Lean Six Sigma Green Belt Training

A

Lean Six Sigma

Green Belt Training

ING Business School

UNIT 2Measure

UNIT 2Measure

ING Business School Ref Unit 2-1

Measure – Learning Objectives

At the conclusion of this unit, you will be able to:

• Create detailed process map/flowchart.• Develop a data collection plan to gather initial

data. • Stratify data to facilitate understanding.• Revise the problem statement based on data.• Assess financial impact of your project.

ING Business School

Create Detailed Process

Map

DEFINE MEASURE ANALYZE IMPROVE CONTROL

Gather Initial Data

Stratify Data

Revise Problem

Statement

Assess Financial

Impact

Overall objective: Narrow the improvement opportunity to a specific problem statement. Overall objective: Narrow the improvement

opportunity to a specific problem statement.

Ref Unit 2-2a

Measure – Major Activities

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Key Deliverables: Commonly Used Tools:

Ref Unit 2-2b

Measure – Key Deliverables/Commonly Used Tools

•Detailed process flowchart •Upstream metrics/ Targets/Specification limits•Data collection plan•Operational Definitions•Metric stratification•Defect definitions•Process sigma•Revised problem statement•Statement of cost/benefit

•Flowchart/Process maps•Line graph/Run chart•Data collection worksheet•Checksheet•Concentration diagram•Pareto chart•Histogram•Control chart•Cost/benefit analysis

ING Business School

Create Detailed Process

Map


Gather Initial Data

Stratify Data

Revise Problem

Statement

Assess Financial

Impact

Ref Unit 2-3a


ING Business School Ref Unit 2-3b

Process Mapping Benefits

• Provides a picture • Promotes consistency • Fosters teamwork• Stimulates ownership• Highlights inefficient and missing activities• Gets everyone singing on the same page

ING Business School

Customer SalesClerk

SalesEngineer

MarketingRep

Sales/Mktg.Manager

Send RFPReceive

RFP

LogRFP

AssignRFP

Wantto Quote

?

No

Prepare Proposal

Yes

Prepare “NoQuote” Letter

UpdateCust. File

AddPricing

SpecsOK?

PriceOK?

A

A

No

Yes

ApproveProposal

ReceiveResponse

Yes

O1

No

SendResponse

Log/Give to Mgr.

Send RFPSend RFPReceive

RFPReceive

RFP

LogRFPLogRFP

AssignRFP

AssignRFP

Wantto Quote

?

Wantto Quote

?

No

Prepare ProposalPrepare Proposal

Yes

Prepare “NoQuote” LetterPrepare “NoQuote” Letter

UpdateCust. File

AddPricing

AddPricing

SpecsOK?

SpecsOK?

PriceOK?

PriceOK?

AA

AA

No

Yes

ApproveProposalApproveProposal

ReceiveResponseReceive

Response

Yes

O1O1

U1

No

SendResponse

SendResponse

Log/Give to Mgr.

Log/Give to Mgr.

Customer SalesClerk

SalesEngineer

MarketingRep

Sales/Mktg.Manager


RFPReceive

RFP

LogRFPLogRFP

AssignRFP

AssignRFP

Wantto Quote

?

Wantto Quote

?

No


Yes


UpdateCust. File

AddPricing

AddPricing

SpecsOK?

SpecsOK?

PriceOK?

PriceOK?

AA

AA

No

Yes



Response

Yes

O1O1

No

SendResponse

SendResponse

Log/Give to Mgr.

Log/Give to Mgr.


RFPReceive

RFP

LogRFPLogRFP

AssignRFP

AssignRFP

Wantto Quote

?

Wantto Quote

?

No


Yes


UpdateCust. File

AddPricing

AddPricing

SpecsOK?

SpecsOK?

PriceOK?

PriceOK?

AA

AA

No

Yes



Response

Yes

O1O1

U1

No

SendResponse

SendResponse

Log/Give to Mgr.

Log/Give to Mgr.

Log/Give to Mgr.

Log/Give to Mgr.

As-isvs.

Should be

“Swimlanes”

Ref Unit 2-4

Functional Flowcharts

• Who is available• Who has knowledge

ING Business School

A

An oval is used to show the inputs to start the process, or to show the output at the end of the process.

A rectangle or box is used to show process activities. Each box should have only one arrow coming in, and one going out. Words in a box should begin with an “action word”, e.g., receives, sends, completes, etc.

An arrow is used to show the direction or flow of process activities.

A diamond is used to show where decisions occur in the process. Decisions have one arrow in and two arrows out (yes/no).

A small circle with a letter or number identifies a break in the same page.

A rectangle or box with dashed lines is used to list clarifying information.

Ref Unit 2-5

Common Flowchart Symbols

ING Business School

See Process Mapping Tips See Process Mapping Tips

Ref Unit 2-6/7

Flowcharting Steps

• Identify and name the process • Identify process boundaries • Determine process level• List process roles• Identify activities for each role• Review and validate

ING Business School

Develop a detailed functional flowchart of your project’s process.

Ref Unit 2-9

Activity

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Create Detailed

Process Map

Stratify Data

Revise Problem

Statement

Assess Financial

Impact

Gather Initial Data

Why do we need data?Where does the data come from?

Ref Unit 2-10


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ProcessSuppliers Customers

Products

Services

Products

Services

Inputs Outputs

Outcome Metrics

Is process acceptable?

Ref Unit 2-11

Collect Outcome Metric Data

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Do you know the name of this tool?

What does it tell us?

% of Polic ies Not Issued On Time -

0%

2003 2004

Target = <2%

1st Qtr. 2nd Qtr. 3rd Qtr. 4th Qtr. 1st Qtr. 2nd Qtr. 3rd Qtr. 4th Qtr. 1st Qtr. 2nd Qtr.

5%

10%

20%

25%


15%

GAP Source: X Files

2005

Period

% N

ot

Issu

ed

on

Tim

e

Good

TitlesTitlesData over timeData over time Good arrowGood arrow

Target info.Target info.

Time on X axisTime on X axis

Source info.Source info.

Ref Unit 2-12

The Gap


Line Graph/Run Chart

• Helps identify cycles, shifts and trends in process performance

• Highlights gaps between process targets and actual performance

• Clarifies before and after changes in process performance

• Aids in predicting future performance

ING Business School

ProcessSuppliers Customers

Products

Services

Products

Services

Inputs Outputs

Upstream Metrics

Outcome Metrics

Upstream Metrics are measures of performance within a process.

Upstream Metrics are measures of performance within a process.

Ref Unit 2-14

Upstream Metrics

ING Business School

Y = f(x)Y = f(x)

Ref Unit 2-15a

Upstream Metrics

Sources of Upstream Metrics• Inputs from suppliers• Outputs from sub-processes • Handoffs from one employee or

department to another• Key decisions • Inspection points

(cont’d.)

ING Business School

Critical Customer

Requirement

Service Characteristic

OutcomeMetric

TargetUpstream

MetricTarget

“I want my policy issued

quickly.”

Turn Around Time

% of policies not issued on

time

<2% not on time

Time to complete

underwriting

<2 working days

Operational Definitions: 1. On time means within 5 working days starting the day after the

policy is received from agent.

2. Time to complete underwriting begins at receipt of policy from Sales, ends when underwriting approved

Ref Unit 2-15b

Process Metrics and Targets Example

ING Business School

Send RFPReceive

RFP

LogRFP

AssignRFP

No

Yes

Prepare “NoQuote” Letter

UpdateCust.. File

AddPricing

SpecsOK?

PriceOK?

PriceOK?

A

No

Yes


ReceiveResponse

Yes

O1O1

U1

No

Log/Give to Mgr.

Customer SalesClerk

SalesEngineer

MarketingRep

Send RFP

Wantto Quote

?

No

Prepare Proposal

A

No

Yes


Yes

No

SendResponse

Sales/Marketing Manager

Upstream Metric

Outcome Metric

Ref Unit 2-16

Process Metrics and Flowchart

ING Business School

Create Line Graph for Outcome Metric & Define Upstream

Metrics/Targets

Ref Unit 2-17

Activity

ING Business School Ref Unit 2-18a

Overview of Data Collection

Information to be Covered• What types of data will you need?• How can you ensure the data is accurate?• How much data is necessary?• What tools are useful for data collection?


Planning for Data Collection

Overall Approach• Plan - Who, What, When, Where, and How

the data will be collected (4Ws and 1H) • Ensure - data integrity• Analyze – compare first samples taken

with what is needed• Adjust - data collection plan as needed


Types of Data

Continuous data: variables, measurable, e.g. time, distance, weight, money.

Attribute data: discrete, counted; measures presence or absence of a characteristic, e.g. good/bad # of defects.

ING Business School

Analyze Types of Data

Ref Unit 2-20

Activity


Ensure Data Integrity

• Precise/Accurate – Data from the measurement method or device does not vary much from the actual value

• Repeatable – Repeated measurements of the same item or characteristic by the same person lead to the same result

• Reproducible – Two or more people measuring the same characteristic in the same way get the same result

• Stable over time – Data and measurements taken at different times by different people are done in the same way (the measurement system doesn’t change over time)


Operational Definitions

DefinitionAn operational

definition is a precise description of how to get a consistent value for the characteristic

you are trying to measure.

Characteristics• Specific • Clear • Measurable • Reflects customer Perspective

Givens • Test and refine the definition after

it is first developed• Training is required to ensure

that results from using the definition are consistent


Operational Definitions

Defect – process output that is not acceptable to the customer; not within specificationDefect Opportunity – A point where a CCR could be missed each time something moves through a process

• An opportunity is a place where a defect can reasonably occur

• The number of defect opportunities is related to the complexity of the process.

• The number of defect opportunities per unit must stay constant before and after DMAIC improvement

(cont’d.)

ING Business School

Create operational definitions for your Outcome & Upstream Metrics.

Ref Unit 2-24

Activity

ING Business School

• Relates to the problem you’re studying – the data can be directly linked to the Preliminary Problem Statement

• Answers questions about current process performance – when is it occurring, who is involved, where is it occurring, what is happening, how is it happening

• Provides information about related conditions – a defect occurred on Thursday; on Thursday we were short-staffed; also on Thursday the system was down for 2 hours

• Cost effective – the value of the data collected is worth more than the cost of collecting the data

How Much Data to CollectHow Much Data to Collect

Ref Unit 2-25

Characteristics of Useful Data

ING Business School

Population -“N” Usually

unknown

Sample – “n”

Take a sample

To make inferences about…

Sampling is collecting some of the data from a process to make inferences about all of the data.

Ref Unit 2-26

Develop a Sampling Plan



Reliable Samples• The data is representative

of the population• Every item has a known

and usually equal chance of being included

• There are no systematic differences between the data you collect and the data you do not collect

Unreliable Samples• Samples are only

collected when/where convenient

• The sample collection method has a pattern

• The process changes during data collection

• Faulty measurements • Only a portion of people

you need data from respond to your request for information

(cont’d.)


Calculations and other information related to determining sample size is covered in BlackBelt training.


What does sample size depend upon?• Desired sampling error – how precise you

want to be (± 4%) • Desired confidence level – how confident

that true population rate falls between selected error rate (95%)

• Variation of the population – characteristics, make-up

• Size of the population – larger populations, larger sample

(cont’d.)

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The use of tools in data analysis is critical – they help you visualize!

Ref Unit 2-29a

Tools for Collecting Data

• Gaps between process targets and actual performance

• Cycles and trends in process performance

ING Business School

Total

Liquidity Account

Bond Fund

Hedge Fund

2241222110

28IIII IIII IIIIIIII IIIII III54

11IIIIIIIIII7

4IIII13

17IIIIIIIIIIII IIII36

TotalBeneficiaryDate of issueCalculationSignature

# new accounts

Liquidity Account

Bond Fund

Hedge Fund

New Equity Issue

2241222110


11IIIIIIIIII7

4IIII13



# new accounts

Pro

duct

60

Type of ErrorType of Error

Total

Liquidity Account

Bond Fund

Hedge Fund

2241222110


11IIIIIIIIII7

4IIII13



# new accounts

Liquidity Account

Bond Fund

Hedge Fund

New Equity Issue

New Equity Issue

2241222110


11IIIIIIIIII7

4IIII13



# new accounts

Pro

duct

60

Type of ErrorType of ErrorType of ErrorType of Error

Source: X files

Report Preparation Confirmation Checksheet

Step Done?

Completion DataPlanned date

Actual date

Planned duration

Project completed

Client review &approval

Final report, draft

Final report review

Final report revisions

Desktop publishing ofreport

Final report submission

6-12

6-17

6-30

7-12

7-21

7-28

7-30

6-26

7-6

7-21

7-28

8-2

Actualduration

5d

13d

12d

9d

7d

2d

10

15d

7d

5d

N/A N/A

Notes

Cust requestedchanges

Client personnelon vacation

Minor changesrequested

Source: X files

Correct cashier error OK check

Checkout Line DelaysCashier Date

Reason Frequency Comments

Price check needed

No cashier available

Register out of tape

Not enough money

Forgot item

Wrong item

Manager assistance needed

Other

Wendy May 19

Source: X files

Frequency Plot ChecksheetPackage Weight

16.0 16.1 16.2 16.3 16.4 16.5 16.6 16.7 16.8Weight in ounces

Source: X files

Ref Unit 2-29b/30

Checksheets

ING Business School

E E E E

E

R RR R RR

E E E E E

E E E E R R R E A

M M T MF

E E E E A E A E E A E EA E

E E E E EA E E A

E: Entry missingR: Receipt missingM: “Misc.” not explainedT: “Trans” no explainedA: Arithmetic error

Expense ReportName: _____________________ Week ending ___________ 20__July 2 05

Date ProjectCode

Hotel Trans Meals Misc Total Comments

Totals

Ref Unit 2-33

Concentration Diagram


Checksheets

Implementation• Are all appropriate, high

potential categories of data being considered?

• Is the right data being gathered?

• Does everyone understand how to fill out the checksheet?

• Has management clearance been given?

• Do the people being observed understand why the checksheet is being used?

Interpretation• Look for trends and

patterns in the data • Look for something

unusual • Other tools can be used

with the Checksheet to help interpret the data, e.g., line graph, pareto chart, etc.

• Extract summary data by sub-totaling, or stratifying the data

(cont’d.)

ING Business School

Create or review the data collection plan for your project.

Ref Unit 2-34

Activity

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Data Collection Plan Project ________________________

How measured1

What questions do you want to answer?

Data Operational Definition and Procedures

What Measure type/ Data type

Related conditionsto record2

Sampling notes

Notes

1) Be sure to test and monitor any measurement procedures/instruments. 2) “Related factors” are stratification factors or potential causes you want to monitor as you collect data.

What is your plan for starting data collection? (attach details if necessary)

How will the data be displayed?(Sketch below)

How/where recorded (attach form)

Ref Unit 2-35

Activity (cont’d.)

ING Business School

Gather Initial Data


Create Detailed

Process Map

Stratify Data

Revise Problem

Statement

Assess Financial

Impact

Ref Unit 2-36a



What is Stratification?

Stratification is classifying and separating data into related groups:

• Stratify Outcome Metric data sources of the gap

• Goal of stratification is a search for significance

• Use stratification to organize resources

• Stratification helps identify the value of the improvement

ING Business School

Answer Questions with DataAnswer Questions with Data

Ref Unit 2-37

% N

ot

Issu

ed o

n T

ime

Good

% of Policies Not Issued On Time

0%

2003 2004

Target = <2%


5%

10%

20%

25%


15%

GAP Source: X Files

2005 Period

% N

ot

Issu

ed o

n T

ime

Good

Stratify By Asking Questions

• When did the data spike, then go down?

• What are reasons for the data to cycle?

• Is it possible to track where the policies that took the longest were written?

• Who wrote the policies that were not issued on time?

• How did it happen?

ING Business School

200410-200502

non-Medical Exam Policies not issued on time

0

20

40

60

80

100

120

140

MissingInfo. on

Application

Unable tocontact agentwith question

Insufficientfollow up

Lack ofpremium

Secondrequirements

Delayed bydelivery

Large amount

coverage

System error0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

N=149

Source: X files

Ref Unit 2-38

Pareto Chart


Interpreting Pareto Chart

• Isolate the significant few from the trivial many – example: 80% in first bar.

• The first category (bar) does not have to contain 80% of the total under analysis.

• A flat or somewhat level pareto chart usually means examine the data further.

• Stratify the data in different ways (frequency, severity, impact) in order to identify the most significant category.

• Ensure that each category has the same opportunity to contribute.

ING Business School

How else could this data be stratified?Reasons Proposals Take Longer Than 3 Days

To Present to Manager for Approval

0

10

20

30

40

50

60

70

80

Wai

ting fo

r

price

info

.

Wai

ting fo

r

serv

ice

spec

s.

Not awar

e

of dea

dline

Resea

rchin

g

serv

ice

info

.

RFP not

assi

gned Other

# L

ate

Pro

po

sals

100%n = 80

0

10

20

30

40

50

60

70

0

10

20

30

40

50

60

70

80

0%

50%

Sales & Marketing Dept.

Jan – Jun, 2003

Ref Unit 2-41

Pareto Practice

ING Business School

Complete Pareto analysis with data provided

Ref Unit 2-42a

Activity

ING Business School

48

40

30

20

10

0

XYZ Loan Processing

N = 48

12

Type of Error

10/23-27/01

Number of Defects

107 6 6

3 4

Not le

gibl

eSig

natu

reCal

cula

tion

Late

sub

mitt

alApp

rova

lM

issin

g in

fo.

Oth

er

100%

50%

25%

75%

Source: Loan Dept.10/23-27/99S. Martinez

Ref Unit 2-42b

Activity – Answer 1

ING Business School

Source: Loan Dept.10/23-27/99S. Martinez

XYZ Loan Processing - Cost of Defects

Extended Cost - Type of Defect

Not le

gibl

e

Appr

oval

Late

sub

mitt

al

Sign

atur

e

Calcul

atio

n

Oth

er

Missing

info

.

$3008

2500

2000

1500

1000

0

N = $3008

1158Co$t of Defects

469 456408 350

63 104

500

100%

50%

25%

75%

Ref Unit 2-42c

Activity – Answer 2

ING Business School

Fund Redemption

1998 1999J F M A M J J A S O N D J F M A M J J A S O N D

8

10

12

14

16

18

20

22

24

Period

GOOD

Fund Redemption

1998 1999J F M A M J J A S O N D J F M A M J J A S O N D

8

10

12

14

16

18

20

22

24

Period

GOOD

0

20

40

60

79

54

8 7 4 60%

25%

50%

75%

100%

Reasons

N = 79

Source: Fund Records

Fund Redemption - PerformanceJan.-Jul. 1999

0

20

40

60

79

54

8 7 4 60%

25%

50%

75%

100%

Reasons

N = 79


Fund Redemption - PerformanceJan.-Jul. 1999

Fund Redemption

0

20

40

60

80

100Jan.-Jul. 1999


19

100

75

50

0%

25

79

10 823

N = 139

Reasons

Fund Redemption

0

20

40

60

80

100Jan.-Jul. 1999


19

100

75

50

0%

25

79

10 823

N = 139

Reasons

0

10

20

30

40

63 3 4

0%

25%

50%

75%

100%N = 5450

Source: Customer. Survey – 8/99

38

Fund Redemption – AwarenessJan.-Jul. 1999

54

Reasons

0

10

20

30

40

63 3 4

0%

25%

50%

75%

100%N = 5450

Source: Customer. Survey – 8/99

38

Fund Redemption – AwarenessJan.-Jul. 1999

54

Reasons

Ref Unit 2-44

Multi-level Stratification

ING Business School

Avg. Time Request to Patient Pickup

Avg. Request to Pickup Time (in minutes) at Holy Cross Hosp.Taken by Transporter Dispatcher Jan-Feb, 2004

Ref Unit 2-45a

Histogram

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Claims Processing (Hours)

Frequency Total

1:00-2:30 32:31-4:00 12

4:01-5:30 16

5:31-7:00 117:01-8:30 68:31-10:00 110:01-11:30 1

TOTAL 50

Ref Unit 2-45b

Histogram/Frequency Chart


Histogram Interpretation

• Focus on the center of the data distribution

• Look at the data spread

• Consider the typical histogram shapes

ING Business School

Nothing unusual. Try to find causes of variation (data spread). Determine if data

is centered properly.

Stratify the data to avoid two populations of data; create two histograms.

Recalculate and draw the histogram again.

Investigate “outliers”. Determine why the data is not centered near the average.

Determine why part of the data is missing. Check your calculations.

Collect more data if necessary.

CLASSES OF DATA

FR

EQ

UE

NC

Y (y)

(x)

CLASSES OF DATA(x)F

RE

QU

EN

CY (y)

(x)

CLASSES OF DATA

(y)

(x)

FR

EQ

UE

NC

Y

(y)

(x)

FR

EQ

UE

NC

Y

CLASSES OF DATA

(y)

(x)FR

EQ

UE

NC

Y (y)

(x)

CLASSES OF DATA

(y)

FR

EQ

UE

NC

Y(y)

Normal

Bi-modal

Comb

Skewed

Precipice

Ref Unit 2-48/49

Histogram Interpretation (cont’d.)

ING Business School

# Days RFP's are Late

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8 9 101112131415161718192021

# Days Late

# o

f O

ccu

rren

ces

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8 9 101112131415161718192021

# Days RFP's are Late

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8 9 101112131415161718192021

# Days Late

# o

f O

ccu

rren

ces

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8 9 101112131415161718192021

Would you change anything?

Ref Unit 2-50

Histogram Interpretation(cont’d.)

ING Business School

Days to Prepare Response

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10# Days to Prepare Response

# o

f O

ccu

rren

ces

X = 5.07

Days to Prepare Response

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10# Days to Prepare Response

# o

f O

ccu

rren

ces

XX = 5.07

How would you proceed?

Ref Unit 2-51

Histogram Interpretation(cont’d.)

ING Business School

Defects Defects

Too early Too late

Delivery Time

Reduce Variation

Delivery Time

Too early Too lateTarget Target

Spread of variation is too wide compared to customer

specifications

Spread of variation is narrow compared to

customer specifications

Less Variation is DesiredLess Variation is Desired

Expect Variation in Everything

Ref Unit 2-52

Understand Data Variation

ING Business School

Mean:The sum of the data set values, divided by the number of values in the data set.

)x(

)x~(

Measure Definition: Example:

(12,14,16,17,14,11,18,17,24,26,23,27,14,14)Ordered Data =(11,12,14,14,14,14,16,17,17,18,23,24,26,27)Mode = 14

(11,12,14,14,14,14,16,17,17,18,23,24,26,27)

(11,12,14,14,14,14,16,17,17,18,23,24,26,27)Median = (16 + 17)/2 = 33/2Median = 16.5

6.1714

247

14

)27...1211(

n

xxMean i

Mode:The most frequently occurring value in the data set

Median:The value that falls exactly in the middle of the ordered data set, half of the numbers fall above, and half below.

Ref Unit 2-53

Measures of Central Tendency

ING Business School

The data below are a sample of days of absence over a year in a department. Determine the mean, median, and mode of this sample:1, 2, 3, 3, 4, 5, 7, 9, 10, 11, 120

Mode =

Median =

Mean =

If this data set were handed to you to analyze, what might be your reaction?

3

5

15.9

Ref Unit 2-54

Activity – Calculating Central Tendency

ING Business School

minmax xx

1

)( 2

n

xxs i

Measure / Definition Example

Range - The difference between the highest and lowest (greatest and smallest) values.

(12,14,16,17,14,11,18,17, 24, 26, 23, 27, 14, 14)

Ordered Data = (11,12,14,14,14,14,16,17,17,18, 23, 24, 26, 27)

Range: (27 – 11) = ________

Standard Deviation (sd) – The calculated variation between each data point in the set and the mean.

(3,5,7,9,11,12,4,5,13,1,16)

Ordered Data = (1,3,4,5,5,7,9,11,12,13,16)

Xbar = ________

s = 4.73

Note: Standard deviation calculations are covered in BlackBelt training.

16

7.82

Ref Unit 2-55

Measures of Dispersion

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68.26% of all data should fall within a distance of ±1sd from the mean95.45% within a distance of ±2sd from the mean99.73% with a distance of ±3sd from the mean

Ref Unit 2-56

The Empirical Rule

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99.73% confidence level:Xbar = 5s = 1Upper range limit = 5+(3x1) or 8Lower range limit = 5 - (3x1) or 2

95.45% confidence level:Xbar = 5s = 1Upper range limit = 5+(2x1) or 7Lower range limit = 5 - (2x1) or 3

Ref Unit 2-57

Activity – Predicting Process Output

Assuming the average number of days to process a new insurance policy is 5, and the standard deviation is 1.

• How many days can you expect it to take to process a new insurance policy?

• In other words, within what range would you expect to find 99.73% of the data?

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Special Cause: something different happening at a certain time or place

Common Cause: always present to some degree in the process

Ref Unit 2-58

Causes of Variation

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UCL - Upper Control Limit

0

20

30

40

50

60

70

80

90

100

Pro

cess

Met

ric

J A S O N D J F M A M J J A S O N D J F M

LCL - Lower Control Limit

Center Line, mean, or Xbar

10Control limits - calculated from data = ± 3 sd

Data points plotted over time

Data patterns are formed by two types of variation…Data patterns are formed by two types of variation…

Ref Unit 2-59

Control Charts

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Common Cause Variation

0

10

20

30

40

50

1 3 5 7 9 11 13 15 17 19 21 23 25

Special Cause

0

10

20

30

40

50

1 3 5 7 9 11 13 15 17 19 21 23 25

Ref Unit 2-60

Control Charts

Common Causes• Inherent in any system• Process with only common causes:

stable, predictable, in control

Special Causes• Special, specific, assignable causes. • Process with common causes plus

factors that induce variation above that inherent to the system: unstable, unpredictable, not in statistical control

• 6 Signals that special causes are present

(cont’d.)

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Ref Unit 2-61a

Control Charts

1. Point Outside Control LimitAny point on or outside the limits is abnormal and requires investigation

2. RunSeven points continually on one side of the center line is abnormal, Also, 10 out of 11, 12 out of 14 or 16 out of 20 points on one side of the center line is abnormal

3. TrendingSeven points in a continuous upward or downward direction is abnormal

(cont’d.)

6 Signals of Special Cause

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Ref Unit 2-61b

Control Charts

4. Approaching CL (hugging)Most points within center line and 1.5σ* indicates mixing of data from different populations, making the control limits too wide and stratification necessary

5. CyclingAny repeated up and down trend is abnormal and requires investigation

6. Approaching Control Limits2 of 3 points outside 2σ(σ= standard deviation) line is abnormal

6 Signals of Special Cause

(cont’d.)

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Notice that the control limits are more narrow after variation is reduced. Can you explain why?

Goal of Process Improvement

Ref Unit 2-62

Additional information related to control charts is covered in BlackBelt training.

Control Charts (cont’d.)

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70

80

90

100

110

120

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

UCL

LCL

Upper Spec

Lower Spec

Ref Unit 2-63

Control Limits, Specifications & Targets

• Control limits (VOP)• Calculated from the data• Describe what the process

is capable of achieving

• Specification limits (VOC)• Set by the customer,

management, or other requirements• Describe what you want a process to achieve

• Target lines (VOB)• Based on objectives external to the process• Not statistically calculated (not shown)

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LSL USL

Not In Control( Not Stable)

In Control(Stable)

Not meeting Customer

Specifications(Not Capable)

Meeting Customer

Specifications(Capable)

CustomerSpecification

Control Limit

LSL USLLSL USL

LSL USL

Voice of the Process

Vo

ice

of

the

Cu

sto

mer

Ref Unit 2-64

Possible Process Conditions

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Stratify Upstream Data to Search for Significant Contributors to the GAP in

the Outcome Metric

Ref Unit 2-65

Activity

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Create Detailed

Process Map

Gather Initial Data

Stratify Data

Revise Problem

Statement

Assess Financial

Impact

Ref Unit 2-66a


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Final Problem Statement: 77% non-MedExampolices not issued on time involved missing or unclear information on the policy application.

200410-200502Non-MedExam Policies not Issued on Time

0

20

40

60

80

100

120

140

MissingInfo. on

application

Unable to contact agent with question

Not follow upor not follow

up enough

Lack ofpremium

Secondrequirements

Delayed bydelivery

Large amountcoverage

System error0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

N=149

Data you have collected and stratified so far focuses your project on the most significant

reasons for the performance gap.

Ref Unit 2-66b

Revise Problem Statement

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DO

Ref Unit 2-67a


• Make sure problem is of size/scope that is solvable

• State the problem in quantitative measurable terms

(cont’d.)

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DO Not

Ref Unit 2-67b


• State a preconceived idea of what the cause(s) may be

• Imply a particular solution

• Affix blame on a person or group

(cont’d.)

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Preliminary Problem StatementRevised Problem Statements are

more Focused

New products miss target launch dates 45% of the time.

Nine out of the last ten new bond funds missed target launch dates

by an more than three months.

20% of late payments come from product XYZ customers

85% of product XYZ customers in the U.S. account for 75% of

late payments.

Storage personnel are involved in 75% of back strain cases,

Storage personnel were lifting something when 92% of the back

strain cases occurred.

Ref Unit 2-68

Narrow the Focus

Revise Problem Statement(cont’d.)

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Identify good problem statements

Ref Unit 2-69

Activity


Calculate Process Sigma

What is Process Sigma? • A reflection of the variation in your process

• Based on the “Yield” - how much of your process output is within specification, e.g., acceptable to the customer

• Requires operational definitions for both defects and defect opportunities

• Calculated for the outcome metric(s)

• Related to the revised problem statement

• Determined using a table



1. Compute First Time Right yield (see Lean Six Sigma Overview, page 16)% YIELD = 1 – (defects / total defect opportunities) X 100

2. Look up % YIELD in a Process Sigma Table (Appendix)

Example:

Assume 100 units, 5 opportunities, 7 defects% YIELD = (1 – 7 / (5 x 100)) X 100% YIELD = (1 – (7 / 500) X 100% YIELD = (1 – .014) X 100% YIELD = 98.6%, and 3.7 Process Sigma



Process Sigma Target • If process sigma is greater than 3.0, set a 2x

defect improvement goal e.g., 1,000 DPMO to 500 DPMO.

• If process sigma is less than 3.0, set a 10x defect improvement goal e.g., 1,000 DPMO to 100 DPMO.

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Develop a Revised Problem Statement, Calculate Process Sigma,

Establish an Improvement Target

Ref Unit 2-72

Activity

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Create Detailed

Process Map

Gather Initial Data

Stratify Data

Revise Problem

Statement

Assess Financial

Impact

Ref Unit 2-73



Cost of Poor Quality (COPQ)

• COPQ is the sum of costs to maintain or satisfy customer requirements after a defect has occurred.

• Understanding process COPQ will help identify the financial impact of the project.

Get help, if needed.

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Conformance (Value-Adding)

Appraisal costs (necessary, value adding)

Prevention costs

Non-conformance(Non-Value- Adding)

Appraisal costs (not needed, non-value- adding)

Internal failure costs

External failure costs

Ref Unit 2-75

Types of Costs

Cost of Poor Quality(cont’d.)


Cost of Poor Quality

Appraisal Costs - May be Non-Value- Adding or Value-Adding

• Maintenance/calibration of inspection/test equipment or procedures

• Materials/supplies used in inspection/test• Incoming inspection/test• In-process inspection/test• Final inspection/test• Product/service audits

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Internal External

•Inspections or tests•Rework•Avoidable process losses•Down time/Delays •Sorting•Failure analysis •Repair•Waste

•Complaint handling/adjustment•Returned items•Failure analysis•Warranties and Allowances •Lost business•Lost sales•Lost customers •Waste

Ref Unit 2-77

Failure Costs – Non-Value Add

Cost of Poor Quality (cont’d.)



Prevention Costs – Value Adding • New product review• Process quality planning• Process control• Quality system audits• Training• Supplier review and qualification• Fool proofing so defects cannot be made, e.g.,

automatic seat-belt restraints

(cont’d.)



Project Benefits

• See calculations from Define step.• Add newly identified benefits.• See list in workbook.

(cont’d.)

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Estimate of Project Annual Financial Impact

Benefit total: (+)

COPQ total:

Conformance (forecast of value-adding costs)

(-)

Non-conformance (forecast of non-value-adding cost reductions)

(+)

Project total:

Ref Unit 2-80


Financial Impact Summary • Estimate annual impact• Indicate if increasing stream of benefits• Save worksheets

(cont’d.)


What costs are Involved in Your Project?

Activity


Measure Step Tollgate Questions

A. Does the process map clearly convey responsibility for each activity?

B. What is the plan for data collection?

C. How has the data been documented?

D. How was the data stratified to identify any significant reasons for the preliminary problem statement?

E. What is the revised problem statement?

F. What is the forecast financial impact of the project?


Unit Summary

Having completed this unit, here are some questions you should be able to answer:

1. What are two types of data?

2. List four sources of data to be addressed in a data collection plan.

3. Name three tools that can be used to collect and stratify data?

4. What should not be included in a revised problem statement? What should?

5. What are three types of costs to be addressed in a COPQ assessment?

6. How is a control chart different from a run chart?

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Calculate Performance

Stratify Data

Frequency Plots

UCL

LCL

UCL

LCL

Run Chart

Plot Outcome Defect Data

A

Mary

John

Sally

Jim

B C E

Develop Data Collection Plan

Data Collection Plan Project ________________________

What questions do you want to answer?

Data Operational Definition and Procedures What Measure

type/ Data type

How Measured 1

Related conditions to

record 2

Sampling notes

How/where recorded (attach

form)

Create Detailed Process Map

F1

F3

F4

F2

Assess Financial

Impact (COPQ)

Revise Problem

Statement

Control Chart

Check Sheet

Collect Upstream Data

Overall objective: Narrow the improvement opportunity to a specific problem statement. Overall objective: Narrow the improvement opportunity to a specific problem statement.

DPMO_____

Yield _____

Sigma _____

Goal _____

Understand Variation

Pareto Chart

To Analyze Step

From Define Step

Ref Unit 2-83b

Project Components: MEASURE

Lean Six Sigma Green Belt Training

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