Implement industrial analytics to build new business models
Once you have connected and have models of your processes as well as right data from them, you can start to identify productivity pockets and optimise all aspects of the operation.
Whether it relates to one machine in a Machine Learning application or a complete material flow in a scheduling solution, the basic methodology is the same when applying analytics to the relevant data.
Using all types of Analytics including AI and ML, you can build new strategies for your Production scheduling, find the right Predictive maintenance model or build a whole fleet of Digital Twins.
With the goal to achieve high-quality control in the production process, Skjern Paper turned to Novotek and GE Digital’s Proficy CSense, an industrial advanced analytics software package that can predict future asset and process performance.
Operators quickly understood the tools in order to gain immediate value and benefit from real-time AI optimisation.
Read how Vattenfall prepares for sustainable energy growth by reaching operational efficiency with ThingWorx and Novotek.
With a need to scale up and laying solid foundation for future integrations with other applications, implementing a centralised platform was more crucial than ever.
Today, staying competitive means progressing with machine learning and analytics.
In this webinar you will learn the journey to success doesn’t require that process engineers need to be data scientists.