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We give a primer on how comprehensive data collection from across the plant floor, coupled with digital process signature analysis, can achieve the sometimes contradictory goals of reduced production costs and improved quality.

In this blog post, Sciemetric's Rob Plumbridge discusses what data you should be collecting on your production line, and what data is the most important to determine root cause when there is an issue with quality or throughput. Read the post to learn more.

When we talk to manufacturers, sometimes we find that the difference between machine data and actual part production data has gone unnoticed. While both have their role to play, one is not a substitute for the other, as they don’t measure the same things. This blog post discusses the pros and cons of these different data systems to help you decide what's right for you.

Brake pads can be constructed of a variety of materials, which need to be securely bonded to a backing plate to function properly. Failure to ensure a proper bond between these materials can result in costly scrap and rework. In this blog post, Sciemetric's Jason Koval discusses how to detect delamination of brake pads using impact response testing.

Paper-based data management and reporting workflows tend to limit the line’s capacity and scalability, and are prone to manual error. When these workflows start creating noticeable bottlenecks on your line, it’s time to look for a more streamlined solution. Learn more.

Data is everywhere on today’s production lines. The challenge is that though manufacturers may be collecting large volumes of data, it often isn’t being used proactively or efficiently to solve day-to-day problems. Consider the following best practices to start making better use of your production line data. 

An engine manufacturer was having end of line (EOL) engine function test failures and couldn’t figure out why. Sciemetric systems enabled analysis that pinpointed the problem, and informed new feature checks to catch these problems in the future. See how this could be applied to your line!

We teamed with Smart Manufacturing Magazine on a survey to better understand how readers use data collected from the production line versus how they would like to use it, to drive productivity, quality and profitability in their plants. Here is what the results tell us.

When a product comes back with a warranty claim, the next step is to determine root cause. Robert Ouellette discusses how digital process signature analysis is the key to trace root cause, address the problem fast, contain the scope of a recall and ensure it never happens again.

Question: Dear John,  Now that I’ve collected all this production and signature data, what can I do with it?  Answer: That data is a goldmine, just waiting to be mined.  Let’s start by looking at the pass/fail data, which can be translated into yields. Comparing …