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Enterprise Data Management (EDM) for Manufacturing from Sightline Systems Equips Quality and Process.

[ClickPress, Fri Feb 10 2017]

Sightline Systems announces the release of its highly anticipated EDM for Manufacturing. The predictive analytics software is designed specifically to help manufacturers take advantage of IIoT system and device data streams, enabling them to proactively address costly challenges that can develop throughout the production process.

EDM for Manufacturing assists process and quality engineers by correlating thousands of data points collected in real time from sensors, SCADA systems, historians, PLCs and more. EDM for Manufacturing displays the information on a highly visualized dashboard, providing an accurate picture of the entire manufacturing operation, enabling floor personnel to more accurately and efficiently identify trends and patterns that affect output quality.

Additionally, manufacturers will be able to leverage the data to better forecast demand, automate manual processes and perform preventive maintenance.

The software uses advanced machine learning techniques to significantly simplify predicting future operational performance. By reviewing historical data which has been collected, EDM for Manufacturing learns the best statistical model to provide a reliable forecast of future behavior.

Forecasts are automatically generated on easy-to-understand dashboards. Alerts can also be created, giving process and quality engineers early warning to problems. Behavioral alerts can also be generated if real time data does not conform to predictions, adding another layer of alerts to a system or process.

Additionally, with EDM for Manufacturing, gone are the days when manual copying and manipulating industrial data by hand or in spreadsheets is the norm. EDM correlates vast amounts of data in microsecond intervals and provides actionable intelligence on a highly visualized dashboard, enabling manufacturers to quickly remedy issues reducing cost, eliminating waste, diminishing production flaws, managing risk and ensuring safety and compliance.

Each incident that results in downtime can cost a manufacturing operation hundreds of thousands of dollars, commented Brandon Witte, president and CEO of Sightline Systems. The advanced analytics capability within EDM for Manufacturing quickly correlates thousands of data points at a sub-second level and quickly presents the information in multiple, easy to understand formats, enabling industrial operators to automate processes and zero in on a potential issue before it becomes a problem. EDM for Manufacturing is changing the entire manufacturing landscape as it facilitates proactive measures resulting in increased gains and profitability in the long run.

EDM for Manufacturing also recognizes anomalies that could indicate that a machine is likely to break down in the near future providing the manufacturer with an opportunity to perform the needed maintenance in non-emergency conditions without shutting down production, saving valuable production time and resources. EDM looks at the history of machine failures and compares those instances to the sensor data the machine is sending to identify trends and patterns that could signal a problem before the breakdown.

To learn more about EDM for Manufacturing, visit www.sightline.com/edm-mfg.

About Sightline Systems

Sightline Systems provides sophisticated, yet remarkably easy-to-use advanced analytics solutions that leverage time series data, predictive analysis, visualization, advanced alerting capability and more, in real time, to provide our customers with a higher level of insight than any other software company can. Customers that implement our solutions enhance efficiency, improve accuracy and increase profitability. For nearly 15 years, Sightline has steadily increased its customer-base to include Fortune 500 and Global 2000 companies in manufacturing, finance, telecommunications, travel, retail as well as federal, state and local governments. For more information, visit: http://www.sightline.com/

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Date:Feb 10, 2017
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