TrendMiner

The Next Gen Production Client for Process Manufacturing

Process manufacturing companies continuously strive to optimize overall equipment effectiveness, performance, and profitability, while complying with new and ongoing regulations. With the right tools and expertise, the wealth of data generated by these companies from sensors, instruments and assets could be analyzed and used to optimize processes. But traditional “big data” solutions require complex IT projects and data scientists to build and maintain models. Aside from being costly and time-consuming, this approach can create resource bottlenecks and at the same time underutilize their own process and asset experts.

TrendMiner from Software AG enables domain experts to analyze, monitor and predict the performance of manufacturing processes via an intuitive user interface, without the need for support from data scientists or IT. With TrendMiner, process engineers and operators can easily search for trends, question their process data directly, create fingerprints for early warnings, and predict the performance of batch, grade, and continuous production processes.

 With TrendMiner, process and asset experts can, with unprecedent ease and speed:

  • Solve previously unsolved questions, such as identifying the root causes of performance drops.
  • Test and verify the validity of a hypothesis, so it can be addressed or ruled out.
  • Find new ways to improve performance through insights obtained from data.
  • Use actionable dashboards to monitor operational performance in real-time.

Use contextual information from 3rd party business applications to gain additional awareness into operational performance.

Process Engineer 4.0

To benefit from the full potential of industry 4.0, process and asset experts must be empowered with analytics to answer their own day-to-day questions. With use of our self-service advanced analytics solution, process engineers will become analytics aware and enabled. Process Analytics Engineer will be better equipped to improve overall equipment effectiveness.

Self-Service Analytics

Classical model-based predictive analytics typically require expensive projects and data science expertise. For many analytics questions, it is faster and more efficient to use a self-service analytics tool, designed with operational end users in mind. Robust algorithms and familiar interfaces maximize ease of use for non-data scientist users.

Actionable Intelligence

TrendMiner reads all the industrial data generated by your production processes (time series data from sensors, instruments and assets). This big data can then be analysed to identify trends and create actionable information to solve production issues, troubleshoot problems and monitor processes and assets in real-time.

About TrendMiner, the company

TrendMiner, of Software AG, delivers advanced analytics software to optimize process performance in industries such as chemical, petrochemical, oil & gas, pharmaceutical, food & beverages, metals & mining, water & wastewater, and other process manufacturing industries. TrendMiner unlocks the full advantage of their (IIoT) data infrastructure, regardless of vendor, and taps into the available human intelligence for making data-driven decisions. We offer standard integrations with a wide range of data sources such as OSIsoft PI, Yokogawa Exaquantum, AspenTech IP.21, Honeywell PHD, GE Proficy Historian and Wonderware InSQL, Cumulocity, OSIsoft OCS, AWS S3, SiteWise, Timestream, Microsoft ADL, ADX, TSI and SAP S/4 HANA DMC. 

TrendMiner empowers everyone in manufacturing operations, across locations, with powerful yet intuitive capabilities to iteratively generate and validate real-time context-aware time-series insights, both as individual and as a collaborative team. Search, diagnostic, and predictive capabilities enable users to speed up root cause analysis, define optimal processes, and configure early warnings to monitor production 24/7. The outcome is an increased quality and velocity in decision making to help drive business outcomes and increasing overall profitability. 

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