SixSigma GB+Module

7/29/2019 SixSigma GB+Module

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6 SigmaQUALITY The Need

With the advent of Globalization every organization relentlessly

pushes themselves for the processes that gives them error freeproducts in a competitive environment. With the growth of

competitive environment - error free products acts as aQualifying parameter only. However the survival demands more.

What is more?

More is defined by the Customer and the competitive environment.

- Critical to Customer- Critical to Delivery

- Critical to Cost

- Critical to Process etc.


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6 SigmaQUALITY Early Growth

Motorola suffered a severe downturn when a Japanese manufacturer invaded

the chip market in 1985-86. In 1987, Motorola started process improvementprojects to counter the invasion. Initial finding was the process cycle time,

but quality fell off the radar. Later the managers began to recognized the

quality problem was due to wait times, inventory mismanagement etc.

Motorolas biggest customer Ford Motors took considerable interest in theprocess improvement and Six Sigma kick-off.

Lots of people participated in the invention of Six sigma over a long period

of time. The Six Sigma methodology package was rolled out in 1987-88.

Motorola was honored with Malcolm Baldrige National Quality Award in 1988.


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6 SigmaQUALITY Project Priority

Priority #3

Cause Known

Solution Unknown

Priority #1

Cause Unknown

Solution Unknown

Cause Known

Solution Known

Priority #2

Cause Unknown

Solution Known

Cause

Soluti

on

Known Unknown

Unknown

K

nown


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6 SigmaQUALITY Methodology

Definewhats important

Measure

how were doing

Analyzewhats wrong

Improveby fixing whats wrong

Controlto guarantee performance


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AnalyzeOpportunity

PlanImprovement

FocusImprovement

DeliverPerformance

ImprovePerformance

6 SigmaQUALITY Parallel-Lean Six Sigma

Lean Six Sigma

Lean Six Sigma is combination of Lean Method and Six Sigma. The method

builds on the knowledge, methods and tools derived from decadesof operational improvement research and implementation.

Lean approaches focus on reducing cost through process optimizations.

Its aim is effectiveness, not just efficiency. Broadly Lean Six Sigma is for

operational improvements - refining existing processes to reduce cost.

Six Sigma is about meeting customer requirements, stakeholder expectation

and improving quality by measuring and eliminating defects.


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6 SigmaQUALITY Six Sigma Jargon

Champion -The Champion is a person responsible for instilling the vision of Six Sigma and communicating

it across the firm. They are usually the upper management or executive officers. The assistin dedicating the resources and choosing the projects.

Master Black Belt -The MBB people are those, who have extensive experience in Six Sigma methodology.

MBBs acts as a coach to its team member in project planning and result evaluation.

The BB people leads the project on fulltime basis. BBs are certified people with hands on

Six Sigma projects. They have strong understanding of statistical methods of data collection

and analysis. They are the project managers and are responsible for all traditional roles.

Black Belt -

Green Belt -The GBs acts as an assistant to BBs in their job. GBs have the basic understandings of

statistic but dont have the expertise and experience like BBs. GBs does the legwork of BBsin project realization. However, they lead the project on a part time basis.


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6 SigmaQUALITY Define Phase

Involves:

ProjectCharter

To develop a Project Charter. Objective Develop focus and purpose of the team.

VOC

Use Voice of Customer to identify customer needs.

Objective Identify the actual pain / opportunity.

CTQs

Translate customer needs into Critical To Quality elements. Objective Identify Project CTQs.

ProcessMap

Translate customer needs into Critical To Quality elements. Objective Identify Project CTQs.


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6 SigmaQUALITY Define Phase

Project Charter: Elements

BusinessCase

Problem

Statement

GoalStatement

Scope

Milestone

- Why is the project worth doing

- Consequence of not doing the project

- Description of problem

- What is not meeting customers requirement

- Improvement the team desires

- What lies in scope and whats out of scope

- Project plan with timeline


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6 SigmaQUALITY Define PhaseProject Charter: RACI Model

Responsibility Charting is a technique for identifying functional areas where there are

process ambiguities . It brings the differences out in open and resolving them through a

cross-functional collaborative effort. It defines the participation roles to different departments

linked with the project.

Responsible (R) doerThe doer(s) is responsible for action/implementation. The doers are those who actually

completes the task.

Accountable (A) the buck stops hereThe accountable persons are those who are actually answerable for activity.

This includes yes or no authority. Only one A is assigned to an action.

Consult (C) in the loopThe consultants are consulted prior to a final decision or action.

Inform (I) keep in the pictureThe communication to this individual is a one-way. They are just informed about the changes

and are expected to take action as a result of the changes.


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6 SigmaQUALITY Define PhaseRACI Charting: Example

RACI Closing Guideline:

1. Place the Accountability & Responsibility at lowest feasible level.

2. There can be only one accountable individual per activity.

3. Authority must accompany accountability.

4. Minimize the number of Consults & Informs.

5. Horizontal & Vertical Analysis of RACI chart indicates flaws in roles/responsibilities.

Mother Father Sam Jenny Clark Kids

A C R

I I A R

C A/R R

C A/R R

C A/R

C A R

Feed the Dog

Play with Dog

Morning Walk

Evening walk

Clean the Dog

Clean the Table


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6 SigmaQUALITY Define PhaseVOC / CTQs if we can find it, we can focus on it

Voice of customer (internal/external) is tracked through various means. Customer Surveys

and Complaints are generally used for external customers while executive level discussions

are intended for refining/improving internal processes. Benchmark data is essential for

process developments/innovations with respect to peers.

Customer Surveys Complaints Executive leveldiscussions

Benchmark Data

Example- CTQs

Critical To Quality(CTQ)

Dimensions

Critical To Delivery(CTD)

JIT Compliance

Critical To Cost(CTC)

RM/Scrap/Inventory

Critical To Process(CTP)

Process Cycle Time

Definition of CTQs changes as per projects, However, it should be very clear and straight-

forward as it forms the base of project.


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6 SigmaQUALITY Define PhaseProcess Mapping: y = f(x, x, x ..)

Process Mapping begins with SIPOC map. Once SIPOC is prepared, Process part is again

fragmented into a high level process diagram involving all work centers that affects Output.

Majority of this process comes underMeasure Phase, but to prioritize and link CTQs to

various process inputs.

Supplier Input Process Output Customer

High level Process Map

S I P O C


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6 SigmaQUALITY Measure PhaseTools:

Measure Phase is a pioneer stage in quantifying, qualifying and validating the Six Sigma

needs. It is in this phase that a more refined data evolves in terms of CTQs. And the phase

translates the physical behavior into statistical problem. The phase also makes an

assessment of current process performance.

Many companies have their own methodology of recording day-to-day process data

however, methods of measurement revolves around above stated five tools.

Process Mapping YX MatrixMeasurement

Systems Analysis(MSA)

Capability Analysis


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6 SigmaQUALITY Measure Phase

High Level Process Mapping: Example-Drilling

A high level process mapping is very important in understanding the interaction of various

process interaction and linkage of CTQs with the process centers. Majority of concentration

is put on in eliminating the excess or the wastage of cycle time as against to the optimalrequirement.

Start Work OrderQTY/PartRefer theDrawing

Use Jig

Jig

Required?

Go forMarking

Prepare

Tooling

Start

Drilling

Is

Drilling

OK

End

Rework

Possible

Rejection

Report

Rework

Report

Rework Area

Rejection

No

YesNo

Yes

No

Yes


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6 SigmaQUALITY Measure PhaseYX Matrix:

The YX Matrix is an exhaustive process study to zero in on to the process characteristic

that has greater impacts on CTQs. Hence, we deduce the weight of different process

impacting CTQs.

Output Variable (Y)

# Rank=

Score x Weight

Weld

Strength

Weld

Appearance

Pinhole

Density

Weld

FlexibilityRank

Weight 10 8 7 4

Weld

Frequency8 4 7 5 181

Power

Amplitude8 8 4 9 208

Initial Gap 5 2 3 4 103

Contact

Pressure2 7 10 2 154I

nputVariable(X)


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Process

Variation

Actual ProcessVariation

MeasurementProcess Variation

Variation due toOperator Variation due toEnvironmentVariation due to

Instrument

6 SigmaQUALITY Measure PhaseMeasurement System Analysis:

MSA is used to separate the variations of measurement system in the process.

Process MeasurementProcessInput Output

Repeatability: Variation when one person repeatedly measures same unit with same system

Reproducibility: Variation when two people measuring same unit with same system

Tolerance: Expectation of error range


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6 SigmaQUALITY Measure PhaseGage R & R Analysis: Example

Gage R & R analysis presented above seems to be complicated , but in fact its not. It is simply aprocess where Repeatability & Reproducibility is measured.

R & R < 10% :

Satisfactory

R & R 10 - 30% :

May be Satisfactory

R & R > 30% :

Unsatisfactory


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6 SigmaQUALITY Measure PhaseCapability Analysis:

Process

Incapable

Process CapabilityCp

Cp is the ratio of tolerance width to short termprocess spread.

Estimates instantaneous capability of theprocess.

Process PerformanceCpk

Cpk is the ratio of the distance measuredbetween process mean and specificationlimits closer to half the total process spread.

Estimates measure of capability of a processto meet established customer requirement.

LSL USL

Cp


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6 SigmaQUALITY Measure PhaseCp & Cpk :

Cpk = Cp : Process Mean is on target.

Cpk = 0 : Process Mean falls on one of the specification limits. 50% of process falls

beyond specified limits.

Cpk < -1 : Process Mean is completely out of specified limits.

Cp = 2 : It implies the accomplishment of short term objective.

Short Term process capability = 3 x Cp.

At Cp = 2, Process Capability = 6

Cp =USL LSL

6STCpl =

X LSL3ST

-Cpu =

USL X3ST

Cp Cpk

-

Cpk = min {Cpl, Cpu}

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6 SigmaQUALITY Analyze PhaseObjective: Brainstorming

Determine X that causes costly defects. Identify the various Hypothesis.

Select tools to approve/disprove the Hypothesis.

Perform statistical test and check for significance.

If cause is statistically and practically significant recommend for Improve phase.

Analyze phase is one of the most important phase of any project. Process data obtained inMeasure phase is analyzed and cross functional teams are consulted for brainstorming

sessions. The session particularly aims in capturing ideas of CFTs that will help in narrowing

the scope of issue process flaws. However, when a very large CFTs are concerned then theideas are not always uniform in structure. To bring uniformity Nominal Group Process

technique is used. NGP implies the segregation of the ideas and grouping them in accordanceto the process/department to which it belongs. This is called as Affinity Diagram of ideas.

After the brainstorming sessions, we arrive at number of causes. These causes are put

together further analysis in the Improve phase.

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6 SigmaQUALITY Analyze PhaseFishbone Diagram:

Fishbone diagram is a Cause & Effect (C&E) diagram which resembles the shape of a fish

bone. It is also called as Ishikawa diagram, named after its creator Kaoru Ishikawa .

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6 SigmaQUALITY Analyze PhaseFMEA:

The main objective of FMEA is to identify ways the product or process can fail and to plan

in order to prevent those failure. FMEA in general supports Analysis Phase.Characteristics of FMEA:

1. Identify potential failure modes.

2. Rate the severity of their effect.

3. Identify potential causes.

4. Evaluate objectively the probability of occurrence of these causes.5. Evaluate the ability to detect the causes.

6. Rank order process deficiencies.

7. Focus on eliminating / controlling these variables.

8. Evaluate objectively the probability of occurrence of these causes.

Severity x Occurrence x Detection = Risk Priority Number (RPN)RPN > 80 Critical need for immediate action.

Severity > 5 Safety related defects.High RPN & Low Detection Flaws in internal tests.

High Occurrence Poor process capability.

Detection (1 to 10) Good Control to Poor Control

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6 SigmaQUALITY Analyze PhaseFMEA: Example

FMEA is a dynamic document and each process owner keeps updating it whenever required.

Process Action Revised Result

ProductFunction

PotentialFailure

Mode

PotentialEffects

ofFailure

Severity

(1-10)

PotentialCauses

Occurrence

(1-10)

CurrentControl

Detection

(1-10)

RPN

Recommended

Action

Responsibility&

TargetDates

ActionTaken

Severity

1-10

Occurrence

(1-10)

Detection

1-10

RevisedRPN

Stock

Inventory

Wrong

Location

Delayin

Finding

5

Correct

LocationFull

7

Checkedtwic

e

ayear

9315

MonthlyStoc

k

Audit

Raghu,

10th

July2011

AuditPlan

Developed

Stock

Inventory

Damaged

Insufficient

S

tock

7

SupplierDefect

3

Inc

oming

Inspection

8168

Pr

ocess

Ins

ection

Padm

avati,

10th

July2011

Plan

Dev

eloped

StockInventory

Damaged

Insufficient

Stock

7

HandlingError

5

Standard

Procedure

9315

ReviewSOP

Sushmit,

5th

July2011

SOPRevised

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6 SigmaQUALITY Analyze PhasePareto Analysis:

Vilfredo Pareto, a 19th century Italian economist who discovered that 80% of the land in Italy

was owned by 20% of the population, established the principle. Later it was redefined as

80% of the problems has their roots in 20% of the causes. Hence Pareto Analysis will give

the vital few 20% that accounts for massive 80% defects in the process.

Example Lets consider delays in credit card processing by a bank with following reasonsand frequency of occurrences.

Category Frequency

No Address 9

Illegible 22

Current Customer 15

No Signature 40

Other 8

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Reorganize the frequency data in decreasing order . Then calculate the relative percentage

on 100 percent level then calculate the cumulative percentage.

Category FrequencyRelative

Percentage

Cumulative

Percentage

No Signature 40 43% 43%

Illegible 22 23% 66%

Current Customer 15 16% 82%

No Address 9 10% 92%

Other 8 8% 100%

Run the Pareto analysis tool in Sigma XL or in XL select the graphical plotting tool that

resembles the graph shown in next slide.

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Result:

The Breaking Point: The breaking point divides the vital few from trivial many in the chart.

Normally, the vital few starts fro 70% and above. In the above result it is No Signature,

Illegible.

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6 SigmaQUALITY Analyze PhaseHypothesis Testing:

Hypothesis testing is a value judgment made about a circumstance, a statement made about

a population. As the name suggests, it is a technique to test the efficacy of changes in anyprocess. Lets assume that we have a data of different types of crime in a city. Now, ahypothesis can be made for the trends in a span of 10 years. But to ascertain this hypothesis

a standard error based hypothesis testing using is conducted, which validates the hypothesis

of crime in span of 10 years. However, in actual applications when certain data

tends to be normal i.e. the data follows normal distributions, then standard test called ast-testis applied to validate the hypothesis. To have a better command over this test one

needs have a clear idea of right tools and have better understanding of statistical tools.

Common Tools:

ANOVA

Normal Data More thantwo group of

equalVariance.

Welch

ANOVA

Normal Data More thantwo group of

unequalVariance

Paired t-test

Normal Data For matched

two groups.

t-test Normal Data

Forunmatchedtwo groups

Chi-Square

For DiscreteData

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6 SigmaQUALITY Analyze PhaseHypothesis Testing: Elements

1. Null Hypothesis (Ho): Null hypothesis is the hypothesis to be tested. In the earlierexample Ho : Crime trends in a span of 10 years.

2. Alternate Hypothesis (H orHa):Alternate hypothesis is opposite to null hypothesis.It is assumed when null hypothesis is rejected after test. In earlier example

H : No trends in crime in a span of 10 years.

3. Sample t-test: Sample t-test is conducted to reject or fail to reject null hypothesis.

4. Level of Risk : Level of risk implies the kinds of error associated while making an

inference from hypothesis test. Two types of error can be made. The experiment

can falsely reject a hypothesis that is true. In this case we say the error is Type Ior the (alpha) error. If test actually fails the hypothesis that is actually false

then it is Type II or (beta) error.

5. Decision Rule: Decision pertains to condition of rejecting or failing to reject the hypothesis.

Predefined confidence level helps in decision.

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Correct Decision

Innocent Individual GoesFree

Incorrect Decision

Guilty Individual Goes FreeType II or Error

Incorrect Decision

Innocent Individual IsDisciplined

Type I orError

Correct Decision

Guilty Individual IsDisciplined

6 SigmaQUALITY Analyze PhaseHypothesis Testing: & error

The above example shows the condition of Type I and Type II error. The decision rule for

above verdict is based on the confidence level of and .

When p-value < 0.05: Reject H0 and when p-Value > 0.05 : Fail to Reject H0

The American Trial SystemIn Truth, the Defendant is:H0: Innocent HA: Guilty

Innocent

Guilty

Verdict

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6 SigmaQUALITY Analyze PhaseChi-Square Test: Discrete data

Chi-Square test applies where discrete data is available for hypothesis testing. Chi-Squaretest is usually conducted in a situation where customer feedback/VOC trends are compared

with expected trends.

Example Lets consider a company X makes customer survey for satisfaction quarterlyand compares them with the previous expected surveys. Assume the sample size be 80.

Category Q3-FY10 Q4-FY10

Excellent 8 8

Very Good 36 37

Good 12 11

Fair 4 7Poor 8 9

Very Poor 12 8

Here we will inspect the case in one variable only that is Category.H

0

: Result Similar and Ha

: Results not Similar. In this test Null & Alternate hypothesisalways remains same.

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6 SigmaQUALITY Analyze PhaseChi-Square Test: Result

Chi-Square test run gives following result: Chi-Sq = 1.734, DF = 5, P-Value = 0.885

Hence, we observe that , P-Value = 0.885i.e P-Value > 0.05. Therefore, it fails to reject

Null hypothesis ( H0 : Result Similar) and the data of Q2-FY10 & Q3-FY10 are similar.

Which implies that company X sustained its confidence level in the market.

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6 SigmaQUALITY Analyze PhaseAnalysis of Variance (ANOVA):

In 1920, Sir Ronald A. Fisher invented a statistical way to compare the data sets. Fisher

called this method as Analysis of Variance which is popular as ANOVA. The F-ratio produced

by ANOVA is named afterFisher. The t-test mentioned earlier had a limitation i.e. only two

data sets can be analyzed. However, in ANOVA minimum of two data sets and maximum of

infinite data sets can be analyzed.

Scope of ANOVA: Whenever we want to compare more than two sets of data in order to

conclude the better one then ANOVA plays great role.Example: Lets consider a case where a company wants to analyze the efficiency of drillingmachine. Company collects the total time taken to complete similar jobs in three locations.

Drilling time in minute in a span of 5 days

A B C

15 28 26

17 25 23

18 24 20

19 27 17

24 25 21

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ANOVA Test:When ANOVA test is run, we get numerical output and graphical output. Former critically

examines the individual data for differences and later plots them for comparison.

One-way ANOVA: A, B, C

Source DF SS MS F P

Factor 2 131.73 65.87 7.81 0.007Error 12 101.20 8.43

Total 14 232.93

S = 2.904 R-Sq = 56.55% R-Sq(adj) = 49.31%

Numerical data examines the relation of data with each other and gives the P-value that

indicates the confidence of test. In above case it is 0.007 and hence P-value < 0.05 which

implies that Null hypothesis is rejected and data given are different. However, in what way

the data differ is explained by box plot in next step.

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ANOVA Test: Understanding differences

In a box plot circle marks the average, horizontal line is median, box reaches +/- one

standard deviation and vertical line is range of data. A close analysis of graphical plot reveals

a lot. Location A & C are statistically similar but B differs from A & C. Location A is more

efficient in drilling. Location B requires special attention. In one particular day at location A the

drilling is least efficient and if rectified it will be very efficient. Location C has less consistency.

Hence, we can conclude in a similar manner for different data.

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6 SigmaQUALITY Analyze PhaseRegression Analysis:

Regression analysis is a statistical tool to analyze the relationship between quantitativevariable. It helps in predicting the process behavior when the variables are change, for

instance how JIT is affected when inventory is reduced. However, the analysis does not

provides the optimal level of inventory rather it gives the level of inventory required to maintain

the desired JIT compliance. Regression transforms the physical process into a

mathematical model that will establish the relation between dependent and independent

variable.In above case: JIT = A (Inventory) + B , value of A and B decides how strong the relationship

exist between JIT and Inventory. When the independent variable is more than one then it is

called as multiple regression analysis.

Example: Lets consider a company X is a vendor of company Y. However, company X is

poor in managing its optimal inventory level to support JIT. But since the purchase trends ofcompany Y quarterly varies so they could not find the optimal inventory requirement.With poor inventory management the operational cost started increasing. To reduce the

financial pain, top management wanted to analyze the effect of inventory over operational cost.

They collected data of past one year, and wanted to know the optimal inventory level for

particular operational cost.

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Data: Operational cost against inventory level in a one year span.

Month Operational Cost

00000 `Inventory

MT

Jan 22 10

Feb 20 8

Mar 30 24Apr 28 20

May 26 19

Jun 29 23

Jul 31 26

Aug 37 28

Sep 38 30

Oct 40 34

Nov 42 37

Dec 54 45

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Result: Regression Analysis gives following result. R-Sq indicates strength of relation.The regression equation is

Operational Cost = 11.5 + 0.882 Inventory (Relation)Predictor Coef SE Coef T P

Constant 11.474 2.512 4.57 0.001

Inventory 0.88202 0.09518 9.27 0.000

S = 3.23748 R-Sq = 89.6% R-Sq(adj) = 88.5%

3210-1-2-3

99

95

90

80

70

60

5040

30

20

10

5

1

Standardized Residual

Perc

ent

Normal Probability Plot

(response is Operational Cost)

Action: Let the management fixes

the operational cost to be 25 lakh.Equation:

25 = 11.5 + 0.882 Inventory

Solution:Inventory 15.3 MT

Note: The red dots represents the

variation of relation form the

regression model adopted.

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6 SigmaQUALITY Improve PhaseStatistical Solution:

Improve phase is all about improvement. All the previous steps are used to narrow down the

scope of actual cause causing costly pain. Lets say that we have run the six sigma methodto improve the product quality. Later we came to know that the profile cutting machine has

some fault. All the cut parts have slight variation. Now, what we do next? Change the

machine or further drill on to the subject. Obviously, the later option is economically viable.

Further, we sorted out some key factors of oxy machine that affects dimension:

Cutting Speed. Kerf value.

Nozzle quality.

Plate Thickness.

Program fault.

But, still its quite difficult to narrow the scope of improvement. Lots of question still arises How cutting speed affects product and what damage it causes alone.What is the net impact of kerf value.

Net effect cutting speed and kerf value.

Do we need to change the nozzle and what will be the frequency.

Do we need to reboot the program.

All these answers are best addressed by Design of Experiment.

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6 SigmaQUALITY Improve PhaseDesign of Experiment (DOE):

The process is always behaves in a manner of how or what the input is given and what arethe external affecting parameters. To model the behavior of each individual factor and the

behavior in interactions with other factors, for the desired output is best done by DOE.

In the previous example, suppose we want to figure out, how beneficial it would be if we would

replace the old nozzles. After all no one wants useless expenses in the name of solution.Example:

Lets say that the company wants to know the effect of cutting speed and kerf value to avoidthe costly expenses. They want to analyze the defects corresponding these factors to

establish the standard parameters for quality product. Company conducted a test run for

10mm thick plate for which they qualified the kerf value and cutting speed as High or Low.After conducting practical experiment, company tabulated the defect quantity as given:

Cutting Speed (mm/m) High Low

High 5 10

Low 9 14

Kerf Value (mm)

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Solution:

However, for validating the experiment they repeated the experiment.Total factors are 3 and level is 2, so total runs = 2. DOE calls for codification of High as (+)

and Low as (-). The combination Cutting Speed x Kerf Value is coded as +, - means firstfactor is high while other is low.

:Cutting Speed Kerf Value

Cutting Speed x

Kerf ValueResponse

+ + + 5 6

+ - +, - 10 11

- + -, + 9 8

- - + 14 12

Main Effect : We can clearly see the impact of cutting speed and kerf value over the

response.

The impact is significant and this is called as main effect. However, the statistical model will

give the relation of factors and response more appropriately.

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3210-1-2-3

99

95

90

80

70

60

50

40

30

20

10

5

1

Standardized Residual

Percent

Normal Probability Plot(response is Response)


Statistical Solution:

When DOE test is run for the given problem, the solution is as follows:Analysis of Variance for Response, using Adjusted SS for Tests

Source DF Seq SS Adj SS Adj MS F P

Cutting Speed 1 15.125 15.125 15.125 17.29 0.014

Kerf Value 1 45.125 45.125 45.125 51.57 0.002

Cutting Speed*Kerf Value 1 0.125 0.125 0.125 0.14 0.725

Error 4 3.500 3.500 0.875

Total 7 63.875

S = 0.935414 R-Sq = 94.52%R-Sq(adj) = 90.41%

Interpretation: The p-value is 0.725 for effectof interaction, hence it is least significant for

consideration. But p-value for cutting speed and

kerf value is less than 0.05 so its significant.If we see the main effects plot for responses , we

get more relation that is :

Increase in cutting speed results decrease in

rejections and increase in kerf value also results in

decrease in rejections. Further, to design the

parameter we require following regression model:

Response = 9.3 + 1.3(Cutting Speed) 2.3(Kerf)

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6 SigmaQUALITY Improve PhaseDesign of Experiment (DOE): Insight

DOE is not so easy as it seems to be in earlier example of cutting machine. DOE is not onlycrucial part of six sigma project but also serves as validating the changes for Control Phase.

There are many ways to conduct DOE, for the sake of understanding we followed the simplest

path. Other complex tests are used when the factors are more or it is related to the

physiological traits for instance predicting customers buying behavior, determining

competitive pricing, predict and optimize response rates to advertisements.Concept of DOE was laid by Ronal Fisher and is now the most vital tool in todays world.Many key figures of DOE test reveals the vital information about how the process is affected

by several factors. Which is why, the tool is so vital in business management.

Robust design method, also called as Taguchi method is quite vital in manufacturing

industries. Taguchi method mainly deals with the factors on which we do not possess control

like wear and tear of tool etc.

This the reason why some of the manufacturing industry exceeds in process excellence over

other.

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6 SigmaQUALITY Control PhaseStatistical Process Control (SPC):

Improvement phase had addressed the factors causing costly pain. Control Phase is all about

sustaining the improvement. Consider a manufacturing unit produces certain variety of partsfor instance profile, drilled and bent parts. Also, the unit is equipped with profile cuttingmachine, vertical drilling machine and press brake. If the unit makes its inspection only at final

step i.e. before the product is delivered to the customer then three cases arises parts ok,parts to be reworked or parts to be rejected. hence the cost of production will include cost of

ok parts, cost of defective parts and cost of rework leading to high operational cost.If we consider a system in which only the process producing parts are monitored and

controlled, then the cost of rework, cost of rejection and cost of inspection can be drastically

reduced. The technique is called as Statistical Process Control or SPC. SPC is all about

managing the production process. Dr. Walter Shewhart and Dr. Edward Deming developed

the SPC technique.SPC helps in :

keeping production processes stable.

predicting cause of variation before defective production.

fixing maintenance frequency to avoid production loss.

prevent defects.

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6 SigmaQUALITY Control PhaseStatistical Process Control (SPC): variation causes defects

Assignable cause of variation: When the cause of variation causes differences in the output

which is significant as per customers specification, it is called as assignable causes.Example Poor quality of RM, improper machine calibrations etc.

Common cause of variation: When the variations are uncontrollable like weather conditions

and causes insignificant damages to output of product, then it is called as common causes.

They are random in nature and are predictable. However, if these causes are unpredictable

and process is sensitive then it can affect the quality of output.

Process monitoring tool:

Control Charts.

Histograms.

Scatter Plots.

Process Checklist.

C & E Diagram.

A3 Reports etc.

These are the commonly used tools in process monitoring. But, for the sake of

understanding we will se the Control charts only. As the others are bit complex at present.

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6 SigmaQUALITY Control PhaseControl Chart:

Control Chart is used to capture the behavior of products over a span of time. Samples are

randomly picked from the production line and checked for the conformance. When theprocess fails it will have an impact on the parts, which will be captured in the control chart.

Then the variation is analyzed for immediate rectification.Elements of Control Chart:

CTQ or X The product characteristic that we check in random inspection.

Process Mean or The process mean of observed sample.n Sample quantity in random pick. Standard Deviation (SD) of observed X.UCL Upper Control Limit. They are 3 SD above .LCL Lower Control Limit. They are 3 SD below .

Relation:

= (xi) / n. = (Xi ) / n UCL = + 3 LCL = - 3 and UCL-LCL = 6 (Six Sigma Controlled)

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6 SigmaQUALITY Control PhaseControl Charts:

WECO Rule talks about the sensitivity of variations like when 4 out of 5 dots are between

1and 2 sigma away from mean. The rule is set by Western Electric Company.Capability Trend of 6: the six sigma way 99.7% defect free

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6 SigmaQUALITY Control PhaseX and R Chart:

The process is about taking small sample from production line and then taking mean of it andplotting against range (R). The key question depending on DOE are

what is the sample size.

time to order the sampling.

how many samples.

The sample size depends on rational sub grouping i.e. depends on process cycle time.Further, the time to order should be in such a way that variations does not causes major

problems. Similarly the sample size is chosen. X = Sample Mean = Xi / n

R = X (Highest) X(Lowest)

Center Line:

Center Line for R chart: R = R / t, t is the number of sample.

Center Line for X chart: X = X / t.

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6 SigmaQUALITY Control PhaseX and R Chart:

The process is about taking small sample from production line and then taking mean of it andplotting against range (R). The key question depending on DOE are

what is the sample size.

time to order the sampling.

how many samples.

The sample size depends on rational sub grouping i.e. depends on process cycle time.Further, the time to order should be in such a way that variations does not causes major

problems. Similarly the sample size is chosen. X = Sample Mean = Xi / n

R = X (Highest) X(Lowest)

Center Line: Center Line for R chart: R = R / t, t is the number of sample.

Center Line for X chart: X = X / t.

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gQUALITY Control PhaseMistake proofing:

Mistake-proofing is required to assure the process is self driven and unintentional variations

made/done by any people does not affects the end result. Key features are :

Mistake-proofing is applied in Improve and Control phase with respect to DOE.

It is not only applied to human error but also to software or other variations.

Standard Operating Procedure (SOP), Checklists, maintenance logs are means of control.

FMEA enlists the possible cause of failure and is controlled on day to day basis.

Awareness:

All the steps discussed so far are ineffective if the quality awareness does not translates well

down the line. Six sigma way results not only defect free processes but is also the source of

financial savings. Majority of managers find difficulties in statistical approach because they

trust their experience. But, six sigma digs were we fail to perceive the problem. Most of thebusiness leaders believes in increasing business, but what about sustaining the business.

Endangered businesses situation is a result of either tough market or failure in quality as per

customer. Six sigma process requires awareness to all the people linked to the end product.

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gQUALITY

With increasing globalization, every steel company must innovate to prosper and

compete in this new environment. POSCO was in difficult situation you might almost

say a crisis a few years ago as we faced this new global competitive threat. As amanagement team, we felt that Six Sigma was a good vehicle to change all

employees way of thinking, current working styles and mind-sets- Ku-taek Lee, Chairman and CEO, POSCO.

Thank You

By Rahil, TSPDL.Tada

SixSigma GB+Module

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