New scalable analytics platform for Industrial IoT Applications

08 December, 2017

Rockwell Automation has a stated goal of enabling advanced analytics for manufacturing. Project Scio is the next step along that continuum. 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.

Making better decisions

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.

“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 of 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.”

Key attributes of Project Scio include:

• Device Auto-Discovery: Manually mapping software to each plant-floor device can be a time-consuming and error-prone process. The platform can auto-discover Rockwell Automation devices and tags, as well as third-party device data, to save time and help reduce risk. 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.

• Leave Isolated Analytics Behind: 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.

• Flexible Machine Learning (ML): Use the right ML algorithm for the right use case. The Project Scio platform is configurable to support many industry-leading algorithms, including SparkML, MLLib and Python.

• Closed-Looped Analytics: 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.