What’s SPC (Statistical Process Control) and MSA (Measurement System Analysis)?
MSA stands for Measurement System Analysis, and SPC (statistical process control) measures / monitors various data that have a strong influence on the quality of finished products, and discovers / solves process abnormalities before defective products are produced. MSA guarantees that the measurement data is correct.
(Duration: 5:09)
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“Please put some videos on MSA and SPC.”
Hi, this is Mike Negami, Lean Sigma Black Belt.
I received a video topic request for the first time. I’m very glad about this. If you have request, please leave it in the comment section below.
The request is “Please put some videos on MSA and SPC”. MSA stands for Measurement System Analysis. SPC stands for Statistical Process Control.
Both of those are usually used in the manufacturing industry, but one of my goals is to evolve these concepts to be used by people in the service industry or by regular business people. Let’s think of how we can do that together!
What’s SPC (Statistical Process Control?
I’ll talk about SPC first. Before SPC was developed, all completed products used to be inspected to see if any piece had defects. If so, the piece was excluded. However, it took time and cost a lot. Also, their defect rates were very high.
On the other hand, with SPC, even during manufacturing, you record and monitor CTQ (Critical To Quality) data, which has a strong effect on the completed products’ quality. Then, you can find and solve process issues before making defective products. It aims to not allow defects to be produced.
In order to achieve this, you use the power of statistics. A typical example is one of the 7 QC Tools, the Control Chart. The horizontal axis is a time line. The vertical axis is CTQ’s measurements.
(Click the image below to enlarge the image.)
The central horizontal line is an average and the area between the other two lines is the acceptable range. When your measurements get out of the range, it’s out of control. Also, even if your measurements are in the range, you can still find statistical instability. I made a video and Excel template about the Control Chart Excel template. Please click the link below for that.
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SPC is an awesome system, isn’t it? In fact, because of SPC, human beings have improved defect rates and productivity dramatically. I introduced Dr. Edwards Deming in the last video. He actually taught Japanese companies this SPC.
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Why do we need MSA: Measurement System Analysis?
SPC is great, but it has one serious problem. When you measure CTQ, you may get variations. You probably know the words, “Garbage in, garbage out”. What if you put the wrong measurements in your Control Chart?
Of course, it will lead to making the wrong decision. Even worse, measurement errors occur all the time. There are many possible biases from operators, measuring devices, measuring methods and so on. Even if the same operator measures the same object with the same device with the same method twice, he may get different measurements.
In order to deal with this problem, MSA, Measurement System Analysis was developed. A tool that is mainly used in MSA is the Gage R&R. Since I didn’t think that it would be used in service operation projects, I haven’t made a template. If you really need the template, please write a request in the comments.
With MSA, you can find how much variation your measurement system’s data includes and if that is acceptable or not.
Applications of SPC and MSA to service operation improvements
These are explanations about SPC and MSA. Now how can we apply these concepts to our service operation improvement? I know it’s not easy. However, if you try to collect and utilize numeric data where you hadn’t had that kind of practice before, you would find great improvement opportunities.
As you apply SPC, it’s very effective to monitor and control signs of mistakes to prevent actual mistakes. One example is, in order to prevent a late delivery, just monitor and control on-time delivery departure.
As you apply MSA, the concept of “Garbage in, garbage out” is an important point on many occasions. Always keep it firmly in mind, especially when you do a data analysis.
Today I talked about SPC and MSA.
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