Deploy self service analytics on prem or in the cloud
Next generation productivity through self-service industrial analytics
Empower process and asset experts with advanced analytics to Analyze, Monitor and Predict the operational performance of batch, grade and continuous manufacturing processes. TrendMiner is an intuitive web-based self-service analytics platform for rapid fire visualization of time-series based process and asset data.
Become a Process Engineer 4.0
Analytics is essential to support process and asset experts in their day-to-day challenges. Using TrendMiner’s self-service advanced analytics solution, SME’s will become analytics enabled or even expert. This supports improving KPI’s like OEE, product quality and reduces costs.
Leverage the power of self-service analytics
Classic model-based analytics requires data science expertise and is often time consuming. Enabling SME’s to use more advanced analytics, decreases “time-to-insight. This “democratization of analytics” supports people with a deep understanding of the process to improve the performance of the plant.
Use actionable intelligence to drive your business
TrendMiner analyses both timeseries data and contextual data. User can find relevant data, identify trends and create actionable information to solve production issues. Users can troubleshoot problems and monitor processes and assets in real-time to make better decisions, faster.
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.
Illuminate your time-series data with context, to get an clear view on operational behavior. Contextual information may reside in various data silos, such as your LIMS, MMS or OEE system.