Twinit has been architected to be fully composable, adaptable, and flexible to enable enterprises and systems integrators to use it as a platform for continuous improvement and innovation. Twinit allows an agile approach to implementing applications, focussing delivery of outcomes on known requirements. Based on learnings, the applications can be enhanced progressively, increasing in sophistication and value.
Digital Twin enabled applications can grow in capability based on the sophistication of the underlying digital twin, based on a maturity model that can be described in five levels.
Level 01: Descriptive Twin
Twinit enables the creation of data models, data integrations, knowledge graphs and role-based user interfaces with multi-modal visualisations to describe discrete entities like a singular product or a composite entity like a building or an infrastructure asset that is made of multiple discrete entities. It allows the creation of descriptive virtual replica of such physical assets.
Level 02: Informative Twin
Through its telemetry channels and integration options, applications composed on Twinit can integrate time series performance data, and transactional data from external systems to a descriptive twin. Applications can integrate analytics tools to enable insight and intelligence to enhance the application’s knowledge graph. The notification service and Twinit’s eventing infrastructure can be used to drive condition-based insights for different user groups.
Level 03: Predictive Twin
Applications built on Twinit can serve as a single source of truth for third party AI powered simulation models and Machine Learning tools. The resulting simulation results can be represented in Twinit for the application to power experiences that enable users to visualise predicted outcomes based on defined process variables, allowing users to make proactive interventions.
Level 04: Prescriptive Twin
Twinit can enable applications to leverage AI powered simulation models to generate prescriptive recommendations by accessing data via the application’s knowledge graph published using Twinit’s Item Service. These recommendations can be presented to the user for decision support.
Level 05: Transformative Twin
As simulation models mature, applications on Twinit will be able to leverage Twinit’s Data Sources service to drive autonomous actions on integrated systems that manage the physical world. An example would be the autonomous control of traffic management systems based on prescriptions generated by the simulation models using data from AI powered traffic cameras with edge analytics feeding information to the digital twin.