The following webinar preview has been approved for all audiences.
Movies are making a comeback in 2021. That also means movie trailers are making more prevalent appearances. Unlike the movie industry, our webinar series had no need to slow down during the pandemic. We’re still going strong this year with topics that’ll get you excited for the future of quality and manufacturing. Our August release, “A Practical Look at How AI Will Change Quality and Manufacturing,” will show you what we can do with artificial intelligence (AI) now and how it’ll change your future.
The best way to talk about where we are with AI is to talk about its forebearers. Advanced analytics paved the way for AI and some of what we can do with these technologies blurs the lines between advanced analytics and AI. A good example of this is predictive and prescriptive analytics. It’s certainly possible to look at data and analyze it to predict future events without using AI, but it is only one piece of the puzzle. Regardless of which technology is being used, the important point is what AI and analytics can do for quality and manufacturing.
A major use case for quality lies in quality event management (QEM). Manual QEM is an arduous process, but by using analytics and/or AI, quality professionals can cut much of the frustration out of the process. We’re talking about an application that tells you if an event should be escalated to a corrective action/preventive action (CAPA), or if that event is similar to others that have occurred. A more advanced application can tell you what action to take and what the likely results will be. This eliminates the guesswork from the process and also ensures you don’t inadvertently make things worse.
For manufacturing, knowing when you might have downtime and avoiding it are benefits of AI. With enough data, an application can go beyond telling you how to avoid problems and can focus on making improvements. For example, it can tell you which employees should be placed on a specific line to improve yield or quality, and the percentage of improvement that combination will be expected to produce.
Those scenarios are possible with the technology we have right now. This isn’t theoretical. Using analytics and AI properly does require a bit of leg work beforehand. The data needs to be digital for it to be advantageous but cleaning it up first is important. Just as digitizing bad processes just helps you do the wrong thing faster, digitizing bad data just means you can quickly draw the wrong conclusions from it. Since AI is trained on data, the more faulty data the application is exposed to, the more incorrect it becomes. In this case, using AI will have the opposite effect that it’s supposed to have.
That being said, how does one go about cleaning up data? And, short of hiring data engineers, how does a company use AI? Especially a small- or medium-sized company? Those are all valid questions that we want to help you answer.
You can get answers to those questions during our August 4 webinar. Erin Wright, director, product management at MasterControl, will help webinar attendees understand how they can make AI work for them. Wright will address how to prepare a company’s data for AI and the tools available to help, including a new product from MasterControl. Be sure to join us for Wright’s presentation and learn how you can implement these advanced technologies in your organization. Register here.
Enjoying this blog? Learn More.
Novelty Becomes Necessity 2021 Analytics TrendsDownload Now