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Common Cause - Special Cause60.9 KB Common Cause - Special Cause - Solution
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Quick reference
Common Cause - Special Cause
SPC Control Charts are designed to differentiate between special cause variation and common cause variation. In order to understand the importance of this and the implication for control, this lesson explains and illustrates the difference.
When to use
Common cause variation is always present in a process. Special cause variation is present in an unstable process. Whenever a process manager seeks to control a process, he or she needs to separate the variation into the appropriate categories so that appropriate actions can be taken.
Instructions
SPC control charts are used to identify the differences between common cause variation and special cause variation. Once the process manager has determined the root cause for special cause variation and eliminated it, the remaining common cause variation is placed under statistical control in order to maintain a predictable process. For this reason, it is important that we clearly understand the differences between these two types of variation.
Common Cause
Common cause variation is the variation that is inherent in the design of the process characteristics. It is the typical variation between process operators, or the variation in equipment performance due to normal usage, it could even be differences in process performance due to environmental factors like heat, cold, or time of day. In one sense this variation is random, in another sense it is not. It is random to the extent that we don’t know if it will be higher or lower than the average value. However, the magnitude is not totally random. There is a normal range for the random variation that it will not exceed. Because of this, we can statistically model and predict the impact of the random variation on process performance. Finally, random variation can only be changed by making a fundamental process change so that the factors outlined earlier are different.
Special Cause
Special cause variation is not due to the inherent variation within the process design. It has as a unique root cause that is outside the inherent process operational characteristics. This root cause is not something the operator is normally monitoring and controlling. The occurrence of this root cause is unpredictable in timing and the magnitude of the impact is also unpredictable. That is why we say that the presence of a special cause variation leads to an unstable or unpredictable process. This unpredictability cannot be mathematically modeled and used to predict normal process performance. It is important to note that special cause variation is not always negative variation. Special causes may change important process parameters that reduce the inherent common cause variation within a process. This is referred to as the Hawthorne Effect – named after a research study conducted in Hawthorne, Illinois. In this study of worker productivity, it was determined that the primary influence on worker productivity was the special cause of having all the study specialists observing the worker’s behavior. Since they knew they were being studied, the worker changed their normal practice to be much more careful – but only when they were being watched.
Common Cause – Special Cause – Control Charts
Control charts are designed to differentiate between common cause variation and special cause variation. This is so the process managers and operators can remove the special cause variation which makes the process predictable. At that point, they can either maintain the normal variation or consider a fundamental change to the process to reduce the magnitude of the normal variation in the process. The operator should never chase the ups and downs of normal variation. Doing that will introduce process tampering which often leads to overcontrolling of the output and makes the overall performance even worse.
Hints & tips
- Identify and resolve special cause variation before attempting to make changes to the process and influencing common causes.
- Beware if tampering – it is an easy trap to fall into. It may give a short term benefit, due to the Hawthorne effect. But unless the process is fundamentally changed, it will go back to the original levels of common cause variation.…
- 00:04 Hi, I'm Ray Sheen.
- 00:06 Let's review the concepts of common cause and special cause variation.
- 00:11 A primary purpose of SPC charts is to identify the presence and
- 00:15 the magnitude of each of these variation categories.
- 00:19 I’ll start with what I mean by categories of variation.
- 00:23 All process variation can be divided into one of two categories.
- 00:27 The first is common cause variation.
- 00:30 This variation is always present, even in a stable process.
- 00:33 It’s inherent in the physical design and operation of the process.
- 00:37 Because it is always present, we can measure it and
- 00:40 establish a baseline of the normal variation using statistical techniques.
- 00:44 The specific instance of variation on any process run is random, but
- 00:49 is always within a set of boundaries that are predictable.
- 00:52 There are limits to this normal variation, which we can calculate and
- 00:55 plot on our control charts.
- 00:58 The second category of variation is special cause variation.
- 01:01 This is associated with unstable processes because it is not predictable.
- 01:06 It is due to something unusual.
- 01:08 Often, that something is poor process management by the business managers or
- 01:12 the process operators.
- 01:14 This type of variation is not predictable.
- 01:17 We can neither predict when it will occur or
- 01:19 what the magnitude of the value will be when it does occur.
- 01:22 Therefore, when it happens it creates an unexpected process performance, and
- 01:26 often requires special action to restore performance.
- 01:30 Let's look at each of these in a little more detail.
- 01:33 I'll start with common cause.
- 01:35 Common cause variation is always present and
- 01:37 is predictable with respect to the magnitude.
- 01:39 Therefore, it should be accounted for when setting process performance targets, and
- 01:44 allowed for within the tolerances on those performance targets.
- 01:48 Let me clarify the nature of randomness of common cause variation.
- 01:52 It is random with respect to any specific occurrence of variation.
- 01:56 Might be a little high or a little low as compared to normal performance, but
- 01:59 that variation is always within predictable magnitude range.
- 02:03 That means we can establish a normal range and a predictable variation.
- 02:08 What we can't do is eliminate common cause variation by taking some form of special
- 02:12 corrective action to chase the variation.
- 02:15 By that I mean if we follow the process and results are a little higher than
- 02:19 normal, we try to tweak the process to be a little lower to compensate.
- 02:23 Think about the young teenager learning to drive.
- 02:26 They over control the car while trying to stay in their lane and
- 02:29 end up going way outside the lines.
- 02:31 Trying to compensate for each occurrence of common cause variation creates
- 02:35 process tampering as a special cause, and normally results in an unstable process.
- 02:41 Okay, now let's look at special cause variation.
- 02:44 When Special Cause variation is present, we say the process is unstable.
- 02:48 That's because the process performance is no longer predictable.
- 02:51 Special Cause variation is not controllable by the process operator.
- 02:56 Therefore, they don't know what the process results will be on any given
- 02:59 run of the process.
- 03:00 Now, you may be thinking, well, that's true for common cause variation also.
- 03:04 The part of that is true for common cause variation is that
- 03:07 you don't know precisely what the process result will be, but you do
- 03:11 know that it will fall within predictable limits of common cause variation.
- 03:15 The problem with special cause is that it falls outside those limits
- 03:19 of common cause variation and you have no ability to predict how far outside.
- 03:24 Special Cause variation is unpredictable, but it's not random.
- 03:27 It has a clear cause that precipitates the variation.
- 03:30 There is a clear, identifiable root cause that if it had not occurred,
- 03:34 there would have been no Special Cause variation.
- 03:37 The good news about that is that sometimes we can find out
- 03:39 what created that underlying root cause and we can take actions to prevent it and
- 03:44 eliminate the Special Cause variation from occurring again.
- 03:48 One other point, the Special Cause variation is not always bad.
- 03:51 Sometimes it is special good.
- 03:53 This means that there is some root cause that is preventing normal cause variation
- 03:57 from occurring.
- 03:58 This can give us a false sense of the range for normal variation.
- 04:02 There's a famous research study conducted in Hawthorne, Illinois,
- 04:05 that illustrated this point.
- 04:07 The purpose of the study was to determine the effect of adding lighting
- 04:10 to the workplace of an industrial assembly line.
- 04:12 Well, first this test was done to establish a baseline without
- 04:15 lighting present.
- 04:16 Then lighting was added and the productivity was measured.
- 04:19 It had improved and
- 04:21 the sponsor of the research, a lighting company, was excited by the results.
- 04:25 However, the researchers did one more assessment.
- 04:28 They turned the lights off and measured productivity again, and
- 04:31 it had improved even more.
- 04:33 So the research said that the best productivity was
- 04:35 achieved when you installed lighting but turned it off.
- 04:39 Yeah, I agree.
- 04:40 That doesn't make much sense.
- 04:41 But with further study,
- 04:43 they found that while lighting helped, the biggest effect on productivity was
- 04:47 having all those researchers there in the factory collecting data all the time.
- 04:51 This was a special cause that disrupted the normal work habits of the people on
- 04:55 the production line.
- 04:56 So let's wrap this up with a comparison of common cause and Special Cause variation.
- 05:02 Common cause is the normal and predictable variation that occurs within a process.
- 05:07 It's predictable in the sense of the magnitude always falls within limits or
- 05:10 boundaries of process performance.
- 05:13 It is always present because it is inherent in the process design.
- 05:17 The predictable aspect allows us to mathematically model this variation,
- 05:20 which let's us establish defined limits for it.
- 05:23 One last point,
- 05:24 it cannot be eliminated by the process operators taking special action.
- 05:28 The only way to improve it is to fundamentally change the process
- 05:33 to one with different physical characteristics, and
- 05:35 therefore, lower levels of random variation.
- 05:39 In contrast, the special cause variation is unpredictable.
- 05:43 The process operator does not know when it will occur.
- 05:45 And if it does occur, the operator is unable to predict the process results
- 05:50 due to the impact of the special cause.
- 05:52 It is outside of the control of the process operator,
- 05:55 which is why we say it makes the process unstable.
- 05:59 The occurrence and magnitude cannot be mathematically predicted.
- 06:02 It is based upon some external unique root cause.
- 06:05 Now, although the operator cannot control the effect,
- 06:08 sometimes they can control the conditions that allow the unique root cause to occur.
- 06:13 In that case, it can be preventable.
- 06:16 This review of the principles of common cause variation and special cause
- 06:20 variation was necessary because we are about to see that SPC control charts
- 06:24 are designed to differentiate between these two types of variation.
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