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Canary Labs has published a white paper that describes how companies can gain insight into how their process operates, find problems and improve quality by using process analytical software.

Process analytics and intelligence – sometimes called manufacturing intelligence – has transformed the way companies produce goods, understand their manufacturing processes and ensure a quality product.

Best-in-class manufacturers use large volumes of real-time process data, generally stored in a process historian, as the foundation to drive real-time analytics and dashboards that improve their ability to detect and react to process bottlenecks or quality issues.

In many cases, real-time analytics have replaced the legacy concept of running reports.

Reports that represent a static picture of a process at a fixed point in time are great tools for compliance audits and long-term warranty analysis.

However, they may not accurately represent the ‘as-is’ state of a process.

With many reports, the end consumer is often expected to know how to interpret the data and has a limited ability to drill down and view data by a subcategory or sub-classification.

Analytics, on the other hand, are generally used to summarise and further dissect the data in forms that require minimal input from the user.

Instead of users having to interpret the data, it is presented in graphical form to enable them to more easily drill down to explore the data and draw a conclusion based on real-time information.

Real-time process analytics and intelligence has the potential to expose a greater amount of process data than traditional reporting is capable of delivering.

Reports that provide data to knowledgeable users allows those users to interpret and draw conclusions from the data based on their domain knowledge and understanding of the process.

However, these reports may not be the best vehicle to deliver information to the majority of company personnel.

Real-time analytics helps to bring process issues into focus.

While graphical analytics may not answer all the questions, they help to expand the understanding of the issues behind the data.

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