All processes contain an inherent degree of variability that challenges predictability and consistency. No two outputs, whether products or services, will ever be exactly identical, even under controlled conditions. Understanding the source and nature of this variation is foundational to any process improvement effort aimed at increasing quality and reducing waste. The majority of this everyday fluctuation is categorized as common cause variation, which defines the natural performance boundaries of any established system. This analysis defines common cause variation, distinguishes it from other process deviations, and outlines the systematic approach required for its identification and reduction.
Defining Common Cause Variation
Common cause variation, often referred to as random variation or system variation, represents the natural, expected fluctuation inherent in any process operating in a stable state. This variability is the background noise of the system, resulting from the cumulative effect of numerous small, non-identifiable factors that are always present. These factors might include minor temperature shifts, slight differences in raw material consistency, or the normal performance variability among different human operators. Common cause variation is predictable probabilistically, meaning the process output will consistently fall within a certain range over time, even if individual data points appear random.
The existence of only common cause variation indicates that a process is statistically in control and performing as designed. This inherent variation establishes the system’s current capability and determines the expected range of performance output. Since these causes are built into the design of the system itself, they affect all process outcomes equally and cannot be traced back to a single, fixable event. Reducing this fluctuation requires fundamental changes to the process structure rather than simple operational adjustments.
The Critical Distinction: Common Cause Versus Special Cause Variation
Distinguishing between common cause variation and special cause variation is a core concept in quality management, as the correct response strategy depends entirely on the source of the deviation. Common cause variation is internal to the system, stable, and affects the entire process output in a consistent, though random, manner. Its presence signals a system that is functioning normally, even if the resulting quality or output level is unsatisfactory for customer needs. This stability means the process is predictable within its established upper and lower control limits.
Special cause variation, also known as assignable cause variation, results from external, unusual, or sporadic events that are not part of the process’s normal operation. Examples include a sudden machine malfunction, a batch of substandard raw material, or inadequate operator training. This variation is unpredictable and causes the process to move out of its normal, stable range, often appearing outside the control limits on a statistical chart. When a special cause occurs, immediate investigation and targeted action are required to identify and remove the specific root cause and restore the system to stability.
Confusion between these two types often leads to counterproductive behavior known as “tampering,” a term popularized by quality theorist W. Edwards Deming. Tampering occurs when an operator reacts to common cause fluctuations, mistakenly treating them as special events requiring adjustment. Intervening in a stable process introduces unnecessary instability and increases the overall level of variation, wasting time and resources. Deming emphasized that management is responsible for addressing common cause variation by changing the system, while frontline workers address special causes as they arise.
Identifying Common Cause Variation Through Statistical Process Control
Statistical Process Control (SPC) provides the primary methodology for determining whether observed process variation is due to common or special causes. The fundamental tool of SPC is the control chart, which plots process data over time against statistically calculated upper and lower control limits. These limits are derived directly from the process data, defining the expected range of performance when only common cause variation is present.
When all data points fall randomly within these control limits, without displaying any non-random patterns such as trends or sudden shifts, the process is said to be “in statistical control” or stable. This pattern confirms that the only variation currently impacting the process is the inherent, expected common cause variation. Conversely, a point falling outside the control limits, or a run of consecutive points on one side of the center line, signals that a special cause has likely entered the system. Control charts are used to separate the common causes that define the system from the special causes that disrupt it.
Real-World Examples of Common Cause Variation
Common cause variation is present across all sectors, manifesting as expected fluctuations in daily operations. In a manufacturing environment, an example is the slight variation in the weight of a molded plastic part due to the normal wear and tear of machine components. Another instance is the minor difference in the tensile strength of a product between batches, which results from small, acceptable variations in raw materials supplied by a vendor. These variations are inherent to the current equipment and supply chain design and cannot be eliminated without a systemic upgrade.
In the service industry, common cause variation appears as small differences in customer waiting times at a bank or call center. Even with consistent staffing and procedures, factors like the normal flow of foot traffic, the varying complexity of customer issues, and an operator’s standard momentary pauses collectively create this natural range of wait times. Similarly, in administrative processes, the time required for a data entry task fluctuates slightly depending on the operator’s keyboarding speed or minor inconsistencies in source documents.
Strategies for Reducing Common Cause Variation
Reducing common cause variation is a strategic undertaking that requires leadership commitment and a fundamental redesign of the process. Since this variation is systemic, management is responsible for effecting permanent changes to the core components of the system. This contrasts sharply with the localized, immediate action required to eliminate special cause variation.
Strategies include investing in better technology or equipment, such as upgrading machines with tighter operational tolerances. Another approach is redesigning the process flow to simplify steps, eliminating opportunities for human or environmental fluctuation. Establishing stricter specifications for input materials can narrow the range of expected variation entering the system. Providing comprehensive, standardized training for all staff minimizes human performance variability, thereby raising the overall capability and predictability of the process.