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.
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Assignable causes of variation have an advantage (high proportion, domination) in many known causes of routine variability. For this reason, it is worth trying to identify the assignable cause of variation , in such a way that its impact on the process can be eliminated, of course, assuming that project managers or members are fully aware of the assignable cause of variation. Assignable causes of variation are the result of events that are not part of the normal process. Examples of assignable causes for variability are (T. Kasse, p. 237):
- incorrectly trained people
- broken tools
- failure to comply with the process
- 1 Identify data of assignable causes
- 2 Types of data for assignable causes
- 3 Determining the source of assignable causes of variation in an unstable process
- 4 Examples of Assignable cause
- 5 Advantages of Assignable cause
- 6 Limitations of Assignable cause
- 7 Other approaches related to Assignable cause
- 8 References
Identify data of assignable causes
The first step you need to take when planning data collection for assignable causes is to identify them and explain your goals . This step is to ensure that the assignable causes data that the project team gathers provides the answers that are needed to carry out the ' process improvement ' project efficiently and successfully. The characteristics that are desirable and most relevant for an assignable causes are for example: relevant, representative, sufficient. In the planning process for collecting data on assignable causes, the project team should draw and mark a chart that will provide the findings before actual data collection begins. This step gives the project team an indication of what data that can be assigned is needed (A. van Aartsengel, S Kurtoglu, p. 464).
Types of data for assignable causes
There are two types of data for assignable causes, qualitative and quantitative . Qualitative data is obtained from deseriography resulting from observations or measures of different types of characteristics of the results of the process in terms of narrative words and statements. However, the next group of data, which are quantitative data on assignable causes, are derived from the description of observations or measures of process result characteristics in terms of measurable quantity in which numerical values are used (A. van Aartsengel, S. Kurtoglu, p. 464).
Determining the source of assignable causes of variation in an unstable process
If an unstable process occurs then the analyst must identify the sources of assignable cause variation. The source and the cause itself must be investigated and, in most cases, unfortunately also eliminated. Until all such causes are removed, then the actual capacity of the process cannot be determined and the process itself will not work as planned. In some cases, however, assignable cause variability can improve the result, then the process must be redesigned (W. S. Davis, D. C. Yen, p. 76). There are two possibilities for making the wrong decision, which concerns the appearance of assignable cause variations: there is no such reason (or it is incorrectly assessed) or it is not detected (N. Möller, S. O. Hansson, J. E. Holmberg, C. Rollenhagen, p. 339).
Examples of Assignable cause
- Poorly designed process : A poorly designed process can lead to variation due to the inconsistency in the way the process is operated. For example, if a process requires a certain step to be done in a specific order, but that order is not followed, this can lead to variation in the results of the process.
- Human error : Human error is another common cause of variation. Examples include incorrect data entry, incorrect calculations, incorrect measurements, incorrect assembly, and incorrect operation of machinery.
- Poor quality materials : Poor quality materials can also lead to variation. For example, if a process requires a certain grade of material that is not provided, this can lead to variation in the results of the process.
- Changes in external conditions : Changes in external conditions, such as temperature or humidity, can also cause variation in the results of a process.
- Equipment malfunctions : Equipment malfunctions can also lead to variation. Examples include mechanical problems, electrical problems, and computer software problems.
Advantages of Assignable cause
One advantage of identifying the assignable causes of variation is that it can help to eliminate their impact on the process. Some of these advantages include:
- Improved product quality : By identifying and eliminating the assignable cause of variation, product quality will be improved, as it eliminates the source of variability.
- Increased process efficiency : When the assignable cause of variation is identified and removed, the process will run more efficiently, as it will no longer be hampered by the source of variability.
- Reduced costs : By eliminating the assignable cause of variation, the cost associated with the process can be reduced, as it eliminates the need for additional resources and labour.
- Reduced waste : When the assignable cause of variation is identified and removed, the amount of waste produced in the process can be reduced, as there will be less variability in the output.
- Improved customer satisfaction : By improving product quality and reducing waste, customer satisfaction will be increased, as they will receive a higher quality product with less waste.
Limitations of Assignable cause
Despite the advantages of assigning causes of variation, there are also a number of limitations that should be taken into account. These limitations include:
- The difficulty of identifying the exact cause of variation, as there are often multiple potential causes and it is not always clear which is the most significant.
- The fact that some assignable causes of variation are difficult to eliminate or control, such as machine malfunction or human error.
- The costs associated with implementing changes to eliminate assignable causes of variation, such as purchasing new equipment or hiring more personnel.
- The fact that some assignable causes of variation may be outside the scope of the project, such as economic or political factors.
Other approaches related to Assignable cause
One of the approaches related to assignable cause is to identify the sources of variability that could potentially affect the process. These can include changes in the raw material, the process parameters, the environment , the equipment, and the operators.
- Process improvement : By improving the process, the variability caused by the assignable cause can be reduced.
- Control charts : Using control charts to monitor the process performance can help in identifying the assignable causes of variation.
- Design of experiments : Design of experiments (DOE) can be used to identify and quantify the impact of certain parameters on the process performance.
- Statistical Process Control (SPC) : Statistical Process Control (SPC) is a tool used to identify, analyze and control process variation.
In summary, there are several approaches related to assignable cause that can be used to reduce variability in a process. These include process improvement, control charts, design of experiments and Statistical Process Control (SPC). By utilizing these approaches, project managers and members can identify and eliminate the assignable cause of variation in a process.
- Davis W. S., Yen D. C. (2019)., The Information System Consultant's Handbook: Systems Analysis and Design , CRC Press, New York
- Kasse T. (2004)., Practical Insight Into CMMI , Artech House, London
- Möller N., Hansson S. O., Holmberg J. E., Rollenhagen C. (2018)., Handbook of Safety Principles , John Wiley & Sons, Hoboken
- Van Aartsengel A., Kurtoglu S. (2013)., Handbook on Continuous Improvement Transformation: The Lean Six Sigma Framework and Systematic Methodology for Implementation , Springer Science & Business Media, New York
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SOURCES OF VARIATION: COMMON AND ASSIGNABLE CAUSES
If you look at bottles of a soft drink in a grocery store, you will notice that no two bottles are filled to exactly the same level. Some are filled slightly higher and some slightly lower. Similarly, if you look at blueberry muffins in a bakery, you will notice that some are slightly larger than others and some have more blueberries than others. These types of differences are completely normal. No two products are exactly alike because of slight differences in materials, workers, machines, tools, and other factors. These are called common , or random, causes of variation . Common causes of variation are based on random causes that we cannot identify. These types of variation are unavoidable and are due to slight differences in processing.
Random causes that cannot be identified.
An important task in quality control is to find out the range of natural random variation in a process. For example, if the average bottle of a soft drink called Cocoa Fizz contains 16 ounces of liquid, we may determine that the amount of natural variation is between 15.8 and 16.2 ounces. If this were the case, we would monitor the production process to make sure that the amount stays within this range. If production goes out of this range—bottles are found to contain on average 15.6 ounces—this would lead us to believe that there ...
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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
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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.
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.
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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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If your control chart has plotted points that are not within the limit or show a non-random pattern in variation, this is considered assignable cause variation. You should be able to assign a cause or cause to it, as special cause variation can occur unexpectedly and is caused by something other than randomness.
A control chart can identify one of two types of variation: assignable cause (also known as a special cause) and common cause. Let’s look at what assignable cause variation looks like and compare it to common cause variation. This article will explain how to determine if your control signals an assignable cause, and how to respond if it does.
A control diagram shows two types of variation. Common cause variation is a random variable that results from process components or 6Ms. special cause variation can be assigned.
Your process variable is considered unstable or out of control when your control chart signals assignable causes variation. The Western Electric rules can help you identify signals of assignable cause variation. They include:
- One point beyond the upper limit or below the limit
- A trend that has 6 or 7 points consecutively increasing or decreasing
- A repeating or cycle
- A series of 8 or more points consecutively on either side of the average or center line.
Assignable cause variation may be due to a defect or fault, mistake, delay in processing, accident, or shortage. It could also be due to a unique combination of factors that work together to improve the process. Your process can be unpredictable if there are no assignable causes. It is important to identify and search for the exact assignable cause. Your process may have been improved by it. If so, you should incorporate it into your process to ensure that improvement is maintained and retained. It can harm your process, so you should seek to get rid of it.
What is the importance of an assignable cause?
Provides direction for action.
You need to be able to identify the causes and understand what they mean. You shouldn’t ignore assignable or special causes.
Every unusual point does not have an assignable cause
You may also throw two dice at the craps tables at your casino. Are there any determinable reasons for throwing an 11 or 10? Or is it just a random chance? You would not expect the process to roll a pair of fair dice to reveal 10s or 11s. But what about a 13. It would be an unexpected result and most likely the result of something strange happening with the dice. This is also true for your process. If your control chart does not indicate it, don’t assume that an assignable special cause is being assumed.
This is useful for determining if your improvements were successful.
Your control chart should transmit signals of special cause variation when you are trying to improve the process. You can connect that signal to the specific cause of your improvement and you will know it worked.
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Theory of Variation
Variation is life, or life is variation. The arrival of your bus or train will vary from day to day, as will your car journey. The start and duration of the seasons varies from year to year. We have good and bad summers (mostly bad in the UK). Likewise, all business processes and their outputs are subject to variation. The number of calls a Contact Centre Agent will handle per day will vary naturally about some mean, as will the outcomes, e.g. sales achieved, problems resolved.
Common and Special Causes of Variation
Way back in 1925, Western Electric Company in Chicago USA, a Dr Walter A. Shewhart invented a new way to think about variation and how to use it to drive improvement.
He identified two types of variation – variation from assignable (common) causes and variation from chance (special) causes. Common causes of variation stay the same day to day. Whereas, special causes of variation result from factors outside normal events.
A process is said to be in a ‘stable state’ and under ‘statistical control’ when the variation is predictable. In this state the process has a definable capability. Costs, performance, quality and quantity are predictable. If the process is not predictable, then it is in an unstable state.
Shewart put his theory to practical application through the development and use of Control, or Capability Charts. These record the variation of a given process or product over time. He applied Control or Capability Limits, emcompassing the common causes of variation, with any data points falling outside these limits being deemed to be due to special causes of variation.
A process can be improved by either reducing the degree of variation and/or by moving the mean value to the optimum point, determined by the point of greatest economic gain, called the Nominal Value (see “ Loss Function “).
Shewhart highlighted two mistakes that are made when attempting to improve results – to react to an outcome as if it came from a special cause, when actually it came from a common cause of variation; and to treat an outcome as if it came from a common cause of variation, when actually it came from a special cause.
We will cover Capability Charts and their application in more detail under Methods. Their use and the application of Statistic Process Control (SPC) is probably one of the most important methods in our process improvement armoury.
A very accessible introduction to the theory of variation and the use of related measures and data analysis. Go for a second-hand copy!
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