Common Causes vs Special Causes of Variation

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In Lean Six Sigma and the Control Phase, understanding variation is critical for process management. Variation in any process comes from two primary sources: common causes and special causes. Common Causes of Variation are inherent to the process itself and represent the natural, predictable variation that occurs within a stable system. These causes are consistently present in day-to-day operations and include factors like equipment wear, raw material inconsistencies, and environmental conditions. Common causes result in a process that is in statistical control, creating a baseline or background noise. Since they are built into the process, eliminating them requires fundamental process redesign or improvement initiatives. Common causes typically account for 85-95% of variation in a process. Special Causes of Variation are unusual, unpredictable events that temporarily disrupt the process. Also called assignable causes, these are sporadic and identifiable factors such as equipment malfunction, operator error, incorrect procedure execution, or unusual external circumstances. Special causes signal that something abnormal has occurred and make the process statistically out of control. They typically appear as sudden spikes or dips in control charts. The distinction is vital during the Control Phase of DMAIC (Define, Measure, Analyze, Improve, Control). When a control chart shows points outside control limits or non-random patterns, special causes are present and must be investigated and eliminated immediately. Once special causes are removed, the process stabilizes and true capability can be measured. Management strategy differs between the two: special causes require immediate investigation and removal, while common causes demand systematic process improvement. Control charts, such as X-bar and R charts, help identify which type of variation exists. A Black Belt must teach the organization to distinguish between these causes, as responding inappropriately—treating special causes as common causes or vice versa—leads to ineffective corrective actions and wasted resources.

Common Causes vs Special Causes of Variation in Six Sigma Control Phase

Understanding Common Causes vs Special Causes of Variation


Why This Concept Is Important


In Six Sigma and quality management, understanding the difference between common causes and special causes of variation is critical for effective process control and improvement. This distinction determines:


  • How you respond to process variation
  • Whether management or operational-level intervention is needed
  • The appropriate improvement strategy to employ
  • Whether a process is in statistical control
  • Resource allocation for problem-solving efforts

Black Belt candidates must master this concept as it forms the foundation of control chart interpretation and process improvement decisions.


What Are Common Causes and Special Causes?


Common Causes of Variation


Common causes (also called assignable causes or random variation) are:


  • Inherent fluctuations present in every process under normal operating conditions
  • The accumulated effect of many small, routine factors
  • Random, unpredictable variations within the system
  • Caused by the process design, equipment, materials, and methods as currently configured
  • Relatively small individual impacts that collectively create baseline variation

Examples of common causes:

  • Normal wear and tear of equipment
  • Slight temperature variations in a room
  • Minor fluctuations in raw material properties
  • Natural human performance variation
  • Normal electrical voltage fluctuations
  • Routine humidity changes

Special Causes of Variation


Special causes (also called assignable causes or exceptional variation) are:


  • Unusual, identifiable factors that are NOT part of the normal process
  • One-time or sporadic events that significantly impact the process
  • Sources that can be traced to specific circumstances or events
  • Often large in magnitude compared to common causes
  • Indicate the process is out of statistical control

Examples of special causes:

  • Equipment malfunction or breakdown
  • New, untrained operator on the line
  • Contaminated batch of raw materials
  • Power outage or electrical surge
  • Tool breakage or failure
  • Supplier change or quality issue
  • Incorrect setup or configuration
  • Environmental disaster or extreme weather

Key Differences at a Glance


AspectCommon CausesSpecial Causes
SourceBuilt into the process systemExternal or unusual events
PredictabilityPredictable in aggregate; unpredictable individuallyUnpredictable and sporadic
MagnitudeSmall, routine variationsLarge, noticeable deviations
FrequencyAlways presentOccasional or one-time
Process StateProcess is in statistical controlProcess is out of statistical control
ResponsibilityManagement and system improvementOperator identification and investigation
ResponseImprove the system; redesign processFind and eliminate the cause
Statistical PatternRandom, within control limitsExceeds control limits or unusual patterns

How Control Charts Detect These Causes


Control charts are the primary tool for distinguishing between common and special causes:


Common Causes on Control Charts


  • Points fall randomly within the control limits (typically ±3 sigma from centerline)
  • Show random scatter with no discernible pattern
  • Approximately 99.73% of points should fall within control limits in a stable process
  • Process exhibits statistical stability
  • No action needed except long-term process improvement

Special Causes on Control Charts


  • Points fall outside control limits
  • Show non-random patterns such as:
  • Runs (6+ points on one side of centerline)
  • Trends (6+ points consistently increasing or decreasing)
  • Clusters or unusual groupings
  • Extreme points far from centerline
  • Process is out of control
  • Immediate action required to identify and eliminate the cause

The Statistical Basis


The theoretical foundation comes from Walter Shewhart's work on control charts:


  • A stable process with only common causes produces a predictable distribution of output
  • Control limits are set at ±3 standard deviations (sigma) from the mean
  • When a special cause is present, the process mean shifts or variance increases, moving points outside these limits
  • The probability of a point exceeding 3-sigma limits by chance alone is approximately 0.27% for each tail

How to Answer Exam Questions on This Topic


Exam Tips: Answering Questions on Common Causes vs Special Causes of Variation


1. Read Carefully for Clues


  • Look for words like 'unexpected', 'unusual', 'sudden', 'one-time' → Special Cause
  • Look for words like 'routine', 'normal', 'inherent', 'ongoing' → Common Cause
  • If it describes something built into the system → Common Cause
  • If it describes an external event or failure → Special Cause

2. Consider the Source


  • Ask: 'Is this part of the normal process design?'
  • Yes = Common Cause
  • No = Special Cause

3. Identify Traceability


  • Can you point to a specific event or condition that caused the variation? → Special Cause
  • Is it caused by multiple routine factors accumulated? → Common Cause

4. Think About Control Chart Position


  • Question mentions data outside control limits → Indicates Special Cause
  • Question mentions data randomly within control limits → Indicates Common Cause
  • Question mentions unusual pattern or trend → Indicates Special Cause

5. Connect to Required Action


  • If the answer indicates immediate investigation and problem-solving → Special Cause
  • If the answer indicates long-term system improvement → Common Cause

6. Frequency and Consistency Matter


  • Always present = Common Cause
  • Sporadic or one-time occurrence = Special Cause

7. Watch for Scenario-Based Questions


Example Scenario: 'A manufacturing process normally produces widgets with a diameter of 10mm ± 0.5mm. Today, one batch came from a new supplier and shows diameters averaging 12mm.'


Answer Strategy:

  • The new supplier is an identified, external source → Special Cause
  • This is not part of normal operations
  • Action: Investigate supplier, reject batch, implement supplier control

8. Process Capability vs Control Questions


  • If asked about what can be improved to reduce variation:
  • Common causes require process redesign, equipment upgrades, or system changes
  • Special causes require identification and elimination

9. Know the SPC (Statistical Process Control) Context


  • Questions about control chart interpretation require understanding these definitions
  • The primary purpose of control charts is to distinguish between common and special causes
  • A process is 'in control' when only common causes are present

10. Multiple Choice Strategy


When faced with multiple choice options:

  • Eliminate options describing routine, ongoing conditions if special cause is correct
  • Eliminate options describing specific, identifiable events if common cause is correct
  • Look for the option describing the most manageable or systematic source of variation for common causes
  • Look for the option describing the most unusual or exceptional source for special causes

11. Avoid Common Mistakes


  • DO NOT confuse 'assignable cause' with 'special cause'—they can mean either depending on context
  • DO NOT assume all large variations are special causes; some can result from common causes in aggregate
  • DO NOT miss the importance of reproducibility: special causes are often one-time; common causes are recurring
  • DO NOT forget that eliminating special causes brings process into control, but reducing common causes improves capability

12. Essay/Short Answer Strategy


For open-ended questions, structure your answer:


  1. State the definition clearly
  2. Provide a relevant example from the scenario
  3. Explain why it is classified that way
  4. Describe the appropriate response or action
  5. Connect to the broader improvement goal

Example Answer Structure:
'This is a special cause because [reason]. An example is [specific event]. The appropriate action is [investigation/elimination]. This differs from common causes, which [contrast statement].'


Real-World Application Scenarios for Exam Preparation


Scenario 1: Manufacturing Defect Rate Increase


  • If defect rate increased after equipment maintenance was missed → Special Cause
  • If defect rate shows routine variation between 2-4% daily → Common Cause

Scenario 2: Call Center Response Time


  • If response time jumped due to a new phone system crash → Special Cause
  • If response time varies normally by 30 seconds per call → Common Cause

Scenario 3: Chemical Process Temperature


  • If temperature spiked due to thermostat failure → Special Cause
  • If temperature naturally fluctuates ±2°C throughout the day → Common Cause

Key Takeaways for Exam Success


  • Common causes are inherent to the process; special causes are external or unusual
  • Common causes keep process in control; special causes push it out of control
  • Use control charts to visually identify which type is present
  • Different actions apply: system improvement for common causes, investigation for special causes
  • The ability to distinguish between them is fundamental to Six Sigma methodology
  • Always look for identifiable, traceable sources for special causes
  • Always think about what is normal vs abnormal for the process
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CSSBB - Common Causes vs Special Causes of Variation Example Questions

Test your knowledge of Common Causes vs Special Causes of Variation

Question 1

A manufacturing team notices that their filling machine produces bottles with weights varying between 499g and 501g consistently over several weeks. One morning, the operator finds three consecutive bottles weighing 510g, 512g, and 508g. The team investigates and discovers a worn gasket in the filling valve. Which statement best describes the correct classification of these two patterns of variation?

Correct Answer: The 499-501g range represents common cause variation inherent to the process, while the 510g+ readings represent special cause variation from the worn gasket

The consistent 499-501g range reflects the natural, predictable variation built into the process (common cause), while the sudden shift to 510g+ bottles signals an assignable, identifiable source of variation (special cause) traced to the worn gasket.

Classifying the stable range as special cause is incorrect because it shows no abnormality and requires no intervention. Calling both patterns common cause incorrectly treats an identifiable, assignable event (the worn gasket) as normal process behavior. Saying the 510g+ readings are common cause with a shifted mean ignores that an assignable root cause was found, which is the defining characteristic of special cause variation.

Key concept tested: distinguishing common cause variation from special cause variation using process behavior patterns.

Question 2

A team monitors a packaging process using X-bar and R charts. The control chart shows several points beyond the upper control limit, and a runs test reveals a pattern of seven consecutive points above the centerline. Management asks you to recommend the best course of action. Given that the process also exhibits consistent, random scatter within the control limits during other periods, what should the team prioritize first?

Correct Answer: Investigate and eliminate the assignable causes driving the out-of-control signals before attempting to reduce the baseline variation

When a control chart displays out-of-control signals such as points beyond control limits and runs of seven consecutive points above the centerline, the team must first identify and remove the assignable (special) causes responsible for these signals before addressing common cause variation.

Tightening tolerances does not address root causes and confuses specification limits with control limits. Investigating common cause variation first is incorrect because special causes must be eliminated before the process can be considered stable, which is a prerequisite for meaningful common cause reduction. Performing a capability study on an unstable process yields unreliable results since capability analysis requires a process in statistical control.

Key concept tested: priority of eliminating special cause variation before addressing common cause variation in SPC.

Question 3

Which type of control chart signal would indicate that a special cause of variation should be investigated using root cause analysis tools?

Correct Answer: A point falling beyond the upper or lower control limit on the chart

A point falling beyond the upper or lower control limit is one of the primary signals of a special cause of variation, which warrants investigation using root cause analysis tools such as fishbone diagrams or 5 Whys.

Random fluctuation around the centerline represents normal common cause variation in a stable process. A stable pattern within control limits indicates the process is in statistical control with only common cause variation present. Natural process spread reflecting inherent capability describes common cause variation, which is addressed through process redesign rather than root cause analysis of assignable causes.

Key concept tested: distinguishing special cause signals from common cause variation on control charts.

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