lean six sigma green belt training manual a quick reference … · 2020-03-26 · 1.1 learning...

LEAN SIX SIGMA

GREEN BELT TRAINING MANUAL

A Quick Reference Guide for Lean Six Sigma Practitioners

Dr.Gopal Sivakumar

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CONTENTS

S. No. Topics Page

1 .1 Learning Objectives 1

1.2.1 Introduction VOC, VOB and COPQ 3

1.2.2 Basic Statistics for Six Sigma 2

1.3.1 Step 1 Generate Project Ideas 8

1.3.2 Step 2 Select Project 2

1.3.3 Step 3 Project Charter and High-Level Map 9

1.4.1 Activity 1.1 Classifying Statements in to VOB and VOC 10

1.42 Activity 1.2 Classifying Cost of Poor Quality 11

1.4.3 Activity 1.3 Sample Variance S2 and Sample Standard Deviation S 12

1.4.4 Activity 1.4 Performance Comparison of two teams 13

1.4.5 Activity 1.5 KAIZEN – Achieving Astonishing Results with micro changes 14

1.4.6 Activity 1.6 DPMO Calculation 15

1.4.7 Activity 1.7 KANO Model Analysis – VOC 16

1.4.8 Activity 1.8 Project Charter 17

1.4.9 Activity 1.9 SIPOC Diagram 18

1.4.10 Activity 1.10 High Level Process Map 19

1.5.1 Data table 1.1 Z table (between mean and Z) 20

1.5.2 Data table 1.2 Z table (Left Area) 21

1.5.3 Data table 1.2 DPMO vs Sigma Level table 22

Lean Six Sigma Green Belt Training Manual 1

Dr.Gopal Sivakumar

1.1 Learning Objectives 1

Welcome to the MSME Program for Lean Six Sigma Green Belt Certification course.

During this course you will learn each one of the following statements and able to implement in a Lean Six Sigma Green Belt Level Project.

Learning Objectives in DEFINE phase – Day 1

Table 1.1 Learning Objectives in DEFINE Phase Major Steps *CompletionStep 1 Generate Project Ideas ⊡ Step 1 Generate Project Ideas ⊡ Step 1 Generate Project Ideas ⊡ Important Questions to be Answered 1 Is your Project important to business? ⊡ 2 Has the chosen project been aligned with organization goals? ⊡ 3 Does a business case exist in the chosen project? ⊡ 4 Is the project goal measurable and achievable? ⊡ 5 Have you identified the customers and their requirements? ⊡ 6 Have you identified the requirements of business stakeholders ⊡ 7 Are the project plan and key milestones are defined? ⊡ Main Deliverables 1 Project Charter ⊡ 2 SIPOC Diagram ⊡ 3 High Level Process Map ⊡

Remarks: Put a Tick in the box once the objective and its implementation are completed

Learning Objectives in MEASURE phase – Day 2

Table 1.2 Learning Objectives in MEASURE Phase Major Steps *CompletionStep 1 Finalize CTQ (Y) measure Performance Characteristics ⊡ Step 1 Validate Measurement System for Y ⊡ Step 1 Measure Baseline Performance and Target Gap ⊡ Important Questions to be Answered 1 Have you identified the inputs (X) and the output (Y) that needs to be

measured? ⊡

2 Do you have an appropriate data collection plan? ⊡ 3 Is your Measurement System capable and adequate? ⊡ 4 Is the data collected credible for analysis? ⊡ 5 How far is the project goal from the analysis? ⊡

⊡ ⊡

Main Deliverables 1 Baseline Performance ⊡ 2 Gage R&R ⊡



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Learning Objectives in ANALYSE phase – Day 3

Table 1.3 Learning Objectives in ANALYSE Phase Major Steps *CompletionStep 1 Identify Potential Factors. ⊡ Step 1 Validate Critical Factors. ⊡ Step 1 Verify Sufficiency of Critical Factors. ⊡ Important Questions to be Answered 1 Have you identified the source of waste, risk and variation? ⊡ 2 Have you identified the Potential X factors and the critical ones that affect

your Y (CTQ )? ⊡

3 Have you statistically validated the root cause of the problem? ⊡ 4 Does the validated X factors have the capability to realize the targeted

improvement in Y? ⊡

Main Deliverables 1 Validated Root Cause/Causes ⊡


Learning Objectives in IMPROVE and CONTROL phase – Day 4

Table 1.4 Learning Objectives in IMPROVEMENT and CONTROL Phase Major Steps *CompletionStep 1 Generate and Evaluate Solutions ⊡ Step 2 Select and Optimize Best Solution ⊡ Step 3 Pilot, Implement and Validate Solution ⊡ Step 4 Implement Control System for Critical X ⊡ Step 5 Document Solutions and Benefits ⊡ Step 6 Handover to Project Owner and Project Closure ⊡ Important Questions to be Answered 1 Does the proposed solution address the vital root causes? ⊡ 2 Is your pilot study successful? ⊡ 3 Has the solution been effectively implemented and documented to sustain the

benefits? ⊡

4 Has the financial summary been calculated and approved? ⊡ 5 Have any new risk to project success been identified and added in the risk

mitigation plan? ⊡

6 Has the process been transferred to the process owner along with appropriate control plan?

⊡

Main Deliverables 1 Selected Solution with Cost/Benefit Analysis ⊡ 2 Process Control Plan and Documentation ⊡



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1.2.1 Define VOC, VOB and COPQ 3

Table 1.5 Introduction to VOC, VOB and COPQ Terms Explanation Voice of Customer [VOC] VOC provides a detailed understanding of the customer’s

requirements. The data helps you discover what your customers think of your business, product or service.

Voice of Business [VOB] VOB provides a detailed understanding of the requirements of the stakeholders in the business. The data helps you discover the needs, wants, expectations, and preferences, both spoken and unspoken, of the people who run the business (shareholders, officers, all departments involved in corporate governance) itself.

Cost of Poor Quality [COPQ] or

Cost of Quality [COQ]

COQ or COPQ provides a detailed understanding of the cost incurred in prevention, appraisal, internal failure and external failure activities.

Prevention costs Include cost of actions taken to prevent poor quality Appraisal costs include cost of actions taken to ensure conformance to standards and

performance requirements. Internal Failure costs occur before product is shipped or the service is completed, to the

customer. External Failure costs occur after the product is shipped or the service is complete.

Table 1.6 Classification of CTQs and Metrics Examples CTQ Metric VOC VOB COPQ

On-time delivery Ease of serviceability Appealing Aesthetics Revenue Growth Market Share Zero defects Zero waste Employee motivation Prevention cost Quality control plan Capability studies FMEA Source of Inspection First Article Inspection Measurement System Analysis Scrap and Rework Safety accidents Supplier returns Reprocessing Warranty claims Product recalls Lost sales

Capturing VOC can increase upselling and cross-selling success rates by 15% to 20%. “Companies that actively engage in VOC programs, spend 25% less on customer retention than those that don’t” – Gartner research


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https://www.superoffice.com/blog/customer-retention-tips-with-crm-software/

1.2.2 Basic Statistics for Six Sigma 4

Table 1.7 Basic Statistics for Six Sigma Statistical

Terms Explanation Numerical Summary Graphical Summary / Remarks

Descriptive Statistics

Provides a clear and concise summary of data

Mean: 10 min.

Standard deviation: 1.25 min.

Range: 6 to 13 min.

N : 50

Inferential statistics

Predicting the population characteristics using the sample characteristics

- -

Mean A single value representing the center of the data. Otherwise mean is the sum of all observations divided by the number of observations

The waiting time of five customer in a store are: 4, 3, 5, 2, and 3 minutes. The mean waiting time is: 4 + 3 + 5 + 2 + 3 = 17/5 = 3.4 min

On an average, a customer waits 3.4 minutes for service at the store

Median The middle of the range of data: half the observations are less than or equal to it and half the observations are greater than or equal to it.

If the data set contains an odd number of values then, median = value of middle term value.

If the data set contains an even number of values, median = average of middle two terms

For example, the median of data set 22,29,33,34,36 is 33

For example, the median of data set 22,29,33,34,36,43 is average of 33 and 34, which is 33.5

Whenever a data set contains outlier or skewed data, Median is the best measure of central tendency. It is not sensitive to extreme data values.

Mode The value that occurs most frequently in a set of observations is called Mode

In the data set 3,5,6,6,6,6,7,7,7,7,8,8,8,8,8,8,8,8,8,9,9,9,9,9,9,10,10. The mode is 8


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Table 1.7 Basic Statistics for Six Sigma contd. Variance A measure of dispersion, which

is the extent to which a data set or distribution is scattered around its mean.

Variance (s) is a squared quantity. For example, a sample of waiting times in a bank have a mean of 15 minutes and a variance of 9 minutes2. Variance is often converted to standard deviation (s). A variance of 9 minutes2 is equivalent to a standard deviation of 3 minutes.

Standard deviation

Standard deviation estimates the "average" distance of the individual observations from the mean.

The greater the standard deviation, the greater the spread in the data.

Range The difference between the largest and smallest data values.

The waiting time of five customers in a bank are: 4,3,5,2 & 3. R = 5-2 = 3 min

Waiting time ranges from 2 to 5 minutes

Quartiles Quartiles are values that divide a sample of data into four equal parts. First quartile (Q1): 25% of the data are less than or equal to this value.

Second quartile (Q2): The median. 50% of the data are less than or equal to this value. Third quartile (Q3): 75% of the data are less than or equal to this value. Interquartile range: The distance between the first and third quartiles (Q3-Q1); thus, it spans the middle 50% of the data.

For a dataset of 6,8,15,35,38,44,44,45,47,50

Q1 = 13.25;

Q2 = Median = 41

Q3 = 45.5;

IQR = 32.25

Normal distribution

A bell-shaped curve that is symmetric about its mean. The normal distribution is the most common statistical distribution because approximate normality arises naturally in many situations. The normal distribution is also known as the Gaussian distribution. The mean (μ) and the standard deviation (σ) are the two parameters that define the normal distribution.

Approximately, 68% of observations are within +/- 1 standard deviation of the mean; 95% are within +/- 2 standards deviations of the mean; and 99% are within +/- 3 standard deviations of the mean.

50

40

30

20

1 0

0

C3

p


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Table 1.7 Basic Statistics for Six Sigma contd. Probability Distribution Plot

Probability Distribution Plots can help you: 1. Clearly visualize

distribution shapes.2. See how changing a

parameter value affects thecurve

3. Compare two differentdistributions to see howwell one approximatesanother.

4. View the probabilitiesassociated with specificareas under the curve.

This graph actually plots probability density functions (PDF) which describes the likelihood of each data value. Typically, you specify the distribution, parameter values, and, optionally, a region of interest to shade under the curve.

Z score table: It helps to know the percentage of values below (to the left) a Z score in a standard normal distribution and the percentage of values above (to the right) a Z score in a standard normal distribution. It also helps to know the percentage of values between two Z score values in a standard normal distribution.

Types of Z score tables:

1. Positive Z score table: This means that the observed value is above the mean of total values.2. Negative Z score table: This means that the observed value is below the mean of total values.

Z score and Calculation of Z score

A Z score is the number of standard deviations from the mean. The Z score is calculated using the relation 𝑍𝑍 =𝑥𝑥−𝜇𝜇𝜎𝜎

, where x is the test value, µ is the mean value and σ is the standard deviation.

Example: The test scores of students in a class test has a mean of 70 and with standard deviation 12. What is the probable percentage of students scored more than 85?

The Z score for the given data is, 𝑍𝑍 = 𝑥𝑥−𝜇𝜇𝜎𝜎

= 85−7012

= 1.25

From the Z table, the fraction of data within this score is 0.8944.

Therefore 89.44 % of the students in the class are within the score of 85.

Hence the percentage of students who are above the test score of 85 is given by

Students % = 100 − 89.44 = 10.56 %

Find the percentage data lying between Z score (–2.575) to Z score (+2.575)

Area to the left of Z value -2.575 = 0.05; Area to the right of Z value +2.575 = 0.05;

Area in between Z value (-2.575 and +2.575) = 1 – 0.1 = 0.99

Hence 99 % data lie between Z value (-2.575 and +2.575)

(Note: 2 (2.575) = 5.15 is the sigma level used at times for 99 % confidence level)

0.30

0.25

0.20

0.15

0.10

0.05

0.00

X

Den

sity

6.5

0. 6827

9.58

Distribution PlotNormal, Mean=8, StDev=1 .5


Dr.Gopal Sivakumar

1.3.1 Define Generate Project Ideas 1

The idea of Six Sigma projects is not to expand the scope and examine every problem in the process. It is to find the critical one that is having the most impact.

Business stake holders and other departmental heads also play a part here. The people working within the process every day will have the most valuable insight into how to improve it.

You might find that the best Six Sigma project is one that doesn’t yet exist in your organization. It might make more sense to create a new product, service or process from scratch. In this case, you may want to approach the Six Sigma process from another angle.

“In these scenarios, you can use a variation of Lean Six Sigma: Define, Measurement, Analysis, Improvement and Control.

Identify what the “organization is trying to accomplish,” and they’re the best starting point for defining the project’s goals and metrics.

With Six Sigma, the best issues to tackle are process problems and it is best to keep the target as solving process errors.

Get from the Voice of Customers, what frustrates them the most and how they’d like to see it change.

Listen to stakeholders to identify fixable pain points after that product, service or hardware goes live. Six Sigma tools are most effective if they’re used to solve an internal error that directly impacts customer or stakeholder satisfaction.

Consult the people working within the process every day as they will have the most valuable insight into how to improve it.

1.3.2 Select Project 2

Identify the variations but consider the impact: Identify the processes with the most variation and then use that list to determine which problems have the most significant effect on the overall business. Identify the variations but consider the impact: Identify the processes with the most. Project ideas can be prioritized based on senior leadership’s strategic business goals and metrics”.

1.3.3 Project Charter and High-Level Map 3

The Project Charter is a living document that outlines a process improvement project for both the team as well as leadership. Teams use the charter to clarify the process issue being addressed, the reason for addressing it and what “success” looks like for those working on it. It’s also used to clarify what’s not being addressed. It is the first step in a Lean Six Sigma project, and therefore takes place in the Define phase of DMAIC. The Project Charter is periodically reviewed and refined throughout the project.


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Project Charter Elements

Problem Statement: What is wrong, not working and not meeting customer’s needs? When and where do the problems occur? What is the frequency of the problem? What is the impact of the problem on our business, employees and customers? What is the impact of the project and / or problem?

Business Case and Benefits What is the business reasons for going to this project?

Goal Statement What is the target of the process measurement?

Timeline When each project phase will take place and completed?

Scope First/Last and In/Out What is in and what is out of the project?

Team Members Who are the participants in this project?

1.4.1 Define Classifying Statements in to VOB and VOC 19

Table 1.8 Activity 1.1 - Classifying Statements into VOC/VOB and CTQ S. No. Description VOC VOB CTQ

1 Loan application document was incorrectly filled 2 We need faster response to our service request 3 Service quality should be the best 4 I want the pizza to be delivered hot 5 We must double our revenue growth 6 Product delivery should be as fast as possible 7 We have a longer cash conversion cycle 8 Not a lot of people are visiting our shopping mall 9 We want our call to be answered quickly 10 Additional raw material procurement should be reduced 11 Overtime cost should be reduced 12 Cost of scrap items must be reduced 13 Warranty claims and penalty are too high 14 We need very good customer service representative 15 We aren’t able to process transactions within time 16 We lost lot of money in Transportation of return stock 17 We lost sale due to stock unavailability


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1.4.2 Classifying Cost of Poor Quality 20

Table 1.9 Activity 1.2 - Classifying Cost of Quality S.

No. Description Prevention Appraisal Internal

Failure External Failure

CTQ

1 Quality control plans

2 Internal Auditing of product, process or services

3 Scrap and Rework 4 Supplier evaluations 5 Process failures

6 Policies, procedures, and work instructions

7 Quality improvement meetings, projects, events

8 Failure Modes Effects Analysis (FMEA)

9 Quality awareness, education, and training

10 Source inspection 11 Lost sales 12 Capability studies (Cp, Cpk) 13 Warranty claims 14 Voice of the Customer audits

15 Measuring and test equipment calibration

16 New product reviews 17 Re-inspection and Re-testing 18 Safety accidents 19 In-process inspection or final testing 20 data entry errors, missing information 21 Missed process steps 22 Product recalls 23 Processing customer complaints,

claims, or returns

1.4.3 Activity 1.3 Sample Variance S2 and Sample Standard Deviation S 21

Runs scored by Mohit Sharma in 10 consecutive innings in a recent T20 series was recorded. Compute the variance and standard deviation of his batting performance and write your inference.

Innings 1 2 3 4 5 6 7 8 9 10 Runs 38 48 42 51 69 58 22 8 59 44

Table 1.10 Variance Calculation Innings 1 2 3 4 5 6 7 8 9 10 Mean Runs [Xi] 38 48 42 51 69 58 22 8 59 44 𝑋𝑋� = 𝑋𝑋𝑖𝑖 − 𝑋𝑋� Variance

(𝑋𝑋𝑖𝑖 − 𝑋𝑋�)2

Variance = 𝑆𝑆2 = ∑(𝑋𝑋𝑖𝑖−𝑋𝑋�)2

(𝑛𝑛−1)=

Sample standard deviation = 𝑆𝑆 = �∑(𝑋𝑋𝑖𝑖−𝑋𝑋�)2

(𝑛𝑛−1)=


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Inference

1.4.4 Activity 1.4 Performance Comparison of two teams 22

Table 1.11 Activity 1.4 - Comparing Performance of Two Teams Two cab agents (A&B) provide cabs for a client. Both companies serve 1000 cab request in a month. We recorded the reporting time of 100 randomly selected cab request per company. The service level agreement states that reporting should be done within 30 minutes. (for data refer cab companies.mtw)

1. If both companies have similar average reporting time against a promised time of 30 minutes, would you be correct in saying that they are equally good?

2. To comment on which one is better, would you like to consider variation in reporting time? If yes, how would you like to measure variation?

3. What’s the probability of failure in both cases?

4. Consider 12000 cab request processed by each company every year, and any late reporting attracts $150 penalty, what’s the estimated loss for each company?

5. What are the graphical plots that you will prefer to display this data to your team and stakeholders?


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1.4.5 Activity 1.5 KAIZEN – Achieving Astonishing Results with micro changes 23 Table 1.12 Activity 1.5 - KAIZEN - Achieving Astonishing Results with micro changes

Carry out the given activity in the space below. Duration: 45 seconds

Round-1

Scenario:

Observation:

Suggestion:

Result:

Time taken: Your Score (Out of 10):

Round-2

Scenario:

Observation:

Suggestion:

Result:


Round-3

Scenario:

Observation:

Suggestion:

Result:


Round-4

Scenario:

Observation:

Suggestion:

Result:


Can you realize the following?

1. Reduction in the work complexity- YES / NO 2. Improvement in the performance - YES / NO 3. Reduction in the process time -YES / NO

There are no big problems, there are just a lot of little problems – Henry Ford


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1.4.6 Activity 1.6 DPMO and Process Sigma Level 24

Table 1.13 Activity 1.6 - DPMO and Process Sigma Level Calculation

Box No. Defect - 1 Defect – 2 Defect - 3 Defect – 4 Defect - 5 Total Defects

1 2 3 4 5 6 7 8 9

10 Sum of Defects Z =

Procedure to find Sigma level:

Number of boxes = 𝑁𝑁𝑏𝑏

Number of units inspected per box = 𝑁𝑁𝑢𝑢𝑏𝑏

Total number of units inspected,𝑇𝑇 = 𝑁𝑁𝑏𝑏(𝑁𝑁𝑢𝑢𝑏𝑏)

Total number of defects = 𝑍𝑍

Defects per unit,𝐷𝐷𝐷𝐷𝐷𝐷 =𝑍𝑍𝑇𝑇

Opportunity for error = 𝑂𝑂𝑂𝑂𝑂𝑂

Defects per opportunity,𝐷𝐷𝐷𝐷𝑂𝑂 =𝑍𝑍

𝑇𝑇(𝑂𝑂𝑂𝑂𝑂𝑂)

Defects per million opportunities = 𝐷𝐷𝐷𝐷𝑂𝑂 × 106

Process Sigma Level σ ST= Refer Table DPMO vs Sigma Level table and obtain the required (short term or long term) value. Log term Sigma Level is obtained by the relation

𝛼𝛼𝐿𝐿𝐿𝐿 = 𝜎𝜎𝑆𝑆𝐿𝐿 − 1.5


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1.4.7 Activity 1.7 KANO Model Analysis – VOC 26

Table 1.14 Activity 1.7 - KANO MODEL ANALYSIS – Voice of Customer Theme:

Team members:

Must Be Factors (If Present: No impact; If absent: Customer dislikes)

More is Better (If Present: Customer likes; If absent: Customer dislikes)

Delight Factors (If Present: Customer likes; If absent: No impact)

Kano model was developed by Professor Noriaki Kano in 1980s, which classifies customer preferences into prioritized categories.


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1.4.8 Activity 1.8 Project Charter 27

The Project Charter acts as an agreement between the project team and Stakeholders, Defining the Business Case, Problem Statement, Objective, Scope, Timeline, and Team.

Table 4.1 Activity 1.8 - Project Charter BUSINESS CASE: From [time1] to [time2], we experience [pain metric]. With a target of [target metric], this gap of [##] leads to a COPQ of [$$].

Example:

PROBLEM STATEMENT: In the period (when), causes [ activities] resulted in X measures [## Rs or ## %] to [company/customers]. The current X factor as compared to historical value represents [## %] more pain [## %]. This negatively affects our CTQ measures by (concern or consequence in ## % or Rs).

Example: In the last six months (when) 20% of our repeat customers were late, over 60 days, paying our invoices (what). The current rate of late payments is up from 10% in 2017 and represents 30% (how big) of our outstanding receivables (where). This negatively affects our operating cash flow by $150,000 (concern or consequence).” GOAL STATEMENT: By the end of [period/ completion date] vital CTQ measure will reach [what level] from [current level].

Example: Increase (verb) employee motivation survey scores (what) from the current 42% to 70% (improvement) by end of 3rd. Quarter 2017 (completion date).

SCOPE STATEMENT:

Example: In scope - Account Receivable Department Seattle facility, Receivables greater than $1m. Out of Scope - All other Departments, Receivables less than $1m.

Project Leader: Stake Holders: Team members: Key Contacts:

PHASE START DATE END DATE % COMPLETION

On time Delayed Stopped

CURRENT HEALTH

Define

Measure

Analyze

Improve

Control


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1.4.9 Activity 1.9 SIPOC Diagram 28

A SIPOC template maps Suppliers, Inputs, Process, Outputs and Customers. It helps you to gain a better understanding and broader view of the value stream of your process.

Figure 1 Activity 1.9 - SIPOC Diagram for Process: Packing an item for dispatch

Figure 2 Activity 8 SIPOC Diagram

Copy to: Customer, You, File, Notice Board

S1

S2

S3

S4

S1,

S2,

S3,

S4,

P1,

P2,

P3,

P4,

O1

O2

C1

C2

SUPPLIER INPUT PROCESS OUTPUT CUSTOMER

SUPPLIER INPUT PROCESS OUTPUT CUSTOMER


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1.4.10 Activity 1.10 High Level Process Map 29

Figure 3 Activity 1.10 - High Level Process Map

Figure 4 Activity 8 - High Level Process Map

Packing an item for dispatch


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1.5.1 Define Z table (between mean and Z) 52


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1.5.2 Define Z table (Left Area) 52


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1.5.2 Define Z table (Left Area) contd 52


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1.5.3 DPMO vs Sigma Level table 53

Table 5.1 DPMO vs Sigma Level – Comparison Table Sigma Level

Without 1.5 sigma shift With 1.5 sigma shift DPMO Yield Defect Rate DPMO Yield Defect Rate

1 317310 68.2690000% 31.7310000% 697612 30.23880% 69.76120% 1.1 271332 72.8668000% 27.1332000% 660082 33.99180% 66.00820% 1.2 230139 76.9861000% 23.0139000% 621378 37.86220% 62.13780% 1.3 193601 80.6399000% 19.3601000% 581814 41.81860% 58.18140% 1.4 161513 83.8487000% 16.1513000% 541693 45.83070% 54.16930% 1.5 133614 86.6386000% 13.3614000% 501349 49.86510% 50.13490% 1.6 109598 89.0402000% 10.9598000% 461139 53.88610% 46.11390% 1.7 89130 91.0870000% 8.9130000% 421427 57.85730% 42.14270% 1.8 71860 92.8140000% 7.1860000% 382572 61.74280% 38.25720% 1.9 57432 94.2568000% 5.7432000% 344915 65.50850% 34.49150% 2 45500 95.4500000% 4.5500000% 308770 69.12300% 30.87700%

2.1 35728 96.4272000% 3.5728000% 274412 72.55880% 27.44120% 2.2 27806 97.2194000% 2.7806000% 242071 75.79290% 24.20710% 2.3 21448 97.8552000% 2.1448000% 211927 78.80730% 21.19270% 2.4 16395 98.3605000% 1.6395000% 184108 81.58920% 18.41080% 2.5 12419 98.7581000% 1.2419000% 158686 84.13140% 15.86860% 2.6 9322 99.0678000% 0.9322000% 135686 86.43140% 13.56860% 2.7 6934 99.3066000% 0.6934000% 115083 88.49170% 11.50830% 2.8 5110 99.4890000% 0.5110000% 96809 90.31910% 9.68090% 2.9 3731 99.6269000% 0.3731000% 80762 91.92380% 8.07620% 3 2699 99.7301000% 0.2699000% 66810 93.31900% 6.68100%

3.1 1935 99.8065000% 0.1935000% 54801 94.51990% 5.48010% 3.2 1374 99.8626000% 0.1374000% 44566 95.54340% 4.45660% 3.3 966 99.9034000% 0.0966000% 35931 96.40690% 3.59310% 3.4 673 99.9327000% 0.0673000% 28716 97.12840% 2.87160% 3.5 465 99.9535000% 0.0465000% 22750 97.72500% 2.27500% 3.6 318 99.9682000% 0.0318000% 17864 98.21360% 1.78640% 3.7 215 99.9785000% 0.0215000% 13903 98.60970% 1.39030% 3.8 144 99.9856000% 0.0144000% 10724 98.92760% 1.07240% 3.9 96 99.9904000% 0.0096000% 8197 99.18030% 0.81970% 4 63 99.9937000% 0.0063000% 6209 99.37910% 0.62090%

4.1 41 99.9959000% 0.0041000% 4661 99.53390% 0.46610% 4.2 26 99.9974000% 0.0026000% 3467 99.65330% 0.34670% 4.3 17 99.9983000% 0.0017000% 2555 99.74450% 0.25550% 4.4 10 99.9990000% 0.0010000% 1865 99.81350% 0.18650% 4.5 6 99.9994000% 0.0006000% 1349 99.86510% 0.13490% 4.6 4 99.9996000% 0.0004000% 967 99.90330% 0.09670% 4.7 2 99.9998000% 0.0002000% 687 99.93130% 0.06870% 4.8 1 99.9999000% 0.0001000% 483 99.95170% 0.04830% 4.9 0.96 99.9999040% 0.0000960% 336 99.96640% 0.03360% 5 0.574 99.9999426% 0.0000574% 232 99.97680% 0.02320%

5.1 0.34 99.9999660% 0.0000340% 159 99.98410% 0.01590% 5.2 0.2 99.9999800% 0.0000200% 107 99.98930% 0.01070% 5.3 0.116 99.9999884% 0.0000116% 72 99.99280% 0.00720% 5.4 0.067 99.9999933% 0.0000067% 48 99.99520% 0.00480% 5.5 0.038 99.9999962% 0.0000038% 31 99.99690% 0.00310% 5.6 0.021 99.9999979% 0.0000021% 20 99.99800% 0.00200% 5.7 0.012 99.9999988% 0.0000012% 13.35 99.99867% 0.00134% 5.8 0.007 99.9999993% 0.0000007% 8.55 99.99915% 0.00086% 5.9 0.004 99.9999996% 0.0000004% 5.42 99.99946% 0.00054% 6 0.002 99.9999998% 0.0000002% 3.4 99.99966% 0.00034%


Dr.Gopal Sivakumar

lean six sigma green belt training manual a quick reference … · 2020-03-26 · 1.1 learning...

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