Big Data and its Six Sigma Application in Manufacturing

Big Data and its Six Sigma Application in Manufacturing

By Discovery Lean Six Sigma

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As the adoption of big data technology is on the rise, we keep seeing some amazing applications in various industries. It should come as no surprise that manufacturing is one of the prime areas for development in this regard, and we’re already seeing some great improvements in many manufacturing sectors thanks to the correct application of big data practices. Of course, it’s not as simple as flicking a switch and calling it a day – it’s important to ensure that some specific aspects are in place.

Observing All details

Big data makes it easy to take a good look at the big picture while at the same time ensuring that you don’t miss out on any of the important details. You can easily “zoom in” on the data and break it down component by component, something which is very difficult to do with traditional analytical methods. At the same time, you can combine those details and cross-reference them in many new ways that were not possible in the past, giving you complete control over what you’re doing with your data.

This allows certain patterns to emerge that can be very productive in the typical manufacturing environment, leading to an increase in the output of your facilities with relatively little effort.

Gathering Data for the Future

It’s also good to consider the long-term implications of data collection, not just what you can do with the information in the immediate sense. As our analytical systems get better and better, it’s important that we preserve the old data sets we’ve already collected in the past, as we may be able to analyze them in even more detail in the near future.

That way, the data collected now can become even more relevant not too long from now, allowing those who’ve been relying on big data practices since the start to benefit the most. Of course, not all types of data are going to be relevant here, and you’re also going to need to learn to trim the excess.

Identifying Defect Causes

When you have enough historic manufacturing data gathered, it should be relatively easy to pinpoint the exact cause of each defect that comes up in the production. While you’d normally at least have some hunch about the possible issue, big data allows you to narrow down the exact cause with absolute certainty, and you can often do that by simply comparing your current data against what you’ve gathered previously.

Deviations should point towards the culprit very efficiently, allowing you to focus on introducing actual improvements and bringing up the quality of your production. What’s more, you should be able to collect this data for the future and use it to prevent such issues from recurring, making big data an even more powerful tool in quality control.

Stable Quality Improvement

On that note, big data can also allow you to improve the overall quality of all production in your facility without having to guess at any step. Knowing what the values of different variables are, it should not be difficult to adjust them so that they match some perfect criteria and result in an improvement in the overall final quality.

Of course, you won’t always have a clear opportunity for these kinds of improvements, but whenever you spot a window like that, you should definitely utilize it to its full potential. After all, that’s why you’re using big data in the first place.


Big data is going to change the game for manufacturers completely at some point. It may take a while for this to happen properly, but once most companies have caught on and have started to utilize big data practices in their own production, we’re going to see a huge boom in the way manufacturing works as a whole around the world.

The post Big Data and its Six Sigma Application in Manufacturing appeared first on Shmula.

By: Shmula Contributor
Posted: August 29, 2018, 1:30 pm

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