In the world of process improvement and quality management, understanding variation is fundamental to achieving operational excellence. The Analyse phase of the DMAIC (Define, Measure, Analyse, Improve, Control) methodology represents a critical juncture where data transforms into actionable insights. Among the powerful analytical tools available to Lean Six Sigma practitioners, Multi Vari Studies stand out as an exceptional method for identifying and understanding the sources of variation in any process.
Understanding Multi Vari Studies: The Foundation
A Multi Vari Study, also known as a Multi Vari Chart or Multi Vari Analysis, is a graphical tool designed to visually display patterns of variation within a process. The term “Multi Vari” literally means “multiple variables,” and this technique allows practitioners to examine variation across different levels simultaneously. Unlike traditional statistical methods that might focus on a single variable, Multi Vari Studies enable teams to investigate multiple potential sources of variation in a structured, efficient manner. You might also enjoy reading about Analyse Phase: Understanding Regression Analysis for Process Variables in Six Sigma.
The primary objective of conducting a Multi Vari Study is to distinguish between three fundamental types of variation: positional variation (within-unit), cyclical variation (unit-to-unit), and temporal variation (time-to-time). By categorizing variation into these distinct types, practitioners can narrow down the search for root causes and focus improvement efforts where they will have the greatest impact. You might also enjoy reading about Fishbone Diagram Alternative Methods: Text-Based Root Cause Analysis for Problem Solving.
The Three Types of Variation Explained
Positional Variation (Within-Unit)
Positional variation refers to differences that occur within a single unit or sample. For example, in manufacturing a sheet of material, thickness measurements might vary from the left side to the right side of the same sheet. In a baking process, cookies on the same tray might have different levels of browning depending on their position relative to the heating element. This type of variation suggests that something within the immediate production environment is causing inconsistency.
Cyclical Variation (Unit-to-Unit)
Cyclical variation occurs between consecutive units produced under seemingly identical conditions. If one batch of products differs from the next batch, or if parts produced in consecutive cycles show different characteristics, cyclical variation is present. This type of variation often points to issues with machine settings, material lot changes, or process conditions that shift between production runs.
Temporal Variation (Time-to-Time)
Temporal variation represents changes that occur over extended periods. These differences might emerge between shifts, between days, or across weeks and months. Temperature fluctuations throughout the day, operator fatigue, equipment wear, or seasonal changes in raw material properties can all contribute to temporal variation.
Constructing a Multi Vari Study: Step-by-Step Approach
Planning the Study
Successful Multi Vari Studies begin with careful planning. The team must first identify the critical quality characteristic (CTQ) to be measured. This characteristic should be clearly defined, measurable, and relevant to customer requirements or process performance. Next, determine the sampling strategy, including how many units to sample, how many measurements to take per unit, and over what time period the study will be conducted.
Data Collection Strategy
The data collection plan should ensure that all three types of variation can be captured and analyzed. A typical sampling scheme might involve measuring multiple locations on each unit (positional), across multiple consecutive units (cyclical), and at different time periods (temporal). Consistency in measurement methods and environmental conditions during data collection is crucial for valid results.
Practical Example: Manufacturing Process Analysis
Consider a company manufacturing metal brackets where the critical dimension is the hole diameter, which must be 10.00 mm ± 0.05 mm. Quality issues have been reported, and the Six Sigma team decides to conduct a Multi Vari Study to identify the source of variation.
Sample Data Set Structure
The team designs a study to measure hole diameters under the following conditions:
- Three time periods: Morning (8 AM), Afternoon (2 PM), and Evening (8 PM)
- Three consecutive production runs at each time period
- Three measurements per bracket: top position, middle position, and bottom position
Sample Data Collection
Morning Shift (8 AM):
Run 1: Top = 9.98, Middle = 10.01, Bottom = 10.03
Run 2: Top = 9.97, Middle = 10.00, Bottom = 10.02
Run 3: Top = 9.99, Middle = 10.02, Bottom = 10.04
Afternoon Shift (2 PM):
Run 1: Top = 10.02, Middle = 10.05, Bottom = 10.07
Run 2: Top = 10.03, Middle = 10.06, Bottom = 10.08
Run 3: Top = 10.01, Middle = 10.04, Bottom = 10.06
Evening Shift (8 PM):
Run 1: Top = 10.04, Middle = 10.07, Bottom = 10.09
Run 2: Top = 10.05, Middle = 10.08, Bottom = 10.10
Run 3: Top = 10.03, Middle = 10.06, Bottom = 10.08
Interpreting Multi Vari Chart Results
When this data is plotted on a Multi Vari Chart, several patterns become immediately apparent. First, there is clear positional variation, with measurements consistently increasing from top to bottom on each bracket. This suggests that the drilling fixture may not be properly aligned, or there may be vibration that increases as the drill progresses through the material.
Second, there is noticeable temporal variation, with hole diameters progressively increasing from morning to evening. This pattern indicates that time-related factors, such as thermal expansion of the drilling equipment as it warms up throughout the day, may be contributing to the problem.
The cyclical variation (run-to-run within each time period) appears relatively small compared to the other two types, suggesting that batch-to-batch consistency is reasonable once the time and position factors are accounted for.
Benefits of Multi Vari Studies in Process Improvement
Multi Vari Studies offer numerous advantages over other analytical approaches. They provide visual clarity that makes patterns immediately recognizable, even to team members without advanced statistical training. The method is efficient, often requiring fewer samples than traditional design of experiments approaches while still delivering actionable insights.
Furthermore, Multi Vari Studies help teams avoid the costly mistake of addressing the wrong source of variation. In our bracket manufacturing example, without this analysis, the team might have spent resources investigating material properties or operator technique when the real issues were equipment alignment and thermal effects.
Best Practices for Successful Multi Vari Studies
To maximize the effectiveness of Multi Vari Studies, practitioners should follow several best practices. Ensure that measurement systems are capable and validated before beginning the study. Maintain strict consistency in data collection procedures to avoid introducing artificial variation. Document all process conditions during the study period, as this contextual information can be invaluable when interpreting results.
Involve cross-functional team members in both planning and interpretation phases. Operators, engineers, quality personnel, and maintenance staff each bring unique perspectives that can illuminate the meaning behind observed patterns. Finally, use Multi Vari Studies in conjunction with other analytical tools rather than in isolation. They work particularly well alongside hypothesis testing, correlation analysis, and designed experiments.
Common Pitfalls to Avoid
Several common mistakes can undermine the value of Multi Vari Studies. Insufficient sample sizes may fail to capture the true variation patterns, while excessively large samples waste resources without adding proportional value. Failing to randomize sample selection where appropriate can introduce bias. Perhaps most critically, jumping to conclusions based solely on visual patterns without confirming findings through additional analysis can lead to incorrect root cause identification.
Integration with DMAIC Methodology
Within the Analyse phase of DMAIC, Multi Vari Studies occupy a strategic position. They typically follow the validation of measurement systems and preliminary data analysis, and they precede more detailed root cause investigation. The insights gained from Multi Vari Studies directly inform the hypothesis generation process and guide subsequent analytical activities.
By narrowing the field of potential causes, Multi Vari Studies make the Analyse phase more efficient and focused. They help teams transition confidently into the Improve phase with a clear understanding of which process variables require intervention.
Transform Your Problem-Solving Capabilities
Multi Vari Studies represent just one of the many powerful analytical tools available through Lean Six Sigma methodology. Mastering these techniques requires both theoretical knowledge and practical application under the guidance of experienced practitioners. Whether you are seeking to advance your career, drive improvements in your organization, or develop a systematic approach to problem-solving, comprehensive training in Lean Six Sigma provides the framework and tools you need.
Professional Lean Six Sigma training programs offer hands-on experience with Multi Vari Studies and the full range of DMAIC tools. Through real-world case studies, simulations, and project work, you will develop the confidence and competence to lead improvement initiatives that deliver measurable results. The skills you acquire translate directly to bottom-line benefits: reduced defects, lower costs, improved customer satisfaction, and enhanced competitive advantage.
Do not let variation remain a mystery in your processes. Take control of quality and performance through proven analytical methods. Enrol in Lean Six Sigma Training Today and join the ranks of professionals who are transforming organizations worldwide. Whether you are pursuing Yellow Belt, Green Belt, or Black Belt certification, the journey begins with a single step. Invest in yourself and your organization’s future by developing the analytical capabilities that distinguish truly exceptional performers from the rest. The tools await, the methodology is proven, and the opportunity is now. Enrol in Lean Six Sigma Training Today and start your transformation.
