Six Sigma Intro Jan 2005

7/29/2019 Six Sigma Intro Jan 2005

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Introduction to Six Sigma


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Topics (Session 1)

Understanding Six Sigma

History of Six Sigma

Six Sigma Methodologies & Tools

Roles & Responsibilities

How YOUcan use Six Sigma


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Six Sigma is. . .

A performance goal, representing 3.4 defects forevery million opportunities to make one.

A series of tools and methods used to improve ordesign products, processes, and/or services.

A statistical measure indicating the number of

standard deviations within customer expectations.

A disciplined, fact-based approach to managing abusiness and its processes.


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Whats in a name?

Sigma is the Greek letter representing the standarddeviation of a population of data.

Sigma is a measure

ofvariation(the data spread)


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What does variation mean?

Variation means that a

process does not produce

the same result (the Y)

every time.

Some variation will exist in

all processes.

Variation directly affects customer experiences.

Customers do not feel averages!

-10

-5

0

5

10

15

20


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Measuring Process PerformanceThe pizza delivery example. . .

Customers want their pizzadelivered fast!

Guarantee = 30 minutes or less

What if we measured performance and found anaverage delivery time of 23.5 minutes?

On-time performance is great, right?

Our customers must be happy with us, right?


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How often are we delivering on

time?Answer: Look atthe variation!

Managing by the average doesnt tell the whole story. Theaverage andthe variation togethershow whats happening.

s

x

30 min. or less

0 10 20 30 40 50


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Reduce Variation to Improve

PerformanceHow many standard

deviations can you

fit within

customerexpectations?

Sigma level measures how often we meet (or fail to meet)the requirement(s) of our customer(s).

s

x

30 min. or less

0 10 20 30 40 50


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Managing Up the Sigma Scale

Sigma % Good % Bad DPMO

1 30.9% 69.1% 691,462

2 69.1% 30.9% 308,5383 93.3% 6.7% 66,807

4 99.38% 0.62% 6,210

5 99.977% 0.023% 233

6 99.9997% 0.00034% 3.4


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Examples of the Sigma Scale

In a world at 3 sigma. . .

There are 964 U.S. flightcancellations per day.

The police make 7 false arrestsevery 4 minutes.

In MA, 5,390 newborns aredropped each year.

In one hour, 47,283international long distance callsare accidentally disconnected.

In a world at 6 sigma. . .

1 U.S. flight is cancelled every3 weeks.

There are fewer than 4 falsearrests per month.

1 newborn is dropped every 4years in MA.

It would take more than2 years to see the same numberof dropped international calls.


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Topics







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The Six Sigma Evolutionary Timeline

1736: Frenchmathematician

Abraham deMoivre publishesan articleintroducing the

normal curve.

1896: Italian sociologist VilfredoAlfredo Pareto introduces the 80/20rule and the Pareto distribution inCours dEconomie Politique.

1924: Walter A. Shewhart introduces

the control chart and the distinction ofspecial vs. common cause variation ascontributors to process problems.

1941: Alex Osborn, head ofBBDO Advertising, fathers awidely-adopted set of rules for

brainstorming.

1949: U. S. DOD issues MilitaryProcedure MIL-P-1629, Proceduresfor Performing a Failure Mode Effectsand Criticality Analysis.

1960: Kaoru Ishikawaintroduces his now famouscause-and-effect diagram.

1818: Gauss uses the normal curve

to explore the mathematics of erroranalysis for measurement, probabilityanalysis, and hypothesis testing.

1970s: Dr. Noriaki Kanointroduces his two-dimensionalquality model and the three

types of quality.

1986: Bill Smith, a seniorengineer and scientist introducesthe concept of Six Sigma atMotorola

1994: Larry Bossidy launchesSix Sigma at Allied Signal.

1995: Jack Welchlaunches Six Sigma at GE.


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Six Sigma Companies


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Six Sigma and Financial Services


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Topics







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DMAICThe Improvement

Methodology

Define Measure Analyze Improve Control

Objective:

DEFINE the

opportunity

Objective:

MEASURE current

performance

Objective:

ANALYZE the root

causes of problems

Objective:

IMPROVE the

process to

eliminate rootcauses

Objective:

CONTROL the

process

to sustain the gains.

Key Define Tools:

Cost of Poor

Quality (COPQ)

Voice of the

Stakeholder

(VOS)

Project Charter

As-Is Process

Map(s)

Primary Metric

(Y)

Key Measure

Tools:

Critical to Quality

Requirements

(CTQs)

Sample Plan

Capability

Analysis

Failure Modes

and Effect

Analysis (FMEA)

Key Analyze

Tools:

Histograms,

Boxplots, Multi-

Vari Charts, etc.

Hypothesis Tests

Regression

Analysis

Key Improve

Tools:

Solution Selection

Matrix

To-Be Process

Map(s)

Key Control

Tools:

Control Charts

Contingency

and/or Action

Plan(s)


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What is the problem? The problem is the Output (a Y

in a math equation Y=f(x1,x2,x3) etc).

What is the cost of this problem Who are the stake holders / decision makers

Align resources and expectations

DefineDMAIC ProjectWhat is the project?

Six Sigma

Project

Charter

Voice ofthe

Stakeholder

Stakeholders

$

Cost ofPoor

Quality


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DefineAs-Is ProcessHow does our existing process work?

Move-It! Courier Package Handling

Process

Accounting

Finalizing

Delivery

Out-SortSupervisorOut-SortClerkAccounts

Supervisor

Accounts

ReceivableClerkWeightFeeClerkDistanceFeeClerkIn-SortSupervisorIn-SortClerkMail ClerkCourier

Observe package

weight (1 or 2) on

back of package

Look up

appropriateWeight Fee and

write in top middle

box on package

back

Take packages

from W eightFee

Clerk Outbox to

A/RClerkInbox.

Add Distance &

WeightFees

together and writein top right box on

package back

Circle Total Fee

and Draw Arrow

from total to

sender code

Take packages

from A/RClerkOutbox to

Accounts

SupervisorInbox.

Write Total Fee

from package inappropriate

Sender column on

Accts . Supv.s log

Add up Total # ofPackages and

Total Fees from

log and create

clientinvoice

Deliver invoiceto

client

Submit log to

General Manager

at conclusion of

round.

Take packages

from AccountsSupervisor

Outbox to Out-

Sort ClerkInbox.

Draw 5-point Star

in upper right

corner of package

front

Sort packages in

order of Sender

Code beforeplacing in outbox

Take packagesfromOut-Sort

Clerk Outbox to

Out-Sort

SupervisorInbox.

Observe senderand receiv er

codes and make

entry in Out-Sort

Supervisors log

DeliverPackages

to customers

according to N, S,E, W route

Submit log to

General Managerat end of round

Submit log to

General Managerat end of round

Does EVERYONE

agree how the current

process works?

Define the Non Value

Add steps


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DefineCustomer RequirementsWhat are the CTQs? What motivates the customer?

Voice of the Customer Key Customer Issue Critical to QualitySECONDARY RESEARCH

PRIMARY RESEARCH

Surveys

OTM

MarketData

IndustryIntel

ListeningP

osts

IndustryBenchmarking

Focus Groups

CustomerService

CustomerCorrespondence

Obser-vations


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MeasureBaselines and

CapabilityWhat is our current level of performance?

Sample some data / not all data

Current Process actuals measured

against the Customer expectation

What is the chance that we will succeed

at this level every time?50403020100

95% Confidence Interval for Mu

26.525.524.523.522.521.520.519.5

95% Confidence Interval for Median

Variable: 2003 Output

19.7313

8.9690

21.1423

Maximum3rd QuartileMedian1st QuartileMinimum

NKurtosisSkewnessVarianceStDevMean

P-Value:A-Squared:

26.0572

11.8667

25.1961

55.290729.610023.147516.4134

0.2156

1000.2407710.238483

104.34910.215223.1692

0.8540.211


95% Confidence Interval for Sigma


Anderson-Darling Normality Test

Descriptive Statistics


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MeasureFailures and RisksWhere does our process fail and why?Subjective opinion mapped into an objective risk profile number

Failure Modes and Effects Analysis (FMEA)

Process or

Product Name:Prepared by: Page ____ of ____

Responsible: FMEA Date (Orig) ______________ (Rev) _____________

Process

Step/Part

Number Potential Fai lure Mode Potential Fai lure Effects

S

E

V Potential Causes

O

C

C Current Controls

D

E

T

R

P

N

Actions

Recommended Resp. Actions Taken

S

E

V

O

C

C

D

E

T

R

P

N

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

Process/Product

X1

X2

X4

X3

etc


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Six Sigma

AnalyzePotential Root CausesWhat affects our process?

y = f (x1, x2, x3 . . . xn)

Ishikawa Diagram(Fishbone)


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AnalyzeValidated Root CausesWhat are the key root causes?

Six Sigma

y = f (x1, x2, x3 . . . xn)

Critical Xs

Process

Simulatio

n

DataStratification

RegressionAnalysis

Experimental Design


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ImprovePotential SolutionsHow can we address the root causes we identified?

Address the causes, not the symptoms.

y = f (x1, x2, x3 . . . xn)

Critical Xs

Decision

Evaluate

Clarify

Generate

Divergent | Convergent


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ImproveSolution SelectionHow do we choose the best solution?

Time

Qualit

y

Cost

Solution Sigma Time CBA Other Score

Six Sigma

Solution

Implementatio

n Plan

Solution Selection Matrix

Nice

Try

Nice

Idea X

SolutionRight Wrong

Implementation

Bad

Good


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ControlSustainable BenefitsHow do we hold the gains of our new process?

Some variation is normal and OK

How High and Low can an X go yet not materially impact the Y

Pre-plan approach for control exceptions

0 10 20 30

15

25

35

Observation Number

Individua

lValue

Mean=24.35

UCL=33.48

LCL=15.21

Process Owner: Date:

Process Description: CCR:

Measuring and Monitoring

Key

Measure

ments

Specs

&/or

Targets

Measures

(Tools)

Where &

Frequency

Responsibility

(Who)

Contingency

(Quick Fix)Remarks

P1 - activity

duration,

min.

P2 - # of

incomplete

loan

applications

Process Control System (Business Process Framework)

Direct ProcessCustomer:

Flowchart

Customer Sales Branch Manager ProcessingLoan Service

Manager

1.1

Application&

Review

1.2

Processing

1.3

Creditreview

1.4

Review

1.5

Disclosure

Apply for

loan

Review

appliation for

completeness

ApplicationComplete?

Complete

meeting

information

No


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DFSSThe Design MethodologyDesign forSix Sigma

Uses

Design new processes, products, and/or services from scratch

Replace old processes where improvement will not suffice

Differences between DFSS and DMAIC

Projects typically longer than 4-6 months

Extensive definition of Customer Requirements (CTQs) Heavy emphasis on benchmarking and simulation; less emphasison baselining

Key Tools

Multi-Generational Planning (MGP)

Quality Function Deployment (QFD)

Define Measure Analyze Develop Verify


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Topics







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Champions

Promote awareness and execution of Six Sigmawithin lines of business and/or functions

Identify potential Six Sigma projects to beexecuted by Black Belts and Green Belts

Identify, select, and support Black Belt and

Green Belt candidates

Participate in 2-3 days of workshop training


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Green Belts

Use Six Sigma DMAIC methodology and basictools to execute improvements within theirexisting job function(s)

May lead smaller improvement projects withinBusiness Unit(s)

Bring knowledge of Six Sigma concepts & tools totheir respective job function(s)

Undergo 8-11 days of training over 3-6 months


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Subject Matter Experts

Provide specific process knowledge to Six Sigma teams

Ad hoc members of Six Sigma project teams

Financial Controllers

Ensure validity and reliability of financial figures used

by Six Sigma project teams Assist in development of financial components of initial

business case and final cost-benefit analysis

Other Roles


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Questions?


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Topics for Detailed Discussion

Problem Identification

Cost of Poor Quality

Problem Refinement

Process Understanding Potential X to Critical X

Improvement


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If it aint broke, why fix itThis is the way weve always done it


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First Pass Yield Roll Throughput Yield

Histogram

Pareto


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Problem IdentificationFirst Pass Yield (FPY):

The probability that

any given unit can gothrough a system

defect-free without

rework.

Step 1

Step 2

Step 3

Step 4

Scrap 10 Units

100 Units

100

90

87

Scrap 3 Units

Scrap 2 Units

85

Outputs / Inputs

100 / 100 = 1

90 / 100 = .90

87 / 90 = .96

85 / 87 = .97

At first glance, the yield would seem to be

85% (85/100 but.)

When in fact the FPY is (1 x .90 x .96 x .97 =

.838)


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Step 1

Step 2

Step 3

Step 4

Re-Work

10 Units

100 Units

Re-Work

3 Units

Re-Work

2 Units

Rolled

Throughput

Yield (RTY):

The yield of

individual

process steps

multiplied

together.

Reflects thehidden factory

rework issues

associated with

a process.

Outputs / Inputs

90 / 100 = .90

97 / 100 = .97

98 / 100 = .98

.90 x .97 x .98 = .855

100 Units

100 Units

100 Units

100 Units


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RTY Examples - Widgets

Function 1

Function 2

Function 3

Function 4

50

5

10

5

50

50

50

50

Roll Throughput Yield

50/50 = 1

(50-5)/50 = .90

(50-10)/50 = .80

(50-5)/50 = .90

1 x .90 x .80 x .90 = .65

Put another way, this process is operating

a 65% efficiency


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RTY Example - Loan Underwriting

Roll Throughput Yield

50/50 = 1

(50-7-2)/50 = .82

(43-6)/43 = .86

(43-1-2)/43 = .93

1 x .82 x .86 x .93 = .66

Put another way, this process is operating

a 66% efficiency

Application

Underwrite

Complete Full

Paperwork

Close

50

Fails

Underwriting

Decide not to

borrow

2

6

2

7

1

42

50

43

43



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HistogramA histogram is a basic graphing tool that displays the

relative frequency or occurrence of continuous data values showing

which values occur most and least frequently. A histogram illustrates theshape, centering, and spread of data distribution and indicates whether

there are any outliers.


5004003002001000

40

30

20

10

0

C8

Frequency

Histogram of Cycle Time


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HistogramCan also help us graphically understand the data


40032525017510025


9484746454


Variable: CT

55.753

61.098

69.947

Maximum

3rd QuartileMedian1st QuartileMinimum

NKurtosisSkewnessVarianceStDevMean

P-Value:A-Squared:

84.494

75.664

90.417

444.000

105.00066.00031.000

1.000

1708.263562.317124569.8167.600380.1824

0.0006.261


95% Confidence Interval for Sigma


Anderson-Darling Normality Test

Descriptive Statistics


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Topics (Session 2)



Problem Refinement


Improvement


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COPQ - The cost involved in fulfilling the gap between the desired and

actual product/service quality. It also includes the cost of lost opportunity

due to the loss of resources used in rectifying the defect.

Examples / Buckets

Roll Throughput Yield Inefficiencies (GAP between desired result and

current result multiplied by direct costs AND indirect costs in the process).

Cycle Time GAP (stated as a percentage between current results and

desired results) multiplied by direct and indirect costs in the process.

Square Footage opportunity cost, advertising costs, overhead costs, etc

Hard Savings - Six Sigma project benefits that allow you to do the same

amount of business with less employees (cost savings) or handle more

business without adding people (cost avoidance).

Soft Savings - Six Sigma project benefits such as reduced time to market,

cost avoidance, lost profit avoidance, improved employee morale,enhanced image for the organization and other intangibles may result in

additional savings to your organization, but are harder to quantify.


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Topics (Session 2)



Problem Refinement


Improvement


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Multi Level ParetoLogically Break down initial Pareto data into sub-

sets (to help refine area of focus)

Problem Refinement

(Web)Others

Non-WEB

1596

13.586.5

100.086.5

100

50

0

100

80

60

40

20

0

Defect

CountPercentCum %

Perc

ent

Cou

nt

Pareto Chart for WEB

Others

OneTimeandOnGoing

OneTime

Annual

16133545

14.711.932.141.3

100.085.373.441.3

100

50

0

100

80

60

40

20

0

Defect

Count

PercentCum %

Percent

Coun

t

Pareto Chart for Type


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Problem StatementA crisp description of what we are trying to solve.

Primary MetricAn objective measurement of what we are attempting

to solve (the y in the y = f(x1, x2, x3.) calculation).

Secondary MetricAn objective measurement that ensures that a Six

Sigma Project does not create a new problem as it fixes the primary

problem. For example, a quality metric would be a good secondary

metric for an improve cycle time primary metric.

Problem Refinement


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Fish Bone Diagram - A tool used to solve quality problems by

brainstorming causes and logically organizing them by branches. Also

called the Cause & Effect diagram and Ishikawa diagram

Problem Refinement

Provides tool for exploring cause / effect and 5 whys


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Topics (Session 2)



Problem Refinement


Improvement


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SIPOCSuppliers, Inputs, Process, Outputs, Customers

You obtain inputs from suppliers, add value through your process, and provide

an output that meets or exceeds your customer's requirements.

Process Understanding


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Process Mapshould allow people unfamiliar with the process to understand

the interaction of causes during the work-flow. Should outline Value Added

(VA) steps and non-value add (NVA) steps.

Process Understanding

Receipt /

Extract

Requal Group

Remit

Data Cap

Inventory

Start Size SortsControl

DocsOpen Pull & Sort

Verify

Pass 1

Key fromimage Balance

Pass 2Rulrs

Perfection

No

Prep cks Ship to IP

Full Form

QCReviewShip to

Cust

Vouch

OK

Prep

Folders /

Box

Yes

No

Vouchers

Full Form

Ck / Vouch

Yes Prep cks,

route

vouch


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Topics (Session 2)



Problem Refinement


Improvement


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Potential X to Critical X

Y is the dependent output of a variable process. In other

words, output is a function of input variables (Y=f(x1, x2,x3).

Through hypothesis testing, Six Sigma allows one to

determine which attributes (basic descriptor (generally

limited or binary in nature) for data we gatherie. day ofthe week, shift, supervisor, site location, machine type,

work type, affect the output. For example, statistically,

does one shift make more errors or have a longer cycle

time than another? Do we make more errors on Fridays

than on Mondays? Is one site faster than another? Once we

determine which attributes affect our output, we determine

the degree of impact using Design of Experiment (DOE).


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A Design of Experiment (DOE) is a structured, organized

method for determining the relationship between factors(Xs) affecting a process and the output of that process (Y).

Not only is the direct affect of an X1 gauged against Y but

also the affect of X1 on X2 against Y is also gauged. In

other words, DOE allows us to determine - does one input

(x1) affect another input (x2) as well as Output (Y).


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Potential X to Critical XDOE Example

P2JamSKDCDELJams

High

LowHigh

LowHigh

LowHigh

Low

1.4

1.3

1.2

1.1

1.0

Elapsed

Main Effects Plot (data means) for Elapsed

1 3 1 3 1 3 1 3

1.00

1.25

1.50

1.00

1.25

1.50

1.00

1.25

1.50

1.00

1.25

1.50Jams

DCDEL

SK

P2Jam

3

1

1

3

1

3

1

3

Interaction Plot (data means) for Elapsed

Main Effects Plot

Direct impact to Y

Interaction Plot

Impacts of Xs on

each other


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DOE Optimizer

Allows us tostatistically predict the

Output (Y) based on

optimizing the inputs

(X) from the Design of

experiment data.


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Topics (Session 2)



Problem Refinement


Improvement


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Improvement

Once we know the degree to which inputs (X) affect our

output (Y), we can explore improvement ideas, focusingon the cost benefit of a given improvement as it relates

to the degree it will affect the output. In other words, we

generally will not attempt to fix every X, only those that

give us the greatest impact and are financially or

customer justified.


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Control

Once improvements are made, the question becomes, are the

improvement consistent with predicted Design of Experiment results

(ieare they what we expected) and, are they statistically different

than pre-improvement results.

1.00.50.0-0.5-1.0

USLLSL

Process Capability Analysis for Sept

% Total

% > USL

% < LSL

% Total

% > USL

% < LSL

% Total

% > USL

% < LSL

Ppk

Z.LSL

Z.USL

Z.Bench

Cpm

Cpk

Z.LSL

Z.USL

Z.Bench

StDev (Overall)

StDev (Within)

Sample N

Mean

LSL

Target

USL

12.62

12.62

0.00

6.35

6.35

0.00

13.04

13.04

0.00

0.38

4.40

1.14

1.14

*

0.51

5.87

1.53

1.53

0.221880

0.166425

23

-0.02391

-1.00000

*

0.23000

Exp. "Overall" PerformanceExp. "W ithin" PerformanceObserved PerformanceOverall Capability

Potential (W ithin) Capability

Process Data

Within

Overall


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Control

Control Chart - A graphical tool for monitoring changes that occur

within a process, by distinguishing variation that is inherent in the

process(common cause) from variation that yields a change to the

process(special cause). This change may be a single point or a series

of points in time - each is a signal that something is different from

what was previously observed and measured.

Sept 20Sept 13Subgroup

0.5

0.0

-0.5IndividualValue

9/259/13Date

2

1

Mean=0.03

UCL=0.5293

LCL=-0.4693

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0MovingRange

1

R=0.1877

UCL=0.6134

LCL=0

I and MR Chart for Sept


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Six Sigma Intro Jan 2005

Documents