Common cause variation: Meaning, Criticisms & Real-World Uses

What Is Common Cause Variation?

Common cause variation refers to the inherent, expected, and natural fluctuations within any stable process or system. It represents the routine variability that is always present due to the many minor, uncontrollable factors that are an intrinsic part of how a process operates. In the realm of statistical process control, understanding common cause variation is fundamental because it distinguishes predictable, system-inherent variability from unusual, external disruptions. When a process exhibits only common cause variation, it is considered to be "in statistical control" and its future performance is predictable within certain limits. These variations are typically small and random, averaging out over time.

History and Origin

The concept of common cause variation, along with its counterpart, special cause variation, was pioneered by Walter A. Shewhart in the 1920s while he was working at Bell Laboratories. Shewhart, a physicist and statistician, developed the fundamental principles of quality control to improve manufacturing processes. His breakthrough in 1924 involved recognizing two distinct types of variation in production data and devising methods, primarily the control chart, to differentiate between them²²,²¹.

Shewhart initially referred to these as "chance causes" and "assignable causes." Later, W. Edwards Deming, a student and collaborator of Shewhart, popularized and refined these concepts, renaming them "common causes" and "special causes." Deming emphasized the critical importance of distinguishing between these two types of variation for effective management and continuous process improvement. His work underscored that reacting to common cause variation as if it were a special cause often leads to "tampering" with the system, making performance worse rather than better²⁰.

Key Takeaways

Common cause variation represents the inherent, normal, and predictable variability within a stable process.
It arises from numerous small, random factors that are always present within the system.
When only common cause variation is present, a process is considered "in statistical control" and its future output is predictable within defined limits.
Addressing common cause variation requires making fundamental changes to the underlying system itself, rather than reacting to individual data points.
Misinterpreting common cause variation as special cause variation can lead to ineffective and potentially detrimental interventions.

Formula and Calculation

Common cause variation itself does not have a single, standalone formula. Instead, it is identified and understood in relation to the statistical limits calculated for a control chart. A control chart visually represents data points over time, along with a central line (mean or average) and upper and lower control limits.

These control limits are typically set at plus or minus three standard deviation units from the central line. When all data points fall within these statistically derived upper control limit (UCL) and lower control limit (LCL), and show no discernible patterns, the variation observed is considered common cause variation. This indicates a stable and predictability process.

The control limits are calculated based on the historical variability of the process:

For an X-bar chart (monitoring the average of subgroups):

UCL = \bar{\bar{X}} + A_2 \bar{R}

LCL = \bar{\bar{X}} - A_2 \bar{R}

Where:

(\bar{\bar{X}}) = Grand average of all subgroup averages
(\bar{R}) = Average of subgroup ranges
(A_2) = A constant factor based on subgroup size, used to calculate 3-sigma limits for the mean

For an R chart (monitoring the range of subgroups):

UCL = D_4 \bar{R}

LCL = D_3 \bar{R}

Where:

(D_3) and (D_4) = Constants based on subgroup size

These formulas help establish the expected range of common cause variation. Points falling outside these limits, or exhibiting non-random patterns within them, signal the presence of special cause variation.

Interpreting the Common Cause Variation

Interpreting common cause variation means recognizing that the fluctuations observed are a normal, inherent part of the system's current design and operation. When a process displays only common cause variation, it signifies that the process is stable and its output is predictable within its natural limits. In this state, the focus should not be on reacting to every minor fluctuation, as such reactions would constitute "tampering" with the process and could actually increase overall variation.

Instead, to reduce common cause variation and improve performance, efforts must be directed at fundamentally changing the system itself. This might involve redesigning processes, investing in new technology, improving training, or modifying the broader operational environment. For example, if a company's customer service response time consistently varies between 3 to 7 minutes (and this is deemed acceptable and stable), attempts to "fix" a 6-minute call when the average is 5 minutes would be an overreaction to common cause variation. True improvement would come from a systemic change, such as optimizing call routing or improving agent tools to shift the entire average lower.

Hypothetical Example

Consider a hypothetical online brokerage firm that aims to process all new account applications within 24 hours. The operations team collects data analysis on the time taken for each application.

Over a month, they find that the average processing time is 18 hours, but individual application times naturally vary between 16 and 20 hours due to factors like varying client responsiveness, slight differences in data entry speed among employees, or minor network latency. These small, random fluctuations are examples of common cause variation. They represent the typical, expected variability given the current structure of their account opening process stability.

If an application takes 19 hours to process, this is within the expected range of common cause variation. Reacting to this single instance by, for example, reprimanding the individual processor or scrambling to re-check that specific application, would be inappropriate. The variation is inherent to the system. To reduce the average processing time to, say, 15 hours, the firm would need to implement systemic changes, such as automating parts of the application process, providing advanced training, or improving backend infrastructure. These changes target the common causes within the system, leading to a new, lower level of inherent variability.

Practical Applications

While originating in manufacturing, common cause variation and the principles of statistical process control have significant applications in finance and business operations. Financial institutions can apply these concepts to monitor various financial metrics and processes, thereby enhancing efficiency and managing risk.

For example, in banking, the time taken to approve loans, the rate of errors in transaction processing, or daily fluctuations in customer service call volumes can exhibit common cause variation. By using control charts, financial managers can determine if a process is stable and predictable or if unusual events (special causes) are impacting performance¹⁹. This helps in areas such as:

Risk Management: Monitoring the daily volatility of a portfolio or the frequency of certain operational incidents can help differentiate normal market noise or routine errors (common causes) from significant, unusual events that require immediate attention (special causes). Statistical control charts have been applied in financial market surveillance and portfolio monitoring to identify such deviations¹⁸,¹⁷.
Operational Performance: Financial firms can track key performance indicators like data entry accuracy, fraud detection rates, or trade execution times. Understanding common cause variation in these metrics allows management to identify the baseline performance of their operations. For instance, analyzing financial statements using statistical process control tools can help monitor changes in aggregated financial ratios, distinguishing normal fluctuations from significant shifts¹⁶.
Compliance: Ensuring adherence to regulatory requirements often involves processes with inherent variability. Control charts can track compliance-related error rates, helping firms maintain quality and identify when unusual deviations occur, prompting targeted investigation rather than reactive "firefighting" for every minor issue¹⁵.

Limitations and Criticisms

While distinguishing between common and special cause variation is crucial for effective management, there are limitations and potential criticisms to consider. One primary challenge is the human tendency to attribute common cause variation to special causes, leading to inappropriate actions. As W. Edwards Deming frequently noted, reacting to every peak and trough in a stable process (i.e., treating common cause variation as if it were a special cause) leads to "tampering," which can increase overall variability and degrade process stability¹⁴,¹³. This can result in wasted resources, increased frustration among employees, and a lack of true process improvement¹².

Another limitation lies in the difficulty of accurately identifying the true underlying common causes, especially in complex systems. While statistical tools like control charts can signal a stable process, pinpointing the specific elements within the system that contribute to the common cause variation often requires deep subject matter expertise and root cause analysis beyond the charts themselves. Furthermore, processes that appear stable due to common cause variation might still not meet desired performance standards, highlighting that "in statistical control" does not necessarily mean "good enough" or "optimal." It simply means predictable.

Common Cause Variation vs. Special Cause Variation

The distinction between common cause variation and special cause variation is fundamental to statistical process control and effective management.

Feature	Common Cause Variation	Special Cause Variation
Nature	Inherent, natural, and expected fluctuations within a system¹¹.	Unusual, unexpected, and abnormal disruptions to a system¹⁰.
Source	Innate factors within the process (e.g., normal wear and tear, typical operator differences, routine environmental shifts)⁹.	External or sporadic factors outside the typical process (e.g., machine breakdown, new untrained employee, sudden change in raw materials)⁸.
Predictability	Predictable; the process is in statistical control⁷.	Unpredictable; indicates an out-of-control process⁶.
Impact	Contributes to the natural spread of data around the mean; small deviations⁵.	Leads to significant deviations or shifts from the norm; can cause defects or inefficiencies⁴.
Management Action	Requires systemic changes to the process itself (e.g., redesign, training, technology upgrades)³.	Requires immediate investigation and corrective action to identify and remove the specific cause².

The primary confusion arises when managers or decision-makers react to common cause variation as if it were a special cause. This "tampering" can destabilize a predictable process. Conversely, ignoring a special cause by dismissing it as "just normal variation" can allow a serious problem to persist or worsen. Correctly distinguishing between the two dictates the appropriate course of action for process improvement and risk management ¹.

FAQs

Q1: What is the main characteristic of common cause variation?

A1: The main characteristic of common cause variation is that it is inherent and expected within a stable system. It represents the natural, routine fluctuations that occur due to many small, random factors that are always present.

Q2: How can you identify common cause variation in a process?

A2: Common cause variation is typically identified using a control chart. If all data points fall within the calculated upper and lower control limits and show no non-random patterns, the observed variability is considered common cause variation.

Q3: What action should be taken when a process exhibits only common cause variation?

A3: When a process exhibits only common cause variation, the appropriate action is to avoid reacting to individual data points. Instead, to reduce the overall level of variation or shift the process mean, fundamental changes to the underlying system are required. This often falls under the responsibility of management.

Q4: Can a process with only common cause variation still be problematic?

A4: Yes, a process with only common cause variation can still be problematic if its inherent level of variability, though predictable, is too high or if its average performance does not meet desired targets. "In statistical control" simply means predictable, not necessarily optimal.