floor suffices. Simultaneously, it is advisable to undertake projects to connect various machine types, enabling the conveyance of live data from the shop floor to the cloud. Subsequently, the models are transferred to the designated analysis location – on-premises or in the cloud – and linked to the live data stream to commence forecasting. The pivotal stage culminates with integrating findings regarding impending machine failures into existing business processes and transferring them to systems such as SAP ERP/MES.