Quality Tools List

Date: 16/Apr/2012

Author: K. S. Alok Ranjan

About: List of tools, methodologies and formulas

used in Quality Improvement methodologies in Six

Sigma. For any enquiry, please

ask@sevensolutions.in or call +91 7503 03 3057

DMAIC BB Tools, Methodologies and Formulas Apr-2012

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List of Phases involved in DMAIC BB approach with Tools and Methodologies used

1. Define Phase:

a. VOC – Voice of the Customer (Exploring customer CTQ)

b. QFD Diagram– Quality Function Deployment Diagram

c. CQT Drill Down Tree – Critical to Quality Drill Down Tree

d. KANO Diagram

e. Brainstorming and Multi Voting

f. Inside-Outside

g. Theme Selection Matrix

h. Scoping

i. Team Charter

2. Measure Phase:

a. SIPOC Model – Suppliers, Input, Process, Output, Customer Model

b. Process Mapping

c. Cross Functional Process Mapping

d. VOC – Voice of the Customer (Defining Specification and Defect)

e. Data Types – Understanding Data Characteristics

f. Sampling Techniques

i. Random/Probability

1st. Simple Random

2nd. Systematic

3rd. Stratified

4th. Clustered

ii. Non Random/Judgement

g. Gauge RnR

i. ANOVA-GRR – Analysis of Variance-Gauge Repeatability and Reproducibility

ii. Continuous GRR

iii. Attribute GRR

iv. Destructive GRR

h. Test/Retest

i. Calibration

j. X Bar-R Chart

3. Analysis Phase:

a. DPMO Worksheet

b. Sampling

c. Central Limit Theorem

d. Normal Distribution (Normality) Test

e. Run Chart

f. Scatterplot

g. Curve Fitting

h. Homogeneity of Variance

i. Estimates

j. Confidence Intervals

k. t-distribution

l. Benchmarking

m. Hypothesis Testing

n. Discreet Probability

o. t-Test – Statistical Hypothesis test

p. f-Test – Any Statistical test where test statistic has an F-Distribution under the Null Hypothesis.

q. Poisson Probability Distribution

r. Binomial Probability Distribution

s. Chi-Square test

t. ANOVA – Analysis of Variance (1,2,3,4... Way)

u. Multi Voting

v. Fishbone

w. Pareto Chart

x. Correlation

y. Regression Analysis (Simple & Multiple)

z. FMEA – Failure Mode and Effects Analysis

aa. Continuous Zst, Zlt

DMAIC BB Tools, Methodologies and Formulas Apr-2012

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bb. Discreet Zst, Zlt

cc. GLM

4. Improve Phase:

a. Screening DOE – Screening Design of Experiment

b. Optimization DOE – Optimization Design of Experiment (2 level Ascent/Descent & Simplex)

c. 3 level CCD/CCF

d. EVOP – Evolutionary Operations

e. TPC Analysis – Technical, Political and Cultural Resistance Analysis

5. Control Phase:

a. ANOVA-GRR – Analysis of Variance-Gauge Repeatability and Reproducibility

b. X Bar-R Chart

c. Acceptance Sampling

d. Mistake Proofing

e. Control Charts

Formulas and Calculations

a. Sample Mean: Arithmetic average of all Data Points in a data set

– Data Point

– Mean n – Number of Samples

b. Population Mean: Arithmetic average of all Data Points in a data set

– Data Point

– Mean N – Number of Data points

c. Mode: Most frequently occurring data point in a set

d. Median: Middlemost data point in a odd numbered data set of of two middlemost data point in an even numbered

data set.

e. Range: Difference between the maximum and minimum data point.

f. Sample Variance: Measure of Spread. It gives an idea of how widely spread the values of the random variable are

likely to be; the larger the variance, the more scattered the observations on average.

- Variance,

– Mean

n – Number of Data points

g. Population Variance: Measure of Spread. It gives an idea of how widely spread the values of the random variable

are likely to be; the larger the variance, the more scattered the observations on average.

- Variance,

– Mean

N – Number of Data points

DMAIC BB Tools, Methodologies and Formulas Apr-2012

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h. Sample Standard Deviation: Measure of the Spread. Dispersion of the Data

s – Standard deviation

– Variance

i. Population Standard Deviation: Measure of the Spread. Dispersion of the Data

– Population Standard Deviation

– Variance

j. Sample Size Formula for Continuous Data Population:

n – Number of Samples

Z – Calculated field of .

For 0.05 it would be Z97.5 which is 1.96 and for 0.01 it would be Z99.5 which is 2.58. [Need to understand

this bit more]

– Representation error allowed in the example. 0.05 (5%) or 0.01 (1%)

– Population Standard Deviation

- Interval of Error allowed for the Population Mean. E.g. (So is 5) or (So is 2) etc.

k. Sample Size Formula for Discreet Data Population:

n – Number of Samples

Z – Calculated field of .

For 0.05 it would be Z97.5 which is 1.96 and for 0.01 it would be Z99.5 which is 2.58. Thumb rule until I do

some RnD on net and find out.

– Representation error allowed in the example. 0.05 (5%) or 0.01 (1%)

– Average Population Proportion Non Defective

= [Need to understand this bit more]

- Interval of Error allowed for the Population Mean. E.g. (So is 5) or (So is 2) etc.