Watch this webinar and learn how you can support your company on the road to water sustainability by improving asset reliability.
Learn how you can support your company by leveraging the data in TrendMiner to increase the energy efficiency of your plant.
Accelerate Root Cause Analysis & Improve Data-Driven Decision Making
Watch this webinar on demand to see how easy trend analysis can be; how to use the analytics capabilities to quickly find root causes for process anomalies and monitor operational performance and learn why thousands of users LOVE the new production client for each analytics maturity level and how they all can contribute to business outcomes.
In this webinar, TrendMiner Data Analytics Engineer Eduardo Hernandez discusses how to easily analyze process performance and find root causes fast, monitor performance to get early warnings and predict what is likely to happen, capture events and create your own production cockpit for operational storytelling, as well as best practices to become a data-driven factory and quickly gain value.
Watch this webinar on demand and discover how the data captured in your PI System can be leveraged for day-to-day, data-driven decision making when it’s combined with self-service analytics capabilities.
In this webinar, TrendMiner Customer Success Manager Daniel Münchrath discusses how pattern recognition and machine learning is used to accelerate energy transition goals and how it helps to go net-zero. He presents use cases for improving energy efficiency and explains how to perform a post-implementation review of a retrofit towards renewables using self-service analytics.
In this webinar, Ruchika Tawani demonstrates how self-service analytics can improve the productivity, reliability and safety of fertilizer plants.
Discover how how process engineers and other operational experts can do more with their data themselves, without the need for data scientists.
Discover how how process engineers and other operational experts can do more with their data themselves, without the need for data scientists.
