dmaic lean six sigma

© 2010 International Institute for Learning, Inc.SCS Singapore Version 1.0

CSICSI SingaporeSingaporeFollowing the Chain of Evidence (the Facts)

in Lean Six Sigma Process Improvement Projects (DMAIC)

Robert Johnston, Ph.D.Executive Director, Six Sigma

International Institute for Learning, Inc.

© 2010 International Institute for Learning, Inc.

2

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GS-4

SCS Singapore Version 1.0

Who Am I?

Robert Johnston, Ph.D. Statistics, MBB

Philosophy: practicality trumps theory• Utility = (Perfection of idea) * (Probability people will use it)

ExperienceAnimal Feed Products, Pharmaceuticals, GE Capital

Allstate, Coca-Cola, Carlson (Radisson), Caterpillar, Deutsche Bank, DHL, FDMS, Intuit, TRW, Schreiber Foods, StarHub, U.S. Navy

Trained/Coached several hundred Lean Six Sigma practitioners/projects


GS-5

SCS ingapore Version 1.0

What is Lean Six Sigma?

“SIX SIGMA: A comprehensive and flexible system for achieving, sustaining, and maximizing business success. Six Sigma is uniquely driven by close understanding of customer needs, disciplined use of facts, data, and statistical analysis, and diligent attention to managing, improving, and reinventing business processes.”

- “The Six Sigma Way” – Pande p. xi


GS-6


What is Lean Six Sigma?


GS-7


Lean Six Sigma Triad

MainFocus


GS-8

Process Design – DMADV?

DMADV is the recipe for designing new processes/products. Usually more complex/longer than DMAIC, so companies often implement DMADV after successfully completing some DMAIC projects.

D

M

A

D

V

Define the process/product and the

business case

Measure: Define the customer requirements

and prioritize them

Analyze functional requirements, create high-level design

Develop detailed design

Verifyprocess/product

performance

QFDFMEA

Drive Customer Requirements Through

Entire Design Cycle

ManageRisk


GS-9


Measure Performance &

Focus on Critical Areas

What is DMAIC?

Where’s the PAIN to the Customer? The Business?

80% 20%

Drill Down for Root Cause

Pull It Out by the Roots

Monitor & Take Action If Root

Cause Re-appears

DMAIC is the recipe or methodology for improving existing processes; it is the backbone of Six Sigma and the starting point for most companies beginning the Six Sigma journey.

Shortcu

t


GS-10


Use of Data in DMAIC: “It’s all about the evidence”

Data is the bedrock of Six Sigma & DMAIC; it helps separate fact from fiction.

Voice of Customer, Financials

Baseline data, focusing data (Pareto Principle)

Cause & Effect Data

Before / After Data

Real-time Monitoring Data

Coun

t

Perc

ent

LocationCount

7.2 4.3 1.4Cum % 72.5 87.0 94.2 98.6 100.0

50 10 5 3 1Percent 72.5 14.5

OtherMWSWNW

70

60

50

40

30

20

10

0

100

80

60

40

20

0

Month

Erro

rs

DecNovOctSepAugJulJunMayAprMarFebJan

10

9

8

7

6

5

4

3

2

1

6

Experience

Cycl

e Ti

me

98765432

16

14

12

10

8

6

4

2Observation

Cycl

e Ti

me

2018161412108642

18

16

14

12

10

8

6

4

2

0

_X=4.50

UCL=7.71

LCL=1.29

Before After

Observation

Cost

24222018161412108642

14

12

10

8

6

4

2

0

_X=5.84

UCL=12.28

LCL=-0.61


GS-11


Six Sigma & Lean (It’s like Chocolate and Peanut Butter)

Six Sigma Focus on QualityCustomer RequirementsVariation & Defect ReductionData BasedSupport Infrastructure

Lean Focus on SpeedCycle Time ReductionElimination of WasteRapid Project Execution

Six Sigma

Lean


GS-12


Why is it called Six Sigma? (optional)

Sigma (σ, standard deviation ) measures process variation (VOP)

Mean

σ

Customer Requirement

Customer Requirement

Good BadBad

σ σσ σ σ

Compared to Customer Requirements (VOC) shows the % Defects


GS-13


Why is it called Six Sigma? (optional)

Reducing variation means reducing the number of defects

Mean

Six Sigma represents 6 standard deviations from the mean to the upper or lower specification limits of the customer

σ σ σ σ σ σ

CustomerRequirement

σ σ σ σ σ σ

CustomerRequirement

Good BadBad

3.4 Defects per Million


GS-14


DMAIC: Following the Chain of Evidence

Improving Processes


GS-15


Define: Houston, we have a problem!

ID the ProcessIncluding Supplier, Inputs, Outputs, Customer

ID the Customer ,his/her Requirements, and the Performance Gap

Critical To Quality (CTQ)

Make them MeasureableDefine a Defect

D M A I C

Input Output


GS-16


Define: CTQ Identification Example

You’ve just ordered a pizza from a local pizza delivery shop. What are your CTQs ?

D M A I C

Not very specific or measureable Not very specific or measureable ……

40-50oC on delivery

<30 min

4-5 oz cheese…

More specific and measureable More specific and measureable ……


GS-17


Measure: So, how bad is it?

Map Process in detail

Establish data collection plan

Output data (y)Stratification data (x’s)

Check Measurement System

Collect Data

Baseline Process Performance

Focus- stratify

D M A I C


1-18


Process Focus

What is supposed to happen…


1-19


Process Focus

What really happens…

Rework … Inspection … Delays … Work-a-rounds …

“Hidden Factory”


Impact of Hidden Factory on Cycle Time

Process Lead Time (PLT)From Customer request to customer receipt

Value Add Process Time (VAPT)Time spent on tasks customer is willing to pay for

Process Cycle Efficiency (PCE)PCE = VAPT / PLT

GS-20


What is a typical value for PCE?


2-21


WIP & Little’s Law: What is WIP?

WIP stands for Work in Process (or Progress).

If we have too much WIP:Cycle times grow and are unpredictable.Resources are spent handling it.Processes are cluttered so it’s hard to expedite something if necessary.


2-22


Little’s Law

Little’s Law states:

Where…

PLT = Process Cycle TimeWIP = Work In ProcessExit Rate = Units/Time

Like the line at an amusement park:

RateExit WIP PLT =

IN

OUTExit Rate:2 people

minute

Minutes 6 2People 12 PLT

MinutePeople

=

=


2-23


Little’s Law: WIP (1 of 2)

If WIP is reduced, then Lead Time is reduced:

While this is common sense, it is not usually how processes are run. We keep throwing more “stuff” into the process (as fast as orders come) increasing WIP and Lead Time.

But if we don’t throw the orders into the process, what do we do with them and why?

IN


minute Minutes 3 2People 6 PLT

MinutePeople

=

=


2-24


Little’s Law: WIP (2 of 2)

Have a “triage” or waiting area.

Waiting orders can be reprioritized (expedited).

Orders in the process can be found and expedited more easily.

We know exactly how long it will take an order to be processed once it enters the queue.

…but don’t forget, the Customer experiences Waiting Time + PLT

IN


minute Minutes 3 2People 6 PLT

MinutePeople

=

=

This one can be expedited if necessary(can be done in 3 minutes instead of

the original 6 minutes).

Waiting Room


2-25


General Application of Little’s Law to Projects/Initiatives/Work

Project W1 W2 W3 W4 W5

A $ $

B $ $

C $ $

Project W1 W2 W3 W4 W5

A $ $ $ $

B $ $ $

C $ $

Work many things at once

Focus on a few things at a time

Increased Flexibility

D

Increased Value


GS-26


A Word on Planning Data Collection: Avoid a Port-Mortem

1. What is the question?

3. Collect data to go from 1. to 2.

2. What Graph/Summary will answer it?

D M A I C


2-27


Check the Measurement System –Is Our Data Any Good?

Process

Measurement System

XX

D M A I C


2-28


Measurement Systems Analysis (MSA) Exercise

M&M Company wants to improve the quality of their output.

It’s a Good M&M if…Clear/Legible Logo, andUniform/Consistent Color, andNo Cracks in Shell

Otherwise, it’s a Bad M&M.

D M A I C


2-29


Measurement Systems Analysis (MSA) Exercise

Teams of 5 or 6

Make a Team grid, 5x5, place 25 M&Ms in the grid (flip chart paper)

Each team member makes a 5x5 score sheet (8.5x11 or A4)

Independently grade each M&M as Good (G) or Bad (B). No talking, sounds of amazement, etc.

A B C D E1

2

3

4

5

A B C D E12345

G G B G BG B B G GB B G B GB B B G BB G G G B

D M A I C


2-30


When done, choose a spokesperson to read through score sheet one item at a time.

If all Team Members agree, then they get a point.

Report Team Point Total.

A B C D E12345

G G B G BG B B G GB B G B GB B B G BB G G G B

D M A I C

Measurement Systems Analysis (MSA) Exercise Answers


2-31


1 2 3 4 5 6 …Team

% A

gree

men

t 100

75

50

25

0

Desired Results

Typical Results!

D M A I C

Measurement Systems Analysis (MSA) Exercise Answers


MSA Examples

Banking

IT

Manufacturing

GS-32



2-33


Existing Data Sources

There is a lot of data out there

Review whatever you can find

Guidelines for using existing dataHow was the data created?– Using which operational definition? (Yours?)– For which purpose/intention? – Under which circumstances? (Rush, end of the shift, …?)

If the data does not follow your operational definition can it be reformatted to fit your needs? (maybe they collected more data than they showed)


Looking at DataGS-34


Which Regions/Teams are better? Worse?

Fooled you! It’s all generated from an identical source … the differences are just random…not real. Summaries – like averages

or totals – may not tell the whole story


Look at the DataGS-35


Need to start looking at the raw data – not just summaries of the data – variation is important!


Look at the Data: Another ExampleGS-36


Company complaint resolution process:Goal: Resolution <50 daysActual: Average Resolution = 97 days!

CEO decides need major/fundamental process change

Requires fundamentalprocess change

Fundamentally process OK– it’s the exceptions

Which is it? Both have average of 97!


GS-37


Analyze: Find the Root Cause: y=f(x)D M A I C


GS-38


Analyze: Verify Cause & Effect Relationship

Y: E

ffect

Dis

cret

e

Con

tinuo

us

Discrete ContinuousX: Potential Cause or

Stratification Factor

Loan $

Cyc

le T

ime

175000150000125000100000

65

55

45

35

Scatterplot of Cycle Time vs Loan $

Approval TimeLo

cati

on70605040

London

NY

Dotplot of Approval Time vs Location

Each symbol represents up to 2 observations.

Face Time

Sale

70605040

YES

NO

Dotplot of Face Time vs Sale

Each symbol represents up to 2 observations.Sale

Cou

nt

YESNO

25

20

15

10

5

0

YESNO

Region = E Region = W SaleNOYES

Pareto Chart of Sale by Region

ScatterplotStratified•Dotplot•Boxplot•Histogram

Stratified•Dotplot•Boxplot•Histogram

Stratified•Pareto

orTable

•Regression•Multiple Regression

•t-test•ANOVA / ANOM•Test of Equal Variance•DOE

•Test of Two Proportions•Chi-square

•Logistic Regression

Y: E

ffect

Dis

cret

e

Con

tinuo

us

Discrete ContinuousX: Potential Cause or

Stratification Factor

Loan $

Cyc

le T

ime

175000150000125000100000

65

55

45

35

Scatterplot of Cycle Time vs Loan $

Approval TimeLo

cati

on70605040

London

NY

Dotplot of Approval Time vs Location

Each symbol represents up to 2 observations.

Face Time

Sale

70605040

YES

NO

Dotplot of Face Time vs Sale

Each symbol represents up to 2 observations.Sale

Cou

nt

YESNO

25

20

15

10

5

0

YESNO

Region = E Region = W SaleNOYES

Pareto Chart of Sale by Region

ScatterplotStratified•Dotplot•Boxplot•Histogram

Stratified•Dotplot•Boxplot•Histogram

Stratified•Pareto

orTable

•Regression•Multiple Regression

•t-test•ANOVA / ANOM•Test of Equal Variance•DOE

•Test of Two Proportions•Chi-square

•Logistic Regression

D M A I C


GS-39


N/N

Y/Y

Analyze: Verify Cause & Effect Relationship- YY/NN

NO YESPotential Cause (X) Present?

Effe

ct (Y

) Pre

sent

?N

O

YES

D M A I C


GS-40


Causal Relationships- Lurking Variables

Lurking Variables are ones you did not measure, or even consider, that impact your process/data

What’s the Lurking Variable?

0 500 1000# Ice-cream Sales

# D

row

ning

s0

5

1

0

20

25

D M A I C


GS-41


Causal Relationships- Lurking Variables

The number of people at the beach which is a function of Temperature!

50 70 90Temperature

# D

row

ning

s0

5

1

0

20

25

# Ic

e-C

ream

Sal

es0

50

0

1

000

50 70 90Temperature

D M A I C


Examples of Lurking VariablesGS-42


Num

ber o

f Dam

aged

Car

tons

pe

r shi

ft

Training didn’t solve the problem…

It was the fork-trucks! New employees got theold fork-trucks – they had a design flaw


GS-43


Lurking Variables: Aggregated Data

Death Rates in Hospitals

What if account for Patient Condition?

Watch out for Lurking Variables in Causal Analysis!

A B

Deaths 450 (15%)

130 (11.8%)

Patients 3000 1100

A B

Deaths 50 (5%)

100 (10%)

Patients 1000 1000

A B

Deaths 400 (20%)

30 (30%)

Patients 2000 100

Good Condition Poor Condition

D M A I C


GS-44


Improve: Fix It!

Eliminate the Root Cause

Brainstorm solutions

Evaluate Solutions and Select best

Manage Risk

Pilot Solution

Verify Results

D M A I C


Before & After

Many solutions don’t actually help

How will you know if yours did?

GS-45



GS-46


Control: Make it Stay Fixed

Standardize Process

Train on the new Process

On-going Process Monitoring

D M A I C


2-47

Responding to Variation Inappropriately

Rule 1: Do Nothing– Start Funnel at 50– Drop 24 Balls

Rule 2: Compensate– Start Funnel at 50– Drop– Adjust: e.g., if ball drops 3

below target, adjust funnel 3 up, etc.

– Repeat Drop & Adjust cycle 24 times



2-48

Responding to Variation Inappropriately

Rule 1: Do Nothing– Start Funnel at 50– Drop 24 Balls

Rule 2: Compensate– Start Funnel at 50– Drop– Adjust: e.g., if ball drops 3

below target, adjust funnel 3 up, etc.

– Repeat Drop & Adjust cycle 24 times

Rule 1 Results

Rule 2 Results

41% increase in variation!



GS-49


Control: Two Kinds of Variation

Common Cause – events happen sometimes to everyone

Special Cause – events only happen sometimes to some people/processes

D M A I C


2-50

Exercise: Two Kinds of Variation

Sign your name 3 times

Now with other hand

Common Cause

Common Cause(just more of it than with the other hand)

SpecialCause



2-51

Understanding Variation

Why it mattersVariation exists in all processes

There are two fundamental kinds of variation:Special Cause and Common Cause

The correct response depends on whether it is Special or Common Cause…



2-52

Responding to Variation

Type ofVariation?

SpecialCommon

Yes

MeetsRequirements?

No

Do Nothing Use all the data to understand cause of variation. Make fundamental process change.

Respond to individual data points, determine cause, take corrective action

Common Cause VariationCustomer or Internal Requirement

1.2.

3.



Introduction to Control Charts

Distinguishing Common & Special Cause Variation

Example of Standard Business Reporting



2-54

This Month

Last Month

Year-To-

Date

Same Month Last Year

101 108 102 98

Business Performance Report: Sales

Please assess our recent performance• Last month’s performance (108) is better than this month’s (101).

• This month’s performance (101) is about the same as YTD’s (102).

• But this month’s performance (101) is better than theperformance the same month last year (98).

Let’s see if our interpretation changes when we plot our data over time, where variation can be seen and taken into account…



2-55

Month

Scen

ario

1

FebDecOctAugJunAprFebDecOctAugJun

110

105

100

95

90

97.6197.61

Time Series Plot of Scenario 1

This Month

Last Month

Year-To-

Date


101 108 102 98

This chart supports an interpretation of a significant change last month – a special cause.

Scenario 1



2-56

Month

Scen

ario

2


110

105

100

95

90

97.6197.61

Time Series Plot of Scenario 2

Scenario 2

This Month

Last Month

Year-To-

Date


101 108 102 98

Last month’s result doesn’t appear unusual – just common cause variation.



2-57

This Month

Last Month

Year-To-

Date


101 108 102 98

Control Chart for Scenario 1

Month

Indi

vidu

al V

alue


115

110

105

100

95

90

85

80

_X=97.61

UCL=104.96

LCL=90.26

1

I Chart of Scenario 1

Last month’s performance is Special Cause variationSCS Singapore Version 1.0

Control Charts are based on the data and show Common Cause variation


2-58

This Month

Last Month

Year-To-

Date


101 108 102 98

Control Chart Scenario 2

Month

Indi

vidu

al V

alue


115

110

105

100

95

90

85

80

_X=97.61

UCL=114.49

LCL=80.73


Last month’s performance is Common Cause variationSCS Singapore Version 1.0


2-59

This Month

Last Month

Year-To-

Date


101 108 102 98

Control Chart Scenario 2: Tampering

Month

Indi

vidu

al V

alue


115

110

105

100

95

90

85

80

_X=97.61

UCL=114.49

LCL=80.73


If a process with Common Cause variation is adjusted based on individual data points (tampering) then process variation will increase!

Minimum Requirement



2-60

Conclusions: Standard Business Reporting

Two radically different processes, requiring different management approaches, both produce the same standard management report … this should concern you!

Charting data over time gives context.Can see patterns and variation in the data

Control Charts plot data over time and use Control Limits to detect Special Cause variation so appropriate action can be taken.

Do managers and workers in your company understand the difference between common and special cause variation? If not, then tampering is occurring.

Month

Indi

vidu

al V

alue


115

110

105

100

95

90

85

80

_X=97.61

UCL=114.49

LCL=80.73


Month

Indi

vidu

al V

alue


115

110

105

100

95

90

85

80

_X=97.61

UCL=104.96

LCL=90.26

1


This Month

Last Month

Year-To-Date


101 108 102 98



GS-61


Two Kinds of Variation: Responding Appropriately

Management takes a big step forward when it stops asking

workers to explain randomness.

D M A I C


GS-62


Measure Performance &

Focus on Critical Areas

Summary: What is DMAIC?

Where’s the PAIN to the Customer? The Business?

80% 20%

Drill Down for Root Cause

Pull It Out by the Roots

Monitor & Take Action If Root

Cause Re-appears

DMAIC is the recipe or methodology for improving existing processes; it is the backbone of Six Sigma and the starting point for most companies beginning the Six Sigma journey.

Shortcu

t


GS-63


“It’s all about the evidence”

Data is the bedrock of Six Sigma & DMAIC; it helps separate fact from fiction.

Voice of Customer, Financials

Baseline data, focusing data (Pareto Principle)

Cause & Effect Data

Before / After Data

Real-time Monitoring Data

Coun

t

Perc

ent

LocationCount

7.2 4.3 1.4Cum % 72.5 87.0 94.2 98.6 100.0

50 10 5 3 1Percent 72.5 14.5

OtherMWSWNW

70

60

50

40

30

20

10

0

100

80

60

40

20

0

Month

Erro

rs

DecNovOctSepAugJulJunMayAprMarFebJan

10

9

8

7

6

5

4

3

2

1

6

Experience

Cycl

e Ti

me

98765432

16

14

12

10

8

6

4

2Observation

Cycl

e Ti

me

2018161412108642

18

16

14

12

10

8

6

4

2

0

_X=4.50

UCL=7.71

LCL=1.29

Before After

Observation

Cost

24222018161412108642

14

12

10

8

6

4

2

0

_X=5.84

UCL=12.28

LCL=-0.61


Data Specific Concepts (“It’s all about the evidence”)

DefineScoping ProjectsUnderstanding Customer Requirements

Measure

Seeing the ProcessThe Devil’s in the Details (PCE<5%)Impact of Multitasking

The State of DataMSA

Look at the Data (not just summaries of the data)

Analyze

Causal Reasoning (YY/NN)Lurking Variables

Improve

Verify Solutions (Before/After)

Control

Responding to Variation (Special/Common Cause)

GS-64



GS-65


The Games afoot!

If you love…a mystery, andthe thrill of discovery, andthe satisfaction of verifiable, positive, enduring change

ThenLean Six Sigma will add a powerful new dimension to your skills!


GS-66


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