six sigma presentation (2)

Measure Phase – Step 2&3 L1 Version 3.2 Slide 1 Proprietary to Wipro Ltd

A I CMD

DMAIC Steps

Establish Performance Parameters

Validate Measurement System for „Y‟

Establish Process Baseline

Define Performance Goals

Identify Variation Sources

Explore Potential Causes

Establish Variable Relationship

Design Operating Limits

Step 2

Step 3

Step 4

Step 5

Step 6

Step 7

Step 8

Step 9

M

A

I

CValidate Measurement System for „X‟

Verify Process Improvement

Institutionalize New Capability

Step 10

Step 11

Step 12

Step 0

Step 1

Establish CTQ Characteristics

Define a Project D

DMAIC Road-Map

Introduction - MEASURE

A robust measurement system forms the basis of any Six Sigma project

A measurement system has two characteristics

Design of the measurement system

Precision of the measurement system

Step 2 of DMAIC

Step 3 of DMAIC

SIPOC

S I

P

O C

Suppliers Inputs

Process

Outputs Customers

Process Boundary

33

44

5511 22S I

P

O C

Suppliers Inputs

Process

Outputs Customers

Process Boundary

33

44

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SIP

OC

Tool

Baton Changes

S I

P

O CSuppliers Inputs

Process

Outputs Customers

Process Boundary

33

44

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P

O CSuppliers Inputs

Process

Outputs Customers

Process Boundary

33

44

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S I

P

O CSuppliers Inputs

Process

Outputs Customers

Process Boundary

33

44

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P

O CSuppliers Inputs

Process

Outputs Customers

Process Boundary

33

44

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Process 1

Process 2

Benefits of Process Mapping

Tremendous value in having teams just discuss the process

Simple & visible structure for thinking through a complex process

Enables seeing the entire process as a team

Enables seeing that changes are not made in a vacuum and will carry through,

affecting the entire process down the line

Creates a framework for designing performance standards for your project

Magnifies non value-added areas or steps

Identifies cycle times of each step in the process

Helps re-examine (if needed) the scope and charter of your project

Points to Look For

The pain areas (identified at the time of project selection) must be within the

selected scope

Between the “Start” and “End” of the process, there should be logical flow of units

leading towards creating an output

„Walk-through‟ the process

Guard against analyzing the process at this stage, just map as-it-is

Do not map the process as you would like it to be

What is a Unit?

A unit is the tangible & measurable characteristic of a process output

Defects are observed / counted in the output characteristic of a unit (denoted as ‟Y‟)

S I

P

O C

Suppliers Inputs

Process

Outputs Customers

Process Boundary

33

44

5511 22S I

P

O C

Suppliers Inputs

Process

Outputs Customers

Process Boundary

33

44

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Examples

In ticket booking example, each ticket booked could be a unit

In the above example, can „each filled requisition given‟ be a unit?

In a bug fix example , each bug that comes in can be considered as a Unit

2.3 Define Specifications and Defect

Key Concepts

Recall that customers are better off telling you what they do not want

A defect is an imperfection or deficiency in the output unit with respect to specifications

defined by the customer

Quality is absence of defects in the unit identified

Quality goes up as defects come down

Quality is inversely proportional to defects

Defects are defined by customers (VOC table can be used here, however, focus here is

to collect project CTQ’s)

A unit may have multiple defects depending upon customer CTQs

Defect is on a unit

A unit which has defects is called a Defective( Even one defect in a unit will make the unit defective)

What is a Specification?

A specification is a customer-defined tolerance for the output unit characteristics

Specifications can be one sided or two sided

Specifications form the basis of any defect measurement exercise

Specification example: Bug fix productivity should be at least 3 bugs / pw ( only LSL – one sided spec).

USL: Upper Specification Limit for „Y‟,

anything above this is a defect.

LSL: Lower Specification Limit for „Y‟,

anything below this is a defect.

Target: Ideally the middle point of USL & LSL.

LSL Target USL

What is Six Sigma?

High

Probability

of Failure

66807 Defects

Per Million

Opportunities

Much Lower

Probability

of Failure

3.4 Defects

Per Million

Opportunities

6 2‟s

1

6 2‟s

Mean / TargetLower

Specification Limit

Upper

Specification Limit

Higher this

number,

Lower the

chance of

producing a

defect

Higher these

numbers,

Lower the

chance of

producing a

defect

1

3 1‟s 3 1‟s

2.4 Understand Data Characteristics

Why Collect Data?

Successful organizations have a common language to communicate

Common language promotes objectivity in decision-making process

Don‟t come up with great solutions for problems that don‟t exist

A measure of „where we are‟ is critical to determining „where we should be‟

Have you reached where you intended to? -- only data answers that question

A good data collection simplifies the problem solving effort

If the solution costs more than the problem, it‟s not worth it. A good data collection

should concentrate as much on measuring problems as it does on measuring solutions

Key Concepts

Improvement can only occur if we understand where we are & where to go, supported

by a measurement system that validates both situations

If the tool, by which we measure a characteristic, is not appropriate, able, or accurate,

effective improvement will not occur

One must understand and quantify the measurement system

Examples

Discrete data - (Is Countable)

Data that can take a limited number of values (Pass / Fail, OK / Not OK, Win / Loss)

Examples

Number of Days in a week

Number of „yes‟ responses to a satisfaction survey

Number of bugs fixed

Number of test cases passed or number of test cases failed

Continuous Data - ( Is Measurable and can take on fractional Values)

Data that be expressed in either fractions or whole numbers

Examples

Time taken to fix a bug

Time taken to close a call

Productivity

Defect Density

Yield of a process

Temperature in the room

Height of a person

Discrete Data Characteristics

Usually illustrated in tables & graphs

Continuous Data Characteristics

Usually illustrated in tables & histograms / frequency polygons

A histogram or frequency distribution shows the number of data points in a data set that fall

into each of the frequency classes

A frequency polygon is constructed by connecting the mid-points of each of the vertical bar

in the Histogram

90 95 100 115 120 125 130

Continuous Data Characteristics

Location / Central Tendency

It is a measure of the center point of any data set

Spread / Dispersion

It is a measure of the spread of any data set around its center

Shape

It is a measure of symmetry of any data set around its center

Measuring the Location

Mean

Mean is the arithmetic average of all data points in a data set

Mode

Mode is the most frequently occurring data point in a data set

Median

Median is the middle data point of a data set arranged in an ascending / descending order

Y1 + Y2 + Y3 + ………. + Yn

nWhere n = number of data pointsY =

Odd number of data points Even number of data points

Average

Measuring the Spread

Range

Range is the difference between the maximum & minimum data point

Variance / Standard Deviation

Variance & standard deviation measure how individual data points are spread around mean

( Y1 - Y )2 + ( Y2 – Y )2 + ……. + ( Yn – Y )2

( n – 1 )Variance = s2 =

Standard Deviation = s = s2

Importance of Spread

Mean of Curve „A‟ is more representative of its data set as compared to Curves „B‟ & „C‟

Spread outside the specifications may result in defects; this information is not

provided by mean

From a process perspective, individual customers are subject to different behaviors

of the process

A

B

C

Normal Distribution

Introduction to Normal Distribution

It‟s a Probability Distribution, illustrated as N ( µ, σ )

Simply put, a probability distribution is a theoretical frequency distribution

Higher frequency of values around the mean & lesser & lesser at values away from mean

Continuous & symmetrical

Tails asymptotic to X-axis

Bell shaped

Total area under the Normal curve = 1

100 110 120 130908070

Figure 3.01

1 unit

of

standard

deviation

+ -

Normal Distribution with

Mean =100

Standard Deviation = 10

Standard Normal Distribution

Instead of dealing with a family of normal distributions with varying means & standard

deviations, a standard normal curve standardizes all the distributions with a single curve

that has a mean of 0 & standard deviation of 1

It‟s illustrated as N ~ ( 0,1 ), i.e. mean = 0 & standard deviation = 1

µ1 µ2 µ3 0 +1 +2 +3

+ -

-1-2-3

Normal Distribution Property

µ

Figure 3.02

+ -

- 1σ + 1σ

95.46%- 2σ + 2σ

68.26%

- 3σ + 3σ99.73%

+ 4σ99.9937%

- 5σ99.99943%

+ 5σ

- 6σ + 6σ99.999998%

- 4σ

Concept of Z Value

To standardize different measurement units; such as, inches, meters, grams; a

standard Z variable is used.

Where Y = Value of the data point we are concerned with

µ = Mean of the data points

σ = Standard Deviation of the data points

Z = Number of standard deviations between Y & the mean (µ)

Z value is unique for each probability within the normal distribution

It helps in finding probabilities of data points anywhere within the distribution

It is dimensionless

Z = Y - µ

σ

Example

It‟s found that time taken for resolution of customer complaints follow a normal distribution with

mean of 250 hours and standard deviation of 23 hrs. What is the probability that a complaint

resolution will take more than 300 hrs?

250 300

Z = 300 - 250

23= 2.17

Looking up Appendix 1 for Normal Distribution Table,

we find that Z value of 2.17 covers an area of 0.98499 under itself

Thus, the probability that a complaint resolution may take between 0 & 300 hrs is 98.5%

& thus, chance of problem resolution taking more than 300 hrs is 1.5%

Z

Example

For the same data, what is the probability that problem resolution will take between

216 & 273 hrs?

250273

Z1 = 273 - 250

23= 1

From Appendix 1:

Total area covered by Z1 = 0.841344740

Total area covered by Z2 = 1 - 0.929219087 = 0.0707

Intercepted area between Z1 & Z2 = 0.7705

Thus, probability that a problem resolution may take between 216 & 273 hrs is 77.05%

216

Z2 = 216 - 250

23= - 1.47

Z1

Z2


Measuring the Shape

Symmetric Data set

It‟s a data set in which spread of the data set around its mean is identical

For such a data set - mean = mode = median

Asymmetric Data set

Positive / Right skewed Negative / Left skewed

- high spread on the right side of the mean - high spread on the left side of the mean

Mean,

Mode,

Median

Mean

Median

Mode ModeMean

Median

The Complete Picture

On CenterLarge Spread

LSL USLT

On CenterSmall Spread

LSL USLTLSL USLT

Off CenterSmall Spread

Off CenterLarge Spread

LSL USLT

Which Type of Data is Preferable?

Continuous data helps you to understand process variation

Sample size required is less

Discrete data does not allow to understand the process variation. It does not tell you

how bad is bad

You need larger samples with Discrete data

Class Exercise

Given below is the sample data on Customer complaint closure time in hrs.

Compute the Mean & Standard Deviation for each quarter.

Quarter 1 Quarter 2

Sample 1 204 145

Sample 2 202 150

Sample 3 205 140

Sample 4 196 165

Sample 5 198 134

Sample 6 190 130

Sample 7 196 170

Sample 8 205 132

Sample 9 200 145

Sample 10 199 164

Mean 199.5 147.5

Standard

Deviation5 14

2.5 Find Opportunities for Error

Opportunities for Error (OFE)

Opportunities for error in a process is the number of steps / tasks / actions in the

process, where there is a possibility of committing an error, that may result in a defect

OFEs are opportunities inside the process that can contribute to a defect

OFE enables to compare the output quality of dissimilar processes

Concept of OFE is applicable only when defect measurement is discrete

This is because data, on whether or not a defect is created, is discrete type (yes / no)

2.6 Design Sampling Plan for

Establishing Process Baseline

What is Baseline?

After the team has understood the unit & defect definition, it would need to analyze

the current performance level

„Baseline‟ refers to a reference point from where the improvement would be

measured

Sampling plan for baselining attempts to define the data collection approach only

Introduction to Sampling

We do sampling all the time

Populations & Samples

Practical aspects – Cost & Time

Sampling is done to study a representative portion of population

Any term describing the characteristics of a sample is called statistic

Any term describing the characteristics of a population is called parameter

Population

Sample

Sam

plin

g

Tool

Populations & Samples

Table 1.60 Population Sample

DefinitionCollection of items

being considered

Portion of the

population chosen for

study

Characteri

stics

‘Parameter’ ‘Statistic’

Population Size = N Sample Size = n

Population Mean = µ Sample Mean = Y

Population Standard

Deviation = σ

Sample Standard

Deviation = s

Types of Sampling

Random Sampling or Probability Sampling

All items in the population have an equal chance of being chosen in the sample

Example: A customer satisfaction survey team picking the customers to be contacted at random

Non-random Sampling or Judgment Sampling

Personal knowledge & opinion are used to identify items for the sample

It is also used to decide upon how to take a random sample later

Example: A forest ranger may decide on a sample of north-west area to cut lumber

Collect Fresh Data

Fresh data should be collected to ensure that the latest process trend is studied

Historical data may have measurement errors which would be validated in next step of

DMAIC

Sometimes, process may generically improve as compared to the „Define‟ phase

due to increased attention from the owners / error in sampling

In such cases, Champion & BB may review the project, address the „discipline‟ issues &

decide whether project needs to collect another sample or gets abandoned here

How Big a Sample?

Business criteria to select a sample size include cost, time & effort

Statistical criteria include the accuracy of the sample representing the population

Higher the sample size, better the accuracy of the information about the population

parameters ( µ & σ )

There must be a balance between the business & statistical criteria

Z1 with n = 25

Z2 with n = 16

Z3 with n = 4

Determinants of Sample Size - Continuous Data

The sample size is determined by answering 3 questions

How much variation is present in the population? ( σ )

In what interval does the true population mean need to be estimated? ( ± )

How much representation error is allowed in the sample? ( α )

Sample size formula for Continuous Data:

n = Z 1 – (α / 2) * σ

2

Estimating Population Parameter

What population parameters we want to estimate

Cost of sampling (importance of information)

How much is already known

Spread (variability) of the population

Practicality: how hard is it to collect data

How precise we want the final estimates to be

Example 1 - Continuous Data

Let‟s take the weight of fertilizer bags whose std packaging is 7 Kgs with a std

deviation of 3.78. Now if I want to take a sample of few bags & want their mean to be

within ± 2, i.e. 5 & 9, how many bags should I sample ?

= 2

σ = 3.78

Assume α = 0.05

From Appendix 1, Z 97.5 = 1.96

So, sample size n = [ (1.96 * 3.78) / 2 ]

= 14

That means 95% of the samples with size 14 will have its mean between 5 & 9

n = Z 97.5 * 3.78

2

2

2

Standard Sample Size Formula - Continuous Data

Usually, value of α is taken as 5%

Z 97.5 = 1.96

Thus, standardized sample size formula can be written as

n = 1.96 * σ

2

for Continuous Data

Standard Sample Size Formula - Discrete Data

Extending the same logic, we can find out the sample size required while dealing with

discrete population

If the average population proportion non-defective is at „p‟, population standard

deviation can be calculated as

n = 1.96

2

for Discrete Data

σ = p ( 1 – p)

Where = Tolerance allowed on either side of the population proportion average in %

p ( 1 – p)


DMAIC

Step 3

Validate

Measurement System

for „Y‟

Introduction - MEASURE

A robust measurement system forms the basis of any Six Sigma project

A measurement system has two characteristics

Design of the measurement system

Precision of the measurement system

Measurement system for „Y‟ indicates that this step deals with the accuracy of defect

measurement & must be completed before proceeding to establish the process baseline

Step 2 of DMAIC

Step 3 of DMAIC

Deliverables of Step 3

3.1 Perform GRR study

3.2 Analyze results

3.1 Perform GRR Study

Count the Occurrence of letter „I‟ in the Paragraph

A country preacher was walking the back-road near a church. He became thirsty so

decided to stop at a little cottage and ask for something to drink. The lady of the house

invited him in and in addition to something to drink, she served him a bowl of soup by the

fire. There was a small pig running around the kitchen. The pig was constantly running up

to the visitor and giving him a great deal of attention. The visiting pastor commented that

he had never seen a pig this friendly. The housewife replied: "Ah, he's not that friendly.

Actually, that's his bowl you're using!"

Purpose of G R & R

A performance critical system was delivered and the customer complained that the performance

criteria is not met

What is this problem due to?

This could again be due to how one measures performance and whether the

customer sees performance the same way

GR & R prevents the following mistake:

Claiming that we delivered quality when we did not OR Claiming that

quality is bad when it is not. We are eliminating the influence of

measurement error from the performance

Not doing GR & R will cause you to tamper with the process when in fact

the process is fine but only measurement is the issue.

Objectives of a Measurement Study

Obtain information about the type of measurement variation associated with the

measurement system

Establish criteria to accept and release new measuring equipment

Compare measuring one method against another

Form basis for evaluating a method suspected of being deficient

Resolve measurement system variation in order to arrive at the correct baseline

Types of Measurement Errors

Measurement System Bias - Calibration Study

Measurement System Variation - GRR Study

µ total = µ process +/- µ measurement

σ2total = σ2

process + σ2measurement

Sources of Variation

Observed Process Variation

ActualProcessVariation

MeasurementVariation

Short-termProcessVariation

Long-termProcessVariation

Variationwithin aSample

Variationdue to

Operators

Variationdue toGage

RepeatabilityAccuracy

Reproducibility

Stability Linearity

Gage Repeatability

Gage Repeatability is the variation in measurements obtained when one operator uses

the same gage for measuring the identical characteristics of the same part

Repeatability

Gage Reproducibility

Gage Reproducibility is the variation in the average of measurements made by different

operators using the same gage when measuring identical characteristics of the same

part

Reproducibility

Operator 1

Operator 2

Component of GRR Study

Trial

Reading

#1

2

3 4

5 6

12

3 4

5 6

12

3 4

5 6

1

Trial

Reading

#2

Operator

A

Operator

B

Operator

C

Difference leads to

Repeatability Six Parts / Conditions

Difference leads

to Reproducibility

2

3 4

5 6

12

3 4

5 6

12

3 4

5 6

1

Measurement Resolution

What is measurement resolution?

Capability of the measurement system to detect the smallest tolerable changes

Number of increments in the measurement system at full range

Example – Using a truck weighing scale for measuring the weight of a tea pack

As a pre-requisite to GRR, ascertain that your gage has acceptable resolution

Data Collection

Usually 3 operators

Usually 10 units to measure

General sampling techniques should be used to represent the population

Each unit is to be measured 2-3 times by each operator (Number of trials)

Gage should have been calibrated properly

Resolution should have been ensured

First operator should measure all units in random order

Same order should be maintained for all other operators

Repeat for each trial

Methods of Performing GRR Studies

ANOVA Method

Measures operator & equipment variability separately with combined effect as well that better

defines causality

More effective when extreme values are present

ANOVA Method

ANOVA not only separates the equipment & operator variation, but also elaborates

on combined effect of operator & part

ANOVA uses the „standard deviation‟ instead of „range‟, & hence gives a better

estimate of the measurement system variation

ANOVA also may not need the „tolerance‟ value as an input

However, time, resource & cost constraints may need to be looked into

Let‟s see an example

GR

R –

AN

OV

A M

eth

od

Tool

GRR Example

Part Operator Trial Response

1 1 1 475

1 2 1 442

1 3 1 489

1 1 2 479

1 2 2 462

1 3 2 463

2 1 1 369

2 2 1 326

2 3 1 302

2 1 2 368

2 2 2 328

2 3 2 318

3 1 1 398

3 2 1 405

3 3 1 410

3 1 2 415

3 2 2 402

3 3 2 421


Entering Data in Minitab

STAT > Quality Tools > Gage R&R Study (Crossed)


Entering Data in Minitab

STAT > Quality Tools > Gage R&R Study (Crossed) > Options

Input USL-LSL for

two-sided specifications

on „Y‟

%Contribution

Source VarComp (of VarComp)

Total Gage R&R 430.9 9.05

Repeatability 98.4 2.07

Reproducibility 332.4 6.98

Operator 24.2 0.51

Operator*Part 308.2 6.47

Part-To-Part 4329.4 90.95

Total Variation 4760.3 100.00

StdDev Study Var %Study Var

Source (SD) (5.15*SD) (%SV)

Total Gage R&R 20.7572 106.900 30.09

Repeatability 9.9219 51.098 14.38

Reproducibility 18.2323 93.896 26.43

Operator 4.9216 25.346 7.13

Operator*Part 17.5555 90.411 25.44

Part-To-Part 65.7981 338.860 95.37

Total Variation 68.9946 355.322 100.00

Number of Distinct Categories = 4

%Tolerance

(SV/Toler)

42.76

20.44

37.56

10.14

36.16

135.54

142.13

Minitab gives the following output:

ANOVA Method

Here, Reproducibility is broken into two parts

If Tolerance

value is input

(say 250 in

this case),

this column

will appear


M is c :

Toleranc e:

Reported by :

Date of s tudy :

Gage nam e:

0

500

400

300

321

Xbar Chart by Operator

Sam

ple

Mean

M ean=404

UCL=424.9

LCL=383.1

0

40

30

20

10

0

321

R Chart by Operator

Sam

ple

Range

R=11.11

UCL=36.30

LCL=0

321

500

400

300

Part

Operator

Operator*Part InteractionA

vera

ge

1

2

3

321

500

400

300

Operator

By Operator

321

500

400

300

Part

By Part

%Contribution

%Study Var

Part-to-PartReprodRepeatGage R&R

100

50

0

Components of Variation

Perc

ent

Gage R&R (ANOVA) for Response

ANOVA Method

GRR Example - Software

Module Estimator Trial Effort

1 1 1 276

1 2 1 240

1 1 2 278

1 2 2 262

2 1 1 169

2 2 1 126

2 1 2 168

2 2 2 128

3 1 1 198

3 2 1 205

3 1 2 215

Suppose effort for developing 3 different modules are estimated by 2 different people, each estimating

twice and data is tabulated as below.


Entering Data in Minitab – Software Example

STAT > Quality Tools > Gage R&R Study (Crossed)


STAT > Quality Tools > Gage R&R Study (Crossed) > Options

Input USL-LSL for

two-sided specifications

on „Y‟

Entering Data in Minitab – Software Example

ANOVA Method – Software Example

Minitab gives the following output:

%Contribution

Source VarComp (of VarComp)

Total Gage R&R 434.2 11.67

Repeatability 65.9 1.77

Reproducibility 368.3 9.90

Est 213.6 5.74

Est*Module 154.7 4.16

Part-To-Part 3285.0 88.33

Total Variation 3719.1 100.00

StdDev Study Var %Study Var

Source (SD) (5.15*SD) (%SV)

Total Gage R&R 20.8367 107.309 34.17

Repeatability 8.1189 41.812 13.31

Reproducibility 19.1898 98.828 31.47

Est 14.6145 75.265 23.96

Est*Module 12.4365 64.048 20.39

Part-To-Part 57.3146 295.170 93.98

Total Variation 60.9846 314.071 100.00

Number of Distinct Categories = 4

Key Concepts

Minitab output under “%Contribution”, illustrates the percent contribution

from part-to-part as compared to GRR. If former is significantly higher than latter,

it tells you that most of the variation is due to differences between parts; very little is due

to measurement system error

Number of distinct categories in the Minitab output illustrates the „number of groups

within your process data that your measurement system can discern‟

A value of 4 or more denotes a good measurement system

Continuous Data

GRR as a % of Contribution to Variation and Number of Distinct Categories

If GRR as % of contribution is about 10% of the total variation - acceptable

If number of distinct categories is >= 4 - acceptable

If none of the above criteria is met, do not proceed to the next step

If tolerance was known, GRR as a % of Tolerance should be used for decision as

explained in the previous slide

Continuous Data

GRR as a % of Tolerance( study var/Tolerance *100)

Study Var = SD * 5.15

If GRR as % of tolerance is less than 10% - excellent measurement system

If GRR as % of tolerance is between 10% to 30% - acceptable measurement system

However, discretion may be needed depending upon application of the process / equipment

If GRR as % of tolerance is above 30% - unacceptable measurement system

You should not proceed to next DMAIC step. Simplify process / explore root cause

GRR for Discrete Data

ANOVA methods apply to continuous data only

For discrete data, a relatively higher accuracy is desired

Usually, discrete data GRR is measured against the „true‟ value

Worksheet for Discrete Data

Data to be filled only in “YELLOW‟ cells

GR

R –

Dis

cre

te D

ata

Tool

Key Concepts

Operator Consistency (Trial Match)

% of times an operator repeats his observation in trial 2 as compared to trial 1

Mutual Consistency (Operator Agreement)

% of times both operators are in complete sync

Operator Efficiency (True Match)

% of times an operator has both his observations matched with true value

Measurement Efficiency (True Agreement)

% of times both operators are in complete sync with the true value

Class Exercise

Data is given on past matches played by Indian cricket team. Classify each match into „LOST‟

or „WON‟, as applicable to Indian team.

Once this is done, put the values in the „GRR – Discrete Data‟ worksheet given &

compute all the six measurements.

Discrete Data

Discrete data measurement system has to be perfect because of sample size limitations

Measurements for operator consistency & efficiency should be targeted at minimum 90%

failing which team may want to discuss with Champion & Black Belt for proceeding further

Key Concepts

Once the GRR has been found to be reduced to acceptable level, project team can

start collecting data

Using this data to arrive the Sigma multiple of the process shall be discussed in

Analyze phase

GRR Applicability for software

GRR Can be used to Calibrate

The Estimation process

the review effectiveness at start of project

Testing Effectiveness at start of project

In areas where measurement of repeatability and reproducibility is not applicable

The operational definition of measures, Units of measure, Data collection mechanism

Needs to be defined

Tollgate - Measure

Detailed As-is Process

Units, Specifications & Defects

Number of OFE‟s, if discrete data

GRR of the Measurement System

Action plan, if GRR is not acceptable

Reduction of GRR to acceptable level

All the Best

for

the

Quiz!!!!!!!!!

Quiz

MEASURE – Q1

Process Mapping helps in

a) Visualizing the activities

b) Understanding the big picture

c) Identifying bottlenecks

d) All of the above

MEASURE – Q2

„S‟ in SIPOC stands for

a) Sales

b) Supplier

c) Shop floor

d) Specifications

MEASURE – Q3

A unit is

a) Where we observe defects

b) Measurable characteristics of process output

c) Measurable characteristics of process input

d) a & b

MEASURE – Q4

GRR study is done on

a) Two gages

b) More than two gages

c) Only one gage

d) Any number is OK

MEASURE – Q5

Quality

a) Goes up as defects come down

b) Is absence of defects in the unit

c) Is defined by the customer

d) All of the above

MEASURE – Q6

Which are the characteristics of discrete data

a) Location

b) Spread

c) OFE

d) Shape

Appendix 1 – Normal Distribution Table

Area Below +ZLT

Z

Appendix 1 – Normal Distribution Table (contd.)

Z

Area Below +ZLT

six sigma presentation (2)

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