The manufacturing industry is on the cusp of major changes driven by machine learning and artificial intelligence. We’ve previously discussed using algorithms to unlock the potential of predictive maintenance but maintenance isn’t the only domain where developments in data science are impacting manufacturing.
The manufacturing use cases that are posed for the biggest shift revolve around upskilling and empowering human workers to make data-driven decisions.
According to McKinsey, “[T]oday’s downsized teams of control-room operators… must troubleshoot and run tests and trials, to name just a few of the tasks that strain the limits of their human capacity. As a result, many operators take shortcuts and prioritize urgent activities that don’t necessarily add value.”
With machine learning, you can surface insights to human workers via interactive dashboards that allow them to make the right decisions quickly and easily.
In this webinar, you’ll learn:
In this webinar, we cover the topic of process optimization through the lens of RapidMiner’s virtual optimizer—a real-time prescriptive dashboard that lets workers understand the current state of your operation, experiment with potential changes, and then implement the best solution.
… best practices for applying machine learning to manufacturing
… practical ways to optimize manufacturing processes with machine learning
… how RapidMiner’s virtual optimizer upskills domain experts
… that RapidMiner’s virtual optimizer is useable even by those with minimal data science experience