- November 18, 2020
- Posted by: David Marshall
- Category: Management, Measurement
When you measure everything in a digital manufacturing facility, you get pretty good at data collection. Not only does digital manufacturing mean you can easily replicate the same piece without any deviations in quality, there’s plenty of data that tell you exactly how well you’re doing it.
At our Duoline facility where we made fiberglass liners (also called oil field tubulars), we had four work cells, and each cell collected 44,000 pieces of data every single day — 176,000 pieces of data every day — and we had to keep all of it for years afterward. (I’ll tell you why in a minute.)
All that data was linked to the products we made because each piece we produced had a unique identifier number. The artificial intelligence system could be set so that if even one piece we made was out of tolerance by 1/10th of 1 percent, an indicator light would change from green to red anywhere along the process, alerting us to a problem.
That didn’t happen very often though — maybe a fraction of a percentage then, too. The idea was that if something changed the light from green to red, you needed to stop everything and figure out what happened, or else every piece thereafter would be red as well. In other words, instead of continuing to make hundreds of thousands of bad parts, we’d only scrap one piece and fix the problem so we didn’t have to scrap all the others.
The other reason all of this data collection was important was if there was ever a product dispute, a failure, or even a claimed product failure, we had an absolute credible source that could prove our products were made to spec.
In other words, a product failure often meant that the user broke it, the claim was fraudulent, or something else happened to break it. We could show customers our data and at least demonstrate that we had made the product correctly and to their specifications.
But in a product liability lawsuit, they just vacuum up names and sue everyone. From the raw materials manufacturer all the way to the distributor who sold the product, if you were involved in any way of the making or selling of the products, you were involved.
And that meant you had to prove you were innocent, they didn’t have to prove you were wrong. In civil court, you’re guilty until you’re proven innocent.
There were several times we were able to pull out all that data and prove ourselves innocent.
How Data Collection Saved Us From a Major Lawsuit
One time Duoline was involved in a legal dispute with an offshore oil field in Ghana, off the west coast of Africa. The owner-operator of the oil field found that the string of pipes had a leak, and was leaking into the sea. Our fiberglass liners were inside the pipe, so the owner-operator sued the pipe manufacturer who then sued us because that kind of thing just rolls downhill.
We had to get an independent third-party engineering group to go there, pull the string of leaky pipe, and do forensic testing on the system. They also pulled out all our specs and the data from our four work cells to make sure that everything was made as it was supposed to be.
Our products were never designed to be a sealing system because they’re just fiberglass and were not designed to take the pressures of deepwater operations. It’s the steel pipe that’s designed to handle that pressure.
The engineering group discovered the steel manufacturer had a quality problem with their threading of the pipe and that’s what caused the leak. Ours were just designed to be a barrier to slow up and prevent corrosion of the steel pipes.
It was all our data and our designs that showed our products weren’t the problem, which meant they had to look elsewhere for the cause of the problem.
That got us out of a $250 million lawsuit, so you can imagine how I felt about that.
Data collection has become an important part of any manufacturing process. From fiberglass liners to pharmaceuticals and even food. Data collection about ingredients or farming sources gives you full product traceability. This means pharmaceutical manufacturers can trace particular batches of drugs down to the individual users. Food recalls can trace contaminated food sources from the farm to the store, rather than issuing blanket recalls of a food product all over the country.
Or at Duoline, each fiberglass liner has a unique ID that would tell you in the number what machine and what cell it came off of, who the operator was, whose shift it happened on, what day, who the materials suppliers were, and what the batch number of the raw materials were. So if a fiberglass liner failed in the field, we could go back and look at all that data and see if there was some reason why a particular liner had failed when all the others hadn’t.
I’ve been a manufacturing executive, as well as a sales and marketing professional, for a few decades. Now I help companies turn around their own business, including pivoting within their industry. If you would like more information, please visit my website and connect with me on Twitter, Facebook, or LinkedIn.
Photo credit: Ohioduidefense (Pixabay, Creative Commons 0)