lecture 7 six sigma yellow belt

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Six Sigma Yellow Belt Dr. M. Kamran Zaman

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Page 1: Lecture 7 Six Sigma Yellow Belt

Six Sigma Yellow Belt

Dr. M. Kamran Zaman

Page 2: Lecture 7 Six Sigma Yellow Belt

Measure

Define Unit,defect & Defect Opportunity

Set specification limits

Develop Data collection plan

Collect data

Analyze Measure Improve Control Define

Establish

Data Collection

Plan

Define

Performance

Standards

CCR

Gap

Sigma=

X

Page 3: Lecture 7 Six Sigma Yellow Belt

• What is the specific CTQ characteristic you will improve?

• How will you measure the process performance?

• What data do you need – what is the data collection plan?

• Is the data valid and accurate?

Outputs

Process X’s

or Factors

PROCESS

X1

X2

X3

X4

Y1

Y2

Y3

Measure ...measure what you care about;

know your measure is good...

DMAIC

Page 4: Lecture 7 Six Sigma Yellow Belt

Measure

Measure is a logical follow-up to Define and in a

bridge to the next step- Analyze.

Page 5: Lecture 7 Six Sigma Yellow Belt

Overview of Brainstorming Techniques

A commonly used tool to solicit ideas by using categories to stimulate cause and

effect relationship with a problem. It uses verbal inputs in a team environment.

Problem or

Condition

The Y

The X’s

(Causes)

l Categories Material Measurement Environment

People Machine Method

The

Problem

Cause and Effect Diagram

Problem or

Condition

The Y

The X’s

(Causes)

l Categories Material Measurement Environment

People Machine Method

The

Problem

Cause and Effect Diagram

Page 6: Lecture 7 Six Sigma Yellow Belt

The Vital Few

A Six Sigma Belt does not just discover which X’s are important in a

process (the vital few).

– The team considers all possible X’s that can contribute or cause

the problem observed.

– The team uses 3 primary sources of X identification:

• Process Mapping

• Fishbone Analysis

• Basic Data Analysis – Graphical and Statistical

– A List of X’s is established and compiled.

– The team then prioritizes which X’s it will explore first, and

eliminates the “obvious” low impact X’s from further

consideration.

Page 7: Lecture 7 Six Sigma Yellow Belt

The Focus of Six Sigma

Y

Dependent

Output

Effect

Symptom

Monitor

X1 . . . Xn

Independent

Input-Process

Cause

Problem

Control

Y f(X)

Would you control shooter or target to get the Gold Medal at

Olympics

Page 8: Lecture 7 Six Sigma Yellow Belt

Y = F (x)

OUTPUT SIGNAL IN-PROCESS PARAMETERS

RELATIONSHIP or EQUATION

THAT EXPLAINS Y IN TERMS OF X

Distance traveled Car speed, traveling time Determined by

Money to Spend Income, Commitments,

Credit Rating

Determined by

OUTPUT (Y) IS DETERMINED BY THE VALUES

OF THE IN-PROCESS PARAMETERS (X’s)

Controlling the Output

Page 9: Lecture 7 Six Sigma Yellow Belt

We use a variety of Six Sigma tools to help separate

the “vital few” variables effecting our Y from the

“trivial many.”

Some processes contain many, many variables.

However, our Y is not effected equally by all of

them.

By focusing on the vital few we instantly gain

leverage.

Archimedes said: “ Give me a lever big enough and

fulcrum on which to place it, and I shall move the

world.” (X6)

(X2)

(X4)

(X1)

(X7)

(X5) (X3)

(X8)

(X10)

(X9)

Y= F (X)

Page 10: Lecture 7 Six Sigma Yellow Belt

Overview of Process Mapping

In order to correctly manage a process, you must be able to describe it in a way that can be easily understood.

– The preferred method for describing a process is to identify it with a generic name, show the workflow with a Process Map and describe its purpose with an operational description.

– The first activity of the Measure Phase is to adequately describe the process under investigation.

Finish Step A Step B Step C Step D Start

Page 11: Lecture 7 Six Sigma Yellow Belt

1. Process inputs (X’s)

2. Supplier requirements

3. Process outputs (Y’s)

4. Actual customer needs

5. All value-added and non-value added process tasks and steps

6. Data collection points

•Cycle times

•Defects

•Inventory levels

•Cost of poor quality, etc.

7. Decision points

8. Problems that have immediate fixes

9. Process control needs

Information from Process Mapping By mapping processes we can identify many important characteristics and develop information for other analytical tools:

Page 12: Lecture 7 Six Sigma Yellow Belt

There are usually three views of a process

Process Mapping

What you THINK it is..

1

What it ACTUALLY is..

2 3

What it SHOULD be..

Page 13: Lecture 7 Six Sigma Yellow Belt

Define

Performance

Standards

Page 14: Lecture 7 Six Sigma Yellow Belt

Performance Standard

A performance Standard defines

• The customer want

• Clearly whether a process is performing well or not

e.g. loan approval within 24 hours

first call resolution

A Performance Standard translates the “Voice of

Customer” in a measurable metric.

Page 15: Lecture 7 Six Sigma Yellow Belt

The Basic Six Sigma Metrics

• Better: DPU, DPMO, RTY (there are others, but they derive from these basic

three)

• Faster: Cycle Time

• Cheaper: COPQ

In any process improvement endeavor, the ultimate objective is to

make the process:

If you make the process better by eliminating defects you will make it faster.

If you choose to make the process faster, you will have to eliminate defects to be as

fast as you can be.

If you make the process better or faster, you will necessarily make it cheaper.

The metrics for all Six Sigma projects fall into one of these three categories

Page 16: Lecture 7 Six Sigma Yellow Belt

Nomenclature

Number of operation steps = m

Defects = D

Unit = U

Opportunities for a defect = O

Yield = Y

Basic Relationships

Total Opportunities = TOP = U x O

Defects per Unit = DPU = D/U

Defects per Unit Opportunity = DPO = DPU/O = D/UxO

Defects per million Opportunity = DPMO = DPO x 106

The Basic Six Sigma Metrics

Page 17: Lecture 7 Six Sigma Yellow Belt

Unit: The event/transaction produced or processed

Defect: Any event/transaction that does not meet the Customer requirement

Opportunity: Any inputs to event/transaction that can be measured that

provides a chance of not meeting a Customer requirement

Defective: A unit with one or more defects

Specification Limits: Tolerance Limit beyond which customer would be

dissatisfied - VOC

Page 18: Lecture 7 Six Sigma Yellow Belt

Defective and Defect

• A nonconforming unit is a defective unit

• Defect is nonconformance on one of many possible

quality characteristics of a unit that causes customer

dissatisfaction.

• A defect does not necessarily make the unit defective

• Examples:

– Scratch on water bottle

– (However if customer wants a scratch free bottle,

then this will be defective bottle)

Page 19: Lecture 7 Six Sigma Yellow Belt

Defect Opportunity

• Circumstances in which CTQ can fail to meet.

• Number of defect opportunities relate to complexity of unit.

• Complex units – Greater opportunities of defect than simple units

• Examples:

– A units has 5 parts, and in each part there are 3 opportunities of defects – Total defect opportunities are 5 x 3 = 15

Page 20: Lecture 7 Six Sigma Yellow Belt

DPO (Defect Per Opportunity)

• Number of defects divided by number of defect

opportunities

• Examples: – In previous case (15 defect opportunities), if 10 units have 2

defects.

– Defects per unit = 2 / 10 = 0.2

– DPO = 2 / (15 x 10) = 0.0133333

Page 21: Lecture 7 Six Sigma Yellow Belt

Yield

Probability of a part made within specifications.

Yield = e-DPU

DPU = Defects per unit

Example

Five defects are observed in 467 units produced. The number

of defects per unit is 5/467. What will be the yield of the

process?

0.98935 or 98.94%

The number of acceptable is called Yield

Page 22: Lecture 7 Six Sigma Yellow Belt

First Time Yield FTY is the traditional quality metric for yield

– Unfortunately, it does not account for any necessary rework

FTY = Total Units Passed

Total Units Tested

Units in = 100

Units Out = 100

Units in = 100

Units Out = 100

Units in = 100

Units Out = 100 Units Passed = 50

Units Tested = 50

FTY = 100 %

Process A (Grips) Process B (Shafts) Process C (Club Heads) Final Product (Set of Irons)

Defects Repaired

40

Defects Repaired

30

Defects Repaired

20

Page 23: Lecture 7 Six Sigma Yellow Belt

• Traditional metrics when chosen poorly can lead the

team in a direction that is not consistent with the

focus of the business. Some of the metrics we must

be concerned about would be FTY - FIRST TIME

YIELD.

• It is very possible to have 100% FTY and spend

tremendous amounts in excess repairs and rework.

First Time Yield

Page 24: Lecture 7 Six Sigma Yellow Belt

Rolled Throughput Yield RTY is a more appropriate metric for problem solving

– It accounts for losses due to rework steps

RTY = X1 * X2 * X3

Units in = 100

Units W/O Rework = 60

RTY = 0.6

Units in = 100

Units W/O Rework = 70

RTY = 0.7

Units in = 100

Units W/O Rework = 80

RTY = 0.8

Units Passed = 34

Units Tested = 100

RTY = 33.6 %

Process A (Grips) Process B (Shafts) Process C (Club Heads) Final Product (Set of Irons)

Defects Repaired

40

Defects Repaired

30

Defects Repaired

20

Page 25: Lecture 7 Six Sigma Yellow Belt

• Instead of relying on FTY - First Time Yield, a more efficient

metric to use is RTY - Rolled Throughput Yield. RTY has a

direct correlation (relationship) to Cost of Poor Quality.

• In the few organizations where data is readily available, the

RTY can be calculated using actual defect data. The data

provided by this calculation would be a Bi-Nominal

Distribution since the lowest yield possible would be zero.

• As depicted here, RTY is the multiplied yield of each

subsequent operation throughout a process (X1 * X2 * X3…)

Rolled Throughput Yield

Page 26: Lecture 7 Six Sigma Yellow Belt

DPMO Calculations

Characteristic Defects Units Opportunities Total Opportunities Defects per Unit Defects per Total

Opportunities

Defects per Million

Opportunities

D U O U X O D/U D/UxO DPO x 106

Type A 21 327 92 30084 0.06422 0.000698045 698.04547

Type B 10 350 85 29750 0.02857 0.000336134 336.13445

Type C 8 37 43 1591 0.21621 0.005028284 5028.28410

Type D 68 743 50 37150 0.09152 0.001830417 1830.41723

Type E 74 80 60 4800 0.92500 0.015416667 15416.6667

Type F 20 928 28 25984 0.02515 0.000769704 769.704433

Page 27: Lecture 7 Six Sigma Yellow Belt

Quality Level Calculation

Sigma Quality Level = 0.8406 + √ 29.37 – 2.221{ln(dpm)}

Page 28: Lecture 7 Six Sigma Yellow Belt

Example Lets take an Example of a

Coffee Shop

Page 29: Lecture 7 Six Sigma Yellow Belt

What are the things which make a Good Hot Coffee??

Temperature

Aroma

Crockery

Froth

Ambience

Coffee Beans

Service Availability

Price

Blend

Page 30: Lecture 7 Six Sigma Yellow Belt

Example: Defects & Opportunity What happens when the coffee is not HOT & the Service

is poor??

10 Opportunities & 2 Defects

Out of 10 parameters only 8 get fulfilled

• Defects = 2

• Opportunities = 10

• Unit = 1

• D/(U*O)= 0.2

• DPMO =

0.2*1000000

• DPMO = 200000

• Only 2.34 Sigma

Page 31: Lecture 7 Six Sigma Yellow Belt

Sigma DPMO YIELD Sigma DPMO YIELD

6 3.4 99.99966% 2.9 81,000 91.9%

5.9 5.4 99.99946% 2.8 97,000 90.3%

5.8 8.5 99.99915% 2.7 120,000 88.0%

5.7 13 99.99866% 2.6 140,000 86.0%

5.6 21 99.9979% 2.5 160,000 84.0%

5.5 32 99.9968% 2.4 180,000 82.0%

5.4 48 99.9952% 2.3 210,000 79.0%

5.3 72 99.9928% 2.2 240,000 76.0%

5.2 108 99.9892% 2.1 270,000 73.0%

5.1 159 99.984% 2 310,000 69.0%

5 233 99.977% 1.9 340,000 66.0%

4.9 337 99.966% 1.8 380,000 62.0%

4.8 483 99.952% 1.7 420,000 58.0%

4.7 687 99.931% 1.6 460,000 54.0%

4.6 968 99.90% 1.5 500,000 50.0%

4.5 1,300 99.87% 1.4 540,000 46.0%

4.4 1,900 99.81% 1.3 580,000 42.0%

4.3 2,600 99.74% 1.2 620,000 38.0%

4.2 3,500 99.65% 1.1 660,000 34.0%

4.1 4,700 99.53% 1 690,000 31.0%

4 6,200 99.38% 0.9 730,000 27.0%

3.9 8,200 99.18% 0.8 760,000 24.0%

3.8 11,000 98.9% 0.7 790,000 21.0%

3.7 14,000 98.6% 0.6 820,000 18.0%

3.6 18,000 98.2% 0.5 840,000 16.0%

3.5 23,000 97.7% 0.4 860,000 14.0%

3.4 29,000 97.1% 0.3 880,000 12.0%

3.3 36,000 96.4% 0.2 900,000 10.0%

3.2 45,000 95.5% 0.1 920,000 8.0%

3.1 55,000 94.5%

3 67,000 93.3%

Page 32: Lecture 7 Six Sigma Yellow Belt

Establish

Data Collection

Plan

Page 33: Lecture 7 Six Sigma Yellow Belt

Data Collection 1. What is the data source or location? Answers to this question help to

clearly identify the point at which the raw data is collected. The most common

mistake made in answering this question is not to identify where reports come

from. Examples of raw data collection include: on tags, in log books, entered

into data bases, scribbled on surveys, interpreted from phone conversations or

automatically tallied by machinery.

2. Who is the data collector? The answer to this question is typically a front

line employee: operator, clerk, waiter or other. If the data is scanned or

automatically tallied by machinery or computer, then a simple entry of the

method employed is adequate.

3. What is the sampling plan? This question is often confused with reporting.

The question is meant to apply to raw data. How often is data collected?

Examples include: continuously, once per minute, each setup, each shift, each

customer contact, every fifth call.

Page 34: Lecture 7 Six Sigma Yellow Belt

Data Collection

Six Sigma project leaders should develop a sound data

collection plan to gather reliable and statistically valid

data in the DMAIC measurement phase.

Incorporating these steps into a data collection plan will

improve the likelihood that the data and measurements

can be used to support the ensuing analysis.

Page 35: Lecture 7 Six Sigma Yellow Belt

Data Collection Plan

Y - Measure Data Source & Location Sample Size Who When How X data that should also

be collected

Page 36: Lecture 7 Six Sigma Yellow Belt

Step 1: Define Goals And Objectives

A good data collection plan should include

A brief description of the project

The specific data that is needed

The rationale for collecting the data

What insight the data might provide (to a process being studied)

and how it will help the improvement team

What will be done with the data once it has been collected

Being clear on these elements will facilitate the accurate and

efficient collection of data.

Page 37: Lecture 7 Six Sigma Yellow Belt

Step 2: Define Operational Definitions and

Methodology

The improvement team should clearly define what data is to be collected and

how. It should decide what is to be evaluated and determine how a numerical

value will be assigned, so as to facilitate measurement.

How many observations are needed

What time interval should be part of the study

Whether past, present, and future data will be collected

The methodologies that will be employed to record all the data

It is best to obtain complete understanding of and agreement on all the

applicable definitions, procedures and guidelines that will be used in the

collection of data. Overlooking this step can yield misleading results if

members of the improvement team are interpreting loosely defined terms

differently when collecting data. Serious problems can arise for the

organization when business decisions are made based on this potentially

unreliable data.

Page 38: Lecture 7 Six Sigma Yellow Belt

Step 3: Ensuring Repeatability,

Reproducibility, Accuracy and Stability

The data being collected (and measured) will be repeatable if the

same operator is able to reach essentially the same outcome

multiple times on one particular item with the same equipment.

The data will be reproducible if all the operators who are

measuring the same items with the same equipment are reaching

essentially the same outcomes. In addition, the degree to which

the measurement system is accurate will generally be the

difference between an observed average measurement and the

associated known standard value.

Page 39: Lecture 7 Six Sigma Yellow Belt

Step 4: The Data Collection Process

Once the data collection process has been planned and defined, it

is best to follow through with the process from start to finish,

ensuring that the plan is being executed consistently and

accurately. Assuming project lead has communicated to all the

data collectors and participants what is to be collected and the

rationale behind it, he or she might need to do additional

preparation by reviewing with the team all the applicable

definitions, procedures, and guidelines, etc., and checking for

universal agreement. This could be followed up with some form

of training or demonstration that will further enhance a common

understanding of the data collection process as defined in the

plan.

Page 40: Lecture 7 Six Sigma Yellow Belt

Step 5: After The Data Collection Process

The project lead should check to see that the results (data and

measurements) are reasonable and that they meet the criteria. If

the results are not meeting the criteria, then the project lead

should determine where any breakdowns exist and what to do

with any data and/or measurements that are suspect.

Reviewing the operational definitions and methodology with the

participants should help to clear up any misunderstandings or

misinterpretations that may have caused the breakdowns.

Page 41: Lecture 7 Six Sigma Yellow Belt

Collect Visual Data to See the

Problem

Where possible use a Digital or Video Camera and capture the

defect or process problem. “A picture is worth a thousand

words in” understanding and communication of the origin and

nature of problems.

Page 42: Lecture 7 Six Sigma Yellow Belt

Data Segmentation

Segmentation involves dividing data into

logical categories for analyzing data. For

instance, while recording the errors made by a

data entry process, the project manager may

choose to capture the step at which the error

occurred, the operator who made the error and

so on.

Page 43: Lecture 7 Six Sigma Yellow Belt

Sampling

Page 44: Lecture 7 Six Sigma Yellow Belt

Sample

Determining sample size is a very important

issue because samples that are too large may

waste time, resources and money, while samples

that are too small may lead to inaccurate results

Page 45: Lecture 7 Six Sigma Yellow Belt

Sampling

• There are normally two types of studies: population and

process. With a population study, the analyst is interested in

estimating or describing some characteristic of the population

(inferential statistics).

• With a process study, the analyst is interested in predicting a

process characteristic or change over time. It is important to

make the distinction for proper selection of a sampling

strategy.

Page 46: Lecture 7 Six Sigma Yellow Belt

Sampling Strategies

Random sampling

Stratified random sampling

Systematic sampling

Rational sub-grouping

Page 47: Lecture 7 Six Sigma Yellow Belt

Random Sampling

Random samples are used in population

sampling situations when reviewing historical or

batch data. The key to random sampling is that

each unit in the population has an equal

probability of being selected in the sample

Page 48: Lecture 7 Six Sigma Yellow Belt

Stratified Random Sampling

Stratified random samples are used in population

sampling situations when reviewing historical or

batch data. Stratified random sampling is used

when the population has different groups (strata)

and the analyst needs to ensure that those groups

are fairly represented in the sample. In stratified

random sampling, independent samples are

drawn from each group. The size of each sample

is proportional to the relative size of the group.

Page 49: Lecture 7 Six Sigma Yellow Belt

Systematic Sampling

• Systematic sampling is typically used in process

sampling situations when data is collected in real time

during process operation.

• Systematic sampling involves taking samples

according to some systematic rule – e.g., every fourth

unit, the first five units every hour, etc.

Page 50: Lecture 7 Six Sigma Yellow Belt

Rational Sub grouping

Rational sub-grouping is the process of putting

measurements into meaningful groups to better

understand the important sources of variation. Rational

sub-grouping is typically used in process sampling

situations when data is collected in real time during

process operations. It involves grouping measurements

produced under similar conditions, sometimes called

short-term variation. This type of grouping assists in

understanding the sources of variation between

subgroups, sometimes called long-term variation.

Page 51: Lecture 7 Six Sigma Yellow Belt

Sampling Strategy Tips

Use following pointers for sampling strategy for a given process:

• It is always better to collect small sample spread over longer

time period than one large sample over a shorter time period.

• Sample more frequently for unstable process and less

frequently for stable process

• Sample more frequently for process with short cycle time and

less frequently for process with long cycle time

Page 52: Lecture 7 Six Sigma Yellow Belt

Sampling Strategy Tips

To understand the sampling frequency one should understand

the objective of data collection The most important issue to

remember when considering sample frequency is the data

collection objective. The sampling frequency is driven by the

objective of data collection

e.g. if the data collected is for monitoring process one might

want to sample data daily. However if the objective is to

collect data for the same process to study capability one might

want to collect data for few months by sampling few data

points each week or month.

Page 53: Lecture 7 Six Sigma Yellow Belt

Analyze

Sub process mapping

Cause & Effect Diagram

Analyze Measure Improve Control Define

Establish

Process Capability

Identify Variation

Sources

Calculate Process Capability

Page 54: Lecture 7 Six Sigma Yellow Belt

Process Capability

There are two popular measures for

quantitatively determining if a process is

capable:

Process capability ratio (Cp)

Process capability index (Cpk)

Page 55: Lecture 7 Six Sigma Yellow Belt

Process Capability Ratio

• For a process to be capable, its values must fall

within upper and lower specifications.

• Process capability is within ± 3 standard

deviation from the process mean.

• Cp= upper specification – lower spec/6

Cp = (USL-LSL) / 6

Page 56: Lecture 7 Six Sigma Yellow Belt

• A capable process has a Cp of at least 1.0.

• Cp = 1.0 means 99.73 % out puts are within

specifications, it suggests that a very capable

process

• The higher the process capability ratio, the

greater the likelihood the process will be

within design specifications.

Process Capability Ratio

Page 57: Lecture 7 Six Sigma Yellow Belt

A comparison between the specified

limits and the process limits

Your Cp is good ( > 1)

Page 58: Lecture 7 Six Sigma Yellow Belt

Here the process limits fall out

of the specified limits

Your Cp is bad ( < 1)

Page 59: Lecture 7 Six Sigma Yellow Belt

Process Capability Ratio

Page 60: Lecture 7 Six Sigma Yellow Belt

Process Capability Ratio

Page 61: Lecture 7 Six Sigma Yellow Belt

Process Capability

When the process is in statistical control,

process capability is equal to 6

Page 62: Lecture 7 Six Sigma Yellow Belt

Cp

• Cp does not measure process performance in

terms of the nominal or target value.

Page 63: Lecture 7 Six Sigma Yellow Belt

WHAT IF YOUR PROCESS CENTRE IS

SHIFTED FROM YOUR SPECIFICATION

CENTRE ?

Page 64: Lecture 7 Six Sigma Yellow Belt

Shift In The Process Mean

Page 65: Lecture 7 Six Sigma Yellow Belt

Process Capability Index Cpk

• It measures the difference between the desired

and actual dimensions of goods or services

produced.

• Cpk = min of [Upper spec limit –X/ 3,

X - Lower spec limit / 3]

Page 66: Lecture 7 Six Sigma Yellow Belt

Process Capability Ratio

Page 67: Lecture 7 Six Sigma Yellow Belt

Cpk

Page 68: Lecture 7 Six Sigma Yellow Belt

Cpk

Cpk gives additional information about the

centering. Therefore, it is also called process

performance index.

Increasing value of Cpk means that the process

is increasingly becoming capable

Page 69: Lecture 7 Six Sigma Yellow Belt

Cpk

• Cpk > 1 a capable process

• CpK < 1 a not capable process

Page 70: Lecture 7 Six Sigma Yellow Belt

Process Capability Study

• Evaluating of a newly established process

• Evaluating the performance of a new

machinery.

• Reviewing specification based on the

inherent variability of the process

• Process studies

• Studying the effect of adjustments made

to the process

Page 71: Lecture 7 Six Sigma Yellow Belt

Data Analysis

To understand the level of current process,

analyze the collected data with the help of

statistical tools.

Page 72: Lecture 7 Six Sigma Yellow Belt

Baseline Process

• Study data for stability, shape

• Statistically indicate the nature of the problem

• Calculate the baseline capability of the process.

Describe the process by its

• Descriptive statistics

• Nature of distribution

• Understand Specification Limits and Centering or Target

Values

• Calculate: probability of a defect and process capability

Page 73: Lecture 7 Six Sigma Yellow Belt

Setting a Goal for Y metric

Following are approaches which one could adopt to

define the Goal for Y metric:

• Benchmarking: One can target to achieve best in

industry.

• Arbitrary Defect Reduction: This is frequently used

with discrete metric.

E.g.reduce DPMO by 50%

Page 74: Lecture 7 Six Sigma Yellow Belt

Other sources for setting a goal

Following are few more options which drive the goal

for process metric:

• Corporate mandate

• Compliance/legal requirement

• Voice Of Customer

• Industry Standards (e.g. ISO)

Page 75: Lecture 7 Six Sigma Yellow Belt

In Analyze phase, you work with all the information

gathered in the Measure phase to determine potential

causes and to prepare for making key changes to positively

alter each scenario.

Analyze - Summary

Page 76: Lecture 7 Six Sigma Yellow Belt

Thank You