AWS Announces AWS IoT TwinMaker
New service makes it faster and easier to create digital twins of real-world systems like buildings, factories, industrial equipment, and production lines—helping many more customers build applications that improve operational efficiency and reduce downtime
Carrier, Siemens, and Accenture among customers and partners using AWS IoT TwinMaker
Industrial companies collect and process vast troves of data about their equipment and facilities from sources like equipment sensors, video cameras, and business applications (e.g. enterprise resource planning systems or project management systems). Many customers want to combine these data sources to create a virtual representation of their physical systems (called a digital twin) to help them simulate and optimize operational performance. But building and managing digital twins is hard even for the most technically advanced organizations. To build digital twins, customers must manually connect different types of data from diverse sources (e.g. time-series sensor data from equipment, video feeds from cameras, maintenance records from business applications, etc.). Then customers have to create a knowledge graph that provides common access to all the connected data and maps the relationships between the data sources to the physical environment. To complete the digital twin, customers have to build a 3D virtual representation of their physical systems (e.g. buildings, factories, equipment, production lines, etc.) and overlay the real-world data on to the 3D visualization. Once they have a virtual representation of their real-world systems with real-time data, customers can build applications for plant operators and maintenance engineers that can leverage machine learning and analytics to extract business insights about the real-time operational performance of their physical systems. Because of the work required, the vast majority of organizations are unable to use digital twins to improve their operations.
AWS IoT TwinMaker makes it significantly faster and easier to create digital twins of real-world systems. Using AWS IoT TwinMaker, developers can quickly get started building digital twins of devices, equipment, and processes by connecting AWS IoT TwinMaker to data sources like equipment sensors, video feeds, and business applications. AWS IoT TwinMaker contains built-in connectors for AWS IoT SiteWise, Amazon Kinesis Video Streams, and Amazon S3 (or customers can add their own connectors for data sources like Amazon Timestream or Snowflake) to make it easy to gather data from a variety of sources. AWS IoT TwinMaker automatically creates a knowledge graph that combines and understands the relationships of the connected data sources, so it can update the digital twin with real-time information from the system being modeled. Customers can import existing 3D models (e.g. CAD and BIM files, point cloud scans, etc.), directly into AWS IoT TwinMaker to easily create 3D visualizations of the physical systems (e.g. buildings, factories, equipment, production lines, etc.) and overlay the data from the knowledge graph on to the 3D visualizations to create the digital twin. Once the digital twin has been created, developers can use an AWS IoT TwinMaker plugin for Amazon Managed Grafana to create a web-based application that displays the digital twin on the devices plant operators and maintenance engineers use to monitor and inspect facilities and industrial systems. For example, developers can create a virtual representation of a metals processing plant by associating data from the plant’s equipment sensors with real-time video of the various machines in operation and the maintenance history of those machines. Developers can then set up rules to alert plant operators when anomalies in the plant’s furnace are detected (e.g. temperature threshold has been breached) and display those anomalies on a 3D representation of the plant with real-time video from the furnaces, which can help operators make quick decisions on predictive maintenance before a furnace fails. With AWS IoT TwinMaker, many more customers can use digital twins to build applications that simulate their real-world systems to improve operational efficiency and reduce downtime.
“Customers are excited about the opportunity to use digital twins to improve their operations and processes, but the work involved in creating a digital twin and custom applications for different use cases is complicated, expensive, and prohibitive for most,” said
AWS IoT TwinMaker is available today in preview in US East (
Carrier Global is a leading provider of healthy, safe, sustainable, and intelligent building and cold chain solutions. “Today, our objectives extend beyond HVAC and refrigeration and into the development of healthy, safe, and sustainable intelligent buildings. With our Abound platform, we aggregate building performance data from a variety of systems and sensors, offering customers real-time insight into their connected spaces. Enhancing this platform with digital twins of buildings for their owners and operators has been a top priority for us,” said
Siemens is a leader in providing software to create comprehensive digital twins for design, manufacturing, and service. “We are excited to work with AWS and expand connections between our Xcelerator portfolio and AWS services including the new AWS IoT TwinMaker service. Through this collaboration, developers will be able to create digital twin solutions that can scale from the simplest to the most complex use cases by combining our rich application services for low-code, visualization, simulation, and industrial IoT with AWS IoT TwinMaker and other AWS services,” said
Accenture is a global professional services company with leading capabilities in digital, cloud, and security. “Digital transformation of manufacturing is a huge opportunity for our clients who often face challenges with fragmented, siloed, and unstructured industrial data, leaving many proofs of concept unscalable,” said
INVISTA, a subsidiary of Koch industries, is a leading global manufacturer of fiber, resins, and chemical intermediates. “Working closely with AWS over many years, we have been building a strong analytics and data science capability to support our manufacturing operations and find new and better ways to improve our products and processes,” said
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