Manufacturing Analytics with Bryan Sapot

Data is the single important thing—whether it’s quality data, failure data or any other kind of data—that helps you make decisions in the best interest if your assets and the organization. You may have set data points but those don’t mean anything unless you know what you are looking for. When you take unstructured data, analyze it, and then turn it into meaningful information to improve manufacturing, it can be called manufacturing analytics. You can use it to make your reliability processes better or you can use it for performance measurement for your assets or personnel, it works great for both.

What organizations do is that they don’t use the data at the right time and that information just gets lost. Manufacturing analytics gives you that visibility of what happened when and that is something you can’t do with traditional methods. It also notifies you when there is an alarming situation and you can fix it before it gets worse. That saves a lot of time and money that you will spend on a failing machine. It helps you assess why the assets are failing and how you can stop it. So, it’s not just a data collection tool.

Yes, it helps you collect data automatically but it also helps you figure out the context of that data you are going to use it in. Then, it should be able to provide a view over a dashboard where every relevant department can see for themselves what’s happening with the machines, at the ground floor, and in the field. That will help you get everyone on the same page. Once you have done that, you can build different predictive maintenance models around that data. You would require a lot of variables that can be used as indicators or causes of the failure and only structured data would make it possible.

When you have a setup like that, you can easily know what’s happening where, you can send out those dashboards to different departments, and then integrate manufacturing analytics into your weekly production meetings. That should help you assess what went wrong when and why? You can dive into the details and then make your operational performance better. Another technique used for data gathering is OEE but it has limitations. The main idea is to get a clear, long-term view of things and then be able to trend it over time.

The maintenance people should be a part of every meeting so that they would understand what kind of metrics they should be focusing on to derive those changes that would increase the uptime. The organizations can start small with a few machines and use manufacturing analytics to solve any quantifiable problem that they are having. You should have a strong leadership to derive that culture change and set this thing up in the system. It will take some time to make it work but once you reach a tipping point, everyone would be interested in sustaining it.

Eruditio Links:

• Eruditio
• HP Reliability
• James Kovacevic’s LinkedIn
• Reliability Report

Bryan Sapot Links:

• SensrTrx
• Bryan Sapot LinkdIn
• LNS Research
• Manufacturing Analytics White Paper
• SensrTrx Improve Manufacturing Performance, Reduce Downtime Webinar

156 Manufacturing Analytics with Bryan SapotJames Kovacevic

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