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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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Solving Special Cause & Common Cause Problems

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About this lesson

Special cause problems should be resolved first in order to achieve process stability. Then common cause problems are addressed to reduce process variation.

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When to use.

These problem solving guidelines should be followed when the team starts to create solutions in the Improve stage of a Lean Six Sigma project. If both types of causes of the problem are present, all special cause solutions should be implemented and a new process baseline measured before starting to implement common cause solutions. In that manner, the true effect of the common cause solution can be measured and understood.


Special cause root causes and common cause root causes are fundamentally different and require fundamentally different approaches for solution. Special cause root causes are unique events. Their occurrence is random and their magnitude is unpredictable. In many cases, they can be totally eliminated if the enabling factors are controlled. Common cause root causes are always present. The magnitude of their impact is random within some predictable boundaries but their occurrence is continuous. They can only be controlled or eliminated by making a change to the basic characteristics of the process or system.

When special cause root causes are present, they should always be solved first. Their solution could impact the overall process performance. Once their solution is in place, the process performance should again be assessed and if there is still an issue, then common cause aspects of the process must be addressed.

Solving Special Cause Problems

Special cause solutions will either consist of actions to control or eliminate the factors that enable the special cause condition to occur or by monitoring for the special cause occurrence and establishing workarounds for immediate implementation when a special cause is detected. Essentially a preventive action approach and a corrective action approach.

The preventive action approach of eliminating the enabling factors usually requires a 6 M (material, machine, manpower, methods, measurement, Mother Nature) analysis of contributing factors to decide which ones are controllable. The corrective action approach often requires the implementation of a monitoring system and the prepositioning of resources or procedures in the event of a special cause occurrence. Trying to solve a special cause problem with common cause solutions is normally ineffective because the random event will still occur randomly.

Solving Common Cause Problems

Common cause solutions must follow one of three approaches:

  • Either a major change to the existing process to reduce the common cause variation,
  • A change to a totally different process with inherently lower common cause variation,
  • Or in some cases, recalibrating the existing process so that the common cause variation occurs within the allowable performance tolerances.

In each case the variation is continuing, it is just that the magnitude or distribution is no longer an issue. Trying to solve common cause problems with special cause methods will often increase the amount of inherent variations since the process monitoring system is now chasing and over-correcting for normal random variation.

Hints & tips

  • There is a tendency for the organization to want to treat everything like it is a special cause and start to chase normal random variation. Take the time in the analysis phase to understand the type of causes you are facing and then take the appropriate action.
  • Special cause monitoring solutions should be periodically tested to ensure they are able to detect the occurrence of a special cause.
  • Work with process operators and supervisors to ensure their expectations for process performance is reasonable. If solving special cause with a monitoring approach, they should expect that the special cause condition will occasionally occur. When solving common cause problems, they should know that there will still be variation in the system, but that the variation is now at an acceptable level.
  • 00:05 Hi, I'm Ray Sheen.
  • 00:06 When determining a solution approach, you need to know if you're fixing a special
  • 00:10 cause problem or a common cause problem.
  • 00:13 The solution strategy is quite different
  • 00:15 depending upon the nature of the root cause or causes.
  • 00:20 Let me explain the strategy to use for each of those conditions.
  • 00:24 You should always address special cause problems first.
  • 00:27 You can't begin to solve common cause problems when the process is unstable and
  • 00:31 unpredictable.
  • 00:33 So first we go for stability and that means eliminating special cause problems.
  • 00:38 This is the approach embedded in many of the basic problem solving methodologies.
  • 00:42 They assume every problem special cause and try to solve it.
  • 00:46 The approach is easy to understand.
  • 00:48 Analyze the problem until you find the root cause and then fix that root cause.
  • 00:53 Either eliminating it, or screening for that root cause occurrence factors and
  • 00:57 taking appropriate action when they are present.
  • 01:00 Once the special cause problems are resolved and
  • 01:03 the process is stable, if there is still a performance problem,
  • 01:07 then it is time to go after the common cause issues.
  • 01:10 Recall the common cause variation is the normal variation inherit in the process.
  • 01:15 So to reduce common cause variation,
  • 01:17 you can never totally eliminate it .You must make a change in the basic process.
  • 01:22 So once the analysis is determined, the contributing factors or
  • 01:25 drivers of variation, your approach should be to determine which factors you
  • 01:29 can reasonably or realistically control or change.
  • 01:32 In other words, what types of solutions are feasible?
  • 01:35 And then select the change or
  • 01:36 changes needed to reduce variation to an acceptable level.
  • 01:40 Let's look a little deeper at what it takes to fix special causes.
  • 01:44 Of course, the actual solution will depend on the actual problem.
  • 01:48 But there are two approaches you can take to create a solution for
  • 01:51 special cause problems.
  • 01:53 The first approach is to control or eliminate the enabling factors.
  • 01:57 When you do this,
  • 01:58 the situation that creates a special cause problem is prevented.
  • 02:02 For instance, let's say your special cause was due to the change to the new vendor
  • 02:05 and how their component performs in your product.
  • 02:08 Eliminate the new vendor, going back to the old one and
  • 02:11 the special cause problem is solved.
  • 02:14 To use this approach, you must have completed a thorough analysis of
  • 02:17 the problem and determined the root cause or causes.
  • 02:20 Then decide which of these causes you can impact.
  • 02:23 That will usually mean doing something in one of the M categories of material,
  • 02:28 manpower, methods, machines, measurements, or mother nature.
  • 02:32 Design your change or improvement and put it in place.
  • 02:36 The goal is to eliminate or prevent the root cause from occurring.
  • 02:41 The other approach used is when the root cause is not controllable.
  • 02:44 Let's say our root cause was due to a power outage.
  • 02:47 That is an infrequent occurrence.
  • 02:49 It's unpredictable and uncontrollable.
  • 02:52 While it can't stop it, I can put a place, an instant warning signal and
  • 02:56 the actions needed to compensate for this root cause problem.
  • 03:00 So in my example, I get a back up generator.
  • 03:03 Whenever the power goes out, the generator automatically starts.
  • 03:07 I didn't prevent the root cause, but I have a design that recognizes and
  • 03:10 compensates for it.
  • 03:12 This approach requires an active monitoring system and
  • 03:15 I recommend that it also include a periodic test of that system.
  • 03:20 Okay, now lets switch and look at the approach to use for common cause problems.
  • 03:25 Now as we've said many times, there is always going to be common cause variation.
  • 03:30 The problem is when the variation causes our product or
  • 03:33 process to miss its performance goals.
  • 03:36 To reduce or modify the inherent common cause variation,
  • 03:39 will normally require a fundamental change to the system or process.
  • 03:44 There are three tactics that we can use to accomplish this.
  • 03:47 One tactic is to work with the existing product or
  • 03:49 process to reduce the sources of variation.
  • 03:52 If there's only one or
  • 03:53 two sources, then change how those parts of the process work.
  • 03:57 Think through the six Ms again when considering this change.
  • 04:01 If there's several interactive factors, you'll probably need
  • 04:04 to do a design of experiment study and more about that in another module.
  • 04:08 Of course, many times it's just easier to switch to a different process or
  • 04:12 system altogether, than trying to fix the existing one.
  • 04:15 That's especially true if the current system is old or uses outdated technology.
  • 04:20 This will likely take some re-engineering or system design.
  • 04:24 Again, a design of experiments can be helpful here.
  • 04:27 The third approach is to shift the existing process performance so
  • 04:30 that the amount of inherit common cause variation
  • 04:33 all falls within an acceptable level of process performance.
  • 04:37 Many processes in systems have a tendency to drift over time.
  • 04:40 The system may just need to be recalibrated.
  • 04:43 This is the easiest of the three approaches, but it's not always variable.
  • 04:47 So when designing you're improvement, be sure you are using the correct approach.
  • 04:52 Trying to fix a common cause problem with a special cause solution will normally
  • 04:56 just make the inherent variation larger and the problem even bigger.
  • 05:00 And trying to fix a special cause problem with a common cause
  • 05:03 solution will not prevent the power outage from ever happening again.
  • 05:08 I spent a lot of time discussing the difference between special cause and
  • 05:12 common cause in earlier modules.
  • 05:15 The reason was so that the improvement approach selected at
  • 05:18 this point in the project, will be one that is truly effective for the problem.

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Common Cause and Special Cause

Businessman looking at mainframe - Common cause and special cause variation

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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Understanding Data Variation in Six Sigma: Special Cause Variation & Common Cause Variation, & How to Reduce Variation

Understanding Data Variation in Six Sigma: Special Cause Variation & Common Cause Variation, & How to Reduce Variation

The Importance of Data Variation in Six Sigma

To understand the impact of variation on performance, consider the two graphs below. Suppose a pizza company is tracking their delivery times relative to the time that they tell customers to expect their pizza. Certainly customers do not want their pizza to arrive late. But there may even be customers who do not want it to arrive early, for instance if they are placing the order when leaving work to go home, or if they plan to take a quick shower first. The company has decided based on customer feedback that they want to be sure to arrive within 8 minutes of the planned arrival time at a customer’s house.

In the first graph, you see that this company is usually arriving within the window they consider acceptable, as shown by the large portion of the curve that is within the two dotted lines indicating the lower and upper limits. However a percentage of customers are receiving their pizza either more than 8 minutes before or more than 8 minutes after the time they were told to expect delivery.

Often to change process performance, a group will try to shift the average, for instance in this case the company might try to just achieve earlier delivery in all cases. But this won’t please customers who don’t want their pizza too quickly, and there will still be enough variation in the process that if something changes and the average increases again, performance will suffer.

Only by reducing variation will performance be substantially and sustainably improved. In the next graph, you can see the result of an effort to tighten up the pizza delivery process, so that all customers receive their pizza within 8 minutes of the expected delivery time. Furthermore, even if there is a short-term problem or a long-lasting change, the delivery time could increase or decrease somewhat without resulting in increased defects.

Process with low variation

Note that this improvement has occurred without even shifting the average time, or the upper and lower spec limits.

Types of Variation

So how does an organization go about reducing the variation in its process using Six Sigma ? That will depend on the type of variation that is present in the process. Common cause variation is variation in process output that is essentially inherent in the process itself. So for instance, some variation in arrival time for pizza deliveries will occur simply due to variations in traffic. It would not be fruitful to try to understand why one delivery to a specific location took 13 minutes while another took 15 minutes, if 97% of the deliveries to that location are generally within 10-19 minutes. Both those values are well within the typical performance of the process. However it may be feasible to improve the overall process so that variation is reduced, perhaps by changing the way routes are determined, or using a bicycle which can maneuver easily through traffic jams instead of a car for deliveries.

If, however, the delivery to that location occasionally takes 32 or 34 minutes, management would be wise to seek information about the cause for each of those outliers. Was it a traffic accident or construction at that time on that route? Did a problem in the kitchen cause a delay in the delivery vehicle’s departure? Or is there one driver who consistently takes well over the typical amount of time for his deliveries?

The proper means of addressing process improvement and variation reduction depends on whether common cause, special cause, or both types of variation are involved. This is particularly important in settings where employee performance is being evaluated: it is neither fruitful nor motivating to interrogate or discipline employees for doing something more or less quickly than another employee if the variation is all inherent in the process.

Techniques for Reducing Variation

Reacting to common cause variation requires a different mindset and set of techniques than reacting to special cause variation. When special cause variation is present, it means that specific factors exist that can impact the process performance in a specific instance, as compared to performance in other situations. For example, weather problems, power outages, and traffic accidents represent special cause that can impact whether pizza is delivered to a customer within the appropriate amount of time.

In order to reduce variation due to special causes, Six Sigma project teams can put in place a procedure for detecting the presence of those factors in a timely fashion, and using contingency plans to minimize their impact. For example, when a delivery route is blocked due to accident or unexpected construction, a notification can go out to all dispatchers and driver so that another route is employed.

When only common cause variation is present in a process, it does not make sense to try to understand why performance was as it was in one specific instance. It is necessary to look at the process overall and determine how variation can be reduced and performance improved. While special cause may be a sign that a process is fundamentally sound but prone to impact by outside forces, common cause is a sign that substantial variation exists in the process itself and that it needs fundamental changes to meet the performance goals and to reduce variation.

The most common way to understand and impact common cause variation is to determine major factors that vary, such as department performing a function, time of day that the process is performed, or equipment used in the process. Graphing and data analysis techniques are used to separate out these different parts of the process, such as by evaluating performance separately for the day shift and the night shift or based on which type of product is being processed.

Once the factors affecting performance are identified, the DMAIC process can be followed to uncover root causes for defects and variation and to implement sustainable improvements.


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Statistical Process Control stands as a cornerstone in quality control and Lean Six Sigma, playing a vital role in elevating product quality and efficiency across various industries, particularly in manufacturing. This systematic, data-driven approach leverages statistical methods to meticulously monitor, control, and enhance processes and their performance.

At its core, SPC thrives on rigorous data analysis, enabling organizations to decipher trends, patterns, and anomalies in manufacturing or service processes. It’s an approach that prioritizes informed decision-making over assumptions, aiming to identify and mitigate process variability – a key factor in ensuring consistent product quality.

Table of Contents

What is statistical process control.

Statistical Proces Control (SPC) is a key method used in quality control, and Lean Six Sigma use to maintain and improve product quality and efficiency. It is used in various industries but is primarily used in manufacturing as a systematic, data-driven approach to uses statistical methods to monitor, control, and improve processes and process performance. 

SPC is a data-driven methodology that relies heavily on data to analyze process performance. By collecting and analyzing data from various stages of a manufacturing or service process, SPC enables organizations to identify trends, patterns, and anomalies. This data-driven approach helps in making informed decisions rather than relying on assumptions or estimations.

The main aim of SPC is to detect and reduce process variability. Variability is a natural aspect of any process, but excessive variability can lead to defects, inefficiency, and reduced product quality. By understanding and controlling this variability, organizations can ensure that their processes consistently produce items within desired specifications. SPC involves continuous monitoring of the process to quickly identify deviations from the norm. This systematic approach ensures that problems are detected early and can be rectified before they result in significant quality issues or production waste.

History and Background of the Development of SPC

Walter shewhart and control charts.

The foundations of SPC were laid in the early 20th century by Walter Shewhart working at Bell Laboratories. Shewhart’s primary contribution was the development of the control chart , a tool that graphically displays process data over time and helps in distinguishing between normal process variation and variation that signifies a problem. The control chart remains a cornerstone of SPC and is widely used in various industries to monitor process performance.

W. Edwards Deming and Post-War Japan


After World War II, W. Edwards Deming brought the concepts of SPC to Japan, where they played a key role in the country’s post-war industrial rebirth. Deming’s teachings emphasized not only statistical methods but also a broader philosophical approach to quality. He advocated for continuous improvement ( Kaizen ) and total quality management, integrating SPC into a more comprehensive quality management system.

Impact on Manufacturing and Beyond

The implementation of SPC led to significant improvements in manufacturing quality and efficiency. It allowed companies to produce goods more consistently and with fewer defects. The principles of SPC have since been adopted in various sectors beyond manufacturing, including healthcare, finance, and service industries, demonstrating its versatility and effectiveness in process improvement.

Fundamental Concepts of SPC

Understanding process variation.

In SPC, process variation is categorized into two types: common causes and special causes.

  • Common Cause Variation: These are inherent variations that occur naturally within a process. They are predictable and consistent over time. In the image below common cause variation is the variation within the control limits
  • Special Cause Variation: These variations are due to external factors and are not part of the normal process. They are unpredictable and can indicate that the process is out of control. In the image below, the special cause variation is the data point outside the upper control limit 

Common and special cause variation

Control Charts

Control charts are essential tools in SPC, used to monitor whether a process is in control.

  • Graphical Representation: They graphically represent data over time, providing a visual means to monitor process performance.
  • Control Limits: Control charts use upper and lower control limits, which are statistically derived boundaries. They help in distinguishing between normal process variation (within limits) and variations that require attention (outside limits).

Types of Control Charts

Understanding the types of control charts in Statistical Process Control (SPC) is crucial for effectively monitoring and improving various processes. These charts are broadly categorized based on the type of data they handle: variable data and attribute data. Additionally, the implementation of SPC in a process, from data collection to continuous improvement, is a systematic approach that requires diligence and precision. Let’s explore these aspects in more detail.

control chart types

Variable Data Control Charts

Variable data control charts are used for data that can be measured on a continuous scale. This includes characteristics like weight, length, or time.

X-bar and R Chart

  • Purpose: Used to monitor the mean (average) and range of a process.
  • Application: Ideal for situations where sample measurements are taken at regular intervals and the mean and variability of the process need to be controlled.
  • Structure: The X-bar chart shows how the mean changes over time, while the R chart displays the range (difference between the highest and lowest values) within each sample.

Individual-Moving Range (I-MR) Chart

  • Purpose: Monitors individual observations and the moving range between observations.
  • Application: Useful when measurements are not made in subgroups but as individual data points.
  • Structure: The I chart tracks each individual data point, and the MR chart shows the range between consecutive measurements.

Attribute Data Control Charts

Attribute data control charts are used for data that are counted, such as defects or defective items.

P-chart (Proportion Chart)

  • Purpose: Monitors the proportion of defective items in a sample.
  • Application: Ideal for quality characteristics that are categorical (e.g., defective vs. non-defective) and when the sample size varies.
  • Structure: It plots the proportion of defectives in each sample over time.

C-chart (Count Chart)

  • Purpose: Tracks the count of defects per unit or item.
  • Application: Used when the number of opportunities for defects is constant, and defects are counted per item or unit.
  • Structure: It plots the number of defects in samples of a constant size.

Implementing SPC in a Process

Implementing Statistical Process Control (SPC) in a process is a structured approach that involves several key steps: data collection, establishing control limits, monitoring and interpretation, and continuous improvement. Each of these steps is critical to the successful application of SPC. Let’s explore these steps in more detail.

Step 1: Data Collection

First, data must be collected systematically to ensure it accurately represents the process. This involves deciding what data to collect, how often to collect it, and the methods used for collection. The selection of data is important. It should be relevant to the quality characteristics you want to control. For example, in a manufacturing process, this might include measurements of product dimensions, the time taken for a process step, or the number of defects.

The data should be representative of the actual operating conditions of the process. It means collecting data under various operating conditions and over a sufficient period.

The sample size and frequency of data collection should be adequate to capture the variability of the process. It’s a balance between collecting enough data for reliability and the practicality of data collection.

Control Chart Step 1

Step 2: Establishing Control Limits

Control limits are calculated using historical process data. They are statistical representations of the process variability and are usually set at ±3 standard deviations from the process mean.

These limits reflect what the process can achieve under current operating conditions.

To help you calculate your data control limits, you can use our Control Limits Calculator.

Control limits are not fixed forever. As process improvements are made, these limits may be recalculated to reflect the new level of process performance.

When significant changes are made to a process (like new machinery, materials, or methods), it might be necessary to recalculate the control limits based on new performance data.

Control Chart Step 3

Step 3: Monitoring and Interpretation

Regularly reviewing control charts is essential for timely detection of out-of-control conditions. Apart from individual points, it’s crucial to look for patterns or trends in the data, which could indicate potential issues.

When data points fall outside the control limits or exhibit non-random patterns, it triggers a need for investigation. The goal is to identify the root cause of the variation, whether it’s a common cause that requires a process change, or a special cause that might be addressed more immediately.

Step 4: Continuous Improvement

SPC is not just about maintaining control; it’s about continuous improvement. The insights gained from SPC should drive ongoing efforts to enhance process performance.

Based on SPC data, processes can be adjusted, improved, and refined over time. This might involve changes to equipment, materials, methods, or training.

In conclusion, SPC is a key tool in the aim for quality control and process improvement. Its strength lies in its ability to make process variability visible and manageable. From the seminal contributions of Walter Shewhart and W. Edwards Deming, SPC has evolved into a comprehensive approach that integrates seamlessly with various quality management systems.

By continuously monitoring processes through control charts and adapting to the insights these charts provide, SPC empowers organizations to maintain control over their processes and pursue relentless improvement. Thus, SPC not only sustains but also elevates the standards of quality, efficiency, and customer satisfaction in diverse industrial landscapes.

  • Madanhire, I. and Mbohwa, C., 2016. Application of statistical process control (SPC) in manufacturing industry in a developing country .  Procedia Cirp ,  40 , pp.580-583.
  • Gérard, K., Grandhaye, J.P., Marchesi, V., Kafrouni, H., Husson, F. and Aletti, P., 2009. A comprehensive analysis of the IMRT dose delivery process using statistical process control (SPC).   Medical physics ,  36 (4), pp.1275-1285.

Q: What is Statistical Process Control (SPC)?

A : SPC is a method used to monitor, control, and improve processes by analyzing performance data to identify and eliminate unwanted variations.

Q: Why is SPC important?

A : SPC helps ensure processes are consistent and predictable. It aids in early detection of issues, reducing defects, and improving overall product or service quality.

Q: What is a control chart?

A : A control chart is a graphical representation used in SPC to plot process data over time, with control limits that help distinguish between common and special cause variations.

Q: How are control limits determined?

A : Control limits are typically set at three standard deviations above and below the process mean, based on historical data. However, these limits can be adjusted depending on the specific chart type and industry standards.

Q: What's the difference between common cause and special cause variation?

A : Common cause variation is the inherent variability in a process, while special cause variation arises from specific, unusual events and is not part of the normal process.

Daniel Croft

Daniel Croft

Daniel Croft is a seasoned continuous improvement manager with a Black Belt in Lean Six Sigma. With over 10 years of real-world application experience across diverse sectors, Daniel has a passion for optimizing processes and fostering a culture of efficiency. He's not just a practitioner but also an avid learner, constantly seeking to expand his knowledge. Outside of his professional life, Daniel has a keen Investing, statistics and knowledge-sharing, which led him to create the website learnleansigma.com, a platform dedicated to Lean Six Sigma and process improvement insights.

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Six Sigma Control Charts: An Ultimate Guide

  • Written by Contributing Writer
  • Updated on March 10, 2023

six sigma control charts

Welcome to the ultimate guide to Six Sigma control charts, where we explore the power of statistical process control and how it can help organizations improve quality, reduce defects, and increase profitability. Control charts are essential tools in the Six Sigma methodology, visually representing process performance over time and highlighting when a process is out of control.

In this comprehensive guide, we’ll delve into the different types of control charts, how to interpret them, how to use them to make data-driven decisions, and how to become a Lean Six Sigma expert .

Let’s get started on the journey to discover the transformative potential of Six Sigma control charts.

What is a Control Chart?

A control chart is a statistical tool used in quality control to monitor and analyze process variation. No process is free from variation, and it is vital to understand and manage this variation to ensure consistent and high-quality output. The control chart is designed to help visualize this variation over time and identify when a process is out of control.

The chart typically includes a central line, which represents the average or mean of the process data, and upper and lower control limits, which are set at a certain number of standard deviations from the mean. The control limits are usually set at three standard deviations from the mean, encompassing about 99.7 percent of the process data. If the process data falls within these control limits, the process is considered in control, and variation is deemed to be coming from common causes. If the data points fall outside these control limits, this indicates that there is a special cause of variation, and the process needs to be investigated and improved.

Control charts are commonly used in manufacturing processes to ensure that products meet quality standards, but they can be used in any process where variation needs to be controlled. They can be used to track various types of process data, such as measurements of product dimensions, defect rates, or cycle times.

Also Read: What Is Process Capability and Why It’s More Interesting Than It Sounds

Significance of Control Charts in Six Sigma

Control charts are an essential tool in the Six Sigma methodology to monitor and control process variation. Six Sigma is a data-driven approach to process improvement that aims to minimize defects and improve quality by identifying and eliminating the sources of variation in a process. The control chart helps to achieve this by providing a visual representation of the process data over time and highlighting any special causes of variation that may be present.

The Objective of Six Sigma Control Charts

The primary objective of using a control chart in Six Sigma is to ensure that a process is in a state of statistical control. This means that the process is stable and predictable, and any variation is due to common causes inherent in the process. The control chart helps to achieve this by providing a graphical representation of the process data that shows the process mean and the upper and lower control limits. The process data points should fall within these limits if the process is in control.

Detecting Special Cause Variation

One of the critical features of a Six Sigma control chart is its ability to detect special cause variation, also known as assignable cause variation. Special cause variation is due to factors not inherent in the process and can be eliminated by taking corrective action. The control chart helps detect special cause variation by highlighting data points outside control limits.

Estimating Process Average and Variation

Another objective of a control chart is to estimate the process average and variation. The central line represents the process average on the chart, and the spread of the data points around the central line represents the variation. By monitoring the process over time and analyzing the control chart, process improvement teams can gain a deeper understanding of the process and identify areas for improvement.

Measuring Process Capability with Cp and Cpk

Process capability indices, such as Cpk and Cp, help to measure how well a process can meet the customer’s requirements. Here are some details on how to check process capability using Cp and Cpk:

  • Cp measures a process’s potential capability by comparing the data’s spread with the process specification limits.
  • If Cp is greater than 1, it indicates that the process can meet the customer’s requirements.
  • However, Cp doesn’t account for any process shift or centering, so it may not accurately reflect the process’s actual performance.
  • Cpk measures the actual capability of a process by considering both the spread of the data and the process’s centering or shift.
  • Cpk is a more accurate measure of a process’s performance than Cp because it accounts for both the spread and centering.
  • A Cpk value of at least 1.33 is typically considered acceptable, indicating that the process can meet the customer’s requirements.

It’s important to note that while Cp and Cpk provide valuable information about a process’s capability, they don’t replace the need for Six Sigma charts and other statistical tools to monitor and improve process performance.

Also Read: What Are the 5s in Lean Six Sigma?

Steps to Create a Six Sigma Control Chart

To create a Six Sigma chart, you can follow these general steps:

  • Gather Data: Collect data related to the process or product you want to monitor and improve.
  • Determine Data Type: Identify the type of data you have, whether it is continuous, discrete, attribute, or variable.
  • Calculate Statistical Measures: Calculate basic statistical measures like mean, standard deviation, range, etc., depending on the data type.
  • Set Control Limits: Determine the Upper Control Limit (UCL) and Lower Control Limit (LCL) using statistical formulas and tools.
  • Plot Data : Plot the data points on the control chart, and draw the control limits.
  • Analyze the Chart: Analyze the chart to identify any special or common causes of variation, and take corrective actions if necessary.
  • Update the Chart: Continuously monitor the process and update the chart with new data points.

You can use software tools like Minitab, Excel, or other statistical software packages to create a control chart. These tools will automate most of the above steps and help you easily create a control chart.

Know When to Use Control Charts

A Six Sigma control chart can be used to analyze the Voice of the Process (VoP) at the beginning of a project to determine whether the process is stable and predictable. This helps to identify any issues or potential problems that may arise during the project, allowing for corrective action to be taken early on. By analyzing the process data using a control chart, we can also identify the cause of any variation and address the root cause of the issue.

Here are some specific scenarios when you may want to use a control chart:

  • At the start of a project: A control chart can help you establish a baseline for the process performance and identify potential areas for improvement.
  • During process improvement: A control chart can be used to track the effectiveness of changes made to the process and identify any unintended consequences.
  • To monitor process stability : A control chart can be used to verify whether the process is stable. If the process is unstable, you may need to investigate and make necessary improvements.
  • To identify the source of variability : A control chart can help you identify the source of variation in the process, allowing you to take corrective actions.

Four Process States in a Six Sigma Chart

Control charts can be used to identify four process states:

  • The Ideal state: The process is in control, and all data points fall within the control limits.
  • The Threshold state : Although data points are in control, there are some non-conformances over time.
  • The Brink of Chaos state: The process is in control but is on the edge of committing errors.
  • Out of Control state: The process is unstable, and unpredictable non-conformances happen. In this state, it is necessary to investigate and take corrective actions.

Also Read: How Do You Use a Six Sigma Calculator?

What are the Different Types of Control Charts in Six Sigma?

Control charts are an essential tool in statistical process control, and the type of chart used depends on the data type. There are different types of control charts, and the type used depends on the data type.

The seven Six Sigma chart types include: I-MR Chart, X Bar R Chart, X Bar S Chart, P Chart, NP Chart, C Chart, and U Chart. Each chart has its specific use and is suitable for analyzing different data types.

The I-MR Chart, or Individual-Moving Range Chart, analyzes one process variable at a time. It is suitable for continuous data types and is used when the sample size is one. The chart consists of two charts: one for individual values (I Chart) and another for the moving range (MR Chart).

X Bar R Chart

The X Bar R Chart is used to analyze process data when the sample size is more than one. It consists of two charts: one for the sample averages (X Bar Chart) and another for the sample ranges (R Chart). It is suitable for continuous data types.

X Bar S Chart

The X Bar S Chart is similar to the X Bar R Chart but uses the sample standard deviation instead of the range. It is suitable for continuous data types. It is used when the process data is normally distributed, and the sample size is more than one.

The P Chart, or the Proportion Chart, is used to analyze the proportion of nonconforming units in a sample. It is used when the data is binary (conforming or nonconforming), and the sample size is large.

The NP Chart is similar to the P Chart but is used when the sample size is fixed. It monitors the number of nonconforming units in a sample.

The C Chart, also known as the Count Chart, is used to analyze the number of defects in a sample. It is used when the data is discrete (count data), and the sample size is large.

The U Chart, or the Unit Chart, is used to analyze the number of defects per unit in a sample. It is used when the sample size is variable, and the data is discrete.

Factors to Consider while Selecting the Right Six Sigma Chart Type

Selecting the proper Six Sigma control chart requires careful consideration of the specific characteristics of the data and the intended use of the chart. One must consider the type of data being collected, the frequency of data collection, and the purpose of the chart.

Continuous data requires different charts than attribute data. An individual chart may be more appropriate than an X-Bar chart if the sample size is small. Similarly, if the data is measured in subgroups, an X-Bar chart may be more appropriate than an individual chart. Whether monitoring a process or evaluating a new process, the process can also affect the selection of the appropriate control chart.

How and Why a Six Sigma Chart is Used as a Tool for Analysis

Control charts help to focus on detecting and monitoring the process variation over time. They help to keep an eye on the pattern over a period of time, identify when some special events interrupt normal operations, and reflect the improvement in the process while running the project. Six Sigma control charts are considered one of the best tools for analysis because they allow us to:

  • Monitor progress and learn continuously
  • Quantify the capability of the process
  • Evaluate the special causes happening in the process
  • Separate the difference between the common causes and special causes

Benefits of Using Control Charts

  • Early warning system: Control charts serve as an early warning system that helps detect potential issues before they become major problems.
  • Reduces errors: By monitoring the process variation over time, control charts help identify and reduce errors, improving process performance and quality.
  • Process improvement: Control charts allow for continuous monitoring of the process and identifying areas for improvement, resulting in better process performance and increased efficiency.
  • Data-driven decisions: Control charts provide data-driven insights that help to make informed decisions about the process, leading to better outcomes.
  • Saves time and resources: Six Sigma control charts can help to save time and resources by detecting issues early on, reducing the need for rework, and minimizing waste.

Who Can Benefit from Using Six Sigma Charts

  • Manufacturers: Control charts are widely used in manufacturing to monitor and control process performance, leading to improved quality, increased efficiency, and reduced waste.
  • Service providers: Service providers can use control charts to monitor and improve service delivery processes, leading to better customer satisfaction and increased efficiency.
  • Healthcare providers: Control charts can be used in healthcare to monitor and improve patient outcomes and reduce medical errors.
  • Project managers : Project managers can use control charts to monitor and improve project performance, leading to better project outcomes and increased efficiency.

Also Read: What Are the Elements of a Six Sigma Project Charter?

Some Six Sigma Control Chart Tips to Remember

Here are some tips to keep in mind when using Six Sigma charts:

  • Never include specification lines on a control chart.
  • Collect data in the order of production, not from inspection records.
  • Prioritize data collection related to critical product or process parameters rather than ease of collection.
  • Use at least 6 points in the range of a control chart to ensure adequate discrimination.
  • Control limits are different from specification limits.
  • Points outside the control limits indicate special causes, such as shifts and trends.
  • Points inside the limits indicate trends, shifts, or instability.
  • A control chart serves as an early warning system to prevent a process from going out of control if no preventive action is taken.
  • Assume LCL as 0 if it is negative.
  • Use two charts for continuous data and a single chart for discrete data.
  • Don’t recalculate control limits if a special cause is removed and the process is not changing.
  • Consistent performance doesn’t necessarily mean meeting customer expectations.

What are Control Limits?

Control limits are an essential aspect of statistical process control (SPC) and are used to analyze the performance of a process. Control limits represent the typical range of variation in a process and are determined by analyzing data collected over time.

Control limits act as a guide for process improvement by showing what the process is currently doing and what it should be doing. They provide a standard of comparison to identify when the process is out of control and needs attention. Control limits also indicate that a process event or measurement is likely to fall within that limit, which helps to identify common causes of variation. By distinguishing between common causes and special causes of variation, control limits help organizations to take appropriate action to improve the process.

Calculating Control Limits

The 3-sigma method is the most commonly used method to calculate control limits.

Step 1: Determine the Standard Deviation

The standard deviation of the data is used to calculate the control limits. Calculate the standard deviation of the data set.

Step 2: Calculate the Mean

Calculate the mean of the data set.

Step 3: Find the Upper Control Limit

Add three standard deviations to the mean to find the Upper Control Limit. This is the upper limit beyond which a process is considered out of control.

Step 4: Find the Lower Control Limit

To find the Lower Control Limit, subtract three standard deviations from the mean. This is the lower limit beyond which a process is considered out of control.

Importance of Statistical Process Control Charts

Statistical process control charts play a significant role in the Six Sigma methodology as they enable measuring and tracking process performance, identifying potential issues, and determining corrective actions.

Six Sigma control charts allow organizations to monitor process stability and make informed decisions to improve product quality. Understanding how these charts work is crucial in using them effectively. Control charts are used to plot data against time, allowing organizations to detect variations in process performance. By analyzing these variations, businesses can identify the root causes of problems and implement corrective actions to improve the overall process and product quality.

How to Interpret Control Charts?

Interpreting control charts involves analyzing the data points for patterns such as trends, spikes, outliers, and shifts.

These patterns can indicate potential problems with the process that require corrective actions. The expected behavior of a process on a Six Sigma chart is to have data points fluctuating around the mean, with an equal number of points above and below. This is known as a process shift and common cause variation. Additionally, if the data is in control, all data points should fall within the upper and lower control limits of the chart. By monitoring and analyzing the trends and outliers in the data, control charts can provide valuable insights into the performance of a process and identify areas for improvement.

Elements of a Control Chart

Six Sigma control charts consist of three key elements.

  • A centerline representing the average value of the process output is established.
  • Upper and lower control limits (UCL and LCL) are set to indicate the acceptable range of variation for the process.
  • Data points representing the actual output of the process over time are plotted on the chart.

By comparing the data points to the control limits and analyzing any trends or patterns, organizations can identify when a process is going out of control and take corrective actions to improve the process quality.

What is Subgrouping in Control Charts?

Subgrouping is a method of using Six Sigma control charts to analyze data from a process. It involves organizing data into subgroups that have the greatest similarity within them and the greatest difference between them. Subgrouping aims to reduce the number of potential variables and determine where to expend improvement efforts.

Within-Subgroup Variation

  • The range represents the within-subgroup variation.
  • The R chart displays changes in the within-subgroup dispersion of the process.
  • The R chart determines if the variation within subgroups is consistent.
  • If the range chart is out of control, the system is not stable, and the source of the instability must be identified.

Between-Subgroup Variation

  • The difference in subgroup averages represents between-subgroup variation.
  • The X Bar chart shows any changes in the average value of the process.
  • The X Bar chart determines if the variation between subgroup averages is greater than the variation within the subgroup.

X Bar Chart Analysis

  • If the X Bar chart is in control, the variation “between” is lower than the variation “within.”
  • If the X Bar chart is not in control, the variation “between” is greater than the variation “within.”
  • The X Bar chart analysis is similar to the graphical analysis of variance (ANOVA) and provides a helpful visual representation to assess stability.

Benefits of Subgrouping in Six Sigma Charts

  • Subgrouping helps identify the sources of variation in the process.
  • It reduces the number of potential variables.
  • It helps determine where to expend improvement efforts.
  • Subgrouping ensures consistency in the within-subgroup variation.
  • It provides a graphical representation of variation and stability in the process.

Also Read: Central Limit Theorem Explained

Master the Knowledge of Control Charts For a Successful Career in Quality Management

Control charts are a powerful tool for process improvement in the Six Sigma methodology. By monitoring process performance over time, identifying patterns and trends, and taking corrective action when necessary, organizations can improve their processes and increase efficiency, productivity, and quality. Understanding the different types of control charts, their components, and their applications is essential for successful implementation.

A crystal-clear understanding of Six Sigma control charts is essential for aspiring Lean Six Sigma experts because it allows them to understand how to monitor process performance and identify areas of improvement. By understanding when and how to use control charts, Lean Six Sigma experts can effectively identify and track issues within a process and improve it for better performance.

Becoming Six Sigma-certified is an excellent way for an aspiring Lean Six Sigma Expert to gain the necessary skills and knowledge to excel in the field. Additionally, Six Sigma certification can provide you with the tools you need to stay on top of the latest developments in the field, which can help you stay ahead of the competition.

You might also like to read:

How to Use the DMAIC Model?

How Do You Improve Logistics with Six Sigma?

Process Mapping in Six Sigma: Here’s All You Need to Know

What is Root Cause Analysis and What Does it Do?

Describing a SIPOC Diagram: Everything You Should Know About It

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SPC defined

Control Chart Properties

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Responding to Special Causes

An excerpt from SPC DeMYSTiFieD (2011 McGraw-Hill) by Paul Keller

When special causes of variation are detected, determine (in process terms) the cause of the process shift. Similarly, when processes are improved, such as resulting from the efforts of Six Sigma project teams, the control chart should provide evidence of a special cause resulting from that change. For this reason, Six Sigma teams always use statistical control charts in the Measure stage of the Define-Measure-Analyze-Improve-Control (DMAIC) methodology to first baseline the existing process, then again in the Control stage to verify the process improvement. For example, if the Six Sigma team had revised the method of customer service by routing clients to more experienced personnel in an attempt to reduce service times, the control chart in the Control stage should indicate that current service times are identified as a special cause (below the lower control limit or a run test violation) relative to the control limits established during the Measure stage. This special cause would be indicative of an actual sustained improvement in service time resulting from the process change. If special causes are not detected between the pre-project Measure stage data and the post-project Control stage data, the project would be presumed to have little or no effect on improving service time.

  • Isolate the instances of variation due to special causes using the time-ordered nature of the control chart to understand what happened (in process terms) at each point in time represented by special causes.
  • To reduce the variation due to common causes, look to all elements of the system for clues to variation. Ignore the point to point variation, since it represents a combination of factors that is common to all the subgroups.

Chapter 7 includes a number of techniques to aid in this investigation, including the use of designed experiments and multivariate regression analysis to understand the significance and relative effects of these sources of variation.

When special causes have been identified on a control chart, the statistical control limits and center line should be recalculated to exclude their effect. Since special causes represent a different process distribution than that defined by the common cause level of variation, their numerical impact on the calculation of the common cause centerline and control limits represents a statistical bias in the estimates.

  • When subgroups are out of control with respect to variation, remove their impact from both estimates of variation and process location.
  • When subgroups are out of control with respect to process location, remove their impact only from process location estimate, allowing the maximum possible number of subgroups to estimate variation.

special cause six sigma

Example SPC control chart in SPC software with stepped regions to accomodate process shift.

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Common & Special Cause Variation

Common & Special Cause Variation

special cause six sigma

Assignable Cause

Published: November 7, 2018 by Ken Feldman

special cause six sigma

Assignable cause, also known as a special cause, is one of the two types of variation a control chart is designed to identify. Let’s define what an assignable cause variation is and contrast it with common cause variation. We will explore how to know if your control is signaling an assignable cause and how to react if it is.

Overview: What is an assignable cause? 

A control chart identifies two different types of variation: common cause variation (random variation resulting from your process components or 6Ms ) and assignable or special cause variation.

Assignable cause variation is present when your control chart shows plotted points outside the control limits or a non-random pattern of variation. Since special cause variation is unexpected and due to some factor other than randomness, you should be able to assign a reason or cause to it.  

When your control chart signals assignable cause variation, your process variable is said to be out of control, or unstable. Assignable cause variation signals can be identified by use of the Western Electric rules, which include:

  •   One point outside of the upper control limit or lower control limit
  •   A trend of 6 or 7 consecutive points increasing or decreasing
  •   A cycle or repeating pattern
  •   A run of 8 or more consecutive points on either side of the average or center line.

Assignable cause variation can be attributed to a defect, fault, mistake, delay, breakdown, accident, and/or shortage in the process. Or it can be a result of some unique combination of factors coming together to actually improve the process. When assignable causes are present, your process is unpredictable. The proper action and response is to search for and identify the specific assignable cause. If your process was improved as a result of your assignable cause, then incorporate it so that the cause is retained and improvement maintained. If your process was harmed by the assignable cause, then seek to eliminate it.

3 benefits of an assignable cause

Assignable causes can be good or bad. They are signals that something unexpected happened. Listen to the signal.

1. Signals something has happened 

Special or assignable cause variation signals that something unexpected and non-random has occurred in your process.

2. Specific cause

By investigating and identifying the specific cause of your signal, you can narrow in on your next steps for bringing the process back into control.

3. Can become common cause variation 

Good news! You found that your assignable cause for lowered production was due to a power outage. Unfortunately, you may not be able to stop power outages in your community. If nothing is done, your assignable cause becomes a common cause. 

You might not be able to stop power outages, but could you install a back-up generator? Then, if the generator doesn’t kick on, you will have an assignable cause you can do something about.

Why is an assignable cause important to understand? 

Interpreting what an assignable cause tells you is important to understand. 

Provides direction for action

Since an assignable cause can be a signal of something good or bad, you need to understand the different actions. Don’t ignore special or assignable causes. 

Not every unusual point has an assignable cause 

While at your favorite casino, you may throw a pair of dice at the craps table. Is there an assignable cause for throwing an 11 or a 10, or is it random variation? No, you would expect the process of rolling a fair pair of dice to show 10s and 11s. What about a 13? That would be unexpected and probably the result of something unusual happening with the dice. The same is true for your process. Don’t assume an assignable special cause unless your control chart signals it. 

Useful for determining whether your improvements worked

When you improve the process, your control chart should send signals of special cause variation — hopefully in the right direction. If you can link that signal to the specific assignable cause of your improvement, then you know it worked. 

An industry example of an assignable cause 

The accounts receivable department of a retail chain started to get complaints from its customers about overbilling. Fortunately, the manager of the department had participated in the company’s Lean Six Sigma training and had been using a control chart for errors.

Upon closer review, she noticed that errors seemed to occur more on Fridays than the rest of the week. In fact, the chart showed that almost every Friday, the data points were outside the upper control limit. She was concerned that nobody was identifying that as a signal of special cause.

She put together a small team of clerks to identify why this was happening and whether there was an assignable reason or cause for it. The assignable cause was determined to be the extra work load on Fridays. 

The team recommended a change in procedure to better balance the workload during the week. Continued monitoring showed the problem was resolved. She also held an all-hands meeting to discuss the importance of not ignoring signals of special cause variation and the need to seek out an assignable cause and take the appropriate action.

3 best practices when thinking about an assignable cause 

Signals of special cause variation require you to search for and identify the assignable cause.

1. Document your search 

If you’ve identified the assignable cause, document everything. If this cause happens again in the future, people will have some background to act quickly and eliminate/incorporate any actions.

2. Quickly identify the cause 

Time is of the essence. If the cause is resulting in a deteriorating process, act quickly to identify and eliminate the cause. The recommendation is the same if your cause made the process better, otherwise, whatever happened to improve the process will be lost as time goes by.

3. Don’t ignore signals of assignable cause 

Even if you get a single signal of special cause, search for the assignable cause. You may choose not to take any action in the event it is a fleeting cause, but at least try to identify the assignable cause.

Frequently Asked Questions (FAQ) about an assignable cause

1. is an assignable cause always bad .

No. It is an indication that something unexpected happened in your process. It could be a good or bad thing. In either case, search for and identify the assignable cause and take the appropriate action. 

2. What are some sources of an assignable cause? 

Some sources may be your process components such as people, methods, environment, equipment, materials, or information. Your process variation can come from these items and can be the assignable cause of a signal of special cause variation.

3. How do I tell if I should look for an assignable cause? 

Control charts were developed to distinguish between common and special cause variation. If they signal special cause variation in your process, seek out an assignable cause and take the appropriate action of either eliminating or incorporating your assignable cause.

Final thoughts on an assignable cause

All processes will exhibit two types of variation. Common cause variation is random, expected, and a result of variation in the process components. Special cause variation is non-random, unexpected, and a result of a specific assignable cause. 

If you get a signal of special cause variation, you need to search for and identify the assignable cause. Once found, you will either seek to incorporate or eliminate the cause depending on whether the cause improved or hurt your process.

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Ken Feldman

Six Sigma Study Guide

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Ted Hessing

Posted by Ted Hessing

Variation is the enemy! It can introduce waste and errors into a process. The more variation, the more errors. The more errors, the more waste.

What is Variation?

Quick answer: it’s a lack of consistency. Imagine that you’re manufacturing an item. Say, a certain-sized screw. Firstly, you want the parameters to be the same in every single screw you produce. Material strength, length, diameter, and thread frequency must be uniform. Secondly, your customers want a level of consistency. They want a certain size of screw all to be the same. Using a screw that’s the wrong size might have serious consequences in a construction environment. So a lack of consistency in our products is bad.

We call the differences between multiple instances of a single product variation .

(Note: in some of Game Change Lean Six Sigma’s videos, they misstate Six Sigma quality levels as 99.999997 where it should be six sigma = 99.99966 % )

Why Measure Variation?

We measure it for a couple of reasons:

  • Reliability: We want our customers to know they’ll always get a certain level of quality from us. Also, we’ll often have a Service Level Agreement or similar in place. Consequently, every product needs to fit specific parameters.
  • Costs: Variation costs money. So, to lower costs, we need to keep levels low.

Measuring Variation vs. Averages

Once, companies tended to measure process performance by average. For example, average tensile strength or average support call length. However, a lot of companies are now moving away from this. Instead, they’re measuring variation. For example, differences in tensile strength or support call lengths.

Average measurements give us some useful data. But they don’t give us information about our product’s consistency . In most industries, focusing on decreasing fluctuations in processes increases performance. It does this by limiting factors that cause outlier results. And it often improves averages by default.

How Do Discrepancies Creep into Processes?

Discrepancies occur when:

  • There is wear and tear in a machine.
  • Someone changes a process.
  • A measurement mistake is made.
  • The material quality or makeup varies.
  • The environment changes.
  • A person’s work quality is unpredictable.

There are six elements in any process:

  • Mother Nature, or Environmental
  • Man or People
  • Measurement

In Six Sigma, these elements are often displayed like this:

6M's of Six Sigma

Discrepancies can creep into any or all elements of a process.

To read more about these six elements, see 5 Ms and one P (or 6Ms) .

For an example of changing processes contrarily causing variation, see the Quincunx Demonstration .

The process spread vs. centering

Process spread vs centering

Types of Variation

There are two basic types that can occur in a process:

  • common cause
  • special cause

Common Cause

Common cause variation happens in standard operating conditions. Think about the factory we mentioned before. Fluctuations might occur due to the following:

  • temperature
  • metal quality
  • machine wear and tear.

Common cause variation has a trend that you can chart. In the factory mentioned before, product differences might be caused by air humidity. You can chart those differences over time. Then, you can compare that chart to Weather Bureau’s humidity data.

Special Cause

Conversely, special cause variation occurs in nonstandard operating conditions. Let’s go back to the example factory mentioned before. Disparities could occur if:

  • A substandard metal was delivered.
  • One of the machines broke down.
  • A worker forgot the process and made a lot of unusual mistakes.

This type of variation does not have a trend that can be charted. Imagine a supplier delivers a substandard material once in a three-month period. Subsequently, you won’t see a trend in a chart. Instead, you’ll see a departure from a trend.

Why is it Important to Differentiate?

It’s important to separate a common cause and a special cause because:

  • Different factors affect them.
  • We should use different methods to counter each.

Treating common causes as special causes leads to inefficient changes. So, too, does treating a special cause like a common cause. The wrong changes can cause even more discrepancies.

How to Identify

Use run charts to look for common cause variation.

  • Mark your median measurement.
  • Chart the measurements from your process over time.
  • Identify runs . These are consecutive data points that don’t cross the median marked earlier. They show common cause variation.

Control Charts

Meanwhile, use control charts to look for special cause variation.

  • Mark your average measurement.
  • Mark your control limits. These are three standard deviations above and below the average.
  • Identify data points that fall outside the limits marked earlier. In other words, it is above the upper control limit or below the lower control limit. These show special cause variation.


Variation is the square of a sample’s standard deviation .

How to Find the Cause of Variation

So far, you’ve found no significant variation in your process. However, you haven’t found what its cause might be. Hence, you need to find the source.

You can use a formal methodology like Six Sigma DMAIC or a multi-vari chart to identify the source of variation.

How to Find and Reduce Hidden Causes of Variation

DMAIC methodology is the Six Sigma standard for identifying a process’s variation, analyzing the root cause, prioritizing the most advantageous way to remove a given variation, and testing the fix. The tools you would use depend on the kind of variation and the situation. Typically, we see either a “data door” or a “process door” and the most appropriate use techniques.

You could try Lean tools like Kaizen or GE’s WorkOut for a smaller, shorter cycle methodology.

How to Counter Variation

Once you identify its source, you need to counter it. As we implied earlier, the method you use depends on its type.

Counter common cause variation using long-term process changes.

Counter special cause variation using exigency plans.

Let’s look at two examples from earlier in the article.

  • Product differences due to changes in air humidity. This is a common cause of variation.
  • Product differences due to a shipment of faulty metal. This is a special cause variation.

Countering common cause variation

As stated earlier, to counter common cause variation, we use long-term process changes. Air humidity is a common cause. Therefore, a process change is appropriate.

We might subsequently introduce a check for air humidity. We would also introduce the following step. If the check finds certain humidity levels, change the machine’s temperature to compensate. The new check would be run several times a day. Whenever needed, staff would change the temperature of the machine. These changes then lengthen the manufacturing process slightly. However, they also decrease product differences in the long term.

Countering special cause variation

As mentioned earlier, we need exigency plans to counter special cause variation. These are extra or replacement processes. We only use them if a special cause is present, though. A large change in metal quality is unusual. So we don’t want to change any of our manufacturing processes.

Instead, we implement a random quality check after every shipment. Then, an extra process to follow if a shipment fails its quality check. The new process involves requesting a new shipment. These changes don’t lengthen the manufacturing process. They do add occasional extra work. But extra work happens only if the cause is present. Then, the extra process eliminates the cause.

Combining Variation

Rather than finding variation in a single sample, you might need to figure out a combined variance in a data set. For example, a set of two different products. For this, you’ll need the variance sum law .

Firstly, look at whether the products have any common production processes.

Secondly, calculate the combined variance using one of the formulas below.

No shared processes

What if the two products don’t share any production processes? Great! Then, you can use the simplest version of the variance sum law.

Shared processes

What if the two processes do share some or all production processes? That’s OK. You’ll need the dependent form of the variance sum law instead.

Calculate covariance using the following formula.

  • μ is the mean value of X.
  • ν is the mean value of Y.
  • n = the number of items in the data set.


Additional Resources

ANOVA Analysis of Variation

What You Need to Know for Your Six Sigma Exam

Combating variation is integral to Six Sigma. Therefore, all major certifying organizations require that you have substantial knowledge of it. So, let’s walk through how each represents what they expect.

Green Belts

Asq six sigma green belt.

ASQ requires Green Belts to understand the topic as it relates to:

Exploratory data analysis Create multi vari studies . Then, interpret the difference between positional, cyclical, and temporal variation. Apply sampling plans to investigate the largest sources. (Create)

IASSC Six Sigma Green Belt

IASSC requires Green Belts to understand patterns of variation. Find this in the Analyze Phase section.

Black Belts

Villanova six sigma black belt.

Villanova requires Black Belts to understand the topic as it relates to:

Six Sigma’s basic premise

Describe how Six Sigma has fundamentally two focuses– variation reduction and waste reduction that ultimately lead to fewer defects and increased efficiency. Understand the concept of variation and how the six Ms have an influence on the process . Understand the difference between assignable cause and common cause variation along with how to deal with each type.

Multi vari studies

Create and interpret multi vari studies to interpret the difference between within piece, piece to piece, and time to time variation.

Measurement system analysis

Calculate, analyze, and interpret measurement system capability using repeatability and reproducibility , measurement correlation, bias, linearity, percent agreement, precision/tolerance (P/T), precision/total variation (P/TV), and use both ANOVA and c ontrol chart methods for non-destructive, destructive, and attribute systems.

ASQ Six Sigma Black Belt

ASQ requires Black Belts to understand the topic as it relates to:

Multivariate tools

Use and interpret multivariate tools such as principal components, factor analysis, discriminant analysis, multiple analysis of variance, etc to investigate sources of variation.
Use and interpret charts of these studies and determine the difference between positional, cyclical, and temporal variation.

Attributes data analysis

Analyze attributes data using logit, probit, logistic regression , etc to investigate sources of variation.

Statistical process control (SPC)

Define and describe the objectives of SPC, including monitoring and controlling process performance, tracking trends, runs, etc, and reducing variation in a process.

IASSC Six Sigma Black Belt

IASSC requires Black Belts to understand patterns of variation in the Analyze Phase section. It includes the following:

  • Multi vari analysis .
  • Classes of distributions .
  • Inferential statistics .
  • Understanding inference.
  • Sampling techniques and uses .

Candidates also need to understand its impact on statistical process control.

ASQ Six Sigma Black Belt Exam Questions

Question: A bottled product must contain at least the volume printed on the label. This is chiefly a legal requirement. Conversely, a bottling company wants to reduce the amount of overfilled bottles. But it needs to keep volume above that on the label.

variation question

Look at the data above. What is the most effective strategy to accomplish this task?

(A) Decrease the target fill volume only. (B) Decrease the target fill variation only. (C) Firstly, decrease the target fill volume. Then decrease the target fill variation. (D) Firstly, decrease the target fill variation. Then decrease the target fill volume.

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D: Reduce variation in your process first, then try to make improvements. Otherwise, your results from a change can be worse. For example, think of the quincunx demonstration . It shows that just changing your puck placement doesn’t help. In fact, it makes your results worse. This is because you didn’t shrink the dispersion. In other words, you didn’t reduce variation, so your results varied even more.

When you’re ready, there are a few ways I can help:

First, join 30,000+ other Six Sigma professionals by subscribing to my email newsletter . A short read every Monday to start your work week off correctly. Always free.

If you’re looking to pass your Six Sigma Green Belt or Black Belt exams , I’d recommend starting with my affordable study guide:

1)→ 🟢 Pass Your Six Sigma Green Belt​ ​

2)→ ⚫ Pass Your Six Sigma Black Belt  ​​ ​

You’ve spent so much effort learning Lean Six Sigma. Why leave passing your certification exam up to chance? This comprehensive study guide offers 1,000+ exam-like questions for Green Belts (2,000+ for Black Belts) with full answer walkthroughs, access to instructors, detailed study material, and more.

​  Join 10,000+ students here. 

Comments (6)

Ijust wanted to thank you Ive been calling and searching reading etc never could find one source to stay focused on to study. Thanks to you now I have found that course and plan to stay on track any recommendations Thanks for helping and taking the time to help people I really appreciate this really thanks any suggestioins you have for me I appreciate.

May God bless you and thanks

Again, you’re welcome, Anthony. I have a write up on how to approach any Six Sigma exam here.

If during Analyze phase of DMAIC the team undersands that the process has many common causes of variation and the process should be redesigned, can the team switch to DMADV?

Absolutely. Pivoting is essential in many cases as new information is discovered.

I would caution that clear communication with your stakeholders is essential here. You want to ensure that the cost to redesign & deploy the new process doesn’t exceed the benefit you’d achieve.

Hi, the link above under 6-M-pictures does lead to nowhere: “5 Ms and one P (or 6Ms)”.

Thank you, Tatjana! Updated!

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What is Six Sigma? – Certification, Training, Lean

  • Lean Six Sigma

Common Cause Variation

Thoughts on preparing for the turbulence that comes with business.

Any business making legitimate strides toward a positive goal is moving in some direction , and any business that is moving is naturally going to face obstacles and bumps in the road. These bumps in the road range from day-to-day variances to unique, major variances that sway a business away from its primary goal of producing a product or service in a consistent and timely manner. Identifying and defining both common and infrequent obstacles is a critical part of business success and survival.

In this article, we will focus primarily on day-to-day, expected variations in productivity. In the Six Sigma system of process improvement, these are called common cause variations. To help bring understanding to the differentiation, let’s look at a couple of important definitions.

Six Sigma: Primary Types of Variation

In the Six Sigma system of process improvement, two primary types of variations from ideal (or average) productivity are defined:

special cause six sigma

Day-to-day, hour-by-hour variations due to common, daily activities. These variations are unavoidable and built into the process.

  • Special Cause Variations

One-time or infrequent variations caused by rare circumstances, such as disasters. These variations are typically not foreseeable and need corrective action.

The Gravel Road

To illustrate the overall picture, we’ll use the example of a car driving down a gravel road:

special cause six sigma

When you drive down a gravel road, you have a feeling of movement. You know the car is headed where you want to go, despite the somewhat rough ride. In the end, the car is moving in the right direction. Small bits of gravel that cover the road and over which the car rolls do create a constant bumpy ride, but it is bearable. There is no need for oversteering or constant braking to respond to each bit of gravel.

The gravel is your constant, expected turbulence–your common cause variation–that is minor enough to continue forward without disrupting the trip.

Special cause variation, on the other hand, would be like large rocks and potholes that you come across occasionally on the road. To avoid these, substantial steering, swerving, and/or braking is necessary to safely navigate. If one of these is struck, it’s possible that extra steering will be necessary to recover the vehicle’s normal trajectory. These larger obstacles do not pop up often, but it is good to be ready when they do.

What do these variances look like in the business world? It helps to first observe that no business is perfect. The result is that there must be some level of standard variation from ideal productivity that is deemed acceptable.

For example, take a ridesharing service like Uber or Lyft. Riders request many rides in concentrated cities where there are plenty of drivers present to make quick pickups the norm. Let’s say the organization aims for a standard wait time between a rider requesting a ride and a driver arriving for pickup of four minutes. In reality, drivers arrive in three to seven minutes on average. This is because there are stoplights, traffic, pedestrians, weather conditions, and other common obstacles that lie between the driver and the rider–and the amount of delay they cause varies constantly. There is no need to respond to these common delays because these delays are built into the process.

Special Cause Variation

Now consider that a sinkhole occurs in the middle of a main intersection and shuts it down. This causes major delays and backups for everyone, bringing the average wait time for riders to sixteen minutes. This is a major disruption and one that should be responded to with best-case alternative strategy.

How Should Organizations Respond to Variations?

In day-to-day business, there are some occasional issues that warrant a major corrective response and others that do not. We alluded to this in our prior example, pointing out that major response to normal traffic in a city is not needed; it is normal. A disruptive sinkhole does require alternative strategy.

Our focus here is the common. Other examples of common cause variation are a printer running out of paper, an assembly line arm needed to pause for regular maintenance, or a freight truck needing an oil change. These things can cause small variations in production time, but they are expected and planned for. They are not a surprise. An organization does not need to hold a conference call to decide how to respond to an empty printer. A worker pauses, grabs another ream, and pops it in. Back to business.

One other note is that variations can also be positive, warranting a good change in process. But that is a topic for the special variance section.

Prep, Not Avoidance

One might think that a major key to business success is avoiding trouble altogether. However, consider this simple law of physics: Every moving object faces a level of resistance. All the same, any time we are moving–whether it be toward our personal goals or business goals–there will be problems in our way that we must decide how to handle.

The issue at hand is not how to avoid all trouble, but how to respond to it and what to respond to. The key Six Sigma categories of common cause variation and special cause variation are helpful aids in planning how your organization will conserve time and material resources by responding

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