PTC Unveils Broad Platform Innovations that Move Computing to the Edge.
PTC today announced at LiveWorx 2016 that it is expanding its Internet of Things (IoT) technology platform by delivering comprehensively distributed, real-time edge computing capabilities, specifically designed for the rigor of industrial use cases. PTC now offers a pre-integrated edge ‘solution stack’ that collects and aggregates data from sensors, performs highly automated machine learning and predictive analytics, enables web and mobile development, and supports augmented reality (AR) experiences. With these innovations, PTC not only supports edge computing with the technology platform, but additionally supports distributed, hybrid deployments that include capabilities both at the edge and in the cloud.
Turning billions of points of data into actionable information is one of the IoT’s primary value-drivers, but organizations have historically faced challenges with traditional architectural approaches for IoT computing – due to the volume, velocity, and variety of data produced within an IoT ecosystem. PTC addresses this challenge with a robust platform that offers comprehensive IoT computing at the edge – computing that occurs on or near the connected device itself. For organizations with cost, latency, and security considerations for computing, PTC removes reliance on the cloud, keeping the entire IoT computing operation onsite, close to the point of data acquisition.
These edge computing capabilities provide the foundation for an optimized IoT system architecture. Customers now have the flexibility to deploy the IoT platform capabilities they need, in the computing configuration they want – deploying capabilities at the edge for speed and efficiency, and integrating to the cloud for enterprise connectivity and further computing. In situations where customers would like to deploy a distributed architecture, PTC offers pre-integrated access to a number of leading public device clouds with an open platform strategy.
PTC’s edge computing capabilities are part of its IoT technology platform. Primary sensor data from control systems is collected via Kepware and can be aggregated with data from secondary sensors collected via partners. Tight integration with ThingWorx Analytics enables seamless ingestion of sensor data into automated machine learning capabilities, enabling real-time anomaly detection and failure prediction. ThingWorx additionally provides web and mobile application enablement and runtime capabilities, providing rapid development of role-based user experiences. Expanding the user experience further, Vuforia Studio Enterprise enables the creation of both augmented reality and virtual reality experiences tuned for IoT use cases. PTC will work with IoT technology leaders like Hewlett Packard Enterprise (HPE) and National Instruments (NI) to optimize the performance of the PTC technology platform. This optimization enables ease of deployment and rapid time to value in cases where edge computing is required.
“Enterprises of all sizes and in all IoT-related industries cannot afford unplanned downtime that disrupts operations due to a lack of insight about how their devices are working,” said Jim Heppelmann, president and CEO, PTC.
“By introducing real-time edge computing capabilities, PTC is enabling IoT solution builders with incredible flexibility in architecting IoT deployments.”
“In turn, solution builders bring efficiency, speed, cost control, and security to the IoT solutions that they develop for their customers.”
“PTC is a leading player in the industrial IoT data analytics market, including services for the extremely important predictive and prescriptive analytics. Their new real-time, edge analytics offering will add to their already stellar IoT portfolio and provide any enterprise with a holistic set of analytics services, from the edge to the cloud,” said Dan Shey, managing director and vice president, B2B, ABI Research.
These edge computing capabilities are being demonstrated at PTC’s LiveWorx IoT conference on both a Flowserve pump demonstrator and a robotic manufacturing line demonstrator.