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ASSIGNABLE CAUSES OF VARIATIONS

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Assignable causes of variation are present in most production processes. These causes of variability are also called special causes of variation ( Deming, 1982 ). The sources of assignable variation can usually be identified (assigned to a specific cause) leading to their elimination. Tool wear, equipment that needs adjustment, defective materials, or operator error are typical sources of assignable variation. If assignable causes are present, the process cannot operate at its best. A process that is operating in the presence of assignable causes is said to be “out of statistical control.” Walter A. Shewhart (1931) suggested that assignable causes, or local sources of trouble, must be eliminated before managerial innovations leading to improved productivity can be achieved.

Assignable causes of variability can be detected leading to their correction through the use of control charts.

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

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Shewhart, W. A. (1939). Statistical Method from the Viewpoint of Quality Control, Graduate School, Department of Agriculture, Washington.

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(2000). ASSIGNABLE 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_57

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Six Sigma Control Charts: An Ultimate Guide

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  • 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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Volume 8 Supplement 1

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Understanding and managing variation: three different perspectives

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

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

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

Clinical researcher perspective

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

Healthcare manager perspective

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

Patient perspective

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

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

Recommendations

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

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

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

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

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Olsson J, Terris D, Elg M, Lundberg J, Lindblad S: The one-person randomized controlled trial. Qual Manag Health Care. 2005, 14: 206-216.

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Michael E Bowen

Division of General Internal Medicine, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA

Division of Outcomes and Health Services Research, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA

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how assignable variation

How to deal with Assignable causes?

How to deal with Assignable causes?

Across the many training sessions conducted one question that keeps raging on is “How do we deal with special causes of variation or assignable causes”. Although theoretically a lot of trainers have found a way of answering this situation, in the real world and especially in Six Sigma projects this is often an open deal. Through this article, I try to address this from a practical paradigm.

Any data you see on any of your charts will have a cause associated with it. Try telling me that the points which make your X MR, IMR or XBar R Charts have dropped the sky and I will tell you that you are not shooting down the right ducks. Then, the following causes seem possible for any data point to appear on the list.

  • A new operator was running the process at the time.
  • The raw material was near the edge of its specification.
  • There was a long time since the last equipment maintenance.
  • The equipment maintenance was just performed prior to the processing.

 The moment any of our data points appear due to some of the causes mentioned below, a slew of steps are triggered. Yeah – Panic! Worse still, these actions below which may have been a result of a mindless brain haemorrhage backed by absolute lack of data, results in more panic!

  • Operators get retraining.
  • Incoming material specifications are tightened.
  • Maintenance schedules change.
  • New procedures are written.

My question is --- Do you really have to do all of this, if you have determined that the cause is a common or a special cause of variation ! Most Six Sigma trainers will tell you that a Control chart will help you identify special cause of variation. True – But did you know of a way you could validate your finding!

  • Check the distribution first. If the data is not normal, transform the data to make it reasonably normal. See if it still has extreme points. Compare both the charts before and after transformation. If they are the same, you can be more or less sure it has common causes of variation.
  • Plot all of the data, with the event on a control chart.  If the point does not exceed the control limits, it is probably a common-cause event.  Use the transformed data if used in step 1.
  • Using a probability plot, estimate the probability of receiving the extreme value.  Consider the probability plot confidence intervals to be like a confidence interval of the data by examining the vertical uncertainty in the plot at the extreme value.   If the lower confidence boundary is within the 99% range, the point may be a common-cause event.  If the lower CI bound is well outside of the 99% range, it may be a special cause.  Of course the same concept works for lower extreme values.
  • Finally, turn back the pages of the history. See how frequently these causes have occurred. If they have occurred rather frequently, you may want to think these are common causes of variation. Why – Did you forget special causes don’t really repeat themselves?

 The four step approach you have taken may still not be enough for you to conclude if it is a common or a special cause of variation. Note – Any RCA approach may not be good enough to reduce or eliminate common causes. They only work with special causes in the truest sense.

So, what does that leave us with! A simple lesson that an RCA activity has to be conducted when you think even with a certain degree of probability that it could be a special cause of variation. To ascertain that if the cause genuinely was a Special cause all you got to do is look back into the history and see if these causes repeated. If they did, I don’t think you would even be tempted to think it to be a special cause of variation.

Remember one thing – While eliminating special causes is considered goal one for most Six Sigma projects, reducing common causes is another story you’d have to consider. The biggest benefit of dealing with common causes is that you can even deal with them in the long run, provided they are able to keep the process controlled and oh yes, the common causes don’t result in effects.

Merely by looking at a chart, I don’t think I have ever been able to say if the point has a Special cause attached to it or not. Yes – This even applies to a Control chart which is by far considered to be the best Special cause identification tool. The best way out is a diligently applied RCA and a simple act of going back and checking if the cause repeated or not.

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Assignable Cause

Last updated by Jeff Hajek on December 22, 2020

An assignable cause is a type of variation in which a specific activity or event can be linked to inconsistency in a system. In effect, it is a special cause that has been identified.

As a refresher, common cause variation is the natural fluctuation within a system. It comes from the inherent randomness in the world. The impact of this form of variation can be predicted by statistical means. Special cause variation, on the other hand, falls outside of statistical expectations. They show up as outliers in the data .

Lean Terms Discussion

Variation is the bane of continuous improvement . It decreases productivity and increases lead time . It makes it harder to manage processes.

While we can do something about common cause variation, typically there is far more bang for the buck by attacking special causes. Reducing common cause variation, for example, might require replacing a machine to eliminate a few seconds of variation in cutting time. A special cause variation on the same machine might be the result of weld spatter from a previous process. The irregularities in a surface might make a part fit into a fixture incorrectly and require some time-consuming rework. Common causes tend to be systemic and require large overhauls. Special causes tend to be more isolated to a single process step .

The first step in removing special causes is identifying them. In effect, you turn them into assignable causes. Once a source of variation is identified, it simply becomes a matter of devoting resources to resolve the problem.

Lean Terms Videos

Lean Terms Leader Notes

One of the problems with continuous improvement is that the language can be murky at times. You may find that some people use special causes and assignable causes interchangeably. Special cause is a far more common term, though.

I prefer assignable cause, as it creates an important mental distinction. It implies that you…

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Variations in Care

Figure 16-1 . County-level risk-standardized 30-day heart failure readmission rates (%) in Medicare patients by performance quintile for July 2009 to June 2012. (Data from Centers for Medicare & Medicaid Services; available at https://data.medicare.gov/data/hospital-compare .) HISTORY AND DEFINITIONS Variation in clinical care, and what it reveals about that care, is a topic of great interest to researchers and clinicians. It can be divided broadly into outcome variation , which occurs when the same process produces different results in different patients , and process variation , which refers to different usage of a therapeutic or diagnostic procedure among organizations, geographic areas, or other groupings of health care providers . Studies of outcome variation can provide insight into patient characteristics and care delivery that predispose patients to either a successful or an adverse outcome and help identify patients for whom a particular treatment is likely to be effective (or ineffective). Process variation, in contrast, can provide insight into such things as the underuse of effective therapies or procedures and the overuse of ineffective therapies or procedures.   Study of the variation in clinical care dates back to 1938, when Dr. J. Allison Glover published a study revealing geographic variation in the incidence of tonsillectomy in school children in England and Wales that could not be explained by anything other than variation in medical opinion on the indications for surgery. Since then, research has revealed variation among countries and across a range of medical conditions and procedures, including prostatectomy, knee replacement, arteriovenous fistula dialysis, and invasive cardiac procedures. Actual rates of use of procedures, different variability in supply of health care services, and the system of health care organization and financing (health maintenance organizations [HMOs], fee-for-service [FFS], and national universal health care) do not necessarily determine or even greatly affect the degree of variation in a particular clinical practice. Rather, the degree of variation in use relates more to the characteristics of the procedure. Important characteristics include: •  The degree of professional uncertainty about the diagnosis and treatment of the condition the procedure addresses •  The availability of alternative treatments •  Controversy versus consensus regarding the appropriate use of the procedure •  Differences among physicians in diagnosis style and in belief in the efficacy of a treatment   When studying variation in medical practice—or interpreting the results of someone else’s study of variation—it is important to distinguish between warranted variation , which is based on differences in patient preference, disease prevalence, or other patient- or population-related factors ; and unwarranted variation , which cannot be explained by patient preference or condition or the practice of evidence-based medicine . Whereas warranted variation is the product of providing appropriate and personalized evidence-based patient care, unwarranted variation typically indicates an opportunity to improve some aspect of the quality of care provided, including inefficiencies and disparities in care.   John E. Wennberg, MD, MPH, founding editor of the Dartmouth Atlas of Health Care and a leading scholar in clinical practice variation, defines three categories of care and the implications of unwarranted variation within each of them:      1. Effective care is that for which the evidence establishes that the benefits outweigh the risks and the “right rate” of use is 100% of the patients defined by evidence-based guidelines as needing such treatment. In this category, variation in the rate of use within that patient population indicates underuse.      2. Preference-sensitive care consists of those areas of care in which there is more than one generally accepted diagnostic or therapeutic option available, so the “right rate” of each depends on patient preference.      3. Supply-sensitive care is care for which the frequency of use relates to the capacity of the local health care system. Typically, this is viewed in the context of the delivery of care to patients who are unlikely to benefit from it or whose benefit is uncertain; in areas with high capacity for that care (e.g., high numbers of hospital beds per capita) more of these patients receive the care than in areas with low capacity, where the resources have to be reserved for (and are operating at full capacity with) patients whose benefits are more certain. Because studies have repeatedly shown that regions with high use of supply sensitive care do not perform better on mortality rates or quality of life indicators than regions with low use, variation in such care may indicate overuse. Local health care system capacity can influence frequency of use in other ways, too. For example, the county-level association between fewer primary care physicians and higher 30-day hospital readmission rates suggests that inadequate primary care capacity may result in preventable hospitalizations.   Table 16-1 provides examples of warranted and unwarranted variation in each of these categories of care. Table 16-1. Examples of warranted and unwarranted variations in heart failure care.   A second important distinction that must be made when considering variation in care is between common cause and special cause variation . Common cause variation ( also referred to as “expected” or “random” variation) cannot be traced to a root cause and as such may not be worth studying in detail. Special cause variation ( or “assignable” variation) arises from a single or small set of causes that can be traced and identified and then implemented or eliminated through targeted quality improvement initiatives ). Statisticians have a broad range of tests and criteria to determine whether variation is assignable or random and with the increasing sensitivity and power of numerical analysis can measure assignable variation relatively easily. The need for statistical expertise in such endeavors must be emphasized, however; the complexity of the study designs and interpretation of results (particularly in distinguishing true variation from artifact or statistical error) carries a high risk of misinterpretation in its absence. LOCAL VARIATION Although variation in care processes and outcomes frequently is examined and discussed in terms of large-scale geography (among countries, states, or hospital referral regions, as, for example, was shown in the heart failure readmissions national map in Figure 16-1 ), it can be examined and provide equally useful information on a much smaller scale. For example, Figure 16-2 shows variation in 30-day risk-adjusted heart failure readmission rates for hospitals within a single county (Dallas, Texas), ranging from 20% below to 25% above the national average and with three hospitals showing readmission rates that were statistically significantly lower than the national average. Although no hospitals had readmission rates that were statistically significantly higher than the national rate, the poorer performing hospitals might nevertheless be interested in improving. Cooperation among the quality and clinical leaders of the hospitals within Dallas County would enable investigation of differences in practices and resources among the hospitals, which might identify areas to be targeted for improvement for those hospitals with higher readmission rates. Figure 16-2 . Forest plot showing variation in heart failure 30-day risk-standardized readmission rates (HF 30-day RSRR, %) in Medicare patients for hospitals in Dallas County, Texas for July 2009 to June 2012. Hospitals were assigned random number identifiers in place of using names. (Data from Centers for Medicare & Medicaid Services; available at https://data.medicare.gov/data/hospital-compare .)   Local between-provider variation is often encountered in the form of quality reports or scorecards. Such tools seek to identify high versus low performers among hospitals, practices, or physicians to create incentives for high performance either by invoking providers’ competitive spirit or by placing a portion of their compensation at risk according to their performance through value-based purchasing or pay-for performance programs. In other words, they show unwarranted variation in the delivery of care. Care must be taken in presenting and interpreting such variation data, however. For example, league tables (or their graphical equivalent, caterpillar charts), which order providers from the lowest to highest performers on a chosen measure and use CIs to identify providers with performance that is statistically significantly different from the overall average, are both commonly used to compare provider performance on quality measures and easily misinterpreted. One’s instinct on encountering such tables or figures is to focus on the numeric ordering of the providers and assume, for example, that a provider ranked in the 75th percentile provides much higher quality care than one in the 25th percentile. This, however, is not necessarily the case: league tables do not capture the degree of uncertainty around each provider’s point estimate, so much of the ordering in the league table reflects random variation, and the order may vary substantially from one measurement period to another, without providers making any meaningful changes in the quality of care they provide. As such, there may not be any statistically significant or meaningful clinical difference among providers even widely separated in the ranking.   Forest plots, such as Figure 16-2 , for hospitals in Dallas County are a better, although still imperfect, way of comparing provider performance. Forest plots show both the point estimate for the measure of interest (e.g., risk-adjusted heart failure 30-day readmission rates) and its CI (represented by a horizontal line) for each provider, as well as a preselected norm or standard (e.g., national average; represented by a vertical line). By looking for providers for whom not only the point estimate but the entire CI falls to either the left or right of the vertical line, readers can identify those whose performance was either significantly better or significantly worse than the preselected standard. Although Forest plots may be ordered so that hospitals are ranked according to the point estimates, that ranking is vulnerable to the same misinterpretation as in league tables. An easy way to avoid this problem is to order the providers according to something other than the point estimate—for example, alphabetically by name. Because Forest plots are easy to produce without extensive statistical knowledge or programming skills, such an approach can be very useful in situations in which experienced statisticians are not available to assist with the performance comparisons.   The funnel plot is probably the best approach for presenting comparative performance data, but it does require more sophisticated statistical knowledge to produce. In a funnel plot, the rate or measure of interest is plotted on the y axis against the number of patients treated on the x axis; close to the origin, the CI bands drawn on the plot are wide (where the numbers of patients are small) and narrow as the numbers of patients increase. The resulting funnel shape gives its name to the plot. Providers with performance falling outside the CI bands are outliers, with performance that may be statistically significantly better or worse than the overall average. Those that excel can be examined as role models to guide others’ improvement. Those that lag behind their peers can be considered as opportunities for improvement, which might benefit from targeted interventions. And because the funnel plot does not attempt to rank providers (beyond identifying the outliers), it is less open to misinterpretation by readers who fail to consider the influence of random variation.   Control charts (discussed later in detail in the context of examining variation over time) can be used in a manner similar to funnel plots to compare provider performance. In such control charts, the CI bands of the funnel plot are replaced with upper and lower control limits (typically calculated as ±3 standard deviations [SDs] from the mean [or other measure of central tendency]), and providers need not be ordered according to decreasing number of patients in the denominator of the measure of interest. As in the funnel plot, however, the providers whose performance is statistically significantly higher (or lower) than the mean are identified as those for whom the point estimate falls above the upper (or below the lower) control limit. Figure 16-3 shows an example of such a control chart for the risk-adjusted 30-day heart failure readmission rates for the hospitals in Dallas County, Texas. Unlike the forest plot in Figure 16-2 , which compares each hospital’s performance with the national average, Figure 16-3 considers only the variation among the hospitals located in Dallas County. As can be seen, no data points fall outside the control limits. Interpretation of control charts is discussed in greater detail later, but this suggests that all the variation in the readmission rates among these hospitals is explained by common cause variation (not attributable to any specific cause) rather than by any specific difference in the hospitals’ characteristics or practices. This is interesting in light of the Figure 16-2 results, which show that three hospitals’ readmission rates differed significantly from the national average. However, it should be kept in mind, first, that the CIs used to make this determination in Figure 16-2 are set at 95% compared with the control limits in Figure 16-3 which are set at 3 SDs (corresponding to 99.73%) for reasons explained in the following section. Second, Figure 16-3 draws only on the data for 18 hospitals, which is a much smaller sample than the national data, and the smaller number of observations results in relatively wide control limits. Figure 16-3 . Control chart showing variation in heart failure 30-day risk-standardized readmission rates (HF 30-day RSRR, %) in Medicare patients for hospitals in Dallas County for July 2009 to June 2012). Hospitals were assigned random number identifiers in place of using names. LCL, lower control limit; UCL, upper control limit. (Data from Centers for Medicare & Medicaid Services; available at https://data.medicare.gov/data/hospital-compare .)   Finally, variation can be studied at the most local level: within a provider—even within a single physician—over time. Such variation is best examined using control charts, discussed in detail in the next section. QUANTITATIVE METHODS OF STUDYING VARIATION Data-driven practice-variation research is an important diagnostic tool for health care policymakers and clinicians, revealing areas of care where best practices may need to be identified or—if already identified—implemented. It compares utilization rates in a given setting or by a given provider with an average utilization rate; in this it differs from appropriateness of use and patient safety studies, which compare utilization rates with an identified “right rate” and serve as ongoing performance management tools.   A good framework to investigate unwarranted variation should provide:      1. A scientific basis for including or excluding each influencing factor and to determine when the factor is applicable or not applicable      2. A clear definition and explanation of each factor suggested as a cause      3. An explanation of how the factor is operationalized, measured, and integrated with other factors Statistical Process Control and Control Charts Statistical process control (SPC), similar to continuous quality improvement, is an approach originally developed in the context of industrial manufacturing for the improvement of systems processes and outcomes and was adopted into health care contexts only relatively recently. The basic principles of SPC are summarized in Table 16-2 . Particularly in the United States, SPC has been enthusiastically embraced for quality improvement and applied in a wide range of health care settings and specialties and at all levels of health care delivery, from individual patients and providers to entire hospitals and health care systems. Its appeal and value lie in its integration of the power of statistical significance tests with chronological analyses of graphs of summary data as the data are produced. This enables similar insights into the data that classical tests of significance provide but with the time sensitivity so important to pragmatic improvement. Moreover, the relatively simple formulae and graphical displays used in SPC are generally easily understood and applied by nonstatistician decision makers, making this a powerful tool in communicating with patients, other clinicians, and administrative leaders and policymakers. Table 16-3 summarizes important benefits and limitations of SPC in health care contexts. Table 16-2. Basic principles of statistical process control.    1. Individual measurements of any process or outcome will show variation.    2. If the process or outcome is stable (i.e., subject only to common cause variation), the variation is predictable and will be described by one of several statistical distributions (e.g., normal [or bell-shaped], exponential, or Poisson distribution).    3. Special cause variation will result in measured values that deviate from these models in some observable way (e.g., fall outside the predicted range of variation).    4. When the process or outcome is in control, statistical limits and tests for values that deviate from predictions can be established, providing statistical evidence of change. Table 16-3. Benefits and limitations of statistical process control in health care.   Tools used in SPC include control charts, run charts, frequency plots, histograms, Pareto analysis, scatter diagrams, and flow diagrams, but control charts are the primary and dominant tools.   Control charts are time series plots that show not only the plotted values but also upper and lower reference thresholds (calculated using historical data) that define the range of the common cause variation for the process or outcome of interest. When all the data points fall between these thresholds (i.e., only common cause variation is present), the process is said to be “in control.” Points that fall outside the reference thresholds may indicate special cause variation due to events or changes in circumstances that were not typical before. Such events or changes may be positive or negative, making control charts useful both as a warning tool in a system that usually performs well and as a tool to test or verify the effectiveness of a quality improvement intervention deliberately introduced in a system with historically poor performance.   The specific type of control chart needed for a particular measure depends on the type of data being analyzed, as well as the behavior and assumed underlying statistical distribution. The choice of the correct control chart is essential to obtaining meaningful results. Table 16-4 matches the most common data types and characteristics for the appropriate control chart(s). Table 16-4. Appropriate control charts according to data type and distribution.   After the appropriate control chart has been determined, further issues include (1) how the upper and lower control limit thresholds will be set, (2) what statistical rules will be applied to separate special cause variation from common cause variation, and (3) how many data points need to be plotted and at what time intervals.   Broadly speaking, the width of the control limit interval must balance the risk between falsely identifying special cause variation where it does not exist (type I statistical error) and missing it where it does (type II statistical error). Typically, the upper and lower control limits are set at ±3 SDs from the estimated mean of the measure of interest. This range is expected to capture 99.73% of all plotted data compared with the 95% captured by the 2 SDs criterion typically used in traditional hypothesis testing techniques. This difference is important because, unlike in the traditional hypothesis test in which the risk of type I error (false positive) applies only once, in a control chart, the risk applies to each plotted point. Thus, in a control chart with 25 plotted points, the cumulative risk of a false positive is 1 – (0.9973) 25 = 6.5% when 3 SD control limits are used compared with 1 – (0.95) 25 = 72.3% when 2 SD limits are used.   The primary test for special cause variation, then, is a data point that falls outside the upper or lower control limit. Other common tests are listed in Table 16-5 . Although applying these additional tests does slightly increase the false-positive rate from that inherent in the control limit settings, they greatly increase the control chart’s sensitivity to improvements or deteriorations in the measure. The statistical “trick” here lies in observing special cause patterns and accumulating information while waiting for the total sample size to increase to the point where it has the power to detect a statistically significant difference. The volume of data needed for a control chart depends on: Table 16-5. Common control chart tests for special cause variation.

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Six Sigma Study Guide

Six Sigma Study Guide

Study notes and guides for Six Sigma certification tests

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.

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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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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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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Unwarranted clinical variation in health care: Definitions and proposal of an analytic framework

Kim sutherland.

1 Agency for Clinical Innovation, Chatswood New South Wales, Australia

Jean‐Frederic Levesque

2 Centre for Primary Health Care and Equity, UNSW Randwick Campus, Randwick New South Wales, Australia

Associated Data

Rationale, aims, and objectives.

Unwarranted clinical variation is a topic of heightened interest in health care systems around the world. While there are many publications and reports on clinical variation, few studies are conceptually grounded in a theoretical model. This study describes the empirical foundations of the field and proposes an analytic framework.

Structured construct mapping of published empirical studies which explicitly address unwarranted clinical variation.

A total of 190 studies were classified in terms of three key dimensions: perspective (assessing variation across geographical areas or across providers); criteria for assessment (measuring absolute variation against a standard, or relative variation within a comparator group); and object of analysis (using process, structure/resource, or outcome metrics).

Consideration of the results of the mapping exercise—together with a review of adjustment, explanatory and stratification variables, and the factors associated with residual variation—informed the development of an analytic framework. This framework highlights the role that agency and motivation, evidence and judgement, and personal and organizational capacity play in clinical decision making and reveals key facets that distinguish warranted from unwarranted clinical variation. From a measurement perspective, it underlines the need for careful consideration of attribution, aggregation, models of care, and temporality in any assessment.

1. INTRODUCTION

Unwarranted clinical variation is a topic that attracts significant attention in developed health care systems internationally. 1 , 2 , 3 , 4 , 5 , 6 , 7 Interest in the topic is not new, however. Seminal papers by Guy, 8 Codman, 9 Glover, 10 Wennberg and Gittelsohn, 11 and Lewis 12 all shaped the field of enquiry, highlighting variation in either service utilization or outcomes of health care. In the past 15 years, the work of the Dartmouth Institute has been instrumental in influencing measurement and reporting approaches in use around the world, 13 catalysing the development of atlases of variation in multiple jurisdictions. 1 , 2 , 4 , 7

Clinical variation has been quantified across a wide range of acute and chronic care specialties, in primary care and hospital settings, and with regard to diagnosis, treatment, and prescribing practices. 14 , 15 , 16 , 17 , 18 , 19 , 20 Variation has been found in almost all areas of health care where it has been looked for. A 2014 systematic review of medical practice variation in OECD countries found 836 published studies and detailed variation across regions, hospitals, and physician practices for almost every surgical field, condition, and procedure studied. 21

However, despite this widespread and enduring interest in unwarranted clinical variation, the literature lacks strong conceptual frameworks to guide rigorous measurement and remediation efforts; and there are few typologies that systematically map the field. 21 , 22 , 23 While the Dartmouth approach identifies three categories of care—namely, effective care (where variation implies some underuse of valid treatment), preference‐sensitive care (where variation implies more than one option of care is available and the exercising of patient choice), and supply‐sensitive care (where variation implies the volume of care provided is a reflection of capacity rather than patient need)—the distinction between what is warranted and unwarranted clinical variation remains poorly delineated. 24 , 25

This paper addresses this issue and has three main objectives. First, it seeks to describe and classify studies that explicitly refer to “unwarranted clinical variation” or “medical variation.” Second, it draws on these studies to inform the development of an analytic framework to identify factors associated with warranted or unwarranted clinical variation. Third, it discusses key issues to resolve if we are to advance the field of unwarranted clinical variation—in terms of both measurement and action to reduce it.

2.1. Building a definition

In linguistic terms, variation is defined as “something that is slightly different from the usual form or arrangement.” 26 Clinical refers the examination and treatment of patients—that is, focusing on patient‐provider interactions and including preventive, diagnostic, therapeutic, and supportive care. Combining these two terms, clinical variation refers to differences in health care services provided to patients that diverge from the “usual form or arrangement.” Unwarranted clinical variation goes beyond this; however, it is a values‐based concept that requires an informed judgement about the extent to which clinical variation is legitimate. It is primarily concerned with appropriateness of care—whether the right care is provided in the right way and in the right amount to address patients' needs and expectations. Accordingly, we define unwarranted clinical variation as “patient care that differs in ways that are not a direct and proportionate response to available evidence; or to the healthcare needs and informed choices of patients.”

From a theoretical perspective, this definition integrates two disparate schools of thought. First, it aligns with the positivism of evidence based medicine—which has been described as “the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients.” 27 Second, it adopts an interpretive stance—acknowledging the importance of judgement, social context, and values in interpreting available evidence; different types of evidence and the context in which it is acted upon; and engaging with patients to make decisions in light of the available evidence. Interpretivists often align with critics of evidence‐based medicine who consider it to be simplistic and dogmatic—“synthesizing a certainty based on what is statistically probable, which, in the clinical setting, does not represent certainty at all”. 28

2.2. Searching the literature

To assess the ways in which unwarranted clinical variation is characterized in research studies, a search of the PubMed database to December 2017 was conducted, using terms used to refer to “unwarranted clinical variation” including “medical practice variation”, and “clinical variation” (see Appendix A ).

The search yielded 489 papers. Initial screening removed papers that were not relevant (eg, focused on genetic variation) leaving 315 for abstract review. Of these, 190 were empirical studies of unwarranted clinical variation and were included in a mapping process (listed in the supplementary file ). The mapping comprised a seven‐stage process realized by the authors in an iterative manner where disagreements were assessed and resolved through discussion and deliberation:

  • Define “a priori” key study characteristics: disease or patient group; setting (primary care, hospital, etc); unit of analysis (regions, hospitals, clinicians etc); metrics;
  • Categorize included studies using a data extraction tool (deductive process);
  • Identify emergent themes from studies (inductive process): statistical methods used; adjustment variables; residual/unexplained variation; scale of variation reported;
  • Cluster themes and factors contributing to warranted and unwarranted variation;
  • Synthesize and resynthesize concepts;
  • Validate using reclassification of studies using the emergent criteria; and

There are three key analytic dimensions in the UCV literature: (a) perspective (whether variation is measured across geographical areas or between providers); (b) criteria for assessment (whether measurement uses an “absolute” assessment, ie, variation from a predefined standard; or a “relative” assessment, ie, variation within a comparator group); (c) and object of analysis (whether assessment relies on process, structure/resource, or outcome metrics).

Of the 190 studies included in the review, 74% were focused on geographic variation; 90% focused on relative variation; and 73% were focused on processes of care (Figure  1 ). Looking at the various perspectives in combination, our initial mapping exercise identified 12 different combinations across the literature (Figure  2 ). The most frequently seen combination was geographic perspective/relative criteria/process metric—comprising 48% of the studies. The mapping and classification process highlighted measurement issues in each of three key dimensions in the UCV literature: perspective, criteria, and object of analysis.

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Summary of retrieved studies by analytic perspective

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Mapping of thematic combinations in the unwarranted clinical variation literature

3.1. Perspective: Geography or provider

Our review found that the variation literature is dominated by geographical‐area studies, typified by atlas publications. 4 , 13 , 14 Geography‐based studies commonly enumerate utilization rates with differences described in terms of “x‐fold variation.” Atlases are of particular interest to policymakers, as they often reflect allocative issues and provide a broad range of indicators. There are however few attempts to explicitly distinguish warranted and unwarranted variation, or to assess levels of care, relative to patient needs, expectations, and preferences. In general, studies do not define an acceptable level of variation nor is there a way to consider multiple processes simultaneously—which limits the value of atlas‐based approaches in complex and multifaceted care pathways.

There are far fewer unwarranted clinical variation studies focused on differences across providers. Here, comparisons are made in terms of health care delivered to patients—by hospitals, units, teams, or individual professionals. More relevant to clinicians and managers, there are generally efforts made to cluster or stratify similar units for comparison and performance assessment purpose.

3.2. Criteria: Absolute or relative

When there are clear standards about best practice, it is possible to make absolute assessments of care and quantify levels of unwarranted variation. Interpretation is most straightforward when the standard is either to “always” or “never” provide a treatment or achieve an outcome in a clearly defined set of circumstances (for example, to always provide annual foot examinations to diabetic patients or never prescribe antibiotics for viral infections). Assessment is much more difficult when there is uncertainty or clinical equipoise 29 —that is, when there are two or more equally valid approaches to meet patient needs, and the best choice depends on how individuals (both clinicians and patients) value the risks and benefits of treatments. Absolute assessment is also difficult in situations of a dynamic and rapidly evolving evidence base where innovations are diffusing throughout a system and differences in clinicians' readiness to adopt innovations result in variation.

In contrast, relative assessment looks for variation between units and generally requires a range of data items and sophisticated analytical techniques to ensure that fair comparisons are made. Combinations of adjustment variables and factors assumed to be contributing to “residual” variation are highly heterogeneous—meaning there are few shared assumptions about what constitutes warranted and unwarranted variation.

3.3. Objects of analysis: Process, outcome, and resource‐based metrics

Only two studies included in our review used multiple metric types. 30 , 31 About three quarters of the studies used process metrics. These are direct measures of tests, treatments, and procedures provided to patients and reflect differences in clinical decisions. Process metrics are meaningful in terms of variation only when they are clearly linked to the evidence base and interpreted in the context of needs and expectations of patients.

This direct measurement of clinical decision making and care delivery is not always feasible however, nor is it always the most informative approach. Proxy measures or indirect measurement is often the most insightful or efficient way to gauge unwarranted clinical variation and includes resource and outcome metrics. Resource metrics focus on inputs and comparisons are made in terms of dollars or other common units that allow for assessment of different combinations of processes and models of care. Outcome metrics also provide indirect measurement of variation in clinical care and are perhaps the most salient of the metrics, focusing on the consequences of unwarranted clinical variation. Like resource measures, outcome measures can capture complex care pathways, bundles, and the multiple processes inherent within them—one outcome measure can be a reflection of dozens of discrete care processes.

3.4. Identifying themes that help delineate warranted and unwarranted clinical variation

The retrieved literature identified various factors that have been used to categorize variation as either warranted or unwarranted and hence key factors to consider in analyses. While a range of adjustment or stratification variables could be relevant for all variation analyses, the literature suggests the use of these covariates or stratifying approaches is predominantly used in the assessment of relative variation between organizations in resource, process, or outcome metrics. Our inductive process of identifying themes regarding what is considered to constitute unwarranted and warranted clinical variation revealed that within these three metric subgroups of process, outcome, and resource measures, there were clear types of adjustment variables, explanatory and stratification variables, and factors associated with residual variation (Table  1 ).

Adjustment variables, stratification, and residual variation factors

4. DISCUSSION

Following synthesis of the literature, and a combination of deductive and inductive inquiry, an empirically derived analytic framework that delineates warranted and unwarranted variation emerged (Figure  3 ). The model highlights the important roles that patients' and clinicians' agency, scientific and clinical evidence, and personal and organizational capacity play in shaping variation—and how these factors should be considered in assessing if variation is warranted or unwarranted.

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Schematic of warranted and unwarranted clinical variation

4.1. The analytic framework—Warranted variation

In this analytic framework, “agency” encapsulates issues of motivations and for whom clinical decisions are made, focusing particularly on questions about whose needs and expectations drive clinical decisions.

From this agency perspective, clinical care must vary if it is to respond to patients' needs and expectations. Services should be tailored to patients' physical, social, and psychological requirements. Clinicians and clinical teams increasingly seek to provide care that is patient‐centred—eliciting patient preferences, supporting informed choice, and engaging patients in decisions about their care. Value and judgement are used to tailor clinical decisions and actions to the social and psychological needs of patients. 32 This means that the best care for one patient will not be the best care for all patients.

Considering patients' legitimate expectations about care and consent to various options for care is therefore key to assessing clinical variation. Increasingly, with the advance in personalized medicine and shared decision making, clinical variation should be expected and will be warranted if it is based on unbiased discussions and informed consent.

“Evidence,” in our analytic framework, focuses on whether clinical decisions align and resonate with the extant knowledge base and considers questions about the basis on which decisions are made. Not all health problems have a unique clinical solution, and in the context of a paucity of evidence about the effectiveness of an intervention, homogeneity of clinical practice may provide a false reassurance and prevent the emergence of clinical innovations. Similarly, the gradual emergence and testing of innovations may create temporary clinical variation. Hence, an explicit and critical appraisal of the nature of the evidence base in any clinical variation assessment is crucial.

From this evidence perspective, variation can be warranted if following appraisal, evidence‐based recommendations are adapted in order to respond to salient contextual cues. Variation can also be warranted where there is uncertainty within the expert clinical community about a preferred test or treatment. 33 Similarly, as the knowledge base about clinical care is constantly evolving, the concept of the “best” care is dynamic. The introduction of a new treatment, test, or model of care inevitably takes time to be adopted or implemented all across a health care system and the process of diffusion results in variation. 34 In innovation terms, variation is warranted and is often a positive feature—bringing with it opportunities to compare ways to provide care—so that the best option can be adopted into routine practice.

“Personal and organisational capacity” focuses on whether clinicians are able to provide care in the way they seek and includes questions about how decisions are enabled and supported. These relate especially to when variation focuses on clinicians and the need to consider any organizational constraints they face. These issues are further explored in the following sections.

From within the capacity perspective, where there are differences in skill‐mix or types of resources available between different local areas or organizations, variation in care processes can be a reflection of adaptation. Clinicians provide services in different ways, using different models of care within different circumstances—and as long as patients achieve equivalent outcomes, this variation can be regarded as warranted. In instances where there are many acceptable and effective ways to care and cure, where there are no guidelines or where guidelines allow for multiple approaches, clinicians can capitalize on their particular skill sets to provide care. Where there are unanticipated complexities, such as suddenly deteriorating patients, clinicians act as expert problem solvers, responding in real time to developing emergencies that will differ from most other routine care—not unwarranted variation but an example of desirable and appropriate variation.

4.2. The analytic framework—Unwarranted variation

Our model also identifies six key categories of unwarranted clinical variation, organized across the same three perspectives of agency, evidence, and capacity. Considering variation in terms of agency, if decisions are made on the basis of non clinically relevant patient characteristics such as age, gender, race, or socio‐economic status or where insufficient information is provided to patients to support properly informed and shared decision making, variation can be considered to be unwarranted.

More starkly perhaps, variation is unwarranted where clinicians' preferences or financial needs take precedence over the evidence‐base or patient interests. The literature features discussions and empirical examples about variation shaped by providers' expectations (eg, scheduling of procedures at certain times for the sake of clinicians' convenience and overuse of certain procedures for financial benefit). 35 This can result in tests, procedures, and treatments which have been shown to be ineffective, continuing to be provided to patients in ways that are wasteful of resources and place them at unnecessary risk. 36 Variation is also unwarranted if it is a result of responsibility for patient‐care, particularly for complex multimorbid patients, being parsed and resulting in episodes of disjointed and incomplete care. 37

From an evidence perspective, variation is deemed to be unwarranted when practice is clearly at odds with the available knowledge base. 38 , 39 , 40 Unwarranted variation can also stem from “indication creep,” where the use of a procedure or treatment grows beyond the original patient group in which it was trialled and shown to be valuable. 14 In an interesting twist, there are cases where a lack of variation can be unwarranted. An illustrative study by Tang et al 19 measured variability in antipsychotic prescribing patterns among psychiatrists and found that less‐expert providers had more homogeneous prescribing behaviours with some physicians relying heavily on a small number of agents, where appropriately tailored care would elicit different treatment regimens. So variation is unwarranted if there is unjustified deviation from the evidence base—that is, evidence is applied in variable ways despite a lack of key contextual confounders, or evidence is applied in a way that is not responsive to context. This element of unwarranted variation resonates with debate about the evidence‐based medicine movement—which for some threatens to overemphasize the importance of general research in routine clinical practice, devaluing the role of clinical judgement. 28

From a capacity perspective, variation is unwarranted if it is a result of differences in the level of training, competency, and technical proficiency of providers 41 , 42 or of limitations in clinicians' ability to resolve uncertainty. 43 Now, more than ever, the provision of reliable and resilient care to ensure patients' safety is seen as a minimum requirement for health care delivery systems and is not something that can vary according to where patients live or where they are treated.

Unwarranted clinical variation can also be a result of local delivery systems—resulting in some clinicians being unable to provide certain elements of care because of resource constraints. For example, variation in surgical waiting times and surgical outcomes can be a result of differences in resourcing across hospitals. Conversely, patterns of resourcing can also promote unnecessary activity, for example, where additional availability of resources results in greater propensity to treat or admit patients to hospital. This notion of “build it and they will come” underpins the concept of supplier‐induced demand. 13

4.3. Acknowledging and tackling the complexity of measuring unwarranted clinical variation

Our mapping exercise showed that clinical variation studies predominantly focus on process metrics; however, their ability to determine the extent to which measured variation is unwarranted has, to date, been limited. This may be because we need a way to more reliably identify and quantify key factors in play. For example, if there is evidential equipoise, the measurement of variation of a single therapeutic option will be misleading; if there is substitution of services to respond to different contexts, or if there is variation that is a reflection of diffusing innovations or changing models of best practice, processes are not strong measures of unwarranted variation.

While outcome‐based analyses provide a means to overcome many of these issues, appropriate statistical adjustment is key. Underadjustment will lead to an overinterpretation of variation—suggesting that there are opportunities to improve care when in reality, much of the variation may be warranted. Overadjustment represents the corollary case—“adjusting away” the impact of factors that, if addressed, could reduce meaningful variation—masking the impact of modifiable factors that should be tackled to improve care.

The measurement of unwarranted clinical variation is vulnerable to a range of failures in analytic design. Limits of measurement to be acknowledged and mitigated include difficulties in interpreting variation in small units, the recognition of “normal variation” and distinguishing it from “special cause variation” 44 , and regression to the mean. 45 While these concerns are well described in measurement and analytic literature, we need greater cognisance of their implications when interpreting and critically appraising measures of variation.

In addition to such well‐established analytic concerns, there were four key considerations that emerged from our mapping exercise—attribution, aggregation, models of care, and temporality—that are fundamental to enhancing our understanding of variation.

When comparing variation across individual clinicians or units, it is essential to distinguish between contextual factors that are outside direct control at that level and those that are tractable or amenable to change. This means, for example, that while geography‐based analyses can reveal disparities, the charge of unwarranted clinical variation cannot necessarily be applied to individual decision makers. Patients living in lower socio‐economic status (SES) areas are often reported to have worse outcomes in readmission or mortality than patients living in higher SES areas; however, a doctor at an inner city practice may be consistent in his or her decision making regardless of patients' SES. Context—either organizational or more broadly in terms of wider determinants of health—constrains clinicians' ability to provide care that delivers equal outcomes to all patients. Taking account of context does not diminish the unwarranted variation—it still exists—but it reflects a system or allocative issue rather than an issue with individual clinical decision makers.

The assessment of the unwarranted nature of clinical variation therefore requires a consideration of the nexus of control. In situations where organizational context is at the root of variation, and clinicians are constrained by structures, regulations, or intractable resource constraints, any resultant variation in the practice and decision making of those clinicians when compared with other clinicians in different organizational contexts can be considered to be reasonable rather than unwarranted. This does not mean that such variation is acceptable—it is clearly featured in the framework of unwarranted variation but as a reflection of policy and management, rather than clinical decisions.

Furthermore, inappropriate analytic choices regarding the level of analysis can confound assessment. Hospitals may appear to be similar to each other despite significant variation within hospitals. 46 Geographical analyses are especially prone to aggregation issues capturing multiple providers in a single area. Overuse or underuse can be occurring simultaneously and be masked by aggregation.

In addition, variation in provision of care may be warranted if different modalities are used for care delivery (eg, yearly eye exams for diabetics may be provided by ophthalmologists or optometrists and rates of eye exams may vary between regions when looked by types of providers separately but not vary in terms of reception of eye exam by patients between regions). However, variation in provision of care may be unwarranted if such allocative decisions prevent some patients receiving the care they need and choose (eg, yearly exams for diabetics may vary if neither the ophthalmologist nor the optometrist options are available locally). This raises the interesting conceptual question of whether variation in access to care should be considered as unwarranted clinical variation. Fundamentally, unwarranted clinical variation focuses on appropriateness of care—measured directly by care process measures or indirectly through resource use and outcome measures. For access issues, we cannot generally attribute unmet patient needs to individual clinicians. However, from a system perspective, a lack of access to appropriate care can be considered to be unwarranted clinical variation.

Finally, the issue of temporality adds further complexity to the assessment of unwarranted clinical variation. Provision of care may vary if the research base is dynamic and innovations are emerging and being tested. The diffusion of innovations takes time—however, after sufficient time has been allowed for uptake and practice change, continuing variation becomes unwarranted. So time is also important as standards of care are continually changing and innovations constantly emerging. In circumstances where the evidence about current care is either equivocal or suggests poor effectiveness of care, variation may be warranted to ensure innovation can emerge, be evaluated, and eventually disseminated at scale.

4.4. Limitations

Our model identifies six key categories of unwarranted clinical variation. It does not however provide guidance about which category investigators of clinical variation should focus upon—should all categories be explored simultaneously? If not, which category to choose? While this undoubtedly is a limitation, prompting systematic consideration of all six categories, and deliberate choices about measurement approaches in light of those considerations would represent a significant step forward.

As has been acknowledged by other researchers, 23 , 24 despite a considerable and enduring interest in measurement of unwarranted clinical variation, there are few conceptually based frameworks available. This means that there was very little theoretical groundwork on which to build. This paper does not present a fully elucidated theory but represents a step forward in seeking to strengthen the conceptual underpinnings and contribute to academic discourse.

Therefore, there are many unanswered questions—such as when is evidence strong enough to require action? How should we resolve conflicting sources of evidence and patient preferences? How do we place value on different elements of the model? How do we reconcile population based science with personalized medicine? Notwithstanding these limitations, this paper aims to provide a critique of current literature around the concept of unwarranted clinical variation and represents a step towards the development of a rounded theoretically based conceptual model.

5. CONCLUSION

Identifying, quantifying, and reducing unwarranted clinical variation promises to deliver a range of benefits to health care systems and to individual patients—more reliable provision of indicated and evidence‐based care, reduction in wasteful or unnecessary care, improved safety of care, greater system efficiency, and better patient outcomes. It has the potential to move us beyond a naïve view that only patients' needs and provider preferences drive delivery of care.

We know that almost all studies that look for variation find it. This ubiquity means that we need to develop sophisticated ways to prioritize measurement efforts and to more clearly distinguish warranted from unwarranted variation. Realizing potential gains is far from straightforward however. While there has been a great deal of sustained interest in the notion of unwarranted clinical variation internationally, there are few conceptual frameworks to guide investigation, systematic measurement, and change management processes. The complexity of unwarranted clinical variation is legion: Availability of evidence and contextual factors both affect how we judge variation between providers; equipoise or equivocal evidence makes variation uninterpretable; and variation in resourcing can make variation unattributable to the individual clinician. Delineating and defining unwarranted clinical variation place a heavy burden on measurement efforts.

In addition, the current lack of clarity around how best to measure unwarranted clinical variation can result in an overemphasis on ranges reported in atlases and propels the field towards an ever increasing focus on adjusting for comorbidities and health factors, without discussing other potential sources of variation. The predominant measurement approach—one that focus on enumerating and reporting relative process measures—is for the most part limited in its capacity to guide efforts to reduce unwarranted variation and improve care. All providers could be performing poorly and little variation revealed; there could be a mix of overuse and underuse of appropriate care; measurement may not be able to capture contextual factors that shape our judgement about the level of appropriateness in variation.

However, many health care systems are developing more sophisticated approaches to assessing unwarranted clinical variation—in the United Kingdom, the Getting it Right First Time programme encapsulates peer‐led deep dives, tailored feedback, and support for implementation. 47 More broadly, there is renewed interest in audit and feedback 48 —engagement of clinical decision makers in data analyses, fair comparisons, attribution and interpretation, and subsequent quality improvement. These efforts point to a way forward in a highly complex field.

The elements of warranted and unwarranted variation are interrelated and are highly sensitive to context. This means that it is difficult to measure quantitatively using administrative data and assessment requires more nuance and reflexivity, pointing us towards a mixed methods approach that is sensitive to uncertainty, social context, sense‐making, and scientific evidence.

Health care systems are dynamic and complex. Health care is unique in terms of the extent to which it is grounded in science but indelibly shaped by social context and values. It is of paramount importance that we start to look in a more informed and sophisticated way at variation in clinical care and patient outcomes in order to distinguish when variation is expected or desirable and when it is unwarranted. Only then can we start to focus on reducing the unwarranted and potentially harmful variation through quality and safety assurance processes and improvement and clinical innovation programs.

CONFLICT OF INTEREST

Both authors are full time employees of NSW Health. There are no external funding sources. There are no conflicts of interest to disclose.

Supporting information

Data S1 Mapped studies

ACKNOWLEDGEMENTS

This work was supported through a HARC fellowship from the Sax Institute, NSW. Preliminary findings were presented on a scientific poster at the World Hospital Congress, Brisbane Australia in October 2018. We thank our colleagues in the NSW Health Unwarranted Clinical Variation Taskforce for their insight and discussions.

APPENDIX A. 

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Sutherland K, Levesque J‐F. Unwarranted clinical variation in health care: Definitions and proposal of an analytic framework . J Eval Clin Pract . 2020; 26 :687–696. 10.1111/jep.13181 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

how assignable variation

Random Variation

Published: November 7, 2018 by Ken Feldman

how assignable variation

If you’re interested in the statistical concepts surrounding random variation, we will provide the statistical definition and explore how it might apply to your organization. 

For those more interested in what it means in practical terms, we will explore the definition and application in terms of its benefits and how it can be used to better manage your organization.

Overview: What is random variation?  

One of the best definitions for random variation appears in the dictionary of the iSixSigma.com website: 

The tendency for the estimated magnitude of a parameter (e.g., based upon the average of a sample of observations of a treatment effect) to deviate randomly from the true magnitude of that parameter. Random variation is independent of the effects of systematic biases. In general, the larger the sample size is, the lower the random variation is of the estimate of a parameter. As random variation decreases, precision increases.

In other words, everything varies, whether it be the dimensions of your product, your personal weight, your manufacturing processing time, the time to get to work, or your blood pressure. Over time, you would expect the variation of those measurements to form some kind of statistical distribution that would approximate the underlying population of whatever you are measuring. 

That underlying distribution will have a calculated central tendency, variation, and shape. At any point in time, the measurement you take will vary and can come from any place in that distribution. If there is random variation, you will not be able to predict the exact value of the next measurement. You might be able to calculate the probability of what the next value might be — or even calculate a range of values within the next measurement might fall. We can call that a confidence interval .

How random variation affects your processes

While the statistical properties are interesting, what might be more important for you is how the concept of random variation impacts your ability to manage your process. If your process is exhibiting random variation, or what Dr. W. Edwards Deming called common cause variation , then your process is predictable and in what might be called a steady state. Deming distinguished common cause from special cause. Special cause variation is unpredictable and a function of some unexpected intervention in your process.

For example, the fill level of your bottle will have some variation as a function of the variation in your fill equipment, liquid, temperature, and run speed. That is the steady state given the combined effects of the variation in your process elements. It is expected and, over time, will form some distribution. 

However, if one of your fill nozzles starts to clog up, there will be variation in fill that is a function of a specific and assignable cause. That would not be expected or predicted until after its occurrence. That would be non-random variation — or special cause variation.

You can use a control chart to distinguish between a random (common cause, predictable, noise) variation and a non-random (special cause, unpredictable, signal) variation.

3 benefits of paying attention to your variation 

Knowing whether your process is exhibiting random or non-random variation will help you properly respond to the signal you receive from your control chart.

1. Proper response

If your process is exhibiting random variation then any improvement will require a fundamental change in the process. If the process is exhibiting non-random variation, then you will need to identify the reason for that assignable cause and then take action to either eliminate or incorporate changes to maintain an improved state or eliminate a negative impact. 

If you are taking sample measurements and the process is demonstrating random variation, you’ll be able to do some level of prediction of future values.  

3. Assess changes

If your process is demonstrating random variation and you make a change, you will have confidence that, if you see an impact due to your change, it will be real and believable.

Why is random variation important to understand?

The concept of random variation, or noise, is a central concept in statistics. You will want to understand what random variation is and its implications for taking the appropriate actions on your process.

Underlying assumption

Most statistical tests will have an underlying assumption that the data you’re analyzing was created by a random process. If not, your results may be inaccurate because of the influence of non-random variation.

Desired state

You should strive to achieve random variation in your processes. Random variation does not imply that everything is OK or good, but merely that the process is predictable and steady state. From there, you will want to evaluate whether that steady state is satisfactory or needs to be improved.

For example, why do you think your doctor wants you to fast before a blood test? Is it to be mean (especially if your appointment is in the afternoon)? No, your doctor wants you to only exhibit random variation in your body processes and not have the influence of special cause variation, so your test results can be considered representative of your true steady state. That doesn’t mean an elevated blood pressure is good, but at least your doctor knows that it exists. From there, he or she can have the proper response. 

Improper response

Unless you have a good understanding of random variation, you may inadvertently believe you have non-random variation when you don’t. This would cause you to try and find an assignable cause when none exists, or make changes as a result of an individual observation that would be tantamount to tampering with the process.

An industry example of random variation 

Unfortunately, many managers don’t understand or appreciate the concept of random variation. For example, a manager in the finance department of a B2B online business was getting complaints from the CFO that invoices were slow getting out to the customer, and thus cash flow was being negatively impacted.

The LSS Master Black Belt (MBB) investigated and found out that, as a result of their LSS training, the manager was control charting the invoice processing time. That was a good thing. When the MBB started questioning the manager how he uses the control chart, he realized what the problem was. The control chart had all of the points within the upper and lower control limits so the process was demonstrating random variation.

The manager was reacting to high and low points without appreciating whether the process was exhibiting common or special cause variation. It turned out that when the manager saw a “high” point on the control chart he initiated a search for the root cause. And when he was happy with a “low” point, he didn’t do anything except to say “Great job!” 

An example chart showing variation in process time

The manager should have realized that the process was stable and showing random variation so the appropriate response should have been to change the process to reduce the overall variation — and if desired, to lower the average processing time.

3 best practices when thinking about random variation 

To manage your process by properly using the concept of random variation, you should consider the following best practices.

1. Collect your data in a random manner

To get a picture of the true random variation of your process, you should collect your data in a random manner. Introducing any bias in your data collection will impact the randomness of your variation.

2. Use the appropriate statistical tools to determine if you have random variation

As has been explained before, the statistical control chart is the best tool for determining whether your process is generating data in a random pattern or not. 

3. Provide a proper response

You should react to random variation by seeking to improve your process if it’s not capable of meeting your specs, targets, or expectations. If you have non-random variation, you will need to investigate why and then take the appropriate steps to either incorporate or eliminate the reasons why. 

Frequently Asked Questions (FAQ) about random variation

What is an example of random variation vs. non-random variation.

Let’s use a pair of fair dice as an example. If we throw our dice many times, we will experience variation in the numbers we throw. If we threw them even more times, we would get a distribution with an average of 7, a range of 10 (12-2) and a shape that is triangular. That is the hypothetical distribution.

But what if we started to see throws of 8, 7, 9, 10, 9, 12, 11, and 10. They are all above the average. We might be suspicious that this is not random variation. We would investigate and possibly find that the dice are loaded. We would then seek to correct the situation if we wanted the dice to represent random variation.

What is the best way to know if we are seeing random variation?

The statistical control chart is the best tool for distinguishing between random and non random variation.

Must I always react to random variation?

If your process is showing random variation and is operating at a desired level, there is no need for you to react. But if you wish to improve your process, you’ll want your process to be in a steady state of random variation. That way, when you observe a change, you can attribute it to what you did rather than some unknown source.

Random variation in a nutshell

Random variation is the desired state for your process. It is predictable and consistent. But, it does not mean your process is operating at its best, only that it is steady state. 

The control chart is the best tool for distinguishing between random variation and non random variation. If you want to improve your process, then make sure you are only seeing random variation. If you have non random variation, find out why, and deal with the root cause(s). Hopefully then you’ll have a process showing only random variation.

About the Author

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Ken Feldman

Variation in Medical Practice and Implications for Quality Scope and Use of Variation in Healthcare

Variation in Medical Practice and Implications for Quality Scope and Use of Variation in Healthcare

how assignable variation

Healthcare quality researchers use a variety of categories to measure improvements and detect variation in quality of care. Quality in healthcare is measured by its ability to satisfy qualitative standards as well as quantitative thresholds. Institute of medicine (IMO) has established six aims for healthcare improvement to ensure that medical care is safe, timely, effective, efficient, equitable, and patient-centered. As such, clinical indicators that address the timeliness of care, for example, from several clinical domains- Acute myocardial infarction, surgical infection prevention, and community-acquired pneumonia are aggregated to assess the appropriate level of time-dependent quality of care at a medical facility.

Variability plays an obvious role in identifying, measuring, and reporting these quality indicators and process of care improvements. For example, the patient mix may make it difficult to compare the process of care measures across multiple hospitals in the same system, creating the appearance of variation among facilities’ services.

Consequently, some healthcare managers are reluctant to use quality improvement measures and indicators because they perceive them as biased toward academic medical research centers or large healthcare organizations, which are seen as not subject to broad variation.

This assumption is unfortunate and false because quality improvement efforts can be and have been successfully applied to small organizations and practices, including single physician practices.

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COMMENTS

  1. Assignable Cause

    Concepts Assignable Cause Published: November 7, 2018 by Ken Feldman Assignable cause, also known as a special cause, is one of the two types of variation a control chart is designed to identify. Let's define what an assignable cause variation is and contrast it with common cause variation.

  2. ASSIGNABLE CAUSES OF VARIATIONS

    1 Citations Download reference work entry PDF Assignable causes of variation are present in most production processes. These causes of variability are also called special causes of variation ( Deming, 1982 ). The sources of assignable variation can usually be identified (assigned to a specific cause) leading to their elimination.

  3. Common cause and special cause (statistics)

    Definitions Common-cause variations Common-cause variation is characterised by: Phenomena constantly active within the system; Variation predictable probabilistically; Irregular variation within a historical experience base; and Lack of significance in individual high or low values.

  4. Six Sigma Control Charts: An Ultimate Guide

    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 ...

  5. Understanding and managing variation: three different perspectives

    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 ...

  6. PDF The assignable cause The Control Chart Statistical basis of the control

    Managing Variation over Time • Statistical Process Control often takes the form of a continuous Hypothesis testing. • The idea is to detect, as quickly as possible, a significant departure from the norm. • A significant change is often attributed to what is known as an assignable cause. •An assignable cause is something that can be

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

    It is also known as "assignable cause." These variations are unusual, unquantifiable, and are variations that have not been observed previously, so they cannot be planned for and accounted for. These causes are typically the result of a specific change that has occurred in the process, with the result being a chaotic problem.

  8. How to deal with Assignable causes

    Compare both the charts before and after transformation. If they are the same, you can be more or less sure it has common causes of variation. Plot all of the data, with the event on a control chart. If the point does not exceed the control limits, it is probably a common-cause event. Use the transformed data if used in step 1.

  9. Assignable Cause: Learn More From Our Online Lean Guide

    Assignable Cause. An assignable cause is a type of variation in which a specific activity or event can be linked to inconsistency in a system. In effect, it is a special cause that has been identified. As a refresher, common cause variation is the natural fluctuation within a system. It comes from the inherent randomness in the world.

  10. Assignable causes of variation and statistical models: another approach

    This paper presents a fresh approach to the analysis of Shewhart control chart's performance. We consider two different types of assignable causes of variation. One—called type I—affects only the parameters of a model of the underlying distribution. The other—called type X—impacts the type of the original distribution.

  11. Identifying and Managing Special Cause Variations

    Here are a few practices to bear in mind when it comes to special cause variations: 1. Countering special cause variations. Contingency plans can be used to counter special cause variations. With this strategy, additional processes are incorporated into operations that prevent or counter a special cause variation. 2.

  12. Assignable Cause

    An assignable cause refers to a specific, identifiable factor or reason that contributes to a variation or deviation in a process or system's output. In statistical process control and quality management, assignable causes are distinct from random or common causes, as they are usually identifiable and controllable.

  13. Variations in Care

    Special cause variation (or "assignable" variation) arises from a single or small set of causes that can be traced and identified and then implemented or eliminated through targeted quality improvement initiatives). Statisticians have a broad range of tests and criteria to determine whether variation is assignable or random and with the ...

  14. The meaning of variation to healthcare managers, clinical and health

    Go to: Introduction Health managers, clinical researchers, and individual patients need to understand and manage variation in healthcare processes in different time frames and in different ways. In short, they ask different questions about why and how healthcare processes and outcomes change ( table 1 ).

  15. Variation

    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.

  16. Chance & assignable causes of variation

    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.

  17. The Power of Special Cause Variation: Learning from Process Changes

    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 ...

  18. What is Variation?

    / Quality Resources / Variation What is the Law of Variation? Quality Glossary Definition: Variation The Law of Variation is defined as the difference between an ideal and an actual situation. Variation or variability is most often encountered as a change in data, expected outcomes, or slight changes in production quality.

  19. Reducing Clinical Variation to Drive Success in Value-Based Care ...

    Standardized care that reduces clinical variation—defined here as the over-, under-, or unnecessary utilization of healthcare services and resources—has been shown to improve quality and outcomes in the ambulatory, acute, and post-acute settings.

  20. Unwarranted clinical variation in health care: Definitions and proposal

    2.1. Building a definition. In linguistic terms, variation is defined as "something that is slightly different from the usual form or arrangement."26 Clinical refers the examination and treatment of patients—that is, focusing on patient‐provider interactions and including preventive, diagnostic, therapeutic, and supportive care. Combining these two terms, clinical variation refers to ...

  21. Assignable variables

    1. Meaning of assignable variation Assignable variation is a variation whose source can be identified. The source is always a major factor like tool failure and absenteeism. Assignable variation is different from random variation; random variation is a natural variation in the output of process created a countless minor factors like temperature, humidity variation or traffic delays.

  22. Random Variation

    Concepts Random Variation Published: November 7, 2018 by Ken Feldman If you're interested in the statistical concepts surrounding random variation, we will provide the statistical definition and explore how it might apply to your organization.

  23. Variation in Medical Practice and Implications for Quality ...

    Variability plays an obvious role in identifying, measuring, and reporting these quality indicators and process of care improvements. For example, the patient mix may make it difficult to compare the process of care measures across multiple hospitals in the same system, creating the appearance of variation among facilities' services.

  24. Assign mobile device management admin privileges based on

    Creating a custom role, which is assignable at the OU level. Assigning permissions at the OU level. Example experience for an admin with OU level permissions. Getting started. Admins: Visit the Help Center to learn more about administrator roles and mobile device management.