A Platform For Scaling Digital Twin Benefits

Digital Twin Technology Versus a Digital Twin Platform

Some technology & service providers offer digital twin-enabling technologies such as IoT integration, modeling, analytics and simulation tools. Others offer digital twin templates for specific equipment or business applications. This is necessary but not enough.

Owner operators have hundreds of twin-able assets and processes with diverse use cases, model types and data sets. All of these twins must be organized and connected with each other to achieve system-wide value. And they must be continuously adapted and maintained for accuracy. This takes an industrial-strength digital twin platform.

Automation and Intelligence Across the Digital Twin Lifecycle

is a full-lifecycle platform for scaling digital twin benefits across industrial enterprises. We use AI and standardization to automate many of the processes associated with digital twin creation, integration, deployment, and management. nD empowers domain experts and data scientists alike to solve industrial-scale business challenges with ease and speed. All with a seamlessly-integrated user experience.

We Manage Complexity. You Deliver Value.

Build

With nD, create discrete digital twins, integrated systems of digital twins, and standard twin classes for rapid replication. Connect to and validate data sets, build and integrate models of any type, and define use cases and KPIs.

  • Auto contextualize and visualize data
  • Create standard twin classes using structured templates
  • Codify institutional knowledge
  • Rapidly prototype use cases with automated machine learning (ML) frameworks
  • Integrate engineering, physics-based and ML models

Digital Twin Platform Checklist

  • Big Data Management
  • Democratized Access
  • Visualization and Analysis

Deploy

Take your twins live, seamlessly moving from prototype to production application. Let nD’s automated workflows do the heavy lifting to instantiate new twins, apply new insights on live streams, and ensure your digital twins work tirelessly to solve your toughest business problems.

  • Publish twins to real-time data streams
  • Auto-instantiate twins using standard classes
  • Deploy turnkey performance, maintenance and optimization applications
  • Auto-align, validate, and apply new data

Digital Twin Platform Checklist

  • Hyperautomation
  • AI-Powered Applications
  • System-Wide Integration

Operate

Transparency is the name of the game with the nD Portal, a visual way to interact with your digital twins and their key performance indicators (KPIs) as they're operating. Aggregate, filter, benchmark, visualize and explore how your twins are operating across time, place, state and status.

  • Continuously monitor, diagnose, predict, prescribe and optimize
  • Track twin operating status, state, performance, health, and issues
  • Aggregate and filter by twin class, status, health, and location
  • Visualize anomalies, issues, diagnostics, and optimizations
  • Run "What-if" simulations and scenarios

Digital Twin Platform Checklist

  • Transparency
  • Visualization and Analysis
  • AI-Powered Applications

Maintain

Keep your digital twins, and all their classes, versions and models, up to date and producing trusted outcomes with nD.

  • Version control engineering models
  • Continuous learning AI models
  • Release standard class changes to all relevant twins
  • Control new releases with alpha and beta testing

Digital Twin Platform Checklist

  • Full-Lifecycle Management
  • Transparency
  • Big Data Management

nD Digital Twin Platform Checklist

  • Big Data Management

    A digital twin platform must rapidly, reliably and securely process huge data volumes and velocities. It must structure messy streaming data to always be in the right order and apply an event-driven architecture that triggers actions at just the right times.

  • Full Lifecycle Management

    A digital twin platform requires standardized workflows for building, deploying, operating and maintaining huge numbers of digital twins and their associated digital masters and models. It must seamlessly move from prototype to production application. And twins must be constantly updated to reflect reality and drive outcomes based on continuous intelligence.

  • System-Wide Integration

    A platform is required for discrete twins to be seamlessly integrated into composite or organizational twins. Equally important is the need to integrate with other systems to drive action – such as using open APIs and advanced streaming technologies to connect twins with enterprise systems like ERP. 

  • Hyperautomation

    A digital twin platform must incorporate hyperautomation in order to deploy twins at scale, reduce complexity and accelerate value. This requires standardized twin templates and workflows, expert knowledge codification and the ability to automatically learn and evolve using AI. Examples include auto-instantiating new twins when a data match is found and automatically structuring, visualizing and applying machine learning to twin data based on pre-defined standards.

  • AI-Powered Applications

    AI enables digital twin value to extend beyond situational awareness to also predict what’s next, prescribe solutions and take optimized actions in real-time.

  • Transparency

    Digital twins by definition provide greater operational and business transparency. Digital twin platforms must go a step further and provide transparency across the full digital twin lifecycle – making things like model management and twin performance against goals easy to access, understand and share. 

  • Visualization and Analysis

    Related to transparency, digital twin platforms should make it easy to visualize and interact with digital twin data. Users from different perspectives should be able to view relevant results as high-level dashboards or deep drill downs. 

  • Democratized Access

    Digital twins will never reach their full potential if their creation and application rely solely on computer and data scientists. A scalable platform puts the power of AI digital twins in the hands of domain experts who know how to extract business value. It makes it easy for domain experts, business analysts and data scientists to collaborate in a shared environment. Examples of democratization include code-free twin development and AI model wizards.

Ready to maximize the value of your data?