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Every piece of data which is measured will show some degree of variation: no matter how much we try, we could never attain identical results for two different situations - each result will be different, even if the difference is slight. Variation may be defined as “the numerical value used to indicate how widely individuals in a group vary.”
In other words, variance gives us an idea of how data is distributed about an expected value or the mean. If you attain a variance of zero, it indicates that your results are identical - an uncommon condition. A high variance shows that the data points are spread out from each other—and the mean, while a smaller variation indicates that the data points are closer to the mean. Variance is always nonnegative.
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Types of Variance
Change is inevitable, even in statistics. You’ll need to know what kind of variation affects your process because the course of action you take will depend on the type of variance. There are two types of Variance: Common Cause Variation and Special Cause Variation. You’ll need to know about Common Causes Variation vs Special Causes Variation because they are two subjects that are tested on the PMP Certification and CAPM Certification exams.
Common Cause Variation
Special cause variation, become a product leader in 6 months.
Common Cause Variation, also referred to as “Natural Problems, “Noise,” and “Random Cause” was a term coined by Harry Alpert in 1947. Common causes of variance are the usual quantifiable and historical variations in a system that are natural. Though variance is a problem, it is an inherent part of a process—variance will eventually creep in, and it is not much you can do about it. Specific actions cannot be taken to prevent this failure from occurring. It is ongoing, consistent, and predictable.
Characteristics of common causes variation are:
- Variation predictable probabilistically
- Phenomena that are active within the system
- Variation within a historical experience base which is not regular
- Lack of significance in individual high and low values
This variation usually lies within three standard deviations from the mean where 99.73% of values are expected to be found. On a control chart, they are indicated by a few random points that are within the control limit. These kinds of variations will require management action since there can be no immediate process to rectify it. You will have to make a fundamental change to reduce the number of common causes of variation. If there are only common causes of variation on your chart, your process is said to be “statistically stable.”
When this term is applied to your chart, the chart itself becomes fairly stable. Your project will have no major changes, and you will be able to continue process execution hassle-free.
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Common Cause Variation Examples
Consider an employee who takes a little longer than usual to complete a specific task. He is given two days to do a task, and instead, he takes two and a half days; this is considered a common cause variation. His completion time would not have deviated very much from the mean since you would have had to consider the fact that he could submit it a little late.
Here’s another example: you estimate 20 minutes to get ready and ten minutes to get to work. Instead, you take five minutes extra to get ready because you had to pack lunch and 15 additional minutes to get to work because of traffic.
Other examples that relate to projects are inappropriate procedures, which can include the lack of clearly defined standard procedures, poor working conditions, measurement errors, normal wear and tear, computer response times, etc. These are all common cause variation.
Special Cause Variation, on the other hand, refers to unexpected glitches that affect a process. The term Special Cause Variation was coined by W. Edwards Deming and is also known as an “Assignable Cause.” These are variations that were not observed previously and are unusual, non-quantifiable variations.
These causes are sporadic, and they are a result of a specific change that is brought about in a process resulting in a chaotic problem. It is not usually part of your normal process and occurs out of the blue. Causes are usually related to some defect in the system or method. However, this failure can be corrected by making changes to affected methods, components, or processes.
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Characteristics of special cause variation are:
- New and unanticipated or previously neglected episode within the system
- This kind of variation is usually unpredictable and even problematic
- The variation has never happened before and is thus outside the historical experience base
On a control chart, the points lie beyond the preferred control limit or even as random points within the control limit. Once identified on a chart, this type of problem needs to be found and addressed immediately you can help prevent it from recurring.
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Special Cause Variation Example
Let’s say you are driving to work, and you estimate arrival in 10 minutes every day. One day, it took you 20 minutes to arrive at work because you were caught in the traffic from an accident zone and were held up.
Examples relating to project management are if machine malfunctions, computer crashes, there is a power cut, etc. These kinds of random things that can happen during a project are examples of special cause variation.
One way to evaluate a project’s health is to track the difference between the original project plan and what is happening. The use of control charts helps to differentiate between the common cause variation and the special cause variation, making the process of making changes and amends easier.
This article has explained special cause variation vs common cause variation which are the two important concepts in project management when it comes to data validation. Simplilearn offers multiple Project Management training courses like the Post Graduate Program in Project Management and learning paths that can help aspiring project managers get the education they need to pass not only exams like the PMP certification and CAPM® but also real-world knowledge useful for any project management career.
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About the author.
A project management and digital marketing knowledge manager, Avantika’s area of interest is project design and analysis for digital marketing, data science, and analytics companies.
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Assignable Cause
Published: November 7, 2018 by Ken Feldman
Assignable cause, also known as a special cause, is one of the two types of variation a control chart is designed to identify. Let’s define what an assignable cause variation is and contrast it with common cause variation. We will explore how to know if your control is signaling an assignable cause and how to react if it is.
Overview: What is an assignable cause?
A control chart identifies two different types of variation: common cause variation (random variation resulting from your process components or 6Ms ) and assignable or special cause variation.
Assignable cause variation is present when your control chart shows plotted points outside the control limits or a non-random pattern of variation. Since special cause variation is unexpected and due to some factor other than randomness, you should be able to assign a reason or cause to it.
When your control chart signals assignable cause variation, your process variable is said to be out of control, or unstable. Assignable cause variation signals can be identified by use of the Western Electric rules, which include:
- One point outside of the upper control limit or lower control limit
- A trend of 6 or 7 consecutive points increasing or decreasing
- A cycle or repeating pattern
- A run of 8 or more consecutive points on either side of the average or center line.
Assignable cause variation can be attributed to a defect, fault, mistake, delay, breakdown, accident, and/or shortage in the process. Or it can be a result of some unique combination of factors coming together to actually improve the process. When assignable causes are present, your process is unpredictable. The proper action and response is to search for and identify the specific assignable cause. If your process was improved as a result of your assignable cause, then incorporate it so that the cause is retained and improvement maintained. If your process was harmed by the assignable cause, then seek to eliminate it.
3 benefits of an assignable cause
Assignable causes can be good or bad. They are signals that something unexpected happened. Listen to the signal.
1. Signals something has happened
Special or assignable cause variation signals that something unexpected and non-random has occurred in your process.
2. Specific cause
By investigating and identifying the specific cause of your signal, you can narrow in on your next steps for bringing the process back into control.
3. Can become common cause variation
Good news! You found that your assignable cause for lowered production was due to a power outage. Unfortunately, you may not be able to stop power outages in your community. If nothing is done, your assignable cause becomes a common cause.
You might not be able to stop power outages, but could you install a back-up generator? Then, if the generator doesn’t kick on, you will have an assignable cause you can do something about.
Why is an assignable cause important to understand?
Interpreting what an assignable cause tells you is important to understand.
Provides direction for action
Since an assignable cause can be a signal of something good or bad, you need to understand the different actions. Don’t ignore special or assignable causes.
Not every unusual point has an assignable cause
While at your favorite casino, you may throw a pair of dice at the craps table. Is there an assignable cause for throwing an 11 or a 10, or is it random variation? No, you would expect the process of rolling a fair pair of dice to show 10s and 11s. What about a 13? That would be unexpected and probably the result of something unusual happening with the dice. The same is true for your process. Don’t assume an assignable special cause unless your control chart signals it.
Useful for determining whether your improvements worked
When you improve the process, your control chart should send signals of special cause variation — hopefully in the right direction. If you can link that signal to the specific assignable cause of your improvement, then you know it worked.
An industry example of an assignable cause
The accounts receivable department of a retail chain started to get complaints from its customers about overbilling. Fortunately, the manager of the department had participated in the company’s Lean Six Sigma training and had been using a control chart for errors.
Upon closer review, she noticed that errors seemed to occur more on Fridays than the rest of the week. In fact, the chart showed that almost every Friday, the data points were outside the upper control limit. She was concerned that nobody was identifying that as a signal of special cause.
She put together a small team of clerks to identify why this was happening and whether there was an assignable reason or cause for it. The assignable cause was determined to be the extra work load on Fridays.
The team recommended a change in procedure to better balance the workload during the week. Continued monitoring showed the problem was resolved. She also held an all-hands meeting to discuss the importance of not ignoring signals of special cause variation and the need to seek out an assignable cause and take the appropriate action.
3 best practices when thinking about an assignable cause
Signals of special cause variation require you to search for and identify the assignable cause.
1. Document your search
If you’ve identified the assignable cause, document everything. If this cause happens again in the future, people will have some background to act quickly and eliminate/incorporate any actions.
2. Quickly identify the cause
Time is of the essence. If the cause is resulting in a deteriorating process, act quickly to identify and eliminate the cause. The recommendation is the same if your cause made the process better, otherwise, whatever happened to improve the process will be lost as time goes by.
3. Don’t ignore signals of assignable cause
Even if you get a single signal of special cause, search for the assignable cause. You may choose not to take any action in the event it is a fleeting cause, but at least try to identify the assignable cause.
Frequently Asked Questions (FAQ) about an assignable cause
1. is an assignable cause always bad .
No. It is an indication that something unexpected happened in your process. It could be a good or bad thing. In either case, search for and identify the assignable cause and take the appropriate action.
2. What are some sources of an assignable cause?
Some sources may be your process components such as people, methods, environment, equipment, materials, or information. Your process variation can come from these items and can be the assignable cause of a signal of special cause variation.
3. How do I tell if I should look for an assignable cause?
Control charts were developed to distinguish between common and special cause variation. If they signal special cause variation in your process, seek out an assignable cause and take the appropriate action of either eliminating or incorporating your assignable cause.
Final thoughts on an assignable cause
All processes will exhibit two types of variation. Common cause variation is random, expected, and a result of variation in the process components. Special cause variation is non-random, unexpected, and a result of a specific assignable cause.
If you get a signal of special cause variation, you need to search for and identify the assignable cause. Once found, you will either seek to incorporate or eliminate the cause depending on whether the cause improved or hurt your process.
About the Author
Ken Feldman
Statistical Process Control 101
- Understanding Process Variation
Types of Process Variation
- Common cause variation is inherent to the system. This variation can be changed only by improving the equipment or changing the work procedures; the operator has little influence over it.
- Assignable cause variation comes from sources outside of the system. This variation can occur because of operator error, use of improper tooling, equipment malfunction, raw material problems, or any other abnormal disruptive inputs.
Control versus capability
Example of special cause variation.
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Chance & assignable causes of variation.
Links to all courses Variation in quality of manufactured product in the respective process in industry is inherent & evitable. These variations are broadly classified as- i) Chance / Non assignable causes ii) Assignable causes i) Chance Causes: In any manufacturing process, it is not possible to produce goods of exactly the same quality. Variation is inevitable. Certain small variation is natural to the process, being due to chance causes and cannot be prevented. This variation is therefore called allowable . ii) Assignable Causes: This type of variation attributed to any production process is due to non-random or so called assignable causes and is termed as preventable variation . Assignable causes may creep in at any stage of the process, right from the arrival of the raw materials to the final delivery of goods. Some of the important factors of assignable causes of variation are - i) Substandard or defective raw materials ii) New techniques or operation iii) Negligence of the operators iv) Wrong or improper handling of machines v) Faulty equipment vi) Unskilled or inexperienced technical staff and so on. These causes can be identified and eliminated and are to discovered in a production process before the production becomes defective. SQC is a productivity enhancing & regulating technique ( PERT ) with three factors- i) Management ii) Methods iii) Mathematics Here, control is two-fold- controlling the process ( process control ) & controlling the finished products (products control).
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Using control charts to detect common-cause variation and special-cause variation
In this topic, what are common-cause variation and special-cause variation, what special-cause variation looks like on a control chart, using brainstorming to investigate special-cause variation, don't overcorrect your process for common-cause variation.
Some degree of variation will naturally occur in any process. Common-cause variation is the natural or expected variation in a process. Special-cause variation is unexpected variation that results from unusual occurrences. It is important to identify and try to eliminate special-cause variation. Out-of-control points and nonrandom patterns on a control chart indicate the presence of special-cause variation.
Examples of common-cause and special-cause variation
A process is stable if it does not contain any special-cause variation; only common-cause variation is present. Control charts and run charts provide good illustrations of process stability or instability. A process must be stable before its capability is assessed or improvements are initiated.
This process is stable because the data appear to be distributed randomly and do not violate any of the 8 control chart tests.
This process is not stable; several of the control chart tests are violated.
A good starting point in investigating special-cause variation is to gather several process experts together. Using the control chart, encourage the process operators, the process engineers, and the quality testers to brainstorm why particular samples were out of control. Depending on your process, you may also want to include the suppliers in this meeting.
- Which samples were out of control?
- Which tests for special causes did the samples fail?
- What does each failed test mean?
- What are all the possible reasons for the failed test?
A common method for brainstorming is to ask questions about why a particular failure occurred to determine the root cause (the 5 why method). You could also use a cause-and-effect diagram (also called fishbone diagram).
While it's important to avoid special-cause variation, trying to eliminate common-cause variation can make matters worse. Consider a bread baking process. Slight drifts in temperature that are caused by the oven's thermostat are part of the natural common-cause variation for the process. If you try to reduce this natural process variation by manually adjusting the temperature setting up and down, you will probably increase variability rather than decrease it. This is called overcorrection.
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COMMENTS
Assignable versus Chance Variation: ... In statistical analysis there are two types of variations: assignable and chance variations. Assignable variations occur
Variations in data which can be attributed to specific causes. defined by Lean Affiliates. Related terms. Common Cause Variation · Random Variations.
Variation may be defined as “the numerical value used to indicate how widely individuals in a group vary.” In other words, variance gives us an
The variation we see in our process can be categorized a common cause variation or assignable cause variation and it's important to be able
Assignable cause variation is present when your control chart shows plotted points outside the control limits or a non-random pattern of
Assignable cause variation comes from sources outside of the system. This variation can occur because of operator error, use of improper tooling
i) Chance Causes: In any manufacturing process, it is not possible to produce goods of exactly the same quality. Variation is inevitable.
Some degree of variation will naturally occur in any process. Common-cause variation is the natural or expected variation in a process.
Common and special causes are the two distinct origins of variation in a process, as defined ... Non-assignable cause ... Special cause, Assignable cause
Any identifiable factor which causes variation in a process outside the predicted limits, thereby altering quality. McGraw-Hill Dictionary of Scientific &