DMAIC Problem Solving Guide
A comprehensive reference guide for the Six Sigma DMAIC methodology
This guide provides key points and coaching questions for each phase: Define, Measure, Analyze, Improve, and Control. Use it as a reference for data-driven problem solving projects.
Six Sigma DMAIC - Data-Driven Problem Solving
DEFINE / MEASURE / ANALYZE
1
Define
Define the problem, project goals, and customer requirements
Key Points
- •Problem Statement: Clearly articulate the issue, its impact, and why it matters to customers and business
- •Project Scope: Define boundaries - what's included and excluded from this DMAIC project
- •Voice of Customer (VOC): Identify customer requirements and expectations (CTQs - Critical to Quality)
- •Business Case: Quantify financial impact, resources needed, and expected benefits
- •Project Charter: Document team members, timeline, goals, metrics, and stakeholder buy-in
- •High-Level Process Map: Create SIPOC (Suppliers, Inputs, Process, Outputs, Customers) diagram
Coaching Questions
- →Who is the customer and what are their critical requirements?
- →What is the business impact if this problem remains unsolved?
- →Do we have clear project boundaries and executive sponsorship?
- →Is the problem statement focused on the 'what' rather than the 'why' or 'how'?
2
Measure
Measure current process performance and establish baseline data
Key Points
- •Define Metrics: Identify Y (output) and X (input) variables to measure process performance
- •Data Collection Plan: Determine what data to collect, how, when, and who will collect it
- •Measurement System Analysis (MSA): Validate that measurement methods are accurate and repeatable
- •Process Capability: Calculate baseline performance using Cp, Cpk, Sigma level, or defect rates
- •Detailed Process Map: Document current state with value stream or detailed flow diagrams
- •Establish Baseline: Capture current performance data to enable before/after comparison
Coaching Questions
- →Are we measuring outputs (Y) that matter to customers and inputs (X) that drive those outputs?
- →Is our measurement system reliable and repeatable (MSA validated)?
- →What is the current process capability and sigma level?
- →Do we have sufficient historical data or do we need to start fresh data collection?
3
Analyze
Analyze data statistically to identify and verify root causes
Key Points
- •Statistical Analysis: Use graphical tools (histograms, box plots, scatter plots, Pareto charts) to visualize data patterns
- •Variation Analysis: Identify sources of variation using control charts, multi-vari analysis, and capability studies
- •Correlation & Regression: Determine relationships between input variables (Xs) and output variables (Ys)
- •Hypothesis Testing: Validate suspected causes statistically using t-tests, chi-square, ANOVA, or regression analysis
- •Root Cause Verification: Test each suspected cause with data and confirm with statistical significance (p-values, confidence intervals)
- •Prioritize Vital Few: Focus on statistically significant root causes using Pareto principle and effect size analysis
Coaching Questions
- →What does the statistical analysis reveal about root causes vs. symptoms?
- →Have we validated suspected causes with hypothesis tests and confirmed statistical significance?
- →Which input variables (Xs) have the strongest statistical relationship to the output (Y)?
- →What is the magnitude of variation from each root cause (common vs. special cause)?
IMPROVE / CONTROL
4
Improve
Develop, test, and implement solutions to eliminate root causes
Key Points
- •Generate Solutions: Brainstorm countermeasures that address validated root causes
- •Solution Selection: Evaluate options using criteria like impact, feasibility, cost, and risk
- •Pilot Testing: Test solutions on small scale before full implementation (PDCA cycles)
- •Design of Experiments (DOE): Optimize multiple variables simultaneously if applicable
- •Mistake-Proofing (Poka-Yoke): Design solutions to prevent errors from occurring
- •Implementation Plan: Define who, what, when, where, and how solutions will be deployed
- •Change Management: Prepare people, training, and communication for new processes
Coaching Questions
- →Do proposed solutions directly address the root causes we validated?
- →Have we piloted solutions and measured results before full rollout?
- →What might go wrong during implementation and how will we mitigate risks?
- →Are people trained and ready to adopt the new process?
5
Control
Control and sustain improvements over time
Key Points
- •Control Plan: Document how the improved process will be monitored and maintained
- •Statistical Process Control (SPC): Implement control charts to detect shifts or trends
- •Standard Operating Procedures: Update work instructions, visual aids, and documentation
- •Training & Handoff: Train process owners and operators on new standards
- •Monitoring Dashboard: Create visual management system for key metrics (Y and X variables)
- •Response Plan: Define actions to take when process goes out of control
- •Continuous Improvement: Schedule periodic reviews and identify next improvement opportunities
- •Project Closeout: Document results, financial benefits, and lessons learned
Coaching Questions
- →How will we know if the process starts to drift back to old performance?
- →Who owns this process and will sustain the improvements long-term?
- →Are control limits and reaction plans clearly defined and communicated?
- →What are the next improvement opportunities for this process?
Compiled by Art Smalley, Art of Lean, Inc.
For educational and personal use only. Not for commercial distribution without permission.
For educational and personal use only. Not for commercial distribution without permission.