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 varia…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
Aspect
Common Causes
Special Causes
Source
Built into the process system
External or unusual events
Predictability
Predictable in aggregate; unpredictable individually
Unpredictable and sporadic
Magnitude
Small, routine variations
Large, noticeable deviations
Frequency
Always present
Occasional or one-time
Process State
Process is in statistical control
Process is out of statistical control
Responsibility
Management and system improvement
Operator identification and investigation
Response
Improve the system; redesign process
Find and eliminate the cause
Statistical Pattern
Random, within control limits
Exceeds 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:
State the definition clearly
Provide a relevant example from the scenario
Explain why it is classified that way
Describe the appropriate response or action
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