This document provides an overview of the DMAIC methodology for Six Sigma improvement projects. It describes the five phases of DMAIC - Define, Measure, Analyze, Improve, and Control. Key aspects and tools covered include identifying the problem and goals, ensuring reliable measurement, understanding sources of variation, testing solutions, implementing improvements, and sustaining gains through control plans. The overall approach provides a structured process for problem solving and continuous process improvement.

1. DMAICDMAIC
Six Sigma Improvement MethodologySix Sigma Improvement Methodology
DMAICDMAIC
Six Sigma Improvement MethodologySix Sigma Improvement Methodology

2. • A logical and structured approach to problem solving and process improvement
• An iterative process (continuous improvement)
• A quality tool with focus on change management
What is DMAIC ?
EE
Effectiveness
= QQ
Quality
Improvement
x AA
Acceptance

3. The Approach
Practical
Problem
Statistical
Problem
Statistical
Solution
Practical
Solution

4. Identify and state the practical problem
Validate the practical problem by collecting data
Convert the practical problem to a statistical one, define
statistical goal and identify potential statistical solution
Confirm and test the statistical solution
Convert the statistical solution to a practical solution
Methodology

5. VoCVoC - Who wants the project and why ?
The scope of project / improvement
Key team members / resources for the project
Critical milestones and stakeholder review
Budget allocation
Define

6. Ensure measurement system reliability
Prepare data collection plan
Collect data
- Is tool used to measure the output variable flawed ?
- Do all operators interpret the tool reading in the same way ?
- How many data points do you need to collect ?
- How many days do you need to collect data for ?
- What is the sampling strategy ?
- Who will collect data and how will data get stored ?
- What could the potential drivers of variation be ?
Measure

7. Understand statistical problem
Baseline current process capability
Define statistical improvement goal
Identify drivers of variation (significant factors)
Analyze

8. Root Cause Analysis (fish bone)
• A brainstorming tool that helps define and display major causes, sub causes and
root causes that influence a process
• Visualize the potential relationship between causes which may be creating
problems or defects
Analyze – Identify Drivers of Variation

9. Control – Impact Matrix
• A visual tool that helps in separating the vital few from the trivial many
Vital Few
High Control – High Impact
Cost IneffectiveCost Ineffective
Low Control – High ImpactLow Control – High Impact
Cost IneffectiveCost Ineffective
High Control – Low ImpactHigh Control – Low Impact
Trivial ManyTrivial Many
Low Control – Low ImpactLow Control – Low Impact
ControlControl
ImpactImpact
Analyze – Identify Drivers of Variation

10. Pareto Chart
• Pareto principle states that disproportionately large percentage of defects are
caused due to relatively fewer factors (generally, 80% defects are caused by 20%
factors)
0
5
10
15
20
25
30
35
L K A F B C G R D
0%
20%
40%
60%
80%
100%
Frequency Cumulative Frequency
Analyze – Identify Drivers of Variation

11. Process Map Analysis
• Visually highlights hand off points / working relationships between people,
processes and organizations
• Helps identify rework loops and non value add steps
VendorVendorProcess BProcess BProcess AProcess ACustomerCustomer VendorVendorProcess BProcess BProcess AProcess ACustomerCustomer
Analyze – Identify Drivers of Variation

12. Hypothesis Testing
• A statistical tool used to validate if two samples are different or whether a
sample belongs to a given population
Null Hypothesis (HNull Hypothesis (Hoo )) is the statement of the status quo
Alternate Hypothesis (HAlternate Hypothesis (Haa )) is the statement of difference
Analyze – Identify Drivers of Variation
One way ANOVA
Regression
Homogeneity of
Variance Moods Median
Chi-Square

13. Map improved process
Pilot solution
Identify operating tolerance on significant factors
Improve

14. Ensure measurement system reliability for
significant factors
Improved process capability
Sustenance Plan
- Is tool used to measure the input / process variables flawed ?
- Do all operators interpret the tool reading in the same way ?
- Statistical Process Control
- Mistake Proofing
- Control Plan
Control

15. Control Plan
• Have the new operating procedures and standards been documented ?
• What Statistical Process Control (SPC) tools will be used to monitor the process
performance ?
• Who will review the performance of the output variable and significant factors on
closure of the project and how frequently ?
• What is the corrective action or reaction plan if any of the factors were to be out
of control ?
Control – Sustenance Plan