AWS Announces General Availability of AWS IoT TwinMaker

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.