Regular assessment of processing quality and efficiency is critical to the long-term success of any manufacturing operation. A “Process Health” assessment provides an unbiased review of the key drivers of a process along with where and to what extent opportunities exist to improve the process.
How do you do this?
Through the advancement of machine learning in combination with a variety of data capture options you can now quickly and easily run a process optimization analysis. Doing so allows manufacturers to better focus their time on how to make substantial gains in production outputs or reductions in production waste by dealing with root causes rather than just the symptoms.
Human-led analytics efforts alone are simply not sufficient given the myriad operational complexities and massive quantities of data that are generated by increasingly available technologies that capture information about every component of the manufacturing lifecycle. AI and machine learning are critical solutions for innovating within Process Health and allowing manufacturers to achieve true data-driven decision making.
Machine learning tools, such as LityxIQ, can be integrated seamlessly with existing business intelligence or industrial software to take full advantage of data being collected. In fact, machine learning can lead to significant top line revenue or cost savings through continuous analysis of time-series data alone.
When coupled with manufacturing data, predictive analytics enables optimization at a scale and speed that human analysis alone can never achieve. The good news is that many manufacturers can now more easily take advantage of machine learning tools in less time than one might imagine.
Machine learning and data are made for each other. If your organization is capturing vast amounts of operations, process, or production data, you can begin converting that data into insights and optimization models that positively impact your operations and bottom-line in weeks, not months or years.
How long does it take to see progress?
Our team of data scientists can provide a rapid review of your data and suggest quick, relevant proof of concept options that can be conducted in a few weeks. As an AVEVA partner, we know what it takes to succeed with machine learning in the manufacturing environment and can share our experience and best practices.
Let’s start a discussion soon.
Process Health describes the ability to perform and accomplish objectives and goals within a discrete or enterprise process. It is a dynamic condition that results from the continuous adjustment in response to changes happened in the process environment.
Coming in January: How machine learning may be the best tool to help your organization with change management