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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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A project management and digital marketing knowledge manager, Avantika’s area of interest is project design and analysis for digital marketing, data science, and analytics companies.

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Using control charts to detect common-cause variation and special-cause variation

In this topic, what are common-cause variation and special-cause variation, what special-cause variation looks like on a control chart, using brainstorming to investigate special-cause variation, don't overcorrect your process for common-cause variation.

Some degree of variation will naturally occur in any process. Common-cause variation is the natural or expected variation in a process. Special-cause variation is unexpected variation that results from unusual occurrences. It is important to identify and try to eliminate special-cause variation. Out-of-control points and nonrandom patterns on a control chart indicate the presence of special-cause variation.

Examples of common-cause and special-cause variation

A process is stable if it does not contain any special-cause variation; only common-cause variation is present. Control charts and run charts provide good illustrations of process stability or instability. A process must be stable before its capability is assessed or improvements are initiated.

This process is stable because the data appear to be distributed randomly and do not violate any of the 8 control chart tests.

This process is not stable; several of the control chart tests are violated.

A good starting point in investigating special-cause variation is to gather several process experts together. Using the control chart, encourage the process operators, the process engineers, and the quality testers to brainstorm why particular samples were out of control. Depending on your process, you may also want to include the suppliers in this meeting.

• Which samples were out of control?
• Which tests for special causes did the samples fail?
• What does each failed test mean?
• What are all the possible reasons for the failed test?

A common method for brainstorming is to ask questions about why a particular failure occurred to determine the root cause (the 5 why method). You could also use a cause-and-effect diagram (also called fishbone diagram).

While it's important to avoid special-cause variation, trying to eliminate common-cause variation can make matters worse. Consider a bread baking process. Slight drifts in temperature that are caused by the oven's thermostat are part of the natural common-cause variation for the process. If you try to reduce this natural process variation by manually adjusting the temperature setting up and down, you will probably increase variability rather than decrease it. This is called overcorrection.

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Common Cause vs. Special Cause Variation: What’s the Difference?

Published: November 7, 2022 by iSixSigma Staff

What is Common Cause Variation?

Common cause variation is the kind of variation that is part of a stable process. These are variations that are natural to a system and are quantifiable and expected. Common cause variations are those that are predictable, ongoing, and consistent. Major changes would typically have to be made in order to change the common cause variations.

One example of a common cause variation would be when a task takes slightly longer or shorter to accomplish than the mean time. Other examples could be normal wear and tear, computer lag time, and measurement errors.

The Benefits of Common Cause Variations

Since common cause variations are always present, they can be measured to establish a baseline using statistical techniques of the normal variation. These types of variations also fit easily within the control limits of a control chart.

How to Identify Common Cause Variation

You can identify common cause variation points on the control chart of a process measure by its random pattern of variation and its adherence to the control limits.

What is Special Cause Variation?

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.

These causes are typically the result of a specific change that has occurred in the process, with the result being a chaotic problem.

One example of a special cause variation would be a task taking exorbitantly longer than typical due to an unexpected crisis. Other examples would be power outages, computer crashes, and machine malfunctions.

The Benefits of Special Cause Variation

One benefit of special cause variations is that they are typically connected to a defect in the system or process that is addressable. Changes to components, methods, or processes can help prevent the special cause variation from occurring again.

How to Identify Special Cause Variation

You can identify special cause variation on a control chart by their non-random patterns and out-of-control points.

Common Cause vs. Special Cause: What’s the Difference?

Common cause variation and special cause variation are related in that they can both be present in the performance of a process. The difference between these two types of variation lies in how common cause variations are normal and expected variations that do not deviate from the natural order of a process. With common cause variations, a process remains stable. With special cause variations, however, a process is dramatically affected and becomes unstable. In short, common cause variations reflect a stable process, while special cause variations reflect an unstable process.

Common Cause vs. Special Cause: Who would use A and/or B?

Both of these types of variation are important to have an understanding of in project management. You can keep track of a project’s health by observing control charts and being able to spot the differences between common cause variations and special cause variations. The ability to spot the differences allows for knowing if a process is stable or not and if there are variations that need to be addressed by making changes or if they can likely be left alone.

Choosing Between Common Cause and Special Cause: Real World Scenarios

A project manager has been tasked with looking at the performance of a project during the previous quarter. A control chart is drafted that shows any variance that occurred during that quarter. With an understanding of how common cause and special cause variance is displayed on a control chart, the project manager looks for points on the chart that appear non-random and that go outside the control of the chart.

Upon inspection, the project manager finds a group of points that fall well outside the parameters of what is typical. A few of the workers are called, and it is determined that at the time those points fell under, there was a flood that prevented the necessary work from being done.

This adequately explains the presence of special cause variation on the control chart.

Summary/Conclusion

Variation in a process is normal and expected. Over a given period of time, it is essentially unavoidable. Nevertheless, by understanding control charts and being able to recognize variances that are typical for the process and those that are atypical, we can make changes to processes to prevent or safeguard against the same special cause variation in the future.

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Common cause and special cause variation

Published on 13 April 2023 at 16:39

by Suzie Creighton

In past articles in this series of blogs, we have looked at Statistical Process Control (SPC) and how it works within the Quality Improvement (QI) setting. We know that SPC charts are designed to measure how a process or system changes over time.

We’re going to look in a bit more detail about how SPC charts identify the difference between positive or negative variation as well as normal and unusual variation in the data. We’re also going to show you the specific types of variation that can exist within an SPC chart, namely: common cause and special cause variation.

Just to reiterate, SPC charts – also known as control charts – are ‘decision-making tools that provide information for timely decisions concerning recently produced products’ . They enable you to monitor and review the progress of QI projects and to look at trends or variation.

SPC charts have an average or mean line, and two control lines which sit above and below the average line. This enables a greater depth of statistical interpretation.

SPC charts also show individual data points, which allows more granularity. You can also use them alongside PDSA cycles – which you may well be familiar with from your QI work.

The Institute for Healthcare Improvement (IHI) recommends the visual display of data as the best way to learn from variation in QI, saying: ‘For improvement efforts, visual displays of data are often the best approach to learn from variation in data. Images are usually easy and quick to prepare, and they make it possible to access nearly all kinds of potential insight from the data.’

Let’s take a look at the variations we can monitor in our SPC charts: common cause and special cause variation.

What is a common cause variation?

In Quality Improvement, teams consistently use data. Both to learn from the processes they are carrying out and to predict future performance. 'Quality improvement requires using data to learn and to predict future performance. In improvement, it is critical to understand that every process has inherent variation that we want to understand.'

When you are looking at your SPC chart, you should be on the look out for two types of variation: common cause and special cause variation.

Common cause variation is something that is to be expected within your project. ‘Variation that is expected and natural in the system. Whilst this does not indicate that the system is working well, it does mean that the system is stable and predictable’. Common cause variation will always be present and is intrinsic to the whole process.

Here’s how the NHS Institute for Innovation and Improvement's document 'Guide to creating and interpreting run and control charts’ describes the common cause variation.

Common cause variation:

• ’is inherent in the design of the process
• is due to regular, natural or ordinary causes
• affects all the outcomes of a process
• results in a “stable” process that is predictable
• also known as random or unassignable variation’

What is a special cause variation?

Special cause variation stems from external sources and control. This type of variation is not expected and therefore not a consistent part of the process. Special cause variation demonstrates that the process is out of statistical control. They show that while ‘the causes can be often infrequent, they can result in a chaotic problem. The cause could possibly be a defect in the system or a problem that has never happened before.’

Special cause variation:

• is due to irregular or unnatural causes that are not inherent in the design of the process
• affect some, but not necessarily all aspects of the process
• results in an “unstable” process that is not predictable
• also known as non-random or assignable variation’

When you note a special cause, you shouldn't implement PDSA cycles, as it shows that the system is not stable.

How to react when you see variation?

We’ve taken a look at common and special cause variation, but you may well be wondering what you should do when you encounter either type of variation within your QI project? Let’s take a look.

NHS England and Improvement's SPC document explains that it is normal for a process to demonstrate no cause of special variation, and therefore recommends that when you identify common cause variation, you should change the underlying process itself rather than reacting to individual performance changes. This would lead to more variation. By reducing variation you can improve your processes.

The NHS Institute for Innovation and Improvement recommends that if your process is demonstrating special cause variation, you should look into the origin of the cause. They add that: ‘changing the underlying process on the basis of special causes is a waste of resources.’

The East London NHS Foundation Trust (ELFT) use Life QI and have an excellent guide to interpreting SPC charts . The team recommends taking the following steps when you note special cause variation:

• Investigate what caused it? Was it due to internal (i.e. change in process) or external factors (i.e. cyber attack)?
• Determine is any action is needed
• Revise control limits (calculate new CL, UCL and LCL) if appropriate’

Rules in detail

Now let’s take a look at rules pertaining to special and common cause variation, and explore the terms you might well have heard in your QI journey.

What is a shift?

This can take place in a SPC or control chart: ‘a shift in its most basic form is eight points in a row either above or below the centerline.’

• A Shift is a run of eight or more data points in a row above or below the centre line
• Low variation is fifteen consecutive points in the inner third of the chart, between the -1 and +1 sigma limits.’

What is a trend?

A trend is six points in a row in an upward direction or in a downward direction.

‘The use of a shift or a trend is that when seen, it is a ‘smoking gun’ for an area of improvement. The team is really hoping to see a trend or a shift because if a process always stays in the state of statistical control, the opportunity of reducing assignable-cause variation is far more challenging then if assignable-cause variation is evident.’

What is high variation?

This is when two out of three points in the outer third of the chart, between the +2 and +3 sigma or between the -2 and -3 sigma limits.

What is an outlier?

An outlier occurs when there is a single data point above the upper control limit (UCL) or below the lower control limit (LCL).

In this article we’ve had a really good look at variation in SPC charts. As we have seen before, if you are using a software solution such as Life QI, this can make life easier for you and your QI team to interpret and act on your SPC charts to deliver better results.

To finish with the words of NHS England and Improvement's SPC document . ‘There are four rules to interpret SPC charts and if you use specialist software, these rules will be flagged so you don’t need to remember them. If one of the rules has been broken, this means that special cause variation exists in the system and once identified, can be removed.’

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Common Cause and Special Cause

Common Cause and Special Cause in Statistics: Understanding Variability

Statistics is a powerful tool for analyzing data and making informed decisions, but to do so effectively, it’s essential to understand the sources of variability within a process or system.

In the realm of statistical process control , two fundamental concepts come into play: common cause and special cause variation. These concepts help us differentiate between the everyday fluctuations in a process and the exceptional, identifiable factors that can lead to significant deviations from the norm.

Common Cause Variation

Common cause variation, often called random variation or systemic variation, is the inherent variability in any process. It is the everyday, expected variation that occurs when a system is stable and operating under normal conditions.

This type of variation is the result of numerous factors and interactions within a process, and it cannot be traced back to a specific source. Common cause variation is, in a sense, the “background noise” of a process.

Key characteristics of common cause variation include:

• Inherent to the Process: Common cause variation is an inherent part of a process and will always exist to some degree.
• Consistent Patterns: It typically follows consistent, predictable patterns, often resembling a bell-shaped curve (a normal distribution).
• Random and Unpredictable: It is random in nature and cannot be attributed to any specific factor or event. This makes it difficult to control or eliminate entirely.
• Small Fluctuations: Common cause variation results in small, manageable fluctuations around a process’s mean or average value.

Examples of common cause variation can include minor temperature fluctuations in a manufacturing process, small variations in delivery times, or slight variations in the weight of identical products produced on the same assembly line.

Special Cause Variation

Special cause variation, also known as assignable variation or non-random variation, is the opposite of common cause variation. It represents variability in a process that can be traced back to specific, identifiable causes. Unlike common cause variation, which is inherent to the process, special cause variation is due to external factors or events that disrupt the system’s normal functioning.

Key characteristics of special cause variation include:

• Identifiable Causes: Special cause variation can be linked to specific events, actions, or factors that are not part of the usual operation of the process.
• Erratic Patterns: Unlike the consistent patterns of common cause variation, it often exhibits erratic and unpredictable patterns.
• Large Fluctuations: Special cause variation results in significant deviations from a process’s mean or average value.
• Unusual Events: Examples of special cause variation can include equipment breakdowns, power outages, errors in data entry, or major shifts in market demand.

Differentiating Between Common Cause and Special Cause Variation

Distinguishing between common cause and special cause variation is crucial in process improvement and quality control. Understanding the source of variability in a process allows organizations to take appropriate actions.

Here are some guidelines for differentiation:

• Data Analysis: The first step is to collect and analyze data. If the variation observed falls within the expected range of common cause variation, it is likely due to inherent process variability. However, if the data points exhibit patterns or values that deviate significantly from the norm, special cause variation may be present.
• Statistical Tools: Various statistical techniques, such as control charts, can be used to monitor processes and identify abnormal data points that suggest special cause variation. Control charts help in distinguishing between natural process variation and unusual occurrences.
• Root Cause Analysis: When special cause variation is suspected, a thorough root cause analysis is essential. This involves investigating the specific factors that contributed to the variation and taking corrective actions to prevent its recurrence.
• Process Control: Once special cause variation is identified and addressed, process control measures can be put in place to minimize the risk of future occurrences.

How can we Minimize Common Cause Variation?

Minimizing common cause variation is a key goal in statistical process control and quality improvement. While common cause variation is inherent to any process and cannot be completely eliminated, there are several strategies and approaches that can help reduce its impact and maintain greater process stability. Here are some ways to minimize common cause variation:

• A thorough understanding of the process is the first step in minimizing common cause variation. You need to know how the process operates, what factors affect it, and the expected sources of variability.
• Implement process control tools, such as control charts, to continuously monitor the process. Control charts help in distinguishing between common cause and special cause variation. They visually represent the process’s performance over time, making it easier to detect trends or shifts.
• Develop and maintain standardized operating procedures for the process. SOPs ensure that everyone involved follows the same methods and practices, reducing variability in human factors and operational choices.
• Invest in the training and skill development of employees involved in the process. A well-trained workforce is less likely to introduce unnecessary variability through errors or inconsistent practices.
• Regularly maintain and calibrate equipment to minimize common cause variation associated with machinery or tools. Well-maintained equipment is more likely to produce consistent results.
• Use statistical techniques to understand the inherent variability in the process. By analyzing the process’s capability and identifying areas with excessive common cause variation, you can make data-driven decisions to improve it.
• Implement Lean Six Sigma principles to identify and eliminate waste and non-value-added steps in the process. This can help streamline operations and reduce variability.
• Use DOE methodologies to study the impact of various process factors on variability systematically. This approach can help optimize processes and identify which factors have the most significant impact on common cause variation.
• Form cross-functional teams to focus on process improvement. Teams can work together to identify sources of common cause variation, develop solutions, and ensure continuous process optimization.
• Make decisions based on data and evidence rather than intuition. Data-driven decisions allow for a better understanding of the process’s performance and the identification of areas where common cause variation can be reduced.
• Establish feedback loops to ensure that lessons learned from past performance are used to make continuous improvements. Regularly review and update process documentation, procedures, and best practices.
• Compare your process performance to industry benchmarks and best practices. Benchmarking can help identify areas where your process may be underperforming and experiencing excessive common cause variation.
• Encourage employees to provide feedback and suggestions for process improvement. They often have valuable insights into daily operations and can help identify and address common cause variation.

Minimizing common cause variation is an ongoing effort requiring a systematic process improvement approach. Organizations can reduce variability and enhance their processes’ overall quality and performance by consistently monitoring, analyzing, and making data-driven adjustments.

How does Common Cause and Special Cause Apply in Six Sigma Projects

Common cause and special cause variation are fundamental concepts in Six Sigma, a structured and data-driven methodology for process improvement. Understanding these concepts is crucial for identifying, analyzing, and addressing variations within processes to reduce defects and improve overall quality. Here’s how common cause and special cause apply in Six Sigma projects:

• Defining the Problem (Define Phase): In the Define phase of a Six Sigma project, the team identifies the problem that needs to be addressed. At this stage, it’s essential to differentiate between common cause and special cause variation. Common cause variation represents the inherent variability in the process, while special cause variation signifies exceptional factors causing deviations from the norm. This distinction helps in setting realistic improvement goals and understanding the scope of the project.
• Data Collection and Analysis (Measure Phase): The Measure phase involves collecting data to quantify the performance of the process and determine its capability. Six Sigma practitioners use statistical tools and control charts to identify patterns in the data. Control charts help distinguish between common cause and special cause variation. Common cause variation is typically represented by data points within control limits, while data points beyond these limits suggest special cause variation.
• Root Cause Analysis (Analyze Phase): Once special cause variation is identified in the Measure phase, the Analyze phase focuses on determining the specific causes of these exceptional variations. Root cause analysis techniques, such as the “5 Whys,” Fishbone diagrams, or Failure Modes and Effects Analysis (FMEA), are employed to understand the underlying factors responsible for special cause variation. Addressing these root causes is critical for process improvement.
• Improvement Actions (Improve Phase): In the Improve phase , the Six Sigma team devises and implements solutions to eliminate or mitigate the root causes of special cause variation. Improvement actions are carefully planned, tested, and validated to ensure that the process becomes more stable and predictable.
• Monitoring and Control (Control Phase): The Control phase is about sustaining the improvements made during the project. Common cause variation is continuously monitored through control charts, and process performance is measured against the new standards. The control plan, established in this phase, ensures that the process remains in control and that deviations due to common cause variation are promptly identified and addressed.
• Continuous Improvement: Six Sigma is inherently focused on continuous improvement. Common cause variation is always present but can be further reduced and managed through ongoing efforts. Teams conduct periodic reviews and data analysis to detect changes in process performance and address any new sources of special cause variation.

Six Sigma projects involve a structured approach to addressing both common cause and special cause variation. While common cause variation represents the natural variability in a process, special cause variation results from specific, identifiable issues.

A Six Sigma project aims to minimize both types of variation to improve process performance and quality. This requires a combination of data analysis, root cause analysis, process improvement efforts, and ongoing monitoring to ensure that the improvements are sustained over time.

In the world of statistics and quality control, understanding the concepts of common cause and special cause variation is vital for making informed decisions and improving processes. Common cause variation is the inherent, expected variation in a process, while special cause variation represents unusual and identifiable sources of variability. By distinguishing between these two types of variation, organizations can work towards greater process stability, predictability, and overall quality improvement.

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Common Cause Variation Vs Special Cause Variation

Table of contents

What is variance, types of variance, common cause variation vs. special cause variation.

Howmuchever perfection you try to bring into the process, there will always be a little variation. Variance means how the data is distributed about an expected value. A zero variation situation means both results are identical, which is a rare situation. The world of quality management experiences its forms of variation, notably common cause, and special cause variation. Understanding the difference between common cause and special cause variation is like deciphering the ingredients behind the flavors in the baker's creations. Common cause variation implies consistency whereas special cause variation includes the inconsistencies coming in the process. In this blog, we will understand the difference between common cause variation and special cause variation in detail. 

Variance provides us with an idea of the way data is distributed around an anticipated value or mean. If you have the value of a zero variance means the results are similar which is a rare condition. A high variance indicates that the points of data are separated from one another--and also the mean, whereas a smaller variance indicates that the data points are close to the average. Variance is always non-negative.

Every single piece of data that is measured will show a degree of variation. No, regardless of how hard we make it, we will never achieve identical results for two different situations. Each result will differ even if the difference is small. A variation could be described as "the number used to determine how much individuals within a group differ."

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When dealing with variations in their work, professionals must confront knowing the decision of when to act and when to not take action. Since if you react to every change in the process and alter the process, it will be an endless process. Dr. Deming called this "tempering the process." In the end, rather than improving the quality, tempering actually, lowers the quality. Deming demonstrated the effects of tempering using the aid of a funnel study.

Variation can be classified into two categories:

Common Causes

Special Causes

Common Cause Variation refers to the inherent, anticipated variability that occurs naturally in any process. It is caused by the regular interactions between multiple elements within the system and is a contributing factor to the overall stability of the system. This kind of variation is considered to be consistent and predictable since it follows a consistent pattern over time. Common cause variations are similar to the fluctuation of the time a person commutes due to weather, traffic, or other factors that are uncontrollable. While it may cause small variations in the outcome but it is within a safe range and can be managed by making adjustments to the process. Quality control measures are designed at reducing common causes variations by fine-tuning processes and improving overall efficiency. Knowing and dealing with common causes for variations is crucial for maintaining the same quality of service or product while also optimizing operations and ensuring the long-term stability of processes.

Special cause variation refers to the unpredictable fluctuations in a process that are caused by factors outside the usual realm of operation. Unlike common cause variation, which results from inherent but consistent factors within a process, special cause variation arises sporadically due to exceptional circumstances. These circumstances could include equipment malfunctions, sudden changes in the environment, or human errors.

Special cause variation often leads to significant deviations from the norm and can result in outcomes that are far from the expected average. It is characterized by its sporadic nature, as it does not follow a predictable pattern. Identifying and addressing special cause variation is crucial in maintaining process stability and quality. Quality control methods, such as control charts, help to distinguish between common and special causes of variation. By pinpointing the factors responsible for special cause variation, organizations can implement targeted solutions to prevent future occurrences and ensure consistent and reliable process outcomes.

The difference between common cause variation vs. special cause variation is represented below:

The two kinds of variations, though connected, possess distinct features that require precise comprehension and effective management. Understanding their distinct characteristics will illuminate their significance in ensuring the stability of processes and improving overall performance.

Origin and Nature: Common cause variation arises from the inherent fluctuations that are an integral element of every process. It is a reflection of the normal variations that result from numerous factors, including minor variations in input conditions, small equipment changes, and personal operator variations. This kind of variation is stable in time and can be seen as a constant background noise that influences the results of processes.

However, the special cause variation results from external factors that aren't part of the normal process conditions. It's sporadic, and usually caused by external factors like sudden breakdowns of equipment or significant environmental changes, or human mistakes. Special cause variations disrupt the normal process flow and can cause deviations that are different from the usual pattern. Because it is not random, it is important to be identified and addressed promptly.

Impact on Process and Stability: Common causes for variation can contribute to the natural spreading of data points within the mean or average of the process. While it could cause small deviations, these variations can be controlled within the parameters of control over the process. Process improvements and optimization can reduce the effects of common cause variations and increase the overall stability of the process.

However, special cause variations can affect the outcomes of the process. It can lead to outcomes that are different from the average and can result in mistakes, defects, or inefficiencies. The unpredictable nature of special cause variations requires immediate attention to avoid it from happening again and to ensure stability.

Control and Management: Controlling common cause variations involves setting limits of control or acceptable ranges in that the procedure is expected to function. Control of the process using statistical (SPC) tools such as control charts, track the process in time to identify patterns and deviations from the expected pattern. By analyzing the data, businesses can tweak processes and ensure the same quality.

To address the issue of special cause variation, it requires an entirely different method. Root cause analysis is designed to determine the exact causes that cause the non-random variations. Once the root cause is identified, corrective measures are taken to either eliminate or minimize its impact. This proactive approach helps prevent future instances of variation due to special causes and increases the reliability of the process.

Visualizing on Control Charts: Control charts are visual representations of variations. In the case of common cause variation data points change within control limits, showing the normal spread. However, in the case of special cause variation, the data points go beyond these limits, which indicates the existence of an unusual influence that is affecting the process.

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If you're looking to increase your knowledge of quality control and process management understanding these variations is an essential aspect, especially in situations like PMP certification training . Like the way a conductor's skill determines the result of a performance, your skill in identifying common causes and special cause variations can have a significant impact on the effectiveness of your process management procedures. If you can master these concepts, you'll be better prepared to optimize processes, identify issues, and produce the smooth results that are expected of PMP-certified professionals.

Frequently Asked Questions (FAQs):

What are the most common causes of variation? 

Common cause variation refers to the natural and expected variations in a process that result from everyday events. It helps to explain the natural variation of data points within the process average, and it follows a predictable pattern throughout time.

What is the special cause of variance? 

Special cause variation is caused by external and sporadic elements which cause non-random shifts in the process's outcomes. This can cause deviations that are beyond the normal range and disrupt the flow of the process.

What do common cause variations be controlled? 

Common cause variation can be managed by using techniques for process optimization. The use of statistical process control (SPC) tools such as control charts can help monitor and keep the procedure within a reasonable limit.

What methods are used to address the issue of special cause variation? 

Special cause variation requires root cause analysis to determine the specific causes that cause the variances. Once they are identified, corrective measures can be taken to stop their repetition.

What are these concepts and how do they connect to PMP certification? 

Understanding common cause and special cause variations is essential in project management situations such as PMP certification courses. The knowledge gained from these concepts allows professionals to ensure the stability of processes improve outcomes, and achieve the precision that comes with PMP-certified experts.

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