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What is GE Digital Twin?

Employing advanced analytics and machine learning methodologies is vital for reducing operational costs and alleviating risks. Digital twins, a key element of the digital transformation journey, offer exact virtual models of physical assets, systems, and objects that aim to improve productivity, streamline processes, and boost profitability. Generally, a digital twin is seen as a software representation of a tangible asset or system designed to identify, prevent, forecast, and enhance operations through real-time data analysis, ultimately providing considerable advantages for businesses. At GE Digital, we focus on utilizing digital twin software to support our clients across three essential areas: Asset, Network, and Process. By adeptly overseeing, simulating, and controlling an asset or process, organizations can greatly improve system performance. Moreover, it is imperative to prioritize the health and safety of employees and the environment while pursuing business objectives, which entails reducing incidents associated with assets and processes and avoiding unexpected downtimes, thus nurturing a more robust operational structure. The adoption of digital twin technology not only boosts efficiency but also opens doors for innovation across numerous industries. As organizations embrace this technology, they position themselves for sustained growth and competitive advantage in an ever-evolving marketplace.

What is AS-TEG?

Our cutting-edge software creates a comprehensive three-dimensional digital model of both onshore and offshore processing facilities by consolidating various data sources to produce an accurate and detailed digital twin. This state-of-the-art technology optimizes numerous phases, including inspections, repairs, and the management of large-scale projects, facilitating an in-depth design process that reduces the need for regular physical site visits, thereby transforming the traditional approach to continuous on-site mobilizations. The AS-TEGâ„¢ platform provides a tailored digital twin experience, allowing operators and contractors to quickly retrieve and pinpoint as-built, design, and integrity data straight from their computers, no matter where they are located. As a result, this innovation significantly boosts operational efficiency and enhances decision-making capabilities, streamlining complex tasks that previously required extensive time and effort. Furthermore, the integration of this technology fosters better collaboration among teams, ultimately leading to improved project outcomes.

Media

Media

Integrations Supported

APERIO DataWise
Cognite
IBM Maximo Application Suite
Microsoft Power BI
SAP EAM

Integrations Supported

APERIO DataWise
Cognite
IBM Maximo Application Suite
Microsoft Power BI
SAP EAM

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

GE Digital

Date Founded

2015

Company Location

United States

Company Website

www.ge.com/digital/applications/digital-twin

Company Facts

Organization Name

eserv

Date Founded

2015

Company Location

Scotland

Company Website

www.eserv-int.com

Categories and Features

Simulation

1D Simulation
3D Modeling
3D Simulation
Agent-Based Modeling
Continuous Modeling
Design Analysis
Direct Manipulation
Discrete Event Modeling
Dynamic Modeling
Graphical Modeling
Industry Specific Database
Monte Carlo Simulation
Motion Modeling
Presentation Tools
Stochastic Modeling
Turbulence Modeling

Categories and Features

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