For any Six Sigma project, analyzing data is a critical step after measurement and data collection. When using the DMAIC approach in the Six Sigma methodology, teams examine the data to identify the root causes of defects and errors in the Analyze phase.
Understanding the importance of thorough analysis using the right tools can help Six Sigma practitioners extract meaningful insights that are key to the overall goal of process improvement.
In this article, we’ll discuss the Six Sigma DMAIC Analyze phase and explain what it entails, as well as its importance, common tools used, and examples of how statistical analysis techniques are leveraged to find solutions in this stage of the DMAIC roadmap.
Six Sigma DMAIC Phases
Six Sigma typically follows the DMAIC (Define, Measure, Analyze, Improve, Control) problem-solving approach. Each phase has specific goals and deliverables that set the foundation for the next stage.
The Define phase identifies the problem and sets project goals and deliverables. Measurement establishes metrics, collects data on the current process, and determines baselines.
The Analyze phase, which is the focus of this article, involves identifying root causes through statistical analysis and tools like hypothesis testing and ANOVA.
The Improvement phase develops solutions to address the vital few root causes. Finally, the improved process is implemented, monitored, and controlled to hold the gains in the Control Phase.
Understanding the purpose of each DMAIC phase ensures you apply the appropriate tools at the right time. Progression through the phases provides a structure for completing process improvement projects successfully.
What is the DMAIC Analyze Phase in Six Sigma?
As earlier highlighted, the Analyze phase is the third stage in the DMAIC methodology. In this critical phase, the Six Sigma team dives into the data gathered during measurement to uncover the root causes of defects and variations.
The core purpose of analyzing is to make sense of the collected data. Statistical analysis, hypothesis testing, DOE, and other methods identify trends, patterns, and relationships between inputs and outputs. These insights reveal the vital few factors that contribute the most to problems in the process.
Armed with this understanding, the team can then focus on developing targeted solutions that will have the biggest impact. The Analyze phase prevents jumping to conclusions or solutions without first thoroughly investigating the facts. Analyzing the data is essential to set the direction and priorities for improvement.
At the end of this DMAIC stage, you will have clear actionable focus areas to address in the upcoming Improve phase.
Importance of the Six Sigma DMAIC Analyze Phase
The Analyze phase plays a crucial role in Six Sigma DMAIC projects. Here are five key reasons why thoroughly analyzing the data is so important:
Identifying Root Causes
By using statistical analysis tools and techniques, teams can pinpoint the vital few sources contributing the most to defects, errors, and variations in the process.
Understanding the underlying root causes paves the way for developing targeted solutions.
Prioritizing Issues
The analysis provides insights into the factors that are having the biggest negative impact on critical-to-quality metrics. This allows you to focus improvement efforts on addressing the most significant pain points and problems first.
Verifying Assumptions
Actually analyzing the data verifies whether your assumptions and hypotheses about the root causes are correct before moving forward with developing solutions. The data may reveal unexpected findings that go against initial assumptions.
Providing Validation
Rather than relying on subjective opinions or guesses about causes, the Analyze phase provides objective validation and backs up conclusions with hard data.
Setting the Direction
The results of the Analyze phase set the direction for the rest of the project by revealing where and how to improve. This prevents wasting time and resources improving the wrong or insignificant parts of a process.
What Happens in the Analyze Phase of DMAIC?
The Analyze phase involves several key steps you should take to extract meaning from the data gathered during the Measure phase. These are:
1. Reviewing Process Maps
Start by reviewing the process maps created earlier in the DMAIC flow. Look for opportunities to add more detail to capture the current state. Also, identify value-added vs non-value-added activities to pinpoint waste and inefficiency.
2. Identifying Potential Causes
Next, use tools like the Five Whys and fishbone diagrams to identify potential causes of defects and variations. Brainstorming sessions can uncover hypotheses to test.
3. Prioritizing Causes
Filter down the list of potential causes to the vital few factors that likely have the biggest impact. These become the focus for the data analysis.
4. Verifying Root Causes
Here statistical analysis tools come into play. Use methods like hypothesis testing, ANOVA, regression, and DOE to verify the true root causes from your prioritized list. The data reveals if your initial assumptions were correct.
5. Refining the Problem Statement
Based on the findings, the problem statement and goals may need refinement. For example, new root causes could emerge requiring additional data collection.
6. Documenting Results
Finally, document all data analysis, tools, findings, recommendations, and refinements to the project plan. This provides the foundation for the next stage which is the Improve phase.
Six Sigma Analyze Phase Deliverables
The Analyze phase concludes by compiling key deliverables and outputs that guide the rest of the DMAIC project. These deliverables are:
- Verified Root Causes: The core deliverable is the verified list of vital few root causes behind defects, validated through statistical analysis. These become the priority areas to address.
- Updated Process Maps: Enhanced process maps may reveal new problem areas or waste and shape later solutions.
- Data Analysis Results: Document all hypothesis tests, analytical findings, and discoveries from exploring the data.
- Refined Problem Statement: If new discoveries emerge, the problem statement may need adjustment, which would require revisiting the project charter.
- Prioritized Factors: Compile a priority list of the inputs and variables that contribute significantly to process issues to concentrate improvements on.
These outputs provide a solid fact base to drive the Improve phase as the team now knows where to focus solutions and has quantitative insights rather than assumptions.
Common Pitfalls in the Six Sigma Analyze Phase
For all its benefits, the Analyze phase is prone to several pitfalls that can undermine the credibility of your analysis. Being aware of these missteps can help avoid them.
Some pitfalls to watch out for in the Six Sigma Analyze phase include:
Not Gathering Enough Data Initially
Many project teams do not collect enough baseline data during the Measure phase. This provides insufficient information to conduct proper statistical analysis. If this is the case, then go back and gather more complete data sets before analyzing.
Prioritizing Incorrectly
Failing to use Pareto charts and other techniques for proper prioritization can lead teams to chase too many potential root causes without focusing on the vital few sources of the problems.
Relying on Basic Graphical Methods
While histograms, scatter plots, and trend charts provide visualization, relying solely on graphical analysis does not provide enough statistical rigor. Incorporate more advanced tools like hypothesis testing, DOE, regression, and ANOVA.
Biased Sampling
Careful sampling is crucial to the success of your analysis. Using biased, unrepresentative samples or insufficient sample sizes negatively affects the accuracy and validity of your analysis.
Disorganized Documentation
Letting analysis and discoveries get lost without thorough documentation causes rework and lack of continuity into later phases. Document statistical tests, results, recommendations, and decisions meticulously.
Scope Creep
It’s easy to get distracted by branching off into tangents outside the core problem statement. Continuously reference the project charter to avoid these rabbit holes.
Six Sigma DMAIC Analyze Phase Tools
The Analyze phase involves a wide range of statistical and analytical tools to uncover the vital few sources of defects and variations. Here are some examples:
Hypothesis Testing
Hypothesis testing allows you to make statistical decisions using experimental data. For example, you could test if production line speed has a significant effect on defect rate. Based on the test results, you would either reject or fail to reject the null hypothesis.
Regression Analysis
Regression analysis quantifies the relationship between a dependent variable and one or more independent variables. For instance, you could use linear regression to identify which factory environmental factors (temp, humidity, etc.) most influence product failure rates.
DOE (Design of Experiments)
DOE provides a structured framework for systematically varying input factors to determine their effect on process outputs. It helps identify which variables have the greatest impact. Taguchi methods and factorial designs are powerful DOE techniques.
ANOVA
Analysis of variance (ANOVA) evaluates differences between two or more processes or population means. Using ANOVA can reveal if batch oven temperature causes differences in material strength between production lines.
Process Mapping
Enhanced process mapping adds granularity to visualize waste, delays, and root causes. A detailed process map uncovers opportunities for improvement.
Pareto Analysis
The Pareto principle states that 80% of problems stem from 20% of the causes. Pareto analysis focuses efforts on the vital few sources with the biggest impact on quality.
Combining various statistical techniques provides hard data to back up your findings and recommendations.
Six Sigma Analyze Phase Example
Let’s walk through an example of the Analyze phase for a project aimed at reducing defects in a furniture manufacturer’s upholstery process.
The Measure phase generated data on defect rates, production volumes, and process cycle times. Initial Pareto analysis indicates that 80% of defects originate in the sewing workstation.
Enhanced Process Mapping
By observing the sewing station, the team creates a detailed process map showing the steps, inputs, and outputs. This reveals inefficiencies like repetitive motions and wait times.
Hypothesis Testing
The team uses hypothesis testing to evaluate which factors significantly influence stitching defects. The data shows that operator experience, machine age, and thread type all pass the significance threshold.
Design Of Experiments (DOE)
Next, a fractional factorial DOE varies combinations of thread type, stitching speed, and needle size to determine the factor settings that minimize defects.
Linear Regression
Regression modeling indicates the top predictors of upholstery failure rates are thread quality, operator fatigue, and stitch tension.
Documentation
All statistical analysis, tests, findings, recommendations, and discoveries are compiled into a report that shapes the Improve phase of the DMAIC process.
In this example, the Analyze phase zeroes in on the vital few variables that contribute to sewing defects so focused solutions can be developed. The data steers the team away from less impactful factors.
Final Thoughts on the DMAIC Analyze Phase
As discussed in this article, the SIX Sigman DMAIC Analyze phase is the engine that drives effective data-based decision-making in DMAIC projects.
Leveraging the right statistical tools and techniques uncovers the vital few root causes of defects and variations. This provides focus for developing targeted solutions that will have maximum impact.
A robust Analyze stage prevents wasting time and resources on insignificant factors while optimizing improvements. Allowing the data to guide your way sets up the rest of the project phases for success.
