Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), today at AWS Summit San Francisco announced the general availability of AWS IoT TwinMaker, a new service that makes it faster and easier for developers to create digital twins of real-world systems like buildings, factories, industrial equipment, and production lines. Digital twins are virtual representations of physical systems that use real-world data to mimic the structure, state, and behavior of the objects they represent and are updated with new data as conditions change. AWS IoT TwinMaker makes it easy for developers to integrate data from multiple sources like equipment sensors, video cameras, and business applications—and combines that data to create a knowledge graph that models the real-world environment. With AWS IoT TwinMaker, many more customers can use digital twins to build applications that mirror real-world systems that improve operational efficiency and reduce downtime. There are no upfront commitments or fees to use AWS IoT TwinMaker, and customers only pay for accessing the data used to build and operate digital twins. To get started with AWS IoT TwinMaker, visit aws.amazon.com/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—and then ensure the digital twin is kept up to date as conditions change. 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 who can leverage machine learning and analytics to extract business insights about the real-time operational performance of their physical systems. Because the work required is complex, 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 get started quickly 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 Amazon Simple Storage Service (Amazon S3), AWS IoT SiteWise, and Amazon Kinesis Video Streams (or customers can add their own connectors for data sources like Amazon Timestream, Snowflake, and Siemens MindSphere) 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 system 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.
“Sensors for equipment, buildings, and industrial processes are proliferating and generating massive amounts of data. Customers are increasingly eager to use that data to optimize their operations and processes and one way to do that is using digital twins, but they find that building a digital twin and custom applications is difficult, time consuming, and prohibitively expensive to maintain today,” said Michael MacKenzie, General Manager, IoT at AWS. “With AWS IoT TwinMaker, customers can now derive previously unavailable insights about their operations that inform real-time improvements to their buildings, factories, industrial equipment, and production lines, and make accurate predictions about system behavior with minimal effort.”
AWS IoT TwinMaker is generally available today in US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (Frankfurt), and Europe (Ireland) with availability in additional AWS Regions coming soon.
Siemens Digital Industries Software is a leader in industrial software including digital twin solutions that connect information technology and operational technology across the entire product lifecycle through design, manufacturing, production, and service. “Through this collaboration, we can leverage AWS IoT TwinMaker and other AWS services within the low-code, data management, visualization, simulation, and industrial IoT applications in our Xcelerator portfolio, making it easier for customers to create digital twin solutions that can scale from the simplest to the most complex use cases,” said Brenda Discher, Senior Vice President for Global Strategy & Marketing, Siemens Digital Industries Software. “Together, we are helping our customers increase manufacturing productivity and flexibility, optimize material costs, and better meet their energy and sustainability goals.”
Carrier is a leading provider of healthy, safe, sustainable, and intelligent building and cold chain solutions. “At Carrier, we are pushing to drive more innovation and connectivity to make buildings and the cold chain more sustainable, efficient, and comfortable. To enable rapid development of more digital solutions, we embarked on the development of a shared services platform—carrier.io—as the foundation of all Carrier digital services,” said Dan Levine, Senior Director IoT, Cloud, and Software Engineering at Carrier. “AWS IoT services will be a key enabler to accelerating the development of our carrier.io IoT platform, and AWS IoT TwinMaker will be used to provide critical asset modeling for the platform, enabling our applications to easily create and integrate digital twins of real-world systems. These applications allow our customers to use their data alongside advanced machine learning and data analytics to decrease service costs, optimize maintenance schedules, and increase reliability, efficiency, and profitability of their Carrier equipment.”
INVISTA, which is a subsidiary of Koch Industries, transforms daily life through its innovations in the nylon and specialty materials industries used in clothing, carpets, cars, and computers, just to name a few. “INVISTA is using AWS IoT TwinMaker to help our field personnel efficiently address operational notifications and alerts from plant floors across multiple, distributed locations,” said Jerry Grunewald, Vice President of Operations Transformation at INVISTA. “With AWS IoT TwinMaker, we can quickly and easily build a digital twin of our manufacturing operations to give field workers a consolidated view of all assets and operational data. By doing so, INVISTA operations is making significant progress on our vision of the connected worker as the outcome of our transformation effort. For example, a field technician could pinpoint the source of equipment anomalies and identify appropriate corrective actions.”
John Holland is one of Australia’s leading integrated infrastructure, rail, multi-modal transport, and building companies. By finding solutions to complex challenges and opportunities, John Holland transforms communities to make them easier to move around, be more connected, and become better to live in. “By their nature, construction projects create change to their surrounding environment, and through the use of IoT and other emerging technologies, John Holland can expand the capabilities of our people,” said Bastian Uber, Chief Digital and Information Officer at John Holland. “As an example, projects are subject to environmental regulations, evidence collection, and monitoring of environmental factors such as noise, dust, vibration, and air quality. Using AWS IoT TwinMaker to create a construction digital twin, we aim to provide environmental managers a 360-degree view into the environmental impact of their project. Our platform collects, analyzes, and displays environmental data in a realistic 3D representation of the construction site, so environmental managers can monitor performance, investigate claims, and gather comprehensive historical evidence.”
Element helps industrial enterprises achieve cleaner, safer, healthier, and more profitable operations through analytical insights that are made possible by uniting IT/OT metadata in a flexible knowledge graph, speeding time to insight and governing data in context. “Built with industrial organizations in mind, Element Unify uses automated, no-code data pipelines to integrate and contextualize IT/OT metadata and then stores relationships within the Unify Graph,” said Andy Bane, CEO at Element. “AWS IoT TwinMaker enables users to create highly contextualized 3D scenes and digital twin applications for analytical insights and actions across the plant floor, control room, and remote operations center to help teams collaboratively solve problems with data. The relationships stored in Unify Graph are provisioned as data models in AWS IoT TwinMaker, which is an essential part of making all of this work. Unify shrinks digital twin development cycles by up to tenfold, significantly speeding time to value. It improves data quality through graph-based relationships and brings much needed governance to digital twins that rely on data from multiple legacy IT/OT systems blended together with data from new IoT sources.”
FuseForward provides critical energy, education, and health care service providers a clear path to an intelligent digital future, with expertise across cloud, intelligent systems, advanced analytics, data privacy and governance, and cybersecurity. “Recently, FuseForward embarked on a collaboration with a team of academic researchers to create a digital twin of a complete university campus. We started with one building, and the next step is to scale up to all 60, and AWS IoT TwinMaker will make this possible,” said Dr. Michael Lamoureux, VP of Research and Lab Operations at FuseForward. “With AWS IoT TwinMaker, we now have the tools to facilitate the creation of our digital twins at scale. Components of AWS IoT TwinMaker will support digital twin development through secure streaming data ingestion and storage, 3D visualization of buildings and artifacts, display of operating data, and more. These methods will allow our industry to move away from bespoke, one-off, custom-made solutions and move into an automated digital twin creation process for smart cities that rapidly meets the customers’ needs and expectations over the long term.”
Radix is a global technology company that provides consulting, engineering, operations technology, and digital solutions. “In today’s world of highly automated manufacturing, many enterprises seek innovations that help modernize older process equipment to remain competitive. A historical obstacle to the development of digital twins was the effort to locate and build the relationships between the metadata and all of the different pieces of process equipment,” said Elliott Bell, Program Director at Radix Engineering & Software. “We at Radix have found that AWS IoT TwinMaker helps systematically build the relationships that exist within the engineering documentation, process data, business systems, and maintenance systems, without human intervention. When applied correctly, digital twins are the ‘automatic spell checker’ of a manufacturing process. This greatly reduces the activities that don’t add value in creating digital twins. Without the obstacles of mapping the metadata, our engineers are able to assist our customers in reaching higher levels of value creation faster.”
Matterport is a spatial-data company digitizing the built world that unlocks unparalleled spatial-data insights for companies and individuals to better design, build, promote, and manage their most valuable asset. “Using immersive, dimensionally accurate 3D models from Matterport, AWS IoT TwinMaker allows customers to create game-changing 3D experiences for their users,” said Conway Chen, Vice President of Business Development & Strategic Alliances at Matterport. “Through our collaboration with AWS, enterprise customers in the industrial, manufacturing, and smart-building industries can connect their immersive, dimensionally accurate 3D models from Matterport with IoT devices to enhance remote monitoring, increase working efficiencies, and enable root-cause analysis. This collaboration provides digital twin visualization of any space with associated data insights and analytics, as well as real-time and historical data access to their spaces.”
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