Six Sigma Black Belt Wk1-Define & Measure

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Six Sigma Black Belt ProgramDefine & Measure phase

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Objectives of Black Belt Training

Why 6 Sigma in our organization?

Benefits of 6 Sigma

General Introduction

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Objectives of Black Belt training

• To develop professionals in quantitative analytical skills, project management, group dynamics, team building and change management to address the organizational challenges.

• To build quality in to the systems to meet and exceed customer requirements.

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Why 6 Sigma in our organization?

• Overall strategy is to:Accelerate improvements in all processes and services by:

- Identifying customer needs- Crafting a value proposition- Designing business models

• Reduce cost of poor quality by eliminating waste, reducing defects and variations

• To delight the Customers / Clients

• To Grow revenues, Sustain Margins, Improve revenue productivity, Grow human capital

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When the principles and methodologies of Six Sigma are properly applied in a business process, they return positive top line & bottom-line results.

Some of the 6 Sigma Benefits are

* Improved overall customer satisfaction

* Increased productivity and added value

* Improved capacity and output

* Reduced total defects and cycle time

* Increased product and service reliability

* Improved process flow

* Improved ROI

Benefits of 6 Sigma

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Introduction to Quality

Fundamental Principles

Quality Concepts

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Aim So High… You’ll Never Be Bored…

The greatest waste of our natural resources is the number of people who never achieve their potential.

Get out of that slow lane. Shift into that fast lane

If you think you can’t, you won’t. If you think you can there’s a good chance you will.

Even making the effort will make you feel like a new person.

Reputations are made by searching for things that can’t be done and doing them.

Knowledge Skills

AttitudeHabit

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Moving up the competency ladder

1. Unconsciously Incompetent

2. Consciously Incompetent

3. Consciously Competent

4. Unconsciously Competent

Unconsciously Incompetent

Consciously Incompetent

Consciously Competent

Unconsciously Competent

1

2

3

4

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Quality

Simply stated, quality comes from meeting customer expectations. This occurs as a result of four activities:

• Understanding customer requirements

• Designing products and services that satisfy those requirements

• Developing processes that are capable of producing those goods and services

• Controlling and managing those processes so they consistently deliver to their capabilities.

Quality Concepts

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Importance of Quality

Classical Business Model

PROFIT = PRICE - COST

Classical Quality Belief

Better Quality Means:

Modern Technology

Modern Machinery

High Skilled Resources

In short

BETTER QUALITY = HIGH COST

Six Sigma Approach

Better Quality Means:

Less Defects

Less Reworks

Less Buffer

Low Cycle Time

In short

BETTER QUALITY = LOW COST

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Quality•Better products and

services•Improved processes

Product Quality•Reduced Scrap•Improved customer Response time

Process Quality•Reduced Rework•Elimination of in process•inspection

Customer Satisfaction

Productivity•Decreased•Cycle time

•Elimination of setup time

Market Share

Profit

CostOpportunity for

profit

Price

Quality’s contribution to Profitability

Organization

Internal

External

Compete with value

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Typical Waste….

Wastes of Manufacturing Process

• Defects

• Waiting

• Processing

• Over production

• Motion

• Inventory

• Transportation

• Under-utilization

• Safety hazards

• Defects

--- rework

• Unsatisfied Customer

---Customer not satisfied, wrong input

• Under-utilization of Resources

--- Poor usage of infrastructure , manpower

• Over- Processing

--- Over-support to customer ,unwanted information

• Redundant Process steps

--- Wrong processes / methods

Wastes of Service Industry

Wastes Kill ….. Business and ProfitsWastes Kill ….. Business and Profits

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Cost of Poor Quality

Lost Opportunity

Lost sales

Late delivery

Long cycle time

Expediting costs

Excess inventory

Additional Costs of Poor Quality

(intangible)

(tangible)

Lost Opportunity

Scrap

Rework

InspectionWarranty

Rejects

Traditional Quality Costs

(intangible)

(tangible)

25~35% of Sales

(Easily Identified)

(Difficult or impossible to measure)

Lost Customer Loyalty

4~ 6% of Sales

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Cost of Poor Quality (COPQ)?

Costs incurred due to product or process quality not meeting the customer requirement all the time.

Costs that would not exist if there were no defects.

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Cost of Quality Categories

Internal Failure Costs - costs that would disappear if no defects existed in the product

prior to shipment to the customer.

External Failure Costs - costs that would disappear if no defects were shipped to the

customer.

Appraisal Costs - costs incurred to discover the condition of the product (during “first

pass through”).

Prevention Costs - costs incurred to keep failure and appraisal costs to a minimum.

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Internal Failure Costs

Examples

• Scrap (labor and material)

• Rework

• Retest / Recheck/ Re-inspection / Re-testing

• Productivity loss due to defects

• Excess inventories

• Failure analysis

• 100% sorting inspection

Before shipment to customer

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External Failure Costs

Examples

• Lost business

• Warranty

• Dealing with complaints

• Returned product

• Price concessions due to lower

grade product

After shipment to customer

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Appraisal Costs

Examples

• Maintaining test equipment

• Quality audits / Transaction Monitoring

• Materials consumed through destructive testing

• Incoming, In process & Final inspection & testing

• Quality system audits

• Inspection & testing of materials & services

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Prevention Costs

Examples

• Maintaining production/operations equipment

• Process Control & Capability evaluation

• Process improvement: -Error proofing -FMEA -DOE

• Training

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Evolution of Quality

1920s: Invention of Control Charts by Walter A. Shewhart, Bell Labs

Each Phase Built on the Structure and Gains From the Previous Phases

1940s: Statistical Process Control

1960s: Japanese Quality Movements

1980s: Six Sigma

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DEMING’S Philosophy

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Deming’s 14 points for Quality Management (Principles of transformation)

1. Create constancy of purpose for continual improvement of product and service. : Replace short-term reaction with long-term planning.

2. Adopt the new philosophy for economic stability.

3. Cease dependency on inspection to achieve quality. : If variation is reduced, there is no need to inspect manufactured items for defects, because there won't be any.

4. End the practice of awarding business on price tag alone.

5. Improve constantly and forever the system of production and service. : Constantly strive to reduce variation.

6. Institute training on the job. : If people are inadequately trained, they will not all work the same way, and this will introduce variation.

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7. Adopt and institute modern methods of supervision and leadership.

8. Drive out fear. Deming saw management by fear as counter- productive in the long term,

because it prevents workers from acting in the organization's best interests.

9. Break down barriers between departments and individuals. : The concept of the 'internal customer', that each department serves not the management, but the other departments that use its outputs.

10. Eliminate the use of slogans & posters : Another central TQM idea “it's not people who make most mistakes - it's the process they are working within. Harassing the workforce without improving the processes they use is counter-productive”

Deming’s 14 points for Quality Management

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11. Eliminate numerical quotas. : Deming saw production targets as encouraging the delivery of poor-quality goods.

12. Remove barriers that rob the hourly worker of the right to pride in workmanship. : Many of the other problems outlined reduce worker satisfaction.

13. Institute a vigorous program of education and retraining.

14. Define top management’s permanent commitment to ever-improving quality and productivity.

Deming’s 14 points for Quality Management

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Introduction to Six Sigma

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Introduction to 6 Sigma

Six Sigma History

Success Stories

Definitions and Drivers

6 Sigma : What Makes It Different?

6 Sigma : Meaning

6 Sigma : Benefits

6 Sigma : The Organization

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In 1979 during a executive meeting, Motorola engineers stated “The real problem at Motorola is that our Quality stinks”

They had data confirming 10 - 20% of annual revenues was spent on correcting poor Quality, costing the company 800 - 900 million US $ PA

To get rid of this problem Motorola came up with Six Sigma Breakthrough strategy.

Six Sigma History

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And the Results?

1986

4.2

1997

5.6 ~ 16 BillionProducts Manufactured

Motorola

In 1988 Bob Galvin (CEO Motorola) while accepting first “Malcom Baldrige National Quality Award ” for Motorola, described about something called as Six Sigma.

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Six Sigma History

Six Sigma is a Structured, Project based approach to achieve BREAKTHROUGH results, leading to sustainable and significant FINANCIAL impact on our organization through intensive application of statistical tools and techniques by our people.

Since then, hundreds of companies around the world have adopted Six Sigma as a way of doing business.

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Success stories - General Electric

General Electric’s, Jack Welch describes Six Sigma as the most important initiative GE has ever undertaken. GE had an operative income of 10% for decades and they were not able to improve this despite various efforts. After implementation of Six Sigma GE was able to improve its operative income from 10% in 1995 to 16.7% in 1998.

As Jack Welch explains it:The best Six Sigma projects begin not inside the business but outside it, focused on answering the question—how can we make the customer more competitive? What is critical to the customer’s success? . . . One thing we have discovered with certainty is that anything we do that makes the customer more successful inevitably results in a financial return for us.

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Asea Brown Boveri

ABB, after application of Six Sigma has reduced measurement equipment error by 83%. The company also made drastic improvements in material handling resulting in an annual estimated cost savings of US $775000.

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Allied Signal

Allied Signal, which was on the verge of bankruptcy was made profitable by CEO Larry Bossidy through Six Sigma. The company implemented Six Sigma program in 1994. The cumulative impact on the savings in the direct costs during this period was more than US$ 2 billion.

Allied’s leaders view Six Sigma as “more than just numbers—it’s a statement of our determination to pursue a standard of excellence using every tool at our disposal and never hesitating to reinvent the way we do things.”

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A Six Sigma Journey • • 1987 to 2006

Over the past few decades six sigma has evolved from a focus on defects to cost reduction to value creation.

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Companies Implemented 6 Sigma -Worldwide

SonySony

KodakKodak

MicrosoftMicrosoft

DupontDupontBlack and DeckerBlack and Decker

FordFord

Johnson and Johnson

Johnson and Johnson

Asea Brown BoveriAsea Brown Boveri

CitibankCitibank

American ExpressAmerican Express

Johnson ControlsJohnson ControlsCaterpillarCaterpillar

GEGE

ToshibaToshiba

Federal ExpressFederal Express

IBMIBM

Allied SignalAllied Signal

Motorola CorporationMotorola

Corporation

Texas InstrumentsTexas Instruments

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Definition & Drivers

6 Sigma• A comprehensive and flexible system for achieving, sustaining

and maximizing business success

• Close understanding of customer needs

• Disciplined use of facts, data, and statistical analysis

• Diligent attention to managing, improving, and reinventing business processes

6 Sigma Drivers

Six Sigma - A concept for Quality improvement

The Goal of Six Sigma is not to achieve six sigma levels of quality. It is about improving profitability, though improved quality and efficiency are the immediate by-products of Six Sigma.

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Quality is a state in which value entitlement is realized for the consumer and the provider in every aspect of business relationship.

• Entitlement for companies means that they have rightful expectation to produce quality products at the highest possible profits.

• Entitlement for customer means they have a rightful level of expectations to high-quality goods at the lowest possible cost.

Definition of Quality as per Six Sigma

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• Six Sigma metric provides a standard for communicating process status and improvement goals.

• Project selection tied to organizational strategy / balanced scorecard Customer focused / proactive vs. customer driven / reactive.

• Project outcomes / benefits tied to financial reporting system.

• Recognition and reward system established to provide motivation.

• Executives and upper management drive the effort through:– Understanding Six Sigma.– Significant financial commitments.– Actively selecting projects tied to strategy.– Setting up formal review process.– Selecting Champions.– Determining strategic measures.

6 Sigma : What Makes It Different?

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Six Sigma v/s TQM and ISO

Focus on Money

Leadership & Top-Down Support

Continuous Improvement

Deployment Strategy & Guidelines

Measurement Criteria of Quality Goals

Performance Targets

Application of Statistical Tools

Quality Career Path

Extension to Cost, Cycle Time & Other Business Issues

Integration of Business Goals with Quality

Functional Focus

Project Approach

ISO TQM Six Sigma

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6 Sigma: Meaning

• Reduce Variation to the half of Tolerance Band

• Minimize defects to the level of 3.4 defects per million opportunities

6σσ Definition

σσ It is a Greek term which designates the spread or distribution about the mean of any process.

66 It is a metric that indicates how well the monitored business process performs. Higher the no., better the process.

Precise But not Accurate: Process is off target

LSL USL

Defect !

Not Accurate Not Precise : High variation in the process

LSL USL

Defect !

LSL USL Six sigma tool tries to reduce variation in the process and shifts process mean towards the target

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Time taken to process order - Advisor A

3536373839404142434445

1 2 3 4 5 6 7 8 9 10

Order no.

Tim

e in

Min

ute

s

40

Process mean is 40Process mean is 40

USL

LSL

Example 1

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Time taken to procees order - Advisor B

3536373839404142434445

1 2 3 4 5 6 7 8 9 10

Order no

Tim

e in

Min

ute

s

40

Process mean is 40Process mean is 40

USL

LSL

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Time taken to process order - Advisor A & B

3536373839404142434445

1 2 3 4 5 6 7 8 9 10

Order no

Tim

e in

Min

ute

s

40

USL

LSL

Which Advisor will you prefer ?

B

A

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The Goal

FromFixing products

and services to become acceptable

Quantum Leap

6 Sigma GoalTo

Optimized processes that produce defect

free products and services

Influence of 6 Sigma

0

10

20

30

0 10 20 30 40 50

Process before and after Six Sigma

Before After

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USL

LSL

Target

Six Sigma is about on target performance

with reduced variability around the target

A Six Sigma Landing .

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Consider 10,00,000 (1 Million) planes take off per year. The plane crash results…at each Sigma Level will be as follows

2 sigma – 3,08,537 Planes per year

3 sigma – 66,807 Planes per

year

4 sigma – 6,210 planes per

year

5 sigma – 233 Planes per

year

6 sigma - 3 Planes per

year

Sigma LevelUnderstanding

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Sigma LevelDefects Per Million

Opportunities% Defects

1 691,462 69 %

2 308,507 31 %

3 66,807 7 %

4 6,210 0.6 %

5 233 0.02%

6 3.4 0.00034%

Sigma Level and Quality

471 122 3 4 5 6 7 8 9 10 11

1 1 1 1 11

3

Six Sigma Process Predictably twice as good as what the customer wants

LSL USL

6

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Sigma –

A measure of variation from “target”.

One standard deviation around the mean is about 31% of the total “opportunities” included with in specification limit.

1 Std. Dev. / 1 Sigma

LSL USL

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If we can fit six standard deviations on both side of the mean in between our target and the specification limits . . .

….. then

99.99966% of our “opportunities” are included!

1 2 3 4 5 6123456

LSL USL

6 Std. Dev. / 6 Sigma

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• Generates sustained success

• Sets a performance goal for everyone

• Enhances value to customers

• Accelerates the rate of improvement

• Promotes learning

• Executes strategic change

6 Sigma : Benefits

• Cost reduction

• Productivity improvement

• Market-share growth

• Customer retention

• Cycle-time reduction

• Defect reduction

• Culture change

• Product/service development

• And many more.

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Y = f(X)

Which one should we focus on the Y or X?

Y

■ Dependent Function

■ Output

■ Effect

■ Symptom

■ Monitor

X1,.…,Xn

■ Independent Variable

■ Input

■ Cause

■ Problem

■ Control object

6σ Methodology

6σ Application assures that problem is solved by focusing on the factors that cause the problem.

Focus of the 6σσ Approach of Problem solving

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If we don’t know the relationship between the Output (Y) and the Inputs (X’s) all we can do is to monitor and sort the good from the bad Y’s.

And Pray Hard that Y will turn out good !

Implement Six Sigma ...

It discovers relationships between Y & Xs

OrOr

Lets Avoid This Situation !

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Y = f (X)

Everything has a cause. Cause influences the effect

Focus on the cause (x) to change the response

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Pr = f (V)

Problem is any deviation from the defined standards of a distinguishing feature.

Focus on source of variation to eliminate the problem

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V = E - O

Variation is the gap; A deviation from the expectation

Measure the variation. We can’t improve what is not measured.

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act do

docheck

Assurance

6 / ISO / COPC/ e-SCM Performance Management System

Time

PerformanceContinual Improvement

act plan

docheck

Continual Improvement

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Six Sigma

Organization, Roles and Responsibilities

Six Sigma Organization

Six Sigma focuses on reducing the variation in every process , makes intensive use of the statistical tools, enables decisions based on facts & data rather than gut feelings and puts customer in first place .

Every improvement we are doing in house must be ultimately linked with the customer satisfaction whether it is internal or external . There must be strong and clear linkages between the internal processes, which ensures the end customer satisfaction .

To transform our organization into Six Sigma organization , we must ensure that six sigma is used as a philosophy / strategy and not a tool .

In an essence , It's a new way of managing the enterprise .

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6 Sigma : The Organization

Leadership

Commitment at Executive Level

Governance

Review,Enable,Monitor,Institutionalise

e.g. Quality leaders, Master Black Belts etc.

Implementation

Scope, Apply Six Sigma Tools And Enhance Business Processes

e.g. Project sponsors, BU Managers, BB, GB, YB – who apply Six Sigma on-the-job

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Deplo

ym

ent

Cham

pio

n /

Top

Managem

ent

Link Six Sigma to

overall business strategy

Creat Customer Feed back

process

Determine strategic goals and objectives

Allocate appropriate resources

Develop incentive plan & reward system

Establish accountibility in annual performance reviews

Creat a core six sigma

leadership team with defined

responsibilities

Extend the initiative in other group companies

MB

B /

Six

Sig

ma

core

team Design and

implement six sigma deployment

Creat an overall training plan

Design training Material & Periodic updates

Define a project selection process & criteria

Establish project review & project trac mechanism

Define a project validation & project closeout process .

Evaluate cultural obstacles & raise red alerts to management

Design a common database of closed projects & key learnings

Compile the lessons learned & share best practices .

Pro

ject

Cham

pio

n /

BU

Managers

Identify opportunities for breakthrough improvement

Identify appropriate project leaders & arrive at cross functional team

Ensure resource availability for project execution

Review the team progress, remove barriers and resolve issues

Appreciate and recognize good efforrts. Motivate project team

Ensure controls are in place and project gains are sustained forever .

Bla

ck B

elt /

Gre

en

Belt /

Pro

ject

Team DEFINE

the project.Pboblem statement, Objective, Scope, Team, Timeline

MEASURE the response variable Y.Baseline, Target, MSA

ANALYZE the negative effect. Identify root causes(X's) & verify statisticaly

IMPROVE peroformance ( Y's) by implementing the counter solutions for X's

CONTROL the KPIV's to sustain improved Y. Control plan, Control chart etc..

Ispat Industries Ltd. Six Sigma Deployment Process Six Sigma Deployment Model

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Organization / Infrastructure

Sponsor

Senior executive who sponsors the overall Six Sigma Initiative and who is responsible for implementing Six Sigma within the business. Select meaningful business impact projects. Responsible for achieving Six Sigma project results. Identify, prioritize, select & scope projects. Review, track, and report Six Sigma project progress and results. Eliminate project barriers, assure proper project resources. Reward, recognize Six Sigma project team

Achievements.

Master Black Belt

Mentor black belts on their projects. Ensures effective application of DMAIC.

Coach on appropriate, effective use of Six Sigma Tools & effective project

management to achieve on-time results. Helps sponsor in improvement

opportunity identification. Apply Six Sigma skills and expertise to their own

Projects. Train BBs, Sponsors and Managers in Six Sigma.

Black Belt

Highly experienced person with four weeks of classroom training, has managed several projects and is an expert in Six Sigma methods / tools. Organize, plan and lead Six Sigma projects. Escalates project barriers to sponsors. Project tracking, reporting. Apply Six Sigma skills and expertise toproject execution. Responsible for coaching / mentoring / training Green andYellow Belts, team members and for helping the Sponsor keep the initiativeon track

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Organization / Infrastructure

Project leader

Understand DMAIC Process and Sustain Improvements. Provide domain /

Process expertise. Accountable for timely completion of projects. Support

improvements. Identifies project team and ensures their availability.

Green BeltProfessional who leads small scope Six Sigma projects. Typically has one week of classroom training in methods, statistical tools, and (sometimes) team skills, participates in Black Belt project team or leads smaller projects.

Yellow Belt

Typically has two days of classroom training in methods and basic statistical tools, participates on a Green Belt project team or leads smaller

improvement projects.

Team Member

Professional who has general awareness of Six Sigma. (through no formal training) and who brings relevant experience or expertise to a particular project.

Apply Six Sigma tools with help of Black / Green / Yellow Belts Contribute ideas during meetings and carry out action items. Collect and analyze data

Lead small activities such as process capability studies, measurement system studies, verifying causes and solutions. Implement and sustain solutions.

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Green Belt • Participates in Black Belt project team.• Leads smaller projects

Yellow Belt• Leads smaller improvement projects

Master Black Belt- Mentor black belts on their

projects

- Ensures effective application

of DMAIC- Helps sponsor in improvement opportunity identification

- Apply Six Sigma skills & expertise to their own projects

- Train BBs, Sponsors & Managers in Six Sigma

Sponsor

- Sponsors the overall Six Sigma initiative

- Select meaningful business impact projects

- Eliminate project barriers, assure proper project resources.

- Reward, recognize Sigma project team achievements

Six sigma Roles

Black Belt• Organize, plan & lead Six Sigma projects• Project barrier escalation to sponsors• Project tracking & reporting coaching / mentoring / training Green & Yellow Belts, team members

Team Members• Bring expertise to projects• Contribute ideas during meetings and carry out action items• Collect and analyze data• Lead small activities • Implement & sustain solutions

Project Leader- Understand DMAIC Process

and Sustain improvements

- Accountable for timely

completion of projects

- Provide domain / Process expertise

- Support improvements. Identifies project team and ensures their availability

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Champion or Sponsor

• Representative of Top Management

• Has Authority over the scope of project

• Statistical Knowledge is preferable

• Experience in carrying out Quality improvement Projects is preferable

At Start: * Identify the Goals of the Project* Select the Project Leader or Black Belt* Ensure the Project Scope is under his control* Identify Milestones* Prepare Project Schedule

On Going: • Provide Resources• Conduct Periodic Review & add value• Ensure the Project is on Right Track• Control Budget

At End: • Handle Implementation Issues• Quantify the Project Results • Verify whether the Goals are achieved• Prepare Future Action Plan, if required• Conclude Project

Requirements Responsibilities

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Master Black Belt

• Sound knowledge of Statistical Tools are required

• Good Communication & Teaching Skills are essential

• Should be a Good Consultant


At Start:• Create Six Sigma awareness among top

management• Provide guidance & training for gathering Voice of

Customer’s & Stakeholders • Help to identify projects• Provide guidance to prepare Project Charter

• On Going:• Provide training on various Statistical Tools useful at

different phases of the project life cycle• Give Statistical Consultancy at different phases of

the project life cycle

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Black Belt

• Statistical Knowledge essential

• Experience in Data Collection, Analysis & Interpretation required

• Should posses Leadership Qualities

• Should posses Good Communication Skills


At Start:• Select Team Members• Prepare Project Charter• Identify Ys

On Going:• Analyze Ys & compute Baseline Sigma Value• Identify Xs• Establish relationships between Ys & Xs• Optimize Xs• Devise Control Mechanism to ensure that Xs

are at optimum always.

At End:• Be equivalent to Master Black Belt.• Conducts programs on Statistical tools• Facilitates Business Unit Head for project

selection

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Green Belts \ Team Members

On Going:Collect data on YsHelp BB to analyze Ys data.Be part of identifying XsCollect data on XsHelp BB to establish relationship between Xs &YsHelp BB to optimize & control XsHelp design the new process Drive the project to completion

At End:* Green Belt will be equivalent to Black Belt

Responsibilities

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Team Members

• Guidelines for team members– Manageable team size up to 5 people

– People who are part of the process

– People who are benefited by removal of pain area

– People who have domain knowledge

– People from same location

– Guest members as required

69

Project Teams

Champion

Black Belt

Green Belt

Team Members

B

G

G G

70

Team Dynamics

Mutual Accountability

Small group of people

Individual Accountability

Specific Goals

Common Approach

Meaningful Purpose

Problem Solving

Technical/Functional

Interpersonal

AccountabilitySkills

Commitment

Per

form

ance

R

esu

lts

Collective

Work ProductKnowledge

Enhancement

71

Deployment Strategy

Elements of 6 Sigma Deployment

Role Matrix

Training

Certification Criteria

Project Classification

Project Benefits Evaluation

72

TopManagementCommitment

1.Project Identification -

To contribute to the bottom-line of the organization, client

satisfaction, etc

2. Project Classification

•Black Belt Project•Green Belt Project•Yellow Belt Projects

3.Enhanced Training and certification

•Awareness Program•Yellow Belt Training•Green Belt Training•Black Belt Training

4. Review Mechanisms

•Program Reviews•Project Reviews

5. Project Benefits Evaluation

Project Benefits (QNI) = Project Savings – Project Expenditure

6. Fostering Quality Culture

•Branding• Yearly Awards •dedicated facilities and staff

Six Sigma is being implemented in the organization as a catalyst for change in culture and achieve competitive advantage.

DMAIC methodology is being used to develop and fine tune both core and enabling processes.

Six Sigma Deployment plan is properly documented as PACE guidelines.

Elements of 6 Sigma Deployment

“commitment for Six Sigma” and “Champions/ Sponsors” are the most powerful success factors

“commitment for Six Sigma” and “Champions/ Sponsors” are the most powerful success factors

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Team Structure - Role Matrix

Role DescriptionQuality Team

Operations / Support Team

HR TeamSS Executive

Committee (EC)

Training Curriculum preparation and implementing improvements as per recommendations

√

Preparing the Training Calendar √

Identifying the Trainers √

Certifying the Trainers √

Defining the Selection Criteria and improving the same on an ongoing basis

√

Identifying the Trainees √ √

Selection of Projects based on ongoing experience and Business Goals

√ √

Sponsor for the Project √ √

Project Management / Coordination √ √

Project Execution Respective BB / GB / YB

Project Certification √

Review of training material √ √

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Types of Training Awareness Program – The program will be of a 4 Hrs duration.

This is engagement specific and will be conducted internally by the dedicated certified trainer.

Yellow Belt Training – Two continuous days as stipulated in the program calendar

Green Belt Training – Five Days program spread over 3 months (2days + 2days + 1day) as stipulated in the program calendar

Black Belt Training – 20 Days program spread over 4 months, 5 continuous days every month as stipulated in the program calendar.

For YB/GB/BB trainings all trainees are expected to be fully available during the training period

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All trainees would need to attend a 2 day YB session/ 5 day GB session / 20 days BB session & pass the respective certification examination.

Qualifying mark would be 70% for YB, 75% for GB & 80% for BB

Project Certification will be granted to the BB, GB and YB identified and dedicated for the particular project

Certification will be done internally by the Certification Board. The criteria for certification will be

Complete training

Pass the examination

Meet any of the options as mentioned in the next page.

76


Black Belt Certification

Green Belt Certification

Yellow Belt Certification

OPTIONS

1 3 4 52

Black Belt Project Leader

Green Belt Project Leader

Yellow Belt Project Leader

Yellow Belt/ Green Belt/ Black Belt Project Member

77

Project Classification

Black Belt Projects• Projects that have cross functional scope and are of high impact and

complexity with respect to NET revenue earnings / NET savings. (Expected savings $ 50,000/ annum min).

• Should complete within 4 to 6 months• Base line, target and savings should be validated by Financial

representative

Black Belt Projects• Projects that have cross functional scope and are of high impact and

complexity with respect to NET revenue earnings / NET savings. (Expected savings $ 50,000/ annum min).

• Should complete within 4 to 6 months• Base line, target and savings should be validated by Financial

representative

Yellow Belt Projects• YB projects are Cell wise projects, where cross functional team is not

required and very limited statistical knowledge required to carry out the project

• This projects are taken as value addition to GB projects. YB projects by themselves do not generate / save revenue.

Yellow Belt Projects• YB projects are Cell wise projects, where cross functional team is not

required and very limited statistical knowledge required to carry out the project

• This projects are taken as value addition to GB projects. YB projects by themselves do not generate / save revenue.

Green Belt Projects• GB projects are less complex than BB projects. (Expected savings $10,000-$

50,000/ annum) • Should complete within 2 to 4 months• Base line, target and savings should be validated by Financial

representative

Green Belt Projects• GB projects are less complex than BB projects. (Expected savings $10,000-$

50,000/ annum) • Should complete within 2 to 4 months• Base line, target and savings should be validated by Financial

representative

IMP

AC

T Green Belt Black Belt

Yellow Belt X

Complexity

78

Project Benefits Evaluation

Soft (Intangible)

Benefits

Hard (Tangible) Benefits

Definition: Any measurable Improvement

which cannot be quantified and converted

into Dollar Savings.

Examples:• Increase Customer Satisfaction

Scores• Improve VOC Scores• Improving Federal / State regulatory

Compliance Scores

Definition: Any measurable Improvement which can be Quantified and converted into Dollar Savings.

Examples: • Reduction of 3 FTE’s • 1% market share Increase• Eliminate 2 Temporary Positions• Rework down 20%• Lower vendor cost per transaction• Save 1 temp. worker 2 hrs of work

per week• Eliminate 30% call volume• Decrease AHT of the process by 60

secs• Reduction in past due receivables

by $10MMProject Benefits (QNI) = Project Savings – Project Expenditure1. Project Charter will include Sign-off from Finance Analyst2. Project validation / authentication is done by Sponsor, Head Quality and Finance Dept. The

return on the project is verified.3. Only upon successful completion of the project, the respective BB will be eligible for

certification.

Project Benefits (QNI) = Project Savings – Project Expenditure1. Project Charter will include Sign-off from Finance Analyst2. Project validation / authentication is done by Sponsor, Head Quality and Finance Dept. The

return on the project is verified.3. Only upon successful completion of the project, the respective BB will be eligible for

certification.

79

Six Sigma

Project Identification and Selection

80

Project Identification & Selection

Project Selection approach

Project Sources and Selection Criteria

Kano Model

CTQ Tree

Quality Function Deployment

81

Project Selection Approach

Steps

What is the process ?Who are the potential customers ( Internal, External, Business

owners)What are the process deliverables ?What is the Unit of Measurement ?Is Measurement System in place ?What is the Current Performance (Baseline) ?What is a expected / targeted performance ?Does gap exists ?Does gap carries significant impact to customer / business ?Identify the improvement approach Continuous Improvement OR Re

designed ?

82

Step I Step III Step IV Step V Step VI Step VII Step II

One of the Business / Functional unit (e.g. Operations / Purchase

Study the critical process and the pain areas identified during customer survey

Identify External & Internal (Business Owner & other functions) for the critical process

Categorize the customer expectations (VOC / Pain areas) from critical process obtained form from surveys in to

Deliverables of Quoting process

For each deliverable of critical process identify If the sub process or product / service feature exists

Business UnitBusiness Unit

Business ProcessBusiness Process

Identify Critical ProcessIdentify Critical Process

Study ProcessStudy Process

Identify CustomersIdentify Customers

List out Customer Expectations/Deliverables


Does ProcessExists?

AYes

No B

Approach Steps Tools Approach Description

Business Process Framework

Process Flow Chart

Customer Surveys

Brain storming

Prioritization

High level process mapping

SIPOC

SIPOC

Sub Process Flow Chart

Process flow steps

SS Project Selection Approach – Identifying Critical processes

83

Ensure that the critical process metrics are linked with Business objectives, Strategy & VOC

Collect data for all critical process metrics to define baseline process performance

Conduct a gap analysis between the criticalprocess baseline performance and targeted /

customer expected performance level

If the sub process exists for a metric / feature apply DMAIC for long term approach OR Problem solving approach for Quick wins for early

realization of improvements where solutions are known

For each deliverable of critical process identify unit of measure & measurement system

Does Gap Exists?

If the sub process does not exist for a metric / feature apply DMADV or

Process Re Engineering Approach.

Are Metrics

in place?

Define Metrics & implement

data collection plan



A

Validate the MetricsValidate the Metrics

Process Base liningProcess Base lining

Does Process exists?

No

NoExplore need of

performance excellence

Explore need of performance

excellence

Apply DMADV / Process

Re Engineering


Re Engineering

Long Term Approach – Apply DMAIC


Quick wins – Apply REIS

Quick wins – Apply REIS

Yes

Yes B

Approach Steps Tools Approach Description

SIPOC

Data collection checklists

Dashboard

Process Capability

Control Charts

Enterprise Performance

Management Matrix

Process

Performance

Enhancement

implementation

1. DMAIC

2. DMADV

3. Re Design / Re Engineer

4. Problem Solving Approach

Six Sigma Project Selection Approach (Contd.)

84

SS Project Selection Approach – Identifying Undesirable Conditions

1. Identify the undesirable business process conditions. (Customer complaints, VOC, Quality reports, Monthly performance indicators)

2. Quantify the undesirable conditions in to Defect (%, PPM, DPMO, Sigma level) or Cost .

3. Identify the potential causes for the undesirable conditions.(High level process map, C & E analysis, Pareto analysis)

4. Arrive at the vital few potential causes and prioritize .(NVA's, Multivoting, 80:20 principle )

5. Identify the business process to which each prioritized suspected cause belongs to .

6. Select the core or enabling process for improvement, quantifying the gap between current performance and what customer wants (Process Deliverable).

Apply...

Apply...

Six Sigma Project 1

Define

Measure

Analyze

Improve

Control

Six Sigma Project 2

Define

Measure

Analyze

Improve

Control

Six Sigma Project N

Define

Measure

Analyze

Improve

ControlTarget : Reliable (Stable & controlled) processes, Satisfied Customers, Happy Employees

85

Improvement Methodology Selection

Product / Process Development : Where a sub process

does not exist to take care of the customer requirement

Product / Process Development : Where a sub process

does not exist to take care of the customer requirement

Product / Process Performance Enhancement : Where the sub

process exists but unable to meet the customer requirement with

current process performance level

Product / Process Performance Enhancement : Where the sub

process exists but unable to meet the customer requirement with

current process performance level

Process ImprovementProcess Improvement

DMADV(Define, measure, analyze,

design and validate)

DMADV(Define, measure, analyze,

design and validate)

DMAIC(Define, measure, analyze,

improve and control)

DMAIC(Define, measure, analyze,

improve and control)

Design processes that do not exist for e.g. knowledge

management

Design processes that do not exist for e.g. knowledge

management

Enhance process metrics e.g. turnaround time, customer problem

resolution effectiveness etc

Enhance process metrics e.g. turnaround time, customer problem

resolution effectiveness etc

Product / Process Metric monitoring :

Where the tentative solutions are known and systematic execution is required to ensure the Improved process performance &

sustenance

Product / Process Metric monitoring :

Where the tentative solutions are known and systematic execution is required to ensure the Improved process performance &

sustenance

REIS(Recognize, evaluate, implement

and sustain)

REIS(Recognize, evaluate, implement

and sustain)

Monitor and reduce errors in the process e.g. ineffective call close

etc

Monitor and reduce errors in the process e.g. ineffective call close

etc

86

Six Sigma DMAIC Project Flow Define

Tollgate

DefineDefine

Step 1: Identify customer & their care abouts.

Covert their needs in to Critical to Satisfaction

(CTS) i.e. CTQ – Critical to quality, CTC – Critical to

Cost & CTD Critical to Delivery.

Step 2: Develop Project Charter

MeasureTollgate

MeasureMeasure

Step 3: Take the snapshot of the process,

how the process performing currently & fix

the baseline.

Step 4 : Validate the measurement system from which we collect the

data.

AnalyzeTollgate

AnalyzeAnalyze

Step 5: Identify the key process input variables that affects the outputs

most.

Step 6 : Verify the identified causes to see whether those are real or

not.

ImproveImprove

ImproveTollgate

Step 7: Determine the solutions to optimize the

output & eliminate / reduce defects &

variations.

Step 8 : Implement the solutions partly & statistically verify their impacts on output.

ControlControl

ControlTollgate

Step9 : Put the control in place to sustain the gains

made by the process improvement.

Step 10 : Integrate in daily work by Process Owner &

team

Phase Deliverables

Required• List of Project CTQs• QFD/CTQ Tree• Project Charter• SIPOC

Tools Box• Project Risk Assessment• Stakeholder Analysis• High Level Project Plan• In Scope/Out of Scope• Customer Survey

Methods (focus groups, interviews, etc.)

Required• Process Baseline

capability• Operational definition,

Specification limits, target, defect definition for Project Y(s)

• Measurement System Analysis

Tools Box• Benchmarking• Data Collection Plan• Gage R&R• Process Map• FMEA• Pareto Analysis

• Required• Data Normality Test • List of Statistically

Significant Xs .• List of vital few Xs &

their verification

• Tools Box • RCA / Fishbone

Diagram• Hypothesis Testing• Correlation &

Regression Analysis

Required• Optimization of Xs• Improvement

verification• Tolerances on Vital Few

Xs

Tools Box

• Design of Experiments• Improved Process Maps• FMEA on new process

Required• Post Improvement

Capability• Improvement tracking • Process Control Plan• Process Owner Signoff

Tools Box• Control Charts• Control Plan• Hypothesis Testing• Error Proofing

D M A I C

87

DefineTollgate

DefineDefine

Step 1: Identify new product, Process, Service

Step 2: Develop Project Charter

MeasureTollgate

MeasureMeasure

Step 3: Identify caustomer & their care abouts (CTQ)

Step 4 : Identify , deploy data collection plan & process base lining

AnalyzeTollgate

AnalyzeAnalyze

Step 5: Develop design alternatives

Step 6 : Develop & evaluate high level design capability

DesignDesign

DesignTollgate

Step 7: Optimize the micro level design parameters

Step 8 : Evaluate and verify the micro level design.

ValidateValidate

ValidateTollgate

Step9 : Validate results on full scale & Put the controls in place to sustain the gain.

Step 10 : Integrate in daily work by Process Owner & team

Phase Deliverables

Required• Customer surveys,

Interviews• Project Charter

Tools Box

• High Level Project Plan• In Scope/Out of Scope• Customer Survey


Required• Operational definition, • Specification limits, • Current Performance• Performance target, • defect definition

Tools Box• Benchmarking• Data Collection Plan• CTQ Tree• Affinity Diagram• Process capability• MSA

Required• Design alternatives • Selection of best

alternative• Detail design

requirements• High level design

capability predicated

Tools Box

• Benchmarking• Design scorecard• FMEA• Layout diagrams• Process map / model• Prototyping

Required• Design alternatives • Selection of best

alternative• Detail design

requirements• High level design

capability predicated

Tools Box

• Benchmarking• Design scorecard• FMEA• Layout diagrams• Process map / model• Prototyping

Required• Post Design Capability• Scale up decisions• Full scale process

implementation• Improvement tracking • Process Owner Signoff

Tools Box• Control Charts• Control Plan• Process capability• Design scorecard• Process management

chart• Standards &

Procedures

Required• Post Design Capability• Scale up decisions• Full scale process

implementation• Improvement tracking • Process Owner Signoff

Tools Box• Control Charts• Control Plan• Process capability• Design scorecard• Process management

chart• Standards &

Procedures

D M A D V

Required• Optimization • Of design parameters• Prediction model• Design Verification• Updated design

scorecard

Tools Box

• Design of Experiments• Improved Process Maps• Updated FMEA • Software simulation• Pilot / Test plan• Design score card• Tolerance analysis• Process management

chart

Six Sigma DMADV Project Flow

88

Step 1: Identify Improvement area/ Problem

Step 2: Define Problem statement & Goal statement

RecognizeRecognizeRecognizeRecognize

Step 3: Evaluate the causes for problem

Step 4 : List the vital causes

EvaluateEvaluateEvaluateEvaluate

Step 5: Identify the countermeasures / solutions

Step 6: Implement the countermeasures / solutions

ImplementImplementImplementImplement

Step 7: Validate results on full scale & Put the controls in place to sustain the gain.

Step 8 : Integrate in daily work by Process Owner & team

SustainSustainSustainSustain

Phase Deliverables

Required• Customer surveys,

Interviews• Problem & Goal

statement• Business dashboard

Tools Box

• In Scope/Out of Scope• Customer Survey


• Problem solving charter

Required• Vital causes

Tools Box

• Cause & Effect Analysis

Required• List of

countermeasures/ solutions

• Countermeasure / solution implementation plan

Tools Box

• Countermeasure Matrix• Implementation plan

RequiredImprovement tracking Process Control PlanProcess Owner Signoff

Tools Box

• Control Charts• Control Plan• Visual Management

Phase ExitReview

Phase ExitReview

Phase ExitReview

Phase ExitReview

Phase ExitReview

Phase ExitReview

Phase ExitReview

Phase ExitReview

R E I S

Problem Solving (Quick Win) Project Flow

89

Sources of Project Generation

C-Sat / V-Sat

Short Term / Long Term Organizational Goals (Business and operations)

Service Level

Transaction Quality

Resource Utilization

Engagement Targets client complaints

etc

Six Sigma Project Selection criteria

• Business Filters• contribute to bottom-line

of the organization• Drastic, long term, risk free

Improvement• Solution Not known• No other teams working on

project

• Project Filters• Availability of Data• Measurable• Time bound• Realistic and Attainable

Project Sources and Selection Criteria

90

• Project identification begin not inside the business but outside it• Focus on answering the following questions:

* How can we make the customer more competitive?* What is critical to the customer’s success?

Anything we do that makes the customer more successful inevitablyresults in a financial return for us.

• Kano Model is one of best technique to collect and understand Voice of Customer (VOC)

• Business and operational goals can be analyzed by CTQ Drill down or Quality Function Deployment

Customers Perception

91

Who is a customer?One who is paying for your service or product a person who buys goods or services

Types of customers •External or internal•Lost customers•Prospective customers

Customers

92

What do customer wants?

What do shareholder wants?

Utility:They buy product or service for a needTime : They want it when they need itValue : They pay for it only if they perceive a value

The business must successfully servethe customers “ Wants “ and

Still provide good profits.

What do customer & shareholders want?

customer voice

I need right product,

at right time,

at good price.

Shareholder

voice

I need happy customers

and good profit

93

Performance

Customer Satisfaction

CUSTOMER’S WANTS

•Must Be

•Satisfiers

•Delighters

Must BeThe better I do, the less dissatisfied the customer is.(e.g., airlines get no credit for getting bags to you on-time)

DelightersNo penalty for not doing them However, if you do them, you get bonus points

SatisfiersThe better we do, the happier the customer is (Plane gets to the destination on time)

Kano Model

94

Kano Model : Exercise

Exercise 1:

Classify the following as Must Be, Satisfiers & Delighters

1. Air Conditioner free with every purchase of four wheeler

2. Availability of hot water in 5 star hotel bath room

3. More mileage per liter of fuel

Exercise 2:

1. Identify a Must Be, Satisfier & Delighter from your own process

95

Gather voice of the customer

* Review existing voc data* Decide what to collect* Select tool to collect*Collect data

Voice of Customer : Ways to Capture

• SURVEYS• FOCUS GROUPS• INTERVIEWS• WORD OF MOUTH• COMPLAINTS

96

Surveys :A method of gathering information From a sample representing the population these are comprehensive data driven information vehicles that are useful in capturing customer requirements as well as

measuring performance against those requirements.

Focus groups: in this group you group together similar customers and ask for their opinion on the requirements as well as performance against those requirements

Customer interviews: could be informal or Structured. Informal interviews give good insight into the customer perspective of the product and services and depends on probing open ended questions


97

Word of mouth: the customer feedback comes through direct and different channels. Internal: employee feedback External: reports from known sources

Be a customer yourself : feel the quality of Service yourself


98

Voice Of The Customer

What do we do with VOC input?What do we do with VOC input?

Sample Comments/Data

“I’m Tired Of Having To Call up for this Lousy Product Every Ten Days”

“I simply don’t understand what the Customer Support Professional Talks about ”

“Why Don’t You Guys Get Your Act Together?!”

“The Phone Must Have Rung Ten Times Before I Got An Answer”

“I’m Not Very Happy With Your Service”

99

Translating Customer Needs To Requirements

Voice Of The Customer Key Issue(s) Requirement

I Am Always On Hold Or Transferred To The Wrong Person.

Add Additional Menu Items To Voice System (BAD)

Customer Gets To The Correct Person The First Time (GOOD)

Want To Talk To The Right Person Quickly

I’m Getting My BillAt Different Times Of The Month.

Customer Wants Timely Bill (BAD)

Customer Bill Received Same Day Of Month (GOOD)

Consistent Monthly Bill

Take Too Long To Process The Application.

Customer Wants Fast Loan (BAD)

Customer Receives Approval On Customer Request Date (GOOD)

Speed Up Loan

100

1. Refer to your workbook.

2. You have given a “Voice Of Customer (VOC)”

3. Using these VOC

- Identify key issues customer is facing - Identify the specific requirements which will tackle these issues

Translating Customer Needs (VOCs) To Requirements.

Exercise 1.1

101

Collection of Voice of Customer (VOC)

Step 1: Get the Voice of Customer (VOC) and the importance rating through survey, feedback, market research, etc.

Example: VOC of a BPO company

VOC Rating

Utilization of billable resources should be maximum

Very Important

Abandoned calls should be minimum Important

Right & Complete Resolution Very Important

Talk Time should be reasonable Important

Good Customer Service Skills Important

102

Step 2: Quantify the Customer rating numerically.

VOC Rating

Utilization of billable resources should be maximum 5

Abandoned calls should be minimum 3

Right & Complete Resolution 5

Talk Time should be reasonable 3

Good Customer Service Skills 3

Very Important: 5

Important: 3

Reasonable: 1

Step 3: Get Voice of Stakeholder.

Voice of Stakeholder Rating

Buffer should be minimum Very Important

Reduce loss of login hours Important

Reduce Rework Important

Increase CSat Score Very Important

Reduce Customer Complaints Very Important

103

Step 4: Quantify Stake holder's voice.

Voice of Stakeholder4 Rating

Buffer should be minimum 5

Reduce loss of login hours 3

Reduce Rework 3

Increase CSat Score 5

Reduce Customer Complaints 5

Very Important: 5

Important: 3

Reasonable: 1

104

Step 5: Map the Customer Requirements & Stake Holder Requirements

to Business and Operational Goals.

Requirements Rating QualityCycle Time

TrainingMulti Tasking


5 High High Medium

Abandoned calls should be minimum 3 Medium High Medium

Right & Complete Resolution 5 High Medium

Talk Time should be reasonable 3 High Medium

Good Customer Service Skills 3 High

Buffer should be minimum 5 Medium High

Reduce loss of login hours 3 Medium high

Reduce Rework 3 High Medium

Increase CSat Score 5 High Medium

Reduce Customer Complaints 5 High Medium

105

Step 6: Calculate importance ranking to Business and Operational Goals.

Requirements Rating QualityCycle Time

TrainingMulti Tasking


5 High High Medium

Abandoned calls should be minimum 3 Medium High Medium

Right & Complete Resolution 5 High Medium

Talk Time should be reasonable 3 High Medium

Good Customer Service Skills 3 High

Buffer should be minimum 5 Medium High

Reduce loss of login hours 3 Medium High

Reduce Rework 3 High Medium

Increase CSat Score 5 High Medium

Reduce Customer Complaints 5 High Medium

Rank 124 118 42 55

106

Step 7: Identify Key Process Output Variables or CTQ’s (Ys).

KPOV’s (Ys) Ranking

Quality 124

Cycle Time 118

Identify Six sigma projects to improve these KPOV’s

107


2. Identify Six Sigma projects for the areas identified through VOC

3. Allowable time : ----- Minutes

Six Sigma project identification through VOC

Exercise 1.2 (10 minutes)

108

CTQ (Critical to Quality) & CTQ Tree

Business CTQBusiness CTQ Customer CTQCustomer CTQ

Internal CTQInternal CTQ

Project CTQProject CTQ

The basic reason any process exists for is to satisfy the

requirements of the customer / Stakeholders.

The critical customer satisfaction parameters can be broadly

categorized under Cost, Quality, Delivery, Service, productivity etc which are called as CTQs

CTQ is a CTQ is a Product , Process or Service Product , Process or Service

characteristic characteristic that satisfies a that satisfies a

Customer Requirement Customer Requirement (External & Internal – Business (External & Internal – Business

Owners, all functions )Owners, all functions )

BusinessY

BusinessY

Process Y

Process Y

Project Y

Project Y

109

Business Objective

Voice of customer /

Organizational Goals/ Pain Areas

Identification of Improvement Areas

Customer

Satisfaction

Score

Service Level

Agreement

Transactional

Quality

Development

Cost

Resource

Utilization

Six Sigma Black Belt & Green Belt Projects

Leading to significant Top line improvement with Customer Satisfaction &

Bottom line impact through Revenue generation and cost saving

Business YBusiness Y

Process YProcess Y

Project YProject Y

110

CTQ TREE

A methodology to break the CTQ’s in the over all level to the CTQ’s at sub process level

Example 1:

CTQ Tree for the KPOV: Quality

Quality

Voice Quality

Data Quality

Voice & Accent

Empathy

Culture

Example 2 :

CTQ Tree for the KPOV: Cycle Time

Cycle Time

Handling Time

Waiting Time

111

Examples of Customer CTQs

A Car Purchaser

• Mileage

• Spacious

• Low price/affordable

• High technology

• Loan Facility

A Car Purchaser

• Mileage

• Spacious

• Low price/affordable

• High technology

• Loan Facility

A Prospective Employee

• Good Salary• Location Preference

• Flexible Working Hours

• ESOPs

• Good working place

A Prospective Employee

• Good Salary• Location Preference

• Flexible Working Hours

• ESOPs

• Good working place

• Customer Sat (Ext)

• Customer Sat (Int)

• Service CSat (Ext)

• Service CSat (Int)

• Customer Sat (Ext)

• Customer Sat (Int)

• Service CSat (Ext)

• Service CSat (Int)

Order Management Client

Service Quality

• Yield

• Productivity

• 1st Touch Cycle Time

• Case Res Cycle Time

Service Quality

• Yield

• Productivity

• 1st Touch Cycle Time

• Case Res Cycle Time

112

•Define what is critical (CTQ) to Business Champion, BB•Define your Customer Champion, BB•Explore Customer CTQ Champion, BB•Define Internal CTQ / Critical Business Process Champion, BB

CTQ Drill Down responsibility Matrix

113


2. Draw a CTQ tree for your any one of the project you have selected

3. Allowable time : ----- Minutes

Drawing a CTQ Tree


114

What is QFD?

• QFD begins with Customer. It is also called House of Quality

• QFD links the needs of the customer with design, development, engineering, manufacturing and service functions. It helps organizations seek out both spoken and unspoken needs, translate these into actions and designs, and focus various business functions toward achieving this common goal. QFD empowers organizations to exceed normal expectations and provide a level of unanticipated excitement that generates value.

• The basic idea of QFD is to translate the voice of customer, throughout the marketing, R&D, engineering and manufacturing stages of product development.

Quality Function Deployment (QFD)

115

• It is a structured approach that facilitates the translation of the customers voice into specific requirements.

• These specific requirements are mapped to the design process production process and delivery processes to determine the process and design requirements.

• Quality function deployment allows customers to prioritize their requirements

• Benchmark with competition, help optimize and to attain competitive advantage


116

QFD: When to use ?

• To develop new product or service capability, specially for complex ones.

• For products and services where clarification and prioritization of efforts on key customer wants is needed.

• For developing or refining existing internal systems in order to build or product delivery capability.

• For development of products or services that do not have a clear mapping of the customer requirements and the design attributes.


117

Benefits of QFD

• QFD brings in involvement of all the departments thus improving the communication among them.

• Provides excellent frame work for cross functional deployment of quality cost & delivery.

• Since QFD is a documentation process it helps in reducing mistakes.

• Brings robustness in the product.


118

How to build one?1. The first portion of QFD matrix is the Customer Requirements. All the VOC that have been

translated into tangible requirements form the Customer Requirements part.

2. The second part, on the right hand side of QFD, is the Planning Matrix. It is used to quantify Customers’ requirement priorities and their perception of the performances of existing products. This is done through Importance Weighting of the Customer Requirements.

3. The third part of QFD is the Technical Requirement section. This describes the product or service in the terms of the company and it includes all the measurable characteristics of the product / service that might be related to meet the customers’ requirements. Often an additional row is included to illustrate the direction in change of variables which is supposed to result in improvement in product / Service performance.

4. The fourth part of QFD is Interrelationships section. This is used to translate the customer requirements into the technical characteristics of the product / service. Inter-relationships between each of the Customer requirements and technical characteristics are analyzed. Generally, the level of inter-relationship is shown with the help of symbols denoting a 3 point scale (High, medium, low ). Each level of inter-relationship is assigned a score. Generally a score of 5-3-1 is used to denote High-medium-low-none.


119

5. The fifth part of QFD is the Roof. This is used to denote the areas where the technical requirements characterizing the product or service support or inhibit each other. Where there is a deterioration because of interaction, “-” sign is used. Where there is an improvement in one characteristic because of the other, “+” sign is used.

6. The final component of QFD is the Targets. It summarizes the conclusions drawn formthe matrix and team’s discussion. It consists of three parts: a) Technical Priority b) competitive Benchmarks and c) Targets.

a) Technical Priority signifies the relative importance of each of the technical requirement in meeting the Customers’ specified needs. It is calculated by summing up the products of interrelationships weightings with the overall weighting in the planning matrix.

b) Competitive Benchmarking: Each of the technical requirements identified as important characteristic of the product or service are compared vis-à-vis the existing product and the competitors’ product.

c) Targets: The final outcomes of QFD are the targets. These are a set of target engineering values to be met by the new product or service.


120

Structure

CustomerRequirements

INTER- RELATIONSHIPS

Targets

Technical Requirements

Roof

PlanningMatrix

A I CD M

Techincal Priorities

1

2

3

5

4

6

7


121



Targets


Roof

PlanningMatrix


1

2

3

5

4

6

7

Customer Requirement : Basically focus on desired deliverable/outcomes from the

process

Customer Importance : Rating of Customer requirement on a scale of 1-5

Complete Resolution 5first Time Resolution 5good Customer Service 4Ontime delivery 5Aware of support boundaries 3Quick Response 5Customer Education 3Technical Priorities% of total

Benchmarking all productsCompetitor product ACompetitor product BDesign Targets

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gCustomer Requirement

Technical Requirement

STEP - 1

122



Targets


Roof

PlanningMatrix


1

2

3

5

4

6

7

Technical Requirement : This describes the product or service in the terms of the company and it includes all the measurable characteristics of the product / service that might be related to meet the customers’ requirements ; Basically focus on input parameters which will have direct/indirect impact on deliverable/outcomes of the process;



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Customer Requirement


STEP - 2

123



Targets


Roof

PlanningMatrix


1

2

3

5

4

6

7

Roof : It shows the inter-relationship between two or more inputs Parameters ` + ’ means positive relationship` - ’ means negative relationship



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++

STEP - 3

124



Targets


Roof

PlanningMatrix


1

2

3

5

4

6

7

Inter-Relationship Block : It shows the inter-relationship between inputs parameters and output deliverable` 5 ’ means Strong relationship` 3 ’ means Medium relationship` 1 ’ means Weak relationship` 0 ’ means No relationship

Complete Resolution 5 5 5 1 5 5 1 3first Time Resolution 5 5 5 1 5 5 1 2good Customer Service 4 3 5 1 1 3 5 3Ontime delivery 5 3 3 5 5 1 1 3Aware of support boundaries 3 1 3 1 3 3 1 3Quick Response 5 3 3 5 3 3 3 3Customer Education 3 5 5 1 1 5 3 2Technical Priorities% of total


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STEP - 4

125



Targets


Roof

PlanningMatrix


1

2

3

5

4

6

7

Planning Matrix :

Our Product : Rating of existing product/service on scale of 1-5 against customer requirements

Competitor A/B : Rating of Competitor product/services against our customer requirements

Competitor Rating shall be benchmark rating

Complete Resolution 5 5 5 1 5 5 1 3 4 4first Time Resolution 5 5 5 1 5 5 1 2 4 3good Customer Service 4 3 5 1 1 3 5 3 4 4Ontime delivery 5 3 3 5 5 1 1 3 5 4Aware of support boundaries 3 1 3 1 3 3 1 3 4 4Quick Response 5 3 3 5 3 3 3 3 4 4Customer Education 3 5 5 1 1 5 3 2 4 3Technical Priorities % of total


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STEP - 5

126

Planned Rating : Target rating for customer requirement based on competitor rating

Improvement Factor : Ratio of Planned rating and Our product Rating

Sales Point : Rating of Sales team (sales perception)on ability to sell the product/services based on how well each customer need is met, on scale of 1.1, 1.3 and 1.5

1.1 : Low inter-relationship

1.3 : Medium inter-relationship

1.5 : Strong inter-relationship

Complete Resolution 5 5 5 1 5 5 1 3 4 4 4 1.3 1.3first Time Resolution 5 5 5 1 5 5 1 2 4 3 4 2.0 1.3good Customer Service 4 3 5 1 1 3 5 3 4 4 4 1.3 1.1Ontime delivery 5 3 3 5 5 1 1 3 5 4 5 1.7 1.3Aware of support boundaries 3 1 3 1 3 3 1 3 4 4 4 1.3 1.1Quick Response 5 3 3 5 3 3 3 3 4 4 4 1.3 1.1Customer Education 3 5 5 1 1 5 3 2 4 3 4 2.0 1.3Technical Priorities % of total


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STEP - 5



Targets


Roof

PlanningMatrix


1

2

3

5

4

6

7

127

Complete Resolution 5 5 5 1 5 5 1 3 4 4 4 1.3 1.3 8.7 14.97%first Time Resolution 5 5 5 1 5 5 1 2 4 3 4 2.0 1.3 13.0 22.45%good Customer Service 4 3 5 1 1 3 5 3 4 4 4 1.3 1.1 5.9 10.13%Ontime delivery 5 3 3 5 5 1 1 3 5 4 5 1.7 1.3 10.8 18.71%Aware of support boundaries 3 1 3 1 3 3 1 3 4 4 4 1.3 1.1 4.4 7.60%Quick Response 5 3 3 5 3 3 3 3 4 4 4 1.3 1.1 7.3 12.67%Customer Education 3 5 5 1 1 5 3 2 4 3 4 2.0 1.3 7.8 13.47%Technical Priorities 224 244 131 211 211 112 1133 57.9% of total 20% 22% 12% 19% 19% 10%


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Overall Weighting : Product of Sales Point, Improvement factor and Customer Importance

Technical Priorities : signifies the relative importance of each of the technical requirement in meeting the Customers’ specified needs. It is calculated by summing up the products of interrelationships weightings with the overall weighting in the

planning matrix.

STEP - 6



Targets


Roof

PlanningMatrix


1

2

3

5

4

6

7

5 8.7 43.5

5 13 65

3 5.9 17.7

3 10.8 32.4

1 4.4 4.4

3 7.3 21.9

5 7.8 39

224

128



Targets


Roof

PlanningMatrix


1

2

3

5

4

6

7

Targets : The final outcomes of QFD are the targets. These are a set of target engineering values to be met by the new product or service.

STEP - 7

Complete Resolution 5 5 5 1 5 5 1 3 4 4 4 1.3 1.3 8.7 14.97%first Time Resolution 5 5 5 1 5 5 1 2 4 3 4 2.0 1.3 13.0 22.45%good Customer Service 4 3 5 1 1 3 5 3 4 4 4 1.3 1.1 5.9 10.13%Ontime delivery 5 3 3 5 5 1 1 3 5 4 5 1.7 1.3 10.8 18.71%Aware of support boundaries 3 1 3 1 3 3 1 3 4 4 4 1.3 1.1 4.4 7.60%Quick Response 5 3 3 5 3 3 3 3 4 4 4 1.3 1.1 7.3 12.67%Customer Education 3 5 5 1 1 5 3 2 4 3 4 2.0 1.3 7.8 13.47%Technical Priorities 224 244 131 211 211 112 1133 57.9% of total 20% 22% 12% 19% 19% 10%


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129

4 House QFD

CustomerMeasurements

(HOW’s)

Houseof

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CustomerCustomerHouseHouse

CustomerCustomerHouseHouse

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FunctionFunctionHouseHouse

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Requirements(HOW’s)

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ProcessProcessHouseHouse

ProcessProcessHouseHouse

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ControlControlHouseHouse

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Define Improve Control

130

Points to Remember in QFD

Do’s

Focus on the end-user

Charts as the ends & not the

means

Find reasons to succeed, not

excuses for failure

If there are no “tough spots” the

first time, it probably is not being

done right !

Don’ts

Don’t apply QFD on everything

Avoid too much chart focus

Don’t hurry up & get done

131

A Restaurant Example – Manila Pizza CTQs from VOC (WHAT’s)

– Type of Menu 3– Economic aspect 4– Presentation of staff 3– Type of service 3– Quality of service 4– Quality of Food 5

Measurements on CTQs (HOW’s)

– Varieties available in terms of Chinese, Indian, Continental food– Price – Availability of parking space– Food served at right temperature– Waiting time for food/order– Options to pay ( Cash/Debit card/Credit card)– Cleanliness of cutlery / attendant’s attire– Staff courtesy– Taste of food– Ambience

Importance Rating

132

Type of Menu 3 5 3 3 1 5 1 3 4 4 4 1.3 1.3 5.2 13.10%

Economic aspect 4 3 5 1 5 5 3 2 4 3 4 2.0 1.3 10.4 26.20%

Presentation of staff 3 1 3 1 1 1 5 3 4 4 4 1.3 1.1 4.4 11.08%

Type of service 3 3 3 5 5 1 1 3 5 4 5 1.7 1.3 6.5 16.37%

Quality of service 4 1 3 5 5 3 1 3 4 4 4 1.3 1.1 5.9 14.78%

Quality of Food 5 3 3 1 1 5 1 3 4 4 4 1.3 1.1 7.3 18.47%Technical Priorities 109 140 100 131 143 78 700 39.7% of total 16% 20% 14% 19% 20% 11%

Benchmarking all products 3 5 10 mins3 opts 5 5Competitor product A 3 4 USD15mins2 opts 4 5Competitor product B 4 6USD10mins3 opts 5 3

Design Targets 3 V

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Capturing Customer

Requirement along with importance

rating

Define

133

Taste as per expectation 5 5 5 5 1 5 5 3 5 4 5 1.7 1.3 10.8 20.79%

Temperature of Food 4 5 1 3 5 1 1 3 4 3 4 1.3 1.3 6.9 13.31%

Add-on over ordered food 1 3 3 1 1 5 5 3 4 5 5 1.7 1.1 1.8 3.52%

Effect on Health 5 3 3 1 1 1 5 1 5 4 5 5.0 1.3 32.5 62.38%Technical Priorities 192 164 109 80 103 233 881 52.1% of total 22% 19% 12% 9% 12% 26%

Benchmarking all products min 4yrsPG 5 5 5 5Competitor product A 5yrs PG 5 3 4 3Competitor product B 8yrs PG 5 5 5 5

Design Targets 5yrs

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Improve

134

Freshness of Vegetable used5 5 5 1 1 3 4 4 4 4 1.0 1.3 6.5 28.77%

Quality of Food grain used 4 1 5 3 3 1 5 4 3 4 0.8 1.3 4.2 18.41%

Storage System 1 5 1 1 5 5 4 4 4 4 1.0 1.1 1.1 4.87%

FIFO 5 5 1 1 1 5 3 5 4 5 1.7 1.3 10.8 47.95%Technical Priorities 96 65 31 35 83 311 22.6% of total 31% 21% 10% 11% 27%

Benchmarking all products SOP PG Cert. 5 5Competitor product A SOP PG Cert. 5 5Competitor product B SOP PG Cert. 5 5

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+Control

135


2. Prepare the first two houses of the QFD for a given example

Preparing the first two houses of the QFD


136

Brainstorming & Multi-voting Example

• Below table illustrates the number of group members & total ideas generated

• Now each member gives votes to ideas (maximum one vote to each idea) & below is the vote distribution for ideas

• Top ideas whose vote count adds upto 32 are as below (30 is not possible), there are 7 such ideas

137

Brainstorming & Multi-voting Example

• Take these 7 ideas for further round of multi-voting

• Give each member 4 votes (round off 50% of 7 to next higher integer) & ask them to distribute these 4 votes among these 7 ideas. Below could be the distribution in this fresh round of voting

• Take top ideas whose vote count adds upto 9

• This list is manageable

138

Points to Remember in Brainstorming

• All ideas are important, don’t out rightly reject any idea

• Participation should be ensured from all team members

• To ensure this, project teams could use the round-robin method of idea generation

• It’s advised to use the Black Belt as the facilitator here

139


2. Do a multi-voting exercise for a given example

Multi-voting


140

A Right Project Selection is key to success

Right Project Selection• Selecting the right project

can have a tremendous effect on your business. If done properly,

• Processes will function more efficiently in 3 to 6 months, employees will feel satisfied and

• Appreciated for making business improvements and ultimately stakeholders will see the benefit

Wrong Project Selection• If project selection is

done improperly, a project may be selected that doesn't have the full business buy-in, project roadblocks may not be removed due to other business priorities, the team may feel Ineffective and the end result may be less than ideal. No one wins in this situation

141

Once the potential projects are identified next step is to verify, whether these are six sigma project or not ?

There are two type of filters to qualify the potential projects as six sigma projects. They are 1) Company filters and 2) Six Sigma Filters

Six Sigma Project Selection Filters

1. Company Filters and 2. Six Sigma Filters

142

1. Company Filters

Why do it? Drop

Why do it? Drop

Aligned withcompany Strategy?

Aligned withcompany Strategy?

No

Does project lead USD--- min. per annum?

Does project lead USD--- min. per annum?

No

Scope is too large. Consider making Multiple projects.

Scope is too large. Consider making Multiple projects.

Can it be completed in4 to 6 months?

Can it be completed in4 to 6 months?

Yes

Yes

1) Is it a severe pain in the process 2) Is documental proof available to prove pain 3) Is the pain sensed by process experts

1) Is it a severe pain in the process 2) Is documental proof available to prove pain 3) Is the pain sensed by process experts

No

Move to Six Sigma Filters

Yes (all 3)Yes

No

143

Implement the solution

Implement the solution

Is the solutionalready known?

Is the solutionalready known?

yes

No

yes

no

Implement a datacollection plan

Implement a datacollection plan

Is needed data available to quantify the

problem?

Is needed data available to quantify the

problem?

No

Derive and implement the solution

Derive and implement the solution

Is the rootcause known?

Is the rootcause known?

Yes

Implement the other team’s solution

Implement the other team’s solution

Is someone elseworking on the problem?

Is someone elseworking on the problem?

Yes

Yes

No

Six Sigma Potential Project

No

2. Six Sigma Filters

144

Types of Projects

1. Projects cutting across processes focusing on CTQ.

2. Projects cutting across CTQ focused on process.

3. Projects focusing on a specific CTQ for a process.

SS Project Execution Process MapVoice Of Customer & Pain Areas

MBB

BB

GB & Team

Client & Top Management

Recognition of Improvement opportunity

Initial Financial Validation

Champion

Project Evaluation &Methodology

selection (DMAIC / DMADV)

Problem Definition

Project Agreement& Target setting

Finance Controller

Project Kick Off

(MBB & Champion are secondary resp.)

Project Execution ( DMAIC / DMADV )(MBB & Champion

are sponsor )

Project Validation,

Closure and sign-

off by champion,MBB and Financial controller

Project Charter

(Champion / sponsor)

Project Review at Pre-defined Frequency

(MBB & Champion)

Integration & Deliver

Final Financial Validation

146

Improvement Project Execution

Business Process

Improvement

Business Process

Improvement

REIS Project

DMAIC Project

DMADV Project

1- MBB 1- BB, 1- GB4 - PA

1- BB, 1- GB4 - PA

1- GB4 - PA

Project 1

Project 2

Project 3

Project 1

Project 2

Project 3

Project 1

Project 2

Project 3

147

Six Sigma

DMAIC Methodology

148

Overview of DMAIC Methodology

DMAIC Vs DMADV

DMAIC Methodology– Define– Measure– Analyze– Improve and – Control

149

DMAIC Vs DMADV

DMAIC

DMADV

Process Management

IMPROVEMENT PROCESS

(DMAIC)

Define, Measure, Analyse, Improve and Control

Improve processes, products, services, and organisation to 6 Sigma quality

CREATION PROCESS

(DMADV)

Define, Measure, Analyse, Design and Validate

Create new processes, products, services, and plants to 6 Sigma quality.

It is also Called DFSS

PROCESS MANAGEMENT

Leverage and sustain the gains achieved by improvement and creation with BPMS, QMS, COPC etc

150

Step 5: CONTROL

Step 2: MEASURE

Step 1: DEFINE

Step 4: IMPROVE

Step 3:ANALYZE

DMAIC

DMAIC

Define Define project goals & customer (internal & external) deliverables

Measure Measure the process to determine current performance

Analyze Analyze and determine the root cause of the defects

Improve Improve the process by eliminating defects

Control Control future performance

DMAIC Methodology

151

DefineObjective: is to define the problem in a clear manner and in a way that is related to an internal or external customer.

• Fully trained team, committed to work on improvement project.• Customers identified and defined (CTQ’s)• Project charter and Process map

Deliverables:

Check points:• Trained team

Customers (and CTQ’s)• Project Charter• Business process mapping (SIPOC)• Process Map

152

Objectiveto measure what you care about most, making certain that your measurementapproach is sound and not based on questionable formulas or data.

Deliverables:• Key measures identification, data collection plan, data on process variation

performance baseline, sigma level calculation.

Check points:• Identification of Key measures - Defining high impact defects• Data Collection Plan - Data collection• Measurement system analysis• Process Variation • Long term and short term variability accounted for.• Performance Baseline/Sigma Calculation• Measure baseline process performance (capability, yield, sigma level).

Measure

153

Deliverables:• Data and process analysis, root cause analysis, quantifying the

gap/opportunity.

Check points:• Data and Process Analysis – Identification of gaps between current• performance and the goal performance• Root Cause Analysis- Verify and quantify the root causes of variation• Quantifying the Gap/Opportunity - Determine the performance gap.

Analyze

Objectiveis to look for the critical root causes of the variability by applying statistical tools to determine what factors are contributing to the problem.

154

Objectiveis to determine and confirm the optimal solution.

Deliverables:• Generate (and test) possible solutions, select the best

solutions, design implementation plan

Check points:• Generating (and Testing) Possible Solutions• Selecting the best Solution (s) • Designing Implementation Plan

Improve

155

Objectiveis to be sure the quality improvements remain in effect and the problem

does not recur.

Deliverables:• Documentation and implementation of monitoring plan, standardized

process, documented procedures, response plan established and deployed, transfer of ownership (project closure).

Check points:• Monitoring Plan• Process Standardization • Documented Procedures • Transfer of Ownership (Project Closure)

Control

156

Define Phase

157

1.0 Define

Opportunities

Define

Main ActivitiesStep 1: • Identify customer & their care about. • Convert their needs in to Critical to Satisfaction (CTS) i.e.

CTQ – Critical to quality, CTC – Critical to Cost & CTD Critical to Delivery.

Step 2: • Develop Project Charter reporting improvement

opportunity and effective project team

Main Tools used Define Phase Outcomes

• List of Project CTQs• QFD/CTQ Tree• Signed off Project

charter • Team Charter• CCRs• SIPOC• Macro Level Process

Map

VOC

Project Timeline

Project team

Project Scope

Goal Statement

Business Case

Problem Statement

Project Charter

Baseline

Target

Gap Analysis

Process Map

S I O CProcess

SIPOC

Define

158

Define Phase Topics

Project Charter

Team Charter

SIPOC

Process Mapping– Top Down Model– Cross Functional Process Mapping &– ICOM model

159

Project Charter

160

What Is A Project Charter?

• A project charter is a document that provides purpose and goals for an improvement team

Six Major Elements of a Project Charter1. Business Case

Explanation Of Why To Do This Project

2. Problem And Goal StatementsDescription Of The Problem/Opportunity And objective In Clear, Concise And Measurable Terms

3. Project ScopeDefined project beginning and end points

4. MilestonesKey Steps And Dates To Achieve Goal

5. RolesPeople, Expectations, Responsibilities

6. Financial Impact Savings, impact on bottom-line

Project Charter

161

Business Case

Business Case Development• The business case describes the benefit for undertaking a

project. The business case addresses the following questions:– Does this project align with other business initiatives?– What is the focus for the project team?

• What impacts will this project have on other business units and employees?

• What benefits will be derived from this project?

• Has the value of the benefits been quantified?

162

Problem Statement

Description Of The “Pain”

What Is Wrong Or Not Meeting Our Customer’s Needs?• Who is the customer of the process ?• What is the process ?

When And Where Do The Problems Occur?

How Big Is The Problem?

What’s The Impact Of The Problem?• If I let it be what will happen ?• If I reduce it what will happen ?• If I increase it what will happen ?

163

Problem Statement Example

Poor Example:Weak Problem statementOur Fatal accuracy score is at 80% against the client target of 95% and we Need to improve it

Improved Example:Fatal accuracy score (what) this quarter (when) has been observed to be at 80% for the last 4 months (extent) against the client target 95% which has significant impact on customer satisfaction. (impact)

164

The Problem Statement

Key Considerations/Potential Pitfalls

• Is The Problem Based On Observation (Fact) or Assumption (Guess)?

• Does The Problem Statement Prejudge A Root Cause?

• Can Data Be Collected By The Team To Verify and Analyze The Problem?

• Is The Problem Statement Too Narrowly or Broadly Defined?

• Is A Solution Included In The Statement?

• Would Customers Be Happy If They Knew We Were Working On This?

165

The Goal Statement

Project Objective

• Definition of The Improvement The Team Is Seeking To Accomplish

• Starts With …. Reduce, Eliminate, Control, Increase.

• Tends To Start Broadly – Eventually Should Include Measurable Target And Completion Date

• Must Not Assign Blame, Presume Cause, Or Prescribe Solution!

166

SMART Problem And Goal Statements

Specific

Measurable

Attainable

Relevant

Time Bound

167

Goal Statement Example

Poor Example:Put in place a Transaction monitoring evaluation system to increase the fatal accuracy.

Improved Example:Increase the fatal accuracy from the existing 80% to 95% by the end of first quarter 2005.

168

Project Scope

• What Process Will The Team Focus On?

• What Are The Boundaries Of The Process We Are To Improve? Start Point? Stop Point?

• What Resources Are Available To The Team?

• What (If Anything) Is Out Of Bounds For The Team?

• What (If Any) Constraints Must The Team Work Under?

• What Is The Time Commitment Expected Of Team Members? What Will Happen To Our “Regular Jobs” While We Are Doing The Project?

169

1. Identify the customer • Who receives the process output?• May be an internal or external customer

2. Define customer’s expectations and needs• Ask the customer• Think like the customer• Rank or prioritize the expectations

3. Clearly specify your deliverables tied to those expectations• What are the process outputs?• Tangible and intangible deliverables• Rank or prioritize the deliverables• Rank your confidence in meeting each deliverable

4. Identify CTQ’s for those deliverables• What are the specific, measurable attributes that are most critical in

the deliverables• Select those that have the greatest impact on customer satisfaction

8 Steps to Scope a Project

170

5. Map your process• The process of producing the deliverables• The process as it is working prior to the project• If you are delivering something, there is a process, even if it has not

been formalized6. Determine where in the process the CTQ’s can be most seriously

affected• Use a detailed flowchart• Estimate which steps contain the most variability

7. Evaluate which CTQ’s have the greatest opportunity for improvement• Consider available resources• Compare variation in the processes with the various CTQ’s• Emphasize process steps which are under the control of the team

conducting the project8. Define the project to improve the CTQ’s you have selected

• Define the defect to be attacked

8 Steps to Scope a Project

171

Importance of Scoping

• Poor/improper scoping may result in following:

– Team loses interest in the project

– Project becomes difficult to implement

– Even after implementation, the desired/significant benefits are not

seen

– Team focuses on trivial pain areas, and missing out the vital ones

– Process selected is too broad to handle or too small to realise

breakthrough improvements.

172

Milestones

• A preliminary High Level Project Plan with dates

• Tied to phases of DMAIC process

• Should be aggressive (don’t miss “ window of opportunity “)

• Should be realistic ( don’t force yourself into corrective rather than preventative solutions)

Week : 1 2 3 4

Review Charter with Champion X

Collect VOC X X

Complete Map X X

Validate Map X

Collect Data X

1-May 7-May 16-Jun 23-Jun 7-Jul

Define

Measure

Analyze

Improve

Control

173

Take Aways –Project Charter

• Key elements of a charter include: Business Case, Problem and Goal • Statements, Project Scope, Milestones, and Roles.

• The team charter is a vital part of the project’s overall success. It communicates the project direction to all members of the team.

• A Problem Statement describes what is wrong while a Goal Statement defines the improvement objective.

• A charter clarifies what is expected of the project team, keeps the team focused, keeps the team aligned with organizational priorities, and transfers the project from the champion to the improvement team

174

Project Charter

Per

iod

Goal statement:Goal statement:

Problem Statement :Problem Statement :

Responses

Black Belt

Project Leader

Project Sponsor

Name Sign

ME

MB

ER

S

Business caseBusiness case

Revenue Enhancement Expense Reduction

Loss Avoidance

Costs

Start End

Basic ScheduleBasic Schedule

In-scope :In-scope : Measurable Goals :Measurable Goals :

Unit: Percentage

Current Target Current Target

Estimated Saving Expected, ( 1 Fin. Year)Estimated Saving Expected, ( 1 Fin. Year)

50,000 USD

Control 06.05.05

Improve 21.04.05

Analyze 07.04.05

Measure 25.03.05

Define 16.03.05

Out-scope :Out-scope :

Reduction of rework from 38% to 5%

xxxxxx

Xxxx (YB)

xxxxxxxxx

The rework percentage in this quarter (Apr’05 to June’05) has been observed to be at 38% based on the system reports which is leading to over stretching of PE to meet the production targets.

All exiting sub – processes in the process 38 5

jkafkj

jkjklajdxcv

1 Mar 05 30 May 05

Rework is one of the main concern area in our xxx process. Due to rework, most of our PE are over stretching their working hours. Our quality percentage is being maintained below the SLA.

The client also concern about the issue and suggested to take action with in 3 months to improve the same.

Failure of the same may lead to employee attrition and loss of business.

Rework reduction

2.5 4.2Any new sub-process to be adding with in the project period

Example

IMPCHG/QLTY/TMPL/6111 Version 1.0, Copy if printed

MBB

175

Scope the Project GB, BB, ChampionDevelop Team Charter GB, BB

Project Scoping responsibility Matrix

176


2. Define in-scope & out-scope given project example

Project Scoping


177

Team Charter

178

Team Charter explains following •How do you want the champion to work with the team?

• Is the team’s role to implement or recommend?

• When must the team go to the champion for approval? What authority does the team have to act independently?

• What and how do you want to inform the champion about the team’s progress?

• What is the role of the team leader and the team coach?

• Are the right members on the team? Functionally? Hierarchically?

Team Roles Refresh

Sponsor • Review the project progress once a month• Provide/modify direction/alignment with

business realities• Provide resources required from time to time • Remove Roadblocks

Black Belt/MBB • Provide content knowledge on Six Sigma

tools to the team

Project Leader • Keep the team focused. Arrange logistics

and team meetings and raise issues with Sponsor

Team Member (s) • Participate in meetings, collect data, do

analysis using Quality tools, provide subject matter expertise related to process

Team Charter

179


2. Practice writing problem and goal statements

Team Charter – Breakout Activity


181

SIPOC

182

SIPOCSIPOC:• A tool to identify all relevant elements of a process

• Helps to understand a complex process better

•Graphic display of steps, events and operations that constitute a process

S - Suppliers

I - Inputs

P - Process

O - Outputs

C - Customers

183

Suppliers– People who provide input to the process

Inputs– Information, material etc., goes into the process from some other group of

people (supplier)

Process – Process is a series of activities that takes an input, adds value to it and

produces an output for a customer

Outputs

– Output of a process creating a product or service that meets a customer need

Customers

– Users of the output

SIPOC

184

S I P O C

Requirements Requirements

Suppliers Inputs Process Outputs Customers

The Key Quality and Delivery Requirements

Placed On Your Suppliers.

Measures That Are Internal To Your

Process. They IncludeQuality and Delivery

Measures Important ToYour Internal Customers

As Well as Waste andCycle Time Measures.

Output MeasuresAre Measures UsedTo Determine How

Well Customer NeedsAnd Requirements

Are Met.

Input Measures Process Measures Output Measures

FlowThinking

Information Flow & Measures

185

SIPOC: Uses

• To know who supplies input to the process

• To know what are the inputs to the process

• To know step by step flow of process

• To know the outputs of process

• To know the customer of a process

186

Steps to create SIPOC1. Attaining a full understanding of all the steps of a process. This is done by

looking at processes from customer’s point of view.

2. Clearly define the process start and end boundaries

3. Brainstorm list of all process steps. Go on the floor, walk through the process and interview people working on the process as needed.

4. Recorded process steps using a sticky-note method. In this method each step in the process is recorded on a sticky-note and built in front of the individual completing the work.

5. Discuss, review & modify process step sequence to agree on “As Is” process map.

6. Add suppliers, inputs, outputs, customers

7. Add Input, Process, & Output measures

187

SIPOC: Format

Supplier Inputs Process Output Customer

A

B

C

X1

X2

X3

Y1

Y2

Y3

Alpha

Beta

Gamma

188

ProcessInputSupplier

Contract Record

SIPOC LEVEL I Contract Management : Contract Publishing

CustomerOutput

ABC Bank

3.Assign Contract Number

Electronic Documents

Global Sourcing-ABC Retained

Job Aid: Four-Eye principle,

Commodity Classification, Template from Emptoris, A-signatory list

Scanning Team

Vendor folder in shared drive

Vendor Number from SAP, Contract

template from Emptoris

Contract Number.xls,

Contract Information Sheet

6. Review Contract record

1.Receive scanned images

2.Sanity Check

4. Upload Data

5.Create Vendor Folder in the shared drive

7.Publish Contract

8.Update Contract Tracking Sheet

P2P Arthur

SIPOC: Example 1: Contract Management : Contract Publishing

189


Resolution Document

SIPOC LEVEL I Contract Management : Contract Enquiry

CustomerOutput

ABC Bank

1. Receive Query and review Query ReceivedGlobal Sourcing-

ABC Retained

Job Aid

P2P Contract Management

Onshore Team Contract Number.xls, Authorization

Sheet

3. Verify Authorization

4. Check Availability

6. Communicate to the user

P2P Arthur

Communication to the Enquirer

5. Get information/document

2. Update Query tracker sheet

7. Close query in the tracker

Contract Management : Contract Enquiry

190


Review Record

SIPOC LEVEL I Contract Management : Contract Review

CustomerOutput

ABC Bank2. Check Contract Compliance

Electronic Documents

Global Sourcing-ABC Retained

Job Aid

P2P Contract Management

Onshore Team

3. Validate Contract Data

4. Check terms & conditions Vs ABC standard terms & conditions

Emptoris

Contract Number.xls

P2P Arthur

1. View & analyze electronic document

5. Create Contract Summary records

Contract Management : Contract Review

191

Escalate?

S I P O C

Agents Team

ManagersACD

IncomingCall

CompletedCall

PerformanceReports

Client

Caller

Input Measures Process Measures Output Measures

Call Opening

Confirmation

Verification

Assistance

Response

Closing

After Call Work

InterpreterSpanish?Y

Y

N

InternalCustomers

Auditors IVR

Number of agents, Managers, Auditors,

Certified / On training

Experience /Vintage

Process Knowledge

Listening / Accent, speaking & keyboarding Skills

App

Number of ACD calls, Pattern, Q

Spanish?

Nature of Query

App

Response

Uptime of ACD, Alltel

Noise?

No of lines per step, Time per step, Time holding for response, Call Accuracy, Quality, Cycle Time

Speed of answer

Calls abandoned & Time

Quality

Talk Time

Wrap Time

Hold time

Resolved ?

Satisfaction score

ASA

Agent productivity

Talk Time

Wrap Time

% Abandoned

Aband Time

AHT,ACW

SIPOC: Example 2:Call Handling

192


2. Draw the SIPOC for your process

SIPOC


193

Process Mapping

194

Process

PROCESS

INPUT

OUTPUT

RESOURCES

MEASURES

SLAs

“Process is a series of activities that takes an input, adds value to it and produces an output for a customer ’’

195

Process Mapping Definition

Process mapping is a graphical display of

steps, events and operations that constitute a

process.

196

Why Process Mapping?

• Validates our understanding of the process with the client (The way work gets done)

• Identifies hidden process steps

• Helps to understand weak links in the process

• Eliminate the ambiguity & brings standardization

• Helps to identify data collection point during measure phase

• Imparts training to others

• To design the “ to be” process

197

Flowchart Vs. Process Map

Flowchart Process Map

May only shows the connected steps in a process

Goes further showing who is doing what, with whom, when, for how long and with what documents.

It shows how operational decisions are made

198

Process Map Symbols

Symbol MeaningStart or End of Process

Activity or Process Step

Decision or Inspection Point

Delay

Connector

Document

Data

Direction of Flow

199

Basic Types of Process Maps

• Linear Process Maps

It can be used when

– The process is not very complicated – Micro level (detailed) map is “dropped down”

from the macro (high level) process steps.

• Cross Functional Process Maps (also known as “Swim Lane process map”)

It can be used when– The process complicated consists of several

activities between different departments or groups.

Call Opening

Confirmation

Verification

Assistance

Response

Closing

Dept 1

Step 4

Step 1

Step 3

Dept 2

Dept 3

Dept 4

Step 1

200

“As Is” Process Map in define phase

• Objective of “As Is” process map in define phase is to identify hidden process steps in process.

• It helps to understand weak links in the process

• It also helps to identify non value adding process steps in the process

201

Steps to create “As Is” Linear Process map

1. Attaining a full understanding of all the steps of a process. This is done by looking at processes from customer’s point of view.



4. Record individual process steps on the sticky-note / post it.


202

Analyze “As Is” Linear Process map

Next Step : Analysis of As Is process map

1. Analyze process map to identify NVA’s.(identify unnecessary approvals, isolating rework, removing duplicate forms and investigating decisions leading to no results)

2. Identify data collection & decision making points.

3. Compare with “To Be” process map to identify the gaps.

203

“As Is” Linear Process map : Example 1

InterpreterSpanish?

Call Opening

Confirmation

Verification

Assistance

Response

Assistance

Escalate?

Call closing

After call work

A

A

Yes

Yes

No

No

Collect data on % Spanish calls

Collect data on % escalated calls

Collect data on % accuracy

On holdNVA

NVA = Non value added activity

204

“As Is” Linear Process map : Example 2

Business UnitBusiness Unit

Business ProcessBusiness Process

Identify Critical ProcessIdentify Critical Process

Study ProcessStudy Process

Identify CustomersIdentify Customers



A

Yes

No

Does

ProcessExists?

B

Does Gap Exists?

Are Metrics

in place?Define Metrics &

implement data collection plan



A

Validate the MetricsValidate the Metrics

Process Base liningProcess Base lining

Does

Process exists?

No

NoExplore need of

performance excellence

Explore need of performance

excellence


Re Engineering


Re Engineering



Quick wins –Fix it

Quick wins –Fix it

Yes

Yes

B

205

Steps to create “As Is” cross functional map1. Attaining a full understanding of all the steps of a process. This is done by

looking at processes from customer’s point of view.

2. List all departments or groups involved in the process. (Record in left column of swim lanes)



5. Record individual process steps on the sticky-note / post it.


206

Analyze “As Is” Cross functional Process map

Next Step : Analysis of As Is cross functional process map

1. Analyze process map to identify NVA’s.(identify unnecessary approvals, isolating rework, removing duplicate forms and investigating decisions leading to no results)

2. Identify data collection & decision making points.

3. Compare with “To Be” process map to identify the gaps.

4. Identify the cross functional complexities involved in the process.

207

Cross Functional Process Mapping

Function 1

Function 2

Function 3

Function 4

Step 2

Step 3

Step4

Step 5

Step 6 Step 7

Step 8 Step 9Step 1

208

Example 1Voice Of Customer & Pain Areas

MBB

BB

GB & Team

Client & Top Management

Recognition of Improvement opportunity

Initial Financial Validation

Project Sponsor

Project Evaluation &Methodology

selection (DMAIC / DMADV)

Problem Definition

Project Agreement& Target setting

Finance Controller

Project Kick Off

(MBB & Champion are secondary resp.)

Project Execution ( DMAIC / DMADV )(MBB & Champion

are sponsoror.)

Project Validation,

Closure and sign-

off by champion,MBB and Financial controller

Project Charter

(Champion sponsoror.)

Project Review at Pre-defined Frequency

(MBB & Champion)

Integration in Deliver

209

Take Photograph

Return home

Place film in Pre paid envelope Send to Processor

Send to Photographer

1 Day Delay

Process Negative

Produce Prints

1 hr

Inspect

OK ?

Package for postingY

NAwait Photograph

Inspect Photograph

FramePhotographto be framed

Y

NStore in album

3 days

3 days

Photographer Postal System Processor

The flowchart below depicts the activities involved from taking a photo to developing, framing and storing the result.

Example 2 : Photo Process

210

Imports Supplier Finance Forwarder / Clearing Agent

Customs

Planning

Plan & Schedule

Obtain price &Prepare P.O

Approve P.O

Fax / Mail copy to supplier

Prepare Order Ack &Send to Company

Check paymentterms

Request for LC

Open LC & send Copy to Imports

Verify LC

OK

Not OK

LC

Inform & send Copy to supplier

Verify correctnessOf LC & Inform

Follow-up material

A

Direct Payment or Site Draft

Get Options detailsfrom Logistics if reqd.

Example3: Import process

211


Customs

B

A

Arrange material Collect material

Arrange shipment &Details to company/Supplier

Send documents to companyAnd Supplier

Send documentsTo bank

Prepare checklist forInternal circulation

Arrange InsuranceAdvise finance for

Insurance

Send to clearing agentFor preparation of BE

Receive info from Bank

Send to Imports forcertification

Check & certify docs

Arrange for payment &Retire documents

Send documents to Imports

Send documents toClearing agent

Prepare Bill of Entry &Files with Customs


212


Customs

B

Inspect & ascertainCustoms Duty

Inform Company - Imports

Prepare RFC to finance customs duty

Prepare payment and Send to clearing agent

Deposit amount inCustoms & clear the goods

Inform Company-Imports for Collection of material

Inform W.H to collectThe material

W.H – Collect the material& prepare GR

Move toStores.


213

Other Types of Process Maps

• Top Down Model • ICOM

214

Macro to Micro (M2M) Process Map

Step 1 Step 2 Step 3 Step 4

T h e P r o c e s s

The Sub-Process

The Micro-Process

It can be used when - Team wants to pay attention to the important process steps in detail.

215

Example 1 : Quotation Sub-Processes

Research Create Proposal Refine Proposal Accept Quote

PreviousAgreement

Look up

PreviousPurchase

HistoryLook up

PreviousQuote HistoryLook up

Cross geo customer quotingLook up

Guided selling tool maps requirements

to offerings

Create initial proposal with all relevant options

Profile defined

Validate customer and proposal pre-credit check credit

rating lookup

Refine proposal(iterative)

Create final proposal

Escalate concession

request

Concession process

Quote Acceptance

Optional indirect

Optional

216

Other process mapping models (additional information)

ICOM

ProcessInput Output

Control

Mechanism

Input: The material / information which enters in to the system with specified process capability

Control : The systems, policies which control or governs the process.

Output : The value added material / information through the process and entitled for next process

Mechanism : The entity which may consume or act as resource for processing. Eg. Machine, computer, Agent

fsdafdf

Single Process

High Level Process Or Multiple processes

217

ICOM Model (additional information)

Level 1Basic processstructure of the business

Level 2

Level 3

1.1

1.2

1.3

1.3.1

1.3.2

1.3.3

Business/Enterprise

Sub-process

Activities withinSub-process

1

Level 0

IncreasingIncreasinglevel level of detailof detail

It can be used when - Team want to map processes hierarchically

218

ICOM Model – Example (Process : Call Handling )

Soft skillsAgent’s training

Server, SoftwareAgent’s training &Process Knowledge

Peripheral

Agent press Button to

attend call

Customer Call

Peripheral

SOPSLA

Agent Verifies

Caller & authorization

Connected Call

IdentifiedGenuine

caller

Rules of verificationData base

Server, SoftwareAgent training

Identification Of Type of call

Obtaining theRequirement

Of caller

IdentifiedType of call

Rules of verificationData base

Server, SoftwareAgent training

Searching and processing

of information

Agent provides information

to caller

Agent press to button to give

access to attend next call

Agent does after Call work (ACW)

Agent check for the Caller

satisfaction

Termination of call

Need to verify from supervisor?

Hold call and Obtain information

from supervisor

Caller’srequirement

Processedinformation

Information as requested

by caller

Answeredcall

Satisfiedcaller

Completedcall

CompletedACW

Agentready

to takenext call

Yes

No

Soft skills

Database

DatabaseSOP

Server, SoftwareAgent’s training &Process Knowledge

Soft skills

Database

Peripheral

SOPSLA

SOPSLA

SOPSLA

219


2. Draw the process map for your process

Process Mapping


220

Measure Phase

221

Measure

Main ActivitiesStep 3: • Take the snapshot of the process, how the process

performing currently & fix the baseline. Step 4 : • Validate the measurement system from which we

collect the data.

Tools used

Measure Phase Outcomes• Operational

Definitions• Measurement System

Analysis• Data Collection

Formats and Plans• Process Baseline • capability• Specification limits, • target, defect

definition for Project Y(s)

Benchmarking

Gage R&RData Collection Plan

Process Map

Baseline and target setting

PreparationPreparation FMEA ProcessFMEA Process ImprovementImprovementPreparationPreparation FMEA ProcessFMEA Process ImprovementImprovement

1. Select Process Team

2. Develop Process Map & Identify Process Steps

3. List Key Process Outputs To Satisfy Internal And External Customer Requirements

4. List Key Process Inputs For Each Process Step

5. Define Matrix Relating Product Outputs To Process Variables

6. Rank Inputs According To Importance

7. List Ways Process Inputs Can Vary (Causes) and identify associated Failure Modes and Effects

8. List Other Causes (Sources of Variability) And Associated FM&Es

9. Assign Severity, Occurrence And Detection Rating To Each Cause

10. Calculate Risk Priority Number (RPN) For Each Potential Failure Mode Scenario

11. Determine Recommended Actions To Reduce RPNs

12. Establish Timeframes For Corrective Actions

13. Create “Waterfall” Graph To Forecast Risk

Reductions

14. Take Appropriate Actions

15. Re-calculate All RPNs

16. Put controls into place

Process or Product Name:

Responsible:

Process Step/Part Number Potential Failure Mode Potential Failure Effects

SEV Potential Causes

OCC Current Controls

DET

RPN

Actions Recommended Resp.

Failure Modes and Effects Analysis (FMEA)

FMEA

222

Data Collection Plan– Operational Definition– Develop Measurement Plan– Data Collection– Data Display and evaluation of Data

Fundamentals of Minitab

Basic Statistics– Measures of Central Tendency– Measures of Dispersion– Probability Distribution

Measure Phase Topics

223

Measure Phase Topics

Gage R&R– Gage R&R for Continuous Data– Gage R&R for Attribute Data

Process Capability

Process Sigma Level Calculations

224

If we can’t accurately measure something

We don’t know enough about it

We can’t control it

We are at the mercy of chance!!!

Why to Measure ?

225

When you measure what you are speaking about and express in numbers, you know something about it.

Scientific Explanation : Very little progress is possible in any field of investigation without the ability to measure. The progress of measurement is in fact the progress of science !

Non Scientific Explanation : If you can not measure, just forget it ! It will be a sheer waste of time.

Without data you are just a loud mouth with an opinion ..

Science of Six Sigma

226

Data Collection Plan

Foundation of six sigma is Data based decision making, Data drives decisions and actions !!!

227

Data are measurements or observations we record and use to understand, characterize, optimize or control something such as process.

What is data

228

Knowledge is Power

Knowledge is not based on opinion, rather it is derived from facts & data.

In order to efficiently collect the data & effectively analyze it,to extract the maximum knowledge available, one must rely

On statistical techniques.

229

Use of Statistics

Data

Statistics convert

to

Usable Information

230

Decide objective

Step 1Operational definition

Step 2Develop Measurement Plan

Step 3Data Collection

Step 4 Data display

Evaluation of Data

• What, How, by Whom the measurement will be done

• Define a Metric

• Stick to procedure/plan

• By Plotting (Graphing) the Data, the result can be easily understood.

Data Collection Plan

231

An operational definition is a precise description of the specific criteria used for the measures (the what), the methodology to collect the data (the how), the amount of data to collect (how much) and who has the responsibility to measure the data

When developing an operational definition, it is important for the team to fully understand and agree that the DEFINITION reflects exactly what information the team is attempting to gather on the process.

Clarity is more important when developing and selecting the measures that will be used to determine the SIGMA PERFORMANCE of the process.

Operational Definition

232

Example :

Operational definitions may determine whether, a team is required to count all the defects on an invoice (required to calculate defects per million opportunities)

or

the total number of defective invoices (any invoice with any defect) or

the type of defects encountered on an invoice (to eliminate the most common defects first).

Each of these cases may require a very different approach for gathering the data.


233

Operational definition provides the foundation for the team to

1. Reach an agreement on what data to be collected.

2. Build consistency and reliability into data collection.

3. Fully agree on how a particular characteristic of a process is to be measured.


234

Poor Operational Definition: Cycle time of a transaction

Good Operational Definition: Collect data for all transactions processed from 1-Aug-05 to 31-Aug-05.

The cycle time of each transition will be determined by the date and time of transaction download from client server by an agent/CSR to the date and time of the PROCESSED transaction was submitted in client server as per the client server system time.

Example of Operational Definition.

235

Write operational definitions for the following cases

1) Maximization the server availability

2) Reduction of the attrition rate in ABZ

3) Improving the quality percentage a process

4) Reduction of call handling time

5) Minimization of abandoned calls in a call center

Exercise: Operational Definition

236

Develop Measurement Plan

Measurement PlanDetermining current process performance usually requires the collection of data. When developing a measurement plan ensure that:

– The data collected is meaningful– The data collected is valid– All relevant data is collected concurrently

What is the Purpose of Collecting the Data? Will it serve the purpose ? How will you collect the data?

-what result will you measure?-what kind of cause will you analyze for the

ineffective process?What kind of tool will be required?-form, check sheet ?

All related Data collected?-Sample size, frequency, sampling method?Is the Data Collecting method is adequate?-who will collect the data?-where can we collect the data?-when will we collect the data?-what kind of assistance will be necessary?

237

Before data collections starts, classify the data into different types: continuous or discrete.

This is important because it will:

– Provide a choice of data display and analysis tools

– Dictate sample size calculation

– Provide performance or cause information

– Determine the appropriate control chart to use

– Determine the appropriate method for calculation of Sigma

Data Classification

238

Continuous Data Discrete Data

Types of Data

Time (in hours) to process anapplication

% of applications with or without errors.

Number of errors in an application.

Customer satisfaction rating ofcall center service.

Measured on a continuum or scale

Binary : Classified into one of two categories

Count : Counted discretely

Ordered categories : Rankings or ratings

Description

Example

239

Continuous Data

Data generated by– Physically measuring the characteristic– Generally using an instrument– Assigning an unique value to each item

Examples:

1. The time it takes to write a proposal.

2. The time it takes to conduct a feasibility study.

3. The time it takes to close the books each month.

4. Invoice amounts.5. Sales order amounts.6. Handling Time, Time to Certify

PEs, etc.

Continuous Data: Example (Call Waiting Time in Secs)

SL No. Waiting Time

SL No. Waiting Time

1 98 11 102

2 103 12 98

3 100 13 101

4 100 14 101

5 99 15 99

6 101 16 100

7 97 17 101

8 102 18 99

9 100 19 100

10 99 20 102

240

Discrete Data

Data generated by

• Classifying the items into different groups based on some criteria

• All the items classified into a group will have same value

Examples:• Gender, Shade Variation, etc.

• Escalations, Repeat Calls, Defective Transactions, Defects in Transactions etc.

GONOGO

Good

Bad

241

Defects versus DefectiveOut of these 09 Invoices… there are...

3 Defective Invoices

6 Defects

Quantity: AAAAAPrice: $BBBBBDate: YY/YY/YY









242

Example of Opportunities

Errors in the rest ofthe invoice are not critical

Hence, there are 3 opportunities per invoiceeven though the invoice contains more than 3 line items.


Only three line items on thisinvoice are

critical to the customer.( Quantity, Price, Date)

243

Discrete : Binary Data (Binomial)

• Classifying the items into only two groups based on some criteria

• Each item will fall in either of the two groups

• All the items classified into a group will have same value

• Expressed or summarized as proportion p or percentage

Examples:

• Gender, Escalations, Repeat Calls, Defective Transactions, etc• An invoice is either “complete” or “incomplete”.• A delivery is either “late” or “not late”.• A product is either “damaged” or “not damaged”.• A hotel room is either “dirty” or “clean”.• A sales pitch is either a “thumbs up” or “thumbs down”.

244

Binary Data : Example (Month wise Escalation of Transactions)

Month No. of Transactions Processed No. of Transactions Escalated

Jan 2000 20

Feb 2500 30

Mar 1500 14

Apr 3000 27

May 4000 40

Jun 3500 33

Proportion of Escalated Transactions

p = No. of Transactions Escalated / Total No. of Transactions Processed

= (20 + 30 + 14 + 27 + 40 + 33) / (2000 + 2500 + 1500 + 3000 + 4000 + 3500) = 164 / 16500 = 0.0099 0.01 = 1 %

On an average, 1 % of Transactions are escalated

245

Discrete : Count Data (Poisson)

Data generated by

• Counting the exact number of occurrences of the characteristic in a group of items.

• It takes integer values as 0,1,2,-,-,-,

• Expressed or summarized as average number of occurrences

Examples:

• Number of fatal defects in transactions processed• Number of accidents in the city during June 2005• Number of suicides in the city during 2004.• The number of errors on twenty invoices.• The number of computer system failures in a month.

246

Count Data: Example (Data on Defects found during Transaction Audit)

No. of Items Audited

100 50 76 82 172 150 89

No. of Defects

3 4 0 1 5 10 1

Average Number of Defects = Total No. of Defects / Total No. Audited= (3 + 4 + 0 + 1 + 5 + 10 + 1) / (100 + 50 + 76 + 82 + 172 + 150 + 89 )= 0.033

On an average 0.033 defects found per TransactionsOn an average 3.3 defects found per 100 Transactions

247

Type of Data



2. Identify the type of data given in example

248

Data Measurement Plan Format

Performance measure

Operational Definition Data Source & location

Sample size

Who will collect the data

Data collection period

How will date be collected

Other data that should be collected at the same time

Time to process a transaction

Date and time of transaction was download from client server by an agent to the date and time of the PROCESSED transaction was submitted in client server

Client server system time.

256 Raju

Smita

1-Aug-05 to 31-Aug-05

Random selection

Type of transaction, Day of week,

Agent name

The data is being collected to measure the performance is called PERFORMANCE DATA.

On the other hand, CAUSE DATA, focus on why the process performs as it does. Cause data supports the problem solving by helping to isolate root causes of the problems.

Most of the times, however, we won’t know enough about potential causes until we have determined our processes current performance level. Be prepared to document current performance first, then brainstorm potential causes and collect additional data related to those causes at a later date.

Cause Data

249

Data Collection

While collecting data ensure that the data measurement plan is followed. Note any deviations from the plan.

Avoid bias and ensure consistency.

Use various tools like check sheets to record and grouping of the data.

Ensure that the sample selected is representative of the population. If there is any concern on this issue, record the things that may cause the data collected to not be representative of the population.

Poor Information

Data Rich

Ensure Effective and Efficient Data Collection

250

Sampling

251

Sampling Objectives

• Understand the purpose and advantages of sampling

• Understand the application of different sampling techniques to ensure accurate process representation

• Gain experience in asking appropriate questions to ensure a robust sampling plan is implemented effectively and efficiently

• Understand guidelines and formulas used to determine sample size

252

Basic Definitions and Symbols

Population (N): The entire set of objects or activities for a process

μ: the mean (arithmetic average) calculated for a population

σ: the standard deviation calculated for a population

Sample (n): a group that is a part or subset of a population

x: the mean (arithmetic average) of a sample

s: the standard deviation of a sample

253

Sampling Definition

Sampling is the process of:Collecting only a portion of the data that is available or could be available, and drawing conclusions about the total population (statistical inference)

x x xx x x x

x x x x xx x x xx x xx x

x x

x xx x

x x

Population

N = 5000 n = 100

Sample

Example:Estimating the average height of students in a college by measuring the heights of only 250 students (250 is a subset of entire students population).

254

Sample ….. When ?

When to …..• Collecting all the data is impractical or too costly

• Data collection can be a destructive process

• When measuring a high-volume process

When not to ……• A subset of data may not accurately depict the process, leading to a

wrong conclusion (every unit is unique-e.g., structured deals)

255

Kinds of Sampling

Random Sampling• This sampling ensures that the

characteristics of the population are collected with equal possibility.

Stratified Sampling • Make stratifying plan for

population characteristics.• Select the sample among each

stratified group

Group A Group B

256

Frequency of Sampling

• Recommended more often for unstable processes

(Systematic, Subgroup sampling)

• Recommended less than usual for stable processes.

• To make a useful business decision we have to decide the

precision of data and frequency of data.

257

Sampling: Methodology

• Select a sample of items from the population

• Measure the characteristics on each item in the sample

• Calculate the sample statistics

• Provide the sample statistics as an estimate of population statistics

258

Methodology: Example

Select a sample of items from the population, say 250 students

Measure the characteristics on each item in the sample

i.e. measure the height of all the 250 students in the sample

Calculate the sample statistic

i.e Calculate the average height of 250 students ( = 5.5 feet)

Provide the sample statistic as an estimate of population statistics

Estimate of average height of students in the college = 5.5 feet

To estimate the average height of students in a college

259

Methodology: Issues

The following are the Waiting Times (Seconds) values of 36 Calls:

Mean Waiting Time = 28.55

The following data is a sample of 10 from the above data:

Sample Mean = 25.3

Sample Statistics may not be exactly equal to Population Statistics

10 30 30 25 34 15 52 12 30 24 20 50

50 60 40 35 25 30 20 10 40 10 10 20

40 26 34 17 20 50 37 24 16 10 40 32

30 34 17 10 24 12 16 50 50 10

260

The following data is another sample of 10 from the parent data:

Sample Mean = 27.8

The estimate may vary from sample to sample

To overcome these issues Confidence Intervals are developed

26 40 34 30 20 16 10 50 20 32

Methodology: Issues

261

Confidence Interval: Methodology

• Select a sample of items from the population


• Calculate the sample statistics

• Provide two limits: an upper bound & a lower bound to the population statistics such that the true value of population statistics will lie within these limits with a specified level of confidence

262

Continuous Data: CI for Population Mean

• Select a sample of n items from the population


• Calculate the sample Mean & Standard Deviation (SD). Then

(1-) % Confidence interval : Sample Mean Constant (confidence level) x Standard Error (SD of Sample Mean)

263


(1-) % Confidence interval on Mean:

Sample Mean Z /2 x SD / n

Z /2 is the Standard Normal variate for an area of /2 as shown in figure

0

1

2

3

4

-3 -2 -1 0 1 2 3Z /2

/2

264


CI Z /20.05 95 % 1.960.01 99 % 2.570.10 90 % 1.64

Obtained from Z table

265

CI for Population Mean: Example

The following data is a sample of 10 from the above data:

30 34 17 10 24 12 16 50 50 10

Sample Mean = 25.3

Sample SD = 15.34

The following are the Waiting Times (Seconds) values of 36 Calls:

10 30 30 25 34 15 52 12 30 24 20 50

50 60 40 35 25 30 20 10 40 10 10 20

40 26 34 17 20 50 37 24 16 10 40 32

Mean Waiting Time = 28.55

266

CI for Population Mean: Example

95 % Confidence interval on Mean:

Sample Mean 1.96 x SD / n

= 25.3 1.96 x 15.34 / 10

= 15.79 to 34.80

Similarly, 2nd Sample:

Sample Mean = 27.8

Sample SD = 11.94

95 % Confidence Interval on Mean:

27.8 1.96 x 11.94 / 10 = 20.39 to 35.20

26 40 34 30 20 16 10 50 20 32

267


2. Calculate CI for a given example

CI for Population Mean


268

Discrete Data: CI for Proportion

Collect a sample of size n from the population

Calculate sample proportion p

Calculate Standard error (SE): (p(1-p)/n)

Then

(1 - ) % CI interval for Population Proportion:

p Z /2 x (p(1-p)/n)

95 % CI interval for Population Proportion:

p 1.96 x (p(1-p)/n)

269


2. Calculate CI for a given example

CI for Discrete data


270

Sample Size Calculation: Continuous Data

Using 95 % CITrue value of population Mean will lie between

Sample Mean 1.96 SD / nThen Population Mean - Sample Mean < 1.96 SD / n ( with 95 % Confidence)

Hence To estimate the population mean with an accuracy of say 5I.e Population Mean - Sample Mean < 5I.e 5 = 1.96 SD / n n = (1.96 SD / 5)2

Sample Size required to estimate population mean with an accuracy of 5: (1.96 SD / 5)2

271

Sample Size Calculation: Methodology Continuous Data

Collect a small sample

Calculate Sample Mean & Standard Deviation

Equate accuracy required to 1.96 SD / n

Solve for n

272


2. Calculate sample size for a given example

Sample Size Calculation for continuous data


273

Sample Size Calculation: Discrete Data

Using 95 % CITrue value of population proportion will lie between

p 1.96 x p(1-p) / n where p is sample proportionThen Population Proportion - Sample Proportion < 1.96 x p(1-p) / n

Hence To estimate the population proportion with an accuracy of say 0.01I.e Population proportion - Sample proportion < 0.01I.e 0.01 = 1.96 x p(1-p) / n n =1.962 p (1-p) / 0.012

Sample Size required to estimate population proportion with an accuracy of 0.1: 1.962 p (1-p) / 0.012

274

Sample Size Calculation: Methodology Discrete Data

Collect a small sample

Calculate Sample proportion p

Calculate Standard Deviation (p(1-p) / n)

Equate accuracy required to 1.96 (p(1-p) / n)

Solve for n

275


2. Calculate sample size for a given example

Sample Size Calculation for discrete data


276

Data Display and Evaluation

Once we collect the data, it is always preferable to evaluate the data for its accuracy and usage prior to calculation of the capability of the process

As a initial step, display the data using Patero charts, Scatter plot, control charts, Histogram or Normality etc to look for data errors, trends and outliers.

Be prepare to collect more data or different data based on the above observations.

Evaluate the data to confirm that the data is dependable, consistent, reliable and representative.

Also ensure that, we get similar results if we repeat the data collection.

Finally confirm that the data collected provide the information we need.

277

Fundamentals of Minitab (Statistical Package)

278

Once you start Minitab, Minitab opens with two main windows.

Session WindowIt displays the results of your analysis in text format.

Data WindowIt contains an open worksheet, which is similar in appearance to a spread sheet. We can open multiple work sheets.

Column

Row

Cell

279

Column NameTo be written by us

Row Number

Column with Text data

Column with Numeric data

Column with date/time data

All the columns are formatted by default to Numeric data. As per the requirement we can reformat the columns.Right click mouse>Format Column>numeric/text/date.

280

FileMost of the functions of the File Menu are similar to Excel sheet.

Open existing or new filesSave filePrint files, etc.

EditMost of the functions of the File Menu are similar to Excel sheet.

Cuts, Paste cells,Undo, redoClear cells etc.

281

DataIt is very useful function menu in Minitab. to immunize the duplicate data entry in the work sheets.

Using this menu, we can subset the worksheets, split and merge work sheets.Minitab automatically opens multiple data windows.

We can transpose and sort the columns.

Group of the points from the graphs can be selected and corresponding data subset can be stored in separate work sheet.

282

CalcCalculator: Data in various columns can be computed and stored in separate column

Column and Row statistics:Various statistics like mean, SD etc can be calculated for the data listed in the column / row

Random data: Can be generated for all the distributions

Probability Distribution: All the distribution statistics can be calculated.

283

StatAll sorts of statistical analysis can be done for the data stored in various columns in the work sheet.

GraphsVarious graphs can be plotted using this menu

EditorUsed for Formatting of the columns

284

Basic Statistics

285

1. Measures of Central Tendency

1. Mean 2. Median 3. Mode

2. Measures of Dispersion

1. Range 2. Variance 3. Standard Deviation

Describe Sets of Continuous Data

The following three characteristics can describe the continuous data set

3. Shape

1. Histogram

286

Continuous Data: Measures of Central Tendency

1. Mean

2. Median

3. Mode

287

Mean: • Numerical value indicating the central value of data• Sum of all observations / Total number of observations

Suppose x1, x2, - - - xn be the data, thenMean = (x1+ x2 + - - -+ xn ) / n = xi /n

Continuous Data : Example Call Waiting Time

Mean: Sum of all observations / Total number of observations

= (98 + 103 + 100 + 100 + 99 + 101 + 97 + 102 + 100 + 99 + 102 + 98 + 101 + 1.01 + 99 + 100 + 101 + 99 + 100 + 102) / 20

= 200.2 / 20 = 100.1 Minutes

288

Median: • Middle Value

• Value which divides observations arranged in ascending or descending order into two equal halves

Case 1: Total number of observations is odd

Median: Middle ValueCase 2: Total number of observations is even

Median: Average of two middle values

Median: Example Call Waiting Time in Minutes

97 98 98 99 99 99 99 100 100 100

100 100 101 101 101 101 102 102 102 103

Total Number of observations: 20 (even)

The middle Values : 100 & 100 (10th value and 11th value)

Median: Average of 2 middle values = (100 + 100) / 2 = 100

289

Mode:

• The observation which occurs maximum number of times in the data

Example Call Waiting Time in Minutes

97 98 98 99 99 99 99 100 100 100

100 100 101 101 101 101 102 102 102 103

Total Number of observations: 20 (even)

The observation with maximum number of occurrences : 100

Mode: 100

290

Continuous Data :Measures of Dispersion

1.Range

2.Variance

3.Standard Deviation

291

Range: Definition

Range: Maximum value – Minimum Value

Example:

5 4 7 3 2

15 9 8 5 2

Maximum Value = 15Minimum Value = 2Range = 15 – 2 = 13

292

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7 8 9 10

Range

Better measure of Dispersion is “Standard Deviation”

Range: Issues

It depends only on extreme valuesHence affected by outliers

293

Example :

5 4 7 3 2

15 9 8 5 2

Step 1:

Calculate Mean = (5+4+7+3+2+15+9+8+5+2) / 10

Mean = 6

Standard Deviation: Definition

Square root of the average squared deviation from mean

Indicates On an average how much each value is away from the Mean

294

Step 2: Take deviations from Mean

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7 8 9 10

-1 -2 1 -3 -4

9 3 2 -1 -4

Example: Standard Deviation

295

Step 3:

Since some values are positive & rest are negative, while taking sum they will cancel out.

So square the values & Sum

1 4 1 9 16

81 9 4 1 16

Sum of Squares = 142

Example: Standard Deviation

296

Example: Standard Deviation5 4 7 3 2

15 9 8 5 2

Step 2 : Take deviations from Mean

-1 -2 1 -3 -4

9 3 2 -1 -4

Step 1: Calculate Mean, Mean = 6

Step 3: Since some values are positive & rest are negative, while taking sum they will cancel out. So square the values & Sum

1 4 1 9 16

81 9 4 1 16

Sum of Squares = 142

Step 4: Standard Deviation = (Sum of Squares / (n -1))

= (142 / (10 -1))

= 15.77 = 3.972

Variance = (SD)2 = 15.77

297

Standard Deviation: Example 2 Call Waiting Time in Minutes

Data (xi – Mean) (xi – Mean)2

x1 98 -1.9 3.61

x2 103 3.1 9.61

x3 100 0.1 0.01

x4 100 0.1 0.01

x5 99 -0.9 0.81

x6 101 1.1 1.21

x7 97 -2.9 8.41

x8 102 2.1 4.41

x9 100 0.1 0.01

x10 99 -0.9 0.81

Sum 999 28.9

n 10

Mean 99.9

S D = (28.9) / (10 – 1)

= 1.7919

298

Continuous Data: Graphical Representation of Data: Histogram

97 98 98 99 99 99 99 100 100 100

100 100 101 101 101 101 102 102 102 103

Example: Call Waiting Time Data

Total Number of observations : 20

Minimum Value : 97

Maximum Value : 103

Number of Classes : n = 20 = 4.47 5

Class Interval : (Maximum – Minimum) / Number of Classes

: (103 – 97) / 5 = 1.2

299

Construction of Frequency Table

Lower Limit of a Class

• 1st Class Lower Limit : Minimum Value• Lower Limit of any class other than 1st class : Upper Limit of Previous Class

Upper Limit of a Class

• Lower Limit of the Class + Class Interval

Construction of Frequency TableSL No Lower Limit Upper Limit Tally Marks Frequency

1 97 98.2 lll 3

2 98.2 99.4 llll 4

3 99.4 100.6 llll 5

4 100.6 101.8 llll 4

5 101.8 103 llll 4

300

Graphical Representation of Data: Histogram

0

1

2

3

4

5

6

98.2 99.4 100.6 101.8 103

301


2. Calculate Mean, Median, Standard Deviation and construct histogram for a given example

Mean, Median, Standard Deviation and histogram


302

Probability & Normal Distribution

303

Ratio of number of favorable outcomes to total number of outcomes

Probability Definition

ExampleNumber of tosses of a coin = 100Number of times Head occurred = 49Number of times Tail occurred = 51

Probability of getting Head in a toss of coin=Number of Times Head occurred / Total number of tosses= 49/100 = 0.49 = 0.5 (Approximately)

304

Example 1 :The day wise average waiting time in seconds (AWT) of calls for 10 days is given below:

50 58 56 48 62 61 55 55 54 51

a. Calculate the probability that average waiting time > 60 Seconds?

b. Calculate the probability that average waiting time < 50 seconds

a. Probability of AWT > 60

= Number of cases with AWT > 60 / Total number of cases = 2 / 10 = 0.2

20 % of the days AWT will be more than 1 Minute

b. Probability of AWT < 50 seconds = Number of cases with AWT < 50 / Total number of cases = 1 / 10 = 0.1

10 % of the days AWT will be less than 50 seconds

305

Example 2 :The number of transactions processed per day by 40 member team for 12 days during transition is given below:

750 780 760 690 725 743 820 810 750 740 775 796 765 735

Risk of not meeting SLA = Probability that productivity < 720 transactions

= Number of days with Productivity < 720 / Total number of days

= 1 / 14 = 0.071

Risk of not meeting SLA is 7 %

Suppose the SLA on productivity is minimum 720 transactions per day, calculate the risk of not meeting the SLA?

306

Issues

To estimate probability using this method,

huge amount of data is required

Solution

When data is less, identify the underline distribution & estimate probability from the distribution

307

Statistical Distributions

Continuous distribution

•Normal distribution

Discrete distribution

•Binomial distribution

•Poisson Distribution

308

Normal DistributionDefinition: Consider the following data on Average Handling Time (AHT) in minutes of 16 Days:

2.3 2.7 2.4 2.6 2.3 2.7 2.5 2.5 2.5 2.4 2.5 2.6 2.2 2.8 2.4 2.6

Plot of the Data:

0

1

2

3

4

5

2.1 2.3 2.5 2.7 2.9

309

Plot of the Data:

0

1

2

3

2.2 2.3 2.4 2.5 2.6 2.7 2.8

• Bell Shaped• Symmetric• Total Area under the curve is 1

Then : Normal Curve & Data follows Normal Distribution

310

Normality Test : Probability Plot using MinitabStep1: Copy the data to Minitab worksheet column

Step 2: Choose Stat > Basic Statistics > Normality Test

311

Step3: Enter the Column Title to the Variable Text Box and Click OK button

312

Step 4: Minitab Output

Interpretation:

If P-Value ≥ 0.05, then Data is Normal

313

Standard Normal Distribution

If

Data follows Normal Distribution

then

(Data - Mean) / SD will follow Standard Normal Distribution

For Standard Normal Distribution:

Mean = 0

SD = 1

314

Standard Normal Distribution: Example

2.3 2.7 2.4 2.6 2.52.5 2.4 2.5 2.6

Data:

Mean = 2.5

SD = 0.1225

Z : (Data - Mean ) / SD

-1.633 1.633 -0.8165 0.8165 0.000.00 -0.8165 0.00 0.8165

Mean = 0.00

SD = 1.0

315

Standard Normal Distribution: Properties

0

1

2

3

4

-3 -2 -1 0 1 2 3

68.26%

95.46%

99.73%

Between

Mean 1 SD : 68.26 % of Values will lie



316

0

1

2

3

2.3 2.4 2.5 2.6 2.7

If data follows normal distribution, then the probabilities can be estimated from Normal Curve

Example:

The probability that AHT will be more than 2.6 Minutes is the area above 2.6 Minutes in Normal Curve

317

0

1

2

3

2.3 2.4 2.5 2.6 2.7

Example:

The probability that AHT will be less than 2.35 Minutes is the area below 2.35 Minutes in Normal Curve

318

Normal Distribution: Examples

The Time to Certify PE’s is normally distributed with mean 40 days and standard deviation 8 days. If the client wants that all PEs shall be certified within 34 to 48 days, estimate the chance of meeting client requirement?

Mean = 40

SD = 8

Let x be the Time to Certify PE

Case 1: Probability of certifying PEs within 34 days

P(x < 34)

Transforming to Standard Normal

P[((x - Mean) / SD ) < ((34 - 40)/8)] = P ( z < -0.75)

319

Normal Distribution: Examples

Case 1: P ( z < -0.75)

0

1

2

3

4

-3 -2 -1 0 1 2 3

From Standard Normal Tables P(z < -0.75) = 1 – 0.7733 = 0.2266

320

Case 2: Probability of certifying PE in > 48 days

P ( x > 48) = P (z > (48 - 40) / 8) = P ( z > 1)

0

1

2

3

4

-3 -2 -1 0 1 2 3

321

Normal Distribution: ExamplesCase 2: From Standard Normal Tables

P (z > 1 ) = 0.1587

Chance of Meeting Client Requirement = 1 – 0.2266 - 0.1587 = 0.6147 = 61.47 %

0

1

2

3

4

-3 -2 -1 0 1 2 3

Chance of Meeting Client Requirement = 0.7733 - 0.1587 = 0.6147 = 61.47 %

OR

322


2. Calculate probability distribution for a given example

Probability distribution for continuous data

Exercise 1.16.1 (20 minutes)

323

Handing Non normal data

Handling Non Normal Data

324

Binomial Distribution:If the data is binary, then probabilities are estimated using Binomial Distribution

325

Binomial Distribution: Example

On an average, 2 % of the transactions processed in a process are defective. On a particular day, out of 400 transactions audited 21 turned out to be defectives. Is it an indication that the process performance deteriorated?

Let p = 2 % = 0.02

Number of Transactions Audited (n) = 400

Number of Defectives (d) = 21

The probability of getting 21 defectives out of 400 when p = 2 % = 0.02 is calculated using Binomial Distribution as shown below

P(getting x = 21 defectives out of 400 transactions) = nCxpx(1-p)n-x

= 400C210.0221(1-0.02)400-21

326


Let p = 2 % = 0.02

Number of Transactions Audited (n) = 400

Number of Defectives (d) = 21

The probability of getting ≤ 20 defectives out of 400 when p = 2 % = 0.02 is calculated using Binomial Distribution as shown below

P ( x ≤ 20) = P ( x = 0 ) + P ( x = 1 ) + P ( x = 2 ) + - - - + P ( x = 20)

P ( getting x = 20 defectives out of 400 transactions ) = nCxpx(1-p)n-x

= 400C200.0220(1-0.02)400-20

327

Calculation of Binomial Probabilities using Minitab

Step 1:

Copy the defective data to Minitab Worksheet as shown below:

328

Calculation of Binomial Probabilities using Minitab

Step 2:

Go to Calc Probability Distributions Binomial

329

Calculation of Binomial Probabilities using MinitabStep 3:

Select Cumulative probability, Enter Number of trials, Probability of success, Input Column, Option Storage and click ‘OK’ button

330

Calculation of Binomial Probabilities using MinitabStep 4:

Minitab will calculate Binomial Probabilities as display in Optional Storage Column as shown below

Note: % Chance = Probability x 100

331

Binomial Distribution: Examplep = 2 % = 0.02

From Binomial Distribution,

Number Audited (n) Defectives (d) Chance of getting d or less defects (%)

400 0 0.03

400 2 1. 31

400 4 9.73

400 6 31.09

400 8 59.26

400 10 81.79

400 12 93.81

400 14 98.38

400 16 99.66

400 18 99.94

400 20 99.99

332


Let p = 2 % = 0.02

Number of Transactions Audited = 400

From Binomial Distribution,

Probability of getting less than 20 defectives in 400 transactions = 0.9999

Hence Probability of getting 20 or more defectives = 1 – 0.9999= 0.0001 0

i.e. if the process is operating at 2 % defectives:

the chance of getting 21 defectives out of 400 is almost 0 ,

Process performance is deteriorated.

333



Probability distribution for Binomial Distribution


334

Poisson Distribution:If the data is Count, then probabilities are estimated using Poisson Distribution

335

Poisson Distribution: Example

The average number of repeat calls per day in a voice process is 20. On a particular day , there were 25 repeat calls. Is there any problem with the process that day?

Let : Average number of Repeat Calls = 20

The probability of getting x ≤ 24 calls when average number of repeat calls is 20 is calculated using Poisson distribution as follows

P ( x ≤ 24) = P ( x = 0) + P ( x=1)+ P ( x = 2) + - - - + P ( x=24)

P ( x = 24 when = 20) = e-x / x!

= e-202024 / 24!

336

Calculation of Poisson Probabilities using MinitabStep 1:

Copy the different values of repeat calls to Minitab worksheet as shown below:

337


Go to Calc Probability Distributions Poisson

338


Choose Cumulative probability, Enter Mean, Input column & Optional storage as shown below and click “OK” button.

339


Minitab will display the probabilities in the Optional storage column as shown below

Note: % Chance = Probability x 100

340


Average number of repeat calls per day = 20

Average Repeat Calls

Repeat Calls (d)

Chance of getting d defects or less (%)

20 0 0.00

20 5 0.01

20 10 1.08

20 15 15.65

20 20 55.91

20 24 84.32

341


Average number of repeat calls per day = 20

From Poisson Distribution,

Probability of getting less than 25 repeat calls = 0.84

Hence Probability of getting 25 or more repeat calls = 1 - 0.84= 0.16 = 16 %

i.e. if the process is operating at 20 repeat calls per day:

the chance of getting 25 repeat calls is 16 %

16 % is large enough to conclude that there is nothing wrong in the Process.

342



Probability distribution for Poisson Distribution


343

Gauge R&R(Measurement System Analysis)

344

Introduction

IdentifyY(CTQ)

Identify theProject

Define

Gage R&Rfor Y

Data Collection Baseline

MeasureWhen ?

BaselineProcess Capability

• Gage R&R is pre-requisite for data collection / analysis

• Gage R&R study is a method to evaluate measurement system to determine the amount of variation it contributes to the total observed process variation.

• In Manufacturing industries, gages are evaluated for repeatability (of readings when a component is measured multiple times) and Operators / inspectors are evaluated for reproducibility (of same readings when the component is measured by different operations).

• In service industries, Appraisers will be assessed instead of Gage

345

Importance of Gauge R&R

• It is a method to determine how good the data is• A Simple method to aid in improving the measurement system• A simple method to evaluate new gage / agents repeatability• A simple method to quantify measurement reproducibility

Examples

• A black belt wants to reduce the variability in transaction time

• A black belt wants to improve the quotation process

• A black belt wants to assess the process knowledge of the agents

346

What is Measurement ?

To give the value to express specific function of a certain material.

What is Measurement System ?

A given value is called measurement data.

All equipment and tools to get the measured data are termed gage.

Gage, operator, software, measurement method and process are

termed as measurement system.

Measurement System

347

How might measurementvariation affect these decisions?

Verify product/process

conformity to specifications

Verify product/process

conformity to specifications

Assist incontinuous

improvement activities

Assist incontinuous

improvement activities

What if the amount of measurement variation

is unknown

?

Process

Measurement

Process

Measurement

Measurement variation can make our process capabilities appear worse than they are.

Why Worry about Measurement Variation?

Consider the reasons why we measure:

348

Accounting For Changes

While we can come up with many explanations, they would fit into three general categories

• Simple Day-to-day Random Variation

• An Event That Changed the Distribution of Calls Coming in to Agents

• A Difference in How Calls Are Classified Between You and the People Classifying Calls Yesterday

Expected Variation

A Change to the Process

A Change to the Measurement System

How Can We Determine the Cause?

349

Sources of variation

Product Variability(Actual variability)

MeasurementVariability

Total Variability(Observed variability)

350

Long Term Process variation

Short TermProcess variation

Within Sample Variation

ActualPart to Part

Variation

MeasurementVariation

Observed Process Variation

Variation due to operators

Variation due to Gage

σ2Total = σ2

Part-Part + σ2R&R

To study & reduce the process variation the measurement variation has to be identified and separated from process

Repeatability Issue

Reproducibility Issue

351

Accuracy (Bias)

The difference between the observed average of measurements and

the true average of the items measured.

Observed

AverageTrue

Average

Accuracy

352

Repeatability

The variation due to the Gauge.The variation observed when the same Appraiser monitors/evaluates the same transaction repeatedly using same facilities / aids.

Master Value

Master Value

Mean Mean

Good Repeatability

Poor Repeatability

353

Reproducibility

Appraiser to Appraiser VariationThe variation observed when different Agents process the same transaction using the same facilities / aids.

Master Value

Master Value

Good Reproducibility

Operator 1

Poor Reproducibility

Operator 2 Operator 3Operator 1 Operator 2 Operator 3

354

Stability

The variation in the average of at least two sets of measurements obtained with a gage as a result of time on the same pieces.

Time 1 Time 2

Stability

355

Methods of performing Gage R&R Studies.

Continuous Data

Xbar-R Method

ANOVA Method

General use.It does not evaluate the interaction effect.

It evaluates the interaction effect of the agents also.More effective when extreme values are present

Discrete Data

Attribute Agreement Analysis

356

Exercise: Gage R & R– Continuous Data

Given the data below for reading by 3 appraisers on 6 calls with 2 trails, determine whether the measurement system is acceptable

Call ID Appraiser A Appraiser B Appraiser C

1 2 1 2 1 2

1 65 60 55 55 50 55

2 100 100 100 95 100 100

3 85 80 80 75 80 80

4 85 95 80 75 80 80

5 55 45 40 40 45 50

6 100 100 100 100 100 100

357

Gage R & R– Continuous Data

Step 1:

Copy the data to Minitab worksheet as shown below

358


Step 2:

Choose Gage R&R Study (Crossed) from Stat Menu as shown below:

359

Gage R & R– Continuous DataStep 3:

Enter Part Numbers, Operators & Measurement Data.

Choose Xbar and R as shown below

Click “OK” button

360


Step 4:

Minitab will give the following Output

Source Var Comp % Contribution

Total Gage R & R 17.434 4.06

Repeatability 7.338 1.71

Reproducibility 10.096 2.35

Part-To-Part 411.568 95.94

Total 429.002 100

Source StdDev (SD) (6 * SD) (%SV)

Total Gage R & R 4.1754 25.052 20.16

Repeatability 2.7088 16.253 13.06

Reproducibility 3.1774 19.065 15.34

Part-To-Part 20.2871 121.723 97.95

Total Variation 20.7154 124.274 100

If < 20 %. Gage acceptableElse if > 30 %, Gage not acceptableElse some problem with

gage, use with caution

361

Gage R & R– Continuous DataStep 5: Graphical Output 1

Perc

ent

Part-to-PartReprodRepeatGage R&R

100

80

60

40

20

0

% Contribution

% Study Var

Gage name:Date of study:

Reported by:Tolerance:Misc:

Components of Variation

Gage R&R (Xbar/ R) for Data

Graphical Representation of the first table in the previous slide

362


Interpretation:

All points in R chart should be within the control limits for all Appraisers

Xbar chart for all appraisers should have more or less same pattern and most of the points should fall outside control limits.

Sam

ple

Range

10

5

0

_R=3.06

UCL=9.98

LCL=0

A B C

Sam

ple

Mean

100

80

60

40

__X=77.36UCL=83.11

LCL=71.61

A B C



R Chart by Appraiser

Xbar Chart by Appraiser


363


Step 5: Graphical Output 3

Interpretation:

All readings for each call is shown with their means connected. Ideally the variation around mean for different calls should be equal and minimum.

Call Id654321

100

90

80

70

60

50

40



Data by Call Id


364


Step 5: Graphical Output 4

Interpretation:

All readings for each appraiser is shown with their means connected. Ideally the variation around mean for different appraisers should be equal and minimum.

AppraiserCBA

100

90

80

70

60

50

40



Data by Appraiser


365


Interpretation:

Ideally the lines should overlap or at least parallel. Large deviations from parallelism indicates lack of agreement among appraisers with respect different calls.

Call Id

Avera

ge

654321

100

90

80

70

60

50

40

Appraiser

AB

C



Appraiser * Call Id Interaction


366

Example: Gage R & R for Transition Cycle Time

A Team Lead in a finance related data process is responsible to monitor and control the cycle time a sub-process. This sub-process is well established and consumes almost equal amount of time to process each transaction.

Over period of time volumes are increased and agents are also increased proportionately. But he observed that currently, the variation in cycle time is very high and not meeting the SLA some times.

He wondered how it can happen? He is in doubt about the agents capability and likes to measure and assess the same.

He chosen 2 agents and 10 transactions. He has conducted Gage R&R study by processing each transaction twice by each agent. The transactions are selected on random basis for the processing.

Gage R&R for Continuous Data

367

Agent 1 Agent 2 Transaction 1 2 1 2

1 21 20 20 20 2 24 23 24 24 3 20 21 19 21 4 27 27 28 26 5 19 18 19 18 6 23 21 24 21 7 22 21 22 24 8 19 17 18 20 9 24 23 25 23

10 25 23 26 25

Summary of Case Study (Manual Calculations)10 Transactions, 2 AgentsEach Agent processed each transaction twice

Data Collection

368

Gage R & R: Example

Operator 1 Operator 2Part 1 2 Range 1 2 Range

1 21 20 1 20 20 02 24 23 1 24 24 03 20 21 1 19 21 24 27 27 0 28 26 25 19 18 1 19 18 16 23 21 2 24 21 37 22 21 1 22 24 28 19 17 2 18 20 29 24 23 1 25 23 2

10 25 23 2 26 25 1Rbar 1.2 Rbar 1.5

Repeatability: Variation due to measurement instrument

Variation occurs when same operator measures the same part again and again

369

Gage R & R: Example

Repeatability: Variation due to measurement instrument

Variation occurs when same operator measures the same part again and again

Variation due instrument : Average of Rbars

= 1/2(1.2 + 1.5) = 1.35

Repeatability (EV) = K1 x Average Rbar = 1.19681

Trails K1

2 0.8862

3 0.5908

370

Gage R & R: Example

Agent 1 Agent 2 Part 1 2 Mean 1 2 Mean

1 21 20 20.5 20 20 20.0 2 24 23 23.5 24 24 24.0 3 20 21 20.5 19 21 20.0 4 27 27 27.0 28 26 27.0 5 19 18 18.5 19 18 18.5 6 23 21 22.0 24 21 22.5 7 22 21 21.5 22 24 23.0 8 19 17 18.0 18 20 19.0 9 24 23 23.5 25 23 24.0

10 25 23 24.0 26 25 25.5 Mean 1 21.9 Mean 2 22.35

Reproducibility: Variation caused by operators

Variation occurs when same part is measured by different operators

371

Gage R & R: Example

Reproducibility: Variation caused by operators

Variation occurs when same part is measured by different operators

Overall Variation between operators : Difference between xbars

= (22.35 - 21.9) = 0.45

Reproducibility AV) = ((xbar diff x K2)2 – (EV2 / n r))

= ((0.45 x 0.7071)2 – (1.196812 / 10 x 2))

= 0.1739

n: Number of Parts

r: Number of trails

Operators K2

2 0.7071

3 0.5231

372

Gage R & R: Example

Total Gage R & R : Repeatability2 + Reproducibility2

= ( 1.196812 + 0.17392) = 1.2094

373

Gage R & R: Example

Agent 1 Agent 2 Part 1 2 1 2

Mean

1 21 20 20 20 20.25 2 24 23 24 24 23.75 3 20 21 19 21 20.25 4 27 27 28 26 27.00 5 19 18 19 18 16.00 6 23 21 24 21 17.25 7 22 21 22 24 17.25 8 19 17 18 20 18.50 9 24 23 25 23 23.75

10 25 23 26 25 24.75

Rp: Mean max – Mean min = 27.00 – 16.00 = 11

Part Variation:

374

Gage R & R: Example

Rp: Mean max – Mean min = 27.00 – 16.00

= 11

Part Variation (PV): K3 x Rp = 0.3146 x 11

= 3.4606

Part Variation:

Parts K3

2 0.7071

3 0.5231

4 0.4467

5 0.4030

6 0.3742

7 0.3534

8 0.3375

9 0.3249

10 0.314

375

Gage R & R: Example

Total Variation: Gage R &R2 + Part Variation2

Total Variation: 1.20942 + 3.46062 = 3.6658

Total Variation:

376

Gage R & R: Example

Summary Table:

Source SD 5.15 x SD % Study Var

Repeatability 1.19681 6.1636 32.64

Reproducibility 0.1739 0.8956 04.74

Total Gage R &R 1.2094 6.2284 32.99

Part Variation 3.4606 17.8221 94.40

Total Variation 3.6658 18.8791 100

% Study variation is 32.99% > 30% hence variation in processing time is not acceptable.

Reasons shall be investigated and improvement plan shall put in place.

377

Agent 1 Agent 2 Transaction 1 2 1 2

1 21 20 20 20 2 24 23 24 24 3 20 21 19 21 4 27 27 28 26 5 19 18 19 18 6 23 21 24 21 7 22 21 22 24 8 19 17 18 20 9 24 23 25 23

10 25 23 26 25

Summary of Case Study (Using Minitab)10 Transactions, 2 AgentsEach Agent processed each transaction twice

Data Collection

378

Enter the data in Minitab as shown

Select Stat>Quality Tools> Gage Study> Gage R&R (Crossed)

Click on columns as shown

Choose either ANOVA or Xbar&R. It is preferable to chose ANOVA as it also analyses the interaction effect

379

% Study variation is 35.28% > 30% hence variation in processing time is not acceptable.

Reasons shall be investigated and improvement plan shall put in place.

380

Total Gage R&R. Focus only on Green Bars. These represents the % of total variation contributed from the data.The Gage R&R should be only 10% of total variation. Rest should be attributed to within transactions Variation. 35.28% is not acceptable

Within Agent (Repeatability)

Agent to Agent (Reproducibility)

Part-to-part variation (transaction to transaction) (estimate of process variation)

Represents the repeatability. Presence of of assignable causes (point out of control point) indicates stability problem. Excessive common cause variation to be addressed

Represents the reproducibility. The detectable shift in the pattern on X-bar chart and inconsistent pattern are unwarranted.

Remember : Most of the points should fall outside control limits.

381

This graph shows the data for eachAgent for all the Transactions.

Represents the Bias. Ideally, all the lines should overlap each other

This graph shows the data for the 10 transactions for each Agent. It display the raw data and

highlights the average of those measurements

Similar to top graph but the data is presented by Agent instead of Transaction. The graph will help

identify Agent issues

382

Gage R & R– Continuous DataExercise 1.17 (20 minutes)


2. Calculate Gauge R & R for a given example

383

An Engagement Team Lead (TL) considered 10 transaction and chosen 2 appraisers at random for Gage R&R study. The transactions were evaluated on “Correct” or “Incorrect” basis. For all the 10 transactions actual results (Standard) are also available with TL. 2 appraisers processed each transaction twice within gap of one week. The results are as follows. Study the Gage R&R.

TRANSACTION NUMBER

STANDARD RADHA KRISHNA

TRIAL 1 TRIAL 2 TRIAL 1 TRIAL 2

1 CORRECT CORRECT CORRECT CORRECT INCORRECT

2 INCORRECT CORRECT INCORRECT INCORRECT INCORRECT

3 INCORRECT INCORRECT INCORRECT INCORRECT INCORRECT

4 CORRECT INCORRECT INCORRECT CORRECT CORRECT

5 INCORRECT INCORRECT INCORRECT INCORRECT CORRECT

6 CORRECT CORRECT CORRECT CORRECT CORRECT


8 CORRECT INCORRECT CORRECT CORRECT CORRECT

9 INCORRECT INCORRECT CORRECT INCORRECT INCORRECT


Example: Gage R & R for evaluate appraiser process knowledge

Gage R&R for Discrete Data

384

Enter the data in Minitab worksheet.Test1 and test 2 results of same appraiser should be at one place, as shown

Note: Minitab 13 is used. Commands are similar to Minitab 14

385

Select Stat > Quality Tools > Attribute Agreement Analysis

Analyze Results

386

Analyze Results


Enter 2, 2 , Radha, Krishna


387

Percent Repeatable by appraiser(it should be >=80%)

388

Repeatability Vs Standard

389

Percentage ReproducibilityFor all appraisers(it should be >=80%)

Percentage ReproducibilityFor all appraisers Vs Standard(it should be >=80%)

390

KRISHNARADHA

100

90

80

70

60

50

40

30

Appraiser

Per

cent

Within Appraiser

KRISHNARADHA

100

90

80

70

60

50

40

30

Appraiser

Per

cent

Appraiser vs Standard

Assessment AgreementDate of study:Reported by:Name of product:Misc:

[ , ] 95.0% CI

Percent

Pictorial Representation

Since R&R is less than 80%, root causes to be identified and corrective actions to be taken. Re-conduct study to assess the improvement.

391

Case 1: Continuous Data

If Total gage R &R %SV < 20 % , Measurement system is acceptable

If Total gage R &R %SV between 20 % to 30 %

Some problem with measurement system, use with caution

If Total gage R &R %SV between > 30 %

Measurement system is unacceptable.

Rules for Gauge R&R study conclusions

Case 2: Discrete Data

If Gage R &R (Agreement) > 80 %

Measurement system is acceptable

Else, Measurement system is unacceptable.

392


2. Calculate Gauge R & R for a given example

Gage R & R– Attribute (Discrete) DataExercise 1.18 (20 minutes)

393

Process Capability

394

Common Process Capability Indices1. Potential capability Cp2. Achieved capability Cpk

Process CapabilityRefers to the inherent or natural variation of a process

Process Capability Cpk

A methodology to check whether the process have the capability to meet the customer requirements

Customer requirements are also expressed asLower Specification Limit (LSL) = 50 DaysUpper Specification Limit (USL) = 60 Days

Process Capability

395

Potential capability Cp: A measure of the ability/potential to meet the customer specifications

Example 1: Specification: 55 ± 5 DaysAllowed variation = 50 Days to 60 DaysNatural Variation = 52 Days to 58 Days

Natural Variation < Allowed variation

Hence Process have the capability to satisfy customer

Example 2 : Specification: 55 ± 5 DaysAllowed variation = 50 Days to 60 Days Natural Variation = 48 Days to 62 Days

Natural Variation > Allowed Variation

Then Process doesn’t have the capability to satisfy customer

396

Potential capability Cp:

If the data is normally distributed, then

Natural variation : Mean ± 3 SD

Example:

Mean = 55 Days & SD = 1 Day

Natural Variation = 55 – (3 x 1) to 55 + (3 x 1)

= 52 Days to 58 Days

397

Potential capability Cp:

Ratio of allowed variation to Total variation

Cp = Allowed variation / Natural variation

= (USL – LSL) / ((Mean + 3 SD) – (Mean - 3 SD))

= (USL – LSL) / 6 SD

A Process has the capability to meet customer requirements if

Allowed variation > Natural variation

(USL – LSL) > 6 SD Cp > 1

Preferably Cp should be greater than 1.34

398

Potential capability Cp: Example

The Time to Certify Agents in days is given in the table below. If the client requirement on Time to Certify Agents is 50 to 90 days, check whether the process has the capability to meet the client requirement ?

85 75 80 65 75 60 80 70 75 60

80 75 70 70 75 75 85 60 50 65

USL = 90 Days

LSL = 50 Days

Mean = 71.5

SD = 9.2

Cp = (USL – LSL) / 6 SD

= (90 – 50) / (6 x 9.2)

= 40 / 55.2 = 0.72

Conclusion ?

399

Potential capability Cp: Issues

• Cp checks only whether the process has the potential to meet the requirements

• Cp never checks whether the Process is actually meeting requirements

400


Example:

Process: Training Process Characteristic: Time to Certify Agents

Specification : 55 ± 5 Days

Process 1 Process 2 Process 3

Mean 55 52 58

SD 1 1 1

USL – LSL 10 10 10

6 SD 6 6 6

Cp 1.66 1.66 1.66

401



Cp 1.66 1.66 1.66

Allowed variation 50 to 60 50 to 60 50 to 60

Total process variation 52 to 58 49 to 55 55 to 61

Cp = 1.66 for all 3 processes

all 3 process have the capability to meet customer requirement

But

only Process 1 is meeting customer requirement

Hence

Achieved Capability Index is developed

Example:Process: Training Process Characteristic: Time to Certify Agents Specification : 55 ± 5 Days

402

Achieved Capability Index Cpk:

Cpk = Min [Cpl, Cpu]

Cpl = (Mean – LSL) / 3 SD

Cpu = (USL - Mean) / 3 SD

Cpk checks whether the process is centered.

403

Achieved Capability Index Cpk: Graphical Representation

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5 6 7

USLLSL

Mean + 3 SD 3 SD

ba

c d

Cpl = a / c

= (Mean – LSL ) / 3 SD

Cpu = b / d

= (USL - Mean ) / 3 SD

404

Achieved Capability Index Cpk: Example

0

0.2

0.4

0.6

0.8

1

1.2

6 7 8 9 10 11 12

126

Mean + 3 SD- 3 SD

33

3 3

Example:

USL : 12 LSL: 6

Mean : 9 SD : 1

Cpu = 3 / 3 = 1

Cpl = 3 / 3 = 1

Cpk = Min [1 , 1] = 1

Cpk = 1

405


0

0.2

0.4

0.6

0.8

1

1.2

5 6 7 8 9 10 11

126

Mean + 3 SD- 3 SD

42

3 3

Example:

USL : 12 LSL: 6

Mean : 8 SD : 1

Cpu = 4 / 3 = 1.33

Cpl = 2 / 3 = 0.66

Cpk = Min [1.33, 0.66] = 0.66

Cpk < 1, Process doesn’t meet the customer requirements.

406


0

0.2

0.4

0.6

0.8

1

1.2

6 7 8 9 10 11 12

126

Mean + 3 SD- 3 SD

33

3 3

Conclusion:

Cpu = 3 / 3 = 1

Cpl = 3 / 3 = 1

Cpk = Min [1 , 1] = 1

Cp = (USL – LSL) / 6 SD = 6 /6 = 1

When process Mean is at center of Specification then

Cpk =Cp

407


Example:

Process: Training Process Characteristic: Time to Certify Agents

Specification : 55 ± 5 Days


Mean 55 52 58

SD 1 1 1

USL – LSL 10 10 10

6 SD 6 6 6

Cp 1.66 1.66 1.66

Cpk 1.66 0.67 0.67

408

Relationship of Cp and Cpk

Cpk = 2

Cpk = 1.5

Cpk = 1

Cpk = 0

Cpk = -0.5

Cp =2

Cp =2

Cp =2

Cp =2

Cp =2

USLLSL

Mean

409


2. Calculate process capability for a given example

Process CapabilityExercise 1.19 (20 minutes)

410

Process Sigma Level Calculations

411

General Guidelines

1. Ensure that the data collected is free from measurement error

2. Ensure that the data is true representation of the population

3. Ensure that the process is stable

4. Ensure that the data is following Normal distribution.

5. If data is not normal, check for the transformed functions.

6. Still, data is non-normal, recheck the data or check whether the data fit in any non-normal distribution.

Process Capability for Continuous Data

412

Z Calculation for Normal Distribution Data

If data is normal follow the following sequence.

• Calculate Ppk using Minitab.

• Sigma multiple Long term of the process is = Zlt = (3 * Ppk)

• Sigma multiple Short term of the process is = Zst = (3 * Ppk)+1.5

• Note the DPMO also from Minitab output.

Process Capability for Continuous Data

413

Data Transformation

Typically one sided specification processes have skewed data.In such cases we transform the data points and specifications to convert data into a normal data.

Transformed data for Y may be in the form of Y2, Y3, Y0.5, Ln(Y) etc.

We can also use the Box-Cox transformation available in Minitab or apply instead of try out all the combinations.

Transformation used for Y is also applied to LSL and USL

However, test of normality should be done again to check if the transformed data has now become normal

414

Process Capability for Continuous Data Example 1

The cycle time (in Minutes) of each transaction in a day on both the shifts was collected. The SLA for the cycle time of the process is 60min.Calculate the process capability.

Enter the data in Minitab

Cycle Time Shift Cycle Time Shift50 1 60 251 1 55 250 1 49 255 1 53 256 1 46 252 1 51 248 1 50 252 1 41 251 1 51 249 1 58 252 1 54 250 1 57 256 1 50 252 1 49 252 1 41 2

415

Select Stat>Control Charts> Variable charts for Individuals> I-MR chart

Click Cycle time

Click OK

Stability Test

416

Observation

Indiv

idual V

alu

e

28252219161310741

60

55

50

45

40

_X=51.37

UCL=62.65

LCL=40.09

Observation

Movin

g R

ange

28252219161310741

15

10

5

0

__MR=4.24

UCL=13.86

LCL=0

I-MR Chart of Cycle Time_1

All the points in the I-MR control chart are within control limits. No significant trends also.

So, it is a stable process.

Note:If there are any out of control point, the related data to be analyzed and eliminate the assignable cause. Then, remove that data point and continue.

Stability Test

417

Cycle Time_1

Perc

ent

6055504540

99

95

90

80

70

605040

30

20

10

5

1

Mean

0.062

51.37StDev 4.206N 30AD 0.696P-Value

Probability Plot of Cycle TimeNormal

P-Value is great than 0.05.So, it is a normal Distribution.

Since the data is satisfying all the pre-requisites, we can calculate the process capability for this process.

Select Stat>Basic Statistics> Normality Test

Click ‘cycle time’ in ‘Variable’ field

Normality Test

418

Click Cycle time

Enter 60 in Upper Spec.

Enter 1

Click OK

Select Stat>Quality Tools>Capability Analysis>Normal

Capability Test

419

Select Stat>Quality Tools>Capability Analysis

6055504540

USLProcess Data

Sample N 30StDev(Within) 3.76009StDev(Overall) 4.24218

LSL *Target *USL 60Sample Mean 51.3667

Potential (Within) Capability

Overall Capability

Pp *PPL *PPU 0.68Ppk 0.68Cpm

Cp

*

*CPL *CPU 0.77Cpk 0.77

Observed PerformancePPM < LSL *PPM > USL 0.00PPM Total 0.00

Exp. Within PerformancePPM < LSL *PPM > USL 10836.64PPM Total 10836.64

Exp. Overall PerformancePPM < LSL *PPM > USL 20919.46PPM Total 20919.46

WithinOverall

Process Capability of Cycle Time

Ppk =0.68Zlt = (3 * Ppk) = 2.04Zst = (3 * Ppk)+1.5 = 3.45DPMO = 20919.46

Capability Test

420

Process Capability for Continuous Data Example 2

HR is working on reduction of recruitment cycle time. 30 data points are collected to set the baseline capability, as shown below. The SLA is 60 days. Calculate the baseline capability of the process.

Recruitment Cycle time Recruitment Cycle time50 5551 5550 5855 5356 4652 5148 5055 4151 5150 5852 5450 5056 5052 4952 40

Enter the data in Minitab

421

Select Stat>Control Charts> Variable charts for Individuals> I-MR chart

Click Recruitment Cycle time

Click OK

Stability Test

422

Observation

Indiv

idual V

alu

e

28252219161310741

60

55

50

45

40

_X=51.37

UCL=61.45

LCL=41.28

Observation

Movin

g R

ange

28252219161310741

12

9

6

3

0

__MR=3.79

UCL=12.39

LCL=0

11

I-MR Chart of Recruitment Cycle time

Some of the points are crossing control limits. No significant trends also.

So, it is a not a stable process.

Since there are some out of control points, the related data to be analyzed and eliminate the assignable cause. Then, remove that data point and continue.

Let us continue with the same data now.

Stability Test

423

P-Value is less than 0.05.So, it is a non-normal Distribution.

So look for the transformation.


Click ‘Rec. cycle time’ in ‘Variable’ field

Recruitment Cycle time

Perc

ent

6055504540

99

95

90

80

70

605040

30

20

10

5

1

Mean

0.018

51.37StDev 4.115N 30AD 0.911P-Value

Probability Plot of Recruitment Cycle timeNormal

Normality Test

424

Select Stat>Control Charts>Box-Cox Transformation

Click Recruitment Cycle time

Enter 1

Click on Options

Ensure dot on top option

Enter C2It store the transformed data in C2 column

Click Ok

Data Transformation

425

Lambda

StD

ev

5.02.50.0-2.5-5.0

4.2

4.0

3.8

3.6

3.4

3.2

Lower CL

Limit

Lambda

3.03

(using 95.0% confidence)

Estimate 3.03

Lower CL -1.26Upper CL *

Rounded Value

Box-Cox Plot of Recruitment Cycle time

Lambda Value = 3.03

Data Transformation

426


Click ‘Box-Cox’ in ‘Variable’ field

Box-Cox

Perc

ent

25000020000015000010000050000

99

95

90

80

70

605040

30

20

10

5

1

Mean

0.071

155934StDev 35099N 30AD 0.673P-Value

Probability Plot of Box-CoxNormal

P-Value is greater than 0.05.So, The transformed data is following normal Distribution.

Now let us calculate the process capability

Data Transformation

427

Select Stat>Quality Tools>Capability Analysis>Normal

Click Rec. cycle time

Enter 60 in Upper Spec.

Enter 1

Click on Box-Cox

Click Box-Cox button

Click others

Enter Lambda value 3.03

Capability Test

428

22500020000017500015000012500010000075000

USL*

transformed dataProcess Data

Sample N 30StDev(Within) 3.36268StDev(Overall) 4.15021

After Transformation

LSL* *Target*

LSL

*USL* 244230Sample Mean* 155371StDev(Within)* 29657.1StDev(Overall)* 35265.1

*Target *USL 60Sample Mean 51.3667

Potential (Within) Capability

Overall Capability

Pp *PPL *PPU 0.84Ppk 0.84Cpm

Cp

*

*CPL *CPU 1.00Cpk 1.00

Observed PerformancePPM < LSL *PPM > USL 0.00PPM Total 0.00

Exp. Within PerformancePPM < LSL* *PPM > USL* 1366.91PPM Total 1366.91

Exp. Overall PerformancePPM < LSL* *PPM > USL* 5872.39PPM Total 5872.39

WithinOverall

Process Capability of Recruitment Cycle timeUsing Box-Cox Transformation With Lambda = 3

Select Stat>Quality Tools>Capability Analysis

Ppk =0.84Zlt = (3 * Ppk) = 2.52Zst = (3 * Ppk)+1.5 = 4.02DPMO = 5872

Capability Test

429

We can use Capability Analysis (Binomial) if the data meet the following conditions.

• Each item is the result of identical conditions.

• Each item can result in one of two possible outcomes (success/failure, Go/No go)

• The probability of success (or failure) is constant for each item.

• The outcomes of the items are independent of each other.

Process Capability for Attribute Data

430

Process Capability for Attribute Data - Example

A Black belt is working on project to reduce the defective transaction in a process. He has collected data for 30 days as shown below.

Calculate the baseline Capability of the process.

Sl No Trasactions / day Defective Transations Sl No Trasactions / day Defective Transations1 52 9 16 45 142 55 11 17 53 133 51 16 18 55 124 47 11 19 49 105 50 9 20 51 76 49 11 21 46 87 54 9 22 55 98 48 13 23 50 89 53 11 24 53 1110 50 7 25 55 1011 45 10 26 45 1212 50 6 27 49 1213 52 11 28 53 1214 47 7 29 48 915 45 10 30 50 17

Enter the this data in Minitab

431

Select Stat>Quality Tools >Capability Analysis>Binomial

Click on ‘Defective Transactions

Click on ‘No. of Transactions

Target, by default 0.Value can be entered if there is any target.

Click OK

Capability Test

432

Sample

Pro

port

ion

28252219161310741

0.4

0.2

0.0

_P=0.2093

UCL=0.3819

LCL=0.0367

Sample

%D

efe

ctiv

e30252015105

22.8

21.6

20.4

19.2

18.0

Summary Stats

0.00PPM Def: 209302Lower CI : 189000

Upper CI : 230745Process Z: 0.8088Lower CI :


0.7364Upper CI : 0.8816

% Defective: 20.93Lower CI : 18.90Upper CI : 23.07Target:

Sample Size

%D

efe

ctiv

e

555045

30

20

10

35302520151050

6.0

4.5

3.0

1.5

0.0

Tar

Binomial Process Capability Analysis of Defective TransationsP Chart

Tests performed with unequal sample sizes

Cumulative % Defective

Rate of Defectives

Dist of % Defective

The P-Chart Verifies that the process is in a state of control.

In this case there is no out of control point.

The proportion defective is 20.93%.

Cumulative % defective is the running average of the percentage defective.

It verifies that you have collected data from enough samples to have a stable defective estimate.

The rate appears to be stabilizing around 21%

Capability Test

433

Defective rate plot verifies that the % defective is not influenced by the number of items sampled.

Data should appear randomly distributed.

Histogram of % defective displays the over all distribution of the % defectives from the samples collected

Sample

Pro

port

ion

28252219161310741

0.4

0.2

0.0

_P=0.2093

UCL=0.3819

LCL=0.0367

Sample

%D

efe

ctiv

e

30252015105

22.8

21.6

20.4

19.2

18.0

Summary Stats

0.00PPM Def: 209302Lower CI : 189000

Upper CI : 230745Process Z: 0.8088Lower CI :


0.7364Upper CI : 0.8816

% Defective: 20.93Lower CI : 18.90Upper CI : 23.07Target:

Sample Size

%D

efe

ctiv

e

555045

30

20

10

35302520151050

6.0

4.5

3.0

1.5

0.0

Tar




Rate of Defectives

Dist of % Defective

Capability Test

434

Sample

Pro

port

ion

28252219161310741

0.4

0.2

0.0

_P=0.2093

UCL=0.3819

LCL=0.0367

Sample

%D

efe

ctiv

e

30252015105

22.8

21.6

20.4

19.2

18.0

Summary Stats

0.00PPM Def: 209302Lower CI: 189000Upper CI: 230745Process Z: 0.8088Lower CI:


0.7364Upper CI: 0.8816

%Defective: 20.93Lower CI: 18.90Upper CI: 23.07Target:

Sample Size

%D

efe

ctiv

e

555045

30

20

10

35302520151050

6.0

4.5

3.0

1.5

0.0

Tar




Rate of Defectives

Dist of % Defective

Results:P-chart indicates that process is stable as there are no data points out of controlThe chart of cumulative % defective show that the estimate of the overall defective rate appears to be settling down around 21%.

Sample

Pro

port

ion

28252219161310741

0.4

0.2

0.0

_P=0.2093

UCL=0.3819

LCL=0.0367

Sample

%D

efe

ctiv

e30252015105

22.8

21.6

20.4

19.2

18.0

Summary Stats

0.00PPM Def: 209302Lower CI: 189000Upper CI: 230745Process Z: 0.8088Lower CI:


0.7364Upper CI: 0.8816

%Defective: 20.93Lower CI: 18.90Upper CI: 23.07Target:

Sample Size

%D

efe

ctiv

e

555045

30

20

10

35302520151050

6.0

4.5

3.0

1.5

0.0

Tar




Rate of Defectives

Dist of % Defective

The process Z is around 0.8, which is very poor. This process could use a lot of improvement

Capability Test

435

Terminology

Unit:A unit is the tangible & measurable characteristic of a process input / output.Defects are observed / counted in the output characteristic of a unit (Denoted as Y)

Examples:Every Call received by a call center Agent: Unit= CallEach employee recruitment cycle time Unit = EmployeeEach transaction processed by agent Unit = TransactionThe transaction not meeting the customer requirement Unit: TransactionNon-availability of system Unit = System

Defect:• A defect is a failure to conform to requirements • Any type of undesired result is a defect.

A failure to meet one of the acceptance criteria of a customer. • A defective unit may have one or more defects.

Process Sigma Multiple for Discrete Data

436

Specification Vs CharacteristicSpecification is a customer-defined tolerance for the output unit value.There may be two sided specifications.Specification form the basis of any defect measurement exercise on continuous data

A characteristic is a customer-defined expectation on the output unit. Characteristic from the basis of any defect measurement exercise on discrete data There may be multiple characteristics defined on a single unit. It is also possible to have a combination of specifications and characteristic on an single unit

Specification : Continuous Data Characteristic : Discrete Data

Example: Transaction processing

Unit: Each transaction processedSome of the defect definitions may be

1) Transaction not completed before 24min = Specification 2) Transactions not submitted in to client server after processing = Characteristic

3) Transactions submitted with out filling up the amount = Characteristic

437

Opportunity for Defect:

Any critical characteristic which is routinely inspected before passing the item is an opportunity for defect.(or)Opportunity for the error in a process is the number of steps / task / actions in the process, where there is a possibility of committing error, that may result in a defect.Concept of OFD is applicable only when defect measurement is discrete.

Recollect the operational definition >>>>>>“Clarity is more important when developing and selecting the measures that will be used to determine the SIGMA PERFORMANCE of the process.

e.g. Operational definitions may determine if a team is to count all the defects on an invoice (required to calculate defects per million opportunities) or the total number of defective invoices (any invoice with any defect) or the type of defects encountered on an invoice (to eliminate the most common defects first). Each of these cases may require a very different approach for gathering the data”

438

• For example, if client wants to ensure that each transactions to be completed with in 20 min, it can be considered as specification and follow the continuous data path.

• If client is interested in controlling defective transactions, the entire unit is either good or Bad. A proportion can be calculated (Binominal).

• If operation head or client head is interested in minimizing the abandoned calls and team is interested in identifying the steps / task / actions in the process, First team suppose to map the process and identify the steps which results in abandoned calls and those steps can be considered as Opportunity for Defect.

• In some cases, client may scope the improvement area. In that situation,team can consider only that portion and identify the Opportunities for Defects.

Examples:

439

• If the measurement and improvement of process characteristics calls for noting all the defects, each detail of the process to be considered OFD.

• If there is no limit to the number of defects that can be counted, It is not possible to count the non-defects, poison distribution can be used.

• If operation head or client head is interested in minimizing the abandoned calls and team is interested in identifying the steps / task / actions in the process, First team suppose to map the process and identify the steps which results in abandoned calls and those steps can be considered as Opportunity for Defect.

Examples:

440

Exercise for DPMO calculation for Discrete data

The Inspection result for a set of 100 Purchase Orders (PO) are given in the Table below:

Cause of Rejection Number of Defects

Supplier Name Incorrect 1

Supplier Door # Incorrect 1

Quantity Higher than that in Indent 3

Quantity less than that in Indent 1

Price is higher than that in the Indent 2

Price is lower than that in the Indent 2

Number of Defects =

Number of Opportunities for Defects =

441

DPU: Defects Per UnitThe ratio of Number of Defects found to the total Number of Items Inspected

Number of Defects = 10

DPU = Number of Defects / Total Number Units Inspected = 10 / 100 = 0.1








442

DPO: Defects Per Opportunity

Ratio of total number of Defects to the total number of opportunities in the inspected lot.

DPO = Defects / (Opportunities x Total number of Units Inspected)

DPO = 10 / (100 x 4) = 0.025








443

DPMO: Defects Per Million Opportunity

DPMO = DPO x 1000000

DPO = Defects / (Opportunities x Total number Inspected)

DPO = 10 / (100 x 4) = 0.025

DPMO = DPO x 1000000 = 0.025 x 1000000 = 25000








444

Yield: Yield = e-DPU

DPU = Defects / (Total Number Inspected) = 10 / 100

= 0.01

Yield = e-DPU= e-0.01=0.99005 = 99 %








445

ZST: Short Term Sigma Value

Z is the Standard Normal Variate equivalent to DPO obtained from Z table.

DPO = Defects / (Opportunities x Total number Inspected)

DPO = 10 / (100 x 4) = 0.025

ZST = 1.96 From conversion tables








446

ZLT: Long Term Sigma Value

ZLT = ZST - 1.5

DPO = 0.025

ZST = 1.96 From conversion tables

ZLT = ZST - 1.5 = 1.96 - 1.5 = 0.46








447



Sigma level calculationsExercise 1.20 (20 minutes)

448

Some Helpful Hints:

• It is always preferable to deal with continuous data. Continuous data is measured on a continuum or scale.

• Collect the cause data along with performance data for initial quick wins. Once you determine the entire processes, collect the additional data related to those causes.

• Always evaluate the colleted data, before calculating the base line capability

449

Some Helpful Hints:

• Proportion Defective: The entire unit is either good or bad. A proportion can be calculated. Assume Binomial

• Count of defects: There is no limit to the number of defects that can be counted. Assume poison.

• Calculate Zlt value using Ppk (noted in Minitab output) instead of Cpk, as Ppk is represents the long term process capability

• Not all the percentages are discrete or count data. Eg. % system availability. If both the numerator and denominator are determined by measuring the % is considered continuous data.

450

ExerciseD & M Phase deliverables

Table 1: Raw data on Transaction time of express Teller

Woking day Transaction Times(sec)

Appraiser A Appraiser B Appraiser C

1 63 55 56 53 61 64

2 69 63 60 65 61 66

3 57 60 61 65 66 62

4 58 64 60 61 57 65

5 79 68 65 61 74 71

6 55 66 62 63 56 52

7 57 61 58 64 55 63

8 58 51 61 57 66 59

9 65 66 62 68 61 67

10 73 66 61 70 72 78

11 57 63 56 64 62 59

12 66 63 65 59 70 61

13 63 53 69 60 61 58

14 68 67 59 58 65 59

15 70 62 66 80 71 76

16 65 59 60 61 62 65

17 63 69 58 56 66 61

18 61 56 62 59 57 55

19 65 57 69 62 58 72

20 70 60 67 79 75 68

Deliverables

1 Type of data

2 Descriptive Statistics (Mean, Median, Mode, SD, Histogram)

3 Sample Size & confidence interval (Accuracy required 10 Secs)

4 Probability of getting transaction time between 55 to 65 seconds.

5 Probability of getting transaction time > 70 seconds

6 Normality Test

7 Process Capability ( LSL : 58 Secs, USL : 65 Seconds)

8 Sigma level calculation

9 Gauge R & R

451



Sigma level calculationsWeek 1 Define & Measure Phase

452

Thank You