6 Sigma Methodologies

8/7/2019 6 Sigma Methodologies

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What initiated Six Sigma

Re v e nu e

Time

Strategic Inflexion PointValley of Death

Path B - Change

Path A Do Nothing


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What initiated Six Sigma

Forward Thinking companies have been quick to

recognize the concept of a Strategic Inflexion Point (SIP)An SIP occurs when the rules of engagement in a businesschanges thereby putting enormous pressures on theorganization to react and move quickly. Organizations,which are agile and respond accordingly are able tosurvive and achieve huge gains while those opt to ignorethose ominous become obsolete and lose big.

e.g . 1. Swiss watch makers ignored the advent of Quartz technology. Theyfailed to notice Strategic Inflexion Point.

2. Polaroid Camera manufacturer ignored the advent of DigitalCamera.


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A level of performance that reflects significantlyreduced defects in our products

A statistical measurement of our process capability

A set of statistical tools to help us measure,analyze, improve, and control our processes

A commitment to our customers to achieve anacceptable level of performance

Six Sigma . . . What it is


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Understanding customer needs and assessingprocess performance with respect to those

needs

Highly accurate processes always performingexactly on target

Narrowest of narrow tolerances at which

process must always perform

Does not mean always performing at a defectrate of 3.4ppm

Six Sigma . . . What it is


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Motorolas Response to Strategic Inflexion Point Motorola was quick to response the SIP and acted to move

along the B path. Their business strategy came in the form of very aggressive and measurable Quality Deployment strategy called Six Sigma. It is a systematic initiative.

It is guided by simple statistical principles which requiresthat all process and products would have no more than 3.4defects per million opportunities or 3.4 PPM (Parts per million)

The inordinately low 3.4ppm number is based on the factthat the process variability (process width) is reduced to a pointwhere it is considerably narrower than the allowablespecification or customer requirement (specification width).Under these conditions, a wide Design Margin is created thereby

increasing process robustness and resulting in a substantiallylow defect count.e.g. Computer


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Six Sigma Goals

Know what is important to thecustomer.

Control the inputs.

Reduce defect levels.

Centre around the target.

Reduce variation.


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Achieving Six Sigma Quality

Six Sigma approach focus on reducingprocess variability and reaching excellence.

Six Sigma quality can be attained by..Process

standardization.

Process stability.

Capabilityimprovement.

Robust design.


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Accuracy & Precision - The twin

objectives of Six Sigma


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Accuracy & precision of a manufacturing process can be bestexplained by using the analogy of a rifle firing at a target.

Picturization of accuracy & precision


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Accuracy and precision

With the center of the target taken to be the true value of thecharacteristic being measured and by the rifle shots representingthe measured values, there are four combinations of accuracy andprecision as depicted in the following slides.


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Inaccurate and imprecise

+ Mean

USL

LSL


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Accurate and imprecise

+

Accurate refers to clustering of data about a known target


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Precise but inaccurate

+

Precision refers to the tightness of the cluster of data.Envision a target with a cluster of arrows all touching one anotherbut located slightly up and to the right of the bullseye.


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Accurate and precise

+


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The Origin of DefectsConsider product which is being made under a normally distributed process.(Most of the processes follow a Normal Distribution).That product which falls outside the specification limit (SW) as depicted in fig(i) will be considered as defective. In fig (i), the Process Width (+/- 3 sigmasabout the mean) is larger than the Specification Width. In contrast, the processdistribution in fig (ii) is well within the specification limits. This is highlydesirable as the probability of defect generation is virtually zero.

USLLSL

SW

PW

Fig (ii)

LSL USL

RejectsRejects

Fig (i)

PW


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Design MarginConsider fig (ii) where the Process Width is significantly narrower thanthe Specification Width. Consequently, there is a vacant space or Guard band on either side of the probability distribution curve as

shown in fig (iii). This vacant space is a measure of Design Margin or a guard band which has special significance in terms of productperformance, reliability and robustness. The Design Margin behaveslike a safety margin providing an allowance for error.

SW

Design MarginDesign Margin


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4 Sigma process

2 3 4 5 6 7 8 9 1210 16151413111

LSL USLMean=8SD = 1.33

- 4 SD + 4 SD


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2 3 4 5 6 7 8 9 1210 16151413111

LSL USL

Mean=8

SD = 1.07

- 5 SD + 5 SD

5 Sigma process


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LSL USLMean=8SD = 1

- 6 SD + 6 SD

2 3 4 5 6 7 8 9 1210 16151413111

6 Sigma process


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Process Capability (Cp)

Process Capability ratios measure how well the productrequirements and process capability match. The larger thevalue of Cp, the better the match between product and process.

Design Width USL -- LSLCp = ----------------------------- = -----------------------

Process Width UCL LCLIn simple words, it means that in the six-sigma approach, thevalue of (standard deviation of process distribution) is suchthat six multiples of , on either side, are able to cover thedesign limits/specifications so well that the process capabilityis 2.


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Process Capability Index (Cpk)

Process Capability index is used to assess the capability index

of process when process average is centered around mean.So, Cpk indicates both variation and location of processaverage over an extended period of time/long-term basis.

X -- LSL USL -- XCpk = Min ----------------- , ----------------------

3 3

It also means that if process-average is equal to midpoint of thespecification-range, then Cpk = Cp. The higher the value of Cpk, lower would be the amount of product, which is outsidespecification limits.


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Amount of process shift allowed

2 3 4 5 6 7 8 9 1210 16151413111

LSL USL

SD = 1

1.5 SD 1.5 SD


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Level Calculation


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An abstract product (Data of two samples)

Note: Conforming and Non-conforming

Sample 1

Sample 2


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Six Sigma metrics are based on the

defects produced.

DPU for sample 1) 6/5 = 1.2

DPU for sample 2) 4/5 = 0.8

Sample 2is better than

Sample 1

Yield Computations

DPU or Defects Per Unit is the Total number of defectsobserved in a population.

Total Number of DefectsD

DPU = ----------------------------------------- = ------ Total Number of Units produced U

Defects Per Unit - DPU

f


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To convert DPU to DPMO, the calculationstep is

D(1) Defect per opportunity (DPO) = ------------

U x O

(2)DPMO = DPO x 10(or) DPU/(opportunities/unit) * 10

D= ------------ x 10

U x O

Defects Per Million Opportunities

6

6

Opportunities/Unit

6


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Total No. of defects 6Defects per Unit (DPU) = ------------------------------------ = ------- = 1.2

Total No. of units produced 5

DPU 1.2Defects per Million Opportunities = ------------------------- x 10 = ------- x 10 = 2,40,000

(DPMO) Opportunities/Unit 5

= 2.3 Sigma (Refer Table)

6 6

Opportunities/Unit:1. Hole size variation at 4 places (4)2. Plate thickness variation (1) Total Opportunities/Unit = 5

Sigma Conversion Table For 1.5 Process Mean Shift


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Sigma Conversion TableYield % Sigma DPMO

99.9997 6.00 3.4

99.9995 5.92 5

99.9992 5.81 899.9990 5.76 10

99.9980 5.61 20

99.9970 5.51 30

99.9960 5.44 40

99.9930 5.31 70

99.9900 5.22 100

99.9850 5.12 150

99.9770 5.00 230

99.9670 4.91 330

99.9520 4.80 480

99.9320 4.70 680

99.9040 4.60 960

99.8650 4.50 1350

99.8140 4.40 1860

99.7450 4.30 2550

99.6540 4.20 3460

99.5340 4.10 4660

99.3790 4.00 6210

99.1810 3.90 8190

98.9300 3.80 10700

98.6100 3.70 13900

98.2200 3.60 17800

97.7300 3.50 22700

97.1300 3.40 28700

96.4100 3.30 35900

95.5400 3.20 44600

94.5200 3.10 54800

93.3200 3.00 66800

91.9200 2.90 80800

90.3200 2.80 96800

88.5000 2.70 115000

86.5000 2.60 135000

84.2000 2.50 158000

81.6000 2.40 184000

78.8000 2.30 212000

75.8000 2.20 242000

72.6000 2.10 274000

69.2000 2.00 308000

65.6000 1.90 344000

61.8000 1.80 382000

58.0000 1.70 420000

54.0000 1.60 460000

50.0000 1.50 500000

46.0000 1.40 540000

43.0000 1.32 570000

39.0000 1.22 610000

35.0000 1.11 650000

31.0000 1.00 690000

28.0000 0.92 720000

25.0000 0.83 750000

22.0000 0.73 780000

19.0000 0.62 810000

16.0000 0.51 840000

14.0000 0.42 860000

12.0000 0.33 880000

10.0000 0.22 900000

8.0000 0.09 920000

For 1.5 Process Mean Shift

DESIGN FOR ROBUSTNESS


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DESIGN FOR ROBUSTNESS

QC Techniques such as SPC concentrate on quality of conformanceto designed dimension.

Product-failure is, primarily, a function of design-quality. A product, designed to withstand variations in environmental and

operating conditions, is said to be robust or possess robust quality. Product has two factors namely (i) Controllable and (ii) Uncontrollable Controllable factors are design parameters such as materials used,

dimensions, and form of processing. Uncontrollable factors are users control (length of use, maintenance,

settings etc.,)

All the components produced within tolerance may not result intofinished products within tolerance.

All parts within tolerance may be acceptable, they are not all of thesame quality.( 59-60-95 )

LOSS FUNCTION


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LOSS FUNCTION

LSL USL

LOSS

Loss function as assumed by goalpost philosophy

Conventional goalpost philosophy where loss incurs only after crossing lower andupper specifications limit

LOSS

Taguchis quadratic loss function


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Taguchi s quadratic loss function

LSL USL

6 Sigma curve

3 Sigma curve

Nominal Value

Loss Function

Loss Area for 6

Loss Area for 3

This loss is given as a quadratic function:Loss = K. (Y Y 0)2

Where Y 0 = nominal valueY = value of performance

characteristicThis is compared with conventionalgoalpost philosophy where loss incurs onlyafter crossing lower and upper bounds.


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Six Sigma Methodologies

There are two Six Sigma methodologies.One is used for improving existingprocesses and the other to introduce new

processes.

The two methodologies are..DMAIC.

DMADV.


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DMAIC

DEFINE

MEASURE

ANALYZE

IMPROVE

CONTROL

Define the problem and results to be achieved.

Measure to determine baseline performance.

Analyze data to pin point pain & improvement area..

Solutions to optimize performance.

Control procedures to sustain gains.

DMAIC is used to improve existing

processes .


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Six Sigma Methodologies DEFINE PHASE

This phase starts with the creation of aproject charter.A good project charter will include

A problemstatement.

Goals &objectives.

Project scope.Teams & roles.

Team guidelines.

Im lementation


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It is very important to identify the needs andrequirements of the customer before startingto define the problem and identifying the

goals and objectives.

For instance a customer may need 1000 unitsper day from us against the 500 units we are

delivering now.

A problem statement and associated goals toincrease the production to 750 units per day

will not solve the problem to the satisfaction


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SIPOC is a high level work flow diagram.

The five elements of SIPOC.

Supplier.

Input.

Process.

Output.

Customer.

Create SIPOC

Si Sig M th d l gi DEFINE PHASE


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Six Sigma Methodologies DEFINE PHASE Supplier Inputs Process Output Customer

Personnel

Dept.

Payroll Data Input

Consolidation

Pay Sheet Employees

ProcessingSpecs

ProcessingPayroll

Pay slip

AccuracyChecks

Report

Print Pay Sheets

Print Pay slips

Courier Paysheets & slips


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DMAIC

DEFINE Define the problem and results tobe achieved.

MEASUREMeasure to determine baseline

performance.ANALYSEAnalyse data to pin point paid

and improvement areas.IMPROVE Solutions to optimize performance.

CONTROL Control procedures to sustaingains.


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This phase involves selecting product characteristic, mapping respectiveprocess, making necessary measurements and recording the results of theprocess. This is essentially a data collection phase.

Six Sigma Methodologies MEASURE PHASE


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Six Sigma Methodologies MEASURE Phase

Process Mapping A Process Map is a visual representation of process

flow in sequence and is an importantcontinuous

improvement tool.Advantages of Process Maps:

Identify areas for cycle time

improvement.Identify rework and non-value

added steps.Identify process simplification

opportunities.


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How to map processes?

Whole process (6)

Sub process of process 1 (5)

Activity of sub process 1 (4)

Six Sigma Methodologies MEASURE Phase


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DMAIC

DEFINE Define the problem and results tobe achieved.

MEASUREMeasure to determine baseline

performance.ANALYSEAnalyse data to pin point pain

and improvement areas.IMPROVE Solutions to optimise

performance.

CONTROL Control procedures to sustaingains.


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Six Sigma Methodologies ANALYSE Phase

In this phase, the data collected

during the measure phase isanalyzed to determinepossible causes and root causes for

various problems encounteredduring the execution of theprocesses.


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Basic tools for data analysis Trend chart

Pareto analysis

Cross-tabulation

Histogram

Dot plot

Scatter diagram

Cause and Effect Diagram


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A Trend Chart shows values in a timesequence. It is

Trend Chart should be used as a routinebefore doing any

Data should be in time order other wise theanalysis can

Plotting average line on the trend chart

Six Sigma Methodologies ANALYSE Phase Trend Chart

d to show the behaviors of a process over time.

mplex statistical or graphical analysis.

be misleading.

interpretation easy.For any abnormal trends, on the chart, they

indicatepresence of special causes in the process.


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Share Price Movement

0

20

40

6080

Month

R

upees

Series1

Series1 48 47 54 53 55 48 45 46 49 57 58 59

Ja Fe M Ap M Ju Ju Au S O N D

BACK


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Six Sigma Methodologies ANALYZE Phase

Pareto Diagram

It is a simple graphical technique forranking items

The Pareto principle is just a few of

the items

This diagram will differentiat e t he most

om the most frequent to the least frequent.

contribute for the most of the

effect.

(It is also called as 80/20 principle)

from that of least importantNext

7 QC TOOLS Pareto Chart


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7 QC TOOLS Pareto Chart

Pareto Analysis for Fai

64

15 126

3

64%79%

91% 97% 100

0

20

40

60

80

Na ture o f F a il

No. of occurance

0%20%

40%60%80%100%120%

Series2 64 15 12 6 3

Series1 64% 79% 91% 97% 100%

Conversio Data Storag Downloadi Preservatio Uploading

80%

Back


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Six Sigma Methodologies ANALYSE Phase Cross Tabulation

Cross tabulations is used tosummarised data if thereare two or more factors. Factors can

be machine,operator, supplier etc.Row and column summaries can also

be added.

Attribute Data Type

Operator A Operator B Totals

10 20 30

5 7 12

15 27 42

Mach. 1

Mach. 2

TotalsBACK


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Histogram

A Histogram is used to

Display the pattern of variation.

Communicate visually information abo

processbehavior.

Make decisions about where to focus

improvement NEXT


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Model of

Histogram

0 10 20 30 40 50 60

30

25

20

15

10

05

BACK


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Dot plots are probably the oldest way of comparing

sequences.

A dot plot is a visual representation of the similarities

between two sequences. Each axis of a rectangular array

represents one of the

two sequences to be compared.

Dot Plots

NEXT


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Dot Plot

0 1

A-WEEK-1

A-WEEK-2

A-WEEK-3

A-WEEK-4

2 BACK


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Scatter Diagrams

Scatter diagrams are used to study the linear relationship between twovariables. Although these diagrams cannot measure exactly that onevariable causes change in the other, they do indicate the existence of a

relationship, as well as the strength of that relationship. A scatter diagram is composed of a horizontal axis containing themeasured values of one variable(generally the independent variable)and a vertical axis representing the measurements of the other variable(generally the dependent variable). The purpose of the scatter diagram isto display what happens to one variable when another variable ischanged. The diagram is used to test a theory that the two variables arerelated. The type of relationship that exits is indicated by the slope of thediagram.

49NEXT


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Draw the two axes of the diagram. The first variable (the independentvariable) is usually located on the horizontal axis and its values shouldincrease as you move to the right. The vertical axis usually contains thesecond variable (the dependent variable) and its values should increase asyou move up the axis.

Plot the data on the diagram. The resulting scatter diagram may look asfollows:

SCATTER PLOT

53BACK


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1

2

3

4

5

6

7

1 2 3 4 5 6X

Y

a = 3

( X1 , Y1 ) or ( 1, 5)

( X2 , Y2 ) or ( 2, 7 )

a is called Y intercept.b is the slope of the line i.e.,

how much each unit change of the independent X changesthe dependent variable Y.

Equation for straight line

Y = a + b X

1 unit

b

C d Eff Di


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Frequentbreakdowns

Inefficientprocesses

InadequateQ.A

Too much of variation

Unclear specifications

Lack of experience

Inadequatetraining

Poor supplier evaluation

POOR QUALITYOF RAW

MATERIALS

METHODS

MACHINEMEN

LowProductivity

Unclear instructions

High setup times

Poor inspection &testing

Complexdesign

Cause and Effect Diagram

NEXT

DMAIC
http://smaic%20model.xls/http://smaic%20model.xls/


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DMAIC

DEFINE

MEASURE

ANALYSE

IMPROVE

CONTROL

Analyse data to pin point paid and improvemareas.



Measure to determine baseline performance

Define the problem and results to be achieve


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Six Sigma Methodologies IMPROVE Phase

Generate Potential Solutions

The first idea that comes to mind may not be sointeresting but the second and third ideas that flo

from it can be very interesting Edward de Bono

The following are the popular methods togenerate potential ideas and actions.

BrainstormingKJ Method


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Six Sigma Methodologies IMPROVE Phase Brainstorming

rainstorming is a technique for harnessing tcreative

There are two phase of Brainstorminga) The generation phase: The facilitator review

the guidelines & purpose. The team membersgenerate a list of ideas, as exhaustive aspossible.

b) The clarification phase: The team reviews the

list of ideas to make sure that everyone

ng of a team to generate and clarify a list of

ve problems or issues.


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The KJ Method was developed by JiroKawakita as a means of -

Organizing diverse observations and

qualitative information, into usefuldocumented facts.

KJ Method is also called as Affinity

Diagrams.

Affinity Diagram


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Creation of an Affinity Diagram

Step 1 Generate ideas.

Step 2 - Display ideas.

Step 3 Sort ideas intogroups.

Step 4 Create headercards.

Ste 5 Draw finished

Si Si M h d l i IMPROVE Ph


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Six Sigma Methodologies IMPROVE Phase Completing the Affinity Diagrams

rrangements for a Wedding Superheader

Hall

IDEA 1IDEA 2

IDEA 3

Food

IDEA 1IDEA 2

IDEA 3

Dress

IDEA 1

IDEA 2

IDEA 3

Header

Si Si M th d l i IMPROVE Ph


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Six Sigma Methodologies IMPROVE Phase Selection of Best Idea Simple

Method HIGH MEDIUM LOWHigh. Idea should be

adopted.Implementin any way.

Ignore.

Mediu

m.

Find why theidea is difficult

and eliminateobstacle.

Implementin any way. Ignore

.

Low.Find why the

idea is difficult Ignore. Ignore

Effectiveness

Fe a s ib ilit y


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DMAIC

DEFINE

MEASURE

ANALYSE

IMPROVE

CONTROL

Define the problem and results to be achi

Measure to determine baseline performa

Analy se data to pin point paid andimprovement areas .




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Six Sigma Methodologies CONTROL Phase

Control Major Activities

Define the on-line process control

measures.

Modify the documentation system.

Conduct training.

Institute the process audit system.

uantif the ains.

Si Si M h d l i CONTROL Ph


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Six Sigma Methodologies CONTROL Phase

Process Audit System

A Process audit

Is a systematic procedure To determine whether quality activities

and relatedresults comply with planned

arrangements and tocheck whether these arrangements are

implemented

effectively, and are suitable to achieve


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DMADV

DEFINE

MEASURE

ANALYZE

DESIGN

VERIFY

Define project goals and deliverables.

Customer needs and specifications.

Options to meet customer needs.

Solutions to meet customer needs.

Performance and ability.

Design for Six Sigma


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Design for Six Sigma

Customer

Internal Customer

External Customer

Internal Customer: A process owner is thecustomer of previous process. (Output of oneprocess becomes input of another process).

External Customer: Customer outside the

organization, to whom

Voice of C stomer (VoC) (F 100 Bik )


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Voice of Customer (VoC) (For a 100cc Bike)

Identify Customers:

Identify potential customers

Ex. People who are earning more than Rs.20,000/-per month

Identify potential segments

The customers in the age group between 18-40years.

Prioritize the segments

Students, office-goers, small businessmen.

V i f C t (V C) (F 100 Bik )


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Collect Data:

Collect Existing Data Data available in the market, company

regarding customerhabits and purchasing trends.

Identify additional data needs

Change in the customer habits and purchasingtrends.

Plan the data collection & collect

Action plan to collect data from the field and

Voice of Customer (VoC) (For a 100cc Bike) contd..

Methods to determine the Voice of the Customer


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Methods to determine the Voice of the Customer

Personal interviews

Telephonic interviews

Mail surveys

Internet / e-mail based surveys

Focus groups

Observing the customer / Being a customer

Customer complaints

Market research reports


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Voice of Customer (VoC) (For a 100cc Bike) Contd..

Analyze Data:

Organize the data

Arrange the existing and new data, in a orderlyway, say sales

vs.age, sales vs.earnings, earnings vs. paymentetc. Prioritize the Needs

Purchaser may be looking for, mileage, speed,price, resale value,ing capacity, etc. These need to be prioritized base

customer surveys.

Translating the Voice of the Customer


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Translating the Voice of the Customer

Quality FunctionDeployment

(QFD)

EngineeringRequirements

Failure Mode andEffect Analysis

(FMEA)

Customer Satisfaction

Customer Dissatisfaction


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Quality Function Deployment ( QFD )


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QFD

No matter how effectively a company meets the initial needs of thecustomers, it must remain constantly alert and responsive to thechanging needs of the customers. Because if the company is notresponsive to these changing needs, the passage of time will erode theearly competitive advantages.


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Quality Function Deployment ( QFD )

Quality Function Deployment may be defined as asystem for translating consumer requirements intoappropriate company requirements at every stage, fromresearch through product design and development, to

manufacture, distribution, installation and marketing,sales and service.

Why do QFD?


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Why do QFD?

To facilitate and document the thinking process infulfilling customer needs.

To establish a detailed and itemized action list for

satisfying the needs.

To provide a common ground for team work.

To reduce ambiguity in the design process.


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The roof of the house of Quality, calledthe Correlation Matrix is used to identify


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the Correlation Matrix, is used to identifyany interrelationships between each of Technical descriptors


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Competitiveevaluation

Rating1 for worst5 for best


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QFD team ranks each customer requirement by assigning it a rating.1 f l t i t t


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1 for least important10 for very important

AB

=A/BSales point tells QFDteam how well a

customer requirementwill sell. The salespoint is a valuebetween 1.0 and 2.0,with 2.0 being thehighest Absolute Weight = Importance to Customer x Scale-up factor x Sales Point

18 = 7 x 1.3 x 2

Relative Weight:Dot product of the column x column

for absolute weight in prioritized customer


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Degree of Difficulty:Technical difficulty inImplementing eachTechnical descriptor Die Castings 7Sand Castings 3

A B C= A x B X C

Absolute Weight = Dot product of the Column xcolumn for importance to customer

g prequirements.


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Failure Mode &Effect Analysis

Six Sigma Methodologies Analyze Phase


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g g y

FMEA Failure Mode Effect Analysis

FMEA is a tool for preventing problems.

FMEA is one of the most effective low risk techniques for

identifying potential problems and preventingthem in a cost

effective manner. FMEA is a procedure for developing andimplementing

A structured approach for prioritizing, evaluatintracking,

and updating design and process developmen

new or revised designs, processes and services.

Basic Elements of


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Bas c e e ts oFMEA

Failures

Causes

Detection

Occurrence

Effectiveness

S

O

D

R P

N

Severity

Failure Mode and Effects Analysis (FMEA)


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Failure Mode and Effects Analysis (FMEA)

FMEA is a structured analysis for identifying ways & methods in which

the product or processes can fail and then plan to prevent thosefailures. FMEA is a proactive tool for reducing defects and non-conformities.

FMEA


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FMEA is a structured approach in :- Identifying ways in which a product / process can fail to meet critical

customer requirements.

Estimating the risk of causes with regard to these failures. Evaluating control plan for preventing these failures. Prioritizing the actions for improving the process.

FMEA is an extremely important tool for each phase of Six Sigma strategy

viz. Measure, Analyze, Improve, Control.

Advantages of FMEA


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g

For improving the reliability and safety of the products.

For improving customer satisfaction.

Tracking actions to reduce non-conformities.

New product development.

Definition of terms


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Failure Mode : It is a manner in which a part or a process canfail to meet specifications. It is usually associated with defect or

non-conformities.

Examples : Missing part, Oversized, Undersized, Incorrectprice, Offspec parts.

Definition of terms


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Cause : Causes are sources of variation which are associated with keyprocess inputs. Cause can be best defined as a deficiency whichresults in a failure mode.

Examples : Instructions not followed, Lack of experience, Incorrectdocumentation, Poor handling etc.

Definition of terms


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Effect : Effect is the impact on the customer (both internal &external) if the failure mode is not prevented or corrected.

Examples : Customer dissatisfaction, Frequent productbreakdowns, Customer downtime.

Relationship of cause, failure mode & effect


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CauseFailure

ModeEffect

FMEA through Cause & Effect Diagram


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FailureMode

Causes Causes Causes

Causes CausesCauses

Effect

Preventor Detect

Steps in FMEA process


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p p

1. Develop a process map and identify process steps.

2. List key process outputs for satisfying internal and

external customer requirements.

3. List key process inputs for each process steps.

4. List ways the process inputs can vary (causes) and

identify associated failure modes and effects.

5. Assign severity occurrence and detection rating for each cause.

Steps in FMEA processcontd.


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6. Calculate risk priority number ( RPN) for each potential

failure mode.

7. Determine recommended actions to reduce RPNs.

8. Establish time frame for corrective actions. 9. Take corrective actions.

10. Put all controls in place.

Ranking terms used in FMEA calculationsScale:1(Best) to 10 ( Worst)


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Scale:1(Best) to 10 ( Worst)

Severity (SEV) : Severity indicates how severe is the impact of the effect on the customer.

Occurrence (OCC) : This indicates the likelihood of the cause of the failure mode tooccur.

Detection (DET) : This indicates the likelihood of the current system to detect the causeor failure mode if it occurs.

Risk priority number : This number is used to place priority to items for better qualityplanning.

RPN = SEV X OCC X DET

See next slides for specimen best to worst ratings on a 10 point scale.

Best to Worst ratings for FMEA calculations


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Rating Degree of Severity Likelihood of Occurrence Ability to detect

1 Customer will not at all observe Very remote possibility Sure that the potential failurethe adverse effect will be detected & prevented

before reaching the nextcustomer

2 Customer will experience Low failure with supporting Almost sure that the potentialslight discomfort documents failure will be detected before

reachig the next customer

3 Customer will experience Low failure without supporting Less chances that theannoyance because of slight documents potential failure will reach thedegradation of performance next customer undetected

4 Customer dissatisfied due to Occasional failures Some controls may detectreduced performance the potential from reaching the

next customer

5 Customer is uncomfortable Moderate failure rate with Moderate chances that thesupporting documents potential failure will reach the

next customer

Rating Degree of Severity Likelihood of Occurrence Ability to detect

Best to Worst ratings for FMEA calculations


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6 Warranty repairs Modearate failure rate without Controls are not likely to detectsupporting documents or prevent the potential failure

from reaching the nextcustomer

7 High degree of customer High failure rate with supporting Less chances that the potentialdissatisfaction documents failure will be detected or

prevented before reaching

the next customer

8 Vey high degree of customer High failure rate with supporting Very less chances that thedissatisfaction documents potential failure will be detected

or prevented before reachingthe next customer

9 Negative impact on the Failure is almost certain Existing controls will not detectcustomer the potential failure

10 Negative impact on the Assured failure Existing controls will not detectcustomer, people & society the potential failure

Rankings of SEVERITY of effect for Design FMEAEFFECT CREITERIA: SEVERITY OF EFFECT RANKSHazardous Very high ranking when potential failure mode affects safe operation 10


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Without and/or regulation noncompliance. Failure occurs without warningWarningHazardous Very high ranking when potential failure mode affects safe operation 9With Warning and/or regulation noncompliance. Failure occurs with warningVery High Item or product is inoperable, with loss of function. Customer very 8

dissatisfiedHigh Item or product is operable, but with loss of performance. Customer 7

dissatisfied

Moderate Item or product is operable, but with loss to comfort/convenience 6items inoperable. Customer experiences discomfort.

Low Item or product is operable, but with loss of performance of comfort/ 5convenience items. Customer has more dissatisfaction.

Very Low Certain item characteristics do not conform. Noticed by most customers. 4Minor Certain item characteristics do not confirm. Noticed by average customers 3Very Minor Certain item characteristics do not confirm. Noticed by discriminating 2

customersNone No effect 1


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Ranking of likelihood of Detection by Design Control for Design FMEADETECTION CRITERIA: LIKELIHOOD OF DETECTION BY DESIGN CONTROL RANKS

Absolute Design control will not and/or cannot detect a potential 10Uncertainty cause/mechanism and subsequent failure mode: or there


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Uncertainty cause/mechanism and subsequent failure mode: or thereis no design control.

Very Remote Very remote chance the design control will detect a potential 9

cause/mechanism and subsequent failure mode.Remote Remote chance the design control will detect a potential8cause/mechanism and subsequent failure mode.

Very low Very low chance the design control will detect a potential 7cause/mechanism and subsequent failure mode.

Low Low chance the design control will detect a potential 6cause/mechanism and subsequent failure mode.

Moderate Moderate chance the design control will detect a potential 5cause/mechanism and subsequent failure mode.

Moderately High Moderatly high chance the design control will detect a potential 4cause/mechanism and subsequent failure mode.

High Moderatly high chance the design control will detect a potential 3cause/mechanism and subsequent failure mode.

Very High Very high chance the design control will detect a potential 2cause/mechanism and subsequent failure mode.

Almost Certain Design control will almost certainly detect a potential 1cause/mechanism and subsequent failure mode.

Rank severity Rank how well

FMEA Form: ( Column 1 to 9 )


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1 2 3 4 5 6 7 8 9Process Potential failure mode Potential failure effect SEV Potential causes OCC Current Controls DET RPNPart No.

12345

List failuremodes for each

step

List effectsof each

failure mode

List causesfor each failure

mode

Rank severityon 1 to 10

scale

Rank occurrenceon a 1 to 10

scale

List how the causeis presently

being controlled

Rank how wellcause/failure

can be detectedon 1 to 10 scale

RPN=SEV*OCC*DET

Designates people

FMEA Form contd : ( column 10 to 15 )


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10 11 12 13 14 15Act ions Recommended Respons ibilit y SEV OCC DET RPN

List actionsrecommendedon RPN pareto

Designates peopleresponsible for

corrective action

RPN is recalculatedon completion of corrective action

Benefits of Six Sigma


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Benefits of Six Sigma Improved Customer Satisfaction More

Business.

Defect Reduction / Elimination ImprovedProductivity.

Yield Improvement -- Rework Reduced. Reduced Cost of

PoorCost of Quality Cost Control.

Improved Process Capability Cycle time

Benefits of Six Sigma


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Process Understanding Reduces Bottlenecks.

Constant Measurement of Key Metrics Consistent Quality. Breakthrough Improvement More Profit.

Finally


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improves

PRODUCTIVITY and QUALITY

Satisfied customers mean

MORE BUSINESS Improved productivity,

Cost Reduction,consistent quality and

increased businessmeans

Finally..SIX SIGMA

6 Sigma Methodologies

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