Put Big Data into context in real time

May 21, 2018
Sheila Kennedy says make more-strategic decisions thanks to the power of AI and machine learning.

Smart cities, plants, and machines rely on smart analytics to put big data into context in real time and optimize decision-making. Powering today’s analytics tools are advanced algorithms, artificial intelligence (AI), machine learning, and digital twins that pull disorganized, disconnected, and “dark” (captured but unused) data into the mix of available information and make it actionable.

Elevating industrial intelligence

Strategic algorithms and cognitive computing improve operational acuity. Seeq software uses end-user-focused advanced computing algorithms to collect and analyze time-series data from manufacturing systems and process historians in real time. Trending, pattern and limit searches, outlier detection, modeling, and illustrative visualizations are among the capabilities that can be applied directly to the data sources without assistance from data scientists or IT experts.

About the Author: Sheila Kennedy
Sheila Kennedy, CMRP, is a professional freelance writer specializing in industrial and technical topics. She established Additive Communications in 2003 to serve software, technology, and service providers in industries such as manufacturing and utilities, and became a contributing editor and Technology Toolbox columnist for Plant Services in 2004. Prior to Additive Communications, she had 11 years of experience implementing industrial information systems. Kennedy earned her B.S. at Purdue University and her MBA at the University of Phoenix. She can be reached at [email protected].

Seeq customer engineers and experts possess “a gold mine of knowledge and know-how,” says Brian Parsonnet, chief technology officer and founder at Seeq. “The software automates low-level procedural software tasks, empowering users with expertise in plants and processes to leverage their experience to accelerate insights.”

AI-powered predictive analytics software from Canvass Analytics automates the entire data analysis process and creates adaptive, predictive data models. It allows operations teams to “achieve real improvements in asset uptime through predictive maintenance programs, increase energy efficiency, improve quality, and optimize production processes, knowing they have the latest insight from their connected factory floor,” says Humera Malik, CEO of Canvass Analytics.

Knowledge through virtualization

Digital twin capabilities allow analytics to be formed from a virtual mirror of assets or processes. The Element Platform from Element Analytics is a digital-twin management solution that “unlocks ad hoc analysis on any and all operational data,” says Sameer Kalwani, founder and vice president of product for Element Analytics.

Digital twins allow companies to compare assets, predict equipment failures, and optimize process lines using simple business intelligence tools or advanced machine learning, explains Kalwani. “Companies are realizing that building, let alone maintaining, digital twins requires software,” he adds.

The Digital Twin Builder (Advanced) within Sight Machine’s Enterprise Manufacturing Analytics (EMA) solution enables configuration of a unified data model of the enterprise. Sight Machine’s digital manufacturing platform provides “a scalable pipeline to combine factory data into standard manufacturing data models that mirror machines/lines, parts/batches, plants, and supply chains,” says Ryan Smith, vice president of product and engineering at Sight Machine.

Another EMA feature is its Correlation Heatmap tool, which automatically analyzes tens of thousands of data points to identify which variables and machines are affecting a product’s quality. “Using advanced analytics and machine learning, Sight Machine’s platform helps companies increase productivity, improve quality, and provide remote visibility across the manufacturing enterprise,” says Smith.

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