What Is Special Cause?
A special cause, also known as an assignable cause, refers to an unusual or unexpected source of variation within a process that is not part of its normal, inherent fluctuations. These variations are triggered by specific, identifiable factors outside the routine operation of a system, marking a process as unstable or "out of control." Identifying special cause variation is a core concept within quality management and Statistical Process Control (SPC), a broader field that applies statistical methods to monitor and control processes. Unlike common cause variation, which is inherent and random, a special cause points to a distinct event or circumstance that requires investigation and corrective action to restore process stability.
History and Origin
The foundational understanding of special cause variation emerged from the work of American physicist and statistician Walter A. Shewhart in the 1920s at Bell Laboratories. Shewhart, often regarded as the "father of statistical quality control," introduced the concept of two distinct types of variation: common causes and special (or assignable) causes. His seminal work led to the invention of control charts, which provided a visual tool to differentiate between these variations. Shewhart's insights were instrumental in recognizing that effective process improvement required different management responses depending on the type of variation present. His theories were later championed and expanded upon by W. Edwards Deming, who extensively applied and taught these principles, particularly in post-World War II Japan, influencing modern quality and operational efficiency practices globally.5
Key Takeaways
- Special cause variation stems from specific, identifiable, and often external factors impacting a process.
- Its presence indicates that a process is "out of statistical control" and requires targeted intervention.
- Ignoring special cause variation can lead to unpredictable outcomes, increased costs, and diminished quality.
- Control charts are the primary tool used to detect special cause signals.
- Addressing special causes often involves root cause analysis and specific corrective actions.
Interpreting the Special Cause
Interpreting a special cause involves recognizing deviations in process data that fall outside the expected range of normal, inherent variation. When a process is operating under "statistical control," its output variation is due only to common causes, which are stable and predictable. The appearance of a special cause, however, signals an external influence disrupting this stability. On a control chart, a special cause is typically identified by data points that fall beyond the statistically calculated control limits, or by specific non-random patterns within the control limits (e.g., seven consecutive points all above or below the center line, or a consistent trend). Identifying these signals alerts managers that a specific investigation is needed to find the assignable cause and take appropriate problem solving steps. Proper interpretation ensures that efforts are directed at the actual source of the problem, rather than overreacting to normal fluctuations.
Hypothetical Example
Consider a financial institution's business processes for approving consumer loans. Over time, the average processing time for a loan application might be 5 business days, with natural variation around this average due to factors like varying application complexity or reviewer workload. This inherent variability represents common causes.
One week, the average loan processing time suddenly jumps to 12 business days, with several individual applications taking 15 or more days. A control chart monitoring these times would show points far beyond the upper control limit, signaling a special cause. Upon investigation, the institution discovers that a key software system used for credit checks experienced a malfunction, causing significant delays. This software malfunction is a special cause. Its identification allows the institution to focus its problem solving efforts on repairing the system, rather than, for example, retraining all loan officers for what appeared to be a general slowdown. Once the system is repaired, the processing times are expected to return to their normal range of common cause variation.
Practical Applications
Special cause analysis is crucial across various industries, including finance, for maintaining stability and improving financial performance. In banking, for instance, it can be applied to monitor transaction error rates, customer service wait times, or loan approval durations. An unexpected spike in transaction errors (a special cause) might indicate a software glitch or a new employee training deficiency, prompting targeted root cause analysis. Similarly, a sudden drop in call center response times could be a positive special cause, due to a new, highly effective script, which could then be standardized across the organization.
In risk management, identifying special causes helps differentiate between routine operational fluctuations and significant, unusual events that could lead to substantial losses or gains. For example, a sudden, atypical increase in fraud detection alerts within a financial system could signal a new type of cyber threat (a special cause) rather than just an expected daily fluctuation. By analyzing performance metrics using special cause principles, organizations can make data-driven decision-making that leads to more efficient processes, reduced costs, and enhanced customer satisfaction.4 Statistical Process Control (SPC) tools, including those used to identify special causes, are utilized by financial institutions to monitor metrics, adhere to regulatory requirements, and reduce error rates.3
Limitations and Criticisms
While vital for process improvement, the application of special cause analysis, particularly through control charts, is not without potential limitations or criticisms. A common pitfall is "tampering," where managers overreact to normal variation (common causes) as if they were special causes. This leads to unnecessary adjustments to a stable system, which can actually increase unpredictability and costs.2 Conversely, failing to recognize a true special cause can allow a significant problem to persist, leading to continued poor financial performance or quality issues.
Another challenge is the incorrect selection of the type of control chart, or misinterpreting the signals the chart provides. If control limits are not properly calculated or are based on inaccurate historical data, they can either generate false alarms or fail to detect real issues.1 Furthermore, successfully addressing a special cause requires effective root cause analysis and the willingness of management to implement systemic changes, which can be hindered by organizational culture or a lack of resources. The benefit of special cause detection hinges on a comprehensive understanding of statistical principles and a disciplined approach to data analysis.
Special Cause vs. Common Cause
Special cause and common cause are the two fundamental types of variation observed in any process, and distinguishing between them is critical for effective management and decision-making.
Common Cause Variation refers to the natural, inherent, and predictable fluctuations within a stable process. These variations are typically small, random, and due to factors always present in the system, such as slight differences in raw materials, ambient temperature changes, or normal human performance variability. A process operating with only common cause variation is considered "in statistical control," meaning its future performance is predictable within established limits. Addressing common cause variation requires changes to the fundamental design of the business processes themselves.
Special Cause Variation, as discussed, represents unusual, unexpected, and unpredictable deviations from the norm. These are caused by specific, identifiable events or factors that are not inherent to the process. Examples include a machine breakdown, a new, untrained operator, a sudden shift in raw material quality from a new supplier, or an unexpected market event. When a special cause is present, the process is "out of statistical control." The appropriate response to a special cause is to identify its specific root cause analysis and take immediate, targeted corrective action to eliminate or incorporate its effect. Confusing the two types of variation, and applying the wrong management action, often leads to poorer outcomes.
FAQs
Q: How do you identify a special cause?
A: Special causes are typically identified using control charts in Statistical Process Control. Signals of a special cause include data points falling outside the statistically calculated upper and lower control limits, or specific non-random patterns within the limits, such as a long run of points above or below the center line, or a clear trend upwards or downwards.
Q: Why is it important to distinguish between special and common causes?
A: It is crucial because the appropriate management action depends on the type of variation. Trying to fix common causes with individual, reactive measures (like blaming an employee for a systemic issue) is ineffective and can make the process worse. Conversely, treating a special cause as random noise means missing an opportunity to address a critical, identifiable problem. Correct identification guides effective problem solving and process improvement.
Q: Can a special cause be positive?
A: Yes, a special cause can be a positive deviation. For example, a sudden and sustained increase in customer satisfaction scores after implementing a new training program for staff could indicate a positive special cause. Identifying and understanding the factors behind such positive special causes can help an organization implement them systematically to improve overall operational efficiency.
Q: What is "tampering" in the context of special causes?
A: Tampering, as defined by W. Edwards Deming, refers to making adjustments to a process in response to common cause variation, mistaking it for a special cause. This overreaction to normal fluctuations can actually increase the overall variability and lead to a less stable and predictable system, wasting resources and hindering financial performance.