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...
Once you've eliminated redundant and irrelevant testing, you can concentrate on breaking down the data silos so you can make better use of the data that matters. I can't tell you how many times I've seen these data silos in action. Data silos hide critical information and make it very difficult to make meaningful performance improvements.
The key to maximizing data is not about availability or capture but rather having capable solutions for delivering the right intelligence to the right people at the right time.
What is lost in a lot of the buzz surrounding the shake up concerning Big Data is the practical advice we need to know to understand how it will directly impact the way we manage quality. This LNS Research paper identifies 5 critical impacts, and how point-solution SPC software such as GainSeeker Suite can help us wade through it.
This infographic "Closed-Loop Quality Management" from LNS Research, outlines some of the findings drawn from the research data of hundreds of quality executives.
Leading employees toward healthier lifestyles, maintaining a culture geared toward wellness cited as recognition factors Hertzler Systems Inc. was named a Get Fit-Get Healthy 2014 Champion of Wellness Organization on June 9. The company achieved First Place standing within its corporate category for continued commitment and dedication to leading employees toward healthier lifestyles, [...]
Step aside boring, there's a new Chart Skins interface in GainSeeker that may plot a new course for how Control charts are typically displayed. Even the white dress shirt was eventually displaced a few days a week by colored shirts and later, Hawaiian prints on some casual Fridays.
Further, in the quality management arena, people are talking about Big Data—especially with more focus on finding correlations in real-time performance data. Many of today’s quality analytics solutions are designed to support the use of Big Data analytics, so manufacturers can cultivate and analyze massive amounts real-time data generated by SPC and other quality process data sources.
