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# Common Cause, Special Cause, Black Noise

Go to solution Solved by Arunesh Ramalingam,

Black Noise

If something is constant, it is most definitely dead. Variation is a natural phenomenon and it is observed as a fluctuation in the output of the machine / system / process. It is most commonly denoted by σ (sigma). Following are the two types of process variations.

Common Cause Variation - This is the random variation and is natural for the process. As name indicates it is “common” to the process. It is the desirable variation in the process. It is also called as within-group variation or inherent variation. Since it is inherent, there is no assignable cause for this variation.

Special Cause Variation / Black Noise - This is the non-random variation and is not part of the process. As name indicates it is “special” to the process. It is also called as Black Noise. Since it is special, there is always an assignable cause for this variation.

An application oriented question on the topic along with responses can be seen below. The best answer was provided by Arunesh Ramalingam on 28th September 2017.

## Question

Q16. How do you differentiate special cause variation (also called Black Noise) from common cause variation? Why is this differentiation important? Explain how misjudging one of these as the other can create problems in the real world.

Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday.

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• Solution

Variation is the fluctuation in a process’s output. Every measured output may not be identically same and we may notice some variation between multiple readings. Statically it is denoted by Standard Deviation (σ) which indicates the spread of each data point in the data set from the mean/average value.

Example: Consider a machine producing 3.000 mm. diameter bolts. But each bolt may not  measure 3.000 mm. diameter exactly. Some can be 2.999 mm., while some can be 3.001 mm. and there are endless possibilities. The spread of the various measurements around the mean (3.000 mm) is called the standard deviation.

Lower the Standard Deviation or Lesser the variation of the diameters indicates that the data points are closer to the mean and the process is better. Aim of a good process design is to minimise this variation.

There are two types of Variations:

 Common Cause Variation Special Cause Variation This is a Random Variation and is natural for the process. As name indicates it is “common” to the process. Other terms : Noise, non-controllable variation, within-group variation, or inherent variation This is a Non - Random Variation or Assignable cause and is not part of the normal process. As name indicates it is “special” to the process and the variation can be assigned to a reason. Though the value of each point cannot be predicted, the range of this variation is predictable. This range is called the process width or the Control limits. This is unanticipated and sporadic. It is completely unpredictable. Common cause variation is introduced by intrinsic variation in the process - by the variation present in People, Information systems, Machines/Equipment, Measurement, Materials and Environment. Special cause variation is introduced by the external parameters such as Operator not available, Computer crash, Power Outage, Machine malfunction. Generally, the process remains in control i.e. within the control limits and no corrective action may be required. If process deviates the control limits, then corrective actions are required. The process goes out of control. Reason of variation should be identified, analyzed and corrected if possible. If unable to correct alternate solutions should be implemented. E.g. The average normal body temperature is generally accepted as 98.6°F (37°C). Per some studies the "normal" body temperature can have a range, from 97°F (36.1°C) to 99°F (37.2°C). So, if the temperature is within this range and the person is otherwise feeling normal, then he may not need any medication E.g. If a person’s temperature goes beyond this range then there are high chances that he has a fever and might need to take medication.

Why they should be differentiated and how misjudging one of these as the other can create problems?

Common Cause Variations may not cause a process to go beyond control limits and so corrective actions may not be required. If corrections are required, then it would be intrinsic to the process like checking on the Manpower, Material, Method, Measurement, Machine, and Environment, shifting the process mean, adjusting the variance and so on. It exists even after "Special Cause" is removed.

Special Cause Variations always cause the process to go out of control. The reason for the variation or “what has changed?” should be identified and analysed. If possible to rectify then it should be corrected else an alternate solution should be implemented.

Mistake 1: Attribute a variation to Special Cause, when it is actually a Common Cause.

Impact – Over-adjustment when not required. If deviation from target is considered due to special cause and the mean is adjusted for the deviation, then the adjustment will become a cause for further deviations and will worsen the situation.

Mistake 2: Attribute a variation to Common Cause, when it is actually a Special Cause.

Impact – Ignoring the variation and not doing anything. A special cause actually shifts the process mean, but this was ignored and no action taken to correct it. This further increases the variability.

Example: Consider a pizza delivery joint located in locality A and catering to locality A & B and running an offer of “delivery in 30 mins or free”.

• Pizza production time: 10 mins
• Delivery Boy travel time to locality A: 10-20 mins. (Common Cause Variation)
• Delivery Boy travel time to locality B: 15-25 mins. (Common Cause Variation)

Issue:  So, some of the deliveries to locality B are free of cost.

Analysis: This is Common Cause Variation.

If the manager considers this as common cause variation, he can either continue with

• a few free deliveries (if it is not heavy on the business) or
• try to improve the pizza production cycle time

But, if the manager commits Mistake 1 (i.e. considers it a Special Cause while it is a Common cause) then he may consider excluding locality B from the offer, which would have a greater impact on the whole business.

Now consider the same scenario but with a new temporary condition:

• The road connecting locality A and B is undergoing renovation and there is frequent traffic delay of 10-15 mins. (Special Cause Variation)

Analysis: This is Special Cause Variation.

If the manager considers this as Special cause variation, he can decide to temporarily

• excluding locality B from the offer (or)
• modify the offer for locality B to “delivery in 45 mins or free” and communicate the valid reason.

But, if the manager commits Mistake 2 (i.e. considers it a Common Cause while it is a Special cause), then he may:

• put in efforts to reduce pizza production time which would not resolve the issues (or)
• land up NOT taking any corrective correction. This could lead to significant increase in pizzas being delivered for free in locality B and unsatisfied customers in locality B
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Understanding Chance causes and Special causes

In our day to day life, we come across several variations and do our own judgement on what we want to consider as a "normal" or "abnormal" variation.

If your son travels from his school to home every day by bicycle, and usually takes 30 minutes to travel the distance, you would not sense anything abnormal if he takes up to 40 minutes on certain days or if he makes it in 25 minutes on some days. However you will be worried if he doesn't turn up even after 50 minutes...  or you might be surprised if he turns up within 10 minutes. This is because you have set in your mind a certain amount of variation that you consider as 'normal' ,  this time variation maybe due to evarying traffic conditions, slight variations in the energy level of your son's pedaling, variations in the tyre pressure and other conditions of his bicycle etc. The range of variation of the time that is considered as 'normal' is believed to be caused by "Common Causes", and we usually do not worry much about it.

However, if the range of variation is beyond this expectation, say it took 60 minutes on a particular day, you are sure to get concerned and would expect that such a variation has occurred due to a specific assignable cause such as an unusual traffic jam, or his bicycle had a puncture to be attended etc.... and you are certain to enquire about the reason for the delay and would expect a specific answer. Such causes are also referred to as "Special Causes". A special cause may also result in him arriving earlier than usual expectation... Eg. the roads were free of traffic due to some strike and he could ride fast.

Thus, the "Common causes" are expected to prevail every day, whereas the "Special causes" are not expected to prevail everyday.

This concept of variation is applicable to all processes and statistical methods have been used to quantify and induce objectivity in the decision whether any process is under the influence of special causes, or just common causes. This is very important in a production process, where objective decisions have to be taken as to when to act, when not to act. When under the influence of common causes only, the behavior of a process is more predictable than when influenced by special causes.

Significance of Control Charts

One of the most popular tools that has been evolved by Walter Shewhart for distinguishing between Chance causes and Special causes is the statistical Control Chart, which has its limits and decision rules evolved based on the probability of an error happening outside the range of variation based on the standard deviation based limits. The expected  behavior of sample means based on the central limit theorem, combined with the range charts (used for the variable data) set the criteria to decide when our actions for an 'special' cause occurrence will be highly justified.

Why is it important to distinguish the special causes from the chance causes?

This takes us back to a part of the discussion we had on the "False alarm" and "Missed alert". If we keep tweaking the process setting for variations that are actually happening due to Chance causes, we may end up drifting the process unnecessarily, and it could lead to more adverse consequences. Whereas if we fail to identify a genuine mean shift (or range shift) of the process, we would allow the process to operate in the non-optimal setting leading to creation of higher number of errors.

Real world examples

Let me illustrate with a real life example of a retail store. If we have to decide the number of salesmen based on the volume of sales, we would collect the volume of sales for such a product in that region for a period of past few years. We might see that the there could be certain instances of high volumes sales due to panic buying, say.. due to certain unprecedented natural calamities. Those data points (spikes) need to be treated as ones due to "Special causes".  When we evolve estimated average sales volume, we need to remove such data points and do the working. Such a process is called 'homogenizing' the data, i.e. we are removing the influence of the special causes from the data. Otherwise, we would end up with over staffing and overall utilization will be poor.

However, if we take a situation that involves a critical characteristic such as safety, we may have to consider the factor of safety to take into account even an abnormal situation that may arise out of a 'special cause'. For example, if we are designing the braking system for a vehicle, it will not be sufficient to design it based on the chance cause factors alone. We will have to accommodate in the design, the possibility of dealing with an extreme adverse situation, even if it falls into a 'Special Cause' category.

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Scope for variations is designed along with the process boundaries and system structure. So when a system adopts a stream, it blends the respective chance and assignable deviations across the functions and will be reporting the non conformance in isolation or in combination.

When the process is statistically controlled, it will have common cause variations. These are random in nature and are inherent. These are accepted as are well within the control limits of the process. Process reengineering and continuous improvement are the popular practices deployed to ensure that the efficiency is better than the current state.

On the other hand, if the process is out of control it will have special cause variations. These are systematic and are to be analyzed. A failure which was not detected at the error state and not contained when at fault makes us regret. Lean principles (mistake proofing, VSM etc) and 7 QC tools should assist the preventive stage as it’s a flaw in the design of the process that has to be fixed.

Tools and practices like FMEA, TRIPLE LOOP LEARNING  and QFD does give an insight of the potential risks, the impact of the process gaps, the VOC/CTQ needed for the VSM and thoughts over KAIZEN efforts. These are to be acknowledged and reconstructed for effective designing. Outliers in the control charts should insist us on investigating the root cause and point at process entropy which is otherwise unavoidable.

Trains arriving/leaving with variations can be still controlled with standardization, however accidents are to be sorted and prevented.

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Common Cause Variation in a process is also called as natural variation that occurs due to factors that are beyond our control. They are some things that are inherently part of the process. They are also referred to as Noise or Random Causes. They are usually predictable statistically or can be predicted based on historical experience.

Special Causes of Variation are specific issues observed in the process or these are unexpected variations. W. Edwards Deming used the term "Assignable Causes" to describe special causes. Such causes would not have been occurred previously or expected to happen previously and are unpredictable statistically or with probabilities.

It is important to differentiate between the special causes and common causes because common causes cannot be eliminated. You need to manage them within the tolerance limits of the process. For example the travel time between point A and Point B can be defined as 25 -35 mins with a mean of 30 mins. The common causes for the variance of plus/minus 5 mins is due to factors such as traffic on the road, no. of signal lights encountered during the journey etc. which are not in our control and need to be catered to in the process.

A special cause for variation in the journey time on a particular day can be that there has been an accident on the road (and therefore traffic jam) or there could a car breakdown or simply that the car was out of fuel and we had to spend time at the petrol pump to fuel up.

Misjudging the Common and Special causes can result in we spending time and energy in putting additional efforts to eliminate/ reduce the common causes OR sometimes we might simply accept variation due to special causes mistaking them as common causes which could have been otherwise been investigated and eliminated.

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Common cause variation would be historical references to compare with and its natural part of the system on most of the causes. Whereas special cause of variation will be unusual instances which do not have historical reference. Common cause variations impact on system would be considerably less compared to special cause of variations. Common cause of variations are predictable in nature whereas special cause variations are not. Differentiation is important as underestimating a special cause for a common cause may be harmful for the system as it may go out of control. Also taking special cause as a common cause may deny us the chance of using the system to its potential best.  Misjudging of these in real world in case of health care would be a disaster. Say in case the symptoms of unhealthy condition in a body be judged as a common cause where as if its a case of special cause then there is potential of causing more harm to the body and health condition and lost oppurtunity to have proactively acted upon on the symptom. Other way round a common cause also cannot be misjudged as a special and treated which will induce unwanted treatment to the body when it was not needed.

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Change is inevitable, even in statistics. There are two types of Variance: Common Cause Variance and Special Cause Variance.

Common Cause Variation is a type of variation which is natural and inherent to a process. Common Causes act randomly and independently of each other, are difficult to eliminate, and often require changes to a process or system. Common-cause variation is the noise within the system

Special cause variation is an unexpected variation that results from unusual occurrences. It is, therefore, easier to identify and fix without significant modifications to a system. Special cause variation always arrives as a surprise. It is the signal within a system.

Common cause variation is characterized by:

·         Phenomena constantly active within the system;

·         Variation is predictable;

·         Irregular variation within a historical experience base; and

·         Lack of significance in individual high or low values.

Whereas Special cause variation is characterized by

·         New, unanticipated, emergent or previously neglected phenomena within the system;

·         Variation inherently unpredictable;

·         Variation outside the historical experience base; and

·         Evidence of some inherent change in the system or our knowledge of it.

It is important to understand and differentiate common cause and special cause variation. Let me give some examples:

Our body temperature is about 36ºC on average. But it is not 36ºC all the time. It has variation. For some reason, sometimes it goes down to 35.5ºC and sometimes it goes up to 36.5ºC. But this is “normal” or “common”. That is when we are Ok. That is the way our body always behaves. Our body temperature has a controlled or stable variation due to common causes of variation only.
But, a few times, our body temperature suddenly rises to, let’s say, 38ºC. This is not “normal” or “common”. Something “special” has happened to make your body temperature go beyond the usual range. There is an uncontrolled or unstable variation due to a special cause of variation, which can be identified, for example, a virus. Remove the special cause of variation (eliminate the virus) and our body temperature will return to its normal behavior.

The limits, 35.5ºC to 36.5ºC, within which the process normally is when there are no special causes of variation. When the temperature goes beyond these limits it is an indication that a special cause of variation is present, and that a corrective action should be taken to correct it and return it to its normal variation.

Another example can be the Process of baking a Bread Loaf. The slight drift up and down of temperature of the oven’s thermostat can probably the common cause variation but opening the oven door during baking can cause the temperature to fluctuate needlessly is special cause variation.

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

So proper cause identification is very important because the answers may yield different resulting paths for Corrective Action and Preventive Action. Determination of variability type is often absent from problem-solving methods, leading to ineffective actions. It is difficult to create an effective solution if the problem and its cause are not accurately understood.

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Common and special causes are the two distinct origins of variation in a process. "common causes", also called natural patterns, are the usual, historical, quantifiable variation in a system, while "special causes" are unusual, not previously observed, non-quantifiable variation.

An example explains the two types of causes of variation:

1. Common cause: It normally takes me 25-35 minutes to commute to a neighboring town. Note that it does not take me exactly 31.5 minutes each time because there is attribution of common cause variation. The value in the range could be affected by factors such as the number of red lights I hit, traffic volume or weather conditions, such as rain or sun. These are a normal part of the drive. Expected common cause variation may be predicted by a control chart, often with limits of the mean +/-3 standard deviations. Common cause variation is present in every process.
2. Special cause: One day, I arrive at the town in two hours, which is statistically peculiar. There is a special cause associated with this incident that is outside the normal system: On that day, a blizzard contributed to the delay.

To solve a problem with a special cause, the team should be looking for what changed or is different, whereas solving problems attributed to common cause will require reducing the variance, increasing the spec range or shifting the process mean. All of these relate to not what is different, but rather what is the same (intrinsic) in the process.

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### Common Causes of Variance

Referred to as ‘Natural Problems', ‘Noise' and ‘Random Cause' was a term coined by Harry Alpert in 1947. Common causes of variance are the usual quantifiable and historical variations in a system that are natural. Though a problem, they are an inherent part of a process. This kind of variance will eventually creep in, and there is nothing you can do about it. Specific actions cannot be taken to prevent this failure from occurring. It is ongoing, consistent, and predictable.

Characteristics of common causes of Variance are:

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

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

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

### Examples of Common Causes of Variance

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

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

Other examples that relate to projects are inappropriate procedures, as in the lack of clearly defined standard procedures, poor working conditions, measurement errors, normal wear and tear, computer response times, etc.

### Special Causes of Variance

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

These causes are sporadic, and they are a result of a specific change that is brought about in a process resulting in a chaotic problem. They usually relate to some defect in the system or method.  However, this failure can be corrected by making changes in a certain method, component or process.

Characteristics of Special Causes of Variation are:

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

On a control chart, the points lie beyond the preferred control limit or even as random points within the control limit. Once identified on a chart, this type of problem needs to be found and addressed immediately so as to prevent recurrence of it in the project. It is not usually part of your normal process and occurs out of the blue.

### Examples of Special Causes of Variance

An example to better explain Special causes: you are driving to work, and you estimate arrival in 10 minutes every day, but, on a particular day you reach 20 minutes later, since you encountered an accident zone and were held up.

Examples relating to project management are if the operator falls asleep during the execution of your project, or a machine malfunctions, a computer crashes, there is a power cut, etc.

One way to evaluate a project's health is to track the difference between the original project plan and what is actually happening. Use of control charts helps to differentiate between the Common Causes and the Special Causes of Variation making the process of making changes and amends easier.

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In Business excellence/Process excellence/Six sigma projects, One of the common pain in any business is process variation. During continuous improvement project reducing process variation is one of the major goal is reduce variation. To reduce process first causes need to identified. there are 2 types of causes 1) common causes, 2) special causes.

Common Causes

This cause frequently occurs in a process, which can be predictable by historical data

Example

While we are driving to office by a road, we come know the road conditions, traffic jams,  signal timings, due to one of the reason there is delay to office to reach. here road condition, traffic jam, signals are common causes.

Special Causes

This cause is uncertain in a process which cannot be predictable and not controllable have to live with it.

Example.

In the above example drive to office, traffic jam due to CM/PM travelling on same road which will not happen daily. this situation we cannot controllable

Misjudging

if we misjudge common cause as Special cause, we may fail implement controls which can be controllable if we misjudge special cause as common cause, we will undergo improvement project which is not controllable. So categorising the causes in appropriate is more critical to improvement project.

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Every process has variation.The source of process variation can be divided into two categories:special and common.Common cause variability is that which is inherent in the process and generally is not controllable by process operators.Examples of common causes include variation in raw materials and variations in ambient temperature and humidity.In the case of service processes,common causes typically include such things as variation in input data,variations in customer load,and variation in computer operations.Some authors refer to common cause variation as natural variation.Special causes of variation include unusual events they when detected can usually be removed or adjusted.Examples include tool wear,gross changes in raw materials, and broken equipment.Special causes are Sometimes called assignable causes.A principal problem in process management is the separation of special and common causes.If the process operator tries to adjust a process in response to common cause variation,the result is usually more variation rather than less.This is Sometimes called overadjustment or overcontrol.If a process operator fails to respond to the presence of a special cause of variation,this cause is likely to produce additional process variation.This is referred to as underachievement or under control.The principal purpose of control charts is to help the process operator recognise the presence of special causes so that appropriate action can be taken.

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Every data which is measured will show some variation as no two pieces are alike. The variation may be defined as the numerical value used to indicate how widely a piece for wich the data has been taken varies.

We always have a noice factor in a process which cause variation and therefore we get the bell shape curve instead of one vertical line of target (means every dimension is equal to the target without variation).

Thus it is always important to understand what kind of variation is affecting the process as the course of action to be taken depends on the type of variance.

Common cause of variance: it is also referred to as variation due to natural cause, noise, and random cause. Now these causes are an inherent part of a process. This kind of variance will eventually happen and there is nothing we can do about them. Any specific actions taken cannot prevent this failure from occurring. It is ongoing, consistent and predictable.

Taking a day to day example for keeping spare parts for my car at my home...

I know my car needs a tire pressure pump or cleaning kit and vacuum cleaner for cleaning purpose and therefore I will store these products for maintaining my car. I would not store a spare set of tires in case the set of tires get stolen from my car. Therefore the common cause of variation is predictable in nature although sometimes it is difficult to predict its not always obvious. This variation will do more harm to the process over the period of time. They lie within 3 standard deviations from the mean where 99.73% of values are expected to be found.

When shown in control charts these are a few random points shown within the control limit. These variations require a management action to rectify. If your control chart shows only common cause of variation the process is said to be stable.

Let us take another example as a person living in metropolitan cities one always know how much early one should start their travel to reach office in the morning by keeping the margin so they don't get late.

Special cause of variance

In the above example if suddenly a major accident takes place on the road which halts the traffic, then your travel time will become more and you will reach late to office.

So these causes refer to the unexpected variation that effects a process. These are also known as the assignable cause. These are the causes which are unusual or non-quantifiable variation. On control charts these variation lies beyond the preferred control limit. On these variations are identified they are to be addressed immediately so as to prevent recurrence of it in the project. These are not usually part of normal process.

The special cause cannot be taken or treated as a normal cause in a project as it includes cost and since the nature of assignable.cause is unpredictable we will not be able to predict the same.

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There is always some degree of variation in measuring data. Variation gives us an idea on how data is distributed about the the expected mean. Following are the 2 types of variation found in any data.

 COMMON CAUSE VARIANCE SPECIAL CAUSE VARIANCE Also known as Natural Problems, Noise , Random Cause Assignable Cause Found By Harry Alpert W Edwards Deming Definition Common causes of variance are the usual quantifiable and historical variations in a system that are natural. Special Cause of Variance refers to unexpected glitches that affect a process. Characteristic These variations are ongoing, consistent, and predictable.They are an inherent part of a process These are variations that were not observed previously and are unusual, non-quantifiable variations. It is not usually part of your normal process and occurs out of the blue Variation in Control chart On a control chart, they are indicated by a few random points that are within the control limit. On a control chart, the points lie beyond the preferred control limit or even as random points within the control limit. Solution These kinds of variations will require management action since there can be no immediate process to rectify it. You will have to make a fundamental change to reduce the number of common causes of variation. This type of problem needs to be found and addressed immediately so as to prevent recurrence of it in the project.

Why is the differentiation important and misjudging one of these can create problem in real world

The differentiation is important because if common cause of variation stays in a process then process doesn't become unstable i.e. the data is in control limits . The presence of common cause of variation is common and it is not actionable . I. But the presence of special cause makes data unstable i.e. that data lies out of control limits and it immediately requires rectification.

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Common cause are the ones which usually occurs, they are natural causes.

Example for common cause: You estimate 20 minutes to get ready and ten minutes to get to work. Instead, you take five minutes extra getting ready because you had to pack lunch and 15 additional minutes to get to work because of traffic. These would be Common Causes of Variance.

Special cause are unusual and were not observed previously.

Example for Special Cause: You are driving to work, and you estimate arrival in 10 minutes every day, but, on a particular day you reach 20 minutes later, since you encountered an accident zone and were held up.

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Difference between common and special cause of variation –

1. Common Cause - Variation caused by chance causes, by random variation in the system, resulting from many small factors. Common Cause variation is created by many factors, that are commonly part of the process, and are acting totally at random and independent of each other. Their origin can usually be traced to the key elements of the system in which the process operates. (Materials, Equipment, People, Environment, Methods). If only common causes of variation are present, the output of a process forms a distribution that is stable over time.

Example: Variation in work commute due to traffic lights, pedestrian traffic, parking issues.

1. Special Cause - Variation caused by special circumstances or assignable cause not inherent to the system. Special Cause variation is created by a non-random event leading to an unexpected change in the process output. The effects are intermittent and unpredictable. If Special Causes of variation are present, the process output is not stable over time and is not predictable. All processes must be brought into statistical control by first detecting and removing the Special Cause variation

Example: Variation in work commute impacted by flat tyre, road closure, heavy frost/ice.

This Differentiation is important because, unless we identify type of cause we can not truly resolve the problem from root and because they require different approaches to deal with them. There are different strategies to deal with it depends on type of cause. Common cause need long term strategy to know roots, manage it and improve the process. Special causes need immediate corrections and short term measures. Hence tools to be used are different.

Misjudgement - If the type of variation is not identified then it is likely that the wrong tools will be used.

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