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assignable cause in quality control

When Assignable Cause Masquerades as Common Cause

Deciding whether you need capa or a bigger boat.

Published: Wednesday, September 27, 2023 - 11:03

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T he difference between common (or random) cause and special (or assignable) cause variation is the foundation of statistical process control (SPC). An SPC chart prevents tampering or overadjustment by assuming that the process is in control, i.e., special or assignable causes are absent unless a point goes outside the control limits. An out-of-control signal is strong evidence that there has been a change in the process mean or variation. An out-of-control signal on an attribute control chart is similarly evidence of an increase in the defect or nonconformance rate.

The question arises, however, whether events like workplace injuries, medical mistakes, hospital-acquired infections, and so on are in fact due to random or common cause variation, even if their rates follow binomial or Poisson distributions. Addison’s disease and syphilis have both been called “the Great Pretender” because their symptoms resemble those of other diseases. Special or assignable cause problems can similarly masquerade as random or common cause if their metrics fit the usual np (number of nonconformances) or c (defect count) control charts.

The exponential distribution is used as a model for rare events, and the metric is the time between occurrences such as days between lost-worktime injuries. Sufficiently infrequent workplace injuries could conform to this distribution and convince the chart users that they are, in fact, random variation.

We also know it’s important to not try to track more than one process, or in the case of attribute data, more than one kind of defect or nonconformance, on a single control chart. The latter probably makes an out-of-control signal less likely if one of the attributes does begin to cause trouble; if we do get an out-of-control signal, the chart won’t show which attribute is responsible. It’s similarly futile to have a single control chart for an aggregate of safety incidents with wide arrays of underlying causes and effects.

Are control charts applicable to safety incidents or medical mistakes?

Some very authoritative sources recommend using control charts for workplace injuries, medical mistakes, and so on. According to a 2014 public health report , “Statistical process control charts have recently been used for public health monitoring, predominantly in healthcare and hospital applications, such as the surveillance of patient wait times or the frequency of surgical failures [e.g., 1–10]. Because the frequency of safety incidents like industrial accidents and motor vehicle crashes will follow a similar probability distribution, the use of control charts for their surveillance has also been recommended [11–15]. These control chart uses can be extended to military applications, such as monitoring active-duty Army injuries.” 1

This reference includes control charts for “injuries per 1,000 soldiers,” and the points are all inside the control limits. The reference does cite a decrease in the injury rate, and this could well be due to corrective and preventive action (CAPA) that removed the root causes of the incidents in question to prevent recurrence. That is, CAPA for special or assignable cause problems will make them less frequent, so their aggregated count will exhibit a decrease. The presence of control limits could, however, have the unintended consequence of implying that these incidents result from random variation rather than assignable causes.

Another reference claims, “Deming estimated that common causes may be responsible for as much as 99% of all the accidents in work systems, not the unsafe actions or at-risk behaviors of workers.” 2  Although one might be reluctant to challenge W. Edwards Deming, the truth is that almost all safety incidents have assignable causes. I’ve yet to see the Occupational Health and Safety Administration or the Chemical Safety Board write one off to random variation. When OSHA fines somebody for an unsafe workplace, it’s always for an assignable cause because OSHA cites a rule and how it was violated (e.g., no fall protection). If Deming contended that 99% of all incidents are due to management-controllable factors, that’s another matter entirely. But these factors are ultimately special or assignable causes. If a problem has an identifiable root cause, it’s a special or assignable cause by definition.

Rethinking common vs. assignable cause

Quality practitioners equate common cause and random cause variation. Random is exactly what it says because process and quality characteristics always experience some variation. Common cause relates to factors that aren’t controllable by the workers. Deming’s Red Bead demonstration shows why it’s worse than useless to reward or penalize workers for them. If these factors are correctable by management, it might be better to not equate them to random variation.

The Ford Motor Co. presented an outstanding example of this more than 100 years ago. 3 “Even the simple little sewing machine, of which there are 150 in one department, did not escape the watchful eyes of the safety department. Every now and then the needle of one of these high speed machines would run through an operator’s finger. Sometimes the needle would break after perforating a finger, and a minor operation would become necessary. When such accidents began to occur at the rate of three and four a day, the safety department looked into the matter and devised a little 75-cent guard which makes it impossible for the operator to get his finger in the way of the needle.”

The reference says the accidents took place at a rate of three and four a day; let’s assume an average of 3.5 per day. It’s quite likely that the daily count would have fit a Poisson distribution for undesirable random arrivals, and would have probably served as a textbook example for a c (defect count) control chart. If we view common or random cause as something inherent to the system in which people must work, in this case an unguarded moving sharp object, then this was a common cause problem. The fact that it was possible to put a finger under the needle shows, however, that the root cause was in the machine (equipment) category of the cause-and-effect diagram. The fact that installation of the guards (figure 1) eliminated the problem completely underscores the fact that they were dealing with special, assignable, or correctable cause variation.

Make no mistake: CAPA is, or at least should be, mandatory for every safety incident or near miss, regardless of the frequency of occurrence, because it almost certainly has a correctable cause.

assignable cause in quality control

Shigeo Shingo offered several case studies that involved workers forgetting to install or include parts. 4 It’s quite conceivable that these nonconformances might have followed a binomial or Poisson distribution, and their counts could have been tracked on an np (number nonconforming) or c (defect count) chart. This might convince many process owners that this was random or common cause variation, especially if no points were above the upper control limit. Shingo determined, however, that the root cause was machine and/or method (as opposed to manpower) because the job design permitted the mistakes to happen. Installing simple error-proofing controls that made it impossible to forget to do something fixed these problems entirely.

If we accept the premise that something management-controllable, like a job design that allows mistakes, is common cause variation, then these problems were common cause variation. The fact that specific, assignable causes were found and removed, however, argues otherwise.

Is a known cause always a special cause?

Does the fact that we know a problem’s root cause always make it a special or assignable cause? Suppose a 19th-century army recognizes that a musketeer is unlikely to hit his target from beyond 50–100 yards because muskets are inherently incapable of precise fire, as shown in figure 2. The only way to improve the situation is to rearm the entire army with rifles, which everybody eventually did.

assignable cause in quality control

The prevailing variation in musket fire, however, had to be classified as common cause because the tool was simply not capable of better performance. There was no adjustment a soldier could make to improve this performance, and adjustment in response to common or random cause variation (i.e., tampering) actually makes matters worse. If, however, the shot group from a firearm was centered elsewhere than the bull’s-eye, this was special or assignable cause because the back sight could be adjusted to correct the problem the same way a machine tool that is operating off nominal can be adjusted to bring it back to center.

Another example involves particle-inflicted defects on semiconductor devices. These devices are so small that even microscopic particles will damage or destroy them during fabrication. Thus the cause is known, but the only way to improve the situation is to get a better clean room with an air filtration system that will reduce the particle count, or get better process equipment and chemicals; the latter also must be relatively particle-free.

The takeaway from these examples is that if the problem’s root cause is known but we can solve it only with a large capital investment, retooling, or whatever, we can construe it as common cause variation. This is emphatically not true, however, of safety incidents and medical mistakes.

Joseph Juran and Frank Gryna reinforce this perception. 5 “Random in this sense means of unknown and insignificant cause, as distinguished from the mathematical definition of random—without cause.” If a root cause analysis (RCA) in the course of corrective and preventive action can find a cause, it’s assignable and not random.

The fact that nonconformance data—and safety incidents and medical mistakes are obviously nonconformances—may fit an attribute distribution and behave in the expected manner on an attribute control chart doesn’t make them random or common cause variation that we must accept in the absence of major capital investments or other overhauls. We must recognize upfront that the aggregate of multiple special-cause incidents can masquerade as binomial or Poisson data. We also need to realize that OSHA violations involve failures to conform to a very specific regulation or standard (such as fall protection), which are special or assignable causes by definition.

Medical regulatory agencies such as Medicare do not, meanwhile, deny payment for things that “just happen,” like surgery on the wrong body part, surgery on the wrong patient, medication errors, and so on. 6 These are “never events” that should never happen, so common or random cause variation is not an acceptable explanation.

This underscores the conclusion that any accident or near miss requires corrective and preventive action regardless of whether the count or frequency of these events falls inside traditional control limits, and even raises questions as to whether control limits (which imply the presence of a random underlying distribution) should be used at all.

In summary: If the only way to improve the situation involves extensive retooling, capital investments, and so on, as in “You’re going to need a bigger boat” from the movie Jaws , it’s common cause variation. The issue isn’t urgent because it’s not practical to take immediate action on it. But it is important. If a competitor gets a bigger boat, a superior rifle, a better cleanroom, or a tool with less variation, we will eventually be in trouble.

If the issue has an identifiable root cause that can be removed with corrective and preventive action, it’s a special or assignable cause variation regardless of whether the metric is inside control limits. CAPA is mandatory when the issue involves worker or customer safety, and highly advisable when it involves basic quality.

References 1. Schuh, Anna, and Canham-Chervak, Michelle.  “Statistical Process Control Charts for Public Health Monitoring.” U.S. Army Public Health Command, Public Health Report, 2014. 2. Smith, Thomas.   “Variation and Its Impact on Safety Management.” EHS Today, 2010. 3. Resnick, Louis.  “How Henry Ford Saves Men and Money.” National Safety News , 1920. 4. Shingo, Shigeo. Zero Quality Control: Source Inspection and the Poka-Yoke System . Routledge, 1986. 5. Juran, Joseph, and Gryna, Frank.  Juran’s Quality Control Handbook,   Fourth Edition .  McGraw-Hill, 1988. 6. Centers for Medicare & Medicaid Services.  “Eliminating Serious, Preventable, And Costly Medical Errors—Never Events.”  2006.

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About The Author

William a. levinson.

William A. Levinson, P.E., FASQ, CQE, CMQOE, is the principal of Levinson Productivity Systems P.C. and the author of the book The Expanded and Annotated My Life and Work: Henry Ford’s Universal Code for World-Class Success (Productivity Press, 2013).

assignable cause in quality control

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assignable cause in quality control

The Power of Special Cause Variation: Learning from Process Changes

Updated: July 28, 2023 by Marilyn Monda

assignable cause in quality control

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

Overview: What is special cause variation? 

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

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

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

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

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

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

RELATED: COMMON CAUSE VARIATION VS. SPECIAL CAUSE VARIATION

Drawbacks of special cause variation .

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

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

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

Why is special cause variation important to understand? 

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

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

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

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

An industry example of special cause variation 

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

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

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

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

3 best practices when thinking about special cause variation 

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

Identify the source

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

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

Make improvements at the source

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

Document everything

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

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

Frequently Asked Questions (FAQ) about special cause variation

  • Are special causes always bad news? 

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

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

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

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

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

Final thoughts on special causes 

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

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

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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.

See Quality: The implications of W. Edwards Deming's approach ; Statistical process control ; Statistical...

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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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Six Sigma Control Charts: An Ultimate Guide

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

six sigma control charts

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

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

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

What is a Control Chart?

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

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

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

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

Significance of Control Charts in Six Sigma

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

The Objective of Six Sigma Control Charts

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

Detecting Special Cause Variation

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

Estimating Process Average and Variation

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

Measuring Process Capability with Cp and Cpk

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

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

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

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

Steps to Create a Six Sigma Control Chart

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

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

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

Know When to Use Control Charts

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

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

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

Four Process States in a Six Sigma Chart

Control charts can be used to identify four process states:

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

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

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

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

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

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

X Bar R Chart

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

X Bar S Chart

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

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

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

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

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

Factors to Consider while Selecting the Right Six Sigma Chart Type

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

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

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

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

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

Benefits of Using Control Charts

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

Who Can Benefit from Using Six Sigma Charts

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

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

Some Six Sigma Control Chart Tips to Remember

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

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

What are Control Limits?

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

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

Calculating Control Limits

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

Step 1: Determine the Standard Deviation

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

Step 2: Calculate the Mean

Calculate the mean of the data set.

Step 3: Find the Upper Control Limit

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

Step 4: Find the Lower Control Limit

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

Importance of Statistical Process Control Charts

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

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

How to Interpret Control Charts?

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

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

Elements of a Control Chart

Six Sigma control charts consist of three key elements.

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

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

What is Subgrouping in Control Charts?

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

Within-Subgroup Variation

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

Between-Subgroup Variation

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

X Bar Chart Analysis

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

Benefits of Subgrouping in Six Sigma Charts

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

Also Read: Central Limit Theorem Explained

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

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

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

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

You might also like to read:

How to Use the DMAIC Model?

How Do You Improve Logistics with Six Sigma?

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

What is Root Cause Analysis and What Does it Do?

Describing a SIPOC Diagram: Everything You Should Know About It

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SOURCES OF VARIATION: COMMON AND ASSIGNABLE CAUSES

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

images

Random causes that cannot be identified.

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

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assignable cause in quality control

Six Sigma Study Guide

Six Sigma Study Guide

Study notes and guides for Six Sigma certification tests

Ted Hessing

Walter A Shewhart

Posted by Ted Hessing

Willam Shewhart

Walter Shewhart is often called the “Father of Quality Control” – he worked as a process engineer for Western Electric and then at Bell Labs.  He retired in the mid ’50s. Juran actually worked under Shewhart.

We can trace Six Sigma as a measurement standard in product variation back to the 1920s when Walter Shewhart showed that three sigma from the mean is the point where a process requires correction.

Walter A. Shewhart Biography

Walter Andrew Shewhart was an American Physicist and statistician, and he is referred to as the ‘Father of Statistical Quality Control.’ Walter Andrew Shewhart was born to Anton and Esta Barney Shewhart in New Canton, Illinois, in the year 1891. He did his Master’s at the University of Illinois. Later, he attended the University of California at Berkeley to pursue a Ph.D. degree, where he received a Doctorate in physics in 1917. He worked as a professor at both universities. Then, he worked as head of the department of physics at Wisconsin Normal School, Lacrosse.

In 1918, Walter Shewhart worked at the Western Electric Company, one of the largest hardware manufacturers during that time. He assisted engineers in the manufacturing plant in refining the quality of telephone hardware. Later Shewhart went to work at Hawthorne until 1925; then, he worked at the Bell Telephone Research Labs. He would stay there until his retirement.

Walter Shewhart developed modern statistical concepts and scientific methods to minimize human efforts. Shewhart’s methods influenced other statisticians like W Edwards Deming and Joseph Juran . People often refer to these three people – Shewhart, Deming, and Juran – as the three authors of the quality upgrade movements. Shewhart’s work, specifically control charts and the PDSA cycle , influenced the daily work of quality extensively.

Reducing Variation – To Improve Quality

The emphasis on reducing variation to enhance quality is an excellent contribution to quality management. Reducing variation to improve quality resulted in the manufacture of precise things. The concept was applicable in different fields like automobiles, electronics, construction, etc.

Shewhart acknowledged two variation classes, namely ‘special‐cause’ and ‘common‐cause variation. These two categories can also be termed as ‘assignable‐cause’ and ‘chance‐cause’ variations respectively. He designed control charts to explain these two categories of variations. Shewhart proposed new attributes and variables in his control charts. Shewhart proposed to control common-cause to improve quality and reduce scrap. In this way, we can bring any process under statistical control. To distinguish between special cause and common cause variations, we must be sure to meet this criteria. After bringing a process to this state, it would be easy to forecast future outputs and manage processes economically. Shewhart’s principle paved the way for modern scientific analysis of process control.

Economic Incentives

The objective of any industry is to develop economical methods in order to satisfy human needs. We can do this by reducing things to routines requiring little human effort. By using scientific techniques and modern statistical theories, it was possible to set up limits for the results of routine efforts economically. We describe a routine as broken down and no longer economical if the results of any routine process deviate away from the limits. Consequently, we must find the cause of trouble, identify it, and eliminate it in order to make the process economical.

Six Sigma Concept

We use the Greek letter ‘Sigma,’ a Greek letter and mathematical term, to denote standard deviation. It is a standard statistical unit used to measure and describe the distribution of any process about its mean.

Shewhart’s ideas and statistical concepts were embraced in clinical laboratories for several years. Clinical laboratories used these concepts in proficiency testing and quality control operations. Many industries have rediscovered Shewhart’s methods and statistical process control tools, named ‘Six Sigma.’ Recently, Motorola Company has also made use of Six Sigma methods for improving product quality. Motorola Inc. was awarded the Malcolm Baldrige National Quality Award in the manufacturing category for using Six Sigma concepts for quality improvement. In recent times, the Six Sigma concept has acquired public attention extensively. Many other organizations also started using Six Sigma concepts, which has popularized it.

ASQ – American Society for Quality

ASQ of individuals who are passionate about methods of quality control. Members of ASQ contribute to the industry with their ideas of quality control and experience. Shewhart was ASQ’s first honorary associate, and he efficaciously brought together the principles of statistics, economics, and engineering. Individuals working in the field of quality control widely recognize his contributions, particularly because he developed highly effective tools specifically control charts.

Shewhart acquired appreciation in the statistical community for writing Statistical Method from the Viewpoint of Quality Control . Additionally, Bell Laboratories held a number of articles and journals that he had written, but fortunately, they published them later.

Another important element in Shewhart’s achievements was his quality of bringing out ideas and knowledge of other individuals.

Shewhart always believed that statistical theory would serve the needs of industry. After all, he was a man of science who worked patiently to develop ideas that made the world better. Shewhart’s contributions and ideas influenced ASQ intensely. Before his death, Shewhart mentioned to other members of ASQ that he appreciated their contributions. It was their efforts that led to extensive growth in the field of quality control. He also stated that the development, which was beyond his expectations, astonished him.

Comments (8)

There are a lot of misleading articles about the origin of the TQM concepts. You’ve demystified some of my doubts. Thank you!

Glad that helped, Bina!

Should this statement, “Shewhart proposed that to improve quality and reduce scrap, common-cause variation should be controlled,” not be “Shewhart proposed that to improve quality and reduce scrap, special-cause variation should be controlled”?

I think your question is really about the difference between common cause variation and special cause variation.

As you standardize a process you’ll have common cause variations. You can then improve those common cause variations.

Special cause variation are those truly unique situations that are by definition difficult to control. You can try to avoid them (eg with checksheets ) or mitigate them (eg with a FMEA ) – but it’s hard to even tell if your process even has experienced a special cause variation until you’ve standardized the process and brought common cause variation under some kind of control.

Could you please describe methods for statistical analysis and control of quality developed by Walter Shewhart.

Please see our Control Charts Study Guide here.

Also, we have our SPC article here.

I’m finding that I’m getting lost with all the hyperlinks

What is the best way to read through ?

Hi Tehtena,

The hyperlinks are just there in case you don’t understand that topic well and want to dive deeper. If you’re new to the material, I would suggest skipping the hyperlinks and focusing on the core message of the pages.

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Module 8. Statistical quality control

BASIC CONCEPTS OF STATSITICAL QUALITY CONTROL

26.1  Introduction

From the early days of industrial production, the emphasis had been on turning out products of uniform quality by ensuring use of similar raw materials, identical machines, and proper training of the operators.  Inspite of these efforts, the causes of irregularity often crept in inadvertently.  Besides, the men and machines are not infallible and give rise to the variation in the quality of the product.  For keeping this variation within limits, in earlier days, the method used was 100 per cent inspection at various stages of manufacturing.

It was in 1924 that Dr. W.A. Shewhart of Bell Telephone Laboratories, USA developed a method based on statistical principles for controlling quality of products during the manufacturing and thus eliminating the need for 100 per cent inspection.  This technique which is meant to be an integral part of any production process, does not provide an automatic corrective action but acts as sensor and signal for the variation in the quality.  Therefore, the effectiveness of this method depends on the promptness with which a necessary corrective action is carried out on the process.  This technique has since been developed by adding to its armory more and more charts, as a result of its extensive use in the industry during and after the Second World War. In this lesson various terms used in the context of Statistical Quality Control (SQC) have been illustrated.

26.2  Definitions of Various Terms Involved in Statistical Quality Control

The following terms are used to understand the concept of Statistical Quality Control

26.2.1  Quality

The most important word in the term ‘Statistical Quality Control’ is quality. By ‘Quality’ we mean an attribute of the product that determines its fitness for use. Quality can be further defined as “Composite product characteristics of engineering and manufacture that determine the degree to which the product in use will meet the expectations of the customer at reasonable cost.” Quality means conformity with certain prescribed standards in terms of size, weight, strength, colour , taste, package etc.

26.2.2  Quality characteristics

Quality of a product (or service) depends upon the various characteristics that a product possesses. For example, the Kulfi we buy should have the following characteristics.

            (a)  TS  (b)  Sugar  (c)  Flavour   (d)    Body & Texture.

All these individual characteristics constitute the quality of Kulfi .  Of course, some of them are important (critical) without which the Kulfi is not acceptable.  For example Minimum TS, Sugar, Body and Texture score is important.  However, other characteristics such as Colour and Flavour may not be so important. The quality characteristics may be defined as the “distinguishing” factor of the product in the appearance, performance, length of life, dependability, reliability, durability, maintainability, taste, colour , usefulness etc. Control of these quality characteristics in turn means the control of the quality of product.

26.2.3  Types of characteristics

There are two types of characteristics viz., variable characteristics and attribute characteristics.

26.2.3.1  Variable characteristic

Whenever a record is made of an actual measured quality characteristic, such as dimension expressed in mm, cm etc. quality is said to be expressed by variables.  This type of quality characteristics includes e.g., dimension (length, height, thickness etc.),hardness, temperature, tensile strength, weight, moisture percent, yield percent, fat percent etc.

26.2.3.2  Attribute characteristic

Whenever a record shows only the number of articles conforming and the number of articles failing to conform to any specified requirements, it is said to be a record of data by ‘attributes’.  These include:

·          Things judged by visual examination

·          Conformance judged by gauges

·          Number of defects in a given surface area etc.

26.2.4  Control

Control means organizing the following steps:

·            Setting up standards of performance.

·            Comparing the actual observations against the standards. 

·            Taking corrective action whenever necessary.

·            Modifying the standards if necessary.

26.2.5  Quality control

Quality control is a powerful productivity technique for effective diagnosis of lack of quality (or conformity to set standards) in any of the materials, processes, machines or end products. It is essential that the end products possess the qualities that the consumer expects of them, for the progress of the industry depends on the successful marketing of products.  Quality control ensures this by insisting on quality specifications all along the line from the arrival of materials through each of their processing to the final delivery of goods.Quality control, therefore, covers all the factors and processes of production which may be broadly classified as follows:

·          Quality of materials : Material of good quality will result in smooth processing there by reducing the waste and increasing the output.  It will also give better finish to end products.

·          Quality of manpower : Trained and qualified personnel will give increased efficiency due to the better quality production through the application of skill and also reduce production cost and waste.

·          Quality of machines : Better quality equipment will result in efficient working due to lack or scarcity of break downs thus reducing the cost of defectives.

·          Quality of Management : A good management is imperative for increase in efficiency, harmony in relations, growth of business and markets.

26.2.6  Chance and assignable causes of variation

Variation in the quality of the manufactured product in the repetitive process in the industry is inherent and inevitable.  These variations are broadly classified as being due to two causes viz., ( i ) chance causes, and (ii) assignable causes.

26.2.6.1  Chance causes

Some “Stable pattern of variation” or “a constant cause system” is inherent in any particular scheme of production and inspection.  This pattern results from many minor causes that behave in a random manner.  The variation due to these causes is beyond the control of human being and cannot be prevented or eliminated under any circumstance. Such type of variation has got to be allowed within the stable pattern, usually termed as Allowable Variation.  The range of such variation is known as natural tolerance of the process.

26.2.6.2  Assignable causes

The second type of variation attributed to any production process is due to non-random or the so called assignable causes and is termed as Preventable Variation.  The assignable causes may creep in at any stage of the process, right from the arrival of raw materials to the final delivery of the goods.

Some of the important factors of assignable causes of variation are substandard or defective raw material, new techniques or operations, negligence of the operators, wrong or improper handling of machines, faulty equipment, unskilled or inexperienced technical staff and so on.  These causes can be identified and eliminated and are to be discovered in a production process before it goes wrong i.e., before the production becomes defective.

26.3  Statistical Quality Control

By Statistical Quality Control (SQC) we mean the various statistical methods used for the maintenance of quality in a continuous flow of manufactured goods.  The main purpose of SQC is to devise statistical techniques which help us in separating the assignable causes from chance causes of variation thus enabling us to take remedial action wherever assignable causes are present.  The elimination of assignable causes of erratic fluctuations is described as bringing a process under control. A production process is said to be in a state of statistical control if it is governed by chance causes alone, in the absence of assignable causes of variation.

In the above problem, the main aim is to control the manufacturing process so that the proportion of defective items is not excessively large.  This is known as ‘ Process Control’ .  In another type of problem we want to ensure that lots of manufactured goods do not contain an excessively large proportion of defective items.  This is known as ‘ Product or Lot Control ’. The process control and product control are two distinct problems, because even when the process is in control, so that the proportion of defective products for the entire output over a long period will not be large, an individual lot of items may not be of satisfactory quality.  Process Control is achieved mainly through the technique of ‘ Control Charts ’ whereas Product Control is achieved through ‘ Sampling Inspection’ .

26.4  Stages of Production Process

Before production starts, a decision is necessary as to what is to be made.  Next comes the actual manufacturing of the product.  Finally it must be determined whether the product manufactured is what was intended.  It is therefore necessary that quality of manufactured product may be looked at in terms of three functions of specification, production and inspection.

26.4.1  Specification

 This tells us what is to be produced and of what specification.  That is, it gives us dimension and limits within which dimension can vary.  These specifications are laid down by the manufacturer.

26.4.2  Production

Here we should look into what we have manufactured and what was intended to.

26.4.3  Inspection

 Here we examine with the help of SQC techniques whether the manufactured goods are within the specified limits or whether there is any necessity to widen the specifications or not.  So SQC tells us as to what are the capabilities of the production process.

Therefore statistical quality control is considered as a kit of tools, which may influence decisions, related to the functions of specification, production or inspection.  The effective use of SQC generally requires cooperation among those responsible for these three different functions or decisions at a higher level than any one of them.  For this reason, the techniques should be understood at a management level that encompasses all the three functions.

The Techie Talkers

Statistical Quality Control: Importance, Application, Advantages and Disadvantages, Source of Quality Variation – Chance and Assignable Cause

Effective Statistical QC involves identifying the sources of variation, distinguishing between common and assignable causes, and implementing appropriate control measures to minimize undesirable variations. This helps maintain consistent product quality and enables organizations to meet customer expectations.

In this blog we are going to discuss about Statistical Quality Control: Importance, Application, Advantages and Disadvantages, Source of Quality Variation – Chance and Assignable Cause in detail.

Process Control

Acceptance sampling (product control), importance of sqc, application of sqc, disadvantages, advantages and disadvantages table, chance (inherent) cause, assignable causes, measurement variation, environmental factors, material variability, human factors, equipment and machinery, process changes, external factors, faqs from statistical quality control: importance, application, advantages and disadvantages, source of quality variation – chance and assignable cause, statistical quality control.

Statistical Quality Control (SQC) is a set of techniques used to monitor, control, and improve the quality of products and processes. It involves collecting and analyzing data to make informed decisions about the quality of products. SQC is important as it ensures that products meet the desired quality standards and customer expectations.

Types of SQC

SQC can be subdivided into two general areas:

Process control refers to a set of procedures adopted to assess, maintain, and enhance quality standards at various stages of the manufacturing process. It involves continuous monitoring and adjustment of the production process to ensure that products meet desired quality levels. Process control aims to identify and rectify deviations from the set quality standards in real time.

Acceptance sampling is a method employed to assess the quality of an entire lot or batch of products by inspecting a smaller, randomly selected sample. The quality of the entire lot is inferred from the findings of this sample inspection. Acceptance sampling is particularly useful when inspecting large quantities of items is impractical or time-consuming.

  • Error Detection : SQC helps in detecting errors, defects, or deviations from the desired quality standards. By analyzing data, companies can identify issues and take corrective actions.
  • Consistent Quality : SQC aims to achieve consistent and uniform quality in products and processes. It reduces variations and ensures products meet specifications.
  • Customer Satisfaction : High-quality products lead to satisfied customers. SQC helps in delivering products that consistently meet or exceed customer expectations.
  • Cost Savings : By identifying defects early in the production process, SQC reduces rework costs and waste. This results in cost savings.
  • Efficiency Improvement : SQC identifies areas for process improvement. It helps in optimizing processes, leading to better efficiency and productivity.
  • Informed Decision Making : Data obtained from SQC enables data-driven decision-making. It provides insights for making strategic choices to enhance quality.

SQC finds applications across industries:

  • Manufacturing : SQC monitors production processes, detects defects, and ensures products meet quality standards.
  • Healthcare : It’s used to monitor patient outcomes, track medical errors, and maintain accurate medical tests.
  • Financial Sector : SQC monitors financial transactions, identifies anomalies, and ensures accurate records.
  • Software Development : In software engineering, SQC ensures the quality of software products by tracking defects and process deviations.

Advantages and Disadvantages of SQC

  • Enhanced Quality : SQC maintains high-quality products by detecting defects early.
  • Cost Reduction : It reduces rework costs and waste by identifying issues at an early stage.
  • Increased Efficiency : SQC optimizes processes, saving time and resources.
  • Customer Satisfaction : High-quality products lead to happy customers.
  • Complexity : Implementing SQC requires expertise in statistical analysis.
  • Resource Intensive : Collecting and analyzing data can be time and resource-consuming.
  • Resistance to Change : Employees might resist process changes required for implementing SQC.
  • Overemphasis on Data : Focusing solely on statistical data might overlook qualitative aspects.

Sources of Quality Variations

sources-of-quality-variations

Common sources of Quality Variations

Some common sources of quality variation include:

These are inherent to the process and are present even when the process is stable and well-controlled. They result from various factors like minor differences in materials, equipment, environmental conditions, or human skills. Common cause variations typically follow a predictable pattern and are quantified through statistical methods.

These are specific, identifiable factors that lead to unusual or unexpected variations in the process. Assignable causes can include factors like equipment malfunction, operator errors, changes in process conditions, or external disturbances. They are often non-random and result in unpredictable patterns in the data.

Measurement error or variability in data collection methods can lead to variations that are unrelated to the actual process. Inaccuracies in measurement instruments, human errors in recording data, and inconsistencies in measurement procedures can contribute to measurement variation.

Changes in temperature, humidity, lighting, and other environmental conditions can impact process outcomes, leading to variations in product quality.

Variations in raw materials or components used in production can directly affect the quality of the final product. Differences in material properties, composition, or characteristics can lead to variations.

Differences in operator skills, training, and experience can introduce variations in how processes are executed. Human errors or inconsistencies in performing tasks can lead to deviations from expected outcomes.

Variation in machinery performance, maintenance, calibration, or wear and tear can result in fluctuations in product quality.

Introducing changes to processes, equipment, or procedures can lead to variations in quality outcomes until the changes are stabilized and optimized.

External factors such as market demand, supply chain disruptions, regulatory changes, or economic conditions can impact the stability and performance of processes.

What is Statistical Quality Control (SQC)?

Statistical Quality Control (SQC) involves a collection of techniques used to monitor, control, and enhance the quality of products and processes through the collection and analysis of data.

What are the common sources of quality variations?

Common sources include Chance (Inherent) Causes, Assignable Causes, Measurement Variation, Environmental Factors, Material Variability, Human Factors, Equipment and Machinery, Process Changes, and External Factors.

What are Assignable Causes of quality variation?

Assignable Causes are specific factors that result in unexpected variations in the process, often causing non-random and unpredictable patterns.

What are External Factors and how do they influence quality variations?

External Factors, such as market demand, supply chain disruptions, regulatory changes, or economic conditions, can impact the stability and performance of processes.

What is Material Variability and how do they affect quality?

Material Variability refers to the differences found in raw materials or components used in production, which can directly impact the quality of the final product.

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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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Volume 8 Supplement 1

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

  • Proceedings
  • Open access
  • Published: 19 April 2013

Understanding and managing variation: three different perspectives

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

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

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

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

Clinical researcher perspective

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

Healthcare manager perspective

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

Patient perspective

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

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

Recommendations

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

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

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

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

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

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Neuhauser D, Diaz M: Quality improvement research: are randomised trials necessary?. Qual Saf Health Care. 2007, 16: 77-80. 10.1136/qshc.2006.021584.

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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

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assignable cause in quality control

How to Identify Causes of Variation in Statistical Process Control

March 31, 2022.

Tiffany Donica

Tiffany M. Donica

line

Statistical Process Control (SPC) is an industry-standard procedure that utilizes statistical techniques during the manufacturing process. Managers using SPC can access quality data during manufacturing in real-time and plot data on a graph with predetermined control limits. The capacity of the process determines  control limits , and the client’s needs determine specification limits. By implementing SPC, manufacturers use quality data to record and predict deviations in the production environment . Data are plotted on a graph, incorporating factors like control limits (natural process limits) and specification limits (requirements determined by the corporate). When recorded data falls within control limits, it indicates everything is operating correctly.

SPC helps manufacturers  address deviations to reduce defects  and waste from the production line and meet customer expectations. The goal is to minimize rework, scrap, or the recall of one or several batches due to customer dissatisfaction. The customer doesn’t receive the rejected product, but the manufacturer has wasted materials, overhead, and operator hours. SPC proactively   responds to issues, resulting in more minor and cost-effective course corrections. While variation is unavoidable, it remains the focus as manufacturers target quality.

What Are The Common Causes of Variation in SPC?

The causes of variation in statistical process control are generally divided into two categories: common cause and special cause. Common cause variation is always present in processes to some degree. While manufacturers can target continuous improvement to reduce common cause variation, they cannot eliminate it since no process can be perfect. Common cause variation is sometimes called chance cause, and manufacturers can use statistical methods to understand their origins better. Problems of overcorrection arise when manufacturers misdiagnose common cause variation as special cause variation. Manufacturers may also mistakenly hold employees responsible for variation over which they have no control by assuming special cause variation rather than common cause variation.

What Are Special Causes of Variation in SPC?

Special cause variation is, as the term implies, special.  Unusual circumstances in the process  create variation. Manufacturers manage special cause variation by identifying and locating the genesis of the variation. Just as manufacturers can improperly manage common cause variation by assuming it to be special cause, so can misinterpreting special cause variation as common cause create waste and rework. Also called assignable cause variation, special cause variation is challenging to predict with statistical methods alone. It can be challenging to detect special cause variation when the issues are minor, as the ‘noise’ of common cause variation can muffle special cause variation. However, the goal is always to target special cause variation as quickly as possible to eliminate it.

How To Efficiently Monitor and Address SPC Variation with Software?

When manufacturers implement SPC, control charts help differentiate between common cause and special cause variation and establish a baseline for common cause variation.  Implementing SPC software  can create actionable control charts with real-time data that allows manufacturers to more efficiently identify stability issues and eliminate special cause variation much sooner. A robust SPC software platform can uncover root causes of variation so manufacturers can apply the appropriate corrective actions. The causes of variation in statistical process control are addressed before costly waste occurs and before manufacturers deliver product to customers.

If you are interested in reducing waste and increasing efficiency using SPC software, check out  SafetyChain’s on-demand demo .

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Qsutra

Statistical Process Control (SPC)

Introduction.

In our daily life, we utilize a variety of products and services from different outlets. We use products such as mobile, electrical bulbs, clothes, etc. and use different types of services such as health care, transportation, consulting, etc. All these services and products should attain certain specifications when we use it, whether it can be good or bad. We are on the tough competitive world and so the main aim of the manufacturer or provider is to achieve quality assurance where it can meet the customer expectations.

In such situations, we require a tool or technique through which we can control the process. This technique is known as statistical process control. For understanding SPC, first of all, we should understand the concept of process in quality control. A process is a series of operations or actions that transforms input to output.

SPC

What is Statistical Process Control?

SPC is a method which is used for understanding and monitoring the process by collecting data on quality characteristics periodically from the process, analyzing them and taking suitable actions whenever there is a difference between actual quality and the specifications or standard. It is a decision-making tool and widely used in almost all manufacturing processes for achieving process stability to continuous improvements in product quality.

Brief History of the Origin of SPC

Walter_A_Shewhart

During the 1920s , Walter A. Shewhart discovered a way to distinguish between common and special causes of variation in a process. This lead to an invention of the widely known method as statistical process control (SPC) . He pioneered the use of statistical techniques for monitoring and controlling quality. Bell Labs wanted to economically monitor and control the variation in the quality of components and finished products. He recognized that inspecting and rejecting or reworking product was not the most economical way to produce a high-quality product. He demonstrated that monitoring and controlling variation throughout production was the more efficient and economical way.

Shewhart developed a visual tool for monitoring process variation, which came to be known as the control chart or the Shewhart con­trol chart and the concept of a state of statistical control in 1924 at Bell Laboratories.

He has defined chance and assignable causes as the two sources of quality variation. A process that is operating with the only chance cause of variation is said to be in statistical control. A process that is operating with the assignable cause of variation is said to be out of control. The underlying concept of the Shewhart chart is to construct its limits based on variation allowable as it is in – control state and monitor the quality of the product produced.

Bell Labs was widely recognized as the ‘international standard for quality’ by the 1930s, due to the large applications of Shewhart’s techniques in the field of telecommunications. During this period, many initiatives were done by conducting extraordinary research in statistical methods to control and improve process variation. This leads to improve product quality in a great way.

His work was summarized in his books titled “Economic Control of Quality of Manufactured Product” (1931) and “Statistical Method from the Viewpoint of Quality Control” (1939) .

Image – Referred from leansixsigmadefinition.com

Breakthrough in the evolution of SPC

W_Edwards_Deming

SPC was pioneered by Walter A. Shewhart in the 1920s. W. Edwards Deming applied SPC methods in the United States during World War II, to  improve quality in the manufacture of weapons and other important products needed during the war period. After the devastating defeat of Japan in World War II, the United States led the Allies in the occupation and rehabilitation of the Japanese state. In 1947, Deming was involved in early planning for the 1951 Japanese Census. The Allied powers were occupying Japan, and he was asked by the United States Department of the Army to assist with the census. Deming was also instrumental in introducing SPC methods to Japanese industry after the war had ended.

Deming’s mastered the Shewhart’s ideas by implementing it to Japanese industry from 1950 onwards. He developed and added some of his techniques to Shewhart’s methodology. Later he named as the ‘Shewhart cycle’ . Deming’s approach to quality management results in continuous improvement of the production process to achieve conformance to specifications and reduce variability. He identifies two primary sources of process improvement: eliminating common causes of quality problems, such as poor product design and insufficient employee training, and eliminating special causes, such as specific equipment or an operator.

He was widely known for his work contribution for Japanese industry and for the new development era. He received an invitation from the Japanese Union of Scientists and Engineers (JUSE) and worked as an expert to teach statistical control. He trained hundreds of engineers, managers, and  scholars in SPC and concepts of quality.

During the 1960s and 1970s, SPC grew rapidly in Japan and was a successful in quality improvement goals. Later other countries started implementing SPC in their process.

His work was summarized in his books titled “ Out of the Crisis” (1982 – 1986) and “ The New Economics for Industry, Government, Education” (1993) , which includes his System of Profound Knowledge and the 14 Points for Management.

Image – Referred from census.gov

Statistical process control also termed as SPC is a statistical method used to monitor, control and improve processes by eliminating variation from industrial, actuarial, service and many other processes. Here we can determine if an improvement is actually happening and also use them to predict whether it is statistically capable to meet the specific target or not. The main aim of using SPC is to understand where the focus of works needs to be done in order to make a difference. It has now been incorporated by organizations around the world as a primary tool to improve product quality by reducing process variation.

During the initial phase, the SPC was applied only on manufacturing industries for quality improvement and so on. As time evolves by, it was started applying on service industry such as airlines, hospitals, insurance companies, etc. Now on this advanced age of science and information technology, it has started applying on big data analytics to artificial intelligence and much more to advance.  

Methodology

SPC involves following phases of activity –

  • Collection of data from a process.
  • Identification of causes and to eliminate it.
  • Track process variation.
  • Diagnosing the deviated process.
  • Implementing corrective action.

(We use basic quality tools on these phases)

How many Types of variation are there in a process?

SPC is implemented in industry to detect a process variation and to eliminate it for better quality assurance. By monitoring the performance of a process, we can detect trends or changes in the process before they produce non-conforming product and scrap. [By reducing variation]

Variation can be divided as common cause variation and special cause variation.

  • Chance causes are also known as random or natural or common causes . It is due to the natural variation of the process; i.e. Variation due to the way the process was designed and we cannot identify. For example, the fuel efficiency of machine varies slightly; the diameter of a bottle cap varies slightly and so on. (Statistically in control)
  •  Assignable causes are also known as special or non-random or unnatural causes. Causes can be identified and eliminated – poor employee training, equipment nonfunctional, etc. An example of special cause variation is the variation that might result if someone untrained is allowed to work in the process.  (Out of control) 

When to use SPC?

  • To have an overall glimpse of a process.
  • Monitoring a process to check whether it is under control or out of control.
  • To track variation and to eliminate it from a process.
  • Improvement in process capability aspects.
  • To increase production by reducing scrap, rework and inspection cost.

What are the benefits of SPC?

  • Early detection of variation in a process.
  • Establish a consistent level of quality.
  • Continuous improvement in a process by reducing variation.
  • Helps in decision making by giving the insights of process.
  • Reduce or eliminate the need for inspection during the supply chain.
  • Lower investment because of process improvements.
  • It provides real time analysis of a process and so we can focus on areas needed for improvement.
  • Efficiency in data entry, analysis and reporting.

What is Process capability analysis?

It is one of the primary tools in SPC. Suppose in a manufacturing process or any process, we often required information about the process w.r.t its performance or capability.  Basically, it refers to the capability of a process to meet customer requirements or industrial standards on a consistent basis.

Measures of Process Capability – Process capability can be measured by the following methods.

  • Process capability Ratio (C p ) – It is often described as the capability of a process when the process data is centred and specification limits are known.

Cp

USL -> Upper specification limit

LSL -> Lower specification limit

σ  -> Process standard deviation

  • Process capability index (Cpk) – It is described as the capability of a process when the process data is not centered and only one of the specification limits are known.

Cpk

Some important considerations

  •  When Cp=Cpk –> process is centered at the midpoint of specifications.
  • When Cp>Cpk –> process mean is nearer to one specification limit or the other.
  • When Cp< 0 –> process mean lies outside the limit.

In a piston manufacturing industry, quality engineers want to assess the process capability. They collect 25 subgroups of five piston rings and measure the diameters. The specification limits for piston ring diameter are 74.0 mm ± 0.05 mm.

process_capability

Interpretation

All the measurements are within the specification limits. The process is on target and the measurements are approximately centred between the specification limits.

What are Control limits and Specification limits?

Control limits – Control limits describe the behaviour of a process which operates in a normal condition. It is basically a horizontal lines drawn on a control chart to examines the outlook of a process. It consists of UCL (Upper control limit), CL (Control limit) and LCL (Lower control limit). If the points lie beyond the limits, then there is an occurrence of a special cause of variation and henceforth.

Specification limits – Specification limits are the values on which the process should give a response within the range. It is based on customer requirements. It can be a plot in a histogram and consists of USL (Upper specification limit) and LSL (Lower specification limit).

Control limits reflect the real capability of a process whereas specification limits reflect the requirement of a customer. A process under control may not deliver the products under the given specifications.  

What are the Challenges we face while implementing SPC?

In this competitive world, every industry wants to be better than others and to achieve the highest level of success. Suppose it can be in the quality field, continuous improvement in a process, efficient productivity and so on. To achieve this level of success, SPC plays an enormous role in a company and there are some of the challenges one may face while implementing SPC.

Some of them are:-

  • Lack of effective training – Training is an important factor for the successful implementation of SPC. Proper training should be given to all the employees who work on a ground level to topmost level in a process.
  • Lack of basic statistics knowledge – One should have basic knowledge about statistics. So they can relate the background of SPC methods. Suppose if they were using the histogram in a process then they should have basic knowledge about it.
  • Responsibilities should be properly defined – Starting from operators to engineers; everyone should have a clear picture of their responsibility in a process. Engineers should have the basic statistical concepts in SPC whereas operators should be good in measurement and plotting it.
  • Management immense support – Elite members of a company should encourage all the employees in all the levels. Management should give time to implement SPC in a proper way. They should never carry away with the time and cost it took to implement it. And nevertheless, wait for its result. Hard work always paid off.

Where can we apply Statistical Process Control?

assignable cause in quality control

Some of them are discussed below.

  • DMAIC – It is a well-known Six Sigma methodology and focused on improving the process. DMAIC stands for Define Measure Analyze Improve and Control. SPC is widely used in Measure, Analyze and Control phases. During the Measure phase, it is used to set the process baseline by doing control chart analysis and Capability analyses are done to check the capability of a process to meet specifications. During the Control phase, it is used to monitor and improve the process.

assignable cause in quality control

To enhance success in lean manufacturing , six sigma and lean six sigma projects, SPC has to be properly used. Apart from these scenarios, we can use SPC tool individually to check the process capability for continuous improvement. Also, prove useful while conducting DOE in a process.

SPC in Total Quality Management (TQM)

In this competitive world, every industry has to compete with each other in terms of quality, production, revenue and so on. The main terminology which satisfies customer needs is “ quality ” which defines the company standard and values.

Within an organization, when TQM has implemented it helps for continuous improvement of process and gives consistently high-quality products. Total Quality Management is defined by the   Deming Prize Committee  as

  • set of systematic activities
  • carried out by the entire organization to effectively and efficiently
  • achieve the organization’s objectives
  • so as to provide products and services
  • with a level of quality
  • that satisfies customers ,
  • at the appropriate time and price.

Statistical process technique (SPC) is a method used in TQM framework for detecting and reducing variation in a process. It is a very powerful method to detect, control, analyze and improve the process by reducing the source of variation. Hence SPC contributes a lot in TQM goal of continuous improvements.

What kind of Organisations can benefit with SPC?

Statistical process control also termed as SPC is a statistical method used to monitor, control and improve processes by eliminating variation from industrial, actuarial, service and many other processes. When an organization first uses SPC, the main objective is to ensure that the process is stable and capable of producing product or services to the expectations. It is widely known as a decision-making tool.

During the initial phase of SPC, it was used in discrete manufacturing (Telecom, Defense, Automobiles, etc.) and later it was applied to process manufacturing (Glass, Pharmaceutical, Beverage, etc.) too.  It is widely used in almost all manufacturing processes for achieving process stability to continuous improvements in product quality.

But in recent years, SPC has implemented in various service sectors like healthcare, financial institutions, call centres, hotels, etc. The service industry has been an integral part of our life. They offer services which are very essential to us – starting from health care, airlines, call centres, banks and so on. For e.g. we often travel to various destinations for official work on holidays by air and stay in a hotel. When we travel by particular airline and didn’t get the essential services – Will we travel again from that airline? Similarly, when we stay at a particular hotel and didn’t get the required services – Will we stay again at that hotel? Our answer will be no, never. So maintaining healthy growth and improving the service quality will have significant impacts on us. And also excellent service quality is noted as a major factor to make a profit in the service sector.

Some of the examples are:-

  • Healthcare – While implementing SPC we can improve patient care by reducing waiting time, and monitoring clinical trials, operational performance and so on.
  • Banking – While implementing SPC we can improve customer service by reducing waiting time, % errors in customer profile, etc.
  • Customer service – While implementing SPC we can improve customer service by reducing the call waiting time, monitoring the response calls, identification of a process whether it is under time limit or not, etc.

In a can-filling process, the quality engineer wants to know whether the process is in control or not. Each hour, they collect a subgroup of 10 cans. To minimize the variation (within subgroup), they collect the cans for a given subgroup in a short period of time.  

They create an X-bar chart to monitor the weight of the cans.

xbarchart_canweight

With reference from the X-bar chart, one point is out-of-control and they conclude that the process is not stable. Hence the process should be improved.

Attend our Training Program, to know more about Statistics and Statistical Software. We conduct various training programs – Statistical Training and Minitab Software Training. Some of the Statistical training certified courses are Predictive Analytics Masterclass,   Essential Statistics For Business Analytics,   SPC Masterclass, DOE Masterclass, etc. (Basic to Advanced Level). Some of the Minitab software training certified courses are Minitab Essentials, Statistical Tools for Pharmaceuticals, Statistical Quality Analysis & Factorial Designs, etc. (Basic to Advanced Level).

We also provide a wide range of Business Analytics Solutions and Business Consulting Services for Organisations to make data-driven decisions and thus enhance their decision support systems.

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assignable cause in quality control

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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FARS2 Deficiency Causes Cardiomyopathy by Disrupting Mitochondrial Homeostasis and the Mitochondrial Quality Control System

Affiliations.

  • 1 Department of Biochemistry and Molecular Biology, Shaanxi Provincial Key Laboratory of Clinical Genetics, Air Force Medical University, Xi'an, China. (B.L., X.C., T.C., J.Z., Y.L., Y.Y., W.H., M.Z., Y.W.).
  • 2 Department of Neurosciences, Air Force Medical University, Xi'an, China. (F.L.).
  • 3 School of Basic Medicine, Department of Ultrasound, Xijing Hypertrophic Cardiomyopathy Center, Xijing Hospital, Air Force Medical University, Xi'an, China. (B.W., L.L.).
  • 4 Department of Anatomy, Histology and Embryology and K.K. Leung Brain Research Center, Air Force Medical University, Xi'an, China. (K.C.).
  • 5 Department of Clinical Diagnoses, Tangdu Hospital, Air Force Medical University, Xi'an, China. (Y.W.).
  • PMID: 38362779
  • DOI: 10.1161/CIRCULATIONAHA.123.064489

Background: Hypertrophic cardiomyopathy (HCM) is a common heritable myocardiopathy. Although HCM has been reported to be associated with many variants of genes involved in sarcomeric protein biomechanics, pathogenic genes have not been identified in patients with partial HCM. FARS2 (the mitochondrial phenylalanyl-tRNA synthetase), a type of mitochondrial aminoacyl-tRNA synthetase, plays a role in the mitochondrial translation machinery. Several variants of FARS2 have been suggested to cause neurological disorders; however, FARS2-associated diseases involving other organs have not been reported. We identified FARS2 as a potential novel pathogenic gene in cardiomyopathy and investigated its effects on mitochondrial homeostasis and the myocardiopathy phenotype.

Methods: FARS2 variants in patients with HCM were identified using whole-exome sequencing, Sanger sequencing, molecular docking analyses, and cell model investigation. Fars2 conditional mutant (p.R415L) or knockout mice, fars2 -knockdown zebrafish, and Fars2 -knockdown neonatal rat ventricular myocytes were engineered to construct FARS2 deficiency models both in vivo and in vitro. The effects of FARS2 and its role in mitochondrial homeostasis were subsequently evaluated using RNA sequencing and mitochondrial functional analyses. Myocardial tissues from patients were used for further verification.

Results: We identified 7 unreported FARS2 variants in patients with HCM. Heart-specific Fars2 -deficient mice presented cardiac hypertrophy, left ventricular dilation, progressive heart failure accompanied by myocardial and mitochondrial dysfunction, and a short life span. Heterozygous cardiac-specific Fars2 R415L mice displayed a tendency to cardiac hypertrophy at age 4 weeks, accompanied by myocardial dysfunction. In addition, fars2 -knockdown zebrafish presented pericardial edema and heart failure. FARS2 deficiency impaired mitochondrial homeostasis by directly blocking the aminoacylation of mt-tRNA Phe and inhibiting the synthesis of mitochondrial proteins, ultimately contributing to an imbalanced mitochondrial quality control system by accelerating mitochondrial hyperfragmentation and disrupting mitochondrion-related autophagy. Interfering with the mitochondrial quality control system using adeno-associated virus 9 or specific inhibitors mitigated the cardiac and mitochondrial dysfunction triggered by FARS2 deficiency by restoring mitochondrial homeostasis.

Conclusions: Our findings unveil the previously unrecognized role of FARS2 in heart and mitochondrial homeostasis. This study may provide new insights into the molecular diagnosis and prevention of heritable cardiomyopathy as well as therapeutic options for FARS2-associated cardiomyopathy.

Keywords: autophagy; cardiomyopathies; heart failure; ligases; mitochondria; mitochondrial dynamics.

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  2. Statistical Quality Control

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  3. Quality Control Data Representation Tools

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  6. Chance and Assignable Causes of Quality Variation

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COMMENTS

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  2. Assignable Cause

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  6. Common cause and special cause (statistics)

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  15. Walter A Shewhart

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  19. Understanding and managing variation: three different perspectives

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