assignable cause nedir

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atanabilir neden' nedir - assignable cause 🧑‍🔧

Bu soruya kısa bir cevap…

Bir süreçte tahmin edilen sınırların dışında varyasyona neden olan ve dolayısıyla kaliteyi değiştiren herhangi bir tanımlanabilir faktör.

Genellikle benzersizdir, ancak süreçte güçlü rahatsızlıklar üretecek kadar büyüktür. Bir veya ara sıra, ancak düzensiz periyotlarda meydana gelen bir olaydır.

Atanabilir neden, bir kontrol grafiğinin tanımlamak için tasarlandığı iki varyasyon türünden biridir.

Tahsis Edilebilir Nedenler, örneğin bir parçanın aşınmasından kaynaklanan bir makine arızası, kaliteli plastikte çok belirgin bir değişiklik vb. Gibi belirlenebilen ve keşfedilip ortadan kaldırılması gereken nedenlerdir. Bunlar, sürecin istendiği gibi çalışmamasına neden olur ve bu nedenle nedeni ortadan kaldırmak ve işlemi doğru işleme döndürmek gerekir.

Atanamaz veya Ortak Neden Varyasyonu: Yaygın nedenler, süreçteki her günün olağan varyasyonuna katkıda bulunan sürecin girdileri ve koşullarıdır (sürecin doğasında bulunan doğal ve rastgele varyasyon). Sürecin bir parçasıdırlar ve kendileri de değiştiği için çıktının çeşitliliğine katkıda bulunurlar.

İnsanları meraklandıran nedir biliyor musunuz? İşte birkaç soru ve cevap:

atanabilir neden

Atanabilir varyasyonların nedenleri nelerdir..., kontrol grafiğindeki atanabilir neden nedir..., araştırmada atanabilir neden nedir....

Umarım bu gönderiyi beğenmişsinizdir.

Kendime not: Makale (ilk taslak) Tamam.

Aradığınızı bulamadınız mı?

Selam! Ben Gelson Luz, makine mühendisi, kaynak uzmanı ve şu konularda tutkuluyum:

Malzemeler, teknoloji ve köpekler.

Mühendislik hakkında en iyi öğrenme blogu olmak için bu blogu oluşturuyorum!

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Assignable Cause

Last updated by Jeff Hajek on December 22, 2020

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

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

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

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

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

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

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

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Assignable causes of variation have an advantage (high proportion, domination) in many known causes of routine variability. For this reason, it is worth trying to identify the assignable cause of variation , in such a way that its impact on the process can be eliminated, of course, assuming that project managers or members are fully aware of the assignable cause of variation. Assignable causes of variation are the result of events that are not part of the normal process. Examples of assignable causes for variability are (T. Kasse, s.237):

• incorrectly trained people
• broken tools
• failure to comply with the process
• 1 Identify data of assignable causes
• 2 Types of data for assignable causes
• 3 Determining the source of assignable causes of variation in an unstable process
• 4 Examples of Assignable cause
• 5 Advantages of Assignable cause
• 6 Limitations of Assignable cause
• 7 Other approaches related to Assignable cause
• 8 References

Identify data of assignable causes

The first step you need to take when planning data collection for assignable causes is to identify them and explain your goals . This step is to ensure that the assignable causes data that the project team gathers provides the answers that are needed to carry out the ' process improvement ' project efficiently and successfully. The characteristics that are desirable and most relevant for an assignable causes are for example: relevant, representative, sufficient. In the planning process for collecting data on assignable causes, the project team should draw and mark a chart that will provide the findings before actual data collection begins. This step gives the project team an indication of what data that can be assigned is needed (A. van Aartsengel, S Kurtoglu, s.464).

Types of data for assignable causes

There are two types of data for assignable causes, qualitative and quantitative . Qualitative data is obtained from deseriography resulting from observations or measures of different types of characteristics of the results of the process in terms of narrative words and statements. However, the next group of data, which are quantitative data on assignable causes, are derived from the description of observations or measures of process result characteristics in terms of measurable quantity in which numerical values are used (A. van Aartsengel, S. Kurtoglu, s.464).

Determining the source of assignable causes of variation in an unstable process

If an unstable process occurs then the analyst must identify the sources of assignable cause variation. The source and the cause itself must be investigated and, in most cases, unfortunately also eliminated. Until all such causes are removed, then the actual capacity of the process cannot be determined and the process itself will not work as planned. In some cases, however, assignable cause variability can improve the result, then the process must be redesigned (W. S. Davis, D. C. Yen, s.76). There are two possibilities for making the wrong decision, which concerns the appearance of assignable cause variations: there is no such reason (or it is incorrectly assessed) or it is not detected (N. Möller, S. O. Hansson, J. E. Holmberg, C. Rollenhagen, s.339).

Examples of Assignable cause

• Poorly designed process : A poorly designed process can lead to variation due to the inconsistency in the way the process is operated. For example, if a process requires a certain step to be done in a specific order, but that order is not followed, this can lead to variation in the results of the process.
• Human error : Human error is another common cause of variation. Examples include incorrect data entry, incorrect calculations, incorrect measurements, incorrect assembly, and incorrect operation of machinery.
• Poor quality materials : Poor quality materials can also lead to variation. For example, if a process requires a certain grade of material that is not provided, this can lead to variation in the results of the process.
• Changes in external conditions : Changes in external conditions, such as temperature or humidity, can also cause variation in the results of a process.
• Equipment malfunctions : Equipment malfunctions can also lead to variation. Examples include mechanical problems, electrical problems, and computer software problems.

Advantages of Assignable cause

One advantage of identifying the assignable causes of variation is that it can help to eliminate their impact on the process. Some of these advantages include:

• Improved product quality : By identifying and eliminating the assignable cause of variation, product quality will be improved, as it eliminates the source of variability.
• Increased process efficiency : When the assignable cause of variation is identified and removed, the process will run more efficiently, as it will no longer be hampered by the source of variability.
• Reduced costs : By eliminating the assignable cause of variation, the cost associated with the process can be reduced, as it eliminates the need for additional resources and labour.
• Reduced waste : When the assignable cause of variation is identified and removed, the amount of waste produced in the process can be reduced, as there will be less variability in the output.
• Improved customer satisfaction : By improving product quality and reducing waste, customer satisfaction will be increased, as they will receive a higher quality product with less waste.

Limitations of Assignable cause

Despite the advantages of assigning causes of variation, there are also a number of limitations that should be taken into account. These limitations include:

• The difficulty of identifying the exact cause of variation, as there are often multiple potential causes and it is not always clear which is the most significant.
• The fact that some assignable causes of variation are difficult to eliminate or control, such as machine malfunction or human error.
• The costs associated with implementing changes to eliminate assignable causes of variation, such as purchasing new equipment or hiring more personnel.
• The fact that some assignable causes of variation may be outside the scope of the project, such as economic or political factors.

Other approaches related to Assignable cause

One of the approaches related to assignable cause is to identify the sources of variability that could potentially affect the process. These can include changes in the raw material, the process parameters, the environment , the equipment, and the operators.

• Process improvement : By improving the process, the variability caused by the assignable cause can be reduced.
• Control charts : Using control charts to monitor the process performance can help in identifying the assignable causes of variation.
• Design of experiments : Design of experiments (DOE) can be used to identify and quantify the impact of certain parameters on the process performance.
• Statistical Process Control (SPC) : Statistical Process Control (SPC) is a tool used to identify, analyze and control process variation.

In summary, there are several approaches related to assignable cause that can be used to reduce variability in a process. These include process improvement, control charts, design of experiments and Statistical Process Control (SPC). By utilizing these approaches, project managers and members can identify and eliminate the assignable cause of variation in a process.

• Davis W. S., Yen D. C. (2019)., The Information System Consultant's Handbook: Systems Analysis and Design , CRC Press, New York
• Kasse T. (2004)., Practical Insight Into CMMI , Artech House, London
• Möller N., Hansson S. O., Holmberg J. E., Rollenhagen C. (2018)., Handbook of Safety Principles , John Wiley & Sons, Hoboken
• Van Aartsengel A., Kurtoglu S. (2013)., Handbook on Continuous Improvement Transformation: The Lean Six Sigma Framework and Systematic Methodology for Implementation , Springer Science & Business Media, New York

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SPC Glossary

Your quick reference to statistical process control for manufacturing quality management systems.

• SPC GLOSSARY: A-B
• SPC GLOSSARY: C
• SPC GLOSSARY: D,E,F,G
• SPC GLOSSARY: H,I,J,K
• SPC GLOSSARY: L,M,N,O
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Quality Management System Glossary

Every manufacturing quality management professional who uses statistical process control (SPC) runs into questions occasionally. That’s why we’ve compiled this SPC glossary to serve as a quick reference when you’re looking for an answer, need to explain a concept to a colleague—or just can’t remember that term that’s on the tip of your tongue.

Feel free to bookmark this reference so you always have the definition you’re looking for—and be sure to visit our other SPC reference resources.

WHAT IS STATISTICAL PROCESS CONTROL? Learn the definition of SPC and what this industry-standard methodology is used for.

SPC 101  Dig in deeper to understand why and how SPC is used in manufacturing quality control.

DEFINITIVE GUIDE TO SPC CHARTS Learn why and how to use different control charts, see examples, and explore use cases.

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

Published: November 7, 2022 by iSixSigma Staff

What is Common Cause Variation?

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

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

The Benefits of Common Cause Variations

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

How to Identify Common Cause Variation

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

What is Special Cause Variation?

Special cause variations are unexpected glitches that occur that significantly affect a process. It is also known as “assignable cause.” These variations are unusual, unquantifiable, and are variations that have not been observed previously, so they cannot be planned for and accounted for.

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

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

The Benefits of Special Cause Variation

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

How to Identify Special Cause Variation

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

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

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

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

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

Choosing Between Common Cause and Special Cause: Real World Scenarios

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

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

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

Summary/Conclusion

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

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X Bar S Control Chart

Posted by Ted Hessing

What are X Bar S Control Charts?

X Bar S charts often use  control charts  to examine the process mean and standard deviation over time. These charts are used when the subgroups have large sample sizes. Conversely, the S charts provide a better understanding of the spread of subgroup data than the range.

X bar S charts are also similar to  X Bar R Control charts . The basic difference is that X bar S charts plot the subgroup standard deviation, whereas R charts plot the subgroup range.

Selection of an appropriate control chart is very important in  control chart  mapping, otherwise ended up with inaccurate  control limits  for the data.

Manually, it is very easy to compute the X Bar R Control chart, whereas the sigma chart may be difficult due to tedious calculations and large sample sizes. With a large sample size in the subgroup, the standard deviation is a better measure of variation than the range because it considers all the data, not just minimum and maximum values.

It is actually two plots to monitor the process mean and the process range (as described by standard deviation) over time. Additionally, it is an example of statistical process control. These combination charts help to understand the stability of processes and detect the presence of  special cause variation .

The cumulative sum ( CUSUM ) and the exponentially weighted moving average ( EWMA ) charts also monitor the mean of the process. But the basic difference is that, unlike the X bar chart, they consider the previous value means at each point. Moreover, these charts are considered a reliable estimate when a correct standard deviation exists.

X Bar S Control Chart Definitions

X-bar chart:  The mean or average change in the process over time from subgroup values. The control limits on the X-Bar consider the sample’s mean and center.

S-chart:  The standard deviation of the process over time from subgroups values. This monitors the process  standard deviation  (as approximated by the sample moving range)

Use X Bar S Control Charts When:

• The  sampling  procedure is the same for each sample and is carried out consistently.
• When the data is assumed to be  normally distributed.
• The X bar S chart is to be used when rationally collecting measurements in a subgroup size is more than 10.
• X Bar R chart is to be considered if the subgroup size is between two and ten observations. For the I-MR chart, the subgroup size is one only.
• When the collected data is continuous (i.e., Length, Weight), etc. and captures in time order.

How to Interpret the X Bar S Control Charts

• To correctly interpret the X bar S chart, always examine the S chart first.
• The X bar chart control limits are derived from the values of S bar (average standard deviation). If the values are out of control in the S chart, the X bar chart control limits are inaccurate.
• If the points are out of control in the S chart, then stop the process. Identify the special cause and address the issue. Remove those subgroups from the calculations.
• Once the S chart is in control, then review the X bar chart. Afterward, you should interpret the points against the control limits.
• All the points are to be interpreted against the control limits but not specification limits.
• If any point is out of control in X bar chat. Identify the special cause and address the issue.

Steps to follow for X bar S chart

Objective of the chart and subgroup size.

• Determine the objective of the chart and choose the important variables
• Choose the appropriate subgroup size and the sampling frequency
• Shewhart suggested collecting 20 to 25 sets of samples with a subgroup size of 10 and above

Note: To demonstrate an example, we just took subgroup size 4 in the below example. But, it is always recommended to take ten and above for the X bar S chart.

Example:  A packing organization monitoring the performance of a packing machine, each container should weigh 35 lb, during the Measure phase, the project team performed the process capability study and identified that the process is not capable(less than one sigma). In Analyze phase collected 12 sets of container weights with a subgroup size of 4.

Compute X bar and S values

• Measure the average of each subgroup i.e X bar, then compute the grand average of all X bar values. This will be the center line for the X bar chart.
• Compute the standard deviation of each subgroup, then measure grand averages of all standard values ie S bar, and this will be the center line for the S chart.

Determine the Control Limits

The first set of subgroups to determine the process mean and standard deviation. These values are to be considered for the creation of control limits for both the standard deviation and mean of each subgroup.

The process is to be in control in the early phase of production.  Special causes are to be identified if any of the points are out of control during the initial phase. Also, the subgroup has to be removed for calculation.

Sometimes in the initial phase, it would also be good to have a few points out of control on the x-bar portion. Otherwise, if all the values are within the control limits may be because of a slope in the measurement system, the team won’t focus on it. Identify appropriate  Measurement System Evaluation (MSE).

• X is the individual value (data)
• n is the sample size
• X bar is the average of reading in a sample
• S is the standard deviation
• S bar is the average of all the standard deviations.
• UCL is the Upper control limit
• LCL is Lower control limit

The below control chart constants are approximate values to measure the control limits for the X bar S chart and other control charts based on subgroup size.

• Refer to  common factors  for various control charts

Example cont:  In the above example n=4

Interpret X bar and S chart

• Plot both the X bar and S chart and identify the assignable causes

Example Cont:  Use the above values and plot the X bar and Sigma chart

From both the X bar and S charts, it is clearly evident that most of the values are out of control. Hence the process is not stable

Monitor the process after improvement

Once the process stabilizes and control limits are in place, monitor the process performance over time.

Example cont:  Control Phase – Once the process is improved and matured, the team identified the X bar S chart as one of the control methods in the  Control plan  to monitor the process performance over the time period.

The following are the measurement values in the Control phase of the project:

Compute X bar and Sigma

Find the control limits

From the both X bar and S charts it is clearly evident that the process is almost stable. During the initial setup of 2nd data set both the S chart and X bar chart values are out of control, the team has to perform the root cause analysis for the special cause and also the process smoothing out from data set number 4. If that continued, the chart would need new control limits from that point.

• Since the S chart is in statistical control, calculate the process standard deviation

• After the process is stabilized, if any point still goes out of control limits, it indicates an assignable cause exists in the process that needs to be addressed. This is an ongoing process to monitor the process performance.

Important notes on X Bar S Control Charts

• A process is “in control” which indicates means of the process is stable, and it can be predictable.
• A process is stable that does not mean it’s a zero-defect process.
• Remember to NEVER put specifications on any kind of  control chart.
• The points on the chart are comprised of averages, not individuals. Specification limits are based on individuals, not averages.
• The operator might tend not to react to a point out of control when the point is within the specification limits.
• X bar S chart helps to avoid unnecessary adjustments in the process

X Bar S Control Chart Videos

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Comments (17)

I am working on a project and not sure how to create the x bar and s chart with the data provided.

Hi Sabrina,

Thanks for reaching out. What’s the problem with the data?

Please help me how I can make evaluation of the results in lab and also how can I evaluate the competence oof operators

Sounds like you want to either do a Designed Experiment or a Hypothesis test.

Hi Mr. Hessing, Thank you for this wonderful explanation about X bar and sigma charts. I have few doubts if you could please explain me: 1. For the C4 factor why the sample 2,3,4,5,6,7,8,9,10 &25 are selected? 2. If the X bar chart show no special cause but in Sigma chart there are special causes. What does it mean for the process? Thank you in advance. Regards Kunchok

I just wanted to know can the LSL for X Bar chart be zero

Hi Adityasaich,

Thanks for reaching out. Before helping you answer I want to draw your attention to a commonly-missed aspect of control charts; LSL is not the same as LCL.

Here’s a good write up:

The UCL or upper control limit and LCL or lower control limit are limits set by your process based on the actual amount of variation of your process. The USL or upper specification limit and LSL or lower specification limit are limits set by your customers requirements. This is the variation that they will accept from your process. (source)

Think about your question again with that understanding. Can the LSL be zero?

Hi Mr. Hessing:

Thank you for this wonderful explanation.

Jorge Zimeri

Glad it’s helpful, Jorge! Thank you for the warm comments.

Hi I want to make X Bar S Control Chart in my hospital for waiting time for (sepsis patient) in Emergency , I collected data for each month as subgroup , but each month I have different number of patients , how I can calculate the UCL & LCL , if the subgroup number changed each month.

Good question, Zobaidi.

I like the guidance suggested here:

In cases like these, it is best to maintain two sets of control charts: one set that monitors each batch, during its course of operation; and a second control chart that plots the average batch parameter, for all batches, as an Individual data value. In this latter control chart, the Moving Range chart is used to monitor the between batch variation.

Hi,thanks for your explanation. May I know, what can I do when the data obtain does not achieve assumption(normality)? After I transform my data using Johnson Transformation,I don’t have idea to continue my x-bar and s chart. Can you please help me to solve this problem? Thanks a lot.

I think you ultimately want to keep trying other types of transformations (eg logarithmic, box cox, etc) until you find something that helps your data approach normality.

In the X Bar R Control Chart article the example has a sample size of 20 and a subgroup size of 4. However, in this article for X Bar S Control Charts the example has a sample size of 12 and a subgroup size of 4. Shouldn’t the subgroup size by greater than or equal to 10 for using X Bar S Control Chart?

You are absolutely right Gabriel Smith, to demonstrate an example, we just took a subgroup of 4 in the example, but it is always recommended to take 10 and above subgroup size for X bar S chart. I have included the note in the article.

Thanks Ramana

Hi, I am confused, because i thought UCL and LCL were 3 std deviations from the center line. Is there a way to compute sigma and multiply it by 3 to get the UCL and LCL, or am i better off just memorizing the formula and using the tables?

Hello Ross Bryant,

Steps to calculate control limits

• First calculate the Center Line. The Center Line equals either the average or median of your data. • Second calculate sigma. The formula for sigma varies depending on the type of data you have. • Third, calculate the sigma lines. These are simply ± 1 sigma, ± 2 sigma and ± 3 sigma from the center line.

+ 3 sigma = Upper Control Limit (UCL) – 3 sigma = Lower Control Limit (LCL)

The formula for sigma depends on the type of data you have: • Is it continuous or discrete? • What is the sample size? • Is the sample size constant?

Each type of data has its own distinct formula for sigma and, therefore, its own type of control chart. Hence we need to refer to the control limits formulas based on the type of data.

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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.

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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.

About the Author

Eshna is a writer at Simplilearn. She has done Masters in Journalism and Mass Communication and is a Gold Medalist in the same. A voracious reader, she has penned several articles in leading national newspapers like TOI, HT and The Telegraph. She loves traveling and photography.

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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.

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.

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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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).

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What is 'assignable cause' 🧑‍🔧

A short answer to this question is…

Any identifiable factor which causes variation in a process outside the predicted limits, thereby altering quality.

It is usually unique, however large enough to produce strong disturbances in the process. It is an event that occurs once or occasionally, but at irregular periods.

Assignable cause is one of the two types of variation a control chart is designed to identify.

Assignable Causes are causes that can be identified and that should be discovered and eliminated, for example, a machine failure due to wear of a part, a very noticeable change in quality plastic, etc. These causes the process to not work as desired and therefore it is necessary to eliminate the cause, and return the process to correct operation.

Non-Assignable or Common Cause Variation: Common causes are the inputs and conditions of the process that contribute to the usual variation of each day in the process (natural and random variation inherent to the process). They are part of the process and contribute to the variation of the output since they themselves also vary.

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Meaning of assignable cause

Assignable cause.

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Meaning and definition of assignable cause :.

Assignable Cause The cause(s) of variation in a process which have a source that is identified, and can be eventually eliminated. [Same as Special Cause]

For the term assignable cause may also exist other definitions and meanings , the meaning and definition indicated above are indicative not be used for medical and legal or special purposes .

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