In the world of process improvement and quality management, distinguishing between systemic and random causes of variation is fundamental to creating meaningful change. This critical skill lies at the heart of the Analyse phase in Lean Six Sigma methodology, where professionals work to identify root causes and determine which problems require intervention and which are simply natural variations in a process.
Understanding this distinction can transform how organizations approach problem-solving, saving countless hours and resources while directing efforts toward changes that truly matter. This comprehensive guide explores the methodologies, tools, and practical applications that help quality professionals make these crucial determinations. You might also enjoy reading about Analyse Phase: Identifying Value Added vs Non Value Added Activities in Lean Six Sigma.
What Are Systemic and Random Causes?
Before delving into identification techniques, it is essential to understand what these terms mean in the context of process improvement. You might also enjoy reading about Process Bottleneck Analysis Methods in the Lean Six Sigma Analyse Phase: A Complete Guide.
Systemic Causes (Special Cause Variation)
Systemic causes, also known as assignable or special causes, are variations in a process that stem from specific, identifiable sources. These causes are not inherent to the process itself but rather result from external factors or unusual circumstances. They create patterns in data that deviate significantly from the expected range of variation.
Examples of systemic causes include:
- Equipment malfunction or calibration issues
- Changes in raw material suppliers or specifications
- Inadequate training of new employees
- Seasonal fluctuations in demand
- Implementation of new procedures without proper testing
Random Causes (Common Cause Variation)
Random causes, alternatively termed common causes, represent the natural variation inherent in any process. These variations occur due to countless small, uncontrollable factors that are built into the process design. They create a stable, predictable pattern over time.
Examples of random causes include:
- Minor temperature fluctuations in a controlled environment
- Slight differences in material properties within specifications
- Normal wear and tear of equipment
- Human variation in repetitive tasks
- Minor measurement system variation
Why This Distinction Matters
The ability to differentiate between these two types of variation determines the effectiveness of improvement efforts. Treating random variation as if it were systemic leads to overreaction, process tampering, and increased variation. Conversely, ignoring systemic causes as random variation allows problems to persist and quality to deteriorate.
Consider a manufacturing example: A company producing plastic bottles notices variation in bottle weight. If they adjust machine settings every time they see a weight outside their ideal target (when the variation is actually random), they may inadvertently create more variation. However, if a supplier change has introduced systemic variation and they ignore it as random, they may continue producing defective products.
Tools for Identifying Systemic vs Random Causes
Control Charts: The Foundation
Control charts represent the most powerful statistical tool for distinguishing between systemic and random causes. These charts plot process data over time and include statistically calculated control limits that define the range of expected random variation.
Let us examine a practical example using sample data from a call center measuring average handle time (AHT) in minutes:
Sample Dataset: Daily Average Handle Time (20 Days)
Day 1: 8.2 minutes | Day 2: 8.5 minutes | Day 3: 8.1 minutes | Day 4: 8.4 minutes | Day 5: 8.3 minutes
Day 6: 8.6 minutes | Day 7: 8.2 minutes | Day 8: 8.4 minutes | Day 9: 8.5 minutes | Day 10: 8.3 minutes
Day 11: 11.2 minutes | Day 12: 11.5 minutes | Day 13: 11.3 minutes | Day 14: 11.1 minutes | Day 15: 11.4 minutes
Day 16: 8.4 minutes | Day 17: 8.3 minutes | Day 18: 8.5 minutes | Day 19: 8.2 minutes | Day 20: 8.4 minutes
When this data is plotted on a control chart with calculated control limits (Upper Control Limit at 9.8 minutes and Lower Control Limit at 7.2 minutes), days 11 through 15 clearly fall outside the expected range. This pattern indicates a systemic cause requiring investigation. In this case, investigation revealed that a new software system was implemented on day 11 and removed on day 16 after technical issues were discovered.
Rules for Identifying Systemic Causes
Quality professionals use several rules to identify systemic causes on control charts:
- Rule 1: One or more points beyond the control limits
- Rule 2: Eight or more consecutive points on one side of the center line
- Rule 3: Six or more consecutive points steadily increasing or decreasing
- Rule 4: Fourteen or more consecutive points alternating up and down
- Rule 5: Two out of three consecutive points beyond two standard deviations from the center line
Histogram Analysis
Histograms provide visual representations of data distribution and can reveal patterns suggesting systemic causes. A normal distribution suggests random variation, while bimodal distributions, skewed patterns, or unexpected shapes often indicate systemic issues.
Using our call center example, if we created a histogram of all 20 days together, we would see a bimodal distribution with one cluster around 8.3 minutes and another around 11.3 minutes, clearly indicating two different process states and a systemic cause affecting days 11 through 15.
The Analyse Phase Approach
Step 1: Data Collection and Verification
The foundation of any analysis begins with reliable data. Ensure your measurement system is accurate, precise, and consistent. Conduct measurement system analysis (MSA) to verify that variation in your data comes from the process, not from how you measure it.
Step 2: Statistical Analysis
Apply appropriate statistical tools based on your data type. For continuous data, control charts, capability analysis, and hypothesis testing provide insights. For discrete data, use proportion charts, Pareto analysis, and chi-square tests.
Step 3: Process Analysis
Map the current process using value stream mapping or process flow diagrams. Identify inputs (X variables) that potentially affect outputs (Y variables). Use cause-and-effect diagrams to brainstorm potential systemic causes.
Step 4: Root Cause Investigation
When systemic causes are identified through statistical analysis, employ root cause analysis techniques such as the Five Whys, fishbone diagrams, or failure mode and effects analysis (FMEA) to understand underlying reasons.
Real World Application: Manufacturing Quality Example
Consider a pharmaceutical company monitoring tablet weight in their production process. They collect samples every hour and record the following data over two shifts:
Shift 1 (Morning): 250.2mg, 250.5mg, 250.1mg, 250.4mg, 250.3mg, 250.6mg, 250.2mg, 250.4mg
Shift 2 (Afternoon): 252.1mg, 252.3mg, 252.4mg, 252.2mg, 252.5mg, 252.3mg, 252.2mg, 252.4mg
While both shifts show acceptable random variation within their respective ranges, the consistent shift upward in the afternoon indicates a systemic cause. Investigation revealed that afternoon temperatures in the production area were higher, affecting material flow properties. This discovery led to climate control improvements, eliminating the systemic variation.
Common Mistakes to Avoid
Organizations frequently make several errors when attempting to distinguish between cause types:
- Tampering: Adjusting processes in response to random variation, thereby increasing overall variation
- Insufficient data: Making conclusions based on too few data points
- Ignoring context: Failing to investigate what changed when systemic causes appear
- Over-reliance on intuition: Making decisions based on gut feeling rather than statistical evidence
Building Capability Through Training
The ability to accurately identify systemic versus random causes requires both theoretical knowledge and practical application. While this article provides an overview, true proficiency comes through comprehensive training and hands-on experience with real-world datasets and scenarios.
Professionals who master these analytical skills become invaluable assets to their organizations, driving improvements that deliver measurable results. They learn to use sophisticated statistical software, interpret complex data patterns, and communicate findings effectively to stakeholders at all levels.
Conclusion
The Analyse phase serves as the critical bridge between understanding current performance and implementing effective solutions. By accurately distinguishing systemic causes from random variation, organizations avoid wasting resources on ineffective solutions while addressing the true drivers of process performance.
This analytical capability transforms reactive firefighting into proactive process management. It enables data-driven decision making, reduces defects, improves customer satisfaction, and ultimately drives competitive advantage in increasingly demanding markets.
The journey from novice to expert analyst requires dedication, but the rewards are substantial. Organizations equipped with these analytical capabilities consistently outperform competitors who rely on guesswork and intuition.
Enrol in Lean Six Sigma Training Today
Ready to master the skills needed to identify systemic and random causes in your processes? Our comprehensive Lean Six Sigma training programs provide the knowledge, tools, and practical experience you need to become a confident process improvement professional.
Whether you are seeking Yellow Belt, Green Belt, or Black Belt certification, our expert instructors guide you through real-world case studies, hands-on projects, and interactive learning experiences. You will gain proficiency in statistical analysis, process mapping, root cause investigation, and solution implementation.
Do not leave process improvement to chance. Invest in your professional development and your organization’s success. Enrol in Lean Six Sigma training today and join thousands of certified professionals who are transforming businesses worldwide. Visit our website or contact our training advisors to discover which certification path is right for you.
