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
Six Sigma Study Guide
Study notes and guides for Six Sigma certification tests
Posted by Ted Hessing
Variation is the enemy! It can introduce waste and errors into a process. The more variation, the more errors. The more errors, the more waste.
What is Variation?
Quick answer: it’s a lack of consistency. Imagine that you’re manufacturing an item. Say, a certain-sized screw. Firstly, you want the parameters to be the same in every single screw you produce. Material strength, length, diameter, and thread frequency must be uniform. Secondly, your customers want a level of consistency. They want a certain size of screw all to be the same. Using a screw that’s the wrong size might have serious consequences in a construction environment. So a lack of consistency in our products is bad.
We call the differences between multiple instances of a single product variation .
(Note: in some of Game Change Lean Six Sigma’s videos, they misstate six sigma quality levels as 99.999997 where it should be 6 sigma = 99.99966 % )
Why Measure Variation?
We measure it for a couple of reasons:
- Reliability: We want our customers to know that they’ll always get a certain level of quality from us. Also, we’ll often have a Service Level Agreement or similar in place. Consequently, every product needs to fit specific parameters.
- Costs: Variation costs money. So to lower costs, we need to keep levels low.
Measuring Variation vs. Averages
Once, companies tended to measure process performance by average. For example, average tensile strength or average support call length. However, a lot of companies are now moving away from this. Instead, they’re measuring variation. For example, differences in tensile strength or support call lengths.
Average measurements give us some useful data. But they don’t give us information about our product’s consistency . In most industries, focusing on decreasing fluctuations in processes increases performance. It does this by limiting factors that cause outlier results. And it often improves averages by default.
How Do Discrepancies Creep into Processes?
Discrepancies occur when:
- There is wear and tear in a machine.
- Someone changes a process.
- A measurement mistake is made.
- The material quality or makeup varies.
- The environment changes.
- A person’s work quality is unpredictable.
There are six elements in any process:
- Mother Nature, or Environmental
- Man or People
- Measurement
In Six Sigma, these elements are often displayed like this:
Discrepancies can creep into any or all elements of a process.
To read more about these six elements, see 5 Ms and one P (or 6Ms) .
For an example of changing processes contrarily causing variation, see the Quincunx Demonstration .
Process spread vs. centering
Types of Variation
There are two basic types that can occur in a process:
- common cause
- special cause
Common Cause
Common cause variation happens in standard operating conditions. Think about the factory we mentioned before. Fluctuations might occur due to the following:
- temperature
- metal quality
- machine wear and tear.
Common cause variation has a trend that you can chart. In the factory mentioned before, product differences might be caused by air humidity. You can chart those differences over time. Then you can compare that chart to weather bureau humidity data.
Special Cause
Conversely, special cause variation occurs in nonstandard operating conditions. Let’s go back to the example factory mentioned before. Disparities could occur if:
- a substandard metal was delivered.
- one of the machines broke down.
- a worker forgot the process and made a lot of unusual mistakes.
This type of variation does not have a trend that can be charted. Imagine a supplier delivers a substandard material once in a three-month period. Subsequently, you won’t see a trend in a chart. Instead, you’ll see a departure from a trend.
Why is it Important to Differentiate?
It’s important to separate a common cause and a special cause because:
- Different factors affect them.
- We should use different methods to counter each.
Treating common causes as special causes leads to inefficient changes. So too does treating a special cause like a common cause. The wrong changes can cause even more discrepancies.
How to Identify
Use run charts to look for common cause variation.
- Mark your median measurement.
- Chart the measurements from your process over time.
- Identify runs . These are consecutive data points that don’t cross the median marked earlier. They show common cause variation.
Control Charts
Meanwhile, use control charts to look for special cause variation.
- Mark your average measurement.
- Mark your control limits. These are 3 standard deviations above and below the average.
- Identify data points that fall outside the limits marked earlier. In other words, above the upper control limit or below the lower control limit. These show special cause variation.
Calculating
Variation is the square of a sample’s standard deviation .
How to Find the Cause of Variation
So far, you’ve found any significant variation in your process. However, you haven’t found what its cause might be. Hence, you need to find the source.
You can use a formal methodology like Six Sigma DMAIC, Use a multi vari chart to identify the source of variation.
How to Find and Reduce Hidden Causes of Variation
DMAIC methodology is the Six Sigma standard for identifying a process’s variation, analyzing the root cause, prioritizing the most advantageous way to remove a given variation, and testing the fix. The tools you would use depend on the kind of variation and the situation. Typically we see either a “data door” or a “process door” and use techniques that are most appropriate.
For a smaller, shorter cycle methodology, you could try Lean tools like Kaizen or GE’s Work Out.
How to Counter Variation
Once you identify its source, you need to counter it. As we implied earlier, the method you use depends on its type.
Counter common cause variation using long-term process changes.
Counter special cause variation using exigency plans.
Let’s look at two examples from earlier in the article.
- Product differences due to changes in air humidity. This is a common cause variation.
- Product differences due to a shipment of faulty metal. This is a special cause variation.
Countering common cause variation
As stated earlier: to counter common cause variation, we use long-term process changes. Air humidity is a common cause. Therefore, a process change is appropriate.
We might subsequently introduce a check for air humidity. We would also introduce the following step. If the check finds certain humidity levels, change the temperature of the machine to compensate. The new check would be run several times a day. Whenever needed, staff would change the temperature of the machine. These changes then lengthen the manufacturing process slightly. However, they also decrease product differences in the long term.
Countering special cause variation
As we said earlier, we need exigency plans to counter special cause variation. These are extra or replacement processes. We only use them if a special cause is present, though. A large change in metal quality is unusual. So we don’t want to change any of our manufacturing processes.
Instead, we implement a random check of quality after every shipment. Then, an extra process to follow if a shipment fails its quality check. The new process involves requesting a new shipment. These changes don’t lengthen the manufacturing process. They do add occasional extra work. But extra work happens only if the cause is present. Then, the extra process eliminates the cause.
Combining Variation
Rather than finding variation in a single sample, you might need to figure out a combined variance in a data set. For example, a set of two different products. For this, you’ll need the variance sum law .
Firstly, look at whether the products have any common production processes.
Secondly, calculate the combined variance using one of the formulas below.
No shared processes
What if the two products don’t share any production processes? Great! Then you can use the simplest version of the variance sum law.
Shared processes
What if the two processes do share some or all production processes? That’s OK, you’ll just need the dependent form of the variance sum law instead.
Calculate covariance using the following formula.
- μ is the mean value of X.
- ν is the mean value of Y.
- n = the number of items in the data set.
Additional Resources
ANOVA Analysis of Variation
What You Need to Know for Your Six Sigma Exam
Combating variation is integral to Six Sigma. Therefore, all major certifying organizations require that you have substantial knowledge of it. So let’s walk through how each represents what they expect.
Green Belts
Asq six sigma green belt.
ASQ requires Green Belts to understand the topic as it relates to:
Exploratory data analysis Create multi vari studies . Then interpret the difference between positional, cyclical, and temporal variation. Apply sampling plans to investigate the largest sources. (Create)
IASSC Six Sigma Green Belt
IASSC requires Green Belts to understand patterns of variation. Find this in the Analyze Phase section.
Black Belts
Villanova six sigma black belt.
Villanova requires Black Belts to understand the topic as it relates to:
Six Sigma basic premise
Describe how Six Sigma has fundamentally two focuses – variation reduction and waste reduction that ultimately lead to fewer defects and increased efficiency. Understand the concept of variation and how the six Ms have an influence on the process . Understand the difference between assignable cause and common cause variation along with how to deal with each type.
Multi vari studies
Create and interpret multi vari studies to interpret the difference between within piece, piece to piece, and time to time variation.
Measurement system analysis
Calculate, analyze, and interpret measurement system capability using repeatability and reproducibility , measurement correlation, bias, linearity, percent agreement, precision/tolerance (P/T), precision/total variation (P/TV), and use both ANOVA and c ontrol chart methods for non-destructive, destructive, and attribute systems.
ASQ Six Sigma Black Belt
ASQ requires Black Belts to understand the topic as it relates to:
Multivariate tools
Use and interpret multivariate tools such as principal components, factor analysis, discriminant analysis, multiple analysis of variance, etc to investigate sources of variation.
Use and interpret charts of these studies and determine the difference between positional, cyclical, and temporal variation.
Attributes data analysis
Analyze attributes data using logit, probit, logistic regression , etc to investigate sources of variation.
Statistical process control (SPC)
Define and describe the objectives of SPC, including monitoring and controlling process performance, tracking trends, runs, etc, and reducing variation in a process.
IASSC Six Sigma Black Belt
IASSC requires Black Belts to understand patterns of variation in the Analyze Phase section. It includes the following:
- Multi vari analysis .
- Classes of distributions .
- Inferential statistics .
- Understanding inference.
- Sampling techniques and uses .
Candidates also need to understand its impact on statistical process control.
ASQ Six Sigma Black Belt Exam Questions
Question: A bottled product must contain at least the volume printed on the label. This is chiefly a legal requirement. Conversely, a bottling company wants to reduce the amount of overfilled bottles. But it needs to keep volume above that on the label.
Look at the data above. What is the most effective strategy to accomplish this task?
(A) Decrease the target fill volume only. (B) Decrease the target fill variation only. (C) Firstly, decrease the target fill volume. Then decrease the target fill variation. (D) Firstly, decrease the target fill variation. Then decrease the target fill volume.
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If during Analyze phase of DMAIC the team undersands that the process has many common causes of variation and the process should be redesigned, can the team switch to DMADV?
Absolutely. Pivoting is essential in many cases as new information is discovered.
I would caution that clear communication with your stakeholders is essential here. You want to ensure that the cost to redesign & deploy the new process doesn’t exceed the benefit you’d achieve.
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Assignable Cause
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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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.
- Why Use SPC in Manufacturing?
- Statistical Process Control (SPC) Implementation
- The Problem with Tampering
- Distributions
- Populations and Sampling
- Process Behavior and Control
- Specification and Control Limits
- SPC Control Charts
- SPC Charting Examples
- Capability and Cpk Manufacturing Charts
- Overcoming Obstacles to Effective SPC
- Statistical Process Control FAQs
Take the first step from quality to excellence
Special Causes of Variation | Assignable causes | Types of variations
Special Causes of Variation are also known as Assignable Causes (un natural) of variation.
If Special cause of variations are present in a process, then the voice of the process is neither stable nor predictable and is said to be out of statistical control.
SPC technique uses Control Charts to monitor and control the Special Cause of variations present in the manufacturing process.
Control Chart is considered as one of the Seven Basic Quality Tools used for product and process improvement.
Table of Contents
Special Causes (Assignable causes)
- Special cause of variation are not always acting on the process.
- Process is not under Statistical control.
- Process output is unpredictable.
- Process is not stable over time.
- Erratic fluctuations and Shift occurred in process.
Type of Special Causes of Variation
- Extreme Variations
- Erratic Fluctuations
- Indication of Trend
Extreme Variations : Extreme variation is recognized by the points falling outside the Upper and Lower control limits.
Causes of Extreme Variations:
- Error in measurements and calculations.
- Wrong setting of machine, tools etc.
- Samples chosen at the start or at the end of an operation.
Erratic Fluctuations : Erratic fluctuation is characterized by ups and downs. This may be due to single cause or a group of causes affecting the process.
Causes of Erratic Fluctuations:
- Frequent adjustment of machine.
- Change in Man, machine , method and material etc.
- Processing of different types of material.
Shift : When a series of consecutive points fall above or below the centre line on the control chart then it is assumed that shift in the process has taken place. It is generally assumed that when 7 consecutive points lie above or below the centre line, the shift is occurred.
Causes of Shift:
- Change in Machine setting.
- Change in Material.
- Loose fixture etc.
- Change in Operator, Inspector and Inspection equipment.
- Unskilled or New operator or carelessness of the operator.
Indication of Trend : If the consecutive points on control chart tend to move steadily either towards Upper Control Limits (UCL) or Lower Control Limit (LCL), then it can be assumed that process is indicating a ‘Trend’ i.e. change is taking place slowly.
Causes of Trend:
- Wear of thread on clamping device
- Clogging of fixtures and holes
- Effect of temperature and humidity.
Common Causes Vs Special Causes
What is Process Capability?
How to Calculate Process Capability ?
What is Statistical Process Control (SPC)?
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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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Assignable Cause
Last updated by Jeff Hajek on December 22, 2020
An assignable cause is a type of variation in which a specific activity or event can be linked to inconsistency in a system. In effect, it is a special cause that has been identified.
As a refresher, common cause variation is the natural fluctuation within a system. It comes from the inherent randomness in the world. The impact of this form of variation can be predicted by statistical means. Special cause variation, on the other hand, falls outside of statistical expectations. They show up as outliers in the data .
Variation is the bane of continuous improvement . It decreases productivity and increases lead time . It makes it harder to manage processes.
While we can do something about common cause variation, typically there is far more bang for the buck by attacking special causes. Reducing common cause variation, for example, might require replacing a machine to eliminate a few seconds of variation in cutting time. A special cause variation on the same machine might be the result of weld spatter from a previous process. The irregularities in a surface might make a part fit into a fixture incorrectly and require some time-consuming rework. Common causes tend to be systemic and require large overhauls. Special causes tend to be more isolated to a single process step .
The first step in removing special causes is identifying them. In effect, you turn them into assignable causes. Once a source of variation is identified, it simply becomes a matter of devoting resources to resolve the problem.
One of the problems with continuous improvement is that the language can be murky at times. You may find that some people use special causes and assignable causes interchangeably. Special cause is a far more common term, though.
I prefer assignable cause, as it creates an important mental distinction. It implies that you…
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Volume 8 Supplement 1
Proceedings of Advancing the Methods in Health Quality Improvement Research 2012 Conference
- Proceedings
- Open Access
- Published: 19 April 2013
Understanding and managing variation: three different perspectives
- Michael E Bowen 1 , 2 , 3 &
- Duncan Neuhauser 4
Implementation Science volume 8 , Article number: S1 ( 2013 ) Cite this article
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Managing variation is essential to quality improvement. Quality improvement is primarily concerned with two types of variation – common-cause variation and special-cause variation. Common-cause variation is random variation present in stable healthcare processes. Special-cause variation is an unpredictable deviation resulting from a cause that is not an intrinsic part of a process. By careful and systematic measurement, it is easier to detect changes that are not random variation.
The approach to managing variation depends on the priorities and perspectives of the improvement leader and the intended generalizability of the results of the improvement effort. Clinical researchers, healthcare managers, and individual patients each have different goals, time horizons, and methodological approaches to managing variation; however, in all cases, the research question should drive study design, data collection, and evaluation. To advance the field of quality improvement, greater understanding of these perspectives and methodologies is needed [ 1 ].
Clinical researcher perspective
The primary goal of traditional randomized controlled trials (RCTs) (ie a comparison of treatment A versus placebo) is to determine treatment or intervention efficacy in a specified population when all else is equal. In this approach, researchers seek to maximize internal validity. Through randomization, researchers seek to balance variation in baseline factors by randomizing patients, clinicians, or organizations to experimental and control groups. Researchers may also increase understanding of variation within a specific study using approaches such as stratification to examine for effect modification. Although the generalizability of outcomes in all research designs is limited by the study population and setting, this can be particularly challenging in traditional RCTs. When inclusion criteria are strict, study populations are not representative of “real world” patients, and the applicability of study findings to clinical practice may be unclear. Traditional RCTs are limited in their ability to evaluate complex processes that are purposefully and continually changing over time because they evaluate interventions in rigorously controlled conditions over fixed time frames [ 2 ]. However, using alternative designs such as hybrid, effectiveness studies discussed in these proceedings or pragmatic RCTs, researchers can rigorously answer a broader range of research questions [ 3 ].
Healthcare manager perspective
Healthcare managers seek to understand and reduce variation in patient populations by monitoring process and outcome measures. They utilize real-time data to learn from and manage variation over time. By comparing past, present, and desired performance, they seek to reduce undesired variation and reinforce desired variation. Additionally, managers often implement best practices and benchmark performance against them. In this process, efficient, time-sensitive evaluations are important. Run charts and Statistical Process Control (SPC) methods leverage the power of repeated measures over time to detect small changes in process stability and increase the statistical power and rapidity with which effects can be detected [ 1 ].
Patient perspective
While the clinical researcher and healthcare manager are interested in understanding and managing variation at a population level, the individual patient wants to know if a particular treatment will allow one to achieve health outcomes similar to those observed in study populations. Although the findings of RCTs help form the foundation of evidence-based practice and managers utilize these findings in population management, they provide less guidance about the likelihood of an individual patient achieving the average benefits observed across a population of patients. Even when RCT findings are statistically significant, many trial participants receive no benefit. In order to understand if group RCT results can be achieved with individual patients, a different methodological approach is needed. “N-of-1 trials” and the longitudinal factorial design of experiments allow patients and providers to systematically evaluate the independent and combined effects of multiple disease management variables on individual health outcomes [ 4 ]. This offers patients and providers the opportunity to collect, analyze, and understand data in real time to improve individual patient outcomes.
Advancing the field of improvement science and increasing our ability to understand and manage variation requires an appreciation of the complementary perspectives held and methodologies utilized by clinical researchers, healthcare managers, and patients. To accomplish this, clinical researchers, healthcare managers, and individual patients each face key challenges.
Recommendations
Clinical researchers are challenged to design studies that yield generalizable outcomes across studies and over time. One potential approach is to anchor research questions in theoretical frameworks to better understand the research problem and relationships among key variables. Additionally, researchers should expand methodological and analytical approaches to leverage the statistical power of multiple observations collected over time. SPC is one such approach. Incorporation of qualitative research and mixed methods can also increase our ability to understand context and the key determinants of variation.
Healthcare managers are challenged to identify best practices and benchmark their processes against them. However, the details of best practices and implementation strategies are rarely described in sufficient detail to allow identification of the key drivers of process improvement and adaption of best practices to local context. By advocating for transparency in process improvement and urging publication of improvement and implementation efforts, healthcare managers can enhance the spread of best practices, facilitate improved benchmarking, and drive continuous healthcare improvement.
Individual patients and providers are challenged to develop the skills needed to understand and manage individual processes and outcomes. As an example, patients with hypertension are often advised to take and titrate medications, modify dietary intake, and increase activity levels in a non-systematic manner. The longitudinal factorial design offers an opportunity to rigorously evaluate the impact of these recommendations, both in isolation and in combination, on disease outcomes [ 1 ]. Patients can utilize paper, smart phone applications, or even electronic health record portals to sequentially record their blood pressures. Patients and providers can then apply simple SPC rules to better understand variation in blood pressure readings and manage their disease [ 5 ].
As clinical researchers, healthcare managers, and individual patients strive to improve healthcare processes and outcomes, each stakeholder brings a different perspective and set of methodological tools to the improvement team. These perspectives and methods are often complementary such that it is not which methodological approach is “best” but rather which approach is best suited to answer the specific research question. By combining these perspectives and developing partnerships with organizational managers, improvement leaders can demonstrate process improvement to key decision makers in the healthcare organization. It is through such partnerships that the future of quality improvement research is likely to find financial support and ultimate sustainability.
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Michael E Bowen
Division of General Internal Medicine, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA
Division of Outcomes and Health Services Research, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, 44106, USA
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Bowen, M.E., Neuhauser, D. Understanding and managing variation: three different perspectives. Implementation Sci 8 (Suppl 1), S1 (2013). https://doi.org/10.1186/1748-5908-8-S1-S1
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IMAGES
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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.
Variation inherently unpredictable, even probabilistically; Variation outside the historical experience base; and; Evidence of some inherent change in the system or our knowledge of it. Special-cause variation always arrives as a surprise. It is the signal within a system. Walter A. Shewhart originally used the term assignable cause.
Types of Variation There are two basic types that can occur in a process: common cause special cause Common Cause Common cause variation happens in standard operating conditions. Think about the factory we mentioned before. Fluctuations might occur due to the following: temperature humidity metal quality machine wear and tear.
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, and how to respond if it does.
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.
F 4. A process is said to be in statistical control when assignable causes are the only sources of variation. F 5. Mistakes stemming from workers' inadequate training represent an assignable cause of variation. T 6. Averages of small samples, not individual measurements, are generally used in statistical process control. T 7.
There are two 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.
Special Causes of Variation are also known as Assignable Causes (un natural) of variation. If Special cause of variations are present in a process, then the voice of the process is neither stable nor predictable and is said to be out of statistical control.
This variation may be classified as one of two types,random or chance cause variation and assignable cause variation. Benefitsof statistical process control include the ability to monitor astable process and identify if changes occur that are due to factors other than random variation. When assignable cause variation does occur, the statistical ...
A normal distribution is generally described by its two parameters: the mean and the range False A process is said to be in statistical control when assignable causes are the only sources of variation False Mistakes stemming from workers' inadequate training represent an assignable cause of variation. True
Common Cause Variation. 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 ...
Depending on the number of process characteristics to be monitored, there are two basic types of control charts. The first, referred to as a univariate control chart, is a graphical display ... But the risk of searching for an assignable cause of negative variation, when none exists, will be reduced. The net result, however, will be an increase ...
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. Common causes of variation Random causes that cannot be identified.
A) Assignable variation is much worse since it has a source B) Random variation will always be larger than assignable variation C) Neither variation is a problem as all processes have them, they just need to be. Question: There are two types of process variation, random variation and assignable variation. Which of the statements below best ...
An assignable cause is a type of variation in which a specific activity or event can be linked to inconsistency in a system. In effect, it is a special cause that has been identified.. As a refresher, common cause variation is the natural fluctuation within a system. It comes from the inherent randomness in the world. The impact of this form of variation can be predicted by statistical means.
because the variation is greater than the natural random variation. The second type of variation that can be observed involves those where the causes can be precisely identified and eliminated. These are called assignable causes of variation. Examples of this type of variation are poor quality in raw materials, an
Assignable variation - in process output, a variation whose cause can be identified Control chart - a time-ordered plot of sample statistics, used to distinguish between random and nonrandom variability Control limits - the dividing lines between random and nonrandom deviations from the mean of the distribution
Managing variation is essential to quality improvement. Quality improvement is primarily concerned with two types of variation - common-cause variation and special-cause variation. Common-cause variation is random variation present in stable healthcare processes.