Common Cause Variation vs Special Cause Variation
The term “variation” is widely used in statistics, quality management, genetics and even biology. It refers to a measurement for a group of numbers that spread out from their average value. Every measured data set involve some degree of variation even if the degree is slight. It is a numerical value specifies how widely datas in a data set vary. A small variance shows that datas are closer to the average and a high variance shows that datas are very different than each other. There are two types of Variation: Common Cause Variation and Special Cause Variation. Note that this is an important subject tested on the PMP and CAPM exams.
Types of Variation
Classification of variance is very important in project quality management. Common cause of variance and special cause of variance have different origins. In order to take an action to improve your process or prevent future problems related with variations, you must know the type of variation that will effect your processes. Walter A. Shewhart developed control charts to distinguish both variations in the 1920’s. He discovered that all the processes involve common cause variation but some processes which are not in control show special cause variation.
Common Cause Variation
According to the studies of Walter A. Shewhart and W. Edwards Deming common causes can be called as natural patterns. Common causes of variances are quantifiable, expected, natural, usual, historical and random causes of variances in a process. Common cause variation shows the process potential when the special cause variation is eliminated from the system. On a control chart common cause variation indicates a random distribution around the control limit (or average limit).
It is possible to predict them probabilistically but specific actions can not be taken to prevent the occurrence of common cause of variances. Therefore a management action is required to make an extensive change on the system to reduce the amount of common cause of variances.
Simply put, common causes of variances are normal, consistent and inherit in the process which can not be eliminated.
Examples for Common Cause Variation
Assume that in a hotel construction project, you estimated 10 days to complete a formwork activity. Due to the climatic conditions, it is completed in 11 days. The completion time is not deviated too much from the mean. This is an example for a common cause variation.
Another example is that your team is working on a software development project. Due to the lack of coordination between team members, unclear scope definition and unexpected errors, you will not complete the project on time.
Below can be some examples for common causes of variances within a project
- Unclear scope definition
- Inadequate design
- Poor management
- Insufficient procedures
- Weather conditions
- Computer response time
- Inadequate working conditions
Special Cause Variation
Differently from the common cause variation, special cause variation refers to known factors that have effects on a process. W. Edwards Deming introduced this concept. Special cause variations are the unusual, non-quantifiable, unexpected variances that were not encountered before in a process. Mostly a specific factor such as a rapid change in conditions or input parameters cause special cause variations.
On a control chart special cause variation indicates a non-random distribution around the control limit (or average limit).
Special cause variations can usually be eliminated with adjustments to the processes, components or methods. They may cause serious problems if they are not eliminated. Special causes of variances are not inherent and usually originate from technical problems.
Simlpy put, special cause variations are caused by unpredictable factors that can not be foreseen with the help of historical experience and records.
Examples for Special Cause Variation
Assume that you are a project manager of a bridge construction project and you estimated 10 days to complete an excavation activity. When you started excavation, a technical problem occured in the hydrolic system of the excavator. This malfunction delayed this activity about 20 days. The problem is solved by fixing up the hydrolic system. This is an example for a special cause variation.
Anorher example is that you were working with a shipping company to transport a generator for a hospital renovation project. Estimated time of arrival was 2 days. But the arrival of the generator took four days because of an accident on the highway.
Below can be some examples for special causes of variances within a project
• Machine fault.
• Power surges
• Operator absent/ falls asleep
• Computer fault.
While analyzing a data set, we see that all the datas are not close to each other. There may be some great and small differences between them. Variance shows that how datas are distributed around an expected or an average value. If the degree of variance is close or equal to zero this means that all the datas are the same or very close to eachother. A high degree of variance indicates that all the datas are far away from each other.
Common and special causes are the two distinct origins of variation in a system. In a process, it is important to determine the type of variance because the course of action you take will depend on the type of variance. Using control charts helps to distinguish Common Cause Variation and Special Cause Variation.
In this article we make a review for Common Cause Variation and Special Cause Variation. We hope that it will be useful for passing PMP and CAPM exams.