1 Ssgb Bsi Intro

8/6/2019 1 Ssgb Bsi Intro

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Module-1

Six Sigma Introduction


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Established by Motorola in the 1980s and still being

developed. Seen as a cornerstone to the companys

culture.

Companies adopting 6 Sigma include General Electric,

Allied Signal, ABB, Sony, Lockheed Martin, Ford,Nissan and many others

It is essential for companies to take responsibility fortheir own (unique) programme.

History of Six Sigma


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A systematic approach to process

improvement. Processes can be related to design,

manufacturing or administrative functions.

It involves the use of statistical tools andtechniques to analyse & improve processes.

The relentless pursuit of variability reduction

and defect elimination.

LSL USL

LSL USL

What is Six Sigma?


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Where can Six Sigma be applied?

Six Sigma can be applied to all company processes A distinction is often made between:

Design applications (Design for Six Sigma)

Manufacturing applications (Operational Six Sigma) Administrative and Service applications (Transactional

Six Sigma)


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Used in statistics as a measure of variation

Sigma=

Standard Deviation

The central philosophy of 6 Sigma is the reductionof variation in all our work processes

The Six Sigma Metric


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The 3 Sigma mentality means 2700 defectives per million!

Lower

Spec.

Limit

Upper

Spec.

Limit

-1 +1

y 1 = 68.26%

+2-2

y 2 = 95.44%

-3 +3

y 3 = 99.73%

y

(Target)

The Normal Distribution


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-6 -5 -4 -3 -2 -1 y +1 +2 +3 +4 +5 +6

Lower

Specification

Limit

Upper

Specification

Limit

Normal DistributionCentred on Target

6 99.999999999 0.002

5 99.99994 0.6

4 99.9937 63

Specification

Limit

Percent within Specification

(Centred Distribution)

Defects Per Million

(Centred Distribution)

3 99.73 2700

The 6 Sigma Metric


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Lower

Spec.

Limit

Upper

Spec.

Limit

2700

Defects per Million

Lower

Spec.

Limit

Upper

Spec.

Limit

0.002

From 3 Sigma to 6 Sigma


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1.5 Shift

Lower

Spec.

Limit

Upper

Spec.

Limit

-6 -5 -4 -3 -2 -1 y +1 +2 +3 +4 +5 +6

Motorolas 6 Sigma Metric


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SpecificationLimit

Percent withinSpecification

(Centred)

Percent withinSpecification(1.5 shift)

Defectsper million(Centred)

Defectsper million(1.5 shift)

1 68.26 30.23 317400 697700

2 95.44 69.13 45600 308700

3 99.73 93.32 2700 66810

4 99.9937 99.38 63 6210

5 99.99994 99.98 0.6 233

6 99.9999998 99.9997 0.002 3.4

Motorolas definition of a 6 Sigma process is one

which achieves 3.4 defects per million or less.

Motorolas 6 Sigma Metric


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A Process with 10 Steps

Each Process Step has a 3 Quality Level = 93.32% Yield

The probability of success (non-defective) at each step = 0.9332

The probability of overall success = 0.933210 = 0.5008

Overall Process Yield = 50.08% (499200 dpm)

6 Sigma & Defect Rates


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Another Process with 10 Steps

Each Process Step has a 6 Quality Level = 99.99966% Yield

The probability of success (non-defective) at each step = 0.9999966

The probability of overall success = 0.999996610 = 0.999966

Overall Process Yield = 99.9966% (34 dpm)

6 Sigma & Defect Rates


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To produce a defect uses production time, production capacity,

energy, raw material.

It must be identified by testing and/or inspection,

transported, stored, re-tested.

It must be reworked and then checked or scrapped and disposed

of

Often this non-value added activity is not shown within the factorymetrics - the hidden factory

This all takes time, people, material, energy, floor space....

The Hidden Factory


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Raw

Materials Mixing Forming Cooling

Finished

Product

FinalInspection

100% Pass

0% FailThis process has 100% yield. Ourcustomers would be very pleased.

Should we be just as happy?

Process Yield


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RTY = 0.925 x 0.94 x 0.95 = 0.826 = 82.6%

0% Fail7.5% of Units

6% of Units

5% of Units

Raw

MaterialsMixing Forming Cooling

Finished

Product

Final

Inspection

100% Pass

0% Fail

Rework

& RepairRework

& Repair

Rework& Repair

Rolled Throughput Yield


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Define

Identify

Opportunity

Identify Key ys

(Outputs)

Measure

y = f(x)

Identify

Critical xs

(Inputs)

Analyse

Optimise

xs

Improve

Control

xs

Control

DMAIC Improvement Process


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Define ImproveMeasure Control Control Critical xs

Monitor ys

Validate ControlPlan

Close Project

1 5 10 15 20

10.2

10.0

9.8

9.6

Upper Control Limit

Lower Control Limit

y

Phase Review

Analyse Characterise xs

Optimise xs

Set Tolerances for xs

Verify Improvement

15 20 25 30 35

LSL USL

Phase Review

y=f(x1,x2,..)

y

x

. . .

. . .

. .. . .. . .

Identify Potential xs

Analyse xs

Select Critical xs

Phase Review

Run 1 2 3 4 5 6 7

1 1 1 1 1 1 1 12 1 1 1 2 2 2 23 1 2 2 1 1 2 2

4 1 2 2 2 2 1 15 2 1 2 1 2 1 26 2 1 2 2 1 2 17 2 2 1 1 2 2 18 2 2 1 2 1 1 2

Effect

C1 C2

C4

C3

C6C5

x

xx

xx

xx

xx

x

x

Select Project

Define ProjectObjective

Form the Team

Map the Process

Identify CustomerRequirements

Identify Priorities

Update Project File

Phase Review

Define Measures (ys)

Evaluate Measurement

System

Determine Process

Stability Determine Process

Capability

Set Targets forMeasures

15 20 25 30 35

LSL USL

Phase Review

DMAIC Improvement Process


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1. Define the Problem

2. Interim Actions

3. Acquire and Analyse Data

4. Determine Root Cause

5. Evaluate Possible Solutions

6. Action Plan and Implement

7. Verify the Results

8. Standardise and Future Actions

Problem Solving

Process


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ReviewTemplates

HistogramPareto

FlowChart

Effect

Man

Maint. Method

Machine

Cause & EffectDiagram

x

xxx

x

xx

xx

x

xx

ScatterDiagram

y=f(x)y

x

RegressionAnalysis

ProcessCapability

MeasurementSystemVariation Reproducibility

Repeatability

Accuracy

StabilityCalibration

Gauge R&R

FMEA

440 500 560 620 680 740

95% Confidence Interval for Mu

568 5 78 5 88 59 8 60 8

9 5 % Co n f id e n c e In te rv a l fo r M e d ia n

V a r i a b l e : S A T

A-Sq u a re d :P-Va lu e :

Me a nStDe vVarianceSk e wn e s sKu r to s isN

Min imu m1 s t Qu a r t i leMe d ia n3 rd Qu a r t i leMa x imu m

5 7 7 .3 2 3

57.159

5 7 0 .7 1 1

0.3290.512

5 9 0 .2 4 065.101

4 2 3 8 .0 82 .6 3 E-0 2-4 .0 E-0 1

100

4 2 6 .0 0 05 4 2 .2 5 05 9 8 .0 0 06 4 0 .0 0 07 4 0 .0 0 0

6 0 3 .1 5 7

75.626

6 0 5 .0 0 0

An d e rs o n -Da rl in g No rma l i ty T e s t

95% Confidence Interval for Mu

9 5 % Co n f id e n c e In te rv a l fo r Sig ma

95% Confidence Interval for Median

Descrip t ive S tat i s ti csMinitab Software

Run yA B C D E F G

1 1 1 1 1 1 1 1 y12 1 1 1 2 2 2 2 y23 1 2 2 1 1 2 2 y34 1 2 2 2 2 1 1 y45 2 1 2 1 2 1 2 y56 2 1 2 2 1 2 1 y67 2 2 1 1 2 2 1 y78 2 2 1 2 1 1 2 y8

Design of Experiments

A1 A2

Analysis of Variance

Robust DesignTolerance Design

CustomerFocus

MistakeProofing

MAIC

ProcessValidation

A Few of the Six Sigma Tools!

1 Ssgb Bsi Intro

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