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Understanding Variation

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Variation is inherent in any process-based project. Hence, the ultimate aim of a Six Sigma project is to eliminate variation and waste through continuous improvement. The main two sources of variation are the assignable causes of variation and the random or common causes of variation. The assignable causes of variations are the result of physical in nature, can result through man, machine, materials, management, methods, procedures, etc., and are generally able to control and eliminate. The random causes of variation are generally an effect of environment and situation specific, and therefore, eliminating complete variation from a process is impossible. The chapter also discusses the necessity of measuring variation and the importance of having a good measurement system in place. The importance of normal distribution in statistical study is emphasized from the process variation point of view. Various measures of variation are also studied to support the understanding of the basics of a processed data.

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Garvin, D.A.: Competing in the Eight Dimensions of Quality. Harvard business Review, Sept–Oct 1987

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Gryna, F.M., Chua, R.C.H., Defeo, J.A.: Juran’s Quality Planning and Analysis for Enterprise Quality, 5th edn. Tata McGraw-Hill, New Delhi (2007)

Montgomory, D.C.: Introduction to Statistical Quality Control. Wiley, India (2003)

Muralidharan, K., Syamsundar, A.: Statistical Methods for Quality, Reliability and Maintainability. PHI Publications, New Delhi (2012)

Oakland, J.S.: Statistical Process Control. Elsevier, New Delhi (2005)

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Muralidharan, K. (2015). Understanding Variation. In: Six Sigma for Organizational Excellence. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2325-2_4

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

Updated: January 14, 2024 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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Volume 8 Supplement 1

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

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Understanding and managing variation: three different perspectives

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

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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.

Article   PubMed Central   PubMed   Google Scholar  

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

Article   PubMed Central   CAS   PubMed   Google Scholar  

Eccles M, Grimshaw J, Campbell M, Ramsay C: Research designs for studies evaluating the effectiveness of change and improvement strategies. Quality and Safety in Health Care. 2003, 12: 47-52. 10.1136/qhc.12.1.47.

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

Article   PubMed   Google Scholar  

Hebert C, Neuhauser D: Improving hypertension care with patient-generated run charts: physician, patient, and management perspectives. Qual Manag Health Care. 2004, 13: 174-177.

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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.

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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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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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, p. 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, p. 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, p. 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, p. 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, p. 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

Author: Anna Jędrzejczyk

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Monday, August 17, 2015

Chance & assignable causes of variation.

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

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