Here is how the question came to me recently:
“So we finally got our MES up and running, and I still can’t get what I need from it. Can you help?”
Cutting acronyms from the herd
Actually, he could have said ERP (Enterprise Resource Planning), QMS (Quality Management System), OEE (Overall Equipment Effectiveness) or a half dozen other acronyms describing business systems that help managers run their operations. All of these systems fill an important need in manufacturing operations, but most of them fail to provide real-time analytics of defects and variation that this quality manager needed to fulfill his responsibilities.
The MES in question is a good system. If you’re in his industry, you’d probably recognize the product. But it left this quality manager high and dry.
He was struggling with all the time his staff spent massaging data to get something that was useful for him. He needed a Data Wrangler, or Data Janitor, to bridge all the data he was collecting in his MES and the insights he hoped to gain from that data.
Data wrangling doesn’t add value
My customer explained the impact on his business:
“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.
“It might be okay for an occasional study, but we need to be looking at this data throughout the day, every day. I can’t afford to have this be someone’s full-time job. A manual process is just not sustainable.”
He was pointing out the obvious: wrangling data is not a value-added activity. Nor is it sustainable for daily operations.
Saddled with Excel
His story illustrates the insight that Mike Roberts of LNS Research shared in his guest post about the Six Reasons to leave Excel Behind for Quality Analytics.
- Excel is error prone
- It lacks agility
- It is not real-time/validated
- It creates more silos of disparate information
- It lets only one user access the data at a time
- It is not scalable
The good news is that a quality analytics solution like GainSeeker Suite solves every one of these challenges. By connecting directly to quality data sources, GainSeeker handles real-time and historical quality data and simultaneous users. Moreover, it is already configured with (validated) algorithms for monitoring, controlling, and modeling data.
The end result is that when you use GainSeeker to automatically wrangle the data, you’re able to take a proactive approach to quality.
To make it easy to evaluate your data wrangling challenges, download our free Easy SPC Resource Kit. It will guide you through all the questions you need to consider to get started on your real-time analytics journey.