- Establish the digital twin as a database for all IoT business processes
- Create complete profiles across the entire lifecycle of your products and map changes in the field
- Use the digital twin as a basis for creating added value for your customers
The digital twin is the virtual counterpart of a physical object in the field in its current state. Ideally, the digital twin ideally accompanies its product instance throughout its entire lifecycle - hence the familiar term "digital shadow". The purpose of this connection of the virtual and real world is to carry out data analyses, simulations and tests. Establish the digital twin as a data basis for digital business models and benefit from comprehensive improvements in your entire value chain!
Improve products and processes
The potential of the digital twin embraces improvements in products as well as production and maintenance processes in every respect: the analyses can initiate performance enhancements through functional changes or quality improvements through product design changes as well as extending machine downtimes, avoiding downtime or accelerating problem solving. Depending on the requirements of the application, the virtual representation is more or less detailed. In the simplest case, these are parameter sets that define the operating parameters of the device. For more complex machines and systems, for example, configuration optimization may require a high degree of granularity of the digital twin, from the operating history to detailed function parameters and the service parts list.
Via cloud-based, open IoT infrastructures you connect devices in the field, for example your machines, to the digital world by means of sensor technology. The field data from the real machine is represented by the digital twin as a virtual counterpart. In addition to operating data and location information, the digital twin can contain time series up to the complete operating history, error messages and data on the desired status.
Mapping changes "As maintained"
What distinguishes the Digital Twin from the service bill of materials is the precise representation of the device's individual properties and changes within the scope of operation in the field. For example, the status "as maintained" can be digitally mirrored down to the last detail. For example, damage, replacement parts, attachments and the like can also be documented.
Originally, the Digital Twin is derived from the Digital Master, the blueprint of a population of devices. The population can consist of product variants or plant-specific variants. In the Elements for IoT dashboard for the digital twin, you link the data relevant to the physical product - the device - from the digital master (geometry models, parts lists, variant configurations) with the field data (technical state, measurement data, construction states) and software configurations.
Increase customer turnover and improve product profitability
The widgets in the dashboard of the digital twin can be configured individually with Elements for IoT: In addition to the master data of the Digital Master and the overview of the devices in the field, you can display maintenance events, charts and analyses, 3D geometries as well as tasks or the activity stream. The analysis of the digital twin forms the basis for better decisions and the creation of added value. With Elements for IoT, make the Digital Twin the linchpin of your digital business models. Increase revenue per customer with services and additional offers as well as by predicting customer behavior and increase your product profitability!
Designing successful products in the closed loop of operation and further development
Analyze and evaluate field data for targeted product improvements
Involve customer communities in the ongoing optimization of products
Use best practices such as ticket systems and quality issues for integration into the development process
Use IoT data for condition monitoring and predictive maintenance
Automate condition monitoring
Offer additional services with predictive maintenance
Analyze whole fleets and environments
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