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Rockwell presents scalable analytics platform for industrial IoT applications

John Genovesi, Rockwell Automation
John Genovesi, Rockwell Automation

Rockwell Automation, a provider of smart manufacturing solutions, has a stated goal of enabling advanced analytics for manufacturing. The firm has unveiled a new IoT tool called, Project Scio, which is designed to offer manufacturers a solution that enables scalable data insight.“Providing analytics at all levels of the enterprise – on the edge, on-premises or in the cloud – helps users have the ability to gain insights not possible before,” said John Genovesi, vice president, Information Software, Rockwell Automation. “When users gain the ability to fuse multiple data sources and add machine learning, their systems could become more predictive and intelligent. Scio puts analytics to work for everyone. The scalable and open platform gives users secure, persona-based access to all data sources, structured or unstructured. And a configurable, easy-to-use interface means that all users can become self-serving data scientists to solve problems and drive tangible business outcomes.”

According to the firm, to make decisions when and where they matter most, a Project Scio platform reduces hurdles to unleashing information. The platform opens access to ad-hoc analytics and performs advanced analysis by pulling structured and unstructured data from virtually any existing source in the enterprise. It can also intelligently fuse related data, delivering analytics in intuitive dashboards – called storyboards – that users can share and view. Users then have the ability to perform self-serve drill downs to make better decisions, dramatically reducing the time to value.

Project Scio features several attributes from being an open platform to having flexible machine learing. The platform can auto-discover Rockwell Automation devices and tags, as well as third-party device data, to save time and help reduce risk, when mapping software to each plant-floor device. Additionally, the auto-discovery process gives users access to more detailed information than is typically available through manual mapping, such as device name, line location and plant location.

Rather than leave data at its source and take database snapshots, the platform brings data into a centralised location and can continually refresh that data. Additionally, connections to data sources only need to be established once. This connection allows users to create custom analytics and refresh them at their preferred rate without the support of a data scientist.

By using the right machine learning (ML) algorithm for the right use case, users can enable flexible ML. The Project Scio platform is configurable to support many industry-leading algorithms, including SparkML, MLLib and Python.

Using either ML or predefined settings, the platform can monitor operations and automatically trigger control adjustments if processes start to fall outside allowable parameters. This can help users optimise control, improve product quality and consistency, and reduce scrap and waste.

Rockwell Automation will introduce an applications marketplace for applications developed in-house and by third parties. The ability to access any data source and create custom analytics for each user’s application is a central feature. However, users can also take advantage of pre-engineered FactoryTalk Analytics applications from Rockwell Automation. These applications allow users to monitor common KPIs, such as OEE and quality, in a standardised way and without any configuration.

This scalable and open-architecture platform is designed to be extended to a full ecosystem of IIoT data sources. The quick connection to the full range of systems that feed data into a connected enterprise includes controllers, MES software and edge devices.

In addition to these information solutions, Rockwell Automation offers a full range of connected services which helps provide customers the ability to ensure network integrity, security, infrastructure design and maintenance, and remote monitoring of equipment including predictive maintenance.

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