GN IoT Asset Monitoring: IoT at the service of manufacturers

GN IoT Asset Monitoring, powered by Cumolocity di Software AG, has been realized to optimize processes and increase production efficiency.

Asset Management

The Internet of Things allows you to transform the heterogeneous panorama of corporate assets (plants, equipment, machinery, devices, software, …) into “intelligent” resources – Digital Twin – that communicate with the world around them. The company can thus automatically collect large quantities of key data and information, deriving from the use of the resources of interest. Constant monitoring and automatic alerting to the occurrence of critical conditions on machinery open up the possibility for companies 4.0 that adopt GN Techonomy solutions to improve process efficiency and identify forecast trends.

Indeed, predictive maintenance represents a great competitive advantage. Predicting, based on built-in reliability algorithms, equipment failures and errors before they occur, minimizes unplanned downtime. Alternatively, the availability of information in real time allows to constantly evaluate the KPIs (integrated and configurable) for maintenance, use and availability of production factors. The company will then be able to draw up intervention plans to resolve problems promptly, so as to optimize the company’s production process.

The data become usage metrics, possibilities for improvement and general overview. They are thus considered as an opportunity to extract more value from the asset and to be able to make informed choices about its production and life cycle.

Asset Monitoring

The use of IoT improves profitability through automatic monitoring and alerting of resources. In fact, the possibility of obtaining real-time insights from the assets connected to the network allows you to have real-time visibility on the integrity and use of resources and, at the same time, to foresee future events. The ability to keep track of any asset, anywhere, anytime, indoors or outdoors, makes the company that adopts this solution capable of constantly monitoring its corporate resources. By digitizing physical assets, it is in fact possible to intelligently monitor the integrity, location and use of the resources themselves. In this way, the company will be able to optimize the company supply chain and improve the customer experience thanks to IoT insights.

GN IoT Asset Monitoring

GN IoT Asset Monitoring allows automatic monitoring of plant operating parameters. This solution was created both to check and act on the status of the equipment installed in the customers’ production sites, and to supervise all the systems in real-time. This allows you to facilitate the monitoring of the levels of each individual installation, facilitating the planning of maintenance, refills, replacements, … This system, in fact, allows you to centralize the detection of the signals and, therefore, to operate in real time. A timely reaction can be implemented thanks to the alerts that come from the plants themselves, also located in remote areas.

The alerts reported by the systems in real time allow, through the remote control system, a rapid intervention to prevent problems before they even occur.

Advantages of GN IoT Asset Monitoring solution developed for real-time asset management and monitoring thousands of plants located all over the world:

• Flexible and fast platform
• Can be integrated directly with ERP systems
• Can be integrated with pre-existing sensors
• Constant monitoring remotely and in real time
• Application (role-based) based on customer-technical-supervisor-planner roles
• No need for external infrastructure / applications: no hardware modification
• Error reduction and time savings
• Performance and productivity improvement
• Automated and role-based provisioning of new plants / sensors
• Real-time analysis for the detection of alarm conditions and dispatching of technicians and for the calculation of performance metrics and usage statistics

Download the presentation with some Use Cases created by GN Techonomy:

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