Descriptive Analytics for Process Manufacturing

As a self-service analytics platform, TrendMiner applies descriptive analytics for any batch, grade, or continuous production process.

Whether you are in the oil and gas, chemical, mining, or any other process industry, TrendMiner software can help you to improve efficiency and quality, reduce waste and energy consumption, and optimize production performance across divisions.

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What is descriptive analytics?

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In general, descriptive analytics is the examination of historical data to better understand the changes that have occurred in a business. In layman’s terms, descriptive analytics refers to the analysis of data to identify ‘what has happened’ or ‘what is happening’.

Descriptive analytics also helps develop a foundation for diagnostic, predictive, and prescriptive analytics – the next crucial steps of the data analytics journey.

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What is descriptive analytics for process manufacturing?

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In the production process industry, descriptive analytics can refer to the evaluation of time-series data gathered during production to provide decision-makers with a holistic view of performance and trends.

In production, data is gathered from a variety of different sources and processes, including production equipment via sensors for equipment and monitoring systems, and more. These data insights allow process engineers to easily analyze their process data to answer questions, such as:

• How is our production process performing?
• How often did this problem occur?
• What is the root cause of the issue?
• Can I monitor deviations of good behavior?
• What is likely to happen next?
• Can I predict when maintenance is needed?

In short, insights gathered from descriptive analytics help to identify areas of strength and weakness in an organization, in turn, helping decision-makers such as plant managers, production managers, and C-suite executives to form the business strategy.

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TrendMiner for descriptive analytics

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TrendMiner applies descriptive analytics by using advanced search algorithms, fast filtering, data visualization modes, and a tag builder to identify causes of process behavior, assess process performance, and find specific issues – essentially establishing ‘what has happened’.

These features can be accessed via TrendMiner’s innovative trend viewer which provides process experts with a graphical representation of a wealth of historical time series data captured in one or more historians.

Process experts understand what the graphical trend lines of their data mean. But when you have thousands of sensor readings over a long period of time, you need additional tools to know what has happened. TrendMiner enables the process engineers to do this tremendously fast and iteratively, so they can find areas for process performance improvements quickly and easily.

TrendMiner’s descriptive analytics capabilities offer detailed insights about energy consumption, production waste, and product quality for your production line, or even your entire business unit. This will help identify new areas for optimizing operational performance increasing overall profitability while better meeting regulatory compliance. As self-service analytics software, TrendMiner is designed for organizations in a variety of different industries

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