In the world of quality management and process improvement, understanding variation is the cornerstone of making meaningful changes. The Measure Phase of the DMAIC (Define, Measure, Analyze, Improve, Control) methodology represents a critical juncture where data collection and analysis transform assumptions into actionable insights. This comprehensive guide explores how organizations can effectively measure process variation sources to drive substantial improvements in their operations.
What Is Process Variation and Why Does It Matter?
Process variation refers to the natural fluctuations that occur in any process output over time. Whether you are manufacturing widgets, processing insurance claims, or serving customers at a restaurant, no two outcomes are exactly identical. This variation can stem from numerous sources, and understanding these sources is essential for improving process performance and consistency. You might also enjoy reading about Measure Phase: A Comprehensive Guide to Conducting Gage Repeatability and Reproducibility Studies.
Every process contains two fundamental types of variation: common cause variation and special cause variation. Common cause variation is inherent to the process and results from the normal functioning of the system. Special cause variation, on the other hand, arises from external factors or unusual circumstances that are not part of the regular process operation. You might also enjoy reading about Measure Phase: Essential Data Validation and Cleaning Techniques for Quality Improvement.
The Foundation of the Measure Phase
The Measure Phase serves as the foundation for all subsequent improvement efforts. During this phase, teams collect baseline data about current process performance, establish measurement systems, and identify the sources contributing to variation. Without accurate measurement, any attempt to improve a process becomes guesswork rather than science.
The primary objectives of the Measure Phase include validating the problem identified during the Define Phase, quantifying the current performance level, and establishing a baseline against which future improvements can be measured. Teams must develop operational definitions for all metrics, ensure measurement system accuracy, and collect sufficient data to characterize process behavior reliably.
Key Sources of Process Variation
People-Related Variation
Human factors represent one of the most significant sources of process variation. Different operators may perform the same task with varying levels of skill, experience, and attention to detail. Consider a customer service center where multiple representatives handle incoming calls. Even with standardized scripts and procedures, response times and customer satisfaction scores will vary based on individual communication styles, product knowledge, and problem-solving abilities.
Method-Related Variation
Variations in how a process is executed can lead to inconsistent results. When procedures are not clearly documented or when multiple methods exist for accomplishing the same task, variation increases. For instance, if some employees use one technique to complete a form while others use a different approach, the time required and error rates will likely differ significantly.
Machine and Equipment Variation
Equipment performance changes over time due to wear, calibration drift, and maintenance schedules. In manufacturing environments, this is particularly evident. A printing press may produce slightly different color densities throughout the day as it warms up, operates at full capacity, and then cools down during breaks.
Material Variation
Raw materials and inputs to a process often vary in quality and characteristics. A bakery purchasing flour from multiple suppliers may notice differences in moisture content, protein levels, or particle size that affect the final product consistency, even when following the same recipe precisely.
Measurement System Variation
Sometimes the variation we observe is not from the process itself but from how we measure it. Inconsistent measurement methods, poorly calibrated instruments, or subjective assessment criteria can introduce artificial variation into our data.
Environmental Variation
External factors such as temperature, humidity, lighting, and noise levels can influence process outcomes. A quality control inspector working in poor lighting conditions may miss defects that would be obvious under proper illumination.
Practical Example: Call Center Performance Measurement
To illustrate these concepts, let us examine a practical example involving a customer service call center. The organization wants to reduce variation in call handling times to improve efficiency and customer satisfaction.
Sample Data Collection
The team collected call duration data over a four-week period for 20 customer service representatives. Here is a simplified representation of their findings:
Week 1 Average Call Times (in minutes):
- Representative A: 8.2 minutes
- Representative B: 12.5 minutes
- Representative C: 7.8 minutes
- Representative D: 15.3 minutes
- Representative E: 9.1 minutes
The overall average call time was 10.6 minutes, but the range spanned from 7.8 to 15.3 minutes. This significant variation prompted the team to investigate the underlying causes.
Measurement System Analysis
Before concluding that the variation was real, the team conducted a measurement system analysis. They verified that the call timing system was accurate and consistent, ensuring that the observed variation reflected actual differences in call duration rather than measurement errors.
Identifying Variation Sources
Through careful analysis, the team identified several variation sources:
People: Representatives with more than two years of experience handled calls 25 percent faster on average than newer employees. Training levels and product knowledge varied considerably across the team.
Methods: Some representatives used a standardized troubleshooting guide consistently, while others relied on memory and intuition. Representatives using the guide demonstrated 30 percent less variation in their call times.
Materials: The quality of information in the customer database varied significantly. When customer history was complete and accurate, calls were resolved 40 percent faster than when representatives had to verify basic information.
Environment: Call times increased by an average of 1.5 minutes during peak hours when representatives felt rushed and stressed. Background noise levels also correlated with longer call durations.
Tools and Techniques for Measuring Variation
Statistical Process Control Charts
Control charts provide a visual representation of process performance over time, distinguishing between common cause and special cause variation. By plotting data points and calculating control limits, teams can identify when a process is stable and when unusual events require investigation.
Capability Analysis
Process capability studies compare the natural variation of a process to specification limits or customer requirements. Capability indices such as Cp and Cpk quantify how well a process can meet expectations, providing a clear metric for improvement opportunities.
Measurement System Analysis
Gauge Repeatability and Reproducibility studies assess the reliability of measurement systems. These studies separate variation caused by measurement error from true process variation, ensuring that improvement efforts focus on the right targets.
Multi-Vari Studies
Multi-Vari charts help identify which families of variation contribute most significantly to overall process variation. By examining within-unit, unit-to-unit, and time-to-time variation, teams can prioritize their investigation efforts efficiently.
Best Practices for the Measure Phase
Successful execution of the Measure Phase requires attention to several critical factors. First, ensure that your data collection plan is comprehensive and well-documented. Clearly define what you will measure, how you will measure it, when measurements will occur, and who will be responsible for data collection.
Second, collect sufficient data to characterize process behavior accurately. Small sample sizes can lead to incorrect conclusions and wasted improvement efforts. As a general guideline, collect at least 30 data points for each condition you wish to analyze, though more complex processes may require larger samples.
Third, validate your measurement system before drawing conclusions from your data. A measurement system that contributes more than 10 percent of total observed variation should be improved before proceeding with process analysis.
Fourth, involve process operators and stakeholders in the measurement planning and execution. Their knowledge of day-to-day operations often reveals variation sources that might otherwise be overlooked.
Moving Forward: From Measurement to Improvement
The insights gained during the Measure Phase establish the foundation for successful process improvement. By accurately quantifying variation and identifying its sources, teams can develop targeted solutions that address root causes rather than symptoms. This data-driven approach minimizes wasted effort and maximizes the return on improvement investments.
Understanding process variation is not merely an academic exercise. Organizations that master these concepts achieve remarkable results: reduced costs, improved quality, increased customer satisfaction, and enhanced competitive positioning. The systematic approach embodied in the Measure Phase transforms good intentions into measurable business outcomes.
Take the Next Step in Your Quality Journey
Mastering the Measure Phase and its associated tools requires both knowledge and practice. While this guide provides a solid foundation, becoming proficient in measuring and analyzing process variation demands comprehensive training and hands-on experience with real-world projects.
Lean Six Sigma training programs offer structured learning paths that take you from fundamental concepts to advanced applications. Whether you are just beginning your quality journey or seeking to enhance existing skills, professional certification provides the credentials and competence that employers value and that drive career advancement.
The methodologies and tools discussed in this article represent just a fraction of what comprehensive Lean Six Sigma training covers. From statistical analysis to change management, from project leadership to advanced problem-solving techniques, formal training equips you with a complete toolkit for driving organizational excellence.
Do not let another day pass watching processes underperform due to uncontrolled variation. The skills you develop through Lean Six Sigma training will serve you throughout your career, regardless of industry or role. You will join a global community of quality professionals who share best practices, support continuous learning, and drive meaningful improvements in their organizations.
Enrol in Lean Six Sigma Training Today and transform your understanding of process variation from theoretical knowledge into practical expertise. Take control of variation, drive measurable improvements, and position yourself as an invaluable asset to your organization. Your journey toward quality excellence begins with a single step. Make that step today and discover how mastering the Measure Phase can unlock your potential and accelerate your career growth.
