Self-Service Industrial Analytics

TrendMiner is an advanced analytics platform for time series data enabling experts in the process manufacturing industry to analyze, monitor and predict process and asset performance in their operational context to improve operational excellence and overall profitability.

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.


  • 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

Key Features of TrendMiner


Perform analysis using discovery tools such as:  Visualizations, statistics, search for conditions and patterns to quickly find the root cause by.



Monitor your manufacturing process by setting up predictive maintenance or AutoML (Automated Machine Learning) anomaly detection for early warnings and alerts.



Based on model-free predictions and Python notebooks, it is possible to predict what is likely to happen.



Put your data into context to continuously improve the performance of your equipment to optimize the manufacturing process. Event and asset data based on your process conditions are categorized, and digital twins can be created.


Report & visualise

Thanks to dashboards and visualization options, you can create clear reports for operational storytelling.

Our TrendMiner services at a glance

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