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SOURCES OF VARIATION: COMMON AND ASSIGNABLE CAUSES

If you look at bottles of a soft drink in a grocery store, you will notice that no two bottles are filled to exactly the same level. Some are filled slightly higher and some slightly lower. Similarly, if you look at blueberry muffins in a bakery, you will notice that some are slightly larger than others and some have more blueberries than others. These types of differences are completely normal. No two products are exactly alike because of slight differences in materials, workers, machines, tools, and other factors. These are called common , or random, causes of variation . Common causes of variation are based on random causes that we cannot identify. These types of variation are unavoidable and are due to slight differences in processing.

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Random causes that cannot be identified.

An important task in quality control is to find out the range of natural random variation in a process. For example, if the average bottle of a soft drink called Cocoa Fizz contains 16 ounces of liquid, we may determine that the amount of natural variation is between 15.8 and 16.2 ounces. If this were the case, we would monitor the production process to make sure that the amount stays within this range. If production goes out of this range—bottles are found to contain on average 15.6 ounces—this would lead us to believe that there ...

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difference between natural and assignable causes of variation

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  • Special Cause vs. Common Cause Variation

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difference between natural and assignable causes of variation

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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difference between natural and assignable causes of variation

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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Table of Contents

Types of variance, common cause variation, common cause variation examples, special cause variation, special cause variation example, choose the right program, common cause variation vs. special cause variation.

Common Cause Variation Vs. Special Cause Variation

Every piece of data which is measured will show some degree of variation: no matter how much we try, we could never attain identical results for two different situations - each result will be different, even if the difference is slight. Variation may be defined as “the numerical value used to indicate how widely individuals in a group vary.” 

In other words, variance gives us an idea of how data is distributed about an expected value or the mean. If you attain a variance of zero, it indicates that your results are identical - an uncommon condition. A high variance shows that the data points are spread out from each other—and the mean, while a smaller variation indicates that the data points are closer to the mean. Variance is always nonnegative.

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Change is inevitable, even in statistics. You’ll need to know what kind of variation affects your process because the course of action you take will depend on the type of variance. There are two types of Variance: Common Cause Variation and Special Cause Variation. You’ll need to know about Common Causes Variation vs Special Causes Variation because they are two subjects that are tested on the PMP Certification  and CAPM Certification exams. 

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Common Cause Variation, also referred to as “Natural Problems, “Noise,” and “Random Cause” was a term coined by Harry Alpert in 1947. Common causes of variance are the usual quantifiable and historical variations in a system that are natural. Though variance is a problem, it is an inherent part of a process—variance will eventually creep in, and it is not much you can do about it. Specific actions cannot be taken to prevent this failure from occurring. It is ongoing, consistent, and predictable.

Characteristics of common causes variation are:

  • Variation predictable probabilistically
  • Phenomena that are active within the system
  • Variation within a historical experience base which is not regular
  • Lack of significance in individual high and low values

This variation usually lies within three standard deviations from the mean where 99.73% of values are expected to be found. On a control chart, they are indicated by a few random points that are within the control limit. These kinds of variations will require management action since there can be no immediate process to rectify it. You will have to make a fundamental change to reduce the number of common causes of variation. If there are only common causes of variation on your chart, your process is said to be “statistically stable.”

When this term is applied to your chart, the chart itself becomes fairly stable. Your project will have no major changes, and you will be able to continue process execution hassle-free.

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Consider an employee who takes a little longer than usual to complete a specific task. He is given two days to do a task, and instead, he takes two and a half days; this is considered a common cause variation. His completion time would not have deviated very much from the mean since you would have had to consider the fact that he could submit it a little late.

Here’s another example: you estimate 20 minutes to get ready and ten minutes to get to work. Instead, you take five minutes extra to get ready because you had to pack lunch and 15 additional minutes to get to work because of traffic. 

Other examples that relate to projects are inappropriate procedures, which can include the lack of clearly defined standard procedures, poor working conditions, measurement errors, normal wear and tear, computer response times, etc. These are all common cause variation.

Special Cause Variation, on the other hand, refers to unexpected glitches that affect a process. The term Special Cause Variation was coined by W. Edwards Deming and is also known as an “Assignable Cause.” These are variations that were not observed previously and are unusual, non-quantifiable variations.

These causes are sporadic, and they are a result of a specific change that is brought about in a process resulting in a chaotic problem. It is not usually part of your normal process and occurs out of the blue. Causes are usually related to some defect in the system or method. However, this failure can be corrected by making changes to affected methods, components, or processes.

Characteristics of special cause variation are:

  • New and unanticipated or previously neglected episode within the system
  • This kind of variation is usually unpredictable and even problematic
  • The variation has never happened before and is thus outside the historical experience base

On a control chart, the points lie beyond the preferred control limit or even as random points within the control limit. Once identified on a chart, this type of problem needs to be found and addressed immediately you can help prevent it from recurring.

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Let’s say you are driving to work, and you estimate arrival in 10 minutes every day. One day, it took you 20 minutes to arrive at work because you were caught in the traffic from an accident zone and were held up.

Examples relating to project management are if machine malfunctions, computer crashes, there is a power cut, etc. These kinds of random things that can happen during a project are examples of special cause variation.

One way to evaluate a project’s health is to track the difference between the original project plan and what is happening. The use of control charts helps to differentiate between the common cause variation and the special cause variation, making the process of making changes and amends easier.

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Volume 8 Supplement 1

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

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Understanding and managing variation: three different perspectives

  • Michael E Bowen 1 , 2 , 3 &
  • Duncan Neuhauser 4  

Implementation Science volume  8 , Article number:  S1 ( 2013 ) Cite this article

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Managing variation is essential to quality improvement. Quality improvement is primarily concerned with two types of variation – common-cause variation and special-cause variation. Common-cause variation is random variation present in stable healthcare processes. Special-cause variation is an unpredictable deviation resulting from a cause that is not an intrinsic part of a process. By careful and systematic measurement, it is easier to detect changes that are not random variation.

The approach to managing variation depends on the priorities and perspectives of the improvement leader and the intended generalizability of the results of the improvement effort. Clinical researchers, healthcare managers, and individual patients each have different goals, time horizons, and methodological approaches to managing variation; however, in all cases, the research question should drive study design, data collection, and evaluation. To advance the field of quality improvement, greater understanding of these perspectives and methodologies is needed [ 1 ].

Clinical researcher perspective

The primary goal of traditional randomized controlled trials (RCTs) (ie a comparison of treatment A versus placebo) is to determine treatment or intervention efficacy in a specified population when all else is equal. In this approach, researchers seek to maximize internal validity. Through randomization, researchers seek to balance variation in baseline factors by randomizing patients, clinicians, or organizations to experimental and control groups. Researchers may also increase understanding of variation within a specific study using approaches such as stratification to examine for effect modification. Although the generalizability of outcomes in all research designs is limited by the study population and setting, this can be particularly challenging in traditional RCTs. When inclusion criteria are strict, study populations are not representative of “real world” patients, and the applicability of study findings to clinical practice may be unclear. Traditional RCTs are limited in their ability to evaluate complex processes that are purposefully and continually changing over time because they evaluate interventions in rigorously controlled conditions over fixed time frames [ 2 ]. However, using alternative designs such as hybrid, effectiveness studies discussed in these proceedings or pragmatic RCTs, researchers can rigorously answer a broader range of research questions [ 3 ].

Healthcare manager perspective

Healthcare managers seek to understand and reduce variation in patient populations by monitoring process and outcome measures. They utilize real-time data to learn from and manage variation over time. By comparing past, present, and desired performance, they seek to reduce undesired variation and reinforce desired variation. Additionally, managers often implement best practices and benchmark performance against them. In this process, efficient, time-sensitive evaluations are important. Run charts and Statistical Process Control (SPC) methods leverage the power of repeated measures over time to detect small changes in process stability and increase the statistical power and rapidity with which effects can be detected [ 1 ].

Patient perspective

While the clinical researcher and healthcare manager are interested in understanding and managing variation at a population level, the individual patient wants to know if a particular treatment will allow one to achieve health outcomes similar to those observed in study populations. Although the findings of RCTs help form the foundation of evidence-based practice and managers utilize these findings in population management, they provide less guidance about the likelihood of an individual patient achieving the average benefits observed across a population of patients. Even when RCT findings are statistically significant, many trial participants receive no benefit. In order to understand if group RCT results can be achieved with individual patients, a different methodological approach is needed. “N-of-1 trials” and the longitudinal factorial design of experiments allow patients and providers to systematically evaluate the independent and combined effects of multiple disease management variables on individual health outcomes [ 4 ]. This offers patients and providers the opportunity to collect, analyze, and understand data in real time to improve individual patient outcomes.

Advancing the field of improvement science and increasing our ability to understand and manage variation requires an appreciation of the complementary perspectives held and methodologies utilized by clinical researchers, healthcare managers, and patients. To accomplish this, clinical researchers, healthcare managers, and individual patients each face key challenges.

Recommendations

Clinical researchers are challenged to design studies that yield generalizable outcomes across studies and over time. One potential approach is to anchor research questions in theoretical frameworks to better understand the research problem and relationships among key variables. Additionally, researchers should expand methodological and analytical approaches to leverage the statistical power of multiple observations collected over time. SPC is one such approach. Incorporation of qualitative research and mixed methods can also increase our ability to understand context and the key determinants of variation.

Healthcare managers are challenged to identify best practices and benchmark their processes against them. However, the details of best practices and implementation strategies are rarely described in sufficient detail to allow identification of the key drivers of process improvement and adaption of best practices to local context. By advocating for transparency in process improvement and urging publication of improvement and implementation efforts, healthcare managers can enhance the spread of best practices, facilitate improved benchmarking, and drive continuous healthcare improvement.

Individual patients and providers are challenged to develop the skills needed to understand and manage individual processes and outcomes. As an example, patients with hypertension are often advised to take and titrate medications, modify dietary intake, and increase activity levels in a non-systematic manner. The longitudinal factorial design offers an opportunity to rigorously evaluate the impact of these recommendations, both in isolation and in combination, on disease outcomes [ 1 ]. Patients can utilize paper, smart phone applications, or even electronic health record portals to sequentially record their blood pressures. Patients and providers can then apply simple SPC rules to better understand variation in blood pressure readings and manage their disease [ 5 ].

As clinical researchers, healthcare managers, and individual patients strive to improve healthcare processes and outcomes, each stakeholder brings a different perspective and set of methodological tools to the improvement team. These perspectives and methods are often complementary such that it is not which methodological approach is “best” but rather which approach is best suited to answer the specific research question. By combining these perspectives and developing partnerships with organizational managers, improvement leaders can demonstrate process improvement to key decision makers in the healthcare organization. It is through such partnerships that the future of quality improvement research is likely to find financial support and ultimate sustainability.

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

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Michael E Bowen

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SPECIAL CAUSES OF VARIATIONS

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W Edwards Deming elaborated on Walter A. Shewhart's argument that variability in manufacturing and service processes can be traced to either common causes or special causes of variations (Shewhart's assignable causes). Special causes variability is beyond the natural variability of the process. Special cause variability can be identified and addressed by operators. Examples of special causes are operator error, faulty setup, or incoming defective raw material. Deming believed that only about 15% of the variation in a process is due to special causes. Deming relied on control charts to describe both the natural variability of the system, and to detect the existence of a special causes of variation. A process that is operating with special causes of variation is said to be “out of statistical control.”

See Quality: The implications of W. Edwards Deming's approach ; Statistical process control using control charts ; Total quality management .

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Deming, W. Edwards (1982). Out of the Crisis, Center for Advanced Engineering Study, Massachusetts.

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(2000). SPECIAL CAUSES OF VARIATIONS . In: Swamidass, P.M. (eds) Encyclopedia of Production and Manufacturing Management. Springer, Boston, MA . https://doi.org/10.1007/1-4020-0612-8_905

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

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

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

Examples of common-cause and special-cause variation

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

difference between natural and assignable causes of variation

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

difference between natural and assignable causes of variation

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

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

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

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

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

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The Power of Special Cause Variation: Learning from Process Changes

Updated: July 28, 2023 by Marilyn Monda

difference between natural and assignable causes of variation

I love to see special cause variation! That’s because I know I’m about to learn something important about my process. A special cause is a signal that the process outcome is changing — and not always for the better.  

Overview: What is special cause variation? 

A control chart can show two different types of variation:   common cause variation (random variation from the various process components) and special cause variation.

Special cause variation is present when the control chart of a process measure shows either plotted point(s) outside the control limits or a non-random pattern of variation.

When a control chart shows special cause variation, a process measure is said to be out-of-control or unstable. Common types of special cause variation signals include:

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

  A special cause of variation is assignable to a defect, fault, mistake, delay, breakdown, accident, and/or shortage in the process. When special causes are present, process quality is unpredictable.

Special causes are a signal for you to act to make the process improvements necessary to bring the process measure back into control.

RELATED: COMMON CAUSE VARIATION VS. SPECIAL CAUSE VARIATION

Drawbacks of special cause variation .

The source of a special cause can be difficult to find if you are not plotting the control chart in real time.  Unless you have annotated data or a good memory, control charts made from historical data won’t aid your investigation into the source of the special cause. 

If a process measure has never been charted, it is almost certain that it will be out of control.  When you first start studying a process with a control chart, you will usually see a variety of special causes. To find the sources, begin a study of the status of critical process components.  

When a special cause source cannot be found, it will become common to the process.  As time goes on, the special causes repeat and cease being special. They then increase the natural or common cause variation in the process.  

Why is special cause variation important to understand? 

Let’s define quality as minimum variation around an appropriate target. The study of variation using a control chart is one way to tell if the process variation is increasing or if the center is moving away from the desired target over time.  

A special cause is assignable to a process component that has changed or is changing. Investigation into the source of a special cause will:

  • Let you know when to act to adjust or improve the process.
  • Keep you from making the mistake of missing an opportunity to improve a process. If the ignored special cause repeats, you still don’t know how to fix it.
  • Provide data to suggest or evaluate a process improvement.

If no special cause variation exists, that is, the process is in control, you should leave the process alone! Making process changes when there is no special cause present is called Tampering and can increase the variation of the process, lowering its quality.

An industry example of special cause variation 

In this example, a control chart was used to monitor the number of data entry errors on job applications. Each day a sample of applications was reviewed. The number of errors found were plotted on a control chart. 

One day, a point was plotted outside the control limit. Upon investigation, the manager noticed it occurred when a new worker started. It was found the worker wasn’t trained.

The newly trained worker continued data entry. A downward trend of errors followed, indicating the training was a source for the special cause! 

The manager issued guidelines for new worker training. Since then, there have been three new workers without the error count spiking. 

3 best practices when thinking about special cause variation 

Special causes are signals that you need to act to move your process measure back into control.  

Identify the source

When a special cause of variation exists, make a timely effort to identify its source.  A good starting point is to check if any process component changed near to the time the special cause was seen. Also, you could ask process experts to brainstorm why the special cause samples were out of control.

For example, a trend up in screw thickness could be caused by a gage going out of calibration.

Make improvements at the source

Implement improvements to the source of special cause variation.  Once you make improvements to the source of the special cause (like re-calibrating that gage), watch what happens as the next thickness samples are plotted.  If the plot moves back toward stability, you know you found the issue!  

Document everything

As you identify recurring special causes and their sources, document them on a control plan so process operators know what to do if they see the special cause again.

For our gage, the control plan could direct a worker to recalibrate the next time the screw thickness trends up, sending the process back to stability.  

Frequently Asked Questions (FAQ) about special cause variation

  • Are special causes always bad news? 

No. A special cause can indicate either an increase or decrease in the quality of the process measure.

If the special cause shows increased process quality (for example, a decrease in cycle time), then you should make its source common to the process.  

  • If a process is in control (no special causes) is it also capable? 

Not always. Control and capability are two different assessments.  Your process measure can be stable (in control) and still not meet the customer specification (capable). 

Once a process measure is in control, you can then assess its capability against the customer target and specification limits. If the data is within customer limits and on target, the process is considered both in control and capable.

Final thoughts on special causes 

Every process measure will show variation, you will never attain zero variability. Still, it is important to understand the nature of variability so that you can use it to better improve and control your process outcomes. 

The special cause variation signal is the key to finding those critical process components that are the sources of variation needing improvement. Use special cause variation to unlock the path to process control.

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Marilyn Monda

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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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Variation & Defect

Variation is everywhere. It probably touches our lives more closely than any other thing! Only the degree of variation may vary.

In automotive parts industry, every cylinder produced has a different diameter, usually within a tolerance limits around a nominal diameter. Traffic conditions on road vary every day. In a shopping mall, number of people changes every day. Response time in telebanking varies during the day. Variation even drives physics - quantum physics. In the words of the living legend of physics, Stephen Hawking, "Quantum mechanics does not predict a single definite result for an observation. Instead, it predicts a number of different possible outcomes and tells us how likely each of these is. Quantum mechanics therefore introduces an unavoidable element of unpredictability or randomness into science." Let us take a simple example to understand variation further.

What time do we reach work in the morning? In a 9 to 5 office environment, do we reach at dot 9AM everyday? Some day it may take a little longer and we reach at 9:10AM, whereas on another day it takes a little shorter to reach and we arrive at 8:50AM. In general, we know that we get to office between 8:50AM to 9:10AM. So there is a variation of 20 minutes! This variation may occur due to traffic signals, and traffic conditions. But we know that the variation is contained with in 20 minutes and seems "natural" or "common". A closer look reveals that this variation does not have a specific reason and is random within 20 minutes range.

Now imagine, we begin to get flat tyre often. This results into further a delay of 20 to 25 minutes every day leading to increase in variation. We finally discover that it is happening due to worn-out tyres. Upon changing the tyres, we are back to our natural variation of 20 minutes because we have removed the "special" or "assignable" cause of variation.

Only way to reduce the natural variation is to change or improve our process of traveling to office. Possibilities are to choose a different route with more predictable traffic conditions, or switch to a 2 wheeler or bicycle to nullify heavy and unpredictable traffic conditions.

Formal Picture

Variation is present in the output(s) of every process. The degree of variation or the distribution pattern of the output is a measure of process capability or maturity. The six key process elements - people, environment, material, method, machinery, and measurement impact variation. It can be classified in 2 categories - common or natural and special or assignable.

The natural variation always occurs and it can not be traced to a specific cause. It is random within a predictable range or in other words, it follows a distribution pattern (we have detailed discussion on distribution later). The natural variation reduction requires fundamental change in the process.

The special variation occurs due to an assignable cause outside the natural variation. It can easily be traced to a specific cause, usually relating to the 6 key process elements. Once detected, its removal is a relatively simple exercise.

At this stage, it may be a good idea to revisit our example of pizza shop in section on "Introduction to Six Sigma".

In our context, a defect is an imperfection of deficiency in a product or a service detected (or perceived) by the customer. In other words, defect occurs when variation in a product or service goes beyond the acceptable limits. We can identify or detect a defect in a product/service only and only if we have a measurable benchmark or target.

Let us go back to our pizza shop example in "Introduction to Six Sigma". Imagine the situation without a 30 minutes delivery target. There was absolutely no way to determine how much of delay is really a delay! Customer perception of delayed delivery would possibly have been a function of his/her hunger level; and management would have had no idea to how to handle such defects.

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Monday, August 17, 2015

Chance & assignable causes of variation.

Links to all courses Variation in quality of manufactured product in the respective process in industry is inherent & evitable. These variations are broadly classified as- i) Chance / Non assignable causes ii) Assignable causes i) Chance Causes: In any manufacturing process, it is not possible to produce goods of exactly the same quality. Variation is inevitable. Certain small variation is natural to the process, being due to chance causes and cannot be prevented. This variation is therefore called allowable . ii) Assignable Causes: This type of variation attributed to any production process is due to non-random or so called assignable causes and is termed as preventable variation . Assignable causes may creep in at any stage of the process, right from the arrival of the raw materials to the final delivery of goods. Some of the important factors of assignable causes of variation are - i) Substandard or defective raw materials ii) New techniques or operation iii) Negligence of the operators iv) Wrong or improper handling of machines v) Faulty equipment vi) Unskilled or inexperienced technical staff and so on. These causes can be identified and eliminated and are to discovered in a production process before the production becomes defective. SQC is a productivity enhancing & regulating technique ( PERT ) with three factors- i) Management ii) Methods iii) Mathematics Here, control is two-fold- controlling the process ( process control ) & controlling the finished products (products control). 

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Six Sigma Study Guide

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Study notes and guides for Six Sigma certification tests

Ted Hessing

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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.

Calculating

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.

https://www.youtube.com/watch?v=0nZT9fqr2MU

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:

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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.

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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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