Companies gamble in small ways every day. They gamble raw materials will arrive on time and will be what was ordered. They gamble customers will continue to buy products with a sustainable financial margin. Will employees be up to the task of running and if needed, adjusting production processes to maintain the quality customers require? We all roll the dice and the odds vary greatly, but in a business context, they all share a common feature, risk.
GainSeeker Suite real-time SPC software can have a profound impact on a business, and even on a whole supply chain. What makes that possible? It seemed to me that we could boil it down to a few really big differences between GainSeeker and most of the rest of the software that is out there for collecting, managing, and analyzing manufacturing quality data. Here is my short list of the top features.
Big Data is already here. Gages, devices, and innumerable sensors produce mountains of data. This equipment is connected by USB ports and various communication protocols in vast networks of operational data. Chances are you are buried under manufacturing big data. OAK is a free tool from Hertzler Systems. It asks the right questions to help you and your team uncover the critical connections between business drivers, strategic outcomes, actions, decisions, and data. One customer said, “The OAK worksheet prompts all the right questions. Fits very well connecting top to bottom. This was a good exercise.” Download a free copy of OAK today.
While I'm naturally inclined to skepticism about the latest buzzword trend, "Systems of Insight" resonates with me. It matches what I see our customers doing with GainSeeker Suite, and the value they're striving to get from working with us. Clearly GainSeeker Suite, as a Manufacturing Intelligence Platform, is aligned with this vision of a System of Insight. Whether we're talking about turning field failures into supply chain leadership, or reducing product giveaway, or knowing where stuff is, or improving manufacturing performance, or any other application of real-time actionable intelligence, the ability to gain insight from data and make better, faster decisions is critical. GainSeeker delivers.
If you’re struggling to make sense of manufacturing big data, you’re not alone. According to research published last year by The Economist, only 42% of manufacturers have what they consider to be a well-defined data-management strategy. Even more striking, 86% report problems in managing the data they are now generating. According to David Line, Managing Editor, Economist Intelligence Unit, “Manufacturing has been at the forefront of data collection and its importance to quality and cost control is well recognised. But collecting too much data, or failing to analyse what you collect, can be counterproductive.”
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.
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.
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.
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.
