special cause definition

Common Cause & Special Cause Variation Explained with Examples

Common Cause & Special Cause Variation pmp

In any business operation, it is important to ensure consistency in products as well as repeatable results. Managers and workers alike have to be aware of the processes and methods on how to produce consistent outcomes at all costs. However, we cannot deny that producing exactly identical products or results is almost impossible as variance tends to exist. Variation is not necessarily a bad thing as long as it is within the standard of the critical to qualities (CTQs) specification limits.

Process variation is the occurrence when a system deviates from its fixed pattern and produces a result which differs from the usual ones. This is a major key as it concerns the consistencies of the transactional as well as the manufacturing of the business systems. Variation should be evaluated as it portrays the reliability of the business for the customers and stakeholders. Variation may also cost money hence it is crucial to keep variation at bay to prevent too much cost spent on variation. It is crucial to be able to distinguish the types of variance that occur in your business process since it will give the lead on what course of action to take. Mistakes in coming up with an effective reaction plan towards the variance may worsen the processes of the business.

There are two types of process variation which will be further elaborated in this article. The variations are known as common cause variation and special cause variation.

Common Cause Variation Definition

Common cause variation refers to the natural and measurable anomalies that occur in the system or business processes. It naturally exists within the system. While it is true that variance may bring a negative impact to business operations, we cannot escape from this aspect. It is inherent and will always be. In most cases, the common cause variant is constant, regular, and could be predicted within the business operations. The other term used to describe this variation is Natural Problems, Noise, or Random Cause. Common cause variance could be presented and analysed using histogram.

What is Common Cause Variation

There are several distinguishable characteristics of common cause variation. Firstly, the variation pattern is predictable. Common cause variation occurring is also an active event in the operations. it is controlled and is not significantly different from the usual phenomenon.

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There are many factors and reasons for common cause variation and it is quite difficult to pinpoint and eliminate them. Some common cause variations are accepted within the business process and operations as long as they are within a tolerable level. Eradicating them is an arduous effort unless a drastic measure is implemented towards the operation.

Common Cause Variation Examples

There is a wide range of examples for common cause variation. Let’s take driving as an example. Usually, a driver is well aware of their destinations and the conditions of the path to reach the destination. Since they have been regularly using the same road, any defects or problems such as bumps, conditions of the road, and usual traffic are normal. They may not be able to precisely arrive at the destination at the same duration every time due to these common causes. However, the duration to arrive at the destination may not be largely differing day to day.

In terms of project-related variations, some of the examples include technical issues, human errors, downtime, high trafficking, poor computer response times, mistakes in standard procedures, and many more. Some other examples of common causes include poor design of products, outdated systems, and poor maintenance. Inconducive working conditions may also result in to common cause variants which could comprise of ventilation, temperature, humidity, noise, lighting, dirt, and so forth. Errors such as quality control and measurement could also be counted as common cause variation.

Special Cause Variation Definition

On the other hand, special cause variation refers to the unforeseen anomalies or variance that occurs within business operations. This variation, as the name suggests, is special in terms of being rare, having non-quantifiable patterns, and may not have been observed before. It is also known as Assignable Cause. Other opinions also mentioned that special cause variation is not only variance that happens for the first time, a previously overlooked or ignored problem could also be considered a special cause variation.

What is Special Cause Variation

Special cause variation is irregular occurrences and usually happens due to changes that were brought about in the business operations. It is not your mundane defects and may be very unpredictable. Most of the time, special cause variation happens following the flaws within the business processes or mechanism. While it may sound serious and taxing, there are ways to fix this which is by modifying the affected procedures or materials.

One of the characteristics of special cause variation is that it is uncontrolled and hardly predictable. The outcome of special causes variation is significantly different from the usual phenomenon. Since the issues are not predictable, it is usually problematic and may not even be recorded in the historical experience base.

Special Cause Variation Examples

As mentioned earlier, special cause variations are unexpected variants that occur due to factors that may affect the business system or operations. Let’s have an example of a special cause using the same scenario as previously elaborated for common cause variation example. The mentioned defects were common. Now, imagine if there is an unexpected accident that happens on the same road you usually take. Due to this accident, the time for the driver to arrive at the same destination may take longer than normal. Hence this accident is considered as a special cause variation. It is unexpected and results in a significantly different outcome, in this case, a longer time to arrive at the destination.

The example of special cause variation in the manufacturing sector includes environment, materials, manpower, technology, equipment, and many more. In terms of manpower, imagine a new employee is recruited into the team and still lacking in experience. The coaching and instructions should be adapted to consider that the person needs more training to be able to perform their tasks efficiently. Cases where a new supplier is needed in a short amount of time due to issues faced by the existing supplier are also unforeseen hence considered a special cause variation. Natural hazards that are beyond predictions may also be categorized into special cause variation. Some other examples include irregular traffic or fraud attack. An unexpected computer crash or malfunction in some of the components may also be considered as a special cause variation.

Common Cause and Special Cause Variation Detection

Control chart

One of the ways to keep track of common cause and special cause variation is by implementing control charts. When using control charts, the important aspect to be considered is firstly, establishing the average point of measurement. Next, establish the control limits. Usually, there are three standard deviations which are marked above and below the average point earlier. The last step is by determining which points exceed the upper and lower control limits established earlier. The points beyond the limits are special cause variation.

Before we get into the control chart of common cause and special cause variation, let’s have a look at the eight control chart rules first. If a process is stable, the points displayed in the chart will be near the average point and will not exceed the control limits.

However, it should be noted that not all rules are applicable to all types of control charts. That aside, it is quite tough to identify the causes of the patterns since special cause variation may be related to the specific type of processes. The table presented is the general rule that could be applied in most cases but is also subject to changes or differences. Studying the chart should be accompanied by knowledge and experiences in order to pinpoint the reasons for the patterns or variations.

A process is considered stable if special cause variation is not present, even if a common cause exists. A stable operation is important before it could be assessed or being improved. We could look at the stability or instability of the processes as displayed in control charts or run charts .

The points displayed in the chart above are randomly distributed and do not defy any of the eight rules listed earlier. This indicates that the process is stable.

The chart presented above is an example of an unstable process. This is because some of the rules for control chart tests mentioned earlier are violated.

Simply, if the points are randomly distributed and are within the limit, they may be considered as the common cause variation. However, if there is a drastic irregularity or points exceeding the limit, you may want to analyse more into it to determine if it is a special cause variation.

Histogram is a type of bar graph that could be used to present the distribution of occurrences of data. It is easily understandable and analysed. A histogram provides information on the history of the processes done as well as forecasting the future performance of the operations. To ensure the reliability of the data presented in the histogram, it is essential for the process to be stable. As mentioned earlier, although affected by common cause variation, the processes are still considered stable, hence histogram may be used on this occasion, especially if the processes undergo regular measurement and assessment.

The data is considered to be normally distributed if it portrays a “bell” shape in the histogram. The data are grouped around the central value and this cluster is known as variation. There are several other examples of more complicated patterns, such as having several peaks in the histogram or a shortened histogram. Whenever these examples of complex structures appear in the histogram, it is fundamental to look into the data and operations more deeply.

The above bar graph is an example of the histogram with a “bell” shape.

However, it should be noted that just because the histogram displays a “bell” shaped distribution, that does not mean the process is only experiencing common cause variation. A deeper analysis should be done to investigate if there were other underlying factors or causes that lead towards the pattern of the distribution displayed in the histogram.

Countering common cause and special cause variation

Once the causes of the variation have been pinpointed, here comes the attempt to combat and resolve it. Different measures are implemented to counter different types of variation, i.e. common cause variation and special cause variation. Common cause variation is quite tough to be completely eliminated. Drastic or long-term process modification could be used to counter common cause variation. A new method should be introduced and constantly conducted to achieve the long-term goal of eliminating the common cause variation. Some other effects may happen to the operations but as time passes, the cause may be gradually solved. As for special cause variation, it could be countered using contingency plans. Usually, additional processes are implemented into the usual operation in order to counter the special cause variation.

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Editorial Team

The meaning of variation to healthcare managers, clinical and health-services researchers, and individual patients

Duncan neuhauser.

1 Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA

Lloyd Provost

2 Associates in Process Improvement, Austin, Texas, USA

3 Centre for Health Improvement, Chalmers University of Technology, Gothenburg, Sweden

Healthcare managers, clinical researchers and individual patients (and their physicians) manage variation differently to achieve different ends. First, managers are primarily concerned with the performance of care processes over time. Their time horizon is relatively short, and the improvements they are concerned with are pragmatic and ‘holistic.’ Their goal is to create processes that are stable and effective. The analytical techniques of statistical process control effectively reflect these concerns. Second, clinical and health-services researchers are interested in the effectiveness of care and the generalisability of findings. They seek to control variation by their study design methods. Their primary question is: ‘Does A cause B, everything else being equal?’ Consequently, randomised controlled trials and regression models are the research methods of choice. The focus of this reductionist approach is on the ‘average patient’ in the group being observed rather than the individual patient working with the individual care provider. Third, individual patients are primarily concerned with the nature and quality of their own care and clinical outcomes. They and their care providers are not primarily seeking to generalise beyond the unique individual. We propose that the gold standard for helping individual patients with chronic conditions should be longitudinal factorial design of trials with individual patients. Understanding how these three groups deal differently with variation can help appreciate these three approaches.

Introduction

Health managers, clinical researchers, and individual patients need to understand and manage variation in healthcare processes in different time frames and in different ways. In short, they ask different questions about why and how healthcare processes and outcomes change ( table 1 ). Confusing the needs of these three stakeholders results in misunderstanding.

Meaning of variation to managers, researchers and individual patients: questions, methods and time frames

Health managers

Our extensive experience in working with healthcare managers has taught us that their primary goal is to maintain and improve the quality of care processes and outcomes for groups of patients. Ongoing care and its improvement are temporal, so in their situation, learning from variation over time is essential. Data are organised over time to answer the fundamental management question: is care today as good as or better than it was in the past, and how likely is it to be better tomorrow? In answering that question, it becomes crucial to understand the difference between common-cause and special-cause variation (as will be discussed later). Common-cause variation appears as random variation in all measures from healthcare processes. 1 Special-cause variation appears as the effect of causes outside the core processes of the work. Management can reduce this variation by enabling the easy recognition of special-cause variation and by changing healthcare processes—by supporting the use of clinical practice guidelines, for example—but common-cause variation can never be eliminated.

The magnitude of common-cause variation creates the upper and lower control limits in Shewhart control charts. 2–5 Such charts summarise the work of health managers well. Figure 1 shows a Shewhart control chart (p-chart) developed by a quality-improvement team whose aim was to increase compliance with a new care protocol. The clinical records of eligible patients discharged (45–75 patients) were evaluated each week by the team, and records indicating that the complete protocol was followed were identified. The baseline control chart showed a stable process with a centre line (average performance) of 38% compliance. The team analysed the aspects of the protocol that were not followed and developed process changes to make it easier to complete these particular tasks. After successfully adapting the changes to the local environment (indicated by weekly points above the upper control limit in the ‘Implementing Changes’ period), the team formally implemented the changes in each unit. The team continued to monitor the process and eventually developed updated limits for the chart. The updated chart indicated a stable process averaging 83%.

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Annotated Shewhart control chart—using protocol.

This control chart makes it clear that a stable but inferior process was operating for the first 11 weeks and, by inference, probably before that. The annotated changes (testing, adapting and implementing new processes of care) are linked to designed tests of change which are special (assignable) causes of variation, in this case, to improvement after week 15, after which a new better stable process has taken hold. Note that there is common-cause (random) variation in both the old and improved processes.

After updating the control limits, the chart reveals a new stable process with no special-cause variation, which is to say, no points above or below the control limits (the dotted lines). Note that the change after week 15 cannot easily be explained by chance (random, or common-cause, variation), since the probability of 13 points in a row occurring by chance above the baseline control limit is one divided by 2 to the 13th power. This is the same likelihood that in flipping a coin 13 times, it will come up heads every time. This level of statistical power to exclude randomness as an explanation is not to be found in randomised controlled trials (RCTs). Although there is no hard-and-fast rule about the number of observations over time needed to demonstrate process stability and establish change, we believe a persuasive control chart requires 20–30 or more observations.

The manager's task demonstrates several important characteristics. First is the need to define the key quality characteristics, and choose among them for focused improvement efforts. The choice should be made based on the needs of patients and families. The importance of these quality characteristics to those being served means that speed in learning and improvement is important. Indeed, for the healthcare manager, information for improvement must be as rapid as possible (in real time). Year-old research data are not very helpful here; just-in-time performance data in the hands of the decision-makers provide a potent opportunity for rapid improvement. 6

Second, managerial change is holistic; that is, every element of an intervention that might help to improve and can be done is put to use, sometimes incrementally, but simultaneously if need be. Healthcare managers are actively working to promote measurement of process and clinical outcomes, take problems in organisational performance seriously, consider the root causes of those problems, encourage the formation of problem solving clinical micro-system teams and promote the use of multiple, evolving Plan–Do–Study–Act (PDSA) tests of change.

This kind of improvement reasoning can be applied to a wide range of care processes, large and small. For example, good surgery is the appropriate combination of hundreds of individual tasks, many of which could be improved in small ways. Aggregating these many smaller changes may result in important, observable improvement over time. The protocol-driven, randomised trial research approach is a powerful tool for establishing efficacy but has limitations for evaluating and improving such complex processes as surgery, which are continually and purposefully changing over time. The realities of clinical improvement call for a move from after-the-fact quality inspection to building quality measures into medical information systems, thereby creating real-time quality data for providers to act upon. Caring for populations of similar patients in similar ways (economies of scale) can be of particular value, because the resulting large numbers and process stability can help rapidly demonstrate variation in care processes 7 ; very tight control limits (minimal common-cause variation) allow special-cause variation to be detected more quickly.

Clinical and health-services researchers

While quality-management thinking tends towards the use of data plotted over time in control-chart format, clinical researchers think in terms of true experimental methods, such as RCTs. Health-services researchers, in contrast, think in terms of regression analysis as their principal tool for discovering explainable variation in processes and outcomes of care. The data that both communities of researchers use are generally collected during fixed periods of time, or combined across time periods; neither is usually concerned with the analysis of data over time.

Take, for example, the question of whether age and sex are associated with the ability to undertake early ambulation after hip surgery. Clinical researchers try to control for such variables through the use of entry criteria into a trial, and random assignment of patients to experimental or control group. The usual health-services research approach would be to use a regression model to predict the outcome (early ambulation), over hundreds of patients using age and sex as independent variables. Such research could show that age and sex predict outcomes and are statistically significant, and that perhaps 10% of the variance is explained by these two independent variables. In contrast, quality-improvement thinking is likely to conclude that 90% of the variance is unexplained and could be common-cause variation. The health-services researcher is therefore likely to conclude that if we measured more variables, we could explain more of this variance, while improvement scientists are more likely to conclude that this unexplained variance is a reflection of common-cause variation in a good process that is under control.

The entry criteria into RCTs are carefully defined, which makes it a challenge to generalise the results beyond the kinds of patients included in such studies. Restricted patient entry criteria are imposed to reduce variation in outcomes unrelated to the experimental intervention. RCTs focus on the difference between point estimates of outcomes for entire groups (control and experimental), using statistical tests of significance to show that differences between the two arms of a trial are not likely to be due to chance.

Individual patients and their healthcare providers

The question an individual patient asks is different from those asked by manager and researcher, namely ‘How can I get better?’ The answer is unique to each patient; the question does not focus on generalising results beyond this person. At the same time, the question the patient's physician is asking is whether the group results from the best clinical trials will apply in this patient's case. This question calls for a different inferential approach. 8–10 The cost of projecting general findings to individual patients could be substantial, as described below.

Consider the implications of a drug trial in which 100 patients taking a new drug and 100 patients taking a placebo are reported as successful because 25 drug takers improved compared with 10 controls. This difference is shown as not likely to be due to chance. (The drug company undertakes a multimillion dollar advertising campaign to promote this breakthrough.) However, on closer examination, the meaning of these results for individual patients is not so clear. To begin with, 75 of the patients who took the drug did not benefit. And among those 25 who benefited, some, perhaps 15, responded extremely well, while the size of the benefit in the other 10 was much smaller. To have only the 15 ‘maximum responders’ take this drug instead of all 100 could save the healthcare system 85% of the drug's costs (as well as reduce the chance of unnecessary adverse drug effects); those ‘savings’ would, of course, also reduce the drug company's sales proportionally. These considerations make it clear that looking at more than group results could potentially make an enormous difference in the value of research studies, particularly from the point of view of individual patients and their providers.

In light of the above concerns, we propose that the longitudinal factorial study design should be the gold standard of evidence for efficacy, particularly for assessing whether interventions whose efficacy has been established through controlled trials are effective in individual patients for whom they might be appropriate ( box 1 ). Take the case of a patient with hypertension who measures her blood pressure at least twice every day and plots these numbers on a run chart. Through this informal observation, she has learnt about several factors that result in the variation in her blood pressure readings: time of day, the three different hypertension medicines she takes (not always regularly), her stress level, eating salty French fries, exercise, meditation (and, in her case, saying the rosary), and whether she slept well the night before. Some of these factors she can control; some are out of her control.

Longitudinal factorial design of experiments for individual patients

The six individual components of this approach are not new, but in combination they are new 8 9

One patient with a chronic health condition; sometimes referred to as an ‘N-of-1 trial.’
Care processes and health status are measured over time. These could include daily measures over 20 or more days, with the patient day as the unit of analysis.
Whenever possible, data are numerical rather than simple clinical observation and classification.
The patient is directly involved in making therapeutic changes and collecting data.
Two or more inputs (factors) are experimentally and concurrently changed in a predetermined fashion.
Therapeutic inputs are added or deleted in a predetermined, systematic way. For example: on day 1, drug A is taken; on day 2, drug B; on day 3, drug A and B; day 4, neither. For the next 4 days, this sequence could be randomly reordered.

Since she is accustomed to monitoring her blood pressure over time, she is in an excellent position to carry out an experiment that would help her optimise the effects of these various influences on her hypertension. Working with her primary care provider, she could, for example, set up a table of randomly chosen dates to make each of several of these changes each day, thereby creating a systematically predetermined mix of these controllable factors over time. This factorial design allows her to measure the effects of individual inputs on her blood pressure, and even interactions among them. After an appropriate number of days (perhaps 30 days, depending on the trade-off between urgency and statistical power), she might conclude that one of her three medications has no effect on her hypertension, and she can stop using it. She might also find that the combination of exercise and consistently low salt intake is as effective as either of the other two drugs. Her answers could well be unique to her. Planned experimental interventions involving single patients are known as ‘N-of-1’ trials, and hundreds have been reported. 10 Although longitudinal factorial design of experiments has long been used in quality engineering, as of 2005 there appears to have been only one published example of its use for an individual patient. 8 9 This method of investigation could potentially become widely used in the future to establish the efficacy of specific drugs for individual patients, 11 and perhaps even required, particularly for very expensive drug therapies for chronic conditions. Such individual trial results could be combined to obtain generalised knowledge.

This method can be used to show (1) the independent effect of each input on the outcome, (2) the interaction effect between the inputs (perhaps neither drug A or B is effective on its own, but in combination they work well), (3) the effect of different drug dosages and (4) the lag time between treatment and outcome. This approach will not be practical if the outcome of interest occurs years later. This method will be more practical with patient access to their medical record where they could monitor all five of Bergman's core health processes. 12

Understanding variation is one of the cornerstones of the science of improvement

This broad understanding of variation, which is based on the work of Walter Shewart in the 1920s, goes well beyond such simple issues as making an intended departure from a guideline or recognising a meaningful change in the outcome of care. It encompasses more than good or bad variation (meeting a target). It is concerned with more than the variation found by researchers in random samples from large populations.

Everything we observe or measure varies. Some variation in healthcare is desirable, even essential, since each patient is different and should be cared for uniquely. New and better treatments, and improvements in care processes result in beneficial variation. Special-cause variation should lead to learning. The ‘Plan–Do–Study’ portion of the Shewhart PDSA cycle can promote valuable change.

The ‘act’ step in the PDSA cycle represents the arrival of stability after a successful improvement has been made. Reducing unintended, and particularly harmful, variation is therefore a key improvement strategy. The more variation is controlled, the easier it is to detect changes that are not explained by chance. Stated differently, narrow limits on a Shewhart control chart make it easier and quicker to detect, and therefore respond to, special-cause variation.

The goal of statistical thinking in quality improvement is to make the available statistical tools as simple and useful as possible in meeting the primary goal, which is not mathematical correctness, but improvement in both the processes and outcomes of care. It is not fruitful to ask whether statistical process control, RCTs, regression equations or longitudinal factorial design of experiments is best in some absolute sense. Each is appropriate for answering different questions.

Forces driving this new way of thinking

The idea of reducing unwanted variation in healthcare represents a major shift in thinking, and it will take time to be accepted. Forces for this change include the computerisation of medical records leading to public reporting of care and outcome comparisons between providers and around the world. This in turn will promote pay for performance, and preferred provider contracting based on guideline use and good outcomes. This way of thinking about variation could spread across all five core systems of health, 12 including self-care and processes of healthy living.

Competing interests: None.

Provenance and peer review: Not commissioned; externally peer reviewed.

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Special Cause vs. Common Cause Variation

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What is the variation?

Whatever measurement we take, there is always a variation between these measurements. No two items or measurements are precisely the same.

The problem with the variation is that it is the enemy of quality. Variation and quality do not go hand in hand. Variation reduction is one of the significant challenges of quality professionals.

Two types of variation, and why is it important to differentiate?

When dealing with variation, the challenge quality professionals face when to act and when not to act. Because if you act on each and every variation in the process and adjust the process, this will be a never-ending process. Dr. Deming called this "tempering the process." Rather than improving the quality, tempering, in fact, reduces the quality. Deming demonstrated the effect of tempering with the help of a funnel experiment.

The causes of variation can be classified into two categories:

Common Causes
Special Causes

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Common Cause Vs Special Cause: Types of Variation

Common cause variation is the natural variation in the process. It is a part of the process. There are "many" causes of this type of variation, and it is not easy to identify and remove these. You will need to live with them unless drastic action is taken, such as process re-engineering.

Common causes are also called n atural causes, noise, non-assignable and random causes .

Special cause variation, on the other hand, is the unexpected variation in the process. There is a specific cause that can be assigned to the variation. For that reason, this is also called as the assignable cause . You are required to take action to address these variations.

Special causes are also called assignable causes .

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Control Charts to identify special causes

If the measurements of a process are normally distributed, then there is a 99.73% chance that the measurement will be within plus and minus three standard deviations. This is the basis of control charts.

If you plot the measurements on a Control Chart, then any measurements which are outside the plus and minus three standard deviation limits are expected to be because of a special cause. These limits are called as the Upper Control Limit (UCL) and the Lower Control Limits (LCL), Once you get such measurement, you are expected to investigate, do the root cause analysis , find out the reason for such deviation and take necessary actions.

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Duncan Neuhauser 1 ,
Lloyd Provost 2 ,
Bo Bergman 3
1 Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA
2 Associates in Process Improvement, Austin, Texas, USA
3 Centre for Health Improvement, Chalmers University of Technology, Gothenburg, Sweden
Correspondence to Charles Elton Blanchard Professor Duncan Neuhauser, Department of Epidemiology and Biostatistics, Medical School, Case Western Reserve University, 10900 Euclid Ave, Cleveland Ohio 44106-4249, USA; dvn{at}case.edu

Control charts
evidence-based medicine
quality of care
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This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode .

http://dx.doi.org/10.1136/bmjqs.2010.046334

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Introduction

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Meaning of variation to managers, researchers and individual patients: questions, methods and time frames

Health managers

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Annotated Shewhart control chart—using protocol.

Clinical and health-services researchers

Individual patients and their healthcare providers

Longitudinal factorial design of experiments for individual patients

The six individual components of this approach are not new, but in combination they are new 8 9

One patient with a chronic health condition; sometimes referred to as an ‘N-of-1 trial.’

Care processes and health status are measured over time. These could include daily measures over 20 or more days, with the patient day as the unit of analysis.

Whenever possible, data are numerical rather than simple clinical observation and classification.

The patient is directly involved in making therapeutic changes and collecting data.

Two or more inputs (factors) are experimentally and concurrently changed in a predetermined fashion.

Therapeutic inputs are added or deleted in a predetermined, systematic way. For example: on day 1, drug A is taken; on day 2, drug B; on day 3, drug A and B; day 4, neither. For the next 4 days, this sequence could be randomly reordered.

Understanding variation is one of the cornerstones of the science of improvement

Forces driving this new way of thinking

Bergman B ,
Lifvergren S ,
Gremyer I ,
Hellstrom A ,
Neuhauser D
Neuhauser D ,

Competing interests None.

Provenance and peer review Not commissioned; externally peer reviewed.

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Common Cause vs. Special Cause: The Basics of Process Variation

By OpEx Learning Team , Last Updated April 26, 2018

When dealing with process variation, you’ll need to learn to recognize the two general types of it, and the ways they impact the operation of your business. In most cases, some level of variance to a process is inevitable: the question is whether the factors that cause this variation are controlled and predicted, and if you can account for that variation in your output projections. If you’re able to work around it, there’s usually no big issue but things tend to get messier once special cause variation is introduced to the mix.

Common Cause Variation

Common cause variation is variation resulting from factors that may or may not be known, but the final impact they have on your output is predictable and controllable, and you would usually know to expect some variation in specific areas when looking at your final reports. As we said above, some level of variation is inevitable for most types of processes, and this is usually the kind you want to have if you want to be sure that you’re in control of the current situation.

Note that in some cases, common cause variation can still be quite high, and this might cause issues in your production. Take steps to reduce it if you are able to identify the root causes, but don’t stress too much if you can’t get to the bottom of the situation.

Special Cause Variation

On the other hand, special cause variation occurs when something out of the ordinary happens in a process. This might be a one-time occurrence, or it can develop into a repeating effect, in which case it might eventually be categorized as common cause variation depending on the circumstances. It’s important to understand that sometimes you will simply not be able to avoid special cause variation, either because the nature of a process is prone to it, or because of external factors in your environment that are beyond your control.

When you’ve identified special cause variation though, it’s important to take prompt steps to deal with it. It may not be such a problematic factor right now, but if you leave it unattended for a long time, this might be a real problem that can bring down some parts of your operations. There are some tools that can help you figure out where some issues are coming from, but you may need to come up with some solutions of your own in some cases, depending on the complexity of the situation.

Identifying Both with the Appropriate Tools

This brings us back to the most important point you need to be aware of what tools are available in your area of work for dealing with problems like that, and know how to apply them to your current situation in order to figure out where different types of variation are coming from, and develop an adequate solution. Special cause variation is typically easier to uncover as it will deviate from the norm more strongly, but there might be some exceptions. On the other hand, when dealing with common cause variation, you might have access to more historic data that can give you a better idea of what’s going on with the help of some statistical analysis.

Once you’ve learned what common cause and special cause variation both are, and how they relate to your line of work, you should be more prepared to identify and deal with them, and you will find them less problematic in the long run as impact on your operations will be minimized and kept to a controllable state where you can drive things in the right direction.

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The Importance of Selecting the Proper Type of Control Chart

Updated: August 10, 2023 by Ken Feldman

You are often faced with the decision of how to react to the variation in your process. Should you investigate for some specific root cause, or make fundamental changes? We will explain the use and benefits of a control chart, plus provide a few best practices for using them to monitor the variation in your processes.

Overview: What is a control chart?

A control chart, also known as a Shewhart or Process Behavior chart, is a time series graph of data collected over time. It is composed of a center line representing the average of the data being plotted and upper and lower control limits calculated from the data. The control limits represent the upper and lower expectations of the process variation.

The control chart was developed by Dr. Walter A. Shewhart while working at Bell Labs in the 1920s. Shewhart was aware that variation existed in the manufacturing processes and was concerned with how people reacted to that variation. He observed that continuously reacting and tampering with the process resulted in greater variation. Shewhart defined two sources of variation: common cause and special cause .

Common cause was defined as the random inherent variation in the process caused by the variation of the process elements. The proper reaction is not to seek a cause for the variation, but to make fundamental changes in the process elements. The source of special or assignable cause variation is an unexpected occurrence. The reaction for special cause variation is to investigate the reason and either eliminate the cause if it is detrimental to the process, or incorporate it if the process was improved.

On May 16, 1924, Shewhart wrote an internal memo introducing the concept of the control chart as a tool for distinguishing between the two causes of variation. Around that time, Shewhart’s work came to the attention of famed statistician Dr. W. Edwards Deming, who was working at the Hawthorne plant of Western Electric. Deming was a strong advocate of Shewhart’s thinking and helped spread the use of the control chart in industry.

Control charts can be used for both continuous and discrete data. While the format and calculations vary as a function of the type and amount of data, the core concepts and purpose are the same. Some of the more common control charts are the Xbar and R chart, ImR chart, P and Np charts, and C and U charts.

Data for the control chart can be selected randomly or over a specified time period. It can be collected as single data points or rational subgroups of data. Below is an example of an Xbar and R chart showing the center line and control limits.

If all the points fall inside the control limits and appear to be random, we can define the variation as common cause, and the process is said to be in-control. If points fall outside the control limits, or display a non random pattern, then you can say the variation is special cause, and the process is out-of-control.

In-control Xbar and R chart

Out-of-control Xbar and R chart

3 benefits of a control chart

The control chart can provide you great insight into your process. Here are a few benefits of the control chart.

1. Helps you distinguish between common and special cause in your process

This is the purpose for the control chart. It will help guide you to the appropriate reaction for the type of variation you are seeing in your process.

2. Tells you when something has changed in your process – and when it hasn’t

If the process is exhibiting common cause variation, then nothing has changed in your process, so don’t look for unique reasons for the variation. If the process starts to show special cause variation, then something has changed, and you should find out what it is.

3. If something changes in your process, you will know when

Since the control chart monitors the process over time, a signal of special cause variation can be linked to a specific time frame of when the data was gathered.

Why is a control chart important to understand?

Since the control chart can provide you valuable information about your process, you need to understand how to construct and interpret the control chart. They are often referred to as the Voice of the Process .

Correct reaction to your variation

You need to take different actions on your process depending on what the control chart tells you about your variation. Taking the wrong action will result in increased variation.

Using the proper type of control chart

The type of control chart you use is dependent on the type and amount of data. Using the wrong control chart will provide misleading and inaccurate information about your process.

Understand the difference between within-sample and between-sample variation

Since you use sampled data to construct your control chart, you need to understand the concepts of signal (between sample) and noise (within sample) so your control chart is sensitive to distinguishing between common and special causes.

An industry example of a control chart

A producer of carbonated beverages used a control chart to monitor the performance of their two suppliers of corrugated containers. Since both had been doing a good job, the purchasing manager didn’t keep the charts up to date. Once the manufacturing manager started to complain about dimensional problems with the containers, purchasing started collecting current data.

As you can see from the two control charts below, Supplier 1 has an in-control process while Supplier 2 is wildly out-of-control.

Supplier 2 was brought in for a conference and told to get their process under control. Until then, Supplier 1 picked up all the business from Supplier 2. Because of the increased volume of business, Supplier 1 provided extra discounts to the company. A win-win for everyone, except Supplier 2.

3 best practices when thinking about a control chart

Here are a few tips to help you use a control chart to monitor your processes.

1. MSA

Since you will be making decisions based on your interpretation of a control chart, you want to be sure the data you are using is valid. Do an MSA (measurement system analysis) before collecting your data so you can have confidence the data properly represents the process.

2. Don’t control chart everything that moves

Identify your critical metrics before deciding on using a control chart. Unless the control chart is meaningful, it will just be another chart on the wall rather than the powerful tool it is.

3. Keep charts up to date

The control chart should be current and up to date for it to be useful.

Frequently Asked Questions (FAQ) about a control chart

What is a control chart used for .

It is used to distinguish between common and special cause variation.

Can a control chart be used for discrete data?

Yes. The control chart was first developed for continuous manufacturing data. Variations were developed to be used for discrete data with applications in almost every type of process and industry.

Does the data need to be normally distributed for the control chart to work?

No. Shewhart developed the control chart to be very robust and practical regardless of the data distribution.

Shewhart stated, “ Some of the earliest attempts to characterize a state of statistical control were inspired by the belief that there existed a special form of frequency function f and it was early argued that the normal law characterized such a state. When the normal law was found to be inadequate, then generalized functional forms were tried. Today, however, all hopes of finding a unique functional form f are blasted.”

Summarizing the control chart

The control chart has been a powerful tool since the mid 1920s to monitor and gain insight into a process and its change over time. Control charts are used to distinguish between the two causes of process variation: common and special. Once you understand which type your process is exhibiting, you can take the appropriate action to address it.

The control chart can be used for continuous and discrete data gathered either singularly or in subgroups. A center line is drawn to represent the average of the data, and control limits are calculated to define the expected range of common cause variation. The proper interpretation of the control chart will tell you what changed in your process (and when) – and what didn’t change.

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Ken Feldman

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.

Neuhauser D, Provost L, Bergman B: The meaning of variation to healthcare managers, clinical and health-services researchers, and individual patients. BMJ Qual Saf. 2011, 20 (Suppl 1): i36-40. 10.1136/bmjqs.2010.046334.

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VA National Quality Scholars Fellowship, Tennessee Valley Healthcare System, Nashville, Tennessee, 37212, USA

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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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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Common Cause - Special Cause

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Quick reference

SPC Control Charts are designed to differentiate between special cause variation and common cause variation. In order to understand the importance of this and the implication for control, this lesson explains and illustrates the difference.

When to use

Common cause variation is always present in a process. Special cause variation is present in an unstable process. Whenever a process manager seeks to control a process, he or she needs to separate the variation into the appropriate categories so that appropriate actions can be taken.

Instructions

SPC control charts are used to identify the differences between common cause variation and special cause variation. Once the process manager has determined the root cause for special cause variation and eliminated it, the remaining common cause variation is placed under statistical control in order to maintain a predictable process. For this reason, it is important that we clearly understand the differences between these two types of variation.

Common Cause

Common cause variation is the variation that is inherent in the design of the process characteristics. It is the typical variation between process operators, or the variation in equipment performance due to normal usage, it could even be differences in process performance due to environmental factors like heat, cold, or time of day. In one sense this variation is random, in another sense it is not. It is random to the extent that we don’t know if it will be higher or lower than the average value. However, the magnitude is not totally random. There is a normal range for the random variation that it will not exceed. Because of this, we can statistically model and predict the impact of the random variation on process performance. Finally, random variation can only be changed by making a fundamental process change so that the factors outlined earlier are different.

Special Cause

Special cause variation is not due to the inherent variation within the process design. It has as a unique root cause that is outside the inherent process operational characteristics. This root cause is not something the operator is normally monitoring and controlling. The occurrence of this root cause is unpredictable in timing and the magnitude of the impact is also unpredictable. That is why we say that the presence of a special cause variation leads to an unstable or unpredictable process. This unpredictability cannot be mathematically modeled and used to predict normal process performance. It is important to note that special cause variation is not always negative variation. Special causes may change important process parameters that reduce the inherent common cause variation within a process. This is referred to as the Hawthorne Effect – named after a research study conducted in Hawthorne, Illinois. In this study of worker productivity, it was determined that the primary influence on worker productivity was the special cause of having all the study specialists observing the worker’s behavior. Since they knew they were being studied, the worker changed their normal practice to be much more careful – but only when they were being watched.

Common Cause – Special Cause – Control Charts

Control charts are designed to differentiate between common cause variation and special cause variation. This is so the process managers and operators can remove the special cause variation which makes the process predictable. At that point, they can either maintain the normal variation or consider a fundamental change to the process to reduce the magnitude of the normal variation in the process. The operator should never chase the ups and downs of normal variation. Doing that will introduce process tampering which often leads to overcontrolling of the output and makes the overall performance even worse.

Hints & tips

Identify and resolve special cause variation before attempting to make changes to the process and influencing common causes.
Beware if tampering – it is an easy trap to fall into. It may give a short term benefit, due to the Hawthorne effect. But unless the process is fundamentally changed, it will go back to the original levels of common cause variation.…
00:04 Hi, I'm Ray Sheen.
00:06 Let's review the concepts of common cause and special cause variation.
00:11 A primary purpose of SPC charts is to identify the presence and
00:15 the magnitude of each of these variation categories.
00:19 I’ll start with what I mean by categories of variation.
00:23 All process variation can be divided into one of two categories.
00:27 The first is common cause variation.
00:30 This variation is always present, even in a stable process.
00:33 It’s inherent in the physical design and operation of the process.
00:37 Because it is always present, we can measure it and
00:40 establish a baseline of the normal variation using statistical techniques.
00:44 The specific instance of variation on any process run is random, but
00:49 is always within a set of boundaries that are predictable.
00:52 There are limits to this normal variation, which we can calculate and
00:55 plot on our control charts.
00:58 The second category of variation is special cause variation.
01:01 This is associated with unstable processes because it is not predictable.
01:06 It is due to something unusual.
01:08 Often, that something is poor process management by the business managers or
01:12 the process operators.
01:14 This type of variation is not predictable.
01:17 We can neither predict when it will occur or
01:19 what the magnitude of the value will be when it does occur.
01:22 Therefore, when it happens it creates an unexpected process performance, and
01:26 often requires special action to restore performance.
01:30 Let's look at each of these in a little more detail.
01:33 I'll start with common cause.
01:35 Common cause variation is always present and
01:37 is predictable with respect to the magnitude.
01:39 Therefore, it should be accounted for when setting process performance targets, and
01:44 allowed for within the tolerances on those performance targets.
01:48 Let me clarify the nature of randomness of common cause variation.
01:52 It is random with respect to any specific occurrence of variation.
01:56 Might be a little high or a little low as compared to normal performance, but
01:59 that variation is always within predictable magnitude range.
02:03 That means we can establish a normal range and a predictable variation.
02:08 What we can't do is eliminate common cause variation by taking some form of special
02:12 corrective action to chase the variation.
02:15 By that I mean if we follow the process and results are a little higher than
02:19 normal, we try to tweak the process to be a little lower to compensate.
02:23 Think about the young teenager learning to drive.
02:26 They over control the car while trying to stay in their lane and
02:29 end up going way outside the lines.
02:31 Trying to compensate for each occurrence of common cause variation creates
02:35 process tampering as a special cause, and normally results in an unstable process.
02:41 Okay, now let's look at special cause variation.
02:44 When Special Cause variation is present, we say the process is unstable.
02:48 That's because the process performance is no longer predictable.
02:51 Special Cause variation is not controllable by the process operator.
02:56 Therefore, they don't know what the process results will be on any given
02:59 run of the process.
03:00 Now, you may be thinking, well, that's true for common cause variation also.
03:04 The part of that is true for common cause variation is that
03:07 you don't know precisely what the process result will be, but you do
03:11 know that it will fall within predictable limits of common cause variation.
03:15 The problem with special cause is that it falls outside those limits
03:19 of common cause variation and you have no ability to predict how far outside.
03:24 Special Cause variation is unpredictable, but it's not random.
03:27 It has a clear cause that precipitates the variation.
03:30 There is a clear, identifiable root cause that if it had not occurred,
03:34 there would have been no Special Cause variation.
03:37 The good news about that is that sometimes we can find out
03:39 what created that underlying root cause and we can take actions to prevent it and
03:44 eliminate the Special Cause variation from occurring again.
03:48 One other point, the Special Cause variation is not always bad.
03:51 Sometimes it is special good.
03:53 This means that there is some root cause that is preventing normal cause variation
03:57 from occurring.
03:58 This can give us a false sense of the range for normal variation.
04:02 There's a famous research study conducted in Hawthorne, Illinois,
04:05 that illustrated this point.
04:07 The purpose of the study was to determine the effect of adding lighting
04:10 to the workplace of an industrial assembly line.
04:12 Well, first this test was done to establish a baseline without
04:15 lighting present.
04:16 Then lighting was added and the productivity was measured.
04:19 It had improved and
04:21 the sponsor of the research, a lighting company, was excited by the results.
04:25 However, the researchers did one more assessment.
04:28 They turned the lights off and measured productivity again, and
04:31 it had improved even more.
04:33 So the research said that the best productivity was
04:35 achieved when you installed lighting but turned it off.
04:39 Yeah, I agree.
04:40 That doesn't make much sense.
04:41 But with further study,
04:43 they found that while lighting helped, the biggest effect on productivity was
04:47 having all those researchers there in the factory collecting data all the time.
04:51 This was a special cause that disrupted the normal work habits of the people on
04:55 the production line.
04:56 So let's wrap this up with a comparison of common cause and Special Cause variation.
05:02 Common cause is the normal and predictable variation that occurs within a process.
05:07 It's predictable in the sense of the magnitude always falls within limits or
05:10 boundaries of process performance.
05:13 It is always present because it is inherent in the process design.
05:17 The predictable aspect allows us to mathematically model this variation,
05:20 which let's us establish defined limits for it.
05:23 One last point,
05:24 it cannot be eliminated by the process operators taking special action.
05:28 The only way to improve it is to fundamentally change the process
05:33 to one with different physical characteristics, and
05:35 therefore, lower levels of random variation.
05:39 In contrast, the special cause variation is unpredictable.
05:43 The process operator does not know when it will occur.
05:45 And if it does occur, the operator is unable to predict the process results
05:50 due to the impact of the special cause.
05:52 It is outside of the control of the process operator,
05:55 which is why we say it makes the process unstable.
05:59 The occurrence and magnitude cannot be mathematically predicted.
06:02 It is based upon some external unique root cause.
06:05 Now, although the operator cannot control the effect,
06:08 sometimes they can control the conditions that allow the unique root cause to occur.
06:13 In that case, it can be preventable.
06:16 This review of the principles of common cause variation and special cause
06:20 variation was necessary because we are about to see that SPC control charts
06:24 are designed to differentiate between these two types of variation.

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Special cause

August 7th, 2013

Instance or event that impacts processes only under “special” circumstances -i.e., not part of the normal, daily operation of the process. See Common Cause; Variation.

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Common cause and special cause (statistics)
Common and special causes are the two distinct origins of variation in a process, as defined in the statistical thinking and methods of Walter A. Shewhart and W. Edwards Deming.
Common Cause & Special Cause Variation Explained with Examples
The variations are known as common cause variation and special cause variation. Common Cause Variation Definition Common cause variation refers to the natural and measurable anomalies that occur in the system or business processes. It naturally exists within the system.
The Power of Special Cause Variation: Learning from Process Changes
A special cause is a signal that the process outcome is changing — and not always for the better. Overview: What is special cause variation? A control chart can show two different types of variation: common cause variation (random variation from the various process components) and special cause variation.
Common Cause vs. Special Cause Variation: What's the Difference?
Special cause variations are unexpected glitches that occur that significantly affect a process. It is also known as "assignable cause." These variations are unusual, unquantifiable, and are variations that have not been observed previously, so they cannot be planned for and accounted for.
Navigating Special Cause Variation in Quality Control
Special cause variation refers to unexpected and assignable events which can affect a process. These are variations that were not observed before and are unusual, non-quantifiable variations. Special cause variation may be sporadic. It is not usually part of your normal process and seems to just unexpectedly occur.
The meaning of variation to healthcare managers, clinical and health
In answering that question, it becomes crucial to understand the difference between common-cause and special-cause variation (as will be discussed later). Common-cause variation appears as random variation in all measures from healthcare processes. 1 Special-cause variation appears as the effect of causes outside the core processes of the work ...
Identifying and Managing Special Cause Variations
Statistical instability If you have a chart that only has common cause variations, it means that your process is likely "statistically stable." Understanding special cause variations on your chart, helps you recognize the inverse. It means that your processes are "statistically unstable" and that modifications may need to occur.
Special Cause vs. Common Cause Variation
Dr. Deming called this "tempering the process." Rather than improving the quality, tempering, in fact, reduces the quality. Deming demonstrated the effect of tempering with the help of a funnel experiment. The causes of variation can be classified into two categories: Common Causes Special Causes Statistical Process Control Bootcamp
Assignable Cause
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.
The meaning of variation to healthcare managers, clinical and health
In answering that question, it becomes crucial to understand the difference between common-cause and special-cause variation (as will be discussed later). Common-cause variation appears as random variation in all measures from healthcare processes.1 Special-cause variation appears as the effect of causes outside the core processes of the work ...
Common Cause vs. Special Cause: The Basics of Process Variation
Special Cause Variation. On the other hand, special cause variation occurs when something out of the ordinary happens in a process. This might be a one-time occurrence, or it can develop into a repeating effect, in which case it might eventually be categorized as common cause variation depending on the circumstances. It's important to ...
The Importance of Selecting the Proper Type of Control Chart
The control chart can provide you great insight into your process. Here are a few benefits of the control chart. 1. Helps you distinguish between common and special cause in your process. This is the purpose for the control chart. It will help guide you to the appropriate reaction for the type of variation you are seeing in your process.
Understanding and managing variation: three different perspectives
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.
Common-Cause vs. Special-Cause
Staff — January 4, 2013. Common-cause variation is where no one, or combination of factors is unduly affected the process variation (random variation). Special-cause variation is when one or more factors are affecting the process variation in a non-random way. With special-cause variation, one should be able to identify, or put their finger ...
Special Cause Definition
Special Cause means: (i) a felony conviction concerning conduct directly related to Purchaser 's business ( including, but not limited to, theft or embezzlement) or (ii) Solomon 's failure to perform his Special Duties, if Solomon does not cure such failure within the later of (x) 30 days of receipt of notice thereof, containing details of such ...
Common Cause Variation Vs. Special Cause Variation
Special 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.
Common Cause
Special causes may change important process parameters that reduce the inherent common cause variation within a process. This is referred to as the Hawthorne Effect - named after a research study conducted in Hawthorne, Illinois. In this study of worker productivity, it was determined that the primary influence on worker productivity was the ...
Special Cause
Special Cause. Definition of Special Cause. A source of variation that is not inherent in the system, and is unpredictable and intermittent. It can be assigned to a defect in the system. On a control chart, it appears as data points outside the control limits or non-random points within the control limits. It is also referred to as Assignable ...
Glossary Term: Special cause
Instance or event that impacts processes only under "special" circumstances -i.e., not part of the normal, daily operation of the process. See Common Cause; Variation. SixSigma.us offers both Live Virtual classes as well as Online Self-Paced training.
Definition of Special Cause Variation
The special cause of variation can be detected by using control charts. Due to the presence of the special cause variation in the process, it becomes more likely to witness the instability, and predicting the outcomes becomes difficult. Unlike the fact that some variables that affect the other variables are common as in the case of common cause ...
Specific cause
specific cause: a cause, the action of which can definitely produce the condition in question.
Special Cause Variation
The following are common special cause variation signals: An exception to the upper limit or below limit. A trend: 6 to 7 points increasing/ decreasing. A repeating or cycle. A run is 8 points or more on either side the average. Special causes of variation can be attributed to any defect, fault, mistake or delay in the process.
Specific cause synonyms, specific cause antonyms
Synonyms for specific cause in Free Thesaurus. Antonyms for specific cause. 107 synonyms for cause: origin, source, agency, spring, agent, maker, producer, root ...

Common Cause & Special Cause Variation Explained with Examples

Common Cause Variation Definition

What is Common Cause Variation

Common Cause Variation Examples

Special Cause Variation Definition

What is Special Cause Variation

Special Cause Variation Examples

Common Cause and Special Cause Variation Detection

Countering common cause and special cause variation

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Introduction

Health managers

Clinical and health-services researchers

Individual patients and their healthcare providers

Longitudinal factorial design of experiments for individual patients

Understanding variation is one of the cornerstones of the science of improvement

Forces driving this new way of thinking

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Common Cause vs. Special Cause: The Basics of Process Variation

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The Importance of Selecting the Proper Type of Control Chart

Overview: What is a control chart?

3 benefits of a control chart

1. Helps you distinguish between common and special cause in your process

2. Tells you when something has changed in your process – and when it hasn’t

3. If something changes in your process, you will know when

Why is a control chart important to understand?

Correct reaction to your variation

Using the proper type of control chart

Understand the difference between within-sample and between-sample variation

An industry example of a control chart

3 best practices when thinking about a control chart

1. MSA

2. Don’t control chart everything that moves

3. Keep charts up to date

Frequently Asked Questions (FAQ) about a control chart

Can a control chart be used for discrete data?

Does the data need to be normally distributed for the control chart to work?

Summarizing the control chart

About the Author

Ken Feldman

Proceedings of Advancing the Methods in Health Quality Improvement Research 2012 Conference

Understanding and managing variation: three different perspectives

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Clinical researcher perspective

Healthcare manager perspective

Patient perspective

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