Common cause variations always exist in process inputs
and outputs therefore it is in the best
interest of manufacturer to reduce these variations and optimize the output performance
as much as possible through various methods. This is also known as a continuous
quality improvement program (CIP) which focus on reducing defects which contributed by
common cause variations and/or bring the process to optimum level to produce
consistent good quality product. Six sigma methodology is a popular method use
for this purpose. This method warrants for systematic phase by phase approach of deploying advance statistic tools to analyze the data collected in a manufacturing process.
The phases are :
Phase

Purpose

Common
tools used

1.
Define

Identify
and understand the problem, establish problem statement. Define SMART goal

Scope
the project
COPIS/SIPOC,
process
flow

2.
Measure

Identify
what parameter to measure, collect current state data to understand what is
the performance of the key parameter
(Y). Calculate cost of quality

Critical
to quality
Gage
Repeatability and reproducibility
GR&R
study
Statistical
control chart
Understand
data distribution
Process
capability study

3.
Analyze

Understand
what the root cause that is contributing to the problem, root cause
analysis, what are the X

Fish
bone diagram
Fault
tree analysis
5
whys
X
and Y correlation
Design
of experiment to identify and quantified process input (root cause)
which contribute to Y

4.
Improve

Establish
improvement plan to address multiple process input x which contributes to poor performance in Y. Pilot run the action plan and track if
there is any significant improvement

PFMEA
Brainstorming tools
Error
proofing
Design
of experiment to optimize Y through control for X
Anova
analysis

5.
Control

Standardized
and sustain
After
improvement action. Document action plan in work instruction and ensure all
follow the same work instruction 
Standardization Track the performance of Y output for a time line to
if the improvement is sustainable, Calculate cost savings

After
improvement control chart
After
process capability analysis
Cost
benefit calculation

6.
Report (optional)

Report
phase 15 to management
Lesson
learned and look for next project

Based on the input and output diagram, process input is termed as X and process
output performance is termed as Y per below :
In six sigma approach, we will need to understand what is the process output, Y which we would like to improve. We will need to collect data to understand which specific process input x have an effect on Y using various statistical tools. Once we have identified process input x which has an impact on Y, we can create action plan to optimize and control x. For example if we would like to improve yield
rate for a product, yield rate is Y, and process input which influence yield rate is x. After we have optimize process input X, then we will need to control X and monitor Y performance.
There are 2 types of process output data (Y) being collected
in a process :
 Attribute data (discrete data)
 Variable data (continuous data)
Data

Attribute

Variable

Also
known as

Discrete

Continuous

Characteristics

Cannot
be divided have decimal point , whole
round number
Clear
operational definition of
Go,
no go,
reject,
accept
pass,
fail

Generated
from a calibrated measuring device and can be divided infinitely
Distance,
Length, weight

Method

Counting
of good and defect parts after going thru inspection such as visual or using
go no go jig

Measure
using measurement device

Example

Yield
rate
Defect
rate
Count
of reject

Dimension
Length, width thickness, strength,
weight etc

The two types of data have very different traits and use
different statistical model use to analyze. During the define and
measurement phase we must understand
what type of process output data we will be using for the six sigma
project. The table below shows the
different statistic models which are used the 2 types of data in various six sigma
phase.
Phase

Statistical
analysis

Attribute
Data

Variable
Data

Measure

G
R&R

Attribute
GR&R

Variable
GR&R

Measure
Improve

Statistical
control charts

P
chart
nP
chart
U
chart
C
chart

X
bar R chart
X
mR chart
X
bar sigma chart

Measure
Improve

Process
performance index

DMPO

Process
capability study

Measure

Distribution

Binomial
Poisson

Normal

Analyze
Improve

Design
of Experiment

Binary
logistic regression

Simple/Multiple
Regression

Improve

Comparative
methods

ANOVA
methods
T
test or Z test

Categorical
analysis
Chi
Square

Attribute data is a count data, therefore a bigger sample size is needed for all the statistical analysis above compare to variable data. In six sigma, variable data is prefer and it always encourage to find a variable metrics from attribute data. An example would be printed circuit board assembly PCBA in circuit test (ICT) which test several electrical metrics at the same time, it would be more beneficial if we can study selected variable electrical metrics rather than just the yield rate of ICT.
In real life, sometimes it is not possible to study every
parts that had been produce especially in high volume manufacturing
environment, hence we will need to use
statistical analysis of sample to made
conclusion on entire population of the part produce through 6 sigma approach. We must maintain the rigour of data
collection and statistical analysis to make a correct conclusion on the
improvement made if it is effective to achieve consistent good quality product
at optimum level.
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