What is Special Cause Variation? Definition, Examples, Importance

What would be the average sum if you threw 2 dice together? You would expect it to be around seven, while numbers around 2-3 or 11-12 would be considered outliers. But what would happen if the sum went above 12? An additional dice is something that you haven’t expected and is not something you have accounted for. 

Such unexpected occurrences are considered special cause variation in any process. Identifying and mitigating these external factors is key to maintaining quality and sanctity of the process. 

Let's look at what special cause variation is, examples, how to identify it and the difference between special cause and common cause variation. Read on to find out. 

What is Special Cause Variation? 

Any variation caused by an identifiable source that is not part of the usual process is considered as a special cause variation. Also known as assignable cause variation, these kind of variations usually tend to indicate that something unexpected has impacted the process. Such variations suggest the involvement of external factors or deviations that warrant a detailed investigation unlike common cause variation. 

Sudden changes in raw material purity, malfunctioning machinery or errors by a new operator are all causes of special cause variations. Since these are not part of inherent changes in the process, they can generally be corrected once they’re identified. 

Dr. W. Edwards Deming, a pioneer in quality management, was the one who coined the term "special cause variation”. He emphasized the importance of differentiating between the two types of variation as a crucial factor in effective management. Taking the incorrect approach to treating variations can lead to wastage of resources spurred by misinformed decision-making. 

Let us look at the difference between special and common cause variation. 

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Special Cause Variation vs Common Cause Variation 

Differentiating between special and common cause variation is quite important to improving process efficiency. 

	Special Cause Variation	Common Cause Variation
1. Definition	Any unusual variation caused by identifiable factors	Natural variations having a cause within limits of the process
2. Predictability	Unpredictable and random	Predictable within identified control limits
3. Frequency	Occasional and sporadic in nature	Identified to be consistent over the course of time
4. Cause	External or assignable to something specific	Something that is built into the process
5. Detection	Identifiable as outliers in control charts	Much more subtle and lie within the control limits
6. Response Needed	Investigation and elimination of the root cause	Improvements on process capability
7. Example	Machine malfunction halting production	Minor fluctuations in temperature or power
8. Control Chart Signal	Trends that extend beyond control limits	Patterns that lie within control limits

Now that we have seen the difference between the two kinds of variation, let us understand the importance of special cause variation in maintaining quality. 

Importance of Special Cause Variation 

It is vital to identify and address the root causes of special cause variation. It helps ensure that quality is maintained. These variations hint that there is something abnormal happening. Ignoring them and letting them continue leads to possibilities like deteriorated product/service quality, dissatisfied customers, and inefficient operations. 

Understanding the reason behind special cause variations helps uncover any issues that were hidden previously. Let us look at that with a relevant example. Increased customer complaints could reveal device-specific bugs after the latest feature was released. This wouldn’t be revealed during the normal testing process. Organizations can quickly patch such issues by investigating and resolving them before they get out of hand. It also helps maintain brand reputation in the long run by addressing unseen issues. 

Special cause variation also plays a tactical role. It acts as an early warning system which helps avoid larger deviations over time. This is extremely crucial in industries like pharmaceuticals, defense, or aerospace, where regulations are important. Ignoring such variations can lead to safety risks or compliance violations. 

All of this also leads to a better data-driven decision-making process. Teams will learn to distinguish between common cause variations and special cause variations. Common cause variations can be given some leeway as they are often within the control limits. This is a crucial part of disciplined quality control initiatives like Six Sigma. 

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Now that you’ve understood the importance of special cause variation, let’s look at different methods to identify them. 

Identifying Special Cause Variation 

There are different ways to identify special cause variations in a process. Let us look at a few of them in detail. 

Control Charts 

One of the most common ways to differentiate special and common cause variations is with the help of control charts. This is done by creating a plot of process data over time. Upper and lower control limits (UCL & LCL) are established based on historical data. These charts provide a visual signal of processes deviating from the norm. 

If a single data point deviates from the control limits, it generally is caused by a common cause variation. On the other hand, if there are patterns like cycles, trends, or sudden shifts, it usually indicates a special cause variation. If consecutive data points show an increase, even within control limits could signify an assignable cause. 

Keeping a well-documented control chart can help take corrective measures before things escalate. It also helps to avoid overreacting to common variation, leading to stable quality management practices. 

Statistical Process Control (SPC) 

Statistical Process Control (SPC) refers to using statistical methods to monitor and control processes. Using  a continuous monitoring process allows for real-time detection and intervention of any special variation. A deeper analysis of any variation is done to identify and remove the root cause. 

This method keeps strong emphasis on understanding variation with the help of statistical evidence. This kind of disciplined approach leads to better understanding of any fluctuations as well as guided decision-making. 

Root Cause Analysis (RCA) 

Conducting a Root Cause Analysis (RCA) is a crucial step to understanding what caused the variation. There are different ways to do this. Some techniques include the 5 Whys, utilizing fishbone diagrams (Ishikawa), Failure Mode and Effects Analysis (FMEA). This is done to trace the variation to its origin and understand how to resolve it. 

A good combination of these tools helps form a comprehensive approach to identify and mitigate the cause of any special variation. Keeping a close eye on such variations ensures that quality is maintained and aligned with business objectives. 

Examples of Special Cause Variation 

Special cause variation can be seen in different industries. Let us look at how it presents itself in a few scenarios. 

• Manufacturing: A company that creates silicon chips notices that the edges of the last few batches are uneven. On closer inspection, the mold has some residue due to overuse. Once the mold is replaced, production continues as normal, revealing an unassigned cause. 
• Healthcare: A spike in wait times is observed on a specific weekend. It is traced to many doctors being on leave. This one-time event is resolved by creating a better schedule for doctor assignments. 
• Call Centers: There is a significant drop in customer satisfaction on a day. It is found that a new script was implemented before reps were trained with it. Once proper training was done, it returned to normal. 
• Food Service: One branch of a fast-food chain has been receiving unusually high complaints about all ice cream. Investigation reveals that the ice-cream machine’s internal thermostat was not working. Once it was fixed, complaints stopped. 
• IT: Users began to report app crashes after a new feature was introduced. The root cause was identified as the service not being able to link with the account, which wasn’t the cause before it was added. 

All of these highlight the random nature of unassigned variations. Since these were outside the normal processes, it required some action for the variation to be addressed. This highlights the need for vigilance and proactive response. 

Now that we’ve understood how special variation occurs in different industries, let us look at the role it plays in quality management within the Six Sigma framework. 

Role of Special Cause Variation in Six Sigma 

Understanding and managing variation is key to improving quality within the Six Sigma framework. This is done by focusing on reducing defects and improving process efficiency. Distinguishing between common and special variance is crucial to this. 

Special cause variation plays a critical role in the Measure and Analyze faces of the DMAIC (Define, Measure, Analyze, Improve, Control) framework. Teams in Six Sigma use tools like control charts and capability analysis to spot any anomalies. Once any special cause is identified, investigation for prioritized. 

Addressing these variations helps stabilize the process and maintain quality. This ensures that outliers from one-time events aren’t included in data used to analyze process efficiency. Ignoring special cause variations can lead to misguided solutions due to distorted analysis. 

Resolving these special variations will lead to quick wins by avoiding escalation. Eliminating these single outliers often leads to improved quality and efficiency without making large-scale changes. 

Constand monitoring for special causes is a crucial part of the Control phase in Six Sigma. This kind of sustained monitoring is needed to manage special variations. A one-time effort isn’t what ensures improved quality in the Six Sigma framework. 

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Summing Up 

Quality control and continuous improvement relies on identifying and mitigating any special cause variations. These specific and identifiable disruptions cause a greater impact on processes when compared to common cause variations. 

Using tools like control charts, SPC, and RCA help analyze, detect, and eliminate special variation in an efficient manner. Managing special variation is also a crucial part of continuous quality improvement in the Six Sigma framework.