TrendMiner is an intuitive web-based self-service analytics platform for rapid-fire visualization of time series-based process and asset data.
Available as SaaS, On-premises, or Private cloud solution, the TrendMiner plug and play software adds value immediately after deployment. It enables cross-site teams to collaborate, learn and improve the overall performance of all production facilities.
Process Manufacturing Challenges
More and more data is being gathered daily from instruments, sensors, and devices.
How can you turn real-time data into real-time performance optimization?
Advanced analytics requires expert knowledge of the asset and process behavior.
Is it really necessary to build complex data models for every case?
Your company’s process engineers are not data scientists and vice versa.
Can you empower process and asset experts with analytics to let them help transform your business?
What if you could…?
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 move from analytics aware to analytics enabled up to analytics expert. The new Process Analytics Engineer will be better equipped to improve overall equipment effectiveness, improve product quality and reduce costs.
What Can You do With TrendMiner?
Search, as easy as using Google for process behavior in the past. Diagnose by instantly finding similar behavior with use of our patent pending pattern recognition technology and find root causes to improve your processes.
Define optimal processes and set fingerprints to monitor production. These can be used to send out automated early warnings to control room staff in case of deviation, or to capture feedback and leverage knowledge across sites.
Why react when you can predict the future instead? Use our soft sensor builder, early warning discovery or a unique model-free predictive mode to predict quality, maintenance or future evolutions of batch runs and transitions.
Context can be captured via monitors, manually added, or via business applications. Now you can illuminate your time-series data with context, to get a clear view of operational behavior and improve Operational Excellence faster.