The Audit Trail has been a part of GainSeeker since the early versions of GainSeeker. This useful module hardly gets noticed. When I train on the System Administration module / Utility module I make sure to focus on this little hidden gem! The Audit Trail makes it easy to see who changed the Specification Limits of a Standard, or who edited or deleted data.
A new case study highlights how a national, multiplant foods company used real-time data to reduce overpack. The effort resulted in a multimillion dollar savings. One striking aspect of this story is the level of frustration our client felt with the way things worked before implementing GainSeeker. Because he was working with old, outdated information, he was stuck with a rear-view mirror perspective. This is something many of our clients have in common: having to make decisions with inadequate knowledge.
If you are in the manufacturing world, measuring process capability ( Cpk ) is extremely important. That's because you're always striving to improve your numbers. It is no wonder people are very curious as to how their Cpk gets calculated in GainSeeker. If GainSeeker's Cpk does not match someone else's calculation of Cpk... we (the technicians) get a call. Believe it or not the technicians at Hertzler Systems receive this question quite a bit. So, why does this happen?
GainSeeker's data brushing feature lets you select the data points you want, then perform actions with those points on the spot. You can draw charts with that data, exclude it, and more. It gives you instantly actionable data.
Did you know that manufacturers with visibility to real-time metrics in manufacturing have a higher average OEE (Overall Equipment Effectiveness) than those who don’t? According to the LNS Research Report: "Big Data - Driving Quality Intelligence at the Speed of Manufacturing", the difference is significant: 5 percentage points higher. You can request your copy of this reporthere.
GainSeeker Get External feature lets you copy and paste data from any text file or table into GainSeeker Suite for a quick and easy way to turn a table of numbers into something far more useful.
Recently we deployed a pilot project for collecting quality check data for a new customer. The pilot is on one line only, but because it involves multiple people, training, custom configurations, and so forth, the detailed deployment plan required a number of steps. The customer's Project Manager for the deployment embodies great project management. He put together a superb plan, and drove it all from a simple spreadsheet. Our project manager, Craig Glick Miller, liked his spreadsheet so much that he made a generic version of it and, with our customer's permission, we offer it here for your review and use.
He needed a Data Wrangler, or Data Janitor, to help him build the bridge between all the data he was collecting in his MES system and the insights he hoped to gain from that data. “I had one of my engineers build some macros in Excel that go into our MES system and extract the data to an Excel workbook. Once it is there, he has to spend hours scrubbing the data before he can do any useful analysis.” He went on to say, “It might be ok for an occasional study, but we need to be looking at this data throughout the day, every day. This manual process is just not sustainable.” My customer was pointing out the obvious: wrangling data is not a value added activity. Not only that, it isn’t sustainable for daily operations.
The age of the IoT (Internet of Things) is upon us. New devices and equipment are coming on line all the time, and it can be a little overwhelming. It all contributes to the feeling that there are too many disparate data sources out there. But the IoT has been coming for a while. This post shares our first experience of it.
There is a lot of evidence that real-time closed loop quality data systems help manufacturers perform at higher levels. But many manufacturers are struggling. They're struggling because they have too much or too little data, or it takes way to much effort to get to their data, or they only get to the data too late to make any difference , or...
