1 Ssgb Amity Bsi Intro

Module 1 - Six Sigma Introduction

Copyright © 2012 BSI. All rights reserved.

Module 1 - Six Sigma Introduction

History of Six Sigma

• Established by Motorola in the 1980’s and still being developed. Seen as a cornerstone to the company’s 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 for their own (unique) programme.

Copyright © 2012 BSI. All rights reserved. 2

What is Six Sigma?

• A systematic approach to process improvement.

• Processes can be related to design, manufacturing or administrative functions.

• It involves the use of statistical tools and techniques to analyse & improve processes.

• The relentless pursuit of variability reduction and defect elimination.

LSL USL


LSL USL

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)


Used in statistics as a measure of variation

σσσσ Sigma=

The Six Sigma Metric


Standard Deviation

The central philosophy of 6 Sigma is the reduction

of variation in all our work processes

Lower

Spec.

Limit

Upper

Spec.

Limit y ±±±± 1σσσσ = 68.26%

y ±±±± 2σσσσ = 95.44%

y ±±±± 3σσσσ = 99.73%

The Normal Distribution


The 3 Sigma mentality means 2700 defectives per million!

-1σσσσ +1σσσσ +2σσσσ-2σσσσ-3σσσσ +3σσσσ

y ±±±± 3σσσσ = 99.73%

y

(Target)

Lower

Specification

Limit

Upper

Specification

Limit

Normal Distribution

Centred on Target

The 6 Sigma Metric


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

±±±±6σσσσ 99.999999999 0.002

±±±±5σσσσ 99.99994 0.6

±±±±4σσσσ 99.9937 63

Specification

Limit

Percent within Specification

(Centred Distribution)

Defects Per Million

(Centred Distribution)

±±±±3σσσσ 99.73 2700

Lower

Spec.

Limit

Upper

Spec.

Limit

2700

Defects per Million

From 3 Sigma to 6 Sigma


Lower

Spec.

Limit

Upper

Spec.

Limit 0.002

±±±±1.5σσσσ Shift

Lower

Spec.

Limit

Upper

Spec.

Limit

Motorola’s 6 Sigma Metric


Limit Limit

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

Calculating Sigma LevelNumber of defects or error opportunities per unit

O 4

Number of Units Processed N 100

Total No. of defects D 16

Defects per Unit DPU D / N 0.16


Defects per Unit DPU D / N 0.16

Defects per opportunity DPO (DPU/O) D . N x O

0.04

Defects per Million Opportunity DPMO DPO x 10,00,000 40,000

Sigma Level 3.25

Yield 1-DPO 96%

SpecificationLimit

Percent withinSpecification

(Centred) σσσσ

Percent withinSpecification(1.5 shift)

Defectsper million(Centred)

Defectsper million(1.5σσσσ shift)

± 1σσσσ 68.26 30.23 317400 697700

± 2σσσσ 95.44 69.13 45600 308700

± 3σσσσ 99.73 93.32 2700 66810

Motorola’s 6 Sigma Metric


± 3σσσσ 99.73 93.32 2700 66810

± 4σσσσ 99.9937 99.38 63 6210

± 5σσσσ 99.99994 99.98 0.6 233

± 6σσσσ 99.9999998 99.9997 0.002 3.4

Motorola’s definition of a 6 Sigma process is one

which achieves 3.4 defects per million or less.

• A Process with 10 Steps

• Each Process Step has a 3σ 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


• Another Process with 10 Steps

• Each Process Step has a 6σ 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


•

The Hidden Factory

• To produce a defect uses production time, production capacity, energy, raw material….

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

• It must be identified by testing and/or inspection, transported, stored, re-tested….

• Often this non-value added activity is not shown within the factory metrics - the “hidden” factory

• It must be reworked and then checked or scrapped and disposed of…


Raw

MaterialsMixing Forming Cooling

Finished

Product

Final

Process Yield


Final

Inspection

100% Pass

0% FailThis process has 100% yield. Our

customers would be very pleased.

Should we be just as happy?

0% Fail

7.5% of Units

Raw

MaterialsMixing Forming Cooling

Finished

Product

Final

Inspection0% Fail

Rework

& RepairRework

& Repair

Rolled Throughput Yield


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

7.5% of Units

6% of Units

5% of Units 100% Pass

& RepairRework

& Repair

Define

Identify

Opportunity

Identify Key y’s

Measure

Analyse

DMAIC Improvement Process


Identify Key y’s

(Outputs)

y = f(x)

Identify

Critical x’s

(Inputs) Optimise

x’s

Improve

Control

x’s

Control

Define ImproveMeasure Control� Control Critical x’s

� Monitor y’s

1 5 10 15 20

10.2

10.0

9.8

9.6

Upper Control Limit

Lower Control Limit

Analyse� Characterise x’s

� Optimise x’s

y=f(x1,x2,..)

y

x

. . .. . .. .. . .. . .

� Identify Potential x’s

� Analyse x’s

Run 1 2 3 4 5 6 7

1 1 1 1 1 1 1 1

Effect

C1 C2

C4

C3

C6C5

� Select Project

� Define Project Objective

� Form the Team

� Map the Process

� Define Measures (y’s)

� Evaluate Measurement System

� Determine Process

DMAIC Improvement Process


� Validate Control Plan

� Close Project

y

Phase Review

� Set Tolerances for x’s

� Verify Improvement

15 20 25 30 35

LSL USL

Phase Review

� Select Critical x’s

Phase Review

1 1 1 1 1 1 1 12 1 1 1 2 2 2 23 1 2 2 1 1 2 24 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

x

xx

xx

xx

xx

x

x

� Identify Customer Requirements

� Identify Priorities

� Update Project File

Phase Review

� Determine Process Stability

� Determine Process Capability

� Set Targets for Measures

15 20 25 30 35

LSL USL

Phase Review

1. Define the Problem

2. Interim Actions8. Standardise and Future Actions

Problem Solving Process


7. Verify the Results 3. Acquire and Analyse Data

4. Determine Root Cause

5. Evaluate Possible Solutions

6. Action Plan and Implement

A Few of the Six Sigma Tools!

Review Templates

FlowChart

Effect

Man

Maint. Method

Machine

Cause & EffectDiagram

y=f(x)y

RegressionAnalysis

MeasurementSystemVariation Reproducibility

Repeatability

Accuracy

StabilityCalibration

Gauge R&R

44 0 5 00 5 60 620 6 80 74 0

95 % C o n fide nc e In te rva l fo r M u

5 6 8 5 7 8 5 8 8 5 9 8 6 0 8

9 5% C on fid en ce In te rva l fo r M ed ian

V a ria b le : S A T

A-S q ua red :P -V a lu e :

M ea nS tD e vVa r ia n ceS k ew n e ssK u rtos isN

M in im um1s t Q u art i leM ed ian3rd Q ua rti leM ax im um

57 7.3 23

5 7 .1 59

57 0 .7 11

0 .32 90 .51 2

5 9 0 .24 0 6 5 .10 1

4 2 38 .0 82 .6 3 E -0 2-4 .0 E -0 1

10 0

4 2 6 .00 05 4 2 .25 05 9 8 .00 06 4 0 .00 07 4 0 .00 0

6 0 3 .15 7

7 5 .62 6

6 0 5 .00 0

A nd ers on -D ar l in g N o rm a lity T es t

9 5 % C on fid e nc e In te rv a l fo r M u

95 % C o n fide nc e In te rv a l fo r S ig m a

95 % C o n fide nc e In te rva l fo r M e d ia n

D e scrip tive S ta tis tic s

Minitab Software

A1 A2

Analysis of Variance

CustomerFocus

MAIC

ProcessValidation


HistogramParetox

xxx

x

xx

xx

x

x

x

ScatterDiagram

y=f(x)y

x

ProcessCapability

FMEA

MistakeProofing

Control Charts

CRCL

Test Statistic

Value

Test Statistic

Distribution

Hypothesis Testing

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