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https://www.trendminer.com/wp-content/uploads/2018/06/DSC_3523.jpg 1500 2250 Edwin van Dijk https://www.trendminer.com/wp-content/uploads/2018/11/RGB-1-trendminerSAG_website.png Edwin van Dijk2018-06-12 16:22:012018-10-23 19:57:50Software AG acquires TrendMiner for analysis and visualization of time series dataEmpower process and asset experts with advanced analytics to Analyze, Monitor and Predict the operational performance of batch, grade and continuous manufacturing processes.
Digitalization
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?
Analytics
Advanced analytics requires expert knowledge of the asset and process behavior.
Is it really necessary to build complex data models for every case?
Transformation
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?
Acceleration
Improving profitability requires acceleration of change.
How can you quickly embed an advanced analytics solutions in your organization and successfully manage the change?
What if you could…?
Process Engineer 4.0
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.
Self-service Analytics
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. As a result, many cases to optimize the operational performance of the plant can be handled by the people who can really interpret the data.
Actionable Intelligence
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. Users can troubleshoot problems and monitor processes and assets in real-time to make better decisions, faster.
Our Software
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 overall performance of all production facilities.
Analyze
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.
Monitor
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.
Predict
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.
Industries Served
Our self-service industrial analytics software is used by leading companies in a wide range of process manufacturing industries, including
Chemicals
Petro-chemicals
Oil & Gas
Pharmaceuticals
Pulp & paper
Metals & Mining
Food & beverage
Utilities
Power Generation
“TrendMiner is a user-friendly self-service analytics tool that provides us with tremendous time savings and delivers better quality results than data models.”
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