Continuous Quality Improvement Program CIP - 6 Sigma Methodology

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 1-5 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 :-

Therefore,  process input (X) will always have an influence on process output (Y).  Most of process inputs can be controlled such as machine parameter setting and it could have an influence on process output Y. 

The simplify relationship of X to Y is Y= f(x)

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 :-

1. Attribute data  (discrete data)
2. 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.

To learn with case study to help you understand how to apply six to improve quality, click on the image or the link below👇:- 

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