Statistical Process Control: Definition & Examples

Statistical Process Control: Definition & Examples
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  • 0:01 Process Control
  • 0:53 Causes of Variation
  • 3:28 Plan, Do, Study, Act
  • 6:17 Lesson Summary
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Lesson Transcript
Instructor: Natalie Boyd

Natalie is a teacher and holds an MA in English Education and is in progress on her PhD in psychology.

One goal of business is to deliver a consistent product. But what happens when there are variations in the product a business is producing? In this lesson, we'll examine the statistical process control method of dealing with variations.

Process Control

Mario owns a shoe company. His factories make shoes that are usually pretty nice, but occasionally one of Mario's shoes comes out with a broken sole or a deformed heel. What can he do to make sure the quality of his shoes is high?

Statistical process control is a way to apply statistics to identify and fix problems in quality control, like Mario's bad shoes. It was first developed by Dr. Walter A. Shewhart at Bell Laboratories in the 1920s, and has since been developed further.

If statistical process control can help Mario solve his quality control issues, he's all for it. But, how does it work? To better understand statistical process control, let's look at the causes of variation and how it can be applied to Mario's shoe factory.

Causes of Variation

Generally, Mario's shoes turn out perfect. But sometimes there's a problem with one of the shoes. What's causing the problems?

In statistical process control there are two causes of variation in products: common and special. A common cause of variation comes from a stable system. That is, the problems are caused by issues in the system that occur pretty much all of the time. Because they're a normal part of the process, they are called common causes. For example, if the materials that Mario uses to make the sole of his shoes are substandard, and that's why some of the shoes end up with holes in the soles, then that is a common cause.

Compare that to the holes being due to the fact that one of the factory machines malfunctioned one day. That's not something that happens all the time, so it's not a common cause of the holes. Instead, it is a special cause of variation, which comes from a specific issue or event, like a malfunctioning machine. Because these causes of variation only happen every once in a while, they are special causes.

In the world of statistics, variations caused by common causes are usually normally distributed. That is, if Mario graphed the number of shoes with holes on the shoes across time, he would expect to see a bell curve of the variations. Special causes, though, usually lie outside the normal distribution. So if Mario's holey problem is caused by a one-time issue - like the machine malfunctioning--then he would expect to see many more holey shoes coming off the assembly line than normal. If he graphed it, the day that the machine malfunctioned would lie outside of the bell curve.

Once Mario has figured out whether the variation of his shoes is being caused by special or common causes, he still has to fix the issue. Common causes of variation must be addressed by fixing or changing the system, and often require management to get involved. For example, Mario might decide to change the materials he's using for the soles of the shoes. Other common cause fixes involve changing a product's design or the manufacturing process.

Special causes, on the other hand, can often be changed quickly, easily and without management intervening. For example, if the machine malfunctioning is the cause of the holes, the machine operator might simply fix the machine and get it working again.

Plan, Do, Study, Act

Okay, Mario understands how to figure out what the cause of the variation in his shoes is, and he knows that common causes involve a larger, systematic change than special causes. But how exactly does he implement change to address issues?

Shewhart developed a four-step system to help fix causes of variation. It's called PDSA or plan, do, study, act. It's also sometimes referred to as PDCA or plan, do, check, act. The four steps of the system are:

1. Plan

The first step is to identify the problem and the cause. In Mario's case, he knows that the problem is that sometimes shoes have holes in the soles. During the planning phase, he'll also have to figure out whether those holes are being caused by substandard materials, a common cause, or a one-time machine malfunction, a special cause.

2. Do

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