The OEE (Overall Equipment Effectiveness) metric has fallen in and out of favor in manufacturing circles over recent years. Beyond the poles of hype and critique, is it possible to navigate a third, thoughtful path that uses the OEE metric to drive business transformation?

A new report by the Automotive Industry Action Group (AIAG) has flagged analytics to identify root causes as one of the critical recommendations in their Quality2020 Survey. The new report is available free from the AIAG website. It looks at data from across the automotive supply chain.

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.

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.