Level 1 - Descriptive Twin: The descriptive twin is a live, editable version of design and construction data—a visual replica of a built asset. Users specify what kind of information they want included and what kind of data they want to extract. It becomes a single source of truth for every possible stakeholder because manuals, notes on part numbers, maintenance schedules, and countless other documentation assets can be ingested and synthesized for universal access.
Level 2 - Informative Twin: This level has an added layer of operational and sensory data. The twin captures and aggregates defined data and verifies data to make sure that systems work together. This data is continuously collected in defined intervals and aggregated to build time-series performance data trending ripe for interpreted insights.
Level 3 - Predictive Twin: This twin can use operational data to gain insights. Think of an IoT reading showing you a vibration in a machining device, warning you that a motor bearing is worn down before you experience problems, providing lower air flow readings warning you that filters need changing, or sharing seasonal energy use metrics informing on your utility bill budget.
Level 4 - Comprehensive Twin: This twin simulates future scenarios and considers “what-if” questions, allowing organizations to assess multiple potential outcomes before making critical decisions.
Level 5 - Autonomous Twin: The most advanced level, this twin has the ability to learn and act on behalf of users. By leveraging AI and ML, an autonomous twin can optimize operations in real time, making adjustments without the need for human intervention.
