sixsigma+wk1

D

M

A

I

C

SIX SIGMA TRAINING

FOR GREEN BELTS

WEEK 1

Six Sigma Operational Excellence

Literature PN 1654520

402-108

16Feb04 Rev CEC 0990-0196-04

Tyco Electronics

Six Sigma Operational Excellence

2100 Paxton Street

Harrisburg, PA 17111 USA

All rights reserved.

This material is “company confidential” and is intended

for internal use in Tyco Electronics only.

Revision C, February 2004

Changes are made periodically to this document.

Changes and technical updates will be added in

subsequent editions.

Rev C February 16, 2004

Order from Literature Distribution Phone 717-558-1495 Document # 402-108 Literature Distribution # 1654520

INTRODUCTION

Six Sigma Training for Green Belts – Week 1Tyco, Six Sigma Operational Excellence

Page 1402-108, Rev. C

Rev. A Printed 3/10/2004© 2003 by Sigma Breakthrough Technologies, Inc.

GB101-1

IntroductionIntroduction

Process Improvement Methodology Process Improvement Methodology Process Improvement Methodology Process Improvement Methodology

Operations Green BeltsOperations Green Belts


GB101-2

SPACERSPACER

S - Safety

P - Purpose

A - Agenda

C - Code of Conduct

E - Expectations

R - Roles and Responsibilities

INTRODUCTION


Page 2402-108, Rev. C


GB101-3

SafetySafety

Safety is always our first priority !

Please be careful of all work hazards, trip hazards, and bio-hazards!

Specific safety issues including:

Safety equipment

Emergency exit procedures

Any other safety related issues


GB101-4

PurposePurpose

Provide an overview of the Green Belt training program

Explore the Define, Measure, Analyze, Improve, Control (DMAIC) process improvement methodology.

Construct a plan and use the tools and methods on the project that has been assigned

INTRODUCTION


Page 3402-108, Rev. C


GB101-5

4:30 - 5:00

4:00 - 4:30

3:30 - 4:00 Basic Quality Tools and Using Minitab

3:00 - 3:30

2:30 - 3:00

Basic Statistics2:00 - 2:30

1:30 - 2:00Action PlanningProcess Mapping

And Value Stream

Mapping

1:00 - 1:30 Introduction to Minitab

12:30 - 1:00

LUNCHLUNCHLUNCHNoon - 12:30

LUNCH11:30 - Noon

11:00 - 11:30Team Building

Introduction to Lean10:30 - 11:00

InterpersonalManagement

Skillsand Dealing with

Resistance

10:00 - 10:30 Kappa Studies & Attribute MSA

Capability Studies

LUNCH

Introduction to SPC9:30 - 10:00 Introduction to Six Sigma

9:00 - 9:30

Use of the Reference Guides8:30 - 9:00 Sampling

FMEAMeasurement

System Analysis

Basic Quality Tools and Using Minitab

(cont.)

Introduction8:00 - 8:30

FridayThursdayWednesdayTuesdayMonday

Effective Team Meetings and Presentation Skills

Introduction to Multi Vari Analysis

C&E Matrix

Summary Week 1

Capability Studies(cont.)

AgendaAgenda –– 11stst SessionSession


GB101-6

AgendaAgenda –– 22ndnd SessionSession

Multi – Vari Analysis

Central Limit Theorem and Minitab

Confidence Intervals

Focused Area Improvement Event

Hypothesis Testing

t – Tests

ANOVA

Chi-Square

Correlation and Regression

Control Plans

Quick Changeover

Project Management

Introduction to Design of Experiments

Techniques for Preparing Presentations

Error Proofing

INTRODUCTION


Page 4402-108, Rev. C


GB101-7

Guidelines / Code of ConductGuidelines / Code of Conduct

Try to visualize what the DMAIC process should look like for your project issues

If you have a question at any time - ask!

Listen as an ally - “How can I apply the DMAIC effectively in my process?”

Parking Lot will be set up to capture ideas

There will be a lot of team activities - we’ll be moving fast!

Housekeeping


GB101-8

Expectations & RolesExpectations & Roles

Expectations

You will have a better understanding of the DMAIC process and the utilization of the various statistical tools and Lean methodologies

You will have a plan for use of the tools in your project areas by Friday

What are your expectations?

Roles

Instructors:

Participants - You

STATISTICS REFERENCE GUIDE


Page 1402-108, Rev. C

GB102-1

QUALITY TOOLS AND

STATISTICS

REFERENCE GUIDE

402-105

Quality Tools and Statistics Quality Tools and Statistics Reference GuideReference Guide

GB102-2

Purpose of the Reference GuidePurpose of the Reference Guide

Provide a supplement to the Power Point slides that are used during the weeks / sessions of Belt training

Provide Tyco Electronics employees with a convenient reference resource for use when working independently or as a team

Provide consistent training and resource material



Page 2402-108, Rev. C

GB102-3

Obtaining Additional CopiesObtaining Additional Copies

Controlled through Tyco Electronics Literature Distribution

Ordering information:

Document Number 402-105

Literature Distribution Number 1307319

Maintained under “document control”

GB102-4

Major TopicsMajor Topics

There are 6 major sections within the Quality Tools andStatistics Reference Guide

The 8 – Discipline Model for Problem Solving

The DMAIC Process

Lean Techniques

Basic Tools

Variability

Glossary of Terms



Page 3402-108, Rev. C

GB102-5

The 8 The 8 –– Discipline Model Discipline Model

for Problem Solvingfor Problem Solving

What is 8-D?

A systematic process that describes, analyzes and identifies the root cause(s) of a problem

Most frequently prescribed format for responding to customer complaints

When is it used?

When the product or process does not meet the documented standards

When a process is operating “out of control”

To resolve incidents of customer reported failures

GB102-6

4Identify &

Verify RootCauses

3ContainProblem

2Describe

Opportunity/Problem

1Define concern,

Organize andPlan

7Prevent

Recurrence

8Celebrate andCommunicate

Success

5Develop

CorrectiveAction Plan

6Implement &

VerifyCorrective

Action

ProblemSolvingProcess

(8D)

PLANACT

DO

CH

EC

K

The 8 StepsThe 8 Steps



Page 4402-108, Rev. C

GB102-7

•STEP 1: Define concern, organize, and plan

1.If needed, assemble a small team of 4-6 people.

2.Ensure that the team has a designated champion.

3.Identify problem topic or opportunity.4.Define purpose, objective, and scope of

project.5.Reach consensus on:

- Charter/Key operating boundaries6.Gather team's ideas and opinions about

the problem area.7.Identify/define action plans.

1.Collect data on production, sales, employee/customer, surveys and feedback, reports, memos, etc. that would establish that a problem exists in a particular area. Determine if data indicates patterns or trends. Are the standards being adhered to?

2.Identify problem topic or opportunity. What is the issue?

3.Relate topic to objectives, business impacts and processes.

4.Define purpose, objective, and scope of project.

Organize and PlanDefine Concern

Reference Guide: Section 1-9Reference Guide: Section 1-9

88––D: Example DetailsD: Example Details

GB102-8

Use For ThisUse For This

Section on 8 Section on 8 -- DD

Can be used for training individuals or teams on this problem solving model

Provides detailed guidance for teams attempting to apply the process to resolve problems

Suggests various tools to apply within the 8 steps

Assists with selecting tools for data collection and decision making

Provides a series of questions to ask at the end of each step



Page 5402-108, Rev. C

GB102-9

DMAIC

The DMAIC model – Define, Measure, Analyze, Improve and Control is a rigorous, disciplined process used by Six Sigma Project Teams.

The DMAIC ProcessThe DMAIC Process

GB102-10

When To Use DMAICWhen To Use DMAIC

Black Belt Projects:

When there is a need to implement breakthrough improvement to a process of strategic importance to the Business

When there is a need to introduce a new product design

Green Belt Projects:

When there is a need to implement breakthrough improvement on projects identified by service or process inefficiencies or product defects

When there is a need for continual improvement on an established process



Page 6402-108, Rev. C

GB102-11

Step 1: DEFINEDescription: Leadership identifies a process that is not meeting the strategic objectives, establishes the scope and boundaries for the project and commissions a Black Belt to lead a Six Sigma project team.

Tasks to do / Questions to AskAre the wasteful processes defined as an output?Which process requires improvement the most?Who is the process owner?Who are the subject matter experts?Who should participate in the improvement effort?What are the boundaries for the process?Who are the customers for this process?What are the customers’ requirements?What is to be produced? Draft specific description of each requirement in terms of physical and measurable attributes of the output.Review agreed-upon customer requirements.Validate the detailed description with the customerDefine the project goals and savings.

Guidelines1.The process owner has

accountability and authority to make changes in the process.

2.Standards are being followed.

3.Identify the current state, the entitlement state, and GAP.

4.Team consists of a Black Belt, process employees, process owner and finance.

5.Process defined as an output statement. Boundaries should be established by identifying suppliers, inputs, customers, and output.

6.Identify internal customers by name.

Step 1: DefineStep 1: Define

GB102-12

What is the Result Of What is the Result Of

the Define Step?the Define Step?

Project commissioned by Executive Champion

Project Charter completed

Improvement opportunity identified

Goals and savings identified

Savings agreed to by Finance

Subject matter experts identified

The improvement team members identified

The customers of the process identified



Page 7402-108, Rev. C

GB102-13

ChecklistChecklist –– Define StepDefine Step

Is this process the most critical facing your organization?

Has the Project Charter been completed?

Has a Black Belt been assigned?

Has a process owner been identified?

Have the team members been identified?

Have the boundaries, inputs and outputs been identified for the project? (Initial Value Stream Map)

Has Finance endorsed the projected savings?

GB102-14

Commission Team1. Who is the Black Belt?2. Who are the subject matter experts?3. Who will be responsible for implementation of this

improvement?4. What are the team’s next steps?

Identify wasteful process1. How have you determined the boundaries to your

process?2. How does this process support external customer

requirements?3. What documented standards are being followed for

the wasteful process?

Define Step Define Step –– Leadership QuestionsLeadership Questions



Page 8402-108, Rev. C

GB102-15

Topics include:

Lean strategies and building blocks

8 wastes (value added vs. non-value added)

Continuous improvement teams (Kaizen)

Cycle time

One-piece flow (JIT)

Pull system (Kanban)

Quality at the source: error proofing (Poka Yoke)

Quick changeover

Reference Guide Reference Guide –– Lean TechnologiesLean Technologies

GB102-16

Topics include:

Standard work; standard operations

Takt time

Total productive maintenance (TPM)

Value Stream mapping

Work flow: visual control and cell design

Workplace organization: 5S

Reference Guide Reference Guide –– Lean Technologies, cont.Lean Technologies, cont.



Page 9402-108, Rev. C

GB102-17

The Basic Quality ToolsThe Basic Quality Tools

Tools for collecting, analyzing and displaying data

Tools for generating ideas

Tools for reaching consensus

Tools for documenting processes and planning actions

GB102-18

The Basic Concepts of StatisticsThe Basic Concepts of Statistics

Types of data

Managing a process

Process Control charts

Process Capability

Measurement System Analysis



Page 10402-108, Rev. C

GB102-19

Glossary of TermsGlossary of Terms

Provides a convenient reference to the terms that are used most frequently within the Six Sigma Performance Excellence initiative

Provides a guide to consistent use of terms

INTRODUCTION TO SIX SIGMA


Page 1402-108, Rev. C

GB103-1

Define Measure Analyze Improve ControlDefine Measure Analyze Improve Control

Climbing to Operational Excellence:Black Belt Training: Introduction to Six Sigma

Training for Operations Training for Operations Green BeltsGreen Belts

GB103-2


Introduction To Six SigmaIntroduction To Six Sigma

History of Six Sigma

Basic Definitions

What is Six Sigma?

Why is Tyco Electronics deploying Six Sigma?

What is the implementation strategy for Tyco Electronics?



Page 2402-108, Rev. C

GB103-3


So many choices,

too many buzz words

GB103-4


The History of Six SigmaThe History of Six Sigma

First publicized in the mid 1980’s by Motorola as a means of organizing their Malcolm Baldrige initiative

Jack Welch adopted in 1994 and implemented within GE

Gained notoriety in 1999 when GE reported that Six Sigma goals were tied to all executive compensation and that promotions within the executive ranks required the achievement of Black Belt status

1999 annual report stated that GE saved $2 Billion over 5 years because of Six Sigma projects



Page 3402-108, Rev. C

GB103-5


Broad AcceptanceNew Technologies

Competitive Awareness

ResultsRefinementDesign

Merrill LynchECMWirecraft

Lockheed Martin

Avery Dennison

FordPolaroidSiebe

AMEXCraneNokiaGEMotorola

DupontSonyBombardierAllied SignalABBTI

1999 - 20021997 - 19981996 – 19971994 – 19951993 – 19941985 - 1992

The History of Six Sigma, cont.The History of Six Sigma, cont.

GB103-6


The History of Six Sigma, cont.The History of Six Sigma, cont.

Motorola

Texas Instruments

IBM / DEC / Kodak

ABB

Allied Signal

GE

Whirl Pool, Siebe, Polaroid

3M

Research & DevelopmentResearch & Development

ManufacturingManufacturing

ServiceService



Page 4402-108, Rev. C

GB103-7


Basic DefinitionsBasic Definitions

Sigma

A mathematical term used to designate the distribution or spread of any process around the average (mean) as expressed in “standard deviations”

For a business or manufacturing process, the sigma value is a metric used to indicate how well the process is performing

GB103-8


Basic Definitions, continuedBasic Definitions, continued

Six Sigma Process

A disciplined methodology of defining, measuring, analyzing, improving and controlling the quality of products, processes and transactions with the goal of eliminating virtually all defects

Six Sigma Process Improvement

A comprehensive, flexible, but essential system to make our business processes more responsive, competitive and profitable



Page 5402-108, Rev. C

GB103-9



Entitlement

The optimal performance level that can be achieved by a process

How would the process operate if it was centered and did not move

Lean

A series of qualitative tools that focus on process optimization through cycle time reduction and the elimination of waste

DMAIC

Define, Measure, Analyze, Improve, Control: disciplined methodology utilized to manage Six Sigma projects

GB103-10



Process

The defined way by which all work gets completed

Any activity or group of activities that takes an input, adds value and provides an output

Manufacturing Process

The defined way utilized to produce the physical product that must conform to a defined set of requirements

Transactional Process

All non-manufacturing processes



Page 6402-108, Rev. C

GB103-11



Six Sigma Champions

Organizational executives who are responsible for:

Sponsoring the Six Sigma project

Launching the Six Sigma project

Making site visits to “see the team in action”

Attending the project review report-out meetings

Removing barriers that are impeding project success, such as

• Capital

• Human resources

• “Red tape”

GB103-12



Master Black Belt

The highest level of technical and organizational proficiency

Provides the training for and assists the Black Belts

Black Belt

Key technical leaders responsible for organizational change and development – ideally this would be a full time position

Must have a comprehensive knowledge of statistical techniques, quality management systems, project management techniques and must have excellent problem solving and facilitation skills

Responsible for oversight of several Six Sigma projects



Page 7402-108, Rev. C

GB103-13



Green Belt

Provide the leadership for the Six Sigma Project Teams from concept to completion - rotate back to “normal” assignment when the project is completed, or is assigned to another project

Must have statistical training

Must have good facilitation and problem solving skills

GB103-14


Six Sigma Qualitative ToolsSix Sigma Qualitative Tools

Audits

Benchmarking

Brainstorming

Cause and Effect Diagrams

Design Review

FMEA

Housekeeping

Process Mapping

Quality Function Deployment

Total Employee Involvement

Workplace Organization



Page 8402-108, Rev. C

GB103-15


Six Sigma Quantitative ToolsSix Sigma Quantitative Tools

Capability Analysis

Check Sheets

Control Plans

Design For Six Sigma

Design Of Experiments

Gage Reliability and Reproducibility Studies

Histograms

Lead Time Analysis

Pareto Analysis

Statistical Process Control

Statistical Tolerance Analysis

GB103-16


Six Sigma Implementation ToolsSix Sigma Implementation Tools

Cellular Manufacturing

DMAIC

Design For Manufacturability


Just In Time (JIT)

Kan Ban

One Piece Flow

Poka Yoke (Error Proofing)

Predictive Maintenance

QOS – Quality Operating System Reviews

Quick Changeover

Supply Base Development



Page 9402-108, Rev. C

GB103-17


What is Six Sigma?What is Six Sigma?

An approach to sustainable improvement that:

Moves us toward our goal of being a world-class company

Fosters a common language and interplant cooperation

Uses a disciplined process to drive capability improvement and eliminate waste

Helps Tyco:

Develop expert personnel and future managers

Increase capacity with minimal capital

Meet productivity goals

Improve customer service

GB103-18


Six Sigma Is…Six Sigma Is…

A Business Improvement Process That Is Lead By Organizational Executives and Senior Management

A Holistic Approach To Management

Must Be Applied In All Areas Of The Company

Manufacturing

Transactional

• Service / Administrative

• Engineering / Design

Communications Intensive

Fact Based And Results Oriented

6



Page 10402-108, Rev. C

GB103-19


Six Sigma Six Sigma –– New and OldNew and Old

What’s new

Some analysis tools (C&E matrix, Multi-vari studies)

Software (Minitab)

DMAIC process integrates the use of the tools

Train - Apply - Review format

Application to Non-manufacturing and Design

What’s not

Some tools (DOE, Gage R&R)

Team structure

Continuous Improvement methods (Kaizan)

GB103-20


Why Six Sigma FailsWhy Six Sigma Fails

Lack of visible support from Executive and Senior Managers

Lack of understanding of the Six Sigma process

Business metrics not clearly defined

Viewed as “another quality program” or “a manufacturing” program

Reward system not linked to the outcomes of Six Sigma

Inadequate / infrequent communications

Not requiring the disciplined process (shortcuts)

Insufficient resources



Page 11402-108, Rev. C

GB103-21


Count the number of times the 6th letter of the alphabet appears in the following text:

The necessity of training farm hands for the first class farms in the fatherly handling of farm live stock is foremost in the eyes of the farm owners. Since the forefathers of the farm owners trained the farm hands for first class farms in the fatherly handling of farm live stock, the farm owners felt they should carry on with the family tradition of training farm hands of the first class farmers in the fatherly handling of farm live stock because they believe it is the basis of good fundamental farm management.


The Inspection ExerciseThe Inspection Exercise

GB103-22


No matter how good your inspection and test processes are, the more defects you create, the more defects escape to the customer

Total Defects Per Unit

Esc

ap

ing

Def

ects

Theory of Escaping DefectsTheory of Escaping Defects




Page 12402-108, Rev. C

GB103-23


Six Sigma …..Six Sigma …..

A ProcessA Process

ImprovementImprovement

MethodologyMethodology

Based onBased on

“Statistical“Statistical

Thinking”Thinking”

GB103-24


Statistical ThinkingStatistical Thinking

A habitual way of looking at work that:

Recognizes all activities as processes

Recognizes that processes have inherent variability and probably have non-value adding steps

Uses data to understand variation / waste and to drive decisions to improve these processes



Page 13402-108, Rev. C

GB103-25


The “Normal Curve”

+/- 3 : The Traditional

Quality Standard

23 11 2 3

34.13% 34.13%

2.1% 2.1%13.6% 13.6%

68.3%

95.5%

99.7%

Six Sigma As a Statistical ConceptSix Sigma As a Statistical Concept

GB103-26


Three-Sigma Process

LSL USL3 standard deviations




“Sigma” refers to the number of standard deviations between the center of the process and

the nearest specification limit.

Four-Sigma Process

Five-Sigma Process

Six-Sigma Process



Page 14402-108, Rev. C

GB103-27


2 308,5373 66,8074 6,2105 2336 3.4

PPM

Process

Capability

Defects per

Million Opp.

The Sigma LevelThe Sigma Level


GB103-28


Close your eyes and listen to a 6 sigma process, followed by a 4 sigma process.

The Sound of Quality…The Sound of Quality…




Page 15402-108, Rev. C

GB103-29


LSL USL

Defects

Process Capability

Inadequate DesignMargin

Inadequate Process

Capability

SuppliedMaterial Variation

Inadequate Measurement

Capability

Defects

Dissecting Process CapabilityDissecting Process Capability


GB103-30


ProcessImprovement That Is:

Methodical

Significant -essentially “error free”

Abundant

3.4

Why Deploy Six Sigma?Why Deploy Six Sigma?



Page 16402-108, Rev. C

GB103-31


6

5

4

3

2

5% 10% 15% 20% 25% 30%

Cost of Quality (% of Sales)

3 Cost of Quality : 10-15% of Sales3 Cost of Quality : 10-15% of Sales

What’s Your Cost Reduction Opportunity ?What’s Your Cost Reduction Opportunity ?

Sig

ma

Le

vel

Sigma Level and Cost of QualitySigma Level and Cost of Quality


GB103-32



Customers Want

Price

Delivery

Quality

We Focus On

Cost

Cycle Time

Reduction of Defects



Page 17402-108, Rev. C

GB103-33


The Basic ApproachThe Basic Approach

The Right Projects

+The Right People

= Better, Faster Results

+The Right Roadmap

and Tools

+The Right Support


GB103-34


A Proven ApproachA Proven ApproachAimed at infrastructure to produce institutionalization

Leadership training to create understanding, goals, process review

Champions form guiding coalition

Black Belts lead projects and become coaches and trainers

Well documented, step-by-step roadmap that works

Process analysis

Application of tools

Establishment of process control

Training with aggressive project selection and results

Action learning: “Plan - Train - Apply - Review” format



Page 18402-108, Rev. C

GB103-35


Practical ProblemPractical Problem Statistical ProblemStatistical Problem

Statistical SolutionStatistical SolutionPractical SolutionPractical Solution

),...,,( 21 kxxxfy

Overall ApproachOverall Approach


GB103-36


Define

Measure

Analyze

Improve

Control

D

M

A

I

C

The Six Sigma ProcessThe Six Sigma Process



Page 19402-108, Rev. C

GB103-37


Step 1: DefineStep 1: Define

Identify the “gap” in meeting the Business strategy or objective

Establish the scope and boundary for the project

Identify the Black Belt and the project team members

Establish the project goals and savings

Obtain approval of the Senior Leaders

GB103-38


Step 2: MeasureStep 2: Measure

A functioning project team lead by a Black Belt

A detailed process map reflecting the key process variables

Measurement capability has been determined

Baseline short term and long term process capability has been determined

Initial determination of the appropriate qualitative, quantitative and implementation tools

Confirmation of the project goals and savings

Confirmation that the team consists of the right members



Page 20402-108, Rev. C

GB103-39


Step 2: Measure Typical ActivitiesStep 2: Measure Typical Activities

Process mapping

Process FMEA

Evaluate / implement a data collection process

Evaluate / implement a control plan

Process characterization / identification of the primary sources of variation

Measurement R & R studies

Process capability determination

GB103-40


Step 3: AnalyzeStep 3: Analyze

Comprehensive analysis of the process variables and the relationship to the product requirements

Understanding of which inputs affect which output



Page 21402-108, Rev. C

GB103-41


Step 3: Analyze Typical Step 3: Analyze Typical

Update process map, process FMEA and control plan

Identification of the root cause of the variation through

Pareto Charts that analyze the data from several perspectives

Cause and Effect Diagrams

SPC Charts

Multi-Vari Studies

Correlation and Regression

GB103-42


Step 4: ImproveStep 4: Improve

Key process variables have been confirmed through the use of data and statistical experimentation

Trial solutions are identified – the efforts required and risks involved are documented

Trial solutions must attack the root cause

Solution must solve the problem / close the “gap”

Project plan to transfer the revised process into sustaining operation



Page 22402-108, Rev. C

GB103-43


Step 4: Improve Typical ActivitiesStep 4: Improve Typical Activities

Process optimization using implementation tools

Focused area improvement

One piece flow

Kan-Ban

Poka Yoke (Error Proofing)

Design of Experiments (DOE)

Determination of the new process capability

Updated process map, FMEA, and control plan

GB103-44


Step 5: ControlStep 5: Control

The Six Sigma Operational Excellence project has been completed and has met the stated goals and savings

Long term capability has been established

Operational control plans have been developed and implemented

The process is returned to the process owner for sustained maintenance

Team final report includes identification of lessons learned and future improvement opportunities

Team recognition



Page 23402-108, Rev. C

GB103-45


Step 5: Control Typical ActivitiesStep 5: Control Typical Activities

Update all required documentation to ensure compliance with Quality Management System requirements

Inspection plans / control plans

Process specifications

Quality specifications

Product prints

Workmanship documents

Local documents – work instructions

Routings

FMEA

On going measurement collection defined / implemented

GB103-46


SevenBasic

QualityTools

GreenBelts

StatisticalToolsFor

ProcessImprovement

BlackBelts

Design ForSix Sigma

MasterBlackBelts

Basic Tools Wall Design Wall

2 Sigma 3 Sigma 4 Sigma 5 Sigma 6 Sigma




Page 24402-108, Rev. C

GB103-47


Six Sigma Improvement FocusSix Sigma Improvement Focus

People

Skills

Capabilities

Knowledge

Material

Reduce Variability

Improve Delivery

Reduce Cost

Processes

Reduce Variability

Improve Capability

Improve Cycle Time

Reduce Errors / Defects

Increase Yields

Match Process to Design

GB103-48


Some Benefits of Six SigmaSome Benefits of Six Sigma

Cost Reduction

Productivity Improvement

Market Share Growth

Customer Retention

Cycle Time Reduction

Reduction in Errors / Defects

Culture Change

Improved Product and Service Development



Page 25402-108, Rev. C

GB103-49


Why 3 Sigma Isn’t Good EnoughWhy 3 Sigma Isn’t Good Enough

20,000 lost articles of mail per hour

Unsafe drinking water for 15 minutes each day

2 unsafe landings at most major airports each day

5,000 incorrect surgical procedures each week

No electricity for almost 7 hours each month

22,000 checks deducted from the wrong account each hour

200,000 prescriptions filled incorrectly each year

50 newborns dropped at birth each day

» Source: QCI International

GB103-50


Six Sigma Will Improve EBITSix Sigma Will Improve EBIT

Improve Rolled

ThroughputYield

LowerTotal

Cost ofQuality

Improve ThroughputVolume and

Delivery

Meet CustomerExpectationsAt A Lower

Cost

Increased RevenueIncreased Margins

Lower Cost ofCapital

ImprovedEBIT



Page 26402-108, Rev. C

GB103-51


Design

Manufacturing Defects

Scrap

Rework

Variance

Suppliers

Administrative Activities

Cycle Time

Reliability

Areas of Opportunity Areas of Opportunity

Within Tyco ElectronicsWithin Tyco Electronics

GB103-52


The “Program Du Jour”

An Easy Fix

A Broad Brush Approach

The Universal Answer To All Business Problems

A Self-sustaining Initiative

A Statistical Program

Free

What Six Sigma Is Not…What Six Sigma Is Not…



Page 27402-108, Rev. C

GB103-53


Integrating Six Sigma Integrating Six Sigma

Time

Op

era

tio

na

l E

xc

ell

en

ce

Breakthrough

Continuous Improvement

BreakthroughSix Sigma

Continuous Improvement

NewStandard

NewStandard

GB103-54


Six Sigma Operational ExcellenceSix Sigma Operational Excellence

Along with recognized industry standards will provide the baseline for a common approach to Quality Management and continual improvement

Will provide increased emphasis on business process improvement during Operations reviews

Will allow us to “Plan For Quality”

Reduce the variability in our processes

Improve the design process



Page 28402-108, Rev. C

GB103-55



Implementation Strategy: Phase 1Implementation Strategy: Phase 1

“Back To The Basics” – to drive immediate improvement

Preventative control plans for every major manufacturing process are to be reviewed, updated and enforced

Operator certification to be reviewed and updated as required

Utilize the Deployment Champions to manage the corrective action commitments for customer complaints

Formalize analysis of material returned due to a Tyco Electronics error

Implement a manufacturing process audit program

Implement a shipping / dock audit program

GB103-56




“Launch Six Sigma Projects”

Completion of the Executive awareness training

Deploy Champion training on a Regional / Country level

Complete initial training sessions for Black Belts on a Regional level

Initial Six Sigma Black Belt projects completed and assessed



Page 29402-108, Rev. C

GB103-57




“Breakthrough Improvement”

Each Business achieves “self-sufficiency”

Master Black Belts within the business are leading the organization to higher levels of proficiency

Master Black Belts are training Black Belts

Black Belts are training Green Belts

Black Belts are managing multiple projects

Design for Six Sigma is developed and launched

GB103-58


ExecutivesProject Team

Members

Champions

Master BlackBelt

Black Belts

Green Belts

All Employees

•Lead change•Own strategy, direction, results

•Approve, launch, monitor projects•Responsible for project activities

•Train & coach•Tools expert

•Apply concepts within work area

•Work on assigned projects

•Work on assigned projects

•Key project leaders•Responsible for selecting the right tools

Six Sigma RolesSix Sigma Roles

And Responsibilities SummaryAnd Responsibilities Summary



Page 30402-108, Rev. C

GB103-59


Six Sigma Operational Six Sigma Operational

Excellence ConclusionsExcellence Conclusions

Remember -----

Six Sigma is more than the answer to a mathematical formula for calculating variability

Six Sigma is a comprehensive approach to Business Process Improvement

GB103-60


Reduced process variation

Outputs match customer requirements and expectations

Measurable continual improvement

Reduced costs

Enhanced profitability

Primary Benefits of Six Sigma…Primary Benefits of Six Sigma…



Page 31402-108, Rev. C

GB103-61


Secondary Benefits of Six Sigma…Secondary Benefits of Six Sigma…

Quality of products and service is improved

Process investigation evaluates value-added status of each element (inputs, raw materials, operations)

Mistake-proofing added to processes

Better flow of inputs/materials toward output means less money tied up in inventory/WIP.

Company can react more quickly to customer requests

GB103-62


Program launched with outward blessing of management

Provision made for training

Upper managers disappear after launching

Program demands people’s time, but no time authorized

Results fall short of promises

Program pushed aside for next get-well-quick program

“Here comes another one”

Cycle of doomed programs

Obstacles to SuccessObstacles to Success



Page 32402-108, Rev. C

GB103-63


Overcoming the ObstaclesOvercoming the Obstacles

Senior Management visibly involved.

Champion has the responsibility for removing roadblocks & authority to make changes.

Black Belts and Green Belts receive training to lead teams to implement the data-driven improvement changes.

Personal and project accountability and regular reporting to management.

INTRODUCTION TO LEAN STRATEGIES


Page 1402-108, Rev. C

GB104-1

Rev C February 2004© 2003 by Tyco Electronics

LEAN Strategiesfor Operations Improvement

D

M

A

I

C

Rev C January, 2003© 2003 by Tyco Electronics GB104-2

Cycle Time Reductionthrough

Waste Elimination

WHAT IS WHAT IS WASTEWASTE??

Anything that does not add valueReference Guide, Lean, Page 1, ¶ 1 - 2

What Is Lean?What Is Lean?



Page 2402-108, Rev. C


NON-VALUE-ADDING ACTIVITYProcess/Operation that take time,resources or space, but do not addvalue to the product or service

VALUE-ADDING ACTIVITY

Changes the information or product

Quote #1

Done right the first time

Customer is willing to pay for it(Customer cares)S

Reference Guide, Lean, Page 2, ¶ 1 - 3

Value and NonValue and Non--ValueValue

Added ActivitiesAdded Activities


Over-Production

Waiting

Transportation

Processing

Inventories

Motion

Defective Products

Unused Creativity

Reference Guide, Lean, Page 2, middle

Reference Guide, Lean, Page 2, middle

8 Wastes8 Wastes



Page 3402-108, Rev. C


All work is a process of adding value.

The value can be physical (manufacturing) or information (transactional).

If you are the thing or data going through the process, what is your journey like? How often do you wait, take useless trips, or get reworked?

What percent of your total time in the process would be for adding value?

95%

5%Non-Value

Adding

ValueAdding

Reference Guide, Lean, Page 2, BottomReference Guide, Lean, Page 2, Bottom


Delivery Performance from cycle time reduction

Labor savings from standard work, cells

Manufacturing space from layout improvements

Increased capacity from quick change-over

Reduced scrap from error-proofing

Reduced inventory from 1-piece flow, kanban

$ or $ ?Reference Guide, Lean, Page 1, ¶ 2Reference Guide, Lean, Page 1, ¶ 2

Why Lean Implementation?Why Lean Implementation?

What are the Opportunities for Six Sigma Projects?What are the Opportunities for Six Sigma Projects?



Page 4402-108, Rev. C


Traditional Six Sigma deals with quality and capability: “How can we eliminate defects?”

By adding Lean strategies, we also look at capacity:“How can we do the work faster and more efficiently?”

Lean strives for the best combination of human skill and machines in an elegant process flow.

Not all projects need both. The measure step in DMAIC will lead you to a good balance. To have the data to decide, measure the Lean factors of:

• Cycle time with value-added percent, • Yield compared to theoretical, and • Inventory.

Reference Guide, Lean, Page 1, 1Reference Guide, Lean, Page 1, 1


• Total Process Cycle time ( “Throughput time” )

Process step cycle time

• Worker cycle time ( + total for all workers )

• Machine cycle time

• Value added percent ( time )

• TAKT time (customer demand )

• Yield percent of theoretical ( parts )

• Inventory levels before/after each step, + finished goods

• Change-over time

Lean MeasuresLean Measures



Page 5402-108, Rev. C


Category

Value Added Operation

Transportation

Inspection

Delay

Process Delay

Lot Delay

Improvement Method

Operations Improvement

Location & Layout

Source Inspection & Error-Proofing

Continuous Flow

Synchronization (JIT)

1-Piece Flow

5 %

10%

5%

80%

Reference Guide, Lean, Page 1, mid - bottom

Reference Guide, Lean, Page 1, mid - bottom

LEAN METHODS:LEAN METHODS:JIT + AutonomationJIT + Autonomation


LEANLEANMETHODS:METHODS:

JIT + JIT + AutonomationAutonomation

Error Proofing“Poka Yoke”

Quick Change-Over


Standard Operations

Kanban

WorkplaceOrganization

( 5 - S )

Reference Guide, Lean, Page 1, bottomReference Guide, Lean, Page 1, bottom

Lean Tools: Improvement Step ApplicationsLean Tools: Improvement Step Applications



Page 6402-108, Rev. C


As a combined team, implement Lean strategies based on the data and analysis. Include:

TAKT Time p. 8

Standard Operations p. 7

Cycle time p. 3

Pull system, one-piece flow p. 6

Inventory levels p. 5

Cell design and workplace layout p. 9

Plan your improvements, implement the changes, re-run production, measure.

Reference Guide, Lean:

Reference Guide, Lean:

Lean StrategiesLean Strategies


TAKT Time =Total Daily Operating Time

Total Daily Requirement

TAKT Time =60 sec x 60 min x 7.5 hrs x 2 shifts

3,600 pieces / day

TAKT Time =54,000 sec.

3,600 / day= 15 sec./ pc.

Reference Guide, Lean, Page 8Reference Guide, Lean, Page 8

TAKT Time=Customer Demand TAKT Time=Customer Demand

in Time Per Piecein Time Per Piece



Page 7402-108, Rev. C


The objective of Standard Operations

is to provide balanced production among

all processes, with minimum labor and WIP

inventories, through the best combination

of people and machines.


Standard OperationsStandard Operations


Total of Worker Cycle TimesTAKT Time

5 + 10+ 8 sec.15 sec.

= 1.7 (2) Workers Required

That is machine time without any operations improvement. Is worker time different? Could

the work be done with one person?

Reference Guide, Lean, Page 4 - 5Reference Guide, Lean, Page 4 - 5

Number of Workers RequiredNumber of Workers Required



Page 8402-108, Rev. C


Lot size 6 1

Floorspace, equipment _______ _______

Production (min./piece FG) _______ _______

Scrap _______ _______

Inventory WIP _______ _______

FG _______ _______

Operators _______ _______

Utilization of 1, 2, 3 _______ _______

Total cycle time 1 pc (Red block time) _______ _______

TAKT time (set by customer demand) 15 sec. 15 sec.

Improved MeasuresImproved Measures


Changeover time is a hidden waste. It is included in standard costs.

Changeovers of < 10 minutes is the Lean goal.

Set in + adjust = Changeover. (Good part to good part) Adjust is typically 90 %.

50% of the savings can be attained with no costs.


ChangeChange--Over TimeOver Time



Page 9402-108, Rev. C



WPO is sometimes referred to as 5S, from five Japanese words for organizing the workplace:

Japanese 5 S 5 S Translation Tyco Electronics 6 Ts (tools)Seiri Organization Proper arrangementSeiton Orderliness OrderlinessSeiso Cleaning up Clean up Seiketsu Cleanliness CleanlinessShitsuke Discipline Discipline

Safety

Workplace Organization (5S)Workplace Organization (5S)


Look first at the business objectives, then use the matrix to determine the most likely Lean tool

to apply to that measure.

Use in combination with Six Sigma statistical tools. The Lean tools will often be the “how to fix” for the problems identified in the statistical

analysis.


Lean Tools:Lean Tools:Linkage to Business ObjectivesLinkage to Business Objectives



Page 10402-108, Rev. C


Balance the tools and strategies based on data.

LEAN requires capable processes. One-piece flow

only works with good pieces.

Capable processes are no good if the cycle times

create late shipments and unprofitable processes.

Six Sigma + LEANSix Sigma + LEAN

INTRODUCTION TO MINITAB


Page 1402-108, Rev. C

Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc.

GB105-1

Introduction to Introduction to

Minitab Release 14Minitab Release 14

Process Improvement Methodology Process Improvement Methodology

Operations Green Belts

Rev. C January 2004© 2003 by Sigma Breakthrough Technologies, Inc.

GB105-2

ObjectivesObjectives

Introduce basic Minitab functions



Page 2402-108, Rev. C


GB105-3

BackgroundBackground

Minitab first introduced at Penn State in the late 70’s

Started as a DOS based program and migrated to Windows

Heavily used in the academic world

Reason for its use in our Six Sigma training

The “industry standard” for Six Sigma applications

Used in other Six Sigma companies (GE, AlliedSignal, Motorola)

Ease of use for beginning students

Very strong user base in the former Tyco Electronics companies


GB105-4

Starting MinitabStarting Minitab

Start MenuStart Menu

Desktop IconDesktop Icon

Windows 2000 Quick Launch ToolbarWindows 2000 Quick Launch Toolbar

Minitab IconMinitab Icon



Page 3402-108, Rev. C


GB105-5

Data Window:•A Worksheet, not an Excel Spreadsheet•Column names are above first row•Everything in a column is considered to

be from the same group

Data Window:•A Worksheet, not an Excel Spreadsheet•Column names are above first row•Everything in a column is considered to

be from the same group

Session Window:•The Output

Session Window:•The Output

Main ScreenMain Screen


GB105-6

Column HeadersT = Text / D = Date

Column HeadersT = Text / D = Date

Column NameColumn Name

Data Direction ArrowData Direction Arrow

Data WindowsData Windows



Page 4402-108, Rev. C


GB105-7

Enter Data into Minitab by

Typing it in

Cutting & pasting from other programs

Random number generators in Minitab

Importing it

Excel, Text, ASCII, Dbase files, etc….

Data WindowData Window


GB105-8

Minitab File TypesMinitab File Types

Minitab Operating Files

Minitab has training files in its data subdirectory

C:\Program Files\MINITAB 14\Data

Minitab File Extensions

MPJ for Data Files (Projects)

Ex: Data.mpj

MTW For Data Files (Worksheets)

Ex: Data.mtw



Page 5402-108, Rev. C


GB105-9

Worksheet Files versusWorksheet Files versus

Project FilesProject Files

Worksheets = just data

can be added to existing projects

use “Open Worksheet” to open

multiple worksheets must be saved as a project to keep together

Projects = data, command line history, graphs, and analyses

can store multiple worksheets and all aspects of analysis

retain last settings used in dialog box

hold all previous analyses from last save, as well as any graphs that were created

use “Open Project” or toolbar button to open


GB105-10

Projects

Worksheets

Opening / Saving WorksheetsOpening / Saving Worksheets

and Projectsand Projects



Page 6402-108, Rev. C


GB105-11

Open Project (opens to

your default directory)

Open Project (opens to

your default directory)

Save Project (Saves with same file name. Use

“save as” to save as a different file name.)

Save Project (Saves with same file name. Use

“save as” to save as a different file name.)

Opening / Saving Worksheets and ProjectsOpening / Saving Worksheets and Projects


GB105-12

Pull Down MenusPull Down Menus

DATA menu CALC menu

GRAPH menuSTAT menu



Page 7402-108, Rev. C


GB105-13

Stack / Unstack Columns

Sort Data

Copy Columns

Manipulate Worksheets

Code Data

The Pull Down Menus The Pull Down Menus -- DataData


GB105-14

Make Patterned Data

Calculator

Create Random Data

The Pull Down Menus The Pull Down Menus -- CalcCalc



Page 8402-108, Rev. C


GB105-15

The Pull Down Menus The Pull Down Menus -- StatStat


GB105-16

The Pull Down MenusThe Pull Down Menus-- GraphGraph



Page 9402-108, Rev. C


GB105-17

Moving Between Minitab Windows

The Pull Down Menus The Pull Down Menus -- WindowWindow


GB105-18

Help

StatGuide

Tutorials

The Pull Down Menus The Pull Down Menus –– HelpHelp



Page 10402-108, Rev. C


GB105-19

Editing PreferencesEditing Preferences


GB105-20

Increase the value of this number(200 is the maximum allowable value)

Increase the value of this number(200 is the maximum allowable value)

Editing PreferencesEditing Preferences



Page 11402-108, Rev. C


GB105-21

Control C / Control V

Copies data / Pastes data

Control E

Pulls up previous menu

Alt Tab

Moves you from one Windows application to another

Ex:

Minitab to PowerPoint for making presentations

Excel to Minitab for copying data

Same function as Ctrl E

Short Cut KeysShort Cut Keys


GB105-22

Paste with Existing Column NamesPaste with Existing Column Names

When pasting a column with a name that already exists in the worksheet, Minitab automatically resolves the conflict by adding an extension (_#)



Page 12402-108, Rev. C


GB105-23

Indicates there is a worksheet description

Indicates there is a worksheet description

Indicates there is a column description

Indicates there is a column description

Description IndicatorsDescription Indicators


GB105-24

Other EasyOther Easy--toto--Use FeaturesUse Features

Project Manager

ReportPad

StatGuide



Page 13402-108, Rev. C


GB105-25

Tree-like navigator to view your entire Minitab Project or browse different windows

Better able to associate session output, graphs and worksheets

Project ManagerProject Manager


GB105-26

Results of all data analyses andgraphs that you have created

Results of all data analyses andgraphs that you have created

Worksheets associated withyour analyses and graphs

Worksheets associated withyour analyses and graphs

Components ofyour project

Components ofyour project

Project Manager ExampleProject Manager Example



Page 14402-108, Rev. C


GB105-27

Within Project Manager, Pop-up menus let you access

convenient functions. (Right mouse-click on item to access

pop-up menu)

Project ManagerProject Manager


GB105-28

A Project Manager toolbar allows easy browsing of several types of windows

Project Manager: Tool BarProject Manager: Tool Bar



Page 15402-108, Rev. C


GB105-29

Jump to session output by double-clicking title

See graphs associated with session output

Edit titles

Open StatGuide

Delete specific output

etc…

Browse SessionBrowse Session


GB105-30

All data analyses and graphscreated during the Minitab session

All data analyses and graphscreated during the Minitab session Results of one specific data

analysis performed during the session

Results of one specific dataanalysis performed during the session

Browse Session ExampleBrowse Session Example



Page 16402-108, Rev. C


GB105-31

Replaces Manage Worksheets

Tile worksheets

Rename worksheets

Bring worksheet to front

Save worksheets

Close worksheets

etc…

Browse WorksheetsBrowse Worksheets


GB105-32

All worksheets associatedwith the project

All worksheets associatedwith the project

The selected worksheetThe selected worksheet

Browse Worksheets ExampleBrowse Worksheets Example



Page 17402-108, Rev. C


GB105-33

Replaces Manage Graphs

Tile graphs

Tile graph with associated worksheet

Rename graphs

Save graphs

Close graphs

etc…

Browse GraphsBrowse Graphs


GB105-34

All graphs associatedwith the project

All graphs associatedwith the project The selected graphThe selected graph

Browse Graphs ExampleBrowse Graphs Example



Page 18402-108, Rev. C


GB105-35

Provides information about the current worksheet:

Column names

Column counts

Number of Missing Values

Data type (numerical, text, date, or time)

Column description

etc…

Browse InformationBrowse Information


GB105-36

Information ExampleInformation Example

Information about a specific worksheetInformation about a specific worksheet



Page 19402-108, Rev. C


GB105-37

Provides a history of all the commands you have used - in command language format

Replaces separate History window

Browse HistoryBrowse History


GB105-38

History ExampleHistory Example

Complete history of your work in Minitab command language formatComplete history of your work in Minitab command language format



Page 20402-108, Rev. C


GB105-39

Provides statistical guidance after you run a procedure in Minitab

The StatGuide icon will only be available after acalculation has been performed in Minitab

The tone is informal and practical, not like a textbook

StatGuideStatGuide


GB105-40

Version 14 Covers

Basic Statistics

Regression

ANOVA

DOE

Control Charts

Quality Tools

SPC & Quality Tools

Reliability/Survival

Multivariate

Time Series

Power and Sample Size

StatGuide TopicsStatGuide Topics



Page 21402-108, Rev. C


GB105-41

ExampleExample

StatGuide LayoutStatGuide Layout

Detailed descriptionDetailed description

Hot-linked definitionsHot-linked definitions

Relevant output highlighted

Relevant output highlighted

Links to in-depth topicsLinks to in-depth topics


GB105-42

Exercise in the Use of Minitab



Page 22402-108, Rev. C


GB105-43

Under Menu Bar, FILE

OPEN PROJECT

(for opening your previously saved projects)

OPEN WORKSHEET

(for opening just the worksheet with the data)

File >> Open Worksheet >> Wine.mtw >> Open

Note: Multiple worksheets can be open at the same time

Opening the WorksheetOpening the Worksheet


GB105-44

Transformations are typical requirements in many analyses (both engineering and statistical)

Some typical transformations of data needed include :

natural log, square root, exp, log (base 10), and linear/nonlinear combinations

Transformations are the result of simple mathematical calculations – simple or complex

Transformations and calculations are performed with CALC >> CALCULATOR

Calculations and TransformationsCalculations and Transformations



Page 23402-108, Rev. C


GB105-45

Let’s look at the WINE.MTW data

Suppose we want a new variable: Natural Log of Aroma…

CALC > CALCULATOR

“Store result in variable:” (type c8 or a variable name)

“Expression:” (click in this box; then under ‘Functions”, scroll down to find Natural log and double-click on it; then double-click on the variable Aroma)

Click on OK and your transformed data appears in column c8

If you didn’t name the column previously, you can type in a variable name for the new column.



GB105-46

Suppose we want a new variable that is a function of several of the variables.

We’ll make column c9 =

This can be done similar to the previous transformation, only now we can include either typing in the necessary numbers and symbols or using the calculator keyboard with our mouse clicks.

gionRe

Quality3)Aroma(2




Page 24402-108, Rev. C


GB105-47


Click NoClick No

Click here to close the worksheetClick here to close the worksheet


GB105-48

• You can easily generatecolumns in the worksheetcontaining patterned orrandom data.

• This will be useful whencreating data matrices forvarious types of analysis.

• Calc >> Make PatternedData >> Simple Set ofNumbers

Creating Patterned DataCreating Patterned Data



Page 25402-108, Rev. C


GB105-49

A total of 200 measurements have been taken on 10 samples of product. These measurements have been placed into one column of your worksheet. The first 20 measurements are for the first sample, the second 20 are for the second sample, etc.

To identify these measurements with the correct sample you will need a column of numbers consisting of the value 1, repeated 20 times to identify the first 20 measurements as belonging to sample 1, followed by the value 2, repeated 20 times to identify the next 20 measurements as belonging to sample 2, etc.

Calc >> Make Patterned Data >> Simple Set of Numbers

Enter the values as shown in the dialog box at the right and review the result in the Worksheet.

This tells Minitab to start with thevalue 1, end with the value 10, and listeach value 20 times.

This will result in a column containing the value 1 repeated 20 times, followed by the value 2 repeated 20 times, followed by the value 3 repeated 20 times, etc.



GB105-50

Instead of grouping all of the measurements for each sample together, suppose that your 200 measurements are arranged so that the first measurement in the data column belongs to sample 1, the second measurement to sample 2, the third to sample 3, …, the tenth to sample 10. Then this pattern of data entry repeats 20 times.

To identify these measurements with the correct sample you will need a column of numbers consisting of the values 1 – 10 in sequence, repeated 20 times.

Calc >> Make Patterned Data >> Simple Set of Numbers

Enter the values as shown in the dialog box at the right and review the result in the Worksheet.

This tells Minitab to start with thevalue 1, end with the value 10, and listeach value 1 time. Then repeat the list20 times.

This will create a column of numbersconsisting of the values 1 thru 10,repeated 20 times.




Page 26402-108, Rev. C


GB105-51

35025015050

100

50

0

simulated data

Frequency

Minitab has the ability to create random data from many statistical distributions including Uniform, Normal, t, Chi-Squared, Lognormal, Weibull, and more.

This is often of interest or need to perform Monte Carlo simulations or to create an “image” of an assumed population from estimated sample parameters.

Creating Random DataCreating Random Data

1000 observations from a simulated Normal distribution with m (mean) = 200 and s (standard deviation) = 35.


GB105-52

Generate 1000 observations froma Normal Distribution with a Meanof 200 and a Standard Deviationof 35. Store the results in column 3.

Generate 1000 observations froma Normal Distribution with a Meanof 200 and a Standard Deviationof 35. Store the results in column 3.

Generate thedata and reviewyour results inthe Worksheet

Generate thedata and reviewyour results inthe Worksheet




Page 27402-108, Rev. C


GB105-53

Column 3 will consist of 1000 random observations.The distributions of these observations will benormal with a mean of 200 and a standarddeviation of 35.

Note: Your results will not be identical to what isshown here. These are random observations, therefore your set of 1000 random observationswill be different, but they will have the same mean(200) and the same standard deviation (35).

Column 3 will consist of 1000 random observations.The distributions of these observations will benormal with a mean of 200 and a standarddeviation of 35.

Note: Your results will not be identical to what isshown here. These are random observations, therefore your set of 1000 random observationswill be different, but they will have the same mean(200) and the same standard deviation (35).



GB105-54

Click OKClick OK

Plotting the Data Plotting the Data -- DotplotDotplot



Page 28402-108, Rev. C


GB105-55

Plotting the Data Plotting the Data -- DotplotDotplot


GB105-56

View descriptive statistics for thedata.

Stat > Basic Statistics > DisplayDescriptive Statistics

Select C3 as the Variable (yourrandom observations)

View descriptive statistics for thedata.

Stat > Basic Statistics > DisplayDescriptive Statistics

Select C3 as the Variable (yourrandom observations)

Descriptive StatisticsDescriptive Statistics



Page 29402-108, Rev. C


GB105-57

Your results will be similar, but not identical, tothese. The mean should be very close to 200and the standard deviation should be very closeto 35.

Your results will be similar, but not identical, tothese. The mean should be very close to 200and the standard deviation should be very closeto 35.



GB105-58

Generate a graphicalsummary of the descriptive statistics

Generate a graphicalsummary of the descriptive statistics




Page 30402-108, Rev. C


GB105-59

Descriptive Statistics Descriptive Statistics –– Graphical SummaryGraphical Summary


GB105-60

Introduction to MinitabIntroduction to Minitab

This concludes the basic introduction to Minitab.

Additional capabilities of the software will be explored as we move through your weeks of Belt training.

BASIC STATISTICS


Page 1402-108, Rev. C

Rev. C February 2004© 2003 by Sigma Breakthrough Technologies, Inc. GB106-1



BasicStatistics


GB106-2


Introduce the concepts of Shape, Center, and Spread of distributions

Learn about the Normal distribution

BASIC STATISTICS


Page 2402-108, Rev. C


GB106-3

Basic StatisticsBasic Statistics

Fundamentals of Improvement Fundamentals of Improvement

Stability

How does the process perform over time?

Stability is represented by a constant mean and predictable variability over time.

Variability

Is the process on target with minimum variability?

We use the mean to determine if process is on target. We use the Standard Deviation ( to determine variability


GB106-4

VariationVariation

“While every process displays Variation, some processes display controlled variation, while other processes display uncontrolled variation” (Walter Shewhart)

Controlled Variation is characterized by a stable and consistent pattern of variation over time

Associated with Common Causes

Uncontrolled Variation is characterized by variation that changes over time

Associated with Special Causes

BASIC STATISTICS


Page 3402-108, Rev. C


GB106-5

2520151050

75

70

65

Sample Number

Sam

ple

Mea

n

X-Bar Chart for Process A

X=70.91

UCL=77.20

LCL=64.62

2520151050

75

70

65

Sample Number

Sam

ple

Mea

n

X-Bar Chart for Process A

X=70.91

UCL=77.20

LCL=64.62

2520151050

80

70

60

50

Sample Number

Sam

ple

Mea

n

X-Bar Chart for Process B

X=70.98

UCL=77.27

LCL=64.70

Special CausesSpecial Causes

Process A shows controlled variationProcess B shows uncontrolled variation

Variation ExamplesVariation Examples


GB106-6

Co

st

There will always be variability present in any process

We can tolerate variability if:

The process is on target

The total variability is relatively small compared to the process specifications

The process is stable over time

New ViewNew View

LSLLSL

USLUSL

NomNom

USLUSL

Traditional View

Traditional ViewAcceptableAcceptable

Co

st

LSLLSL USLUSLNomNom

Can We Tolerate Variability?Can We Tolerate Variability?

BASIC STATISTICS


Page 4402-108, Rev. C


GB106-7

Ford’sprocess

Mazda’sprocess

LSL USL

Manufacturing ofAutomobile

Transmissions

Manufacturing ofAutomobile

Transmissions

Cost of OffCost of Off--Target ProductionTarget Production


GB106-8

Process ImprovementProcess Improvement

Data Analysis TasksData Analysis Tasks

Determine if process is stable

If process is not stable, identify and remove causes of instability

Determine the location of the process mean - Is it on target?

If not, identify the variables which affect the mean and determine optimal settings to achieve target value

Estimate the magnitude of the total variability - Is it acceptable with respect to the customer requirements (spec limits)?

If not, identify the sources of the variability and eliminate or reduce their influence on the process

We will now review statistics that help this process

BASIC STATISTICS


Page 5402-108, Rev. C


GB106-9

Basic StatisticsBasic Statistics

Types of Data

Measures of the Center of the Data

Mean

Median

Measures of the Spread of Data

Range

Variance

Standard Deviation

Properties of a Normal Distribution


GB106-10

Types of DataTypes of Data

Attribute Data (Qualitative)

Categories

Good / Bad

Machine 1, Machine 2, Machine 3

Shift number

Counted things (# of Errors in a document, # units shipped, etc.)

Variable Data (Quantitative)

Continuous Data (Decimal subdivisions are meaningful)

Time (seconds)

Pressure (psi)

Conveyor Speed (ft/min)

Rate (inches)

etc.

BASIC STATISTICS


Page 6402-108, Rev. C


GB106-11

Attribute Variable

Variable

Attribute

Outputs

Inp

uts

Chi-square Analysis of Variance

Discriminate Analysis

Logistic regression

Correlation

Multiple Regression

There are statistical techniques to cover all combinations of data types

There are statistical techniques to cover all combinations of data types

Selecting Statistical TechniquesSelecting Statistical Techniques


GB106-12

s = Sample Standard Deviation

x = Sample Average = Population Mean

= Population Standard Deviation

Statistics estimate Parameters

Sample Statistics vs.Sample Statistics vs.

Population ParametersPopulation Parameters

BASIC STATISTICS


Page 7402-108, Rev. C


GB106-13

Measures of Central TendencyMeasures of Central Tendency

Mean: Arithmetic average of a set of values

Reflects the influence of all values

The “balance point” or “center of gravity” for the data

Strongly Influenced by extreme values

Median: Reflects the 50% rank - the center number after a set of numbers has been sorted

Splits the data in half. 50% of the data points will be higher than the median and 50% will be lower than the median.

“Robust” to extreme values. A large change in the data may have very little, if any, effect on the median.

n

x

=x

n

1=i

i


GB106-14

$10, 20, 30, 40, 50 ($ in thousands)$10, 20, 30, 40, 50 ($ in thousands)

As head of the university’s Communications Dept. you are asked to summarize the average starting salaries of Communications graduates.

What is the average income (or “center of gravity”)?

What is the median income?

What is the average income (or “center of gravity”)?

What is the median income?

$10, 20, 30, 40, 5000 ($ in thousands)$10, 20, 30, 40, 5000 ($ in thousands)

However, under the advice of the Public Relations Dept. you consider to including one of your former Communications majors:

Shaquille O’Neal (a rather wealthy rookie basketball star)

Mean and Median ExampleMean and Median Example

BASIC STATISTICS


Page 8402-108, Rev. C


GB106-15

Measures of VariabilityMeasures of Variability

Range: the distance between the extreme values of a data set (Highest - Lowest)

Variance ( ): the Average Squared Deviation of each data point from the Mean

Standard Deviation ( ): the Square Root of the Variance

The range is more sensitive to outliers than the variance

The most common and useful measure of variation is the standard deviation - why?

The most common and useful measure of variation is the standard deviation - why?


GB106-16

Population Mean

N

X

= 1

i

N

i

Sample Mean

n

x

=x

n

1=i

i

Population Standard Deviation

N

)(X

=S=

N

1=i

2

i

Sample Standard Deviation

1

2

1

n

xx

s

n

i

i

Computational EquationsComputational Equations

BASIC STATISTICS


Page 9402-108, Rev. C


GB106-17

Calculating SigmaCalculating Sigma

Problem: Using the form above, calculate the standard deviation for the numbers:

2 1 3 5 4

1

2

3

4

5

6

7

8

9

10

Mean

square

1-n

)(XN

1=i

2i X

1-n

)(XN

1=i

2i X

2)X-(XX-XX


GB106-18

1 2 -1 1

2 1 -2 4

3 3 0 0

4 5 2 4

5 4 1 1

6

7

8

9

10

15 10

Mean 3

square 2.5

1.581139

2)X-(XX-XX

1-n

)(XN

1=i

2i X

1-n

)(XN

1=i

2i X

ExampleExample

BASIC STATISTICS


Page 10402-108, Rev. C


GB106-19

The Normal DistributionThe Normal Distribution

The “Normal” Distribution is a distribution of data which has certain consistent and predictable properties

These properties are very useful in our understanding of the characteristics of the underlying process from which the data were obtained

Most natural phenomena and man-made processes are distributed normally, or can be represented as normally distributed


GB106-20

The Normal DistributionThe Normal Distribution

Property 1: A normal distribution can be described completely by knowing only the:

mean, and

standard deviation

Distribution OneDistribution One

Distribution Two

Distribution Two

Distribution ThreeDistribution Three

What is the difference among these three normal distributions?

BASIC STATISTICS


Page 11402-108, Rev. C


GB106-21

43210-1-2-3-4

40%

30%

20%

10%

0%

95%

Pro

ba

bil

ity o

f s

am

ple

valu

e

Number of standard deviations from the mean

The Normal Curve and Probability AreasThe Normal Curve and Probability Areas

Associated with the Standard DeviationAssociated with the Standard Deviation

Property 2Property 2: The area under sections of the curve can be used to estimate the cumulative probability of a certain “event” occurring

99.73%

68%Cumulative

probability of obtaining a value

between two values

Cumulative probability of

obtaining a value between two

values


GB106-22

Number ofStandard

DeviationsTheoretical

NormalEmpiricalNormal

+/- 168% 60-75%

+/- 295% 90-98%

+/- 399.7% 99-100%

The previous rules of cumulative probability apply even when a set of data is not perfectly normally distributed

Let’s compare the values for a theoretical (perfect) normal distributions to empirical (real-world) distributions

Empirical Rules forEmpirical Rules for

the Standard Deviationthe Standard Deviation

BASIC STATISTICS


Page 12402-108, Rev. C


GB106-23

Introduced the concepts of Shape, Center, and Spread of distributions

Introduced the properties of a Normal distribution

SummarySummary

BASIC QUALITY TOOLS


Page 1402-108, Rev. C


GB107-1

Basic Quality Basic Quality

ToolsTools




GB107-2


To introduce you to the 7 Basic Quality Tools

Dotplots / Histograms / Normal Plots

Run charts / Time Series

Pareto Diagrams

Stratification (2nd Level Pareto)

Boxplots

Scatter Plots

Marginal Plots

Show application of these techniques for Data Mining

BASIC QUALITY TOOLS


Page 2402-108, Rev. C


GB107-3

Plotting Your DataPlotting Your Data

The first three rules of data analysis

Rule # 1 Plot your data



Very little can be learned from looking at a long list of numbers.

Statistics, such as mean and standard deviation, can be deceptive if only presented as numbers.

Your data will be much easier to understand and to explain to others if it is plotted as a graph.


GB107-4

Statistical DistributionsStatistical Distributions

We can describe the behavior of any measurable feature by plotting multiple data points for thevariable:

over time

across products

on different machines, etc.

The accumulation of these data can be viewed as a distribution of values

The data can be represented in:

dot plots

histograms

normal curve or other “smoothed” distribution

BASIC QUALITY TOOLS


Page 3402-108, Rev. C


GB107-5

Imagine a painting operation which is engineered to provide a thickness of 21 mils of paint. The actual thickness is measuredon 150 separate samples and plotted above. Each dot represents one “event” of output at a given value. As the dots accumulate,the nature of the painting operation’s actual performance can beseen as a “distribution” of thickness values.

Imagine a painting operation which is engineered to provide a thickness of 21 mils of paint. The actual thickness is measuredon 150 separate samples and plotted above. Each dot represents one “event” of output at a given value. As the dots accumulate,the nature of the painting operation’s actual performance can beseen as a “distribution” of thickness values.

29241914

Thickness

Dotplot DistributionDotplot Distribution


GB107-6

Let’s take a look at the Thickness of paint applied by a sprayer

Open file DM Basic Tools.MPJ, worksheetPainting.MTW

Use the column Thickness.MTW as the variable

Creating a DotplotCreating a Dotplot

BASIC QUALITY TOOLS


Page 4402-108, Rev. C


GB107-7

Now imagine the same data, grouped into “intervals” with the number of times that a thickness data point falls within a giveninterval determining the height of the interval bar.

Now imagine the same data, grouped into “intervals” with the number of times that a thickness data point falls within a giveninterval determining the height of the interval bar.

Histogram DistributionHistogram Distribution


GB107-8

Once again we will use the column Thickness as the variable

Once again we will use the column Thickness as the variable

Creating a HistogramCreating a Histogram

BASIC QUALITY TOOLS


Page 5402-108, Rev. C


GB107-9

Finally, we can view the data as a smoothed distribution (red line), in this example using the “normal distribution” assumption (we’ll discuss this later). It provides an approximation of how the data might look if we were to collect an infinite number of data points.

Finally, we can view the data as a smoothed distribution (red line), in this example using the “normal distribution” assumption (we’ll discuss this later). It provides an approximation of how the data might look if we were to collect an infinite number of data points.

Smoothed (Normal) DistributionSmoothed (Normal) Distribution


GB107-10

Creating a HistogramCreating a Histogram

with a Normal Curvewith a Normal Curve

BASIC QUALITY TOOLS


Page 6402-108, Rev. C


GB107-11

Normal Probability PlotsNormal Probability Plots

We can test whether a given data set can be described as “normal” with a Normal Probability Plot

If a distribution is close to normal, the Normal Probability Plot will be close to a straight line

Minitab makes the normal probability plot easy

Let’s do one for the Painting.MTW data set


GB107-12


BASIC QUALITY TOOLS


Page 7402-108, Rev. C


GB107-13

Example Probability PlotExample Probability Plot


GB107-14

Open the worksheet Distributions.MTWDistributions.MTW

Produce a normal plot of each of the columns of data

Which appear to be normal?

Produce a histogram of each of these columns of data

Notice anything?

You have 5 minutes

ExerciseExercise

BASIC QUALITY TOOLS


Page 8402-108, Rev. C


GB107-15



GB107-16

Time SeriesTime Series

So far we have looked at the data ‘lumped together’

Another way to review the data is over time

Let’s review some commonly used time series methods:

Run Charts

Individuals Charts

Let’s switch back to the Painting.MTW worksheet

BASIC QUALITY TOOLS


Page 9402-108, Rev. C


GB107-17

Run ChartsRun Charts

A Run Chart applies some initial statistical diagnostic tests to the series


GB107-18

Observation

Th

ickn

ess

15010050

29

24

19

14

Number of runs about median:

0.83722

70

Expected number of runs: 76.00000

Longest run about median: 9

Approx P-Value for Clustering: 0.16278

Approx P-Value for Mixtures:

Number of runs up or down:

0.32470

102


Longest run up or down: 3

Approx P-Value for Trends: 0.67530

Approx P-Value for Oscillation:

Run ChartRun Chart

BASIC QUALITY TOOLS


Page 10402-108, Rev. C


GB107-19

IndicationConditionTest

Trends in the dataFewer runs observed than expected

Number of Runs Up or Down

Oscillation (data varies up and down rapidly)

More runs observed than expected

Number of Runs Up or Down

Clustering of DataFewer runs observed than expected

Number of Runs About the Median

Mixture (mixed data from two populations)

More runs observed than expected

Number of Runs About the Median

Run Chart Tests for RandomnessRun Chart Tests for Randomness


GB107-20

Observation

Th

ickn

ess

15010050

29

24

19

14

Number of runs about median:

0.83722

70


Longest run about median: 9

Approx P-Value for Clustering: 0.16278

Approx P-Value for Mixtures:

Number of runs up or down:

0.32470

102


Longest run up or down: 3

Approx P-Value for Trends: 0.67530

Approx P-Value for Oscillation:

Run ChartRun Chart

BASIC QUALITY TOOLS


Page 11402-108, Rev. C


GB107-21

An Individuals Chart looks like a Run Chart but now they apply some Process Control Limits to the data

Individuals ChartIndividuals Chart


GB107-22

Observation

Ind

ivid

ua

l V

alu

e

150100501

30

25

20

15

_X=22.03

UCL=30.57

LCL=13.48

I Chart of Thickness

Individuals ChartIndividuals Chart

BASIC QUALITY TOOLS


Page 12402-108, Rev. C


GB107-23

Data MiningData Mining

So far we have looked at all data together, giving us an overall look at the shape and trends of the data

However, we still haven’t looked into why the data may be shaped the way it appears

We can use some other graphical techniques to start ‘mining’ the data for the reasons as to WHY…


GB107-24

There is another method of looking at the data that may be easier to see differences in the distributions

Boxplots show the spread and center of the data

BE CAREFUL!BE CAREFUL!

The center of the Boxplot is the

MEDIANMEDIAN, not the MEANMEAN

BoxplotsBoxplots

BASIC QUALITY TOOLS


Page 13402-108, Rev. C


GB107-25

Median (50%)

Q3 (75%)

Q1 (25%)

Maximum value

Minimum value

Outlier (extreme value)

Boxplot of ThicknessBoxplot of Thickness


GB107-26

We can also generate boxplots by a variable to look at the variation due to that variable

Nozzle

Th

ickn

ess

1051

30.0

27.5

25.0

22.5

20.0

17.5

15.0

Boxplot of Thickness vs Nozzle

BoxplotsBoxplots

BASIC QUALITY TOOLS


Page 14402-108, Rev. C


GB107-27

Pareto Diagrams are an essential tool to help prioritize improvement targets

Pareto’s allow us to focus on the 20% of the problems that cause 80% of the poor performance

Let’s open worksheet Quality Control.MTW

Pareto DiagramPareto Diagram


GB107-28

Defects CountsMissing Screws 274Missing Clips 59Defective Housing 19Leaky Gasket 43Scrap 4Unconnected Wire 8Missing Studs 6Incomplete Part 10

Pareto DiagramPareto Diagram

BASIC QUALITY TOOLS


Page 15402-108, Rev. C


GB107-29

We can generate a second level Pareto using the Bystatement

This breaks down the overall Pareto by time of day

Second Level ParetosSecond Level Paretos


GB107-30

Second Level ParetoSecond Level ParetoFlaws PeriodScratch DayScratch DayPeel DayPeel DaySmudge DayScratch DayOther DayOther EveningPeel EveningPeel EveningPeel EveningPeel EveningScratch EveningScratch EveningPeel NightScratch NightSmudge NightScratch NightPeel NightPeel NightPeel NightPeel NightOther NightOther NightScratch NightScratch NightPeel NightScratch NightSmudge NightScratch NightOther NightScratch NightScratch NightPeel WeekendPeel WeekendPeel WeekendSmudge WeekendSmudge WeekendSmudge WeekendOther Weekend

BASIC QUALITY TOOLS


Page 16402-108, Rev. C


GB107-31

Relational PlotsRelational Plots

Many times we wish to graphically review the relationship between two variables

Scatter Plots and Marginal Plots are two effective methods for accomplishing this task


GB107-32

ScatterplotScatterplot

Scatterplots study the relationship between two variables

Let’s look at an example where the both the Customer and our QC department measure the temperature of ovens set at 350 degrees

Open worksheet Temperature.MTW

BASIC QUALITY TOOLS


Page 17402-108, Rev. C


GB107-33

QC

Cu

sto

me

r

400390380370360350340330

390

380

370

360

350

340

Scatterplot of Customer vs QC

ScatterplotScatterplot


GB107-34

Marginal plots allow you to combine a Scatterplot and one of three other plots

Histograms

Boxplots

Dotplots

Marginal PlotsMarginal Plots

BASIC QUALITY TOOLS


Page 18402-108, Rev. C


GB107-35

QC

Cu

sto

me

r

400390380370360350340

390

380

370

360

350

340

Marginal Plot of Customer vs QC

Marginal Plot (ScatterplotMarginal Plot (Scatterplot

with Histogram)with Histogram)


GB107-36

Marginal PlotsMarginal Plots

BASIC QUALITY TOOLS


Page 19402-108, Rev. C


GB107-37

Data MiningData Mining

All of the methods introduced have provided insight on how to graphically represent data

Use of the data to answer the question of WHY is the product / process performing in that fashion is Data Mining

Data analysis should follow some basic steps:

Look at the data (raw) to identify any abnormalities (errors, unexpected values)

Analyze the data graphically to get a visual sense of the data

Analyze the data statistically to get a numerical sense of the data

Let’s look into this last point a little deeper


GB107-38

ReviewReview

When we looked at the three different distributions in the Distributions.mtw worksheet, we saw three totally different distributions (graphically)

Let’s look and see what these three distributions look like numerically

We can use Descriptive Statistics to accomplish this task easily

BASIC QUALITY TOOLS


Page 20402-108, Rev. C


GB107-39

Let’s look at the different data sets using some other techniques

We will start with some Descriptive Statistics

This will give us some insight as to the Center, Spread, and Shape of the data



GB107-40

What observations can we make from these numbers?

Look at the Center, Spread, and Shape indicators

The three distributions have the same values for their Mean and Standard Deviation, yet they are not the same

Spread

Center Center

|<--- Shape Indicators --->|(Mean, Median and TrMean)


BASIC QUALITY TOOLS


Page 21402-108, Rev. C


GB107-41

Histograms and NormalHistograms and Normal

Probability PlotsProbability Plots


GB107-42

Open worksheet Mystery.MTW

Generate a Normal Probability Plot for the Mystery variable

What is your conclusion?

Mystery DistributionMystery Distribution

BASIC QUALITY TOOLS


Page 22402-108, Rev. C


GB107-43

Mystery

Pe

rce

nt

200150100500

99.9

99

95

90

80

7060504030

20

10

5

1

0.1

Mean

<0.005

100.0

StDev 32.38

N 500

AD 27.108

P-Value

Probability Plot of MysteryNormal

Normal Probability PlotNormal Probability Plot

for Mystery Datafor Mystery Data


GB107-44

My

ste

ry

150

100

50

Boxplot for Mystery DataBoxplot for Mystery Data

BASIC QUALITY TOOLS


Page 23402-108, Rev. C


GB107-45

Mystery

Fre

qu

en

cy

160140120100806040

60

50

40

30

20

10

0

Histogram for Mystery DataHistogram for Mystery Data


GB107-46

Mystery

15010050

Dotplot for Mystery DataDotplot for Mystery Data

BASIC QUALITY TOOLS


Page 24402-108, Rev. C


GB107-47

Looking at the numbers alone can be confusing

Let’s take a look at the data graphically and add the numbers tothe output

Graphical Descriptive StatisticsGraphical Descriptive Statistics


GB107-48

160140120100806040

Median

Mean

1201101009080

A nderson-Darling Normality Test

V ariance 1048.78

Skewness 0.00716

Kurtosis -1.63184

N 500

Minimum 41.77

A -Squared

1st Q uartile 68.69

Median 104.20

3rd Q uartile 130.81

Maximum 162.82

95% C onfidence Interv al for Mean

97.15

27.11

102.85

95% C onfidence Interv al for Median

82.78 117.66

95% C onfidence Interv al for StDev

30.49 34.53

P-V alue < 0.005

Mean 100.00

StDev 32.38

95% Confidence Intervals

Summary for Mystery

Graphical Descriptive StatisticsGraphical Descriptive Statistics

BASIC QUALITY TOOLS


Page 25402-108, Rev. C


GB107-49

Index

My

ste

ry

500450400350300250200150100501

175

150

125

100

75

50

Time Series Plot for Mystery DataTime Series Plot for Mystery Data


GB107-50

Introduced some graphical and analytical tools for Data Mining

Data breakdown (Pareto, Dotplot, Histogram, Boxplot, Descriptive Statistics)

Time Series methods (Individual Charts, Run Charts)

Relational graphical techniques (Scatter Plots, Marginal Plots)

Pareto diagrams

ALWAYS PLOT YOUR DATAALWAYS PLOT YOUR DATA

SummarySummary

INTRODUCTION TO SPC


Page 1402-108, Rev. C


GB108-1

Intro. to Statistical Intro. to Statistical

Process Control Process Control

(SPC)(SPC)




GB108-2


Link Control Chart methods to the Process Improvement

Discuss different types of variation

Introduce a few basic Control Charting methods

Discuss the interpretation of Control Charts

INTRODUCTION TO SPC


Page 2402-108, Rev. C


GB108-3

The DMAIC ProcessThe DMAIC ProcessDefine• Identify the gap• Establish Scope and Boundary• Assign Black Belt and Team• Establish Project Charter

Measure• Level 0 and Value Stream Map• Determine Baseline Performance

• Initial Capability Studies

• Process Capacity• Initial Control Plan

• Detail Process Map• Measurement System Analysis• C & E Matrix• FMEA

Analyze• Root Cause Analysis

• Multi – level Pareto Diagrams

• 5 Why Diagrams• Identification of Waste

• Multi – Vari Studies• ANOVA• Correlation and Regression

Improve• Implement Process Optimization• Design of Experiments• Identify Root Cause of Variation• Confirm Results• Finalize Value Stream Map

Control• Error Proof and Implement SPC• Update Control Plan• Update all Documentation• Return to Process Owner


GB108-4

* Myth - SPC Is Only Used As A Control Tool* Myth - SPC Is Only Used As A Control Tool

Found Critical X’s

Controlling Critical X’s

All X’s

1st “Hit List”

Screened List

MEASUREMEASURE

ANALYZEANALYZE

IMPROVEIMPROVE

CONTROL*CONTROL*

The Funneling EffectThe Funneling Effect

INTRODUCTION TO SPC


Page 3402-108, Rev. C


GB108-5

Should We Take Action ?

Everyday we are flooded by data and we are forced to make decisions:

Plants Output Decreases By 4%

US Trade Deficit Rises By $40 Billion

Company X’s Earnings Are Off $240 Million From Previous Quarter

We Need Ways of We Need Ways of

Interpreting DataInterpreting Data


GB108-6

Leave It Alone It Ain’t Broke

Pain &Suffering

Pain &Suffering

Lower “Customer”Requirement(Lower Spec)

THIS METHOD

•Tells you where you are in regards to customer’s needs

•It will NOT tell you how you got there or what to do next

Pressure To Achieve Customer Requirements Will Cause One To :

1. Actually fix the process2. Sabotage the process3. Sabotage the data (integrity)

Upper “Customer”Requirement(Upper Spec)

How Do We Manage Data How Do We Manage Data ––

HistoricallyHistorically

INTRODUCTION TO SPC


Page 4402-108, Rev. C


GB108-7

2

3

Scra

p L

evel (%

)S

cra

p L

evel (%

)

1

J F M A J F M A

1996 1997

Party TimeParty Time

• The factory scrap level is at a year low of 1.5%

• Manager presents an award to the plant

• Ceremony in the cafeteria: pizza and refreshments for all!

• “Everyone should be proud of what you’ve accomplished”

Derived from Understanding Variation: The Key To Managing Chaos, Donald J. Wheeler, SPC Press. 1993.

Variation Example:Variation Example:Special vs. Common CauseSpecial vs. Common Cause


GB108-8

2

3

1

J F M A M J J J F M A M J J

1996 1997

Manager wants to take back awardManager wants to take back award

• Three consecutive months of scrap increases

• Manager wishes he could take back the award

• Instead of holding the gains, scrap went right back up

• Manager decides: “Recognition has backfired. This group needs tough management!”

Scra

p L

evel (%

)S

cra

p L

evel (%

)



INTRODUCTION TO SPC


Page 5402-108, Rev. C


GB108-9

2

3

1

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

1996 1997

No more “Soft Management”No more “Soft Management”

• By November, scrap has risen to a value of 2.6% - Year High

• Manager decides to take action

• A “special meeting” is called to solve this problem once and for all

• After a sound lecture on the importance of scrap, the manager leaves. Employees aren’t sure what to do. Besides, they have other metrics which are more important. So they do nothing.

Scra

p L

evel (%

)S

cra

p L

evel (%

)




GB108-10

• Manager has seen reduced scrap levels since the end of last year. “Things are looking-up!” (REMEMBER: Nothing had been done to change the system)

• His take away: “A tough management style gets results!”

Manager concludes:“Tough Love Makes Things Happen

Manager concludes:“Tough Love Makes Things Happen

2

3

1


1996 1997

J F M A M JJ F M A M J

Scra

p L

evel (%

)S

cra

p L

evel (%

)



INTRODUCTION TO SPC


Page 6402-108, Rev. C


GB108-11

What might this data look

like on a control chart?



GB108-12

2

3

1


1996 1997

UCL

J F M M J J A S OJ F M M J J A S O

LCL

Scra

p L

evel (%

)S

cra

p L

evel (%

)


Let the Process do the talking! !

The REAL Story!!The REAL Story!!The Voice of the ProcessThe Voice of the Process

INTRODUCTION TO SPC


Page 7402-108, Rev. C


GB108-13

• Manager “ Hey, I made my decision based on data - How can I be wrong ?”

• Belt“Your decisions were made from observing high and low points as signals, when in reality, it was all noise (Common Cause Variation). Look at the data, there has been no significant change in the process.”

Manager concludes, “Tough Love Makes Things Happen!”

Manager concludes, “Tough Love Makes Things Happen!”

J F M M J J A S OJ F M M J J A S O

2

3

Scra

p L

eve

l (%

)S

cra

p L

eve

l (%

)

1


1996 1997

Party TimeParty Time

Manager WantsTo Take Back Award

Manager WantsTo Take Back Award

No more soft managementNo more soft management

UCL

LCL

The Control Chart TellsThe Control Chart Tells

A Different Story A Different Story –– Why?Why?


GB108-14

“Failure to use control charts to analyze data is one of the best ways known to mankind to increase costs,

waste effort, and lower morale.”

- Dr. Donald J. Wheeler

“Failure to use control charts to analyze data is one of the best ways known to mankind to increase costs,

waste effort, and lower morale.”

- Dr. Donald J. Wheeler

Control Chart MethodsControl Chart Methods

INTRODUCTION TO SPC


Page 8402-108, Rev. C


GB108-15

Control Chart MethodsControl Chart Methods

Where Did It Come From?Where Did It Come From?

1920’s - Western Electric / Dr. Walter Shewhart

Used to identify Controlled & Uncontrolled Variation

Controlled: a.k.a. Common Cause or Inherent (Noise)

Uncontrolled: a.k.a Special Cause or Assignable (Signals)

Tries to find the process signals in all of the noise

Uses Control Charts as main tool


GB108-16

COMMON CAUSE (Noise)

Is present in every process

Is produced by the process itself (the way we do business)

Can be removed and/or lessened but requires a fundamental change in the process

A process is Stable, Predictable, and In-Control when only Common Cause Variation exists in the process

* note - if subgroups are used, the common cause variation is a function of sub-grouping choices

Types of Variation:Types of Variation:Common vs. SpecialCommon vs. Special

INTRODUCTION TO SPC


Page 9402-108, Rev. C


GB108-17

SPECIAL CAUSE (Signals)

Unpredictable

Typically large in comparison to Common Cause variation

Caused by unique disturbances or a series of them

Can be removed/lessened by basic process control and monitoring

A process exhibiting Special Cause variation is said to be Out-of-Control and Unstable

Types of Variation:Types of Variation:Common vs. SpecialCommon vs. Special


GB108-18

DATA PLOTTED OVER TIME

MO

NIT

OR

ED

C

HA

RA

CT

ER

IST

IC

UCL

Center Line

LCL

UCL = Upper Control Limit / LCL = Lower Control Limit

Plotted Data

The Basic Control Chart:The Basic Control Chart:Key ComponentsKey Components

INTRODUCTION TO SPC


Page 10402-108, Rev. C


GB108-19

•What % of data points should fall between the UCL and LCL ?

•If a point falls beyond the UCL or LCL does this mean we aremaking a defect for the customer ?

UCL

LCL

Control Chart ComponentsControl Chart Components

UCL versus LCLUCL versus LCL


GB108-20

Upper Control Limits = UCLLower Control Limits = LCL

Upper Specification Limits = USLLower Specification Limits = LSL

Is The Process Below Making Defects ?

UCL

LCL

TIMETIME

USL

LSL

UCL and LCL vs. USL and LSLUCL and LCL vs. USL and LSL

INTRODUCTION TO SPC


Page 11402-108, Rev. C


GB108-21

Upper Control Limits = UCLLower Control Limits = LCL

Upper Specification Limits = USLLower Specification Limits = LSL

Is The Process Below Making Defects ?

UCL

LCL

TIMETIME

USL

LSL



GB108-22

Process Control Limits are calculated based on data from the process itself

They are based on +/- 3 (99.73% of the process variation is expected to fall between these limits)

Product Specification Limits ARE NOT found on the control chart

Understanding how the process matches up against customer requirements IS important to know

To determine how the process performs to Customer Expectations, a Process Capability Study is required


INTRODUCTION TO SPC


Page 12402-108, Rev. C


GB108-23

# 1) Putting specification limitsspecification limits on a Control Chart

#2 ) Treating UCL and LCL as a specification limit

When you do either of these the control chart becomes just an inspection tool - it’s no longer a control chart

UCL / LCL are not directly tied to customer defects !

TWO BIG CONTROL CHART ERRORS



GB108-24

Variables Charts

X-Bar / R Chart

X-Bar / s Chart

I-MR Chart

Attribute Charts

I-MR Chart

p-Chart

np-Chart

c-Chart

u-Chart

Major Types of Control ChartsMajor Types of Control Charts

INTRODUCTION TO SPC


Page 13402-108, Rev. C


GB108-25

VARIABLEVARIABLE -

The data are continuous (measured)

Results from the actual measuring of a characteristic such as diameter of a hose, electrical resistance, weight of a unit , etc.

ATTRIBUTEATTRIBUTE -

The data are generally counted

Results from using go/no-go gages, inspection of visual defects, quantity of missing parts, pass/fail or yes/no decisions, etc.

Two General Kinds of DataTwo General Kinds of Data


GB108-26

(1) Length of connector housings produced by a molding press

(2) Average production rate of the manufacturing lines in a building

(3) Number of printing defects on a package label

(4) Number of typographical errors per Sales Contract

(5) Number units failing test in monthly production

(6) Percent failed units in Monthly Production

(7) Amount of time taken to close an Account Receivable

(8) Number of parts with defects per 100 parts produced

Exercise: What Type of Data?Exercise: What Type of Data?

INTRODUCTION TO SPC


Page 14402-108, Rev. C


GB108-27

DiscreteVariable

What Type Of Data ?

Data Collected In Groups or Individuals ?

CountingSpecific Defects orDefective Items ?

GROUPS(Averages)

(n>1)

INDIVIDUALVALUES

(n=1)

X-Bar RX-Bar S

IndividualsMoving Range

SpecificTypes Of “Defects”

DefectiveItems

Is The Probability OfA Defect Low ?

If You Know How ManyAre Bad, Do You Know How Many Are Good?

Poisson Distribution Binomial Distribution

IndividualsMoving Range

NO

YES YES

Area ofOpportunity Constant

In Each SampleSize ?

YESNO

C ChartU Chart

ConstantSample Size ?

NP Chart

NO YES

P Chart

Choosing the Correct Control Chart Choosing the Correct Control Chart

NOTE: X-Bar S is appropriate

for subgroup sizes (n) of > 10


GB108-28

Minitab Minitab –– Control ChartsControl Charts

INTRODUCTION TO SPC


Page 15402-108, Rev. C


GB108-29

The Tests We Will Use:Test #1: One point outside the UCL or LCL (3-sigma limit) Test #2: Two of three consecutive outside the 2-sigma limit Test #3: Four of five consecutive outside the one-sigma limitTest #4: Nine consecutive on one side of the center linePattern Test: A pattern repeats itself (#cycles x #points in pattern >15)

The Tests We Will Use:Test #1: One point outside the UCL or LCL (3-sigma limit) Test #2: Two of three consecutive outside the 2-sigma limit Test #3: Four of five consecutive outside the one-sigma limitTest #4: Nine consecutive on one side of the center linePattern Test: A pattern repeats itself (#cycles x #points in pattern >15)

A set of standard tests have been created to help identify SPECIAL CAUSE events present in our process

We use the phrase “Out of Control” when the points on the chart have failed a test.

When any of the points on the chart fail a test, it means something “unusual” has happened –

Go Check It Out !!Go Check It Out !!

Control Charts TestsControl Charts Tests


GB108-30

““Rules Of The Road”Rules Of The Road”(1) Start with Test #1 and Pattern Detection test(1) Start with Test #1 and Pattern Detection test

(2) If higher sensitivity is needed, also use tests (2) If higher sensitivity is needed, also use tests

#2, 3, & 4#2, 3, & 4

The Tests We Will Use:Test #1: One point outside the UCL or LCL (3-sigma limit) Test #2: Two of three consecutive outside the 2-sigma limit Test #3: Four of five consecutive outside the one-sigma limitTest #4: Nine consecutive on one side of the center linePattern Test: A pattern repeats itself

The Tests We Will Use:Test #1: One point outside the UCL or LCL (3-sigma limit) Test #2: Two of three consecutive outside the 2-sigma limit Test #3: Four of five consecutive outside the one-sigma limitTest #4: Nine consecutive on one side of the center linePattern Test: A pattern repeats itself

Detecting Lack of ControlDetecting Lack of Control

INTRODUCTION TO SPC


Page 16402-108, Rev. C


GB108-31

1 Sigma

2 Sigma

3 Sigma

1 Sigma

2 Sigma

3 Sigma

60-75%

90-98%

99-99.9%

% of Data PointsUCL

LCL

TIMETIME

The ItemWe Are

Measuring

Rules of Standard Deviation:Rules of Standard Deviation:Where Does the Data Lie?Where Does the Data Lie?


GB108-32

Test #1

Test #2

Test #3

Test #4

Minitab Tests for Lack of ControlMinitab Tests for Lack of Control

Trend

INTRODUCTION TO SPC


Page 17402-108, Rev. C


GB108-33

Control Charts:Control Charts:Individual / Moving RangeIndividual / Moving Range

Use:

When its inconvenient or impossible to obtain more than one measurement per sample

When technology allows for easy measurement of every unit at minimal cost

Data availability is sparse

Variation

Short Term: Represented by the variation from one unit to the next (Moving Range chart)

Long Term: Represented by a long sequence of such events (Individuals chart)

Charts are Based on a Subgroup Size of 1


GB108-34

Open worksheet: Individual MR.MTWin DM SPC.MPJ

Stat > Control Charts > Variables Charts for Individuals > I-MR

Variable = Errors

Minitab Exercise:Minitab Exercise:Individuals / Moving RangeIndividuals / Moving Range

INTRODUCTION TO SPC


Page 18402-108, Rev. C


GB108-35

What are the charts telling you?

Are the process data stable?

Observation

In

div

idu

al

Va

lue

24222018161412108642

10

5

0

_X=4.04

UC L=10.13

LC L=-2.05

Observation

Mo

vin

g R

an

ge

24222018161412108642

8

6

4

2

0

__MR=2.292

UC L=7.488

LC L=0

I-MR Chart of Errors

Minitab Exercise:Minitab Exercise:Individuals / Moving Range OutputIndividuals / Moving Range Output


GB108-36

Requirements for EffectiveRequirements for Effective

Use of Control ChartsUse of Control Charts

Management MUST establish and support an environment that promotes proper action and support to the information collected on the control charts

Control Charts are implemented ONLY on Key Processes on which improvement will bring benefit to the organization and/or the customer

Data collected from the process is validated through the use of a CAPABLE measurement system

Biggest Failure Of Control Chart Programs

Flood the system with control charts, then don’t take action on the data

INTRODUCTION TO SPC


Page 19402-108, Rev. C


GB108-37

Control Charts Control Charts –– SamplingSampling

vs. 100% Inspectionvs. 100% Inspection

Control Chart sampling is a simple and effective method for process understanding

If actions are taken to eliminate special causes (stabilize the process) and capability is proven, 100% Inspection can be eliminated (but beware of customer specified inspection plans)


GB108-38

ExercisesExercises

Open the Minitab Project File DM SPC.MPJ

Practice Creating Control Charts using theWorksheets contained in this project file

INTRODUCTION TO SPC


Page 20402-108, Rev. C


GB108-39

Tests forTests for

Statistical ControlStatistical Control


GB108-40

0 5 10 15 200

1

2

3

4

5

Rbar

Xdbar

LCL

UCL

UCL

LCL

Rang

e

8

9

10

11

12

13

14

ABCCBA

XB

AR

Normal VariationNormal Variation

INTRODUCTION TO SPC


Page 21402-108, Rev. C


GB108-41

0 5 10 15 200

1

2

3

4

5

Rbar

Xdbar

LCL

UCL

UCL

LCL

Ra

ng

e

8

9

10

11

12

13

14

ABCCBA

XB

AR

Zone Test 1: Zone Test 1: Range ChartRange Chart


GB108-42

0 5 10 15 200

1

2

3

4

5

Rbar

Xdbar

LCL

UCL

UCL

LCL

Ra

ng

e

8

9

10

11

12

13

14

ABCCBA

XB

AR

Any Point Outside the Control LimitAny Point Outside the Control Limit

Zone Test 1: Zone Test 1: Average ChartAverage Chart

INTRODUCTION TO SPC


Page 22402-108, Rev. C


GB108-43

Two of Three Points in Zone ATwo of Three Points in Zone A

0 5 10 15 200

1

2

3

4

5

Rbar

Xdbar

LCL

UCL

UCL

LCL

Ra

ng

e

8

9

10

11

12

13

14

ABCCBA

XB

AR



GB108-44

Two of three successive points in Zone ATwo of three successive points in Zone A

0 5 10 15 20

0

1

2

3

4

5

Rbar

Xdbar

LCL

UCL

UCL

LCL

Ra

ng

e

8

9

10

11

12

13

14

ABCCBA

XB

AR


INTRODUCTION TO SPC


Page 23402-108, Rev. C


GB108-45

Four of Five successive points in Zone B and BeyondFour of Five successive points in Zone B and Beyond

0 5 10 15 20

0

1

2

3

4

5

Rbar

Xdbar

LCL

UCL

UCL

LCL

Ra

ng

e

8

9

10

11

12

13

14

ABCCBA

XB

AR



GB108-46

Four of Five successive points in Zone B and BeyondFour of Five successive points in Zone B and Beyond

0 5 10 15 20

0

1

2

3

4

5

R-bar

X-dbar

LCL

UCL

UCL

LCL

Ra

ng

e

8

9

10

11

12

13

14

ABCCBA

XB

AR


INTRODUCTION TO SPC


Page 24402-108, Rev. C


GB108-47

Eight successive points in Zone C and Beyond, on the same Side of the Center Line


0 5 10 15 20

0

1

2

3

4

5

Rbar

Xdbar

LCL

UCL

UCL

LCL

Range

8

9

10

11

12

13

14

ABCCB

A

XB

AR



GB108-48



0 5 10 15 20

0

1

2

3

4

5

Rbar

Xdbar

LCL

UCL

UCL

LCL

Range

8

9

10

11

12

13

14

ABCCB

A

XB

AR


INTRODUCTION TO SPC


Page 25402-108, Rev. C


GB108-49

Seven Consecutive Points Without a Major Change in Direction


0 5 10 15 20

0

1

2

3

4

5

Rbar

Xdbar

LCL

UCL

UCL

LCL

Range

8

9

10

11

12

13

14

ABCCB

A

XB

AR

Trend Test: Trend Test: Range ChartRange Chart


GB108-50



0 5 10 15 20

0

1

2

3

4

5

Rbar

Xdbar

LCL

UCL

UCL

LCL

Range

8

9

10

11

12

13

14

ABCCB

A

XB

AR

Trend Test: Trend Test: Average ChartAverage Chart

INTRODUCTION TO SPC


Page 26402-108, Rev. C


GB108-51

Eight or more points fall on both sides of centerline in Zones A & B, with none in Zone C.

Eight or more points fall on both sides of centerline in Zones A & B, with none in Zone C.

0 5 10 15 20

0

1

2

3

4

5

Rbar

Xdbar

LCL

UCL

UCL

LCL

Range

8

9

10

11

12

13

14

ABCCBA

XB

AR

Mixture Test: Mixture Test: Average ChartAverage Chart


GB108-52

Fifteen or more consecutive points in either C Zone; few or no points beyond Zone C

Fifteen or more consecutive points in either C Zone; few or no points beyond Zone C

0 5 10 15 200

2

4

6

8

10

12

14

Ave

LCL

UCL

Range

4

6

8

10

12

14

16

ABCCBA

Ave

LCL

UCL

XB

AR

Stratification Test: Stratification Test: Average ChartAverage Chart

INTRODUCTION TO SPC


Page 27402-108, Rev. C


GB108-53

A repeating pattern of change.A repeating pattern of change.

0 5 10 15 20 25

0

1

2

3

4

5

Rbar

Xdbar

LCL

UCL

UCL

LCL

Ra

ng

e

8

9

10

11

12

13

14

ABCCBA

XB

AR

Cycle Test: Cycle Test: Range & Average ChartsRange & Average Charts


GB108-54

Linked Control Chart Methods to the process improvement and the DMAIC roadmap

Discussed different types of variation

Introduced a few basic Control Charting methods

Discussed the interpretation of Control Charts

SPCSPC -- SummarySummary

TEAM BUILDING


Page 1402-108, Rev. C

GB109-1

Team Building and Interpersonal

Skills for Black Belts and Green

Belts

D

M

A

I

C

TeamTeamBuildingBuilding

GB109-2

What is the team in this picture?

Why two horses?

Roles?

Team boundaries?

“Form follows function. . . .” Teams provide:• Process knowledge• Process ownership• Time• Creativity

Reference Guide:Reference Guide:Page 2Page 2

TEAM BUILDING


Page 2402-108, Rev. C

GB109-3

CRITICAL FACTORS FOR TEAM SUCCESS

PURPOSE: Why do we have a team? What’s our output?

PROCESS: What process will we follow?

COMMUNICATION: Is our communication complete and appropriate?

COMMITMENT: How is commitment nourished?

INVOLVEMENT: Are the right people on the team?

TRUST: How is trust built and maintained?


GB109-4

Team Size: 5 – 7

Why not more?


TEAM BUILDING


Page 3402-108, Rev. C

GB109-5

Team BuildingStart-up “to do” list:

• Purpose / mission• Form team• Six Sigma training• Plan kick-off meeting• Black / Green Belt as coach / leader

Reference Guide:Reference Guide:

Page 4Page 4

GB109-6

First Team Meeting Agenda

• Management Challenge / Commitment

• Team Charter / Goals

• Team Member Roles And Responsibilities

• Empowerment ( Team’s Budget And Decision-

Making Authority)

• Measures (Team Milestones)

• Ground Rules And Meeting Schedule

• Team Training Needs


TEAM BUILDING


Page 4402-108, Rev. C

GB109-7

GOALPROCESS

ENVIRONMENT

- Physical

- Emotional

- Business / Political

GROUP

Team Dynamics – Group Behavior


GB109-8

NORMING:• Expression of opinions and data flow• Development of cohesion

PERFORMING:• Collaboration and interdependence• Problem-solving

Four phases of team development

B.W. Tuckman, “Developmental Sequence in Small Groups.”

Psychological Bulletin, 63, 1965, pp. 384-399

FORMING:• Orientation• Testing and dependency

ISTORMING:• Emotional response to task demands• Intragroup conflict

II

III

IV

Reference Guide:Reference Guide:Page 5Page 5--77

TEAM BUILDING


Page 5402-108, Rev. C

GB109-9

Achieving Team Synergy

(Lost at Sea exercise)


GB109-10

1P

eop

le9

1 Task 9

S-4 S-1

S-2S-3

9 Maturity 1

1P

eop

le9

1 Task 9

S-4 S-1

S-2S-3

9 Maturity 1

Situational Leadership Styles


PROCESS MAPPING


Page 1402-108, Rev. C

GB110-1Rev. C February 2004© Tyco Electronics

ProcessProcess

MappingMapping



Rev. C January 2004 © Tyco Electronics GB110-2

Process Mapping Process Mapping

Provide an overview of Process Mapping

To show the step-by-step example of Process Mapping

To demonstrate where Process Mapping fits into the DMAIC Process Improvement Model

To provide examples of Process Maps

To perform a Process Mapping exercise

PROCESS MAPPING


Page 2402-108, Rev. C














Critical Input VariablesCritical Input Variables

30+ Inputs

8 - 10

4 - 8

3 - 6

Found Critical X’s

Controlling Critical X’s

10 - 15

All X’s

1st “Hit List”

Screened List

• “0” Process & Value Stream Maps

• Failure Modes and Effects Analysis

• Multi-Vari Studies

• Design of Experiments (DOE)

• Control Plans

• C&E Matrix

The Funneling EffectThe Funneling Effect

MEASUREMEASURE

ANALYZEANALYZE

IMPROVEIMPROVE

CONTROL*CONTROL*

PROCESS MAPPING


Page 3402-108, Rev. C


Types of Process MapsTypes of Process Maps

‘Level 0’ Process Map ( I-P-O or SIPOC )

Used to define the macro level for Suppliers and Customers. (Only 3 or 5 blocks in map)

Value Stream Map

Process and information flow (macro)

Key metrics (e.g. cycle time, yield, inventory) for key process steps and for the overall project. Shows constraints.

Detailed Process Map shows all steps in the process, including non-value adding steps.


Detailed Process MappingDetailed Process Mapping

What is the tool?

Graphical illustration of the work process

What will the tool identify?

All value-added and non-value-added process steps

Process Inputs (X’s)

Process Outputs (Y’s)

Data Collection Points

First Input Variables to put in FMEA

When do you apply process mapping?

Always

What are results of Process Mapping?

Identify systems needing Measurement Studies

Identify Output Variables for Capability Studies

Identify missing elements in the Control Plan

Identify possible non-value-added process steps

PROCESS MAPPING


Page 4402-108, Rev. C


Process MappingProcess Mapping

Should describe:

Major activities / tasks

Sub-processes

Process Boundaries

Input Variables (X’s)

Output Variables (Y’s)

Should be reviewed frequently and updated

),...,,( 21 kxxxfy


Increasing Process Description Detail: Increasing Process Description Detail: Top Down Mapping MethodTop Down Mapping Method

Map – Start with P from SIPOC

Detail – I/O Analysis

Prioritize

C&E Matrix

Common Sense

Map – drill into sub processes

Flow Chart

Cross Functional Map

Cross Resource Map

Detail

Prioritize

Map - drill into sub-sub processes

Flow Chart

Cross Functional Map

Cross Resource Map

Detail

Prioritize

Ro

un

d 1

Ro

un

d 2

Ro

un

d 3

Stop

StartStart

StopStop

PROCESS MAPPING


Page 5402-108, Rev. C


Increasing Process Description Detail:Increasing Process Description Detail:Types of MapsTypes of Maps

Flow Chart (aka Block Diagram)

Shows process steps, without worrying about details

Useful as a first pass to understand process steps

Cross Functional Map (aka “Swim lane”)

Rearranges flow chart process steps into lanes of functions

Clearly shows hand-offs

Process steps move left to right as time progresses

Cross Resource Map

Further drill down into functions, showing actual resource to resource hand offs

customer

No

Yes

No

Yes

Function 1

Function 2

Function 3

Function 4

Function 1

Function 2

Function 3

Function 4

Resource 1

Resource

2


Transactional Non-Manufacturing

Example Manufacturing Example

MACRO Organization Customer

Service

Plant East Berlin

LOCAL Process Customer Orders Process Area Z-Pack Assembly

MICRO Subprocess Order Entry Process Step Work Center

5052

• Start at Macro level and work towards the Micro level, as required

• Focus Local and Micro level work in areas of largest potential gain

• Don‘t go more detailed than you need to – avoid analysis paralysis.

Different Levels of Process MapsDifferent Levels of Process Maps

PROCESS MAPPING


Page 6402-108, Rev. C


Preparing the Process MapPreparing the Process Map

Team Effort – Manufacturing Example:

Manufacturing engineers

Line operators

Line supervisors

Maintenance technicians

Suppliers

Inputs to Mapping

Brainstorming

Manuals, work instructions

Engineering specifications

Operator experience

Fishbone diagrams (6 Ms)

Man, Machine (Equipment), Method (Procedures), Measurement, Materials, Mother Nature (Environment)

• Transactional Example:

Sales force

Engineers

Operators

Supervisors


Setting up the InterviewSetting up the Interview

Contact the people in advance and schedule the interview. Make sure not to conflict with other schedules

Keep in mind that people may be concerned about why you want to talk to them

Keep the interview short

no more than 30-45 minutes

Ask them to collect any forms, documents, lists, or procedures that are normally used

Develop a set of interview questions

Let your team members conduct the interviews too!Let your team members conduct the interviews too!

PROCESS MAPPING


Page 7402-108, Rev. C


Setting up the InterviewSetting up the Interview

Whenever possible hold the interview at the individual’s actual place of work and not a conference room.

At the workplace, make note of its condition.

Is this a quiet, orderly, neat, and efficient work area or is it in chaos

Are there manuals or other documents in the office?

Look around the area and at other work areas:

Make note of the above characteristics for other desks in the area.


Conducting the InterviewConducting the Interview

Begin with questions that are aimed at an understanding of the process as a system

When an entity (work) arrives, what is the first thing that is done?

Then what? Loop until sequence is complete.

When is it done?

As you move through the “Then what?” loop,begin to create a straw-man flow chart of the process.

Loop through all activities in sequence until the end of the process is identified.

Describetask

or activity

Thenwhat?

End

PROCESS MAPPING


Page 8402-108, Rev. C


Conducting the InterviewConducting the Interview

The SOLAR ModelThe SOLAR Model

S - Enter at the Social level.

Put them at ease. Discuss the weather, something from the news. Go easy.

O - Explain the Objective of the meeting, discussion, etc.

Why are you here, what are you doing, why are you doing it?

L - Listen to what they say.

Ensure you’re non-verbal cues show you are interested.

A – Advise or Ask

Influence by recommendations, coaching, persuading, confronting, or asking probing questions for more understanding

R - Record.

Identify and record specific steps or outcomes.

The SOLAR Model is a ‘checklist’ for you to remember as you are in the middle of the interview

The SOLAR Model is a ‘checklist’ for you to remember as you are in the middle of the interview


Interviewing:Interviewing:Notes & Things to RememberNotes & Things to Remember

Be sure to thank the people you interview.

Be sure to thank their manager/supervisor for letting them meet with you.

Remember they can’t be doing their job if they are talking to you.

Be aware of how much time you have spent conducting an interview.

Don’t ask for 30 minutes and stay 60.

PROCESS MAPPING


Page 9402-108, Rev. C


Level 0 Process and Inputs/OutputsLevel 0 Process and Inputs/Outputs

Identify the process in basic terms

Identify External Supplier (Inputs)

Raw materials

Incoming information

Personnel

Identify the Customer Requirements (Outputs)

Process or Product customer specifications or critical attributes.

Business/Organization Performance metrics.

(We need to get a “high level” view of the process first)(We need to get a “high level” view of the process first)


Traditional SIPOC LayoutTraditional SIPOC Layout

Supplier

Supplier

S

ProcessInput

ProcessInput

ProcessInput

“Big X’s”

I

ProcessStep 1

ProcessStep 2

ProcessStep 3

ProcessStep 4

ProcessStep 5

ProcessStep 6

P

ProcessOutput

ProcessOutput

ProcessOutput

“Big Y’s”

O

Customer

Customer

C

PROCESS MAPPING


Page 10402-108, Rev. C


Methods for Making a IMethods for Making a I--PP--O or SIPOCO or SIPOC

Start with the Customers and work to the left

Always keeps the customer as the focus

Can be confusing if Process is unclear

Or

Start with the Process and work outward

More intuitive for team members

Risk less customer focus – you must facilitate the focus!

Try either method … see which works better for you. Try either method … see which works better for you.


Level 0 Examples:Level 0 Examples:

Order Entry

PhoneFAXEmailEDI

PriceAvailabilityConfirmation of

OrderPromise DateOrder number

Inputs Outputs

NonNon-- ManufacturingManufacturing


Stamping

SOPDiesRaw StockEquipmentDie MaintenanceOperator

FlatnessUndamaged PartsDimensionalityTensile StrengthBurr freeCycle Time

Inputs Outputs

PROCESS MAPPING


Page 11402-108, Rev. C


• Do a first draft of your “Level 0” process map.

• Discuss at your table.

Time: 15 min.

ExerciseExercise


METALFORMING

CT: 2 days--------------------------------

Yield: 0.8 %-------------------------------

WIP: 3,500

Value Stream MapValue Stream Map

A Value Stream Map is a special type of process map that includes:

Major process steps and info flow

Lean symbols

Before-and-after map at the macro level

Project measures, activity update

Team membership

The map includes key Lean indicators for

the major process steps, including:

Cycle Time

Inventory

Yield at each major step and

Rolled throughput yield: RTY

(your key project metrics)

PROCESS MAPPING


Page 12402-108, Rev. C


Value Stream Map IconsValue Stream Map Icons


Date:

xx-xx-

Project:

Lodenbel

Black Belt: R. Lee

Team Members: AB, CD, EF, IF, KL

Push Prod. No takt time Pull Production

TAKT

time

from 8'

to 5.6 '

Std.

Work /

Cell

design

Ded. Depts No std. Work

Current State Future State

Metrics Improvement Proposals

Status

by

Lead time Cycle Time WIP / Inventory Scrap Tape Detailed process map, QFD

Base Proposed Base Proposed Base Proposed Base Proposed Base Proposed Measures, validation

30

days2 days 30' 8' 200 19 10% 0.9 9 - 10" 2"

Kaizan 1

Kaizan 2, DOE on cutting opns.

Die cut analysis for 1 and 2

Productivity focus **

Started

Define Measure Analyze Improve Control Completed

Past due

D

M

A

I

C

Precision

Card Co.

RED, Inc

Prod.

Control

Sub Assy

# 1

Sub Assy

# 2

Finishing

Dept

Pink

Blue

White

Precision

Card Co.

RED, Inc

MRP

S

S 2000

pull

B

B 500

-1- -2- -3-

CT: 2 2 4

Yld: 87% 85% 91%

WIP 10 9 0

RTY = 69%

Prod Goal = 1pp/12 min **

current is 1pp/16.8 min

dailydaily

Ship

800 / week

3 people

2 shifts

1

2

3

Kaizan

Cell

Kaizan

Supplier

Certif ied

ValueStreamMap

Project Data

Before/After Maps

Key Metrics:Base /

Current

Project Status

PROCESS MAPPING


Page 13402-108, Rev. C


• Do a first draft of your Value Stream process map. (Current state, measures, team, etc.)

• Use blank form provided. Do in pencil

• Discuss at your table.

Time: 20 min.

ExerciseExercise


Detail Process Mapping StepsDetail Process Mapping Steps

Start with Level 0 and Value Stream Map: the process, its external inputs and customer outputs

Identify all steps in the process graphically, including non-value adding steps like delay.

List key output variables at each step

List key input variables and classify process inputs as controlled or uncontrolled

Add process specifications for Input Variables

PROCESS MAPPING


Page 14402-108, Rev. C


Detail Process MapDetail Process Map

Include all value-added and non value-added steps(Process steps, inspection/test, rework, scrap, delay.)


Answer Phone

Greet customer

Determine parts needed

Identify need date

Identify ship to address

Identify price

Identify shipment method

Internal Information

Get internal P/N

Determine terms

Identify lead time

Complete order worksheet

Order Entry

Input information

Print order confirmation

Determine ship date

Review order

File / Queue

File for end-of-day processing, queue for batch download 10:00

EST next work day

NonNon--ManufacturingManufacturing

Move

Move material from

warehouse

Wait

Store in staging area

Set-up

Set up and adjust die, reeler, etc.

Stamp

4-out die, 6 stations in die

Move

Move to inspection operation,

wait

Inventory

Return to stores to wait

further processing


List Output VariablesList Output VariablesInclude both process and product output variables

ManufacturingManufacturingOutputs

• Cycle Time

• Material properties

• NVA

Move

Move material from

warehouse

Stamping

Wait

Store in staging area

Set up

Set up and adjust die,

reelers, etc.

Move

Move to inspection operation,

wait

Inventory

Return to stores to wait

further processing

• Cycle Time

• NVA

• Cycle Time

• NVA

Outputs

• Cycle Time

• Correct part

• Correct dimensions

• Burr free

• Cycle Time

• NVA

• Cycle Time

• NVA

PROCESS MAPPING


Page 15402-108, Rev. C


List Output VariablesList Output VariablesInclude both process and product output variables

• Prompt answer

• Live body

• Part number

• Availability

• Need date

• Customer number

• Order worksheet

• Pricing


Outputs

Answer Phone

• Greet customer

• Determine P/N

• Identify need date

• Identify ship to address

• Identify ship method


•Get internal P/N

•Determine terms

•Identify lead time

•Complete order worksheet

Outputs

• Order in computer

• All line items complete

• Correct info

• Promise date

• Ordernumber

• Printed confirmation

• Cycle time

• NVA

Order Entry

• Input information

• Print order confirmation

• Determine ship date

• Review order

File / Queue

• File/store for end-of-day processing, queue for batch download 10:00 EST next work day.


List and Classify Input VariablesList and Classify Input Variables

List all key input variables and classify them asControlled inputsUncontrolled inputs

C = Controlled Inputs: Input Variables that can be changed to see the effect on Output Variables. Sometimes called “Knob” variables

U = Uncontrolled Inputs: Input Variables that impact the Output Variables but are difficult or impossible to control (may also be controllable, just not under control currently)

Example: Environmental variables such as humidity

KIPV = Critical Inputs: Input Variables that have been statistically shown to have a major impact on the variability of the Output Variables

PROCESS MAPPING


Page 16402-108, Rev. C


Is it Controlled or Uncontrolled?Is it Controlled or Uncontrolled?

Is it Controllable or Uncontrollable?Is it Controllable or Uncontrollable?

Current System

Controlled UncontrolledP

ossib

le Un

co

ntr

olla

ble

C

on

tro

llable

No short term solution

Long term –possibly implement

technology

Validate with Measurement

System Analysis

Why?

Validate it’s truly uncontrollable with

DOE

OPPORTUNITY!

Put in control system

Label as U*

Label C or U Using Current System


Process Mapping Manufacturing Process Mapping Manufacturing

Example (VExample (V--A only)A only)

Outputs

• Cycle Time


Feed

Feedmaterial from roll

Cutting

Cut material to length

• Cycle Time

• Correct length

• Burr free

Stamping

Stamp piece to size

• Cycle Time

• Correct part

• Burr free


• Tensilestrength

Drawing

Draw features

Outputs

• Cycle Time

• Correct part


• Tensilestrength

Punching

Punch out features

• Cycle Time

• Correct part

• Burr free


• Tensilestrength

Cleaning

Clean metal surface

• Cycle Time

• Clean surface

• Residue free

• Gear speed

• Gear wear

• Material lot


Inputs Type

C

U

U

U

• Shear speed

• Shear wear


• Clampingforce

• Shear force

C

U

U

C

U

• Die wear


• Ram force

• Ram speed

• Die number

• Die hardness

U

U

C

C

C

C

• Drawing speed


• Die number

Inputs Type

C

U

C

• Punch speed

• Punch wear


• Clampingforce

• Ram force

C

U

U

C

U

• Solvent purity

• Solvent age

• Surface roughness

• Contaminantlevel

• Humidity

• Temperature

C

U

U

U

U

U

U

PROCESS MAPPING


Page 17402-108, Rev. C


Process Mapping Transactional Process Mapping Transactional

Example (VExample (V--A only)A only)

(Non(Non--Manufacturing)Manufacturing)

Outputs

Answer Phone

• Greet customer

• Determine P/N

• Identify need date

• Identify ship to address

• Identify ship method


• Get internal P/N

• Determine terms

• Identify lead time

• Complete order worksheet

Order Entry

• Input information

• Print order confirmation

• Determine ship date

• Review order

Order Confirmation

• FAX confirmation to customer

• Verify manufacturing receipt of order

Inputs Type

• Orderworksheet

• Computerentry screens

• Lead time information from mfg

• Shipment method

• Printed confirmationsheet

• Productionschedule

• Customer contact info

• Productioncontact info

• Confirmationprocedure

U

C

U

C

C

U

C

C

C

Inputs Type

• Information fromcustomer

• Greeting script

• Answering procedure

• Telephonesystem

• Cross reference for P/N’s

• Orderinformation

• Plant loading information

• Orderworksheet form

• Pricingalgorithm

C

C

C

U

C

U

U

C

C

• Promptanswer

• Live body

• Part number

• Availability

• Need date

• Customer number

• Orderworksheet

• Pricing

Outputs

• Order in computer

• All line items complete

• Promise date

• Ordernumber

• Printed confirmation

• Orderconfirmationto customer

• Order to production


Controlled Input

VariableTarget

Upper

Spec

Lower

Spec

Die Wear ? 2.56 2.33

Solvent Type ? ? ?

Ram Speed ? 800 750

Clamping Force ? ? ?

Add Specifications for InputsAdd Specifications for Inputs

For Input variables identified as Controlled and for Critical Inputs, we can add the operational specifications and Targets for these variables

This information is the beginnings of the Control Plan!

Use spec numbers if available, not just input category

PROCESS MAPPING


Page 18402-108, Rev. C


Example:Example:Coffee ShopCoffee Shop

When the customer arrives at the shop, they have a need-- a need for a cup of coffee.

That need is translated into a customer order. It is this orderthat actually flows through the coffee process and carries the need.

This customer order may change from information to physical material and equipment as it moves through the process.

As the order moves through the process, each activity adds a degree of fulfillment to that order.

At the end of the process, the fulfilled order is delivered.

To the customer, the output of this process is a cup of coffee.

To the process manager, the output of the process is a successfully completed order.

What is the entity that moves through a coffee shop?What is the entity that moves through a coffee shop?


Coffee Making ExerciseCoffee Making Exercise

You have been identified as a team for instructing future Six Sigma Leaders in the activity of making coffee.

There have been complaints about the coffee, and your mission is to improve the process of making coffee with our existing equipment (Mr. Coffee type maker).

Your first task is to document the process with a Level 0 and Detailed Process Map.

Good luck: The last coffee team was awarded the Bilge-Water Coffee Award and had to drink their own brew. Everyone else is buying from the machine. (The previous team did not use any Six Sigma tools.)

PROCESS MAPPING


Page 19402-108, Rev. C


Uses for Process MapUses for Process Map

Provides inputs to Cause and Effects Matrix

Provides inputs to FMEA

Provides inputs to Control Plan Summary

Provides inputs to Capability Summary

Provides inputs to Multi-Vari Studies

Evaluate Experimental Designs

Tracks variables studied

Allows evaluation of design’s robustness to noise variables

Can also be used to track team activitiesCan also be used to track team activities


Process Map Analysis:

Overview of Opportunity and Strategy

?Capability Issues:

Statistical and Quality Tools

Capacity Issues:Lean Tools

Does the nature of the gap suggest one or the other, or both?

Does the nature of the gap suggest one or the other, or both?

Process Map AnalysisProcess Map Analysis

PROCESS MAPPING


Page 20402-108, Rev. C


Next StepsNext Steps

Process MapProcess Map

1 2 3 4 5 6 7 8 9 10 11

Process Step Process Inputs He

avi

es

in

Pro

du

ct

Lig

hts

in

Pro

du

ct

Mo

istu

re in

P

rod

uc

t

Ac

idity

in

Pro

du

ct

Lo

w C

ap

ac

ity

Fro

m U

nit

Ex

ces

sive

D

ow

ntim

e

Ma

teri

al

Lo

sse

s

Co

rro

sio

n o

f E

qu

ipm

en

t

Po

or

Re

act

or

Pe

rfo

rma

nce

Total

139 Day Tanks Analysis 10 10 9 9 335

9 Reactor Cat./HF Ratio 5 8 7 157

7 Reactor Rxr Temperature 6 5 4 7 149

73 Lights Removal Condenser Leak 4 8 2 4 1 148

74 Lights Removal Reboiler Leak 4 8 2 4 1 148

131 Purification Low Stages 8 8 144

144 Final Storage Containers 3 2 6 6 140

100 Neutralization pH Value 6 6 3 138

16 Catalyst Stripper Pluggage 3 6 5 3 137

111 Drying Decomposition 2 6 3 2 2 134

39 Drier Water Carryover 4 6 5 1 132

34 Drier Molecular Sieve 3 3 2 7 2 125

1 2 3 4 5 6 7 8 9 10 11

Process Step Process Inputs He

avi

es

in

Pro

du

ct

Lig

hts

in

Pro

du

ct

Mo

istu

re in

P

rod

uc

t

Ac

idity

in

Pro

du

ct

Lo

w C

ap

ac

ity

Fro

m U

nit

Ex

ces

sive

D

ow

ntim

e

Ma

teri

al

Lo

sse

s

Co

rro

sio

n o

f E

qu

ipm

en

t

Po

or

Re

act

or

Pe

rfo

rma

nce

Total

139 Day Tanks Analysis 10 10 9 9 335

9 Reactor Cat./HF Ratio 5 8 7 157

7 Reactor Rxr Temperature 6 5 4 7 149

73 Lights Removal Condenser Leak 4 8 2 4 1 148

74 Lights Removal Reboiler Leak 4 8 2 4 1 148

131 Purification Low Stages 8 8 144

144 Final Storage Containers 3 2 6 6 140

100 Neutralization pH Value 6 6 3 138

16 Catalyst Stripper Pluggage 3 6 5 3 137

111 Drying Decomposition 2 6 3 2 2 134

39 Drier Water Carryover 4 6 5 1 132

34 Drier Molecular Sieve 3 3 2 7 2 125

Cause and Effects Matrix

Cause and Effects Matrix

Process

Step/Part

Number

Potential Failure Mode Potential Failure Effects

S

E

V

Potential Causes

O

C

C

Current Controls

D

E

T

RPNActions

RecommendedResp.

COATING &

IMAGING

DIRTY PHOTOMASK MICROCRACKING,

DELAMINATION, STREAKS 8

LOW FREQUENCY OF CLEANING

8

SOP, VISUAL INSPECTION

7 448

INCREASE FREQUENCY

TO ONCE EVERY 20

PANELS

MG

IMPROVE CLEANING

METHOD

PF

PURCHASE OFF-LINE

CLEANING SYSTEM

MG

TEST ON-LINE MASK

REPLACEMENT

PF

FMEAFMEA

Process Step Input Output

Process

Specification

(Target, LSL, USL)

Cpk

Mean - Sigma

Measurement

Technique

%R&R

P/T

Sample

Size

Sample

Frequency

Control

MethodReaction Plan

Coating Dosage 22.5, 22, 23 1.22 UIL-1700 25% 1/hr Auto-timer Cross check

Coating Height

24,23,25 1.54 Micrometer 31%/0.47 35 pts per panel

1/hr Coating & pump

speed

Adjust previous

Coating width

14,12,16 1.78 Laser Measuring Device

1/hr None in place

Coating

length

36,34,38 1.43 Laser Measuring

Device

1/hr None in place

Vacuum 35" Hg Vacuum Gauge 1/hr Monitor Compare

guages, look for blockage

Initial Assessment of Capabilities and Control Plans

Initial Assessment of Capabilities and Control Plans

OUTPUTS

INP

UT

S

Customer Requirement

(Output Variable)

Measurement

Technique

%R&R or P/T

Ratio

Upper

Spec

Limit

Target

Lower

Spec

Limit

Cp CpkSample

SizeDate Actions

Gel Time

ViscosityCleanliness

Color

Homogeneity

ConsistencyDigets Time

Temperature

Solids

Key Process Output Variable

Capability Status Sheet

Input Type Output

Agit speed Contr Stab timeTemperature Contr Stabilize Acid numberPressure Contr Color

Air flow Contr Put in setpoints Viscosity

% O2 in air Noise Slowly reduce press Reactor temp

Air temp Contr Monitor temp Temp profileOffgas flowOffgas compHT coeff

Belt speed Contr Finish timeBelt temp Contr Finish Acid numberFlow rate Contr Color

Nozzle type SOP Check nozzle type ViscosityHole size Contr Put in setpoints Drop PointRoom temp Noise Monitor appearance HardnessAgit speed Contr Pellet appear

Tank temp Contr

Input Type Output

Wax grade SOP Prep timeAmt wax Contr Prepare Reactor Acid numberCharge rate Contr ViscosityAgit speed Contr Charge melted wax Reactor tempRxn temp Contr Bring to reaction temp Temp profilePressure Contr HT coeffAir flow Contr

% O2 in air Noise

Viscosity NoiseWax temp Noise

AN target SOP Oxid timeAgit speed * Contr Oxidize Acid numberTemperature * Contr ColorPressure * Contr Put in setpoints ViscosityAir flow * Contr Sample hourly Reactor temp

% O2 in air Noise Monitor acid number Temp profile

Air temp Contr Offgas flowAir humidity Noise Offgas comp

HT coeffResp time


Initial Assessment of Control PlanInitial Assessment of Control Plan

Start an initial assessment of the control plan after the process map is complete

Add the measurement technique, operating specifications, and targets for the controlled and critical inputs as well as the outputs

Excel template: Control Plan.XLS

Process Step Input Output

Process Spec

(LSL, USL,

Target)

Cpk / Date

(Sample

Size)

Measurement

System

%R&R

or P/T

Current

Control

Method

(from FMEA)

Who Where WhenReaction

Plan

Current Control Plan

PROCESS MAPPING


Page 21402-108, Rev. C


Process Mapping SummaryProcess Mapping Summary

Provided overview of process mapping

Showed the step-by-step example of process mapping

Demonstrated where process mapping fits in the DMAIC Process Improvement Method

Provided examples of process maps

Created draft and sample process maps

MEASUREMENT SYSTEMS ANALYSIS


Page 1402-108, Rev. C


GB111-1

MeasurementSystemsAnalysis




GB111-2


Introduce Measurement Systems Analysis

Define basic measurement terms

Outline procedure for performing a Measurement Systems Analysis

Perform an exercise to practice methodology



Page 2402-108, Rev. C


GB111-3













GB111-4

Measurement ProcessMeasurement Process

The ideal measurement system produces “true” measurements every time

Quality of the measurement system is characterized by statisticalproperties

The measurement process should include:

Design and certification

Capability assessment over Time

Control

Repair and re-certification

Properties

Must be in Statistical Control

Variability must be small compared to product specifications

Variability must be small compared to process variation

Discrimination should be about one-tenth of product specification or process variation



Page 3402-108, Rev. C


GB111-5

Possible Sources of Process VariationPossible Sources of Process Variation

We will look at “repeatability” and “reproducibility” as primarycontributors to measurement error

We will look at “repeatability” and “reproducibility” as primarycontributors to measurement error

To address actual process variability, the variation due to the measurement system must first be identified and separated from that

of the process

To address actual process variability, the variation due to the measurement system must first be identified and separated from that

of the process

Stability Linearity

Long-term

Process Variation

Short-term

Process Variation

Variation

w/in sample

Actual Process Variation

Repeatability Calibration

Variation due

to gage

Variation due

to operators

Measurement Variation

Observed Process Variation


GB111-6

Basic ModelBasic Model

2

SystemtMeasuremen

2

Product

2

Total

The Total Variation is equal to the real product variation plus the variation due to the

measurement system

The Total Variation is equal to the real product variation plus the variation due to the

measurement system

2

ilityReproducib

2

ityRepeatabil

2

SystemtMeasuremen

The Measurement System Variation is equal to the variation due to repeatability plus the variation

due to reproducibility.

The Measurement System Variation is equal to the variation due to repeatability plus the variation

due to reproducibility.



Page 4402-108, Rev. C


GB111-7

Measurement Process ComponentsMeasurement Process Components

Measurement tools:

Hardware

Software

All procedures for using the tools:

Which operators

Set-up and handling procedures

Off-line calculations and data entry

Calibration frequency and technique


GB111-8

Sources of Measurement VariationSources of Measurement Variation

Environme

Mechanical instability

Tool

Environment

Work Methods

Wear

Electrical instability

Algorithm instability

Ease of Data Entry

Operator Training

Calibration Frequency

Maintenance Standard

Sufficient Work Time

Standard Procedures

Operator Technique

‘Measurement Variation’

Humidity

Cleanliness

Vibration

Line Voltage Variation

Temperature Fluctuation



Page 5402-108, Rev. C


GB111-9

Information to be ObtainedInformation to be Obtained

How big is the measurement error?

What are the sources of measurement error?

Is the tool stable over time?

Is the tool capable for this study?

How do we improve the measurement system?


GB111-10

11010090807060504030

15

10

5

0

Observed

Fre

quen

cy

LSL USL

Actual process variation -No measurement error

Observed processvariation -With measurement error

11010090807060504030

15

10

5

0

Process

Fre

quen

cy

LSL USL

How does Measurement Error Appear?How does Measurement Error Appear?



Page 6402-108, Rev. C


GB111-11

Sources of VariationSources of Variation

Product Variability

(Actual variability)

Product Variability

(Actual variability)

Measurement

Variability

Measurement

Variability

Total Variability(Observed variability)

Total Variability(Observed variability)


GB111-12

TerminologyTerminology

Discrimination

Accuracy related terms

True value

Bias

Linearity

Precision related terms

Repeatability

Reproducibility

Linearity

Stability



Page 7402-108, Rev. C


GB111-13

DiscriminationDiscrimination

The number of decimal places that can be measured by the system. Increments of measure should be about one-tenth of the width of the product specification or process variation.

1 2 3 4 5

1 2 3 4 5

Poor DiscriminationRange ofproductvariationcan only

be writtenas 0,1,or 2

Good DiscriminationRange ofproductvariation

can be writtenas 0, 0.1, 0.2,0.3, …1.5, for

a total of16 values.


GB111-14

Precision and AccuracyPrecision and Accuracy

Accurate Precise not Neither Preciseand Precise Accurate or Accurate

Accurate Precise not Neither Preciseand Precise Accurate or Accurate



Page 8402-108, Rev. C


GB111-15

Accuracy TermsAccuracy Terms

Accuracy - Does the average of the measurements deviate from the true value?

True value

Theoretically correct value

NIST standards

Bias

Distance between average value of all measurements and true value

Amount the measuring instrument is consistently off target

Systematic error or offset


GB111-16

AccuracyAccuracy

Instrument accuracy is related to the difference between the observed average value of measurements and the master or “true” value

The master value is an accepted, traceable reference standard (e.g., NIST)

MasterValue

MasterValue

Average Value of MeasurementsAverage Value of Measurements



Page 9402-108, Rev. C


GB111-17

BiasBiasAverage of measurements differ from the Master Value by a fixed amount. Bias effects include:

Operator bias - different operators get detectable different averages for the same thing

Instrument bias - different instruments get detectable different averages for the same thing, etc.

Master Value

Master Value

Average 1

Instrument 1 Instrument 2

Average 2

Instrument 2 Bias

Instrument 1 Bias


GB111-18

Precision TermsPrecision Terms

Total variation in the measurement system

Measure of natural variation of repeated measurements

Repeatability and Reproducibility

222

rpdrptMS



Page 10402-108, Rev. C


GB111-19

RepeatabilityRepeatability

The inherent variability of the measurement device

Variation that occurs when repeated measurements are made of the same variable under absolutely identical conditions

Same operator

Same set-up

Same units

Same environmental conditions

Short-term

Estimated by the pooled standard deviation of the distribution of repeated measurements

Repeatability is always less than the total variation of the system

rpt


GB111-20

RepeatabilityRepeatability

The variation between successive measurements of the same

part, same characteristic, by the same person using the same

instrument. Also known as test - retest error; used as an

estimate of short-term variation.

Master Value

Master Value

Average

GoodRepeatability

Average

PoorRepeatability



Page 11402-108, Rev. C


GB111-21

ReproducibilityReproducibility

The variation that results when different conditions areused to make the measurements

Different operators

Different set-ups

Different test units

Different environmental conditions

Long-term

Estimated by the standard deviation of the averages of measurements from different measurement conditions

rpd


GB111-22

ReproducibilityReproducibility

The difference in the average of the measurements made by different persons using the same or different instrument when measuring the identical characteristic

Master Value

Master Value

GoodReproducibility

Operator 1

PoorReproducibility

Operator 2 Operator 3Operator 1 Operator 2 Operator 3



Page 12402-108, Rev. C


GB111-23

Accuracy vs. PrecisionAccuracy vs. Precision

Suppose we have a reference material with a ‘true’ hardness of 5.0

Method 1 gives the following readings:3.8, 4.4, 4.2, 4.0

Method 2 gives the following readings:6.5, 4.0, 3.2, 6.3

Which method is more accurate?

Which method is more precise?

Which method do you prefer? Why?

Method 2 Method 2 -- average is equal to ‘true’ hardness valueaverage is equal to ‘true’ hardness value

Method 1 Method 1 -- variability is very lowvariability is very low

Method 1 Method 1 -- we can compensate easier for mean shift we can compensate easier for mean shift

than variabilitythan variability


GB111-24

Accuracy vs. PrecisionAccuracy vs. Precision

Related to Difference between Average Measurements and the

True Value

Related to Difference between Average Measurements and the

True Value

Related to the Variance and Standard Deviation of the

Measurements

Related to the Variance and Standard Deviation of the

Measurements

Measurement System Bias -Determined through “Calibration Study”

Measurement System Variability - Determined through “R&R Study”

AccuracyAccuracy

PrecisionPrecision



Page 13402-108, Rev. C


GB111-25

Measurement Capability Index Measurement Capability Index -- P/TP/T

Precision to Tolerance RatioPrecision to Tolerance Ratio

Addresses what percent of the tolerance is taken up by measurement error

Includes both repeatability and reproducibility

Ideal: 8% or less Acceptable: 30% or less

Usually expressed as percent


ToleranceTP MS*15.5

/

Note: 5.15 standard deviations accounts for 99% of MS variation. The use of 5.15 is an industry standard

Note: 5.15 standard deviations accounts for 99% of MS variation. The use of 5.15 is an industry standard


GB111-26

Precision to Tolerance RatioPrecision to Tolerance Ratio

Product Tolerance

LSL USL

measurement system variation

P/T = 20%

P/T = 100%

P/T = 200%



Page 14402-108, Rev. C


GB111-27

Uses of P/T RatioUses of P/T Ratio

The P/T ratio is the most common estimate of Measurement System precision

This estimate may be appropriate for evaluating how well the Measurement System can perform with respect to specifications

Specifications, however, may be too tight or too loose

Generally, the P/T ratio is a good estimate when the measurement system is only used to classify production samples, for process control use, etc.

Even then, if process capability (Cpk) is not adequate, the P/T ratio may give you a false sense of security

Note: In MINITAB, the P/T Ratio is called % Tolerance


GB111-28

Measurement Capability Index Measurement Capability Index -- %R&R%R&R

Addresses what percent of the Total Variation istaken up by measurement error

Includes both repeatability and reproducibility

As a target, look for % R&R less than 28%

Less than 14% is ideal



100&%Total

MSRR



Page 15402-108, Rev. C


GB111-29

% Repeatability & Reproducibility% Repeatability & Reproducibility

(% R&R)(% R&R)


measurement system variation

%R&R = 20%

%R&R = 75%

%R&R = 100%


GB111-30

Uses of %R&RUses of %R&R

The %R&R is the best measure for the Belt

This estimates how well the Measurement System performs with respect to the overall process variation

%R&R is the best estimate when performing process improvement studies

Care must be taken to use samples representing full, but typical, process

variation

Care must be taken to use samples representing full, but typical, process

variation



Page 16402-108, Rev. C


GB111-31

%R&R = 20%

%R&R = 50%


%R&R = 100%

Product Tolerance

LSL USL

Measurement System Variation

P/T = 20%

P/T = 50%

P/T = 100%

3 Sigma Process:3 Sigma Process:P/T Ratio and % R&RP/T Ratio and % R&R


GB111-32

%R&R = 25%

%R&R = 50%


%R&R = 100%

Product Tolerance

LSL USL

P/T = 50%

P/T = 100%

P/T = 200%





Page 17402-108, Rev. C


GB111-33

%R&R = 20%

%R&R = 40%

%R&R = 100%

Product Tolerance

LSL USL

P/T = 10%

P/T = 20%

P/T = 50%

ObservedProcess Variation




GB111-34

Measurement Error EffectMeasurement Error Effect

on Capability Indiceson Capability Indices

The higher the Measurement Error the more dramatic the impact on your

ability to understand the true process capability!



Page 18402-108, Rev. C


GB111-35

Gage R&R Study SetGage R&R Study Set--upup

Generally two to three operators

Generally 10 units to measure

Each unit is measured 2-3 times by each operator

Units are selected to cover the normal full range of the process

Don’t select consecutive units


GB111-36

Sample Size IssuesSample Size Issues

Number of operators

If process uses multiple operators, choose 2-4 at random

If process uses only one operator, or no operators, perform study without operator effects (reproducibility effects ignored)

Number of samples

Select enough samples so that

(number of samples) X (number of operators) > 15

If not practical or possible, choose number of trials so that:

if S x O < 15, trials = 3

if S x O < 8, trials = 3 to 4





Page 19402-108, Rev. C


GB111-37

Study Sample SelectionStudy Sample Selection

Samples should be pulled from the process that span the normal variation of the process

Example: If you produce a material with a mean thickness of 0.020” and a s of 0.001”, get samples that have thickness from 0.017 - 0.023” (99% of the range)

BE CAREFUL!

If you produce the same base material with different thickness with the same process, sub-group them and perform the R&R study

Example:

A process produces a material with thickness 0.070”, 0.085”, and 0.090” all with a +/- 0.005” tolerance, all measured with the same system

Perform 3 R&R studies - one for each thickness target

If you lumped the above samples together, the GR&R value would be artificially low


GB111-38

Procedure for Performing an R&R StudyProcedure for Performing an R&R Study

Calibrate the gage, or assure that it has been calibrated

Have operator 1 measure all samples once in random order

Have operator 2 measure all samples once in random order

Continue until all operators have measured samples once (this isTrial 1)

Repeat steps 2-4 for the required number of trials

Calculate the statistics of the R&R study

Repeatability

Reproducibility

Standard deviations of each of the above

%R&R

P/T Ratio

Analyze results and determine follow-up action, if any



Page 20402-108, Rev. C


GB111-39

Minitab Gage R&R Data MatrixMinitab Gage R&R Data Matrix

Let’s set up a table for a 3 Operators, 10 Sample per trial, 2 Trials gage study

We want to create a Minitab file with the following columns

Part or Sample

Operator

Trial

Measurement

Let’s set up the table to have the correct Trial / Operator / Sample combinations using the MakePatterned Data procedure


GB111-40


Setting up the Gage R&R Data Matrix in the Minitab worksheetis similar to setting up a matrix for a designed experiment.

TRIAL 1 TRIAL 2

OPERATOR 1 OPERATOR 2 OPERATOR 3 OPERATOR 1 OPERATOR 2 OPERATOR 3

SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10 SAMPLES 1 - 10

We have 2 trials to run.During each trial we have 3 operators.

Each operator will measure 10 samples.



Page 21402-108, Rev. C


GB111-41


TRIAL 1 TRIAL 2



We must set up 3 columns in Minitab to fit the matrixColumn 1 (C1) will be named Trials

Column 2 (C2) will be named OperatorsColumn 3 (C3) will be named Samples

Entries in the 3 columns will be usedto identify the factors in the study

Open a new worksheet in Minitab and name the 3 columns


GB111-42


TRIAL 1 TRIAL 2



The matrix above will provide you withinformation on the length of the three columns

The total number of measurements to be made is 60(2 trials X 3 operators X 10 measurements = 60)

This tells us that each of the 3 columnswill have 60 entries (60 rows per column)



Page 22402-108, Rev. C


GB111-43


TRIAL 1 TRIAL 2



Column 3 (Samples) will consist of the numbers1-10, repeated 6 times to identify the 10 samples

measured by each of the 3 operators during each trial


GB111-44

Making Patterned DataMaking Patterned Data



Page 23402-108, Rev. C


GB111-45



GB111-46




Page 24402-108, Rev. C


GB111-47



GB111-48




Page 25402-108, Rev. C


GB111-49



GB111-50

Minitab Gage R&R FileMinitab Gage R&R File



Page 26402-108, Rev. C


GB111-51

Minitab Gage R&R FileMinitab Gage R&R File

Measurement taken during Trial 1by Operator 1 on Sample 6.

Measurement taken during Trial 1by Operator 2 on Sample 8.


GB111-52

Coding VariablesCoding Variables

You can change the Operator values to the actual person’s name and still perform the analysis using a Coding procedure



Page 27402-108, Rev. C


GB111-53



GB111-54




Page 28402-108, Rev. C


GB111-55

Last Checks....Last Checks....

Before running your study, make sure that you have the right number of cells for each operator and sample

To do this we use Cross Tabulation


GB111-56

Cross TabulationCross Tabulation



Page 29402-108, Rev. C


GB111-57

We look for equal cell counts in all

combinations

Table StatisticsTable Statistics

The table should contain equal counts for all operators and all parts before continuing

Rows: Trials Columns: Operators

Dillon Fred Tom All

1 10 10 10 30

2 10 10 10 30

All 20 20 20 60


GB111-58

Gage R&R Example Gage R&R Example

Let’s use Minitab to analyze some data

Open worksheet AIAG MSA Example.MTW in file DMANOVA MSA.MPJ

We will be using Minitab’s Gage R&R Study (Crossed) function



Page 30402-108, Rev. C


GB111-59

Minitab Gage R&R Study (Crossed)Minitab Gage R&R Study (Crossed)


GB111-60

Options / Info ScreensOptions / Info Screens

Enter the specific gage information here for records

Important when doing a number of iterations

Enter the specific gage information here for records

Important when doing a number of iterations

Enter the Process Tolerance in the Options screen

REMEMBER – the tolerance is the entire range of the specifications!

You can add a descriptive Title as well

Enter the Process Tolerance in the Options screen

REMEMBER – the tolerance is the entire range of the specifications!

You can add a descriptive Title as well

Accepted value for 99% of the distribution

Accepted value for 99% of the distribution



Page 31402-108, Rev. C


GB111-61

Minitab OutputMinitab Output

Minitab produces both analytical and graphical analysis information

Analytical Results

ANOVA table(s)

Components of Variation

Percent contribution table

Graphical Results

X-Bar / R chart

Components of Variation

Operator*Part Interaction Plot

By Operator and By Part plots

Let’s look at the analytical first and then return to the graphical


GB111-62

Gage R&R ReportGage R&R Report

Total Gage R&RIdeal: 2% or lessAcceptable: 7.7% or less



Page 32402-108, Rev. C


GB111-63

StdDev Study Var %Study Var %Tolerance

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

Total Gage R&R 0.066615 0.34306 32.66 68.61

Repeatability 0.035940 0.18509 17.62 37.02

Reproducibility 0.056088 0.28885 27.50 57.77

Operator 0.030200 0.15553 14.81 31.11

Operator*Sample 0.047263 0.24340 23.17 48.68

Part-To-Part 0.192781 0.99282 94.52 198.56

Total Variation 0.203965 1.05042 100.00 210.08

Number of Distinct Categories = 4

Gage R&R ReportGage R&R Report

• % R&R (for process improvement efforts)

• %P/T (for acceptance to spec efforts)

• BE CAREFUL - watch for low Distinct Categories! Must be at least 5 for Process Improvement use!

• % R&R (for process improvement efforts)

• %P/T (for acceptance to spec efforts)

• BE CAREFUL - watch for low Distinct Categories! Must be at least 5 for Process Improvement use!


GB111-64

Number of Distinct CategoriesNumber of Distinct Categories

The number of distinct categories of data within the process that the measurement system can detect.

Represents the number of non-overlapping confidence intervals within the range of the product variation and is an indication of the Discrimination of the measurement system.

If the number of categories is less than 2, the measurement system is of minimal value since it will be difficult to distinguish one part from another.

If the number of categories is 2, the measurement system can only divide the data into 2 groups – low and high.

If the number of categories is 3, the measurement system can divide the data into 3 groups – low, medium and high.

A measurement system that is acceptable and useful for process improvement activities must have 5 or more distinct categories. 10 or more is ideal.



Page 33402-108, Rev. C


GB111-65

Number of Distinct CategoriesNumber of Distinct Categories

Control charts, process parameters, capability indices, process improvement

5 or more

Insensitive control charts

Coarse estimates of process parameters and capability indices

2 – 4

Generally of no use, but may provide minimal information on conformance versus nonconformance

0 – 1

Use of Measurement SystemNumber of Categories


GB111-66

Graphical OutputGraphical Output



Page 34402-108, Rev. C


GB111-67

Gage R&R X / R ChartGage R&R X / R Chart

• We want to see variability in the X-bar chart outside the Control Limits

• This indicates Part-to-Part variability

• If there was none, you probably did not get samples that cover the normal range in production

• We want to see variability in the X-bar chart outside the Control Limits

• This indicates Part-to-Part variability

• If there was none, you probably did not get samples that cover the normal range in production


GB111-68

XX--Chart IndicatorsChart Indicators

If the averages for each operator is different, the reproducibility is suspect

We want more averages to fall outside the control limits but consistently for all operators

This indicates more part-to-part variability which is what we want

We want to see the majority of the points on the chart outside the control limits

If this is the case and the R-Chart is in control, then we will be able to determine the percent of the process variability that is consumed by the measurement system



Page 35402-108, Rev. C


GB111-69

Gage R&R X / R ChartGage R&R X / R Chart

• The Range chart should show a process that is in control.

• If a point is above the UCL, the operator is having a problem making consistent measurements.

• The Range chart can also help identify inadequate discrimination

• We want at least 5 possible values within the control limits

• The Range chart should show a process that is in control.

• If a point is above the UCL, the operator is having a problem making consistent measurements.

• The Range chart can also help identify inadequate discrimination

• We want at least 5 possible values within the control limits


GB111-70

RR--Chart IndicatorsChart Indicators

Suspect inadequate Discrimination if:

the range chart has less than 5 distinct levels within the Control Limits

5 or more levels for the range but more than 1/4 of the values are zero

Repeatability is questionable if the range chart shows out-of-control conditions

If the range for an operator is out-of-control and the others are not, the method is suspect

If all operators have ranges out-of-control, the system is sensitive to operator technique



Page 36402-108, Rev. C


GB111-71

• We desire lines that follow the same pattern and are reasonably parallel to each other

• Significant interactions are indicated by crossing lines betweenoperators

• We also want to see that the part averages vary enough that the differences between parts are clear.

OperatorOperator--Part Interaction PlotPart Interaction Plot


GB111-72

• A graphical representation of the data discussed before

• We want the Gage R&R bars to be as small as possible, driving the Part-to-Part bars to be larger

• A graphical representation of the data discussed before

• We want the Gage R&R bars to be as small as possible, driving the Part-to-Part bars to be larger

Components of VariationComponents of Variation



Page 37402-108, Rev. C


GB111-73

By OperatorBy Operator

• The By Operator graph shows the average value (Circle) and the spread of the data for each operator

• We want the grouping to be similar across all operators and a flat line across the means for the operators

• The By Operator graph shows the average value (Circle) and the spread of the data for each operator

• We want the grouping to be similar across all operators and a flat line across the means for the operators


GB111-74

By PartBy Part

• The graph shows the average (circles) and spread of the values for each sample

• We want to see minimal spread for each part, but variability between samples (means shifting)

• The graph shows the average (circles) and spread of the values for each sample

• We want to see minimal spread for each part, but variability between samples (means shifting)



Page 38402-108, Rev. C


GB111-75

Gage Run ChartGage Run Chart


GB111-76




Page 39402-108, Rev. C


GB111-77


• The chart shows the two measurements made by each operator on the 10 parts.

• We want to see consistency of measurement values within each operators results and between the operators.

• The chart shows the two measurements made by each operator on the 10 parts.

• We want to see consistency of measurement values within each operators results and between the operators.


GB111-78

8%8% 2%2% 1010

30%30% 28%28% 557.7%7.7%

P/T

Ratio

P/T

Ratio

R&R %Contribution(Ratio of Variances)

R&R %Contribution(Ratio of Variances)

Number of Distinct

Categories(Discrimination

Index)

Number of Distinct

Categories(Discrimination

Index)

%Study Variation

%Study Variation

Red

Yello

wG

reen

14%14%

%R&R Contribution is more statistically correct than %Study Variation

The latter is based on the standard deviations, which is not accurate

If you follow this chart you will be OK No Matter Which Metrics You Use

GR&R MetricsGR&R Metrics -- Additional MetricsAdditional Metrics



Page 40402-108, Rev. C


GB111-79

Measurement System EvaluationMeasurement System Evaluation

Written inspection/ measurement procedure?

Detailed process map developed?

Specific measuring system and set-up defined?

Trained or Certified Operators?

Instrument calibration performed in a timely manner?

Accuracy?

R&R?

Bias?

Linearity?

Discrimination?

Correlation with supplier or customer where appropriate?


GB111-80

SummarySummary

Measurement systems are important to analyze BEFORE embarking on Process Improvement activities

Be careful when picking samples - watch for correct sub-grouping and sample size requirements

Analyze the measurement system for Operator, Part, and Trial effects

Make sure that the gage system has enough discrimination to be useful in determining different levels in the measured attribute

Always generate a GR&R Report to document findings, methods, and improvement opportunities

Total Variation includes Measurement Error - try to minimize the controllable error in the gage system



Page 41402-108, Rev. C


GB111-81

GR&R ExerciseGR&R Exercise

The Department of Health has decided to let you check your own waste water cleanliness provided you can measure the water volume with acceptable repeatability and reproducibility

Due to the nature of the materials being measured, if you touch the container while measuring you will be exposed to potentiallyhigh levels of biological and chemical toxins.

If you touch the container while measuring, you will not be permitted to perform your own measurements, costing the company thousands of $$ in labor and testing by outside laboratories


GB111-82

GR&R ExerciseGR&R Exercise

Equipment needed:

10 plastic containers

1 measurement device

Specifications:

See container

Procedure:

Fill 10 containers with varying amounts of water between the marked lines on each container (ask instructor)

Have each of three operators independently measure the height of the water three times (use proper MSA techniques), WITHOUT touching the container

REMEMBER - Maintain one operator per trial!

Randomize the measurement order of the containers (i.e., 1,3,7,5,2,...) for each operator

Analyze the results and present to class

CAPABILITY STUDIES


Page 1402-108, Rev. C


GB112-1

CapabilityCapability

StudiesStudies




GB112-2


“Traditional” process capability indexes

Attribute and Variable Capability Studies

Short Term and Long Term Process Capability

CAPABILITY STUDIES


Page 2402-108, Rev. C


GB112-3













GB112-4

We continually gather data on our process and ask:“Is it capable of producing defect free products ?”

Gathering and analyzing the data is called....

A Capability Study

USLUSLLSLLSL

BackgroundBackground -- Capability StudiesCapability Studies

CAPABILITY STUDIES


Page 3402-108, Rev. C


GB112-5

What Type of Data

Do You Have ?

What Type of Data

Do You Have ?

Variables DataVariables DataAttribute DataAttribute Data

Collect Data

On Process

Collect Data

On ProcessCollect Data

On Process

Collect Data

On Process

Analyze Data

In Minitab

Analyze Data

In MinitabAnalyze Data

In Excel

Analyze Data

In Excel

State Capability

DPU, PPM

Cp, Cpk, Pp, Ppk

State Capability

DPU, PPM

Cp, Cpk, Pp, Ppk

State Capability

DPU, PPM

State Capability

DPU, PPM

Capability Roadmap Capability Roadmap -- 30,000 Ft. View30,000 Ft. View


GB112-6

“Defects”( )

1 2 3 4 5 6 7

Number of Mistakes

“Defects”(x > 130 Min)“Non-Defects”

(x < 130 Min)

15 110 115 120 125 130 135 140

Assembly Time (Minutes)

“Defect Free”( )

CustomerRequirement

CustomerRequirement

ATTRIBUTESATTRIBUTES VARIABLESVARIABLES

Classifying Data Classifying DataClassifying Data

CAPABILITY STUDIES


Page 4402-108, Rev. C


GB112-7

Capability StudiesCapability Studies

withwith

Variables DataVariables Data


GB112-8

Standard Normal DistributionStandard Normal Distribution

One Standard Deviation (

This is a standard Normal distribution where:Mean = 0

Standard Deviation = 1

CAPABILITY STUDIES


Page 5402-108, Rev. C


GB112-9

Transformation to the Standard Transformation to the Standard

Normal DistributionNormal Distribution


GB112-10

ZZ--TransformTransform

General form:

This transform produces a value Z on the Standard Normal Distribution where the mean µ = 0 and the standard deviation = 1

The value indicates how far the number is from the mean in units of standard deviations

For example, if Z = 2, that would say that the value in question is 2 standard deviations greater than the Mean

By using this method, we can calculate the proportion of product that is out-of-spec based on the process mean and standard deviation.

)-(x=Z

CAPABILITY STUDIES


Page 6402-108, Rev. C


GB112-11


You may see the equation for the Z-Transform writtenin a number of different ways.

You may see the equation for the Z-Transform writtenin a number of different ways.

)-(x=Z

)x-(x=Z

s

)x-(x=Z


GB112-12

Estimating % NonEstimating % Non--conformingconforming

For estimating percent nonconforming for a process, we will substitute the Lower Spec Limit (LSL) and the Upper Spec Limit (USL) for x

)x-(SL=Z

)x-(x=Z

The result will tell us how far the process mean is from the specification limit in units of sigma

CAPABILITY STUDIES


Page 7402-108, Rev. C


GB112-13

USLUSL

Z

Z ScoreZ Score


GB112-14

What’s the Area Under the Tail ?What’s the Area Under the Tail ?

USLUSL

2.52.5 We get the area under the tail from a Z table ......…or we can use Minitab!

CAPABILITY STUDIES


Page 8402-108, Rev. C


GB112-15

Z TableZ Table

2.0 0.02275 0.02222 0.02169 0.02118 0.02068 0.02018 0.01970

2.1 0.01786 0.01743 0.01700 0.01659 0.01618 0.01578 0.01539

2.2 0.01390 0.01355 0.01321 0.01287 0.01255 0.01222 0.01191

2.3 0.01072 0.01044 0.01017 0.00990 0.00964 0.00939 0.00914

2.4 0.00820 0.00798 0.00776 0.00755 0.00734 0.00714 0.00695

2.5 0.00621 0.00604 0.00587 0.00570 0.00554 0.00539 0.00523

2.6 0.00466 0.00453 0.00440 0.00427 0.00415 0.00402 0.00391

2.7 0.00347 0.00336 0.00326 0.00317 0.00307 0.00298 0.00289

2.8 0.00256 0.00248 0.00240 0.00233 0.00226 0.00219 0.00212

2.9 0.00187 0.00181 0.00175 0.00169 0.00164 0.00159 0.00154

3.0 0.00135 0.00131 0.00126 0.00122 0.00118 0.00114 0.00111

3.1 0.00097 0.00094 0.00090 0.00087 0.00084 0.00082 0.00079

2.0 0.02275 0.02222 0.02169 0.02118 0.02068 0.02018 0.01970

2.1 0.01786 0.01743 0.01700 0.01659 0.01618 0.01578 0.01539

2.2 0.01390 0.01355 0.01321 0.01287 0.01255 0.01222 0.01191

2.3 0.01072 0.01044 0.01017 0.00990 0.00964 0.00939 0.00914

2.4 0.00820 0.00798 0.00776 0.00755 0.00734 0.00714 0.00695

2.5 0.00621 0.00604 0.00587 0.00570 0.00554 0.00539 0.00523

2.6 0.00466 0.00453 0.00440 0.00427 0.00415 0.00402 0.00391

2.7 0.00347 0.00336 0.00326 0.00317 0.00307 0.00298 0.00289

2.8 0.00256 0.00248 0.00240 0.00233 0.00226 0.00219 0.00212

2.9 0.00187 0.00181 0.00175 0.00169 0.00164 0.00159 0.00154

3.0 0.00135 0.00131 0.00126 0.00122 0.00118 0.00114 0.00111

3.1 0.00097 0.00094 0.00090 0.00087 0.00084 0.00082 0.00079

.00621or .621%or 6210 ppm


GB112-16

MinitabMinitab

CAPABILITY STUDIES


Page 9402-108, Rev. C


GB112-17

MinitabMinitab

Cumulative Distribution Function

Normal with mean = 0 and standard deviation = 1.00000

x P( X < = x )

2.5000 0.9938


GB112-18

MinitabMinitab

1 - .9938 = .0062or .62%or 6200 ppm


x P( X <= x)2.5000 0.9938

CAPABILITY STUDIES


Page 10402-108, Rev. C


GB112-19

ExampleExample

Assume that a transmitter contains a chip that must operate at afrequency no higher than 1.030 GHz. (USL = 1.030 GHz)

Our process is producing chips having an average operating frequency of 0.995 GHz, with a standard deviation of 0.010 GHz.

What is the percent nonconforming for the chips produced by the process?

The mean of the process is 0.995 GHz

The standard deviation is .010 GHz

USL – X(bar)Z = —————

s

1.030 - .995= —————— = 3.5

.010


GB112-20

Z TableZ Table

3.0 0.00135 0.00131 0.00126 0.00122 0.00118 0.00114 0.00111

3.1 0.00097 0.00094 0.00090 0.00087 0.00084 0.00082 0.00079

3.2 0.00069 0.00066 0.00064 0.00062 0.00060 0.00058 0.00056

3.3 0.00048 0.00047 0.00045 0.00043 0.00042 0.00040 0.00039

3.4 0.00034 0.00032 0.00031 0.00030 0.00029 0.00028 0.00027

3.5 0.00023 0.00022 0.00022 0.00021 0.00020 0.00019 0.00019

3.6 0.00016 0.00015 0.00015 0.00014 0.00014 0.00013 0.00013

3.7 0.00011 0.00010 9.964E-05 9.577E-05 9.204E-05 8.844E-05 8.498E-05

3.8 7.237E-05 6.951E-05 6.675E-05 6.409E-05 6.154E-05 5.908E-05 5.671E-05

3.9 4.812E-05 4.617E-05 4.429E-05 4.249E-05 4.076E-05 3.909E-05 3.749E-05

4.0 3.169E-05 3.037E-05 2.911E-05 2.790E-05 2.674E-05 2.562E-05 2.455E-05

4.1 2.067E-05 1.979E-05 1.895E-05 1.815E-05 1.738E-05 1.663E-05 1.592E-05

2.0 0.02275 0.02222 0.02169 0.02118 0.02068 0.02018 0.01970

2.1 0.01786 0.01743 0.01700 0.01659 0.01618 0.01578 0.01539

2.2 0.01390 0.01355 0.01321 0.01287 0.01255 0.01222 0.01191

2.3 0.01072 0.01044 0.01017 0.00990 0.00964 0.00939 0.00914

2.4 0.00820 0.00798 0.00776 0.00755 0.00734 0.00714 0.00695

2.5 0.00621 0.00604 0.00587 0.00570 0.00554 0.00539 0.00523

2.6 0.00466 0.00453 0.00440 0.00427 0.00415 0.00402 0.00391

2.7 0.00347 0.00336 0.00326 0.00317 0.00307 0.00298 0.00289

2.8 0.00256 0.00248 0.00240 0.00233 0.00226 0.00219 0.00212

2.9 0.00187 0.00181 0.00175 0.00169 0.00164 0.00159 0.00154

3.0 0.00135 0.00131 0.00126 0.00122 0.00118 0.00114 0.00111

3.1 0.00097 0.00094 0.00090 0.00087 0.00084 0.00082 0.00079

.00023or .023%or 230 ppm

CAPABILITY STUDIES


Page 11402-108, Rev. C


GB112-21

MinitabMinitab


GB112-22

MinitabMinitab



x P( X <= x )

3.5000 0.9998

Remember – Minitab gives the area under the curve to the left of thespecified value (in this case, the proportion conforming). To obtainthe proportion nonconforming we must subtract the result from 1.0.

1.0 – 0.9998 = 0.0002 or .02% or 200 ppm

CAPABILITY STUDIES


Page 12402-108, Rev. C


GB112-23

MinitabMinitab

x P( X <= x)3.5000 0.9998

Tail Probability = 1 Tail Probability = 1 -- .9998.9998

Tail Probability = .0002 or Tail Probability = .0002 or

200 ppm200 ppm

.9998 .0002


GB112-24

ZZ--Transform ExampleTransform Example

If we look at the following, we see that, for 150 Melt Index measurements:

Mean = 10

= 0.5

Let’s say:

LSL = 9

USL = 11

LSL

12111098

4 0 0

3 0 0

2 0 0

1 0 0

0

Population

Freq

uency

USL

Question: What is the estimated percentage of the distribution that will be out of spec?

Question: What is the estimated percentage of the distribution that will be out of spec?

CAPABILITY STUDIES


Page 13402-108, Rev. C


GB112-25


The task is to determine estimates of the proportion of the normal curve that is outside both the upper and lower specification limits. We do that by calculating Z-score for each spec limit.

We can now calculate the areas below the lower spec and above the upper spec using the normal probability function.

Z =(USL- x)

=0.5

= 2.00

UZ =(LSL- x)

=9 - 10

0.5

= -2.00

L

11 - 10


GB112-26

Minitab for Estimating Proportion Minitab for Estimating Proportion

outside Lower Specoutside Lower Spec

Go to Calc > Probability Distributions > NormalCalc > Probability Distributions > Normal

Mean and Sigma

Lower Spec Limit

CAPABILITY STUDIES


Page 14402-108, Rev. C


GB112-27

MinitabMinitab


Normal with mean = 10.0000 and standard deviation = 0.500000

x P( X <= x )

9.0000 0.0228

Since this is the Lower Spec Limit and Minitab gives us the area to theleft of the specified value, this will be the proportion that is below thelower spec limit. It is not necessary to subtract this result from 1.0.


GB112-28

LSL

12111098

4 0 0

3 0 0

2 0 0

1 0 0

0

Population

Freq

uency

USL

Minitab OutputMinitab Output



x P( X <= x)9.0000 0.0228

LSL

CAPABILITY STUDIES


Page 15402-108, Rev. C


GB112-29

Mean and Sigma

Upper Spec Limit

Minitab for Estimating Proportion Minitab for Estimating Proportion

outside Upper Specoutside Upper Spec


GB112-30

MinitabMinitab



x P( X <= x )

11.0000 0.9772

This is the Upper Spec Limit. Minitab provides us with the area underthe curve to the left of this value. We need the area to the right (abovethe spec limit), so we must subtract from 1.0.

1.0 – 0.9772 = 0.0228 or 2.28% or 22,800 ppm

CAPABILITY STUDIES


Page 16402-108, Rev. C


GB112-31

MinitabMinitab



x P( X <= x)11.0000 0.9772

USL

LSL

12111098

4 0 0

3 0 0

2 0 0

1 0 0

0

Population

Freq

uency

USL

1- .9772 = .0228


GB112-32

MinitabMinitab

.0228

LSL USL

.0228

.0228 + .0228 = .0456 or 4.56% or 45,600 ppm

CAPABILITY STUDIES


Page 17402-108, Rev. C


GB112-33

ProblemProblem

Suppose we have a process with:

Mean = 37.5

Standard Deviation = 7.83

Specification: 30 +/- 15

Determine the total estimated percentage outside specification for this process

How many standard deviations is the mean from the upper spec limit?


GB112-34

Process CapabilityProcess Capability

How much of the process output is out of spec?

In the short term?

In the long term?

USLUSLLSLLSL

CAPABILITY STUDIES


Page 18402-108, Rev. C


GB112-35

Voice of The Customer

Voice of The Process

USLUSLLSLLSL

Process Capability RatiosProcess Capability Ratios


GB112-36

Centering – Put The Process On Target

Spread – Reduce The Variation

LSL USL

DefectsDefects

Process Capability Process Capability -- The StrategyThe Strategy

CAPABILITY STUDIES


Page 19402-108, Rev. C


GB112-37

Statisticians Developed 2 key MetricsFor Measuring Capability

Statisticians Developed 2 key MetricsFor Measuring Capability

USLC

- LSL

6p

s

C Min(X-LSL

3

USL- X

3pk

s s, )



GB112-38

USLC

- LSL

6p

s

Total ToleranceC

Process Spreadp


CAPABILITY STUDIES


Page 20402-108, Rev. C


GB112-39

Measurement Error Effect on Measurement Error Effect on

Capability IndicesCapability Indices

Obs

pObs

s

LSLUSLC

6

22

MSActObs sss

226 MSAct

pObs

ss

LSLUSLC

We know that

Therefore:

where

What happens to Cp when the Measurement System Error increases?

CCpp


GB112-40

%R&R Guidelines%R&R Guidelines

Once %R&R has been calculated, you must determine which to target first:

Process variation, or

Measurement systems variation

Ask the question: is your measurement system capable for the process improvement work for which you’ll be using it?

You can assume that the measurement system is capable for use in process improvement activities when the following conditions exist for Observed Process Capability (CpObs) and %R&R:

If CpObs < 1.0 and %R&R < 50%

If 1.0 < CpObs < 1.5 and %R&R < 40%

If CpObs > 1.5 and %R&R < 30%

Note: See detailed graph to follow

CAPABILITY STUDIES


Page 21402-108, Rev. C


GB112-41

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

0.5

0

0.6

0

0.7

0

0.8

0

0.9

0

1.0

0

1.1

0

1.2

0

1.3

0

1.4

0

CpObs

Cp

Act

0%

10%

20%

30%

40%

50%

60%

70%

%R&R Effect on Capability%R&R Effect on Capability

Source: Larry B. Barrentine: Concepts for R&R Studies



GB112-42

Which might need the most attention -

Measurement System or Process Improvement?

Process %R&R CpObs Which do we work on?

1 10% 0.5

2 40% 1.0

3 60% 1.5

4 70% 5.5

Process

Measurement system

*Process 4: Would improving %R&R really be worth the effort ?

Measurement system*

Either one

Process 2: May Benefit more by addressing Measurement system than Process improvements at this point.

%R&R vs. Capability%R&R vs. Capability

CAPABILITY STUDIES


Page 22402-108, Rev. C


GB112-43

p3C=igmaS

LSL USL

xbar = 30 MHzs = 1

27 MHz 33 MHz

CUSL - LSL

6p

s

Cp = __________ Sigma = __________1.01.0 3.03.0

Cp vs. Sigma LevelCp vs. Sigma Level


GB112-44

CUSL - LSL

6p

s

Cp = __________

LSL USL

xbar = 30 MHzs = 1

29 MHz 31 MHz

0.330.33 1.01.0

p3C=igmaS

Sigma = __________


CAPABILITY STUDIES


Page 23402-108, Rev. C


GB112-45

Cp = __________

LSL USL

xbar = 30 MHzs = .333

28 MHz 32 MHz

2.02.0 6.06.0

p3C=igmaS

Sigma = __________

CUSL - LSL

6p

s



GB112-46

LSL USL

28 MHz 32 MHz

xbar = 33 MHzs = .333

Cp = __________2.02.0 6.06.0

p3C=igmaS

Sigma = __________

CUSL - LSL

6p

s


CAPABILITY STUDIES


Page 24402-108, Rev. C


GB112-47

C Min(X-LSL

3

USL- X

3pk

s s, )

CX-LSL

3pL

s

USL- X

3sCpU

A Metric - To Take Into Account Process ShiftA Metric - To Take Into Account Process Shift



GB112-48

To include the effects of process centering, we know

Where and

Therefore,

Measurement Error Effect Measurement Error Effect

on Capability Indices on Capability Indices

Obs

Obs

Obs

Obs

pkObs

s

LSLXor

s

XUSLMINC

33

22

MSActObs sss MSActObs XXX

2222 33 MSAct

MSAct

MSAct

MSAct

pkObs

ss

LSLXXor

ss

XXUSLMinC

Be careful of the direction of the bias (the

sign of the XMS)

Be careful of the direction of the bias (the

sign of the XMS)

What happens to Cpk when the Measurement System Error increases?

Cpk

CAPABILITY STUDIES


Page 25402-108, Rev. C


GB112-49

CpU = ___________CpL = ___________

Cpk = ____________

5.05.0 --1.01.0

--1.01.0

LSL USL

28 MHz 32 MHz

xbar = 33 MHzs = .333

CpL, Cpk, CpUCpL, Cpk, CpU


GB112-50

Can Cp and Cpk be equal in value ?

Can CpL and CpU be equal to each other ?

Can Cpk take on a negative value ?

What’s a controversial, but easy way of increasing Cp ?

Questions …Questions …

CAPABILITY STUDIES


Page 26402-108, Rev. C


GB112-51

The challenge in business is obtaining accurate estimates of true Process Capability

Let’s take a look at an example - Minutes of Production Downtime

Is this process Capable? Is it Stable?

Spec Man went out and gatheredproduction downtime data for the last 30 days

The customer’s (Plt. manager) specification limits :No more than 40 minutes / day

Spec Man went out and gatheredproduction downtime data for the last 30 days

The customer’s (Plt. manager) specification limits :No more than 40 minutes / day

YOU MAKE THE CALL !

ShortShort--Term CapabilityTerm Capability


GB112-52

A Short-term Capability study covers a relatively short period of time (Days, Weeks) generally consisting of 30 to 50 data points. The actual number depends on the subject under study.

A Short-term Capability study covers a relatively short period of time (Days, Weeks) generally consisting of 30 to 50 data points. The actual number depends on the subject under study.

Is The ProcessIn Control ?

Is It Meeting theRequirements?



35302520151050

39

34

29

24

Observation Number

Indiv

idua

l Va

lue

X=30.60

3.0SL=37.36

-3.0SL=23.84

Short Term CapabilityShort Term Capability

CAPABILITY STUDIES


Page 27402-108, Rev. C


GB112-53

Is this process Capable? Is it Stable?

Spec Man is excited, he declares “The process is meeting the downtime requirements!”

However, the ever-wise Belt isn’t ready to declare victory. She suggests Long-Term Validation of the data.

Dejected, Spec Man goes off to perform the long-term study.

Spec Man is excited, he declares “The process is meeting the downtime requirements!”

However, the ever-wise Belt isn’t ready to declare victory. She suggests Long-Term Validation of the data.

Dejected, Spec Man goes off to perform the long-term study.

YOU MAKE THE CALL !

LongLong--Term CapabilityTerm Capability


GB112-54

A Long-term capability study covers a relatively long period of time (Weeks, Months) generally consisting of 100-200 data points. Again, the actual amount depends

on the subject under study.

A Long-term capability study covers a relatively long period of time (Weeks, Months) generally consisting of 100-200 data points. Again, the actual amount depends

on the subject under study.





100500

50

40

30

20

Observation Number

Indiv

idua

l Va

lue

X=33.80

3.0SL=47.12

-3.0SL=20.49

Long Term CapabilityLong Term Capability

CAPABILITY STUDIES


Page 28402-108, Rev. C


GB112-55

What Does This Tell Us About The Way We Look At Data ?


GB112-56

LSLLSL

Short-Term Capability

Long-TermCapability

Over time, a process tends to shift by approximately 1.5Over time, a process tends to shift by approximately 1.5


USLUSL

The Dynamic ProcessThe Dynamic Process



CAPABILITY STUDIES


Page 29402-108, Rev. C


GB112-57

Back to the Process Improvement PlanBack to the Process Improvement Plan

The 6 Sigma Methodology calls for performing a Short-term Capability study during the Measurement Phase to establish the process baseline

Minitab has many tools that will help you in this areaCapability Sixpack, Capability Analysis, Capability Analysis (attribute data)


GB112-58

Capability Analysis Capability 6 Pack

Subgrouped data

Individual Samples

Capability ToolsCapability Tools

CAPABILITY STUDIES


Page 30402-108, Rev. C


GB112-59

Pooled Standard DeviationPooled Standard Deviation

This is the default option for Minitab when subgroups are specified

This is a weighted average of the subgroup standard deviations

Caution: This option is used for best case short-term capability when rational subgroups are used

The results using this option can lead to misleading information about capability

This option is a good method for measuring “instantaneous” capability and may be used to establish process entitlement (Cp & Cpk)


GB112-60

Average Moving RangeAverage Moving Range

This option for estimating the Standard Deviation should be used when you have independent data points

This is typical of a process where:

Each item is measured only once, and

Items produced ‘near’ each other are not reflective of each other

Example:

• Each refrigerator is measured for cool down rate

• Two refrigerators manufactured next to each other can not predict the other’s cool down rate

CAPABILITY STUDIES


Page 31402-108, Rev. C


GB112-61

Capability vs. PerformanceCapability vs. Performance

5040302010

14

13

12

11

10

Index

CO

2-S

hrt

CO2 Levels for 55 Time Points

Process Performance: Total Variation including shifts and

drifts (Pp & Ppk)

Process Performance: Total Variation including shifts and

drifts (Pp & Ppk)

Capability: Only random or short term variability

(Cp & Cpk)

Capability: Only random or short term variability

(Cp & Cpk)


GB112-62

Performance vs. CapabilityPerformance vs. Capability

These data show that the process, if well controlled can perform much better than it currently is.

5040302010

14

13

12

11

10

Index

CO

2-S

hrt

CO2 Levels for 55 Time Points

CAPABILITY STUDIES


Page 32402-108, Rev. C


GB112-63

Process IndicesProcess Indices

We will discuss indexes about process performance and process capability

Process capability is the “Potential” of the process (Cpand Cpk)

Process performance is the processes “Real” performance (Pp and Ppk)


GB112-64

Is it Normal ?

Is ItNormal ?

How does theprocess variationcompare to thespec limits ?

Is it in control ?

What do the last 25 groups look like ?

Is it in control ?

Capability SixpackCapability Sixpack

What are the resultsfor Cpk and Ppk ?

CAPABILITY STUDIES


Page 33402-108, Rev. C


GB112-65

Capability Sixpack Capability Sixpack

What’s happening here ? ___________________


GB112-66

WARNING!!!WARNING!!!

Statistical Assumptions Made In Capability Studies

1. Data Comes From A Stable ProcessData Comes From A Stable Process

If Not, work towards getting the process in control

Don’t despair, you can still make some assumptions about your process in the mean time

2. Has A Normal DistributionHas A Normal Distribution

If Not, transform it (ask the instructor)

If Items #1 and #2 aren’t met, results will be misleading

CAPABILITY STUDIES


Page 34402-108, Rev. C


GB112-67

Capability / Performance IndicesCapability / Performance Indices

Minitab can give both Short-term and Long-term process capability data for the same data set

Use the file DM CAPABILITY.MPJDM CAPABILITY.MPJ, worksheet

CARBAT2.MTWCARBAT2.MTW

Fill in the dialog boxes as follows


GB112-68

2

Minitab Capability StudyMinitab Capability Study

2.

3.

4.

1.

CAPABILITY STUDIES


Page 35402-108, Rev. C


GB112-69

Capability Results for CO2Capability Results for CO2--LongLong

15.614.413.212.010.89.68.4

LSL USL

Process Data

Sample N 155

StDev (Within) 0.51407

StDev (O v erall) 0.84298

LSL 8.00000

Target *

USL 16.00000

Sample Mean 12.64258

Potential (Within) C apability

C C pk 3.02

O v erall C apability

Pp 1.84

PPL 2.14

PPU 1.55

Ppk

C p

1.55

C pm *

3.02

C PL 3.51

C PU 2.54

C pk 2.54

O bserv ed Performance

PPM < LSL 0.00

PPM > USL 0.00

PPM Total 0.00

Exp. Within Performance

PPM < LSL 0.00

PPM > USL 0.00

PPM Total 0.00

Exp. O v erall Performance

PPM < LSL 0.02

PPM > USL 34.05

PPM Total 34.07

Within

Overall

Process Capability of CO2-Long


GB112-70

Computing the Standard DeviationComputing the Standard Deviation

)1(

2

1

n

xx

RM

n

i

ii

1

2

1

n

xx

s

n

i

i

overall

Cp and Cpk

Pp and Ppk

128.1

* RMswithin

* For subgroup size = 1

CAPABILITY STUDIES


Page 36402-108, Rev. C


GB112-71

Capability Study ResultsCapability Study Results

Notice that both Cp / Cpk and Pp / Ppk values are given

The data shows that the Capability of the process (Cpk) is 2.18 and the Entitlement (Cp) is 2.59

This shows that if the process were under good control it could be a world class performer >6 !!

The data also shows the Performance of the process (Ppk)is reasonably capable with a value of 1.33

But, does this tell the entire story?

Perform the analysis on the same data using the Capability Sixpack (Normal) option in Minitab. Do you see any

problems that may need resolution?


GB112-72

Process Performance and Capability Process Performance and Capability

The Ppk can closely approach the Cp when

The Customer specifications truly reflect customer requirements

The process in under statistical control

The data approximate the normal distribution

The Cp is like a benchmark or entitlement

The sigma for capability is driven primarily by random error

We would like Ppk to be very close to Cp

CAPABILITY STUDIES


Page 37402-108, Rev. C


GB112-73

The Idea of Rational SubgroupsThe Idea of Rational Subgroups

There is another way of establishing the sigma for capability and that is through rational subgroups

Goal:

To establish a sampling window small enough to exclude systematic non-random influences (special causes)

Intended Result:

Data exhibiting only common cause variation withingroups of n items and special cause (if it exists) variation between groups


GB112-74

Rational Subgroups Rational Subgroups -- ImplicationsImplications

If done correctly, averaged (Pooled) sigma’s from the subgroups estimate “best case” process capability of the current process

If there is a big difference between the pooled standard deviation and the total

standard deviation, then either the process mean, or the process sigma is changing

over time

If there is a big difference between the pooled standard deviation and the total

standard deviation, then either the process mean, or the process sigma is changing

over time

CAPABILITY STUDIES


Page 38402-108, Rev. C


GB112-75

An Example of Rational SubgroupsAn Example of Rational Subgroups

Open the worksheet RATIONAL SUBGROUPS.MTWRATIONAL SUBGROUPS.MTW

Plot the data using the Time Series plot

Click on Graph > Time Series Plot and fill in the information asshown below.


GB112-76

An Example of Rational SubgroupsAn Example of Rational Subgroups

How does the ‘Within Groups’ Sigma relate to the Total Sigma?

Hint: You can run the Descriptive Stat procedure twice – once for the total data set and then use the BY function with Shift as the BY variable.

How does the ‘Within Groups’ Sigma relate to the Total Sigma?

Hint: You can run the Descriptive Stat procedure twice – once for the total data set and then use the BY function with Shift as the BY variable.

CAPABILITY STUDIES


Page 39402-108, Rev. C


GB112-77



GB112-78

Results of Descriptive StatisticsResults of Descriptive Statistics

What implication does this have in studying process capability? What Sigma represents the “real” process capability? What

Sigma represents the potential process capability?

What implication does this have in studying process capability? What Sigma represents the “real” process capability? What

Sigma represents the potential process capability?

CAPABILITY STUDIES


Page 40402-108, Rev. C


GB112-79

Components of Variation andComponents of Variation and

Rational SubgroupsRational Subgroups

2g

1j

n

1=i

jij

g

1j=

2

j

2g

1j=

n

1=i

ij )X(X)X-X(n)XX(

Total Sum-of Squares = Between Grp SS + Within Grp SS

7654321023222120191817161514131211109876543210

3.5

2.5

1.5

Hour

Outp

ut

Demonstation of Rational Subgroups

Shift is the Grouping Variable

Mean Shift Variability

Pooled Within Grps Variability

Total Variability


GB112-80

Components of Variation andComponents of Variation and

Rational SubgroupsRational Subgroups

CapabilityCapability RandomRandomSpecial CauseSpecial Cause

7654321023222120191817161514131211109876543210

3.5

2.5

1.5

Hour

Outp

ut

Demonstation of Rational Subgroups

Shift is the Grouping Variable

2g

1j

n

1=i

jij

g

1j=

2

j

2g

1j=

n

1=i

ij )X(X)X-X(n)XX(

Total Sum-of Squares = Between Grp SS + Within Grp SS

CAPABILITY STUDIES


Page 41402-108, Rev. C


GB112-81

Visualizing the ComponentsVisualizing the Components

LSLLSL

Over time, a process tends to shift by approximately 1.5Over time, a process tends to shift by approximately 1.5

USLUSL

2g

1j

n

1=i

jij )X(X

2g

1j=

n

1=i

ij )XX(

g

1j=

2

j )X-X(n


GB112-82

Exercises in DiagnosticsExercises in DiagnosticsNow let’s look at 4 long-term capability studies to practice what we’ve learned

Use worksheets Diag 1.MTWDiag 1.MTW, Diag 2.MTWDiag 2.MTW, Diag 3.MTWDiag 3.MTW,

Diag 4.MTWDiag 4.MTW in DM CAPABILITY.MPJDM CAPABILITY.MPJ

Process Target: 70

Process USL: 100

Process LSL: 40

Use the data sets and complete the exercise sheets on the following pages for each

Each data set represents a long term capability study using 150 data points

Use the diagnostics from the Capability Analysis (Normal) and the Capability Six Pack (Normal) to set a course of action

Use the diagnostics from the Capability Analysis (Normal) and the Capability Six Pack (Normal) to set a course of action

CAPABILITY STUDIES


Page 42402-108, Rev. C


GB112-83

Diag 1 WorksheetDiag 1 Worksheet

Cp Cpk

Pp Ppk

Sigma: Capability

Sigma: Performance

Normal ?

In Statistical Control ?


GB112-84


Cp Cpk

Pp Ppk

Sigma: Capability

Sigma: Performance

Normal ?


CAPABILITY STUDIES


Page 43402-108, Rev. C


GB112-85


Cp Cpk

Pp Ppk

Sigma: Capability

Sigma: Performance

Normal ?



GB112-86


Cp Cpk

Pp Ppk

Sigma: Capability

Sigma: Performance

Normal ?


CAPABILITY STUDIES


Page 44402-108, Rev. C


GB112-87

Capability Summary SheetCapability Summary SheetMany times you will be interested in the capability of more than one Key Output or Key Input Variable. Use the Capability Summary Sheet to track the status on the variables of interest. This is found in the Excel spreadsheet: Capability Summary Capability Summary

Sheet.xlsSheet.xls.

Customer

Requirement

(Output Variable)

Measurement

Technique

%R&R or P/T

Ratio

Upper

Spec

Limit

Target

Lower

Spec

Limit

Cp CpkSample

SizeDate Actions



Customer

Requirement

(Output Variable)

Measurement

Technique

%R&R or

P/T Ratio

Upper

Spec

Limit

Target

Lower

Spec

Limit

Cp CpkSample

SizeDate Status

Fire RetardencyUL 700 25% 3 1.5 na

No Data

Available

Selvage Edge

Consistency

On-line Physical

Measurement15% 4.5 4 3.5 1.15 0.85 50 Sep-95

Improvement

Plan in Place

Membrane

Stability

Oven Test None 0.75 0.5 na 1.1 0.65 25 Aug-95

Measurement

Study

Scheduled

Exam

ple

This example shows lack of capability data for the first KPOV, lack of good capability for the second KPOV and lack of measurement

systems evaluation for the third KPOV.

This example shows lack of capability data for the first KPOV, lack of good capability for the second KPOV and lack of measurement

systems evaluation for the third KPOV.


GB112-88

Types of Capability IndexesTypes of Capability Indexes

Instantaneous Capability:

Process Capability over an extremely short period of time

This should represent the very best performance a process is capable of over a short time

This should be a close estimate of process entitlement

Can be estimated using the “best run” of a short-term or long-term study

Short-Term Capability:

Capability study based on 30-50 data points

Usually equal to or greater than long-term capability

Long-Term Capability:

Capability study based on a large number of data points

Best estimate of true process capability

Diagnostics can be made from this data

CAPABILITY STUDIES


Page 45402-108, Rev. C


GB112-89

Goal SettingGoal Setting

This is a generic sequence of process improvement:

Near Term Goal: Move the Ppk to Pp (Center the process)

Mid-Term Goal: Move the Pp to Cpk (Reduce Variation)

Long-Term Goal: Move Cpk to Cp (Random Variation)

Six Sigma Process

Cp = 2.00

Ppk = 1.5


GB112-90

Questions to Ask About Output VariablesQuestions to Ask About Output Variables

Is the measurement system adequate?

For a Key Process Output Variable is:

Nominal Best,

Larger Better

Smaller Better?

Is the concern for

Process Centering

Process Variation

or both?

CAPABILITY STUDIES


Page 46402-108, Rev. C


GB112-91

Questions to Ask About Output VariablesQuestions to Ask About Output Variables

What is the baseline for the process variable?

Mean

Sigma

Is the Output currently in statistical control?

Is the Output affected by time?

Are there multiple responses you’re concerned with?

What’s the priority?


GB112-92












CAPABILITY STUDIES


Page 47402-108, Rev. C


GB112-93

What Type of Data

Do You Have ?

What Type of Data

Do You Have ?

Variables DataVariables DataAttribute DataAttribute Data

Collect Data

On Process

Collect Data

On ProcessCollect Data

On Process

Collect Data

On Process

Analyze Data

In Minitab

Analyze Data

In MinitabAnalyze Data

In Excel

Analyze Data

In Excel

State Capability

DPU, PPM

Cp, Cpk, Pp, Ppk

State Capability

DPU, PPM

Cp, Cpk, Pp, Ppk

State Capability

DPU, PPM

State Capability

DPU, PPM

Capability Roadmap Capability Roadmap -- 30,000 Ft. View30,000 Ft. View


GB112-94

SummarySummary

You should be able to set short, intermediate and long-term goals based on capability data

These tools will be used extensively in the Measurement, Improvement, and Control Phases of process improvement !!

CAPABILITY STUDIES


Page 48402-108, Rev. C


GB112-95


withwith

Attribute DataAttribute Data


GB112-96

Attribute Capability StudiesAttribute Capability Studies

Use the Attribute Sigma Calculator (Excel spreadsheet Attribute Sigma Calculator.xlsAttribute Sigma Calculator.xls)

Use the Minitab functions –

Capability Analysis (Binomial)Capability Analysis (Binomial)

Capability Analysis (Poisson)Capability Analysis (Poisson)

CAPABILITY STUDIES


Page 49402-108, Rev. C


GB112-97

Calculate Defects per Unit (DPU):

ProducedUnitsof#Total

Defectsof#Total=DPU

Go to a Sigma Chart and Estimate the Sigma Levelor

Use the Attribute Sigma Calculator

Calculate Defects per Million Opportunities (DPMO):

000,000,1Unit / Oppty

UnitPerDefects=DPMO x

Calculating the Product SigmaCalculating the Product Sigma--LevelLevel


GB112-98

Attribute Sigma Calculator.XLSAttribute Sigma Calculator.XLS

Capability Tool Capability Tool -- Attribute DataAttribute Data

CAPABILITY STUDIES


Page 50402-108, Rev. C


GB112-99

Attribute Exercises: Attribute Exercises: ManufacturingManufacturing

#1 Defective Bolts

The plant has just completed a run of 40,000 bolts. 100 of the bolts were found to be defective.

#2 Defective Lots

During March, 12,412 Type A motors were assembled into dryers. Each motor has 3 opportunities to be produced correctly(1 opportunity each for Power, Vibration, and Overall Weight). During the month, 200 defective occurences were observed.

#3 Defective Batches

The plant has just completed a run of 400 refrigerators (134 parts per refrigerator). During the build, 12,312 defective (misassembled or damaged) parts were observed.

DPU = 0.0025 DPMO = 2500 Sigma = 2.81

DPU = 0.0161 DPMO = 5371 Sigma = 2.55

DPU = 30.78 DPMO = 229,701 Sigma = 0.74

What’s The DPU, DPMO, & Sigma ?What’s The DPU, DPMO, & Sigma ?


GB112-100

#1 Defective CoilsThe plant has just completed a build of 1291 coils. 104 of the coils were found to be defective.

#2 Defective Sheets of SteelDuring March, 15,514 Type A sheets were manufactured. Each sheet has 5 opportunities to be manufactured in a defective manner (1 opportunity at each station). During the build, 203 defects were observed.

Attribute Exercises: Attribute Exercises: ManufacturingManufacturing

What’s The DPU, DPMO, & Sigma ?What’s The DPU, DPMO, & Sigma ?What’s The DPU, DPMO, & Sigma ?

CAPABILITY STUDIES


Page 51402-108, Rev. C


GB112-101

#1 Defective Purchase OrdersDuring March, 764 purchase orders were placed. 321 of them had defects.

#2 Late ShipmentsFor 1997, 42,100 shipments of spare parts were made. 4,100 arrived late.

#3 Defective Purchase OrdersDuring March, 764 purchase orders were placed. On the forms, there are 8 locations in which to place information. 1234 locations had defective information.

Attribute Exercises: Attribute Exercises:


What’s The DPU, DPMO, & Sigma ?What’s The DPU, DPMO, & Sigma ?What’s The DPU, DPMO, & Sigma ?


GB112-102

MinitabMinitab

Minitab will provide the same calculations asthe Attribute Sigma Calculator and it will provideadditional information, as well.

Minitab will provide –

A control chart of the inspection data, which will allowyou to determine if your process is in control.

A plot of the defect distribution or a binomial plot,which will allow you to test assumptions about the distribution of the defectives.

A plot of the cumulative percent defective, which willtell you if you have taken enough samples and if yourprocess has stabilized.

CAPABILITY STUDIES


Page 52402-108, Rev. C


GB112-103

MinitabMinitab


GB112-104

MinitabMinitab

Open the Minitab project file Attribute Capability.mpjAttribute Capability.mpj

The active worksheet should be Binomial1.MTWBinomial1.MTW. If this is not the active worksheet click on Window > Window >

Binomial1Binomial1.

Perform an attribute capability study on the data in the worksheet using the Capability Analysis (Binomial)Capability Analysis (Binomial)function of Minitab.

CAPABILITY STUDIES


Page 53402-108, Rev. C


GB112-105

MinitabMinitab


GB112-106

MinitabMinitab

Is the process

stable and

in control?

Is the process

stable and

in control?

Have you collected

enough samples to

allow the %Defective

to stabilize?

Have you collected

enough samples to

allow the %Defective

to stabilize?

What are the

capability estimates?

What are the

capability estimates?

Was the sampling

from a binomial

distribution?

Was the sampling

from a binomial

distribution?

What is the

distribution of

the defectives

in the samples?

What is the

distribution of

the defectives

in the samples?

CAPABILITY STUDIES


Page 54402-108, Rev. C


GB112-107

MinitabMinitab

Change the active worksheet to Binomial2.MTWand perform the same analysis on this data.

Change the active worksheet to Binomial2.MTWand perform the same analysis on this data.


GB112-108

MinitabMinitab

CAPABILITY STUDIES


Page 55402-108, Rev. C


GB112-109

MinitabMinitab

Change the active worksheet to Poisson.MTW and performa capability analysis on the data.


The Poisson distribution is associated with the number ofdefects observed in an item where the item occupies aspecified amount of time or space. It is not necessary forthe size of the item inspected to be constant. The size ofthe item may vary.

Examples –

The number of breaks in a piece of wire. The pieces of wire

inspected may be of different lengths.The number of scratches on the surface an appliance.


GB112-110

MinitabMinitab



CAPABILITY STUDIES


Page 56402-108, Rev. C


GB112-111

MinitabMinitab


GB112-112

QuestionsQuestions

Does the performance meet capability?

If performance meets capability there is no reason for looking for process control techniques

If performance is not meeting capability, then there may be opportunities for SPC and APC

Does capability meet the customers needs?

If capability meets customer requirements, then there is no need to work on the process

If capability is not meeting customer requirements, then a process change is required.

CAPABILITY STUDIES


Page 57402-108, Rev. C


GB112-113

Does Performance meet Capability?Does Performance meet Capability?

No (Ppk < Cp) Yes (Ppk = Cp)D

oe

s C

ap

ab

ilit

y m

ee

t C

us

tom

er

Need

s?

Do

es

Ca

pa

bil

ity

me

et

Cu

sto

me

r N

eed

s?

No (Cp < Goal)

Yes (Cp >> Goal)

ChangeProcess

(reduce variability)

Improve Control

ChangeProcess

(reduce variability)

Improve Control

LittleOpportunity

forImprovement

Performance and Capability MatrixPerformance and Capability Matrix

CAUSE AND EFFECTS MATRIX


Page 1402-108, Rev. C


GB113-1

Cause and Cause and

Effects MatrixEffects Matrix




GB113-2


To link the Cause and Effects (C&E) Matrix to the Process Map

To review the steps to create the C&E Matrix

To link the C&E Matrix to further steps in the DMAIC Process Improvement

To create a C&E Matrix



Page 2402-108, Rev. C


GB113-3













GB113-4

Cause and Effects MatrixCause and Effects Matrix

This is a simplified QFD (Quality Function Deployment) matrix to emphasize the importance of understanding the customer requirements

Relates the Key Inputs to the Key Outputs (Customer Requirements) using the process map as the primary information source

Key Outputs are scored as to importance to the customer

Key Inputs are scored as to relationship to key outputs



Page 3402-108, Rev. C


GB113-5

Step 1: List Key OutputsStep 1: List Key Outputs

Translate Into… Requirements

VOC

VOB

Vendor

Customer

Shipper

S

Inventory

Information

Pickers

Packers

Shipping supplies

I

Customer places order

Process Order

Fill Order

Pack Order

Ship Order

P

Shipment

Sales $

Inventory Reduction

O

Customer placing order

C


GB113-6

Cause and Effects Matrix OutputsCause and Effects Matrix Outputs

Pareto of Key Inputs to evaluate in the FMEA and control plans

Input into the Capability Study in the Measurement Phase

Input into the initial evaluation of the Process Control Plan



Page 4402-108, Rev. C


GB113-7

Cause & Effect Matrix StepsCause & Effect Matrix Steps

Identify key customer requirements (Outputs) from process map

Rank order and assign priority factor to each Output (usually on a 1 to 10 scale)

Identify all process steps and materials (Inputs) from the Process Map

Evaluate correlation of each input to each output

low score: changes in the input variable (amount, quality, etc.) have small effect on output variable

high score: changes in the input variable can greatly affect the output variable

Cross multiply correlation values with priority factors and sum for each Input


GB113-8

ExamplesExamples

Order Entry

PhoneFAXEmail

PriceAvailabilityConfirmation of

OrderPromise DateOrder number

Inputs Outputs

NonNon-- ManufacturingManufacturing

Manufacturing

Stamping

SOPDiesSteel MaterialEquipmentDie MaintenanceShiftOperator

FlatnessUndamaged PartsDimensionalityTensile StrengthBurr freeCycle Time

Inputs Outputs



Page 5402-108, Rev. C


GB113-9

Process Mapping ExamplesProcess Mapping Examples

ManufacturingOutputs

• Cycle Time


Feed

Feed material from roll

Cutting


• Cycle Time

• Correct length

• Burr free

Stamping

Stamp piece to size

• Cycle Time

• Correct part

• Burr free


• Tensilestrength

Drawing

Draw features

Outputs

• Cycle Time

• Correct part


• Tensilestrength

Punching

Punch out features

• Cycle Time

• Correct part

• Burr free


• Tensilestrength

Cleaning

Clean metal surface

• Cycle Time

• Clean surface

• Residue free

• Gear speed

• Gear wear

• Material lot


Inputs Type

C

U

U

U

• Shear speed

• Shear wear


• Clamping force

• Shear force

C

U

U

C

U

• Die wear


• Ram force

• Ram speed

• Die number

• Die hardness

U

U

C

C

C

C

• Drawing speed


• Die number

Inputs Type

C

U

C

• Punch speed

• Punch wear


• Clamping force

• Ram force

C

U

U

C

U

• Solvent type

• Solvent purity

• Solvent age


• Contaminant level

• Humidity

• Temperature

C

U

U

U

U

U

U


GB113-10

Rating of

Importance to

Customer

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Total

Process Step Process Input

1 02 0

3 0

4 05 0

6 0

7 08 0

Cause & Effect Matrix FormCause & Effect Matrix Form

Cause and Effect Matrix.XLSCause and Effect Matrix.XLS

1. List Key Outputs



Page 6402-108, Rev. C


GB113-11

Rating of Importance to

Customer

1 2 3 4 5 6

Fla

tness

Un

dam

ag

ed

Part

s

Dim

en

sio

nality

Ten

sile

Str

en

gth

Bu

rr F

ree

Cycle

Tim

e

Total

Process

StepProcess Input

ExampleExample

1. List Key Outputs

1. List Key Outputs

The Outputs are defined in Step 1 of Process MappingThe Outputs are defined in Step 1 of Process Mapping


GB113-12

Rating of

Importance to

Customer

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Total


1 02 0

3 0

4 05 0

6 0

7 08 0


2. Rank Outputs as to

Customer importance


Customer importance



Page 7402-108, Rev. C


GB113-13


Customer7 9 9 4 6 8

1 2 3 4 5 6

Fla

tness

Un

dam

ag

ed

Part

s

Dim

en

sio

nality

Ten

sile

Str

en

gth

Bu

rr F

ree

Cycle

Tim

e

Total

Process

StepProcess Input

ExampleExample

This step should include Marketing, Product Development and Manufacturing. If possible, include customer representatives.

This step should include Marketing, Product Development and Manufacturing. If possible, include customer representatives.


Customer importance


Customer importance


GB113-14

Rating of

Importance to

Customer

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Total


1 02 0

3 0

4 05 0

6 0

7 08 0


3. List Key Inputs by

Process Step


Process Step



Page 8402-108, Rev. C


GB113-15

ExampleExampleRating of Importance to

Customer7 9 9 4 6 8

1 2 3 4 5 6

Fla

tne

ss

Un

da

ma

ge

d

Pa

rts

Dim

en

sio

na

lity

Te

ns

ile

S

tre

ng

th

Bu

rr F

ree

Cy

cle

Tim

e

Total

Process

StepProcess Input

Feed Gear SpeedFeed Gear WearFeed Material lotFeed Material properties

Cutting Shear speedCutting Shear wearCutting Material propertiesCutting Clamping force

This step uses the Process Map inputs directly. Notice the Process Inputs follow the Process map step-by-step.

This step uses the Process Map inputs directly. Notice the Process Inputs follow the Process map step-by-step.


Process Step


Process Step


GB113-16

Rating of

Importance to

Customer

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Total


1 02 0

3 0

4 05 0

6 0

7 08 0


4. Relate Inputs to Outputs




Page 9402-108, Rev. C


GB113-17

Relating Inputs to Customer RequirementsRelating Inputs to Customer Requirements

You are ready to relate the customer requirements to the processinput variables

Correlational scores: No more than 3 levels

1, 3 and 5

1, 3 and 9

Assignment of the scoring takes the most time

To avoid this, spell out the criteria for each score:

0 = No Correlation

1 = The process input only remotely affects the customer requirement

3 = The Process input has a moderate effect on the customer requirement

9 = The Process input has a direct and strong effect on the customer requirement


GB113-18


Customer7 9 9 4 6 8

1 2 3 4 5 6

Fla

tne

ss

Un

da

ma

ge

d

Pa

rts

Dim

en

sio

na

lity

Te

ns

ile

Str

en

gth

Bu

rr F

ree

Cy

cle

Tim

e

Total

Process

StepProcess Input

Feed Gear Speed 3 9 9 9 1 9Feed Gear Wear 0 3 9 1 9 9Feed Material lot 9 1 9 9 0 0Feed Material properties 9 1 9 9 0 0

Cutting Shear speed 9 9 9 1 9 9Cutting Shear wear 3 9 3 0 9 3Cutting Material properties 9 1 1 9 9 9Cutting Clamping force 9 3 9 0 9 1Cutting Shear force 9 1 9 0 9 3

Stamping Die wear 9 3 9 0 9 0

ExampleExample

This is a subjective estimate of how influential the Input Variables are on the Output Variables

This is a subjective estimate of how influential the Input Variables are on the Output Variables





Page 10402-108, Rev. C


GB113-19

Rating of

Importance to

Customer

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Re

qu

ire

me

nt

Total


1 02 0

3 0

4 05 0

6 0

7 08 0


Sum of Rating x Correlational Score values for all Requirements

Sum of Rating x Correlational Score values for all Requirements

5. Cross-multiply and

prioritize


prioritize


GB113-20


Customer7 9 9 4 6 8

1 2 3 4 5 6

Fla

tne

ss

Un

da

ma

ge

d

Pa

rts

Dim

en

sio

na

lity

Te

ns

ile

Str

en

gth

Bu

rr F

ree

Cy

cle

Tim

e

Total

Process

StepProcess Input

Feed Gear Speed 3 9 9 9 1 9 297Feed Gear Wear 0 3 9 1 9 9 238Feed Material lot 9 1 9 9 0 0 189Feed Material properties 9 1 9 9 0 0 189

Cutting Shear speed 9 9 9 1 9 9 355Cutting Shear wear 3 9 3 0 9 3 207Cutting Material properties 9 1 1 9 9 9 243Cutting Clamping force 9 3 9 0 9 1 233Cutting Shear force 9 1 9 0 9 3 231

Stamping Die wear 9 3 9 0 9 0 225

ExampleExample

We now start getting a feel for which variables are most important to explaining variation in the outputs

We now start getting a feel for which variables are most important to explaining variation in the outputs


prioritize


prioritize



Page 11402-108, Rev. C


GB113-21

Process

StepProcess Input

Cutting Shear speed 9 9 9 1 9 9 355Punching Punch speed 9 9 9 0 9 9 351Drawing Drawing speed 9 9 9 9 0 9 333

Stamping Ram speed 9 9 9 3 1 9 315Feed Gear Speed 3 9 9 9 1 9 297

Punching Punch wear 9 3 9 0 9 3 249Cutting Material properties 9 1 1 9 9 9 243

We have sorted on the cross-multiplied numbers and find that the Input variables in the box above are the most

important

We can now evaluate the control plans for these Input Variables

We have sorted on the cross-multiplied numbers and find that the Input variables in the box above are the most

important

We can now evaluate the control plans for these Input Variables

ExampleExample


GB113-22

NonNon--Manufacturing ExampleManufacturing Example

Rating of Importance

to Customer

9 7 10 3 3

1 2 3 4 5

Process Step Process Inputs Ava

ilib

ility

Pri

cin

g

Pro

mis

e D

ate

Ord

er

co

nfir

ma

tion

Ord

er

Nu

mb

er

Total

11 Order Entry

Computer entry

screens 9 9 9 9 3 270

8Internal Info Order worksheet form 9 9 9 1 9 264

3 Answer Phone Answering procedure 9 9 9 9 0 261

10 Order Entry Order worksheet 9 9 9 0 9 261

6 Internal Info Order info 9 9 9 1 3 246

17 Order ConfirmationProduction contact info 9 0 9 9 0 198

1 Answer Phone Info from customer 9 3 9 1 0 195

7 Internal Info Plant loading info 9 3 9 0 0 1925 Internal Info Cross ref for P/N 3 9 9 1 0 183

12Order Entry

Lead time information from mfg 9 1 9 0 1 181

15 Order Confirmation Production schedule 9 0 9 1 0 174

18Order Confirmation

Confirmation procedure 0 0 9 9 9 144

14Order Confirmation

Printed confirmation sheet 3 3 3 9 9 132

13 Order Entry Shipment method 1 3 9 0 0 120

16Order Confirmation Customer contact info 0 0 9 9 1 120

9 Internal Info Pricing algorithm 0 9 0 0 0 63

2 Answer Phone Greeting script 1 1 1 3 0 35

4 Answer Phone Telephone system 0 0 0 1 0 3



Page 12402-108, Rev. C


GB113-23

Focused ApproachFocused Approach

Phase I

Place the Outputs across the top of the matrix and rank

Place the process steps down the side of the matrix

Correlate process step to Outputs

Pareto the process steps

Phase II

Start a new C&E Matrix with the Inputs from the top three or four process steps

Recommended when first starting a project

Focuses the efforts and gives the team a feeling that they’re working on the important process steps first

Gives you a running start at the FMEA and preliminary Control Plan Analysis


GB113-24

Focused Approach, cont.Focused Approach, cont.

Place the Outputs across the top of the matrix and rank

Place Inputs down the side of the matrix starting with the first process step and moving to the last

This approach is okay for small process with relatively few steps

Should only be used for processes with a relatively small number of steps and Inputs



Page 13402-108, Rev. C


GB113-25

Rating of

Importance to Customer

9 9 7 10 10 9 3 2 6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Process Inputs Ge

l Tim

e

Vis

co

sity

Cle

an

line

ss

Co

lor

Ho

mo

ge

ne

ity

Co

ns

iste

ncy

Dig

ets

Tim

e

Te

mp

era

ture

So

lids

Total

1Scales Accuracy

9 8 2 1 1 9 1 1 8 321

2Preheating DICY TK

1 1 1 1 1 1 1 1 1 65

3DMF Load Accuracy

3 8 1 1 1 8 1 3 8 255

4DMF

Cleanliness1 1 4 2 1 2 1 1 1 105

5DMF Raw Materials

1 1 1 1 1 2 1 1 1 74

6DICY Load Accuracy

9 7 1 1 1 9 1 1 2 269

7DICY Envir.

Factors8 5 3 1 1 8 1 1 2 247

8DICY Raw Materials

8 5 1 1 1 9 1 1 2 242

9DICY Mixer Speecd

1 1 1 1 7 1 1 1 1 125

C&E Matrix

FMEA

Process or Product Name:

Prepared by:

Responsible: FMEA Date (Orig) ______________ (Rev) ___

Process

Step/Part

Number Potential Failure Mode Potential Failure Effects

S

E

V Potential Causes

O

C

C Current Controls

D

E

T

R

P

N

Spin Draw Process

Fiber Breakouts Undersized package, High SD panel-hours lost 2

Dirty Spinneret8

Visual Detection of Wraps and broken Filaments 9 144

5Filament motion

2Visual Sight-glass

8 80

8Polymer defects

2Fuzzball Light

9 144

0

Process/Product

Failure Modes and Effects Analysis

(FMEA)

Key Inputs are explored

Capability Summary

Customer Requirement

(Output Variable)

Measurement

Technique

%R&R or P/T

Ratio

Upper

Spec

Limit

Target

Lower

Spec

Limit

Cp CpkSample

SizeDate Actions

Gel Time

Viscosity

Cleanliness

Color

Homogeneity

Consistency

Digets Time

Temperature

Solids



The Key Outputs are listed and evaluated

Outp

uts

Control Plan SummaryProduct: Core Team: Date (Orig):

Key Contact:Phone: Date (Rev):

Process Process Step Input Output

Process

Specification (LSL,

USL, Target)

Cpk /Date Measurement

Technique

%R&R

P/T

Sample

Size

Sample

Frequency

Control

MethodReaction Plan

DICY Turn Steam on Scales

Accuracy

DMF Load DMF DMF Load Accuracy

DMF Load DMF DMF Cleanliness

DICY Load DICY DICY Envir. Factors

DICY Load DICY DICY Load Accuracy

DICY Load DICY DICY Raw Materials

DICY Load DICY DICY Mixer Speecd

DMF Load DMF DMF Raw Materials

DICY Turn Steam on Preheating DICY TK

Control Plan

The Key Inputs are evaluated

Inputs

Linking the C&E Matrix to Other ToolsLinking the C&E Matrix to Other Tools


GB113-26

Next StepsNext Steps

Control Plan Review

Perform an initial assessment of the control plan for high ranked Inputs in the C&E matrix Pareto

Perform the same assessment for high ranked Outputs (customer requirements)

This helps identify “low hanging fruit” at the front end of a Process Improvement project

Capability Review

Review the Capability Summary for those Inputs ranked high in the C&E matrix Pareto

If there are blanks (unknown capability), review measurement systems and collect baseline data

FMEA

High ranked Inputs will be evaluated using the FMEA



Page 14402-108, Rev. C


GB113-27

Control Plan EvaluationControl Plan Evaluation

Standard Operating Procedures

Do they exist?

Are they understood?

Are they being followed?

Are they current?

Is operator certification performed?

Is there a process audit schedule?


GB113-28

Control Plan EvaluationControl Plan Evaluation

Controllable Input Variables:

How are they monitored?

How often are they verified?

Are optimum target values and specifications known?

How much variation is there around the target value?

How consistent are they?

Other Questions:

How often is the Input Variable out of control?

Which Input Variables should have control charts?



Page 15402-108, Rev. C


GB113-29

Is it Controlled or Uncontrolled?Is it Controlled or Uncontrolled?

Is it Controllable or Uncontrollable? Is it Controllable or Uncontrollable?

Current System

Controlled UncontrolledP

ossib

le

Un

co

ntr

olla

ble

C

on

tro

llable

No short term solution

Long term –possibly implement

technology / advanced design

Validate with Measurement

System Analysis

Why?

Could validate it’s truly uncontrollable with DOE/Response

Surface Methods

OPPORTUNITY!

Put in control system

Label as U*

Label C or U Using Current System


GB113-30

SummarySummary

Linked the Cause and Effects (C&E) Matrix to the Process Map

Reviewed the steps to create the C&E Matrix

Linked the C&E Matrix to further steps in the DMAIC Process Improvement

Performed an exercise to create a C&E Matrix



Page 16402-108, Rev. C


GB113-31

Coffee Making C&E Matrix ExerciseCoffee Making C&E Matrix Exercise

Objective:

Take 20 minutes to generate a C&E Matrix using your previous “Coffee Making” process map

Assumptions:

Customer requirements and associated Importance Ranking

Great taste, not bitter - 10

Hot, but not scalding – 8

Condiments - 8

Procedure:

Include all Input Variables

Controlled and Uncontrolled

Deliverables:

Determine ranking of Input Variables

Choose top 3 Input Variables for the next step

FAILURE MODES AND EFFECTS ANALYSIS


Page 1402-108, Rev. C


GB114-1

Failure Modes and Failure Modes and

Effects Analysis Effects Analysis

(FMEA)(FMEA)



Rev. C January 2004 © 2003 by Sigma Breakthrough Technologies, Inc.

GB114-2


Provide insight to the uses of FMEA

Identification of risk sources

Define the different types of FMEA

To learn the steps in developing a Process FMEA

Create an FMEA



Page 2402-108, Rev. C


GB114-3













GB114-4

DefinitionDefinition -- FMEAFMEA

A structured approach to:

identifying the ways in which a product or process can fail

estimating the risk associated with specific modes of failure

prioritizing the actions that should be taken to reduce the risk

evaluating the design validation plan (Product) or the current control plan (Process)

Primary Directive: Identify ways the product or process can fail and eliminate or reduce the risk of failure



Page 3402-108, Rev. C


GB114-5

Where do Risks Come From?Where do Risks Come From?

VagueWorkmanship

Standards

Poor control plans & SOP’s

Raw Material Variation

Poorly developed Specification

LimitsMeasurement

Variation (Online and QC)

Machine Reliability

Potential Safety

HazardsUnclear Customer Expectations

D. H. Stamatis, FMEA:FMEA from Theory to Practice, Quality Press, 1995

Poor Process Capability

Cumulative Risk


GB114-6

Prevent causePrevent cause

Prevent cause from Prevent cause from

leading to modeleading to mode

Prevent mode from Prevent mode from

leading to effectleading to effect

Detect & remove Detect & remove

defective partsdefective parts

Concept Concept firmfirm

ProductionProductionprints completeprints complete

Initial Initial tooling builttooling built ProductionProduction

TIMETIME

Sa

vin

gs

Sa

vin

gs

The Opportunities to Reduce or Eliminate The Opportunities to Reduce or Eliminate

Failures Decrease with TimeFailures Decrease with Time



Page 4402-108, Rev. C


GB114-7

HistoryHistory

First used in the 1960’s in the Aerospace industry during the Apollo missions

In 1974 the Navy developed MIL-STD-1629 regarding the use of FMEA

In the late 1970’s, automotive applications driven by liability costs

Tyco Electronics Manual 402-29 describes the FMEA process in detail


GB114-8

Types of FMEATypes of FMEA

System - used to analyze complete systems and sub-systems in the early concept and design stages

Focuses on potential failure modes, caused by the design, associated with the functions of the system

Design - used to analyze product designs before they are released to production

Focuses on Product Function

Assumes that the product is properly manufactured to the specifications

Process - used to analyze manufacturing and assembly processes

Focuses on Process Inputs (what can go wrong in the manufacturing process)

Assumes that all components meet the design requirements



Page 5402-108, Rev. C


GB114-9

Role of Process FMEARole of Process FMEA

Key tool of process team to improve the process in a preemptive manner (before failures occur)

Used to prioritize resources to insure process improvement efforts are beneficial to customer

Used to document completion of projects

Should be a dynamic document, continually reviewed, amended, updated


GB114-10

Purposes of Process FMEAPurposes of Process FMEA

Analyzes new processes

Identifies deficiencies in the process control plan

Establishes the priority of actions

Evaluates the risk of process changes

Identifies potential variables to consider in Multi-vari and DOE studies

Guides the development of new processes

Helps set the stage for breakthrough



Page 6402-108, Rev. C


GB114-11

FMEA Inputs and OutputsFMEA Inputs and Outputs

Inputs

Process map

C&E matrix

Process history

Process technical procedures

Team experience and knowledge of the process

Outputs

List of actions to prevent causes or to detect failure modes

History of actions taken


GB114-12

FMEA TeamFMEA Team

Team approach is necessary

Responsible Belt leads the team

Recommended representatives:

Design

Practitioners / Operators / Supervisors

Quality

Reliability

Maintenance

Materials

Testing

Suppliers



Page 7402-108, Rev. C


GB114-13

Definition of TermsDefinition of Terms

Failure Mode

Effect

Cause

Current Controls

Severity, Occurrence, Detection

Risk Priority Number (RPN)


GB114-14

Failure ModeFailure Mode

Failure Mode - the way in which a specific process input fails. If the mode is not detected and either corrected or removed it willcause Effect to occur

Can be associated with a defect (in discrete manufacturing) or aprocess input variable that goes outside of specification

Anything that can be wrong with a process input or output is considered a Failure Mode

Examples

Temperature too high

Incorrect PO number

Surface contamination

Dropped call (customer service)

Stubbing with contact receptacle during mating

Paint too thin



Page 8402-108, Rev. C


GB114-15

EffectEffect

Failure Effect - impact on customer requirements -generally external customer focus, but can also include internal customers / downstream processes

Examples

Difficult to mate. High mating force.

Stubbing with contact receptacle during mating

Poor coveragePaint too thin

Customer dissatisfactionDropped call

Poor adhesionSurface contamination

Accounts Receivable traceability errors

Incorrect PO number

Paint cracksTemperature too high

EffectMode


GB114-16

CauseCauseCause

Sources of variation that allow the Failure Mode to become active

Identification of Causes should start with Failure Modes associated with the highest severity ratings

Examples

High solvent contentPaint too thin

Incorrect dimensions specifiedStubbing with contact receptacle during mating

Insufficient number of Customer Service representatives

Dropped call

Lubrication residueSurface contamination

Typographical errorIncorrect PO number

Thermocouple out of calibrationTemperature too high

CauseMode



Page 9402-108, Rev. C


GB114-17

Current ControlsCurrent Controls

Current Controls (Sometimes referred to as Methods of Verification)

Systematized methods/devices in place to prevent or detect Failure Modes

Three types of control methods

Type 1: Prevents the Cause or prevents the Cause from leading to the Failure Mode.

Type 2: Detects the Failure Mode in time to lead to corrective action before the Effect takes place.

Type 3: Detects the Effect of the Failure Mode after it has occurred.

Prevention consists of mistake proofing (Poka Yoke), automated control and set-up verifications

Detection consists of audits, checklists, inspection, laboratory testing, training, SOP’s, preventive maintenance, etc.

Which is more important to process improvement -- prevention or detection?


GB114-18

ControlsControls

FMEA ModelFMEA Model

EffectEffect

External customer or downstream process step

CauseCause

Failure Mode(Defect)


Process Step

Material or process input



Page 10402-108, Rev. C


GB114-19

The Dynamic Nature of the FMEAThe Dynamic Nature of the FMEA

Lossof

Nail

Lossof

Shoe

Lossof

Horse

Lossof

Rider

Lossof

Battle

Why Armies of Knights Lose Battles


GB114-20

FMEA ModelFMEA Model

ControlsControls

EffectEffect


CauseCause



Process Step

Material or process input

Prevention

Detection

Detection

Detection

Which is best case?Which is worst case?

Which is best case?Which is worst case?



Page 11402-108, Rev. C


GB114-21

Linking Failure Modes to EffectsLinking Failure Modes to Effects

Note that the relationship between the Failure Mode and the Effect is not always 1-to-1

Effect 1Effect 1

Effect 2Effect 2Failure Mode 1Failure Mode 1

Failure Mode 1Failure Mode 1

Effect 1Effect 1

Failure Mode 2Failure Mode 2and

Failure Mode 1Failure Mode 1

Failure Mode 2Failure Mode 2Effect 1Effect 1or


GB114-22

Risk Priority Number (RPN)Risk Priority Number (RPN)

The output of an FMEA is the Risk Priority Number

The RPN is a calculated number based on information you provide regarding

the SEVERITY of the EFFECT,

the probability of OCCURRENCE of the CAUSE

the ability of the CURRENT CONTROLS to DETECT the failures modes

It is calculated as the product of three quantitative ratings, each one related to the effects, causes, and controls:

EffectsEffects CausesCauses ControlsControls

RPN = Severity X Occurrence X Detection



Page 12402-108, Rev. C


GB114-23

Risk Priority NumberRisk Priority Number

Risk Priority Number is not sacred

Scaling for Severity, Occurrence and Detection can be locally developed

Other categories can be added

For example, one engineer added an Impact score to the RPN calculation to estimate the overall impact of the Failure Mode on the process


GB114-24

Definition of RPN TermsDefinition of RPN Terms

Severity (of Effect)- importance of effect on customer requirements - could also be concerned with safety and other risks if failure occurs (1=Not Severe, 10=Very Severe)

Occurrence (of Cause)- frequency with which a given Cause occurs and creates Failure Mode. Can sometimes refer to the frequency of a Failure Mode (1=Not Likely, 10=Very Likely)

Detection (capability of Current Controls) - ability of current control scheme to detect or prevent:

the causes before creating failure mode

the failure modes before causing effect

1=Likely to Detect, 10=Not Likely at all to Detect



Page 13402-108, Rev. C


GB114-25

Example Rating ScaleExample Rating Scale

Rating Severity of Effect Probability of Occurrence Ability to Detect

10 Hazardous without warningVery high:

Can not detect

9 Hazardous with warningFailure is almost inevitable

Very remote chance of detection

8 Loss of primary functionHigh:

Remote chance of detection

7Reduced primary function

performance

Repeated failuresVery low chance of detection

6 Loss of secondary functionModerate:

Low chance of detection

5Reduced secondary function

performance

Occasional failuresModerate chance of detection

4Minor defect noticed by most

customers

Moderately high chance of

detection

3Minor defect noticed by some

customers Low:High chance of detection

2Minor defect noticed by

discriminating customers

Relatively few failuresVery high chance of detection

1 No effect Remote: Failure is unlikely Almost certain detection


GB114-26

FMEA ModelFMEA ModelBecause the Detection score is a little difficult to understand, we will do a quick exercise. Give a Detection score for each of the

boxes below:

Det = 10

Det = 7

Det = 1Det = 3

ControlsControls

EffectEffect


CauseCause



Process Step

Material or process

input

Prevention

Detection

Detection

Detection



Page 14402-108, Rev. C


GB114-27

FMEA ScoringFMEA Scoring

There are a wide variety of scoring “anchors”, both quantitative or qualitative

Two types of scales are 1-5 or 1-10

The 1-5 scale makes it easier for the teams to decide on scores

The 1-10 scale allows for better precision in estimates and a wide variation in scores

The 1-10 scale is generally considered to be the best option

The 1-10 scale is generally considered to be the best option


GB114-28

FMEA Form FMEA Form -- Initial AssessmentInitial Assessment

Process

Step/InputPotential Failure Mode Potential Failure Effects

S

E

V

Potential Causes

O

C

C

Current Controls

D

E

T

R

P

N

Actions

Recommended

What is the

process step/

Input under

investigation?

In what ways does the

Input Variable go wrong?

What is the impact on the

Output Variables (Customer

Requirements) or internal

requirements?

Ho

w S

eve

re i

sth

e

effe

ct

to t

he

cus

tom

er? What causes the Input

Variable to go wrong?

Ho

w o

ften d

oes c

au

se

or

FM

occu

r? What are the existing controls

and procedures (inspection and

test) that prevent either the cause

or the Failure Mode? Should

include an SOP number.

Ho

w w

ell

ca

n y

ou

dete

ct

cau

se

or

FM

? What are the actions

for reducing the

occurrence of the

Cause, or improving

detection? Should

have actions only on

high RPN's or easy

fixes.

0

0

0



Page 15402-108, Rev. C


GB114-29

Philosophical ApproachPhilosophical Approach

Focus on Safety Issues

Assume incoming material is perfect and process is not good

Assume process is perfect and incoming material is not good


GB114-30

MethodologyMethodology

Two major approaches:

Starting with QFD/Cause & Effect Matrix

Starting with FMEA directly from the Process Map

We will explain the approach using the C&E matrix, though both approaches are very similar

Spreadsheet tools have been prepared to assist you in the preparation of the FMEA



Page 16402-108, Rev. C


GB114-31

FMEA Methodology FMEA Methodology –– StartingStarting

with C&E Matrixwith C&E Matrix

Advantage: The Cause & Effect Matrix assists the team in defining the important issues that the FMEA should address by helping to prioritize

Important customer requirements

Process inputs that could potentially impact these requirements

Prioritizing the Key Process Inputs according to their impact on the Output variables (We want to focus on Inputs that highly impact a large number of Outputs first)

The C&E Matrix also provides quantitative output that can be used in the determination of the specific severity ratings for the next stage of the FMEA process


GB114-32

FMEA FMEA -- Step by StepStep by Step

1. For each Process Input, determine the ways in which the input can go wrong (Failure Modes)

2. For each Failure Mode associated with the inputs, determine Effects of the Failures on the customer

Remember the internal customers!3. Identify potential Causes of each Failure Mode

4. List the Current Controls for each Cause or Failure Mode

5. Create Severity, Occurrence, and Detection rating scales

6. Assign Severity, Occurrence and Detection ratings to each Cause

7. Calculate RPN’s for each Cause

8. Determine Recommended Actions to reduce high RPN’s

9. Take appropriate actions and recalculate RPN’s



Page 17402-108, Rev. C


GB114-33

Process Mapping ExamplesProcess Mapping ExamplesManufacturing

Outputs

• Cycle Time


Feed

Feed material from roll

Cutting


• Cycle Time

• Correct length

• Burr free

Stamping

Stamp piece to size

• Cycle Time

• Correct part

• Burr free


• Tensilestrength

Drawing

Draw features

Outputs

• Cycle Time

• Correct part


• Tensilestrength

Punching

Punch out features

• Cycle Time

• Correct part

• Burr free


• Tensilestrength

Cleaning

Clean metal surface

• Cycle Time

• Clean surface

• Residue free

• Gear speed

• Gear wear

• Material lot


Inputs Type

C

U

U

U

• Shear speed

• Shear wear


• Clamping force

• Shear force

C

U

U

C

U

• Die wear


• Ram force

• Ram speed

• Die number

• Die hardness

U

U

C

C

C

C

• Drawing speed


• Die number

Inputs Type

C

U

C

• Punch speed

• Punch wear


• Clamping force

• Ram force

C

U

U

C

U

• Solvent type

• Solvent purity

• Solvent age


• Contaminant level

• Humidity

• Temperature

C

U

U

U

U

U

U


GB114-34

Process

StepProcess Input

Cutting Shear speed 9 9 9 1 9 9 355Punching Punch speed 9 9 9 0 9 9 351

Drawing Drawing speed 9 9 9 9 0 9 333Stamping Ram speed 9 9 9 3 1 9 315

Feed Gear Speed 3 9 9 9 1 9 297

Punching Punch wear 9 3 9 0 9 3 249Cutting Material properties 9 1 1 9 9 9 243

Feed Gear Wear 0 3 9 1 9 9 238Drawing Material properties 9 1 9 3 0 9 237

Punching Clamping force 9 9 3 1 9 1 237Cleaning Solvent type 0 9 3 0 9 9 234

Cleaning Solvent purity 0 9 3 0 9 9 234Cutting Clamping force 9 3 9 0 9 1 233

C&E Matrix ExampleC&E Matrix Example

This is the C&E Matrix for the previous Manufacturing Process Map with the Key Inputs sorted by the Total Score

We will address ONLY the top 5 items in the initial FMEA activity



Page 18402-108, Rev. C


GB114-35

FMEA WorksheetFMEA WorksheetBefore we move to the example let’s look at an FMEA worksheet

The information on this sheet is transferred directly to the FMEA form

The purpose of this worksheet is to focus the team on the FMEA inputs and not on scoring

The scoring should be done after the basic inputs have been made

PFMEAPFMEA--FRMFRM--WRKSHT.XLSWRKSHT.XLS

Process

Step

Key Process

Input

Failure Modes - What can go

wrong? Effects Causes

Current

Controls


GB114-36

Determine Failure ModesDetermine Failure Modes

We will only deal with the first two Key Process Input Variables on the C&E Matrix which are Shear Speed and Punch Speed

First we’ll list Shear Speed for the Cutting process step

Process

Step

Key Process

Input



Current

Controls

Cutting Shear Speed Speed too high

Speed too low





Page 19402-108, Rev. C


GB114-37

Determine EffectsDetermine Effects

These effects are internal requirements for the next process and/or to the final customer

Process

Step

Key Process

Input



Current

Controls

Cutting Shear Speed Speed too high Burrs

Damage to blade

and material

Speed too low Insufficient cut

Rounded edge

2. For Each Failure Mode Associated with the Inputs, Determine Effects

2. For Each Failure Mode Associated with the Inputs, Determine Effects


GB114-38

Identify Potential CausesIdentify Potential Causes

In most cases, there will be more than one Cause for a Failure Mode but we’ll keep it simple for this exercise

Process

Step

Key Process

Input



Current

Controls

Cutting Shear Speed Speed too high Burrs Incorrect set point

Damage to blade

and materialPoor calibration

Speed too low Insufficient cut Incorrect set point

Rounded edge Poor calibration

Galling of blade

3. Identify Potential Causes of Each Failure Mode3. Identify Potential Causes of Each Failure Mode



Page 20402-108, Rev. C


GB114-39

Process

Step

Key Process

Input



Current

Controls

Cutting Shear Speed Speed too high Burrs Incorrect set point Operator verification

Damage to blade

and materialPoor calibration Monthly maintenance

Speed too low Insufficient cut Incorrect set point Operator verification

Rounded edge Poor calibration Monthly maintenance

Galling of blade None

List Current ControlsList Current Controls

For each Failure Mode/Cause we list how we are either preventingthe Cause or detecting the Failure Mode

We will list the procedure number where we have a SOP

This is how the FMEA identifies initial holes in the Current Control Plan - process teams can start working on these holes right away

This is how the FMEA identifies initial holes in the Current Control Plan - process teams can start working on these holes right away

4. List the Current Controls for Each Cause4. List the Current Controls for Each Cause


GB114-40

Create ScalesCreate Scales

Example Rating ScaleRating Severity of Effect Likelihood of Occurrence Ability to Detect

10 Hazardous without warningVery high:

Can not detect

9 Hazardous with warningFailure is almost inevitable

Very remote chance of detection

8 Loss of primary functionHigh:

Remote chance of detection

7Reduced primary function

performance

Repeated failuresVery low chance of detection

6 Loss of secondary functionModerate:

Low chance of detection

5Reduced secondary function

performance

Occasional failuresModerate chance of detection

4Minor defect noticed by most

customers

Moderately high chance of

detection

3Minor defect noticed by some

customers Low:High chance of detection

2Minor defect noticed by

discriminating customers

Relatively few failuresVery high chance of detection

1 No effect Remote: Failure is unlikely Almost certain detection

5. Create Severity, Occurrence, and Detection Rating Scales

5. Create Severity, Occurrence, and Detection Rating Scales



Page 21402-108, Rev. C


GB114-41

Assign RatingsAssign Ratings

We are now ready to transfer the worksheet input to the FMEA form

Copy and paste the worksheet columns into the appropriate FMEA form columns

The team then starts scoring each row to compute the RPN values

Notes:

You will only use one Severity value

Determine which Effect has the highest associated Severity and use that SEV value for ALL Effects for the related Failure Mode

Example: If a Failure Mode could lead to either a scratched surface (SEV 3) or an incorrect dimension (SEV 9), then use a SEV of 9 for all of the Effect related to that Failure Mode

6. Assign Severity, Occurrence and Detection Ratings to Each Cause

6. Assign Severity, Occurrence and Detection Ratings to Each Cause


GB114-42

6. RPN Component Value Assignment6. RPN Component Value Assignment

Process


S

E

V

Potential Causes

O

C

C

Current Controls

D

E

T

R

P

N

What is the

process step/

Input under

investigation?

In what ways does the

Input Variable go wrong?




requirements?

How

Seve

re is t

he

effe

ct

to t

he

cusotm

er? What causes the Input

Variable to go wrong?

How

often d

oe

s c

ause

or

FM

occur? What are the existing controls


test) that prevent either the cause



How

well

can y

ou

dete

ct

cause o

r F

M?

Cutting / Shear

Speed

Speed too high Burrs; Damage to blade and

material 9Incorrect set point

7Operator verification

6 378

Cutting / Shear

Speed


material 9Poor calibration

2Monthly maintenance

3 54

Cutting / Shear

Speed

Speed too low Insufficient cut; Rounded

edge 6Incorrect set point


6 144

Cutting / Shear

Speed


edge 6Poor calibration

2Monthly maintenance

3 36

Cutting / Shear

Speed


edge 6Galling of blade

5None

10 300

Punching /

Punch Speed

Speed to high Damage to punch5

Incorrect set point9

None10 450

Punching /

Punch Speed


Poor calibration4

Quarterly maintenance5 100

Punching /

Punch Speed

Speed to low Deformation of material;

Incorrect feature size 10Incorrect set point

3None

10 300

Punching /

Punch Speed

Speed to low Deformation of material;

Incorrect feature size 10Poor calibration

4Quarterly maintenance

5 200

Punching /

Punch Speed

Variable Speed Variable feature sizes9

Current variation6

None10 540



Page 22402-108, Rev. C


GB114-43

7. Calculate RPNs7. Calculate RPNs

Multiply the Severity, Occurrence, and Detection values for each line in the FMEA

Then sort all lines by RPN

Notice that you have to identify all cells so you can carry the Failure Modes, Effects, Causes and Current Controls along with the sort

Process


S

E

V

Potential Causes

O

C

C

Current Controls

D

E

T

R

P

N

Punching /

Punch Speed

Variable Speed Variable feature sizes9

Current variation6

None10 540

Punching /

Punch Speed


Incorrect set point9

None10 450

Cutting / Shear

Speed


material 9Incorrect set point


6 378

Cutting / Shear

Speed


edge 6Galling of blade

5None

10 300


GB114-44

RPN ReviewRPN Review

Once you calculate the RPN for each Failure Mode / Cause / Controls combination, review the results and look for insights

Do the gut check - does the Pareto of items make sense?

If not, maybe the ratings given are varying

Determine potential next steps:

Data collection

Experiments

Process improvements

Process control implementations



Page 23402-108, Rev. C


GB114-45

Determine Recommended ActionsDetermine Recommended Actions

Now fill in recommended actions for top RPNs

Actions are recommended for only the high RPN’s

The key is FOCUS!

Process

Step/InputPotential Failure Mode Potential Failure Effects Potential Causes Current Controls

R

P

N

Actions

RecommendedResp.

Punching /

Punch Speed

Variable Speed Variable feature sizes Current variation None540

Investigate line

condition systems

XYZ by 4/15

Punching /

Punch Speed

Speed to high Damage to punch Incorrect set point None450

Determine set point

and put in SOP 1234

CA by 3/31

Cutting / Shear

Speed


material

Incorrect set point Operator verification378

Determine max set

point; instruct workers

CA by 3/31

Cutting / Shear

Speed


edge

Galling of blade None300

Include on

maintenance schedule

RBM by 4/09

8. Determine Recommended Actions to Reduce High RPN’s

8. Determine Recommended Actions to Reduce High RPN’s


GB114-46

Act and RecalculateAct and Recalculate

We have recorded the action taken and the impact on the RPN

Notice that this is a nice way to track past activities

The FMEA should be re-evaluated by the group as new recommended actions are identified, completed and recorded

R

P

N

Actions

RecommendedResp. Actions Taken

S

E

V

O

C

C

D

E

T

R

P

N

540Investigate line

condition systems

XYZ by 4/15 Installed line conditioner

5/1 9 3 2 54

450Determine set point

and put in SOP 1234

CA by 3/31 SOP updated, operators

being audited 4/12 5 4 6 120

378Determine max set

point; instruct workers

CA by 3/31 SOP updated, operators

being audited 4/3 9 5 5 225

300Include on

maintenance schedule

RBM by 4/09 Maintenance schedule

updated 4/01 6 5 5 150

9. Take Appropriate Actions and Recalculate RPNs9. Take Appropriate Actions and Recalculate RPNs



Page 24402-108, Rev. C


GB114-47

NonNon--Manufacturing ExampleManufacturing Example

Process


S

E

V

Potential Causes

O

C

C

Current Controls

D

E

T

R

P

N

Actions

Recommended

What is the

process step/

Input under

investigation?

In what ways does the Key

Input go wrong?


Key Output Variables

(Customer Requirements) or

internal requirements?

How

Seve

re is t

he e

ffect

to t

he c

usotm

er? What causes the Key Input to

go wrong?

How

often d

oes c

ause o

r

FM

occur? What are the existing controls


test) that prevent eith the cause



How

well

can y

ou d

ete

ct

cause o

r F

M? What are the actions

for reducing the

occurrance of the

Cause, or improving

detection? Should

have actions only on

high RPN's or easy

fixes.

Order Entry Computer entry screens

missing information

Incomplete order, incorrect

information, missing

information

9

Critical data not required to

move to next entry field 7

None

10 630

Make certain fields

mandatory in software


with incorrect information



information

9

Typographical error

5

None

10 450

Training / auditting /

metrics for typos


missing information



information

9

Definition of information needed

not clear 4

None

10 360

Improve Help in

computer software

Answering

Procedure

Do not follow procedure Missing / incorrect

information for order9

Method too complicated7

Audit to procedure XXXXXXX5 315

Answering

Procedure



Poor training6

Audit training with 'blind' callers5 270


missing information



information

9

Too many screens

3

None

10 270





information

9

Incorrect information from

customer 3

Confirmation rejection by

customer 9 243

Answering

Procedure



Documented method out of

date3

Revision audit every 6 months8 216





information

9

Information wrong on order

worksheet 4

Review worksheet for

completeness before entry 5 180

Answering

Procedure



New person answering phones3

Apprentice system / training5 135


missing information



information

9

System overrides

2

Overrides only permitted to

Supervisor / password protected 3 54





information

9

Automatic computer cross

reference data incorrect 2

System audits monthly

3 54


GB114-48

Process

Step

Key Process

Input



Current

Controls

Pour into

glassBeer volume Overflow

Wasted Beer/

Wet LapDrunk None

Glass too small Visual

Not paying

attentionNone

Too much foam

Bad Taste /

Don't get as

drunk

No tilt Visual

Pouring too highVisual and operator

training

Pouring too fastVisual and operator

training

No Foam

No beer

mustache/ Poor

taste

Flat beer Expiration date

Tilted glass Visual

Slow Pour Operator training

Empty glass No drink Too drunk None

Broken Glass Visual

No Money

Job / Process

Excellence - big

bonus

No Friends Personality

A Well Loved Process FMEA!!A Well Loved Process FMEA!!



Page 25402-108, Rev. C


GB114-49

Approaches to FMEAApproaches to FMEA

Approach One (C&E Matrix Focus)

Start with Key Inputs with the highest scores from the C&E Matrix analysis

Fill out the FMEA worksheet for those Inputs

Calculate RPN’s and develop recommended actions for the highest RPN’s

Complete the Process FMEA for other Inputs over time

Approach Two (Customer Focused)

Fill out the Failure Mode and Effects columns of the worksheet. Copy to FMEA form and rate Severity.

For High Severity Ratings, List Causes and rate Occurrence for each Cause

For the highest Severity * Occurrence Ratings, evaluate Current Controls

For Highest RPN’s develop recommended actions


GB114-50

Approaches to FMEAApproaches to FMEA

Approach Three (Comprehensive)

Good approach for small processes

Fill out the FMEA worksheet beginning with the first process step and ending with the last

Score SEV, OCC and DET for all causes

Develop recommended actions for highest RPN’s

Approach Four (Super Focused)

Pick the top Pareto defect item (Damaged Components) or Failure Mode (Variability in Temperature)

Focus the FMEA process on only that defect or Failure Mode

Purpose: To “Kill” that Failure Mode



Page 26402-108, Rev. C


GB114-51

OverviewOverview

Process


S

E

V

Potential Causes

O

C

C

Current Controls

D

E

T

R

P

N

Actions

Recommended

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

What is the Input

What What

can go can go

wrong wrong

with the with the

Input?Input?

What can What can

be done?be done?

What is What is

the Effect the Effect

on the on the

Outputs?Outputs?

What are What are

thethe

Causes?Causes?

How can How can

these be these be

found or found or

prevented?prevented?

How How

Bad?Bad?How How

Often?Often?How How

well?well?


GB114-52

SummarySummary

Provided insight to the uses of FMEA

Identified sources of risk

Defined the different types of FMEA

Introduced the steps in developing a Process FMEA

Practiced creating a FMEA



Page 27402-108, Rev. C


GB114-53

Coffee Making FMEA ExerciseCoffee Making FMEA Exercise

Get back into your teams and perform a FMEA on your three top ranked input variables from your Coffee Making C&E Matrix

Identify all potential causes for each failure mode and the current controls

For the top 3 items in the FMEA, identify recommended actions

Report results on a flip chart or on the Excel spreadsheet using the computer display

List lessons learned as part of the team presentation

You have 20 minutes!


KAPPA STUDIES

Page 1402-108, Rev. C

Rev. C February 2004©2003 by Sigma Breakthrough Technologies, Inc.

GB115-1

Attribute MSAAttribute MSA



Rev. C January 2004©2003 by Sigma Breakthrough Technologies, Inc.

GB115-2

Kappa TechniquesKappa Techniques

For classifications of attribute/categorical data

Used when a quality criteria is difficult or impossible to define

Several units must be classified more than once and by more than one “rater”

If there is substantial agreement, there is the possibilitythat the ratings are accurate

If agreement is poor, the usefulness of the ratings is EXTREMELY LIMITED


KAPPA STUDIES

Page 2402-108, Rev. C


GB115-3


Non-Parametric approaches provide some tools to deal with subjective attribute data

Applies to all types of distributions, so it does not matter if the distribution is normal, binomial, etc.

Requires only that the observations be independent

Are less efficient than parametric approaches, and require larger sample sizes to achieve the same results

Hypotheses are less precise, and yield less information in conclusions

Example: we may know that two distributions are not the same, but we do not know if they are different in terms of central tendency or in terms of variability


GB115-4


Used when a measurement system is needed to classify items in a nonquantitative manner

Kappa techniques treat al l misclassifications equally

Kappa techniques do not assume ratings are equally distributed across the possible range

Requirements for use:

Units to be measured are independent from one another

Raters inspect and classify independently

The rating categories are mutually exclusive and exhaustive


KAPPA STUDIES

Page 3402-108, Rev. C


GB115-5

Studying Measurement SystemsStudying Measurement Systems

Once we have the right data we must determine our ability to measure it

Information to be attained



Is the measurement system stable over time?

Is it capable for this study?



GB115-6

Attribute Data MSAAttribute Data MSA

If many people are evaluating the same thing they need to agree:

With each other

With themselves

Attribute data contains less information than variables data, but often it is all that’s available

Therefore, we must be diligent about the integrity of attribute measurement systems

The issue is

Can I rely on the data coming from my measurement system?


KAPPA STUDIES

Page 4402-108, Rev. C


GB115-7

Attribute MSA GoalsAttribute MSA Goals

Determine:

% overall agreement

% agreement within appraisers (Repeatability)

% agreement between appraisers (Reproducibility)

% agreement with known standard (Accuracy)

Kappa (how much better the measurement system is than random chance)


GB115-8

Attribute Measurement Studies:Attribute Measurement Studies:SamplesSamples

Need 30-50 samples which span the normal extremes of the process

The majority should be from the ‘gray’ areas

Remainder should be clearly good or clearly bad

Example: Invoice Errors

We collect a sample of 30 invoices

5 units will be clearly defective (a single large defect or enough smaller defects to reject it)

5 units will be clearly acceptable (everything correct)

The remaining samples vary in the quantity and type of defects


KAPPA STUDIES

Page 5402-108, Rev. C


GB115-9

Attribute Measurement Studies:Attribute Measurement Studies:MethodsMethods

Select 2-3 people who normally conduct the assessment

Randomly provide the samples to one person (without indicating which sample is which) and have the person rate each of the items

Once the first person has reviewed all items, repeat with the remaining people

Once everybody has rated each item, repeat the steps above for asecond trial

Note: All possible combinations of appraisers, items, and “trials” should be represented

Each appraiser must examine all of the items

Each appraiser must examine those items the same number of times (trials)


GB115-10

Attribute MSA Example: Attribute MSA Example: Spots on FilmSpots on Film

A film plant was having problems with spots on one of its products. Operators made the decision as to whether to reject rolls for spots. There had been considerable operator turnover, and engineers questioned how well the operators were trained in recognizing rejectable spots.

50 samples were selected – some clearly acceptable, some clearly unacceptable, and some on both sides of “borderline” conditions

Four operators and an “expert” (the tech service representative) recorded Reject or Accept decision


KAPPA STUDIES

Page 6402-108, Rev. C


GB115-11

Sample A B C D

1 R R R A

2 A A R A

3 A A A A

4 A A A A

5 A A A A

6 A A A A

7 A A A A

8 A A A A

9 A A A A

10 A R R R

11 R A A R

12 A A R A

13 R A A R

14 A R A A

15 A A A A

16 A A A A

17 A A A A

18 A A A A

19 A A A A

20 A A A A

Agreement Among Inspectors?

Agreement Among Inspectors?


• Results indicate definite problems(Results for 20 of the 50 samples are shown)

• Of the 50 samples, the inspectors actually agreed unanimously on only one “Rejected” sample

• They agreed unanimously on 29 “Accepted” samples

• %Parts with disagreement =

20/50 = 40%


GB115-12

Sample A B C D Expert

1 R R R A A

2 A A R A A

3 A A A A A

4 A A A A A

6 A A A A A

7 A A A A A

8 A A A A A

9 A A A A A

11 R A A R A

14 A R A A A

15 A A A A A

16 A A A A A

17 A A A A A

18 A A A A A

19 A A A A A

20 A A A A A

5 A A A A R

10 A R R R R

12 A A R A R

13 R A A R R

% Correct Decisions Overall = 150/200 = 75%

Inspector vs. Expert Results

%Bad Accepted %Good Rejected

A 9/13 = 69% 4/37 = 11%

B 9/13 = 69% 4/37 = 11%

C 9/13 = 69% 4/37 = 11%

D 8/13 = 62% 3/37 = 8%

Agreement With the Expert?

Agreement With the Expert?



KAPPA STUDIES

Page 7402-108, Rev. C


GB115-13


In an Attribute MSA study, Kappa is used to summarize the level of agreement between raters after agreement by chance has been removed

If there is substantial agreement, there is the possibility thatthe ratings are accurate

If agreement is poor, the usefulness of the ratings is extremely limited

Requirements for use:

Units to be measured are independent from one another

Raters’ inspect and classify independently

The rating categories are mutually exclusive and exhaustive

Minitab computes the value of Kappa as part of the output for anAttribute Agreement Analysis


GB115-14

Kappa (K) is defined as the proportion of agreement between raters after agreement by chance has been removed:

where:

Pobserved = Proportion of units classified in which the

raters agreed

Pchance = Proportion of units for which one would

expect agreement by chance

KappaKappa

chance

chanceobserved

P

PPK

1


KAPPA STUDIES

Page 8402-108, Rev. C


GB115-15

Kappa Value Suggested Interpretation

-1 to 0.0 Random agreement

> 0.60 Marginal - Significant effort required

> 0.70 Good - Improvement warranted

> 0.90 Excellent

Kappa will be between -1 and +1

A Kappa value of +1 means perfect agreement

General RuleGeneral Rule: If K<0.70, the measurement system needs attention!



GB115-16


Kappa can also be applied in cases of multiple assessors and multiple classification categories

Customer Returns example:

A Green Belt had a project to reduce customer returns. A reason code is assigned to every customer return by a Customer Service Representative (CSR). There was concern that CSRs weren’t consistent in their assignment of codes.

Three assessors were asked to assign reason codes to the same 30 returns. Each assessor made the assignments twice (once on each of two different days).

The Green Belt also asked a division expert to evaluate the returns


KAPPA STUDIES

Page 9402-108, Rev. C


GB115-17

Minitab Attribute Minitab Attribute

Agreement AnalysisAgreement Analysis

Project: BB MSA.MPJBB MSA.MPJ

Worksheet: Customer Customer

Returns.MTWReturns.MTW

Design:

3 CSRs (Appraisers)

2 Days

30 Returns(Samples)

Codes are in column CategoryCategory

Expert values are in column ExpertExpert


GB115-18



Use the Attribute Agreement Analysis to analyze attribute measurement system results


KAPPA STUDIES

Page 10402-108, Rev. C


GB115-19



Fill in the appropriate column information first

Fill in the appropriate column information first

Make sure that the Kappa option is checked in the Results window

Make sure that the Kappa option is checked in the Results window

Include expert’s ratingsInclude expert’s ratings


GB115-20

Graphical AnalysisGraphical Analysis

Note: These two graphs have different scales!

Note: These two graphs have different scales!

Agreement Within Appraiser is like repeatability

Here CSR 1 agreed with herself 90% of the time. CSR 2 was internally consistent 86.7% of the time, and CSR 3 matched himself on 96.7% of the returns

Graph shows the 95% CI for the % agreement

This graph shows % agreement between the CSRs and the expert opinion


KAPPA STUDIES

Page 11402-108, Rev. C


GB115-21

Numerical Output: Numerical Output: Within AssessorWithin Assessor

Within Appraiser

Assessment Agreement

Appraiser # Inspected # Matched Percent (%) 95.0% CI

A 30 27 90.0 ( 73.5, 97.9)

B 30 26 86.7 ( 69.3, 96.2)

C 30 29 96.7 ( 82.8, 99.9)

# Matched: Appraiser agrees with him/herself across

trials.

Attribute Gage R&R Study

Attribute Gage R&R Study for Category

Within Appraiser



A 30 27 90.0 ( 73.5, 97.9)

B 30 26 86.7 ( 69.3, 96.2) C 30 29 96.7 ( 82.8, 99.9)

# Matched: Appraiser agrees with him/herself across trials.

Kappa Statistics

Appraiser Response Kappa SE Kappa Z P(vs > 0)

A Administrati 0.8693 0.1826 4.7612 0.000

Damaged 1.0000 0.1826 5.4772 0.000

Incorrect Pr -0.0169 0.1826 -0.0928 0.537

Overstock 1.0000 0.1826 5.4772 0.000

Planned Adju 0.7917 0.1826 4.3361 0.000 Pricing 0.7917 0.1826 4.3361 0.000

Quality 1.0000 0.1826 5.4772 0.000

Quantity * * * *

Overall 0.8768 0.0855 10.2533 0.000

B Administrati 0.6078 0.1826 3.3293 0.000

Damaged 1.0000 0.1826 5.4772 0.000 Incorrect Pr 1.0000 0.1826 5.4772 0.000

Overstock 1.0000 0.1826 5.4772 0.000

Planned Adju 0.6296 0.1826 3.4486 0.000

Pricing 0.7689 0.1826 4.2116 0.000

Quality 1.0000 0.1826 5.4772 0.000

Quantity * * * * Overall 0.8346 0.0845 9.8785 0.000

C Administrati 1.0000 0.1826 5.4772 0.000

Damaged 0.8383 0.1826 4.5914 0.000

Incorrect Pr 1.0000 0.1826 5.4772 0.000

Overstock 1.0000 0.1826 5.4772 0.000

Planned Adju 1.0000 0.1826 5.4772 0.000

Pricing 0.8383 0.1826 4.5914 0.000 Quality 1.0000 0.1826 5.4772 0.000

Quantity 1.0000 0.1826 5.4772 0.000

Overall 0.9597 0.0803 11.9524 0.000

* When no or all responses across trials equal the value,

kappa cannot be computed.

Each Appraiser vs Standard



A 30 23 76.7 ( 57.7, 90.1) B 30 20 66.7 ( 47.2, 82.7)

C 30 22 73.3 ( 54.1, 87.7)

# Matched: Appraiser's assessment across trials agrees with standard.

Kappa Statistics


A Administrati 0.3847 0.1291 2.9802 0.001

Damaged 1.0000 0.1291 7.7460 0.000

Incorrect Pr 0.4915 0.1291 3.8073 0.000

Overstock 0.6491 0.1291 5.0281 0.000 Planned Adju 0.8958 0.1291 6.9391 0.000

Pricing 0.8952 0.1291 6.9345 0.000

Quality 0.8295 0.1291 6.4256 0.000

Quantity -0.0345 0.1291 -0.2671 0.605

Overall 0.7561 0.0583 12.9782 0.000

B Administrati 0.2357 0.1291 1.8256 0.034 Damaged 1.0000 0.1291 7.7460 0.000

Incorrect Pr 1.0000 0.1291 7.7460 0.000

Overstock 0.4643 0.1291 3.5963 0.000

Planned Adju 0.5673 0.1291 4.3943 0.000

Pricing 0.7016 0.1291 5.4348 0.000

Quality 0.8137 0.1291 6.3026 0.000 Quantity -0.0345 0.1291 -0.2671 0.605

Overall 0.6352 0.0574 11.0724 0.000

C Administrati 0.3750 0.1291 2.9047 0.002

Damaged 0.9191 0.1291 7.1196 0.000

Incorrect Pr 1.0000 0.1291 7.7460 0.000

Overstock 0.6491 0.1291 5.0281 0.000


Quality 0.7054 0.1291 5.4640 0.000

Quantity 0.6491 0.1291 5.0281 0.000 Overall 0.7011 0.0558 12.5715 0.000

Between Appraisers


# Inspected # Matched Percent (%) 95.0% CI

30 17 56.7 ( 37.4, 74.5)

# Matched: All appraisers' assessments agree with each other.

Kappa Statistics

Response Kappa SE Kappa Z P(vs > 0)

Administrati 0.4778 0.0471 10.1365 0.000 Damaged 0.9412 0.0471 19.9650 0.000

Incorrect Pr 0.7943 0.0471 16.8493 0.000

Overstock 0.8761 0.0471 18.5844 0.000

Planned Adju 0.6800 0.0471 14.4250 0.000

Pricing 0.6397 0.0471 13.5708 0.000

Quality 0.8399 0.0471 17.8166 0.000


All Appraisers vs Standard


# Inspected # Matched Percent (%) 95.0% CI 30 15 50.0 ( 31.3, 68.7)

# Matched: All appraisers' assessments agree with standard.

Kappa Statistics



Incorrect Pr 0.8305 0.0745 11.1424 0.000


Pricing 0.7603 0.0745 10.2000 0.000

Quality 0.7829 0.0745 10.5033 0.000

Quantity 0.1934 0.0745 2.5945 0.005

Overall 0.6975 0.0330 21.1414 0.000

• Similar information as left graph (see previous slide)

• Adds specific numbers of matched and inspected


GB115-22

Numerical Output:Numerical Output:Appraiser vs. ExpertAppraiser vs. Expert




A 30 23 76.7 ( 57.7, 90.1)

B 30 20 66.7 ( 47.2, 82.7)

C 30 22 73.3 ( 54.1, 87.7)

# Matched: Appraiser's assessment across trials agrees

with standard.

This indicates how often the assessor is giving the “correct” answer

We want 90% or better agreementWe want 90% or better agreement



Within Appraiser



A 30 27 90.0 ( 73.5, 97.9)

B 30 26 86.7 ( 69.3, 96.2) C 30 29 96.7 ( 82.8, 99.9)


Kappa Statistics


A Administrati 0.8693 0.1826 4.7612 0.000

Damaged 1.0000 0.1826 5.4772 0.000

Incorrect Pr -0.0169 0.1826 -0.0928 0.537

Overstock 1.0000 0.1826 5.4772 0.000


Quality 1.0000 0.1826 5.4772 0.000

Quantity * * * *

Overall 0.8768 0.0855 10.2533 0.000

B Administrati 0.6078 0.1826 3.3293 0.000


Overstock 1.0000 0.1826 5.4772 0.000

Planned Adju 0.6296 0.1826 3.4486 0.000

Pricing 0.7689 0.1826 4.2116 0.000

Quality 1.0000 0.1826 5.4772 0.000


C Administrati 1.0000 0.1826 5.4772 0.000

Damaged 0.8383 0.1826 4.5914 0.000

Incorrect Pr 1.0000 0.1826 5.4772 0.000

Overstock 1.0000 0.1826 5.4772 0.000

Planned Adju 1.0000 0.1826 5.4772 0.000

Pricing 0.8383 0.1826 4.5914 0.000 Quality 1.0000 0.1826 5.4772 0.000

Quantity 1.0000 0.1826 5.4772 0.000

Overall 0.9597 0.0803 11.9524 0.000






A 30 23 76.7 ( 57.7, 90.1) B 30 20 66.7 ( 47.2, 82.7)

C 30 22 73.3 ( 54.1, 87.7)


Kappa Statistics


A Administrati 0.3847 0.1291 2.9802 0.001

Damaged 1.0000 0.1291 7.7460 0.000

Incorrect Pr 0.4915 0.1291 3.8073 0.000


Pricing 0.8952 0.1291 6.9345 0.000

Quality 0.8295 0.1291 6.4256 0.000

Quantity -0.0345 0.1291 -0.2671 0.605

Overall 0.7561 0.0583 12.9782 0.000


Incorrect Pr 1.0000 0.1291 7.7460 0.000

Overstock 0.4643 0.1291 3.5963 0.000

Planned Adju 0.5673 0.1291 4.3943 0.000

Pricing 0.7016 0.1291 5.4348 0.000


Overall 0.6352 0.0574 11.0724 0.000

C Administrati 0.3750 0.1291 2.9047 0.002

Damaged 0.9191 0.1291 7.1196 0.000

Incorrect Pr 1.0000 0.1291 7.7460 0.000

Overstock 0.6491 0.1291 5.0281 0.000


Quality 0.7054 0.1291 5.4640 0.000


Between Appraisers



30 17 56.7 ( 37.4, 74.5)


Kappa Statistics



Incorrect Pr 0.7943 0.0471 16.8493 0.000

Overstock 0.8761 0.0471 18.5844 0.000

Planned Adju 0.6800 0.0471 14.4250 0.000

Pricing 0.6397 0.0471 13.5708 0.000

Quality 0.8399 0.0471 17.8166 0.000






Kappa Statistics



Incorrect Pr 0.8305 0.0745 11.1424 0.000


Pricing 0.7603 0.0745 10.2000 0.000

Quality 0.7829 0.0745 10.5033 0.000

Quantity 0.1934 0.0745 2.5945 0.005

Overall 0.6975 0.0330 21.1414 0.000


KAPPA STUDIES

Page 12402-108, Rev. C


GB115-23

Numerical OutputNumerical Output

All Appraisers vs. Standard gives an indication of the effectiveness of the inspection standards:

Between AppraisersAssessment Agreement


30 17 56.7 ( 37.4, 74.5)





30 15 50.0 ( 31.3, 68.7)

# Matched: All appraisers' assessments agree with

standard.

Between-Appraisers agreement is like Reproducibility:



Within Appraiser



A 30 27 90.0 ( 73.5, 97.9)

B 30 26 86.7 ( 69.3, 96.2) C 30 29 96.7 ( 82.8, 99.9)


Kappa Statistics


A Administrati 0.8693 0.1826 4.7612 0.000

Damaged 1.0000 0.1826 5.4772 0.000

Incorrect Pr -0.0169 0.1826 -0.0928 0.537

Overstock 1.0000 0.1826 5.4772 0.000


Quality 1.0000 0.1826 5.4772 0.000

Quantity * * * *

Overall 0.8768 0.0855 10.2533 0.000

B Administrati 0.6078 0.1826 3.3293 0.000


Overstock 1.0000 0.1826 5.4772 0.000

Planned Adju 0.6296 0.1826 3.4486 0.000

Pricing 0.7689 0.1826 4.2116 0.000

Quality 1.0000 0.1826 5.4772 0.000


C Administrati 1.0000 0.1826 5.4772 0.000

Damaged 0.8383 0.1826 4.5914 0.000

Incorrect Pr 1.0000 0.1826 5.4772 0.000

Overstock 1.0000 0.1826 5.4772 0.000

Planned Adju 1.0000 0.1826 5.4772 0.000

Pricing 0.8383 0.1826 4.5914 0.000 Quality 1.0000 0.1826 5.4772 0.000

Quantity 1.0000 0.1826 5.4772 0.000

Overall 0.9597 0.0803 11.9524 0.000






A 30 23 76.7 ( 57.7, 90.1) B 30 20 66.7 ( 47.2, 82.7)

C 30 22 73.3 ( 54.1, 87.7)


Kappa Statistics


A Administrati 0.3847 0.1291 2.9802 0.001

Damaged 1.0000 0.1291 7.7460 0.000

Incorrect Pr 0.4915 0.1291 3.8073 0.000


Pricing 0.8952 0.1291 6.9345 0.000

Quality 0.8295 0.1291 6.4256 0.000

Quantity -0.0345 0.1291 -0.2671 0.605

Overall 0.7561 0.0583 12.9782 0.000


Incorrect Pr 1.0000 0.1291 7.7460 0.000

Overstock 0.4643 0.1291 3.5963 0.000

Planned Adju 0.5673 0.1291 4.3943 0.000

Pricing 0.7016 0.1291 5.4348 0.000


Overall 0.6352 0.0574 11.0724 0.000

C Administrati 0.3750 0.1291 2.9047 0.002

Damaged 0.9191 0.1291 7.1196 0.000

Incorrect Pr 1.0000 0.1291 7.7460 0.000

Overstock 0.6491 0.1291 5.0281 0.000


Quality 0.7054 0.1291 5.4640 0.000


Between Appraisers



30 17 56.7 ( 37.4, 74.5)


Kappa Statistics



Incorrect Pr 0.7943 0.0471 16.8493 0.000

Overstock 0.8761 0.0471 18.5844 0.000

Planned Adju 0.6800 0.0471 14.4250 0.000

Pricing 0.6397 0.0471 13.5708 0.000

Quality 0.8399 0.0471 17.8166 0.000






Kappa Statistics



Incorrect Pr 0.8305 0.0745 11.1424 0.000


Pricing 0.7603 0.0745 10.2000 0.000

Quality 0.7829 0.0745 10.5033 0.000

Quantity 0.1934 0.0745 2.5945 0.005

Overall 0.6975 0.0330 21.1414 0.000


GB115-24

Numerical Output:Numerical Output:

KappaKappa

Kappa and Kappa values for each category of defect are provided for Between-Appraisers (and other sections)

How well can we identify the defect types?

Between AppraisersAssessment Agreement


30 17 56.7 ( 37.4, 74.5)


Fleiss’ Kappa Statistics


Administrati 0.4778 0.0471 10.1365 0.000

Damaged 0.9412 0.0471 19.9650 0.000

Incorrect Pr 0.7943 0.0471 16.8493 0.000

Overstock 0.8761 0.0471 18.5844 0.000

Planned Adju 0.6800 0.0471 14.4250 0.000

Pricing 0.6397 0.0471 13.5708 0.000

Quality 0.8399 0.0471 17.8166 0.000

Quantity 0.1910 0.0471 4.0520 0.000

Overall 0.7106 0.0210 33.8869 0.000

The overall Kappa (between appraisers) represents the study-wide agreement (across appraisers and categories)

Measures how much better than chance agreement is the matching you see on a sample by sample basis



Within Appraiser



A 30 27 90.0 ( 73.5, 97.9)

B 30 26 86.7 ( 69.3, 96.2) C 30 29 96.7 ( 82.8, 99.9)


Kappa Statistics


A Administrati 0.8693 0.1826 4.7612 0.000

Damaged 1.0000 0.1826 5.4772 0.000

Incorrect Pr -0.0169 0.1826 -0.0928 0.537

Overstock 1.0000 0.1826 5.4772 0.000


Quality 1.0000 0.1826 5.4772 0.000

Quantity * * * *

Overall 0.8768 0.0855 10.2533 0.000

B Administrati 0.6078 0.1826 3.3293 0.000


Overstock 1.0000 0.1826 5.4772 0.000

Planned Adju 0.6296 0.1826 3.4486 0.000

Pricing 0.7689 0.1826 4.2116 0.000

Quality 1.0000 0.1826 5.4772 0.000


C Administrati 1.0000 0.1826 5.4772 0.000

Damaged 0.8383 0.1826 4.5914 0.000

Incorrect Pr 1.0000 0.1826 5.4772 0.000

Overstock 1.0000 0.1826 5.4772 0.000

Planned Adju 1.0000 0.1826 5.4772 0.000

Pricing 0.8383 0.1826 4.5914 0.000 Quality 1.0000 0.1826 5.4772 0.000

Quantity 1.0000 0.1826 5.4772 0.000

Overall 0.9597 0.0803 11.9524 0.000






A 30 23 76.7 ( 57.7, 90.1) B 30 20 66.7 ( 47.2, 82.7)

C 30 22 73.3 ( 54.1, 87.7)


Kappa Statistics


A Administrati 0.3847 0.1291 2.9802 0.001

Damaged 1.0000 0.1291 7.7460 0.000

Incorrect Pr 0.4915 0.1291 3.8073 0.000


Pricing 0.8952 0.1291 6.9345 0.000

Quality 0.8295 0.1291 6.4256 0.000

Quantity -0.0345 0.1291 -0.2671 0.605

Overall 0.7561 0.0583 12.9782 0.000


Incorrect Pr 1.0000 0.1291 7.7460 0.000

Overstock 0.4643 0.1291 3.5963 0.000

Planned Adju 0.5673 0.1291 4.3943 0.000

Pricing 0.7016 0.1291 5.4348 0.000


Overall 0.6352 0.0574 11.0724 0.000

C Administrati 0.3750 0.1291 2.9047 0.002

Damaged 0.9191 0.1291 7.1196 0.000

Incorrect Pr 1.0000 0.1291 7.7460 0.000

Overstock 0.6491 0.1291 5.0281 0.000


Quality 0.7054 0.1291 5.4640 0.000


Between Appraisers



30 17 56.7 ( 37.4, 74.5)


Kappa Statistics



Incorrect Pr 0.7943 0.0471 16.8493 0.000

Overstock 0.8761 0.0471 18.5844 0.000

Planned Adju 0.6800 0.0471 14.4250 0.000

Pricing 0.6397 0.0471 13.5708 0.000

Quality 0.8399 0.0471 17.8166 0.000






Kappa Statistics



Incorrect Pr 0.8305 0.0745 11.1424 0.000


Pricing 0.7603 0.0745 10.2000 0.000

Quality 0.7829 0.0745 10.5033 0.000

Quantity 0.1934 0.0745 2.5945 0.005

Overall 0.6975 0.0330 21.1414 0.000


KAPPA STUDIES

Page 13402-108, Rev. C


GB115-25


For this example, the following Kappa values are calculated

Within appraiser

The individual category Kappa represents how consistently each appraiser rates the same samples over multiple trials

The overall Kappa for each appraiser represents that appraiser’s consistency across all categories

Between appraisers

The individual category Kappa represents how consistently all the appraisers categorized the samples in that category

The overall Kappa represents the study-wide agreement (across appraisers and categories)


GB115-26

Improving this measurement system would likely requirechanging operational definitions for each defect type,

retraining the CSRs, or both

Improving this measurement system would likely requirechanging operational definitions for each defect type,

retraining the CSRs, or both


Interpreting the Results

The individual Kappa values range from 0.19 to 0.94, meaning that agreement among raters is:

EXCELLENT for Damaged;

UNACCEPTABLY LOW for Administrative and Quantity;

MARGINAL for Planned adjustments and Pricing

GOOD for Incorrect product, Overstock, and Quality

The overall Kappa was 0.7106, indicating that the system could use some improvement


KAPPA STUDIES

Page 14402-108, Rev. C


GB115-27

Improving Attribute Improving Attribute

Measurement SystemsMeasurement Systems

Sense multipliers (devices to improve human senses)

Masks / templates (block out unimportant information)

Checklists

Automation

Reorganization of work area

Visual aids


GB115-28

Measurement Systems:Measurement Systems:Questions to AskQuestions to Ask

Have you picked the right measurement system? Is this measurement system associated with either critical inputs or outputs?

What do the precision, accuracy, and stability look like?

What are the sources of variation and what is the measurement error?

What needs to be done to improve this system?

Have we informed the right people of our results?

Who owns this measurement system?

Who owns trouble shooting?

Does this system have a control plan in place?

What’s the training frequency? Is that frequent enough?

Do identical systems match?


KAPPA STUDIES

Page 15402-108, Rev. C


GB115-29

SummarySummary

Measurement systems are important to analyze BEFORE embarking on Process Improvement activities

Be careful when picking samples – watch for correct sub-grouping and sample size requirements

Analyze the measurement system for Operator / Assessor, Part / Item, and Trial effects

Make sure that the measurement system has enough resolution to distinguish between samples

Always generate a Gage R&R report to document findings, methods, and improvement opportunities

Total observed variation includes measurement error –try to minimize the controllable error in the measurement system


GB115-30

Exercise:Exercise:Candy InspectionCandy Inspection

Objective:Evaluate the M&M inspection system

Tools:

3 inspectors

Bag of M&Ms

Procedure:

BEFORE opening the bag, operationally define the defects you will be concerned with

Have your ‘inspectors’ evaluate the materials in the samples of candies

Evaluate the measurement system

Report any recommended improvement actions

TEAM MEETINGS AND PRESENTATION SKILLS


Page 1402-108, Rev. C

GB116-1Rev C February 2004

Team Meetings andTeam Meetings andPresentation SkillsPresentation Skills

DMAIC Process ImprovementDMAIC Process Improvement

For Operations Green BeltsFor Operations Green Belts

GB116-2


Discuss techniques for conducting effective team meetings

Introduce technical presentation Do’s and Don’ts

Content

Format and Structure

Slide Design

Review basic presentation skills Do’s and Don’ts

Verbal

Visual

Energy



Page 2402-108, Rev. C

GB116-3

Conducting Effective Meetings Conducting Effective Meetings

(Refer to the Team Building and Interpersonal Skills Reference Guide page 20)

PREPARE

Determine the objective of the meeting

Gather background information and data

Decide who needs to be invited

Prepare an Agenda (Item, its outcome, and who will present or lead discussion)

Assign the amount of time to be dedicated to each topic

Distribute the agenda and background information to participants before the meeting. This will give them a chance to prepare for their participation

GB116-4

Begin The MeetingBegin The Meeting

Begin the meeting on time (even if people are late).

Review the Objective, the Agenda and the Time assigned to each item

Assign Roles (optional, as needed)

Leader - Focuses on the business objective. Encourages participation.

Timekeeper - Keeps the team on the agreed upon time track (critical)

Scribe - Records ideas, suggestions, and action items (critical)

Facilitator - Assists the leader.

Process Guide - If using 8D, DMAIC, or other process, keeps the team on track



Page 3402-108, Rev. C

GB116-5

Conduct The MeetingConduct The Meeting

Review background information and data

Ask participants for new data or ideas, discuss

If meeting is for problem solving, identify root causes, then brainstorm possible solutions

If meeting is for continuous improvement of a process, Brainstorm suggestions/ideas

Strive for quantity

No criticizing/evaluating ideas during brainstorming.

Build on others’ ideas.

Clarify to make sure everyone understands the suggestions/ideas

Combine similar ideas

GB116-6

Conduct The Meeting (Cont.)Conduct The Meeting (Cont.)

Determine best solutions

If the team cannot come to consensus, you could have everyone vote--3 points for their first choice, 2 points for their second choice, 1 point for their third choice

If issues arise that are separate from the meeting or cannot be resolved in the meeting, record them on a parking lot for later resolution

Determine an action or implementation plan

Assign action items and completion dates (Who will do What by When)



Page 4402-108, Rev. C

GB116-7

Close The MeetingClose The Meeting

End the meeting on time, even if you are not complete; or re-negotiate ending time.

Summarize and review action plans, setting dates and times for follow-up or future meetings

Thank all participants

Issue minutes with action items, times, and dates documented and send to all participants and others who need to know the results of the meeting

GB116-8

Why Should You Care About Why Should You Care About

Making Presentations?Making Presentations?

Each of you will have to make a presentation about your project.

Part of your project’s success will be dependent on the support you get based on the your technical results…

… and part will be based on your ability to present your findings in order to gain support from:

Management

Process Owners

Peers

Team Members



Page 5402-108, Rev. C

GB116-9

Successful Presentations Depend On …Successful Presentations Depend On …

Good communications skills

Ability to present technical material to an audience with differing backgrounds and levels of technical ability

Verbal and non-verbal communications

Advance preparation

Presentation material must carry the message to your audience

Delivery must be enthusiastic and convey knowledge of the subject

Team Reference Guide, page 27

GB116-10

Two Types Of MeetingsTwo Types Of Meetings

Information meetingAdviseUpdateSell

Decision Making MeetingGoal settingProblem Solving



Page 6402-108, Rev. C

GB116-11

Key Differences In Types Of MeetingKey Differences In Types Of Meeting

ELEMENTS INFORMATION

MEETING

DECISION MAKING

MEETING

Number of

attendees

Any number Small size

Who should

attend ?

Those who need to know Those responsible and those who

can contribute

Communication

process

One way from leader to

participants with opportunities

for questions

Interactive discussion among all

attending

Meeting room

setup Participants facing front of

room-classroom style

Participants facing each other-

conference style

Most effective

style of leadership Authoritative Participative

Emphasis should

be on :

Content Interaction and problem solving

Key to success Planning and preparation of

information to be presented

Meeting climate that support open,

free expression

GB116-12

Selecting ParticipantsSelecting Participants

FOR INFORMATION MEETINGS :- Those who need to know the information to

be presented

FOR DECISION MAKING MEETING :- Those who have knowledge to contribute- Those who have authority over the area

affected by the decision- Those you need the commitment to carry out

the decision- Those who have diversity of view-point

(People with expressiveness and open-mindedness)

Danielle MUYL .AMP.GEP



Page 7402-108, Rev. C

GB116-13

ContentContent

Most presentations fail due to their lack of focus

Content is essential – know what your audience wants and needs!

Do’s

Use simple graphical representations

Use bullet statements

Highlight meaningful information

Don’ts

Include information that is too complex

Write and read paragraphs of information

Clutter the presentation with busy graphics

GB116-14

Organizing Your PresentationOrganizing Your Presentation

It is a good idea to start by developing objectives. Once this is done you need to thoroughly analyze the audience. You must complete these steps before you separately brainstorm the main points and the sub points of your presentation. If it’s a persuasive presentation, then you must state the benefits. You then gather factual information and prepare a blueprint of your presentation. Also prepare any visual aids, handouts and notes that you will need. And don’t forget to practice.

What is your reaction to this slide?



Page 8402-108, Rev. C

GB116-15

Organizing Your PresentationOrganizing Your Presentation

Develop objectives

Analyze the audience

Brainstorm

Introduction

Main ideas and benefits

Sub ideas

Conclusions

Develop visuals, handouts and notes

Practice

GB116-16

ContentContent

So how do you know what to include?

Some guidelines:

Project description and targeted value

Key learning's and the tools used to accomplish them

Next steps / action plan / timing

Roadblocks to success needing resolution by the audience

Recognition of contributors

Results to date (financial and learning's)




Page 9402-108, Rev. C

GB116-17

Format And StructureFormat And Structure

One of the best ways to structure a presentation is to:

Tell them what you’re going to tell them

Tell them

Tell them what you told them

Identify the 3 key concepts, actions, or issues that you want the audience to address

Work from your conclusion and build the supporting materials around these key points

GB116-18

Presentation SequencePresentation Sequence

Suggested flow for a presentation:

Introduction

Main Ideas and Sub Ideas

Benefits

Conclusion

Questions and Answers

Review Sentence



Page 10402-108, Rev. C

GB116-19

The Last SlideThe Last Slide

Make sure that your 3 key points are summarized in the Review Sentence

This can be viewed as your Elevator Speech!

Characteristic of a great presentation:

If your presentation time is cut short and you can only present one slide, you can convey your entire presentation in the last slide

GB116-20

Using Visuals / GraphicsUsing Visuals / Graphics

Use visuals and graphics to:

Focus the attention of the audience

Reinforce your verbal message

Stimulate interest

Illustrate factors that are hard to visualize

Graphically represent data



Page 11402-108, Rev. C

GB116-21

Using Visuals / GraphicsUsing Visuals / Graphics

Don’t use visuals or graphics to:

Try and impress the audience with overly detailed tables or graphs

Avoid interaction with the audience

Make more than one point

Present simple ideas that can be presented verbally

GB116-22

Slide Design Do’s And Don’tsSlide Design Do’s And Don’ts

Do’s

KISS (Keep It Simple and Short)

Present one key point per visual

Use bullets, not full sentences

Statements on the slide should only be a reference to you and drive a key point




Page 12402-108, Rev. C

GB116-23


Do’s

Highlight key items in a graphical item

Show only part of the C&E Matrix or top items in the FMEA, not the entire item

State the conclusions that you reach based on the graphic

Use simple animation – too much can degrade the validity of the message

GB116-24


Don’ts

Use Yellow text (e.g. Yellow)

It is not easily read except on a very dark background

Use more than three colors per graph

Use more than 3 curves per graph

Present rows and columns of data

Provide summary information only



Page 13402-108, Rev. C

GB116-25


Don’ts

Make text and graphs too small

Rule of Thumb: If you can read everything on your computer screen from 8 feet away, it is good

Have too much text per slide

Rule of Thumb: 50% white space on the slide

Add clipart and pictures that don’t clearly link to the idea presented

GB116-26

Presentation Skill ReviewPresentation Skill Review

Some of you have never had to speak in public before and many feel uncomfortable speaking in front of a group

Regardless, each of you will have to present in front of many different groups throughout your project and your career

The following material contains some reminders on techniques that will ease your anxiety



Page 14402-108, Rev. C

GB116-27

PreparationsPreparations

Organize

Your thoughts, your materials, your presentation

Memorize

Key points, key messages, your elevator speech

Visualize

The success of your presentation and the expected outcome

Positive Affirmations

Remind yourself that you CAN do this!

Release Tensions Exercises

Stretching, breathing, meditation prior to speaking

Practice

GB116-28

AppearanceAppearance

Posture

Standing upright = authoritative

Leaning / sitting = personal

Movement

Purposeful - not anxious

Shoulder Orientation

Squared up - not slouching

Gestures

Purposeful, natural - not erratic

Attire

Appropriate for audience




Page 15402-108, Rev. C

GB116-29

During The PresentationDuring The Presentation

Remember to Breathe

If you find yourself stressing or speaking too fast, simply stop and take a breath

Move

Use the space that you have effectively

Eye Contact

Make the presentation personal by connecting visually with the audience

Do NOT have any caffeine before or during the presentation

Remember - You are surrounded by friends!

GB116-30

DeliveryDelivery

Enthusiasm is contagious

If you are not enthusiastic about your presentation, no one else will be!

Relate with your audience

Make a common bond through examples

Voice projection

Be clear and loud

Use inflection in your voice

Use conversational speed and tone

Don’t read every word on every slide



Page 16402-108, Rev. C

GB116-31

Eye ContactEye Contact

Good eye contact –

Opens the channel of communication with the audience

Establishes and builds rapport

Involves the audience

Talk to the audience – not to the projected material

GB116-32

DeliveryDelivery

Place yourself at “center stage”

Orient your shoulders toward the audience

Leave some light on in the room so you can be seen

Use pointers sparingly to draw attention to a specific item or to trace the relationship on a visual / graphic

Nervousness is amplified when using a pointer

Especially evident when using laser pointers

Don’t “play” with the pointer – when not using the pointer, lay it down



Page 17402-108, Rev. C

GB116-33

Keeping Their AttentionKeeping Their Attention

The first 120 seconds is “free”

The words “in conclusion” draws them back

Use “energizers” to hold them in the middle

People speak at 120 - 200 words per minute, and comprehend at 600 words per minute

GB116-34

Examples Of EnergizersExamples Of Energizers

Enthusiasm

Movement

Visual Aids

Samples, something that they can touch or see

Animation

Pictures / clipart

Humor

Interactive Questions

Voice and Body Language




Page 18402-108, Rev. C

GB116-35

Questions And AnswersQuestions And Answers

Prepare for them

Welcome them

Listen to the question and clarify if necessary

Restate for the whole audience

Respond to the whole audience

Be honest

GB116-36

Benefits Of Good Benefits Of Good

Presentation MaterialsPresentation Materials

Audience is 43% more likely to be persuaded

You can cover the material in 25-40% less time

Learning is improved 200%

Retention is improved 38%

You are perceived as professional, persuasive, credible, interesting, and prepared



Page 19402-108, Rev. C

GB116-37

Practice, Practice, PracticePractice, Practice, Practice

Key words on notes pages

Mental run through for flow of ideas

Stand up rehearsal using the presentation material

Video tape or rehearse with a friend

GB116-38

Final ThoughtsFinal Thoughts

Smile; enjoy sharing your information

x Tell jokes unless you are good at it

x Waste your first 120 seconds thanking them or making apologies.

x Read your presentation

Remember the audience wants you to succeed

INTRODUCTION TO MULTI-VARI STUDIES


Page 1402-108, Rev. C


GB117-1

Introduction to Multi-Vari Studies




GB117-2














Page 2402-108, Rev. C


GB117-3

TopicsTopics

Overview of Multi-Vari Studies

Noise Variables and Analysis Introduction

Planning Multi-Vari’s

Data Collection

Note: The name “Multi-Vari” was given to this methodology by L. A. Seder in the paper titled, “Diagnosis with Diagrams” which appeared in Industrial Quality Control in January and March, 1950.


GB117-4

MultiMulti--Vari StudiesVari Studies

Method of characterizing the baseline capability of a process while in production mode

Data collection is passive in that the process is monitored in its natural state

Data is collected for a short period of time and analyzed to determine capability, stability and relationships between Input Variables and Output Variables

Multi-Vari study should continue until the full range of the output variable is observed

Span from Low to High as observed in the short-term capability or historic data

Identifies inherent process capabilities and limitations



Page 3402-108, Rev. C


GB117-5

Why MultiWhy Multi--Vari?Vari?

To determine with high statistical confidence the capability of the Output Variables of a process

To identify assignable causes of variability

To obtain initial components of variability (Shift-to-Shift, Run-to-Run, Operator-to-Operator)

To get a first-look at process stability over time

To provide direction and input for Design of Experiments (DOE) activities


GB117-6

PhasesPhases

Phase I:

Perform Short-term Capability Study

Derive a plan for more in-depth study of the process based on data and notes from the Short-term Capability Study

Phase II:

Initial study of the effects of controlled, uncontrolled (noise), and material input variables on the output variable

Focus on characterizing the effect of major uncontrolled (noise) variables in the system

Initial relationships between controlled inputs and outputs



Page 4402-108, Rev. C


GB117-7

MAJOR FOCUSMAJOR FOCUS

To study the uncontrolled Noise Variables first!To study the uncontrolled Noise Variables first!

Variation in the Noise variables produces chronic and acute mean shifts and changes in variability that lead to process instability

We must remove, if possible, these sources of variation first before we can leverage the important controlled input variables in a systematic way


GB117-8

Noise VariablesNoise Variables

We can classify Noise Variables into three main families of variation:

Positional - Differences in variation due to similar processes across a production line

Reactor-to-reactor differences

Line-to-line differences

Press to Press differences

Production location to location differences

Operator-to-Operator differences

Temporal - Differences in variation of a process over time

Shift-to-Shift

Day-to-Day

Week-to-Week

Sequential - Differences across a series of processes

If the output variable is affected by several different processes, we test Process-to-Process variability



Page 5402-108, Rev. C


GB117-9

Analyzing Noise Variables Analyzing Noise Variables

For Continuous Processes

Test for Variability within a time span

Example: Four Measurements per Shift

Test for Variability across short time spans

Example: Variability across shifts

Test for Variability across longer time times

Example: Variability across days, weeks and/or months

For Discrete Processes

Test for Variability within a piece

Example: Four measurements per oven cavity

Test for Variability within a batch

Example: Variability across cavities within a batch

Test for Variability across batches

Example: Variability across batches within a month


GB117-10

Positional Noise TreePositional Noise Tree

… and any other plants making this product

Plant 1

Mold 2Mold 1 Mold 3

… and other cavities

Cavity 1 Cavity 2 Cavity 3

…and other molds



Page 6402-108, Rev. C


GB117-11

Material Noise TreeMaterial Noise Tree

Lot/Batch 1 Lot/Batch 3Lot/Batch 2

Part/Spool 2 Part/Spool 3Part/Spool 1

Supplier A … and other suppliers

Location 1 Location 2 Location 3 … and other locations within a part

Measurement 1 Measurement 2

… and other measurements within a location


GB117-12

Temporal Noise TreeTemporal Noise Tree

Month 1 Month 3Month 2

Year 1

Day 1 Day 2 Day 3 … and other days within weeks

Shift 1 Shift 2

Week 2 Week 3Week 1 Week 4

Hour 1 Hour 4 … and other hours within Shift



Page 7402-108, Rev. C


GB117-13

Studying Noise Variables Studying Noise Variables

Sampling

We would sample several points within a shift, several shifts over several days or weeks

Let’s look at a sample data collection sheet on the next page for a process with:

Two controlled Inputs (Temperature, Pressure)

One Output (% Impurities)

Three Noise variables (Day, Shift and Time within Shift)

We’ll sample over two days


GB117-14

Data SheetData SheetDay Shift Time Input: Temperature Input: Pressure Output: % Impurities

1 1 1 91 48 0.020000

1 1 2 97 52 0.020000

1 1 3 88 44 0.020000

1 1 4 87 43 0.010000

1 2 1 109 50 0.060000

1 2 2 98 45 0.040000

1 2 3 103 55 0.030000

1 2 4 99 49 0.050000

2 1 1 111 55 0.010000

2 1 2 103 53 0.010000

2 1 3 106 54 0.040000

2 1 4 93 55 0.000000

2 2 1 101 46 0.050000

2 2 2 93 48 0.040000

2 2 3 97 54 0.010000

2 2 4 99 49 0.030000

Notice that this data sheet format is the same format as a Minitab data file

With this sheet we can easily investigate differences in % Impurities due to time-to-time, shift-to-shift and day-to-day Variations

We can also correlate Temperature and Pressure with Impurities



Page 8402-108, Rev. C


GB117-15

MultiMulti--Vari Phase I:Vari Phase I:Capability StudyCapability Study

1. Set up the process to your “best guess”“best guess” setup and record the values of your process Input Variables

2. Identify a reasonable way to create rational subgroups; i.e, how will you select the samples?

3. Run the product over a short period of time to remove as much external variation as possible

Approximately 30 time points30 time points is the target for data collection

4. Have your team carefully observe the process and take plenty of notes

5. Measure and record values for the process Output Variable(s)


GB117-16

MultiMulti--Vari Phase I:Vari Phase I:Capability StudyCapability Study

6. Run Capability SixCapability Six--packpack and Review:

Normal Plot, Histogram

SPC Charts (Check for Stability, Accuracy)

7. Run the Test for NormalityTest for Normality on the data

8. Run the Capability StudyCapability Study and identify both the Short Term and Long Term capability indices

9. Diagnose for Mean Shift and Variance Inflation

10. Develop action plan based on diagnostics



Page 9402-108, Rev. C


GB117-17

MultiMulti--Vari Phase II:Vari Phase II:PlanningPlanning

1. Determine Objective

2. Identify Input and Output Variables to be studied

3. Identify Measurement Systems for each variable

Which should be studied to assure capability?

4. Determine sampling plan

5. Determine data collection, formatting and storage procedure

6. Description of procedure and settings used to run process

7. Team assigned and trained

8. Clear responsibilities assigned

9. Outline of data analysis to be performed


GB117-18

MultiMulti--Vari Phase II:Vari Phase II:Execution and AnalysisExecution and Analysis

1. Run process and collect data

2. Analyze data:

Is the process stable, in control?

Which are the key noise variables affecting the output variable?

Which are the key controlled variables that influence the output variable?

3. Validate results with follow-up DOE

4. Conclusions

5. Reporting results, recommendations



Page 10402-108, Rev. C


GB117-19

PlanningPlanningCritical Step

Provides for:

Statement of Objective

List of Input Variables and Output Variables to be studied

Ensure Measurement Systems are capable

Description of sampling plan

Method of data collection, formatting and storage

Description of procedure and settings used to run process

Team assigned and trained

Clear responsibilities assigned

Outline of data analysis to be performed

Teams are crucial !

Up-front communication of Objectives essential !

Teams are crucial !

Up-front communication of Objectives essential !


GB117-20

ObjectiveObjective

Objective is usually stated in terms of the Input’s effects on the Outputs

Objective should tie to the Team Charter

Examples:

1 Study the effects of temperature and pressure on Percent Impurities

2 Study the stability and capability of the lead wire drawing process

3 The Objectives of the study are to:

Study the effects of furnace operating variables on fractures

Exercise: Each team write each objective on a flip chart



Page 11402-108, Rev. C


GB117-21

Identify Inputs and OutputsIdentify Inputs and Outputs

ProcessProcessProcess Key Process Outputs

Key Process Key Process

OutputsOutputs

Noise Inputs

(Discrete)

Noise InputsNoise Inputs

(Discrete)(Discrete)

Examples

• Different Operators

• Different Machines

• Different Shifts

Examples

• Different Operators

• Different Machines

• Different Shifts

Noise Inputs

(Continuous)

Noise InputsNoise Inputs

(Continuous)(Continuous)

Examples

• Room Temperature

• Barometric Pressure

• Relative Humidity

• Raw Material Characteristics

Examples

• Room Temperature

• Barometric Pressure

• Relative Humidity

• Raw Material Characteristics

Examples

• Temperature

• Pressure

• Time

Examples

• Temperature

• Pressure

• Time

Controlled Inputs

Controlled Controlled

InputsInputs

Tools

• Scatterplots

• Correlation

• Regression

Tools

• Scatterplots

• Correlation

• Regression

Tools

• Boxplots

• Main Effects and Interaction Plots

• ANOVAs, T-test

Tools

• Boxplots

• Main Effects and Interaction Plots

• ANOVAs, T-test

Tools

• C&E Matrix

• FMEA

• Fishbone

• Short-term Capability

Tools

• C&E Matrix

• FMEA

• Fishbone

• Short-term Capability


GB117-22

Controllable in real life

Controllable in real life

ControlFactors &

“HeldConstant”

Factors

Noise Factors that we can

afford to manipulate

for the experiment

only

Influential Noises that

are not controlled

Factors with no

impact on the

response

Factors that are controlled

in the experiment

Factors that are controlled

in the experiment

(Entire right circle)

Factors that are

influential on the

response

Factors that are

influential on the

response

(Entire left circle)

Remember, Noise is in the Eyes of the BeholderRemember, Noise is in the Eyes of the BeholderRemember, Noise is in the Eyes of the Beholder

Controlled vs. UncontrolledControlled vs. Uncontrolled



Page 12402-108, Rev. C


GB117-23

Measurement System CapabilityMeasurement System Capability

Perform Gage R&R on important Inputs and, especially, critical Outputs

Use standard Gage R&R procedures

Remember, a noisy gage distorts your view of the trueprocess variation

2

tmeasuremen

2

actual

2

observed


GB117-24

Sampling PlanSampling Plan

A good sampling plan will capture all relevant sources of noise variability

Lot-to-lot, batch-to-batch

Different shifts, operators, machines or processes

Sample Size rule of thumb: 30

Input Variables do not always have to be measured for each sample

Example:

Samples are drawn for an output variable measurement every hour

Ambient humidity (input variable) is assumed to be constant over a 4 hour period



Page 13402-108, Rev. C


GB117-25

Sampling DesignsSampling Designs

You can use one or more of the following sampling designs

Sampling designs help insure a representative sample of the process without having to collect extreme amounts of data

Nielson Rating are based on about 1500 viewers

Those viewers are chosen to best represent all viewers in the country

Sampling designs:

Simple Random Sample

Stratified Sampling

Cluster Sampling

Systematic Sampling

Subgroup Sampling


GB117-26

Sampling Plan WorksheetSampling Plan Worksheet

Key Outputs: Variable How Measured When Measured

1

2

3

Noise Variables: Variable How Measured When Measured

1

2

3

4

5

Controllable Inputs Variable How Measured When Measured

1

2

3

4

5

Overall Sampling Plan:



Page 14402-108, Rev. C


GB117-27

Discrete ExampleDiscrete Example


1. Surface Quality Pits Visual Sample

Dents Visual Sample


Section to Section Section id. Count

Sheet to Sheet Sheet number Count

Pack to Pack Pack number Count


Copper Exposure Exposure time Timer Each Pack

Infeed Optimization Infeed ratio Meter Each Pack

Equipment cleaning Cleanliness rating Visual Each Pack


Pits and Dents will be measured in each Section of Sheets A, B, C on Packs 1, 7, 13.

Controllable Inputs will be recorded for each Pack 1, 7, 13.


GB117-28

Fibers ExampleFibers Example


1. Uptime / Quality Breakouts Operator On Occurrance

Breaking Strength Lab Breakout Sample

Broken Filaments Lab/Visual Breakout Sample


Fiber Batch Batch number Recorded

Polymer Batch Batch number Recorded

Panel Panel number Recorded

Day Day Recorded

Shift Shift number Recorded


1

2

3

4

5


For each Breakout occurance, Breaking Strength and Broken filaments will be

measured.

Fiber and Polymer Batch will be recorded along with panel number, day, shift and time.



Page 15402-108, Rev. C


GB117-29

Data FormatsData Formats

Data quickly logged into a database (e.g. Minitab)

Data should include a column for each of:

ID Information (Time, Lot#, Shift, Site, etc.)

Uncontrolled (Noise) inputs

Process Inputs

Process outputs

Comments


GB117-30

Fiber Breakouts

Objective To identify the key variables affecting fiber breakouts

Metrology At each breakout occurrence:

1) Take sample of material for lab testing of breaking strength and fiber breakouts

2) Record all other (known) information as indicated below

Occurrence data Lab Test Results Cause (if known)

Me

as

ure

Data

Point Fiber Batch

Polymer

Batch Panel Day Shift Time

Time

between

failures

Breaking

strength

Broken

filaments

Dirty

Spinneret

Slow/fast

Holes

Oil on

L.E.D

Rough/Dirty

Roll

Surface

Threadpath

Alignment

Filament

Motion

Process

Variability

Fo

rma

t

control

number batch # batch # i.d.# MM/DD # HH:MM (calc.) lbs. % broken

(Description of

variable, if known)

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

X’s Y’s Comments

Looking For X’s That Cause Machine ShutdownLooking For X’s That Cause Machine Shutdown

Manufacturing Quality Data SheetManufacturing Quality Data Sheet



Page 16402-108, Rev. C


GB117-31

X’s

Looking For X’s That Cause Dirty/Unsafe TrailersAttributes or Variables Present Here?

Looking For X’s That Cause Dirty/Unsafe TrailersAttributes or Variables Present Here?

Transactional Quality Data SheetTransactional Quality Data Sheet

Date:

Branch:

Observer:

Process Inputs Perform Inspection

Inspection Other

Insp. Area Weather Interrupted Activity

C)ollection

D)evon

# trailers P)hone C)ustomer

D)irt to be C)ust V)endor

Unit Arr. P)aved P)uddle R)ain insp. V)end B)reakdown

Unit # Lease # Time G)ravel N)o Pdl. Insp. Name Temp. D)ry In & Out # M)ech C)omputer

Du

ratio

n (

min

.)

Dri

ver

part

ic.

inin

sp. (Y

/N)


GB117-32

ExerciseExercise

Objective: Establish a sampling plan for your project’s first Multi-Vari study

Procedure:

Identify key output variable(s) to be studied

Identify key input variables to be studied

Controlled

Uncontrolled (Noise)

• Continuous / Variable

• Discrete / Attribute

Identify measurement systems for each output and input variable; identify those which need to be evaluated

Specify your initial sampling plan using the worksheet provided (see next slide)



Page 17402-108, Rev. C


GB117-33

Sampling Plan WorksheetSampling Plan Worksheet

Purpose of the Experiment: ___________________________________________________

Output : _________________________ How will you measure it ? ___________________

Inputs:

Controlled How / When will you measure it ?1) ______________________ _______________________2) ______________________ _______________________3) ______________________ _______________________4) ______________________ _______________________

Uncontrolled How / When will you measure it ?1) ______________________ _______________________2) ______________________ _______________________3) ______________________ _______________________4) ______________________ _______________________


GB117-34

Rules for Effective Data CollectionRules for Effective Data Collection

Team must follow sampling plan consistently

Do a short Pilot Run to test your procedures

Note changes in operating conditions that are not part of the normal or initial operating conditions

Maintain monitors on gauges for key process inputs

Record any events that are out of the ordinary

Log data into database quickly

Keep a log book

Run your MV study until Y varies over full range!Run your MV study until Y varies over full range!



Page 18402-108, Rev. C


GB117-35

1) Passive observation provides too narrow a range of “X” behavior

2) Interactions are present, but we only investigate one “X” at a time

3) Multi-colinearity (Confounding) is present

.9 .95 1 1.05 1.1

X=Car Weight (tons)

Y=Gas Mileag

e(mpg)

30

25

20

0 .5 1 1.5 2X=Car Weight (tons)

Y=Gas Mileage(mpg)

30

20

10

You SeeYou SeeYou See

13 13.5 14 14.5 15

X=Age of Car (Yrs)

Y=Selling

Price(Thousa

nds)

6

4

2

1 6 14 22 30X=Age of Car (Yrs)

Y=Selling

Price(Thousa

nds)

35

25

5

You SeeYou SeeYou See

We See...X (Heat)Looks Unimportant

Reality...X (Heat) Is

Important and Influenced by Shift

7654321

37

32

27

22

Heat

Yie

ld

7654321

37

32

27

22

Heat

Yie

ld

SHIFT

12

MultiMulti--Vari PitfallsVari Pitfalls

Counter to 1:

Run your MV study until Y varies over full range!

Counter to 1:

Run your MV study until Y varies over full range!

Counter to 2 & 3:

Run DOE’s to verify Key X’s & interactions

Counter to 2 & 3:

Run DOE’s to verify Key X’s & interactions


GB117-36

SummarySummary

Overviewed Multi-Vari studies

Reviewed noise variables and analysis introduction

Planning Multi-Vari studies

Identified methods for data collection


SAMPLING

Page 1402-108, Rev. C


GB118-1

SamplingSampling




GB118-2

TopicsTopics

Overview Sampling

Introduce Sampling guidelines

Discuss Sampling techniques and sample sizes


SAMPLING

Page 2402-108, Rev. C


GB118-3

It is not always feasible or possible to analyze 100% of the population...

But, we can draw conclusions about the population through statistically relevant samples

Why Sample?Why Sample?

Population

Sample


GB118-4

A good sample is a miniature version of the population - just like it, only smaller

A good sample is a miniature version of the population - just like it, only smaller

What is a Sample?What is a Sample?

Collecting only a portion of the data that is available or could be available

Using the data from the sample to draw statistical inferences

A faster, less costly way to gain insight into a process or large population

Remember: Even if we collect 100% of the data points for our process or population during a certain period of time, it is still only a sample of the whole population


SAMPLING

Page 3402-108, Rev. C


GB118-5

When Does Sampling Work?When Does Sampling Work?

Each member of the population has an equal chance of being selected

Selecting one member doesn’t influence likelihood of another member being selected or not

There aren’t any significant differences between those selected and those that weren’t

You have a large enough sample to find what you’re looking for. If it’s a rare event or you want to be very precise, you’ll need a large sample.


GB118-6

Process vs. Population Determines Sampling Approach, Sample Size Calculations, and Other Considerations

Process or Population?Process or Population?

A Process . .A Process . . ..

Back guard assembly

Porcelain spraying

Quoting activity

Inventory counts maintenance

A Population . . .A Population . . .

All the back guards produced

Product made using one supplier lot

Deals that exceed margin targets

Items with discrepancies

What is a Process and what is a PopulationWhat is a Process and what is a Population


SAMPLING

Page 4402-108, Rev. C


GB118-7

Sampling: How, What, WhenSampling: How, What, When

Purpose Considerations Sample Size Approach

Process Take action or predict future

- In Control?

- Capable?

- Improve?

Where to sample

Frequency

Grouping

Representative

Cost

Use Sample Size by Tool or Statistic guidelines

Subgroup Sampling

Systematic Sampling

Large Population

Describe or Quantify Characteristics of the Population

Precision (+/- )

Amount of characterisitic’s variation: or P

- Confidence level

- Representative

- Cost

Use Minitab procedures

Random Sampling

Stratified Random Sampling

Systematic Sampling

Cluster Sampling

Sampling Reference MatrixSampling Reference Matrix


GB118-8

Simple Random Sample (SRS)Simple Random Sample (SRS)

If all possible samples of n experimental units are equally likely to be selected, then the procedure is a simple random sample

Example: We generate 15 random numbers from 1 to the number of items produced in a shift for the enamel baking process

These items identified are then evaluated with respect to the output measures

Characteristics of Simple Random Sample

Unbiased: Every experimental unit has the same chance of being chosen

Independence: The selection of an experimental unit is not dependent on the selection of another

In continuous processes, using a SRS is very difficult because there is no clear experimental unit. Generally we sample from astream of material.


SAMPLING

Page 5402-108, Rev. C


GB118-9

Stratified SampleStratified Sample

Divide the population into homogeneous groups and randomly sample from within each group

Example: Stamping Batches

There are a large number of batches (batched by steel lot) going through the stamping press with each press batch having many items created

Randomly sample 2 items from each batch

This sample will effectively represent each batch’s effect on the variability of the output variables

Another example

If you have 2 paint lines, randomly sample product from each line

This sample allows you to represent the effect of each line on the variability of the output


GB118-10

Cluster SampleCluster Sample

Divides the sample into smaller homogeneous groups, then the groups are randomly sampled

Example: Paint Lines

Assume there are a number of positions for product within each paint line

Number each paint line and randomly sample a subset of the paint lines

Then randomly sample positions within the selected paint lines (within the subset)

This represents line effects without having to sample from all lines


SAMPLING

Page 6402-108, Rev. C


GB118-11

Systematic SampleSystematic Sample

Start with a randomly chosen unit and sample every kth

unit thereafter

Example:

For a assembly shift, there are N number of product produced

Pick a number at random to start and then select every 15th unit to evaluate

This method is good because its simple


GB118-12

Subgroup SampleSubgroup Sample

Sample output of step or activity with some frequency - usually a time increment

Remember the Sources of Variation -

Will a subgroup show the variation you’re interested in?

Frequency - predetermined, corresponds to control chart sampling

Example:

Pull 5 ice machines at 10 a.m., 12 p.m., 2 p.m., and 4 p.m.


SAMPLING

Page 7402-108, Rev. C


GB118-13

To Sample or Not to Sample?To Sample or Not to Sample?

For some populations, sampling may not be the best approach

Example: If you have only 25 sales over $10 million per year, would you sample?

You would probably look at ALL those sales (or units)

This is NOT sampling

It’s known as a census and has its own set of rules


GB118-14

When In Doubt, Ask For Help!When In Doubt, Ask For Help!

Standard Sampling StrategiesStandard Sampling Strategies

Consider what main factors or Sources of Variation are on your suspect list

Will your strategy allow you to see what you want to see?

The more complex the strategy, the harder it is to plan, execute, and control

Only use the degree of sophistication you really need. Don’t use a complex sampling scheme if you don’t need one.

Sampling can be a complicated question

Review your approach with a mentor or Master Black Belt before you start data collection

Which Strategy Is Right For My Project?Which Strategy Is Right For My Project?


SAMPLING

Page 8402-108, Rev. C


GB118-15

Considerations To Keep In MindConsiderations To Keep In Mind

Sampling from ProcessesSampling from Processes

Where you Sample

Location in the process where your process variable affects the output variable

Sample as far upstream as practically and logically possible

Frequency of Sampling

Often enough to catch it going from good to bad

It is better to have several small samples over time than one large sample at single point in time

Sample more frequently if process stability is questionable

Subgrouping:

Minimize opportunity for special cause within subgroup

Want special causes to change between subgroups

Keep track of what, where, when, who, etc.

For subgroups or Sources of Variation, you’ll need at least 5 data points for each category of the variable


GB118-16

Tool or Statistic Minimum Sample Size

Average 5 - 10

Standard Deviation 25 - 30

Proportion Defective (P) 100 and nP >= 5

Histogram or Pareto 50

Scatter Diagram 25

Control Chart 20

Remember these are MINIMUMS for sampling from Processes. More data points means higher confidence in conclusions you can draw from your data

Sample Size By Tool or StatisticSample Size By Tool or Statistic


SAMPLING

Page 9402-108, Rev. C


GB118-17

Common Issues With Sample QualityCommon Issues With Sample Quality

Selection Bias

No structure to determine which items you sample

Convenience sampling; Systematic sampling matches some structure

Changes in the Environment

Environmental changes make the sample no longer representative

Non-Response Bias

Especially in surveys, characteristics of those who don’t respond are different in a significant way from those who do (vocal minority)

Measurement Bias

Often related to unequal batch sizes - tendency to select larger batches to sample from

Non-representative batches . . . long overdue bills without samplingquickly paid accounts

Sampling Plan Executed Improperly

Try to be aware of biases and other quality problems that can creep in and affect your data!


GB118-18

Sampling from PopulationsSampling from Populations


SAMPLING

Page 10402-108, Rev. C


GB118-19

Team ExerciseTeam Exercise

Divide into teams

Evaluate the sampling examples on the next page

Identify strengths and weaknesses for each example

Think of at least one suggestion to improve the approach for each example

You have 15 minutes


GB118-20

Sampling ExamplesSampling Examples

1. A team is studying “First Call Resolution”. Approximately 15% of callers are sampled to complete a customer survey. Only 10% of those sampled were willing to complete the survey.

2. A team is working on SAP information input accuracy. They have decided to collect a sample of transactions processed from 4:00 -5:00 p.m. every day for the next four days.

3. A team studying cycle time for product changeovers is going to sample data from the one production line that has data readily available.

4. A team is looking at packaging defects. They plan to sample by pulling every 200th unit processed over the next 30 days.

5. A team is working on material shortages. They’ve developed a subgroup sampling approach for all items in the database.


SAMPLING

Page 11402-108, Rev. C


GB118-21

SummarySummary

Provided an overview to sampling

Introduced some sampling guidelines

Discussed sampling techniques and sample sizes

INTERPERSONAL MANAGING SKILLS


Page 1402-108, Rev. C

GB119-1

Rev C February 2004©Tyco

Interpersonal Managing Skills

& Dealing with Resistance

D

M

A

I

C


Green BeltsGreen Belts

GB119-2

Rev C January, 2003©Tyco

Team Reference Guide, Page 17Team Reference Guide, Page 17

When:• You’ll make a decision or take action based on the

information, opinion, or suggestion offered, or• Your immediate impulse is to reject, ignore, or disagree

with what you are hearing.

How:

Clarify by seeking additional information about:

What has been said, and/or

Why

Confirm by stating your understanding of:

What has been said, and/or

Why

Clarifying and ConfirmingClarifying and Confirming



Page 2402-108, Rev. C

GB119-3



When:• You want someone to change his or her performance or

suggestion, and • You have confirmed your understanding.

How:• Give balanced feedback:

• Specify the merits you want to see retained• Specify the concerns you want to see eliminated

• Explore ideas for retaining merits and eliminating concerns• Invite suggestions• Make suggestions

Constructive CriticismConstructive Criticism

GB119-4


Team Reference Guide, Page 18


When• You think a difference exists

How:• Define the difference

• State what’s important to you, and why• Clarify / Confirm what’s important to the other

person, and why

Managing DifferencesManaging Differences



Page 3402-108, Rev. C

GB119-5



WhenYou are willing and able

to consider alternatives

HowDiscuss the difference:

Explore ideas to find alternatives

WhenYou are unwilling or unable

to consider alternatives, orYou’re unable to reach a

mutually acceptable decision

How:Terminate the discussion:

Acknowledge the other person’s right to differ

Explain what you’ve decided and why

No longer a difference

Managing Differences ( Cont. )Managing Differences ( Cont. )

GB119-6


WhenThe work of someone whose performance matters

to you: • Exceeds expectations • Consistently meets expectations • Meets expectations not usually met by that person.

How• Give specific examples of performance• Mention personal qualities that contributed to performance• Mention resulting benefits to you, the department, and/or the organization.


CreditingCrediting



Page 4402-108, Rev. C

GB119-7


Write Your Own Role Play…a “real” Write Your Own Role Play…a “real”

situation you may be insituation you may be in

Describe the situation and enough information so your partner can play your role and practice the skills.

You will play the other person.

The third person in the triad will be the coach for the skill practice. Create

Triads

CreateTriads

GB119-8


VISIONDRIVERS

LEADERSHIPPARTICIPATION

COMMUNICATIONTRAINING AND EDUCATION

REINFORCEMENT

WestinghouseResearch:

Change video


Managing Change: Managing Change:

Dealing With Resistance to ChangeDealing With Resistance to Change



Page 5402-108, Rev. C

GB119-9


• Wants more detail• Flood with detail• Time• Attack• Impracticality• I’m not surprised• Confusion• Silence• Intellectualizing• Moralizing• Compliance• Methodology

Team Reference Guide, Page 42-43

Team Reference Guide, Page 42-43

Faces of Resistance Faces of Resistance

GB119-10




Write Your Own Role Play… a “resistance” Write Your Own Role Play… a “resistance”

situation you may be insituation you may be in

COST SAVINGS GUIDELINES


Page 1402-108, Rev. C

GB121(Appendix)-1

Tyco InternationalTyco International

Cost Savings GuidelinesCost Savings Guidelines


For Operations Green BeltsFor Operations Green Belts

GB121-2

Calculating Six Sigma Project

Financial Impact



Page 2402-108, Rev. C

GB121-3

Why is the Financial Impact Important ?Why is the Financial Impact Important ?

Whether we are trying to improve a transactional process, the quality of a product or plant’s yields or efficiency, there is always a financial impact.

Measurement is important for Tyco, as a Company to determine how we are continually improving. (Note that we have to measure consistently as “One Company.”)

Measurement is critical for tracking individual performance and division performance as well.

We must track and measure each quarter and report the financial impact as a company.

- A tracking mechanism is being established using a PowerSteering tool. Training on this tool may be separate from this training.

GB121-4

Where does Financial Impact trackingWhere does Financial Impact tracking

Fit in to a Project ?Fit in to a Project ?Process Map (See Next Page)

Defines and schedules expected involvement from project leaders and finance community

Defines project timeline and maps it to DMAIC structure

Critical points include:

Initial Measurement of the Project Estimate

Updating estimates during steady-state or at end of Control State (Committed)

At end of each month, implemented savings need to be updated

At end of Each Quarter, Finance approves implemented savings

The Period of Measurement should be 12 months. The actual start date of the measurement should be defined by the Project Leader and validated by Finance



Page 3402-108, Rev. C

GB121-5

1. 2. 3. 4. 5.

Define Measure Analyze Improve Control

BB enters project into tracking tool, project costs are estimated and entered as they occur

Projected Savings are estimated (Finance consulted and briefed on project costs and benefits)

Enter steady-state of savings during or at end of Control and estimate Committed Savings

At the end of each month, BB enters Implemented Savings based on actual results.

At the end of each quarter, Finance approvesImplemented Savings and enables Roll-up!!

BBEngaged

Project LifecycleProject Lifecycle

Projected Savings – Net savings estimated to be achieved over 12 month period

Committed Savings – Net savings identified as a result of a signed contract, pricing agreement, or proposed Budget Reduction

Implemented Savings – Net Savings realized through buying off contracts, release of POs, or reductions in budget

GB121-6

Keys Points

Six Sigma Financial Impact SummaryAs part of measuring the financial impact of Six Sigma projects, Tyco International and its subsidiaries have adopted common categorization and measurement standards to determine savings related to each project.

The financial impact of every project is versus the prior year or current year established baseline. However, all financial impacts need to be reviewed and approved by the financial individual responsible for the department and/or division Operating Plan (Budget).

All project specific costs must be netted against the project savings (cost reductions or margin associated with revenue increase). General costs (e.g. Six Sigma resources and training, external consultants, license fees, etc…) will be netted against the aggregate net savings for reporting purposes.



Page 4402-108, Rev. C

GB121-7

Six Sigma Financial Impact Classification Matrix

Prior Year or Current Year Baseline Impact

Hard Soft

Category (Impact or change vs. (No Impact or change

Baseline) vs. Baseline)

Revenue Growth * Increase Revenue Avoid Loss of Revenue

Cost Productivity Reduce Costs Avoid Additional Cost

Working Capital/

Cash Flow Increase Cash (Sustainable) Avoid Additional Investment

* The Hard Budget impact of revenue growth will be the margin associated with

increase in revenue

GB121-8

Detailed Project Classification Criteria

Revenue Growth *

* The Hard Budget impact of revenue growth will be the margin associated with the increase in revenue

Category Area Discussion Comments How to Measure

Revenue

Growth -

Hard *

Increased Mfg. Flexibility * -Translates into lead time

reduction which can be tied

to revenue growth and fall

through.

-“Hard” savings is revenue

growth due to additive business

without additional capacity.

-Look for revenue growth due

to ability to meet customer

requested dates.

-Delivery performance

improvement does not equate

to increasing inventory.

Revenue

Growth -

Soft*

Customer Returns / Customer

Complaints

-Project the potential loss of

business.

-Savings associated with lost

business due to poor quality

Revenue

Growth -

Hard *

Revenue

Growth -

Hard *

Delivery Performance

Administrative Errors -Reduction in customer

returns and complaints.

-Increases revenue due to

elimination of errors.

-“Hard” savings is revenue

growth due to additive business

without additional capacity.

NOTE: The information above are only guidelines and should serve as a starting point only



Page 5402-108, Rev. C

GB121-9


Cost Productivity - HardCategory Area Discussion Comments How to Measure

-Purchased price reduction is

“hard” savings.

-Reduction in receiving

inspection is a “hard” savings –

only if the person / position is

eliminated.

-Monitor the people

component.

-Redeployment of people may

result in “soft” savings.

-Report the equivalent

people’s work that we don’t

need to pay.

-Reduction in overtime results in

“hard” savings.

-Reduction in use of agency

results in “hard” savings.

-Possible savings from: -“Hard” savings if less raw

material is purchased.

-Not needing to buy a certain

amount of raw material.

-Calculate the savings using the

standard manufacturing costs.

-Calculate the direct costs

associated with the

replenishment process

-Must be cautious of the use

of variable vs. fixed costs.

Productivity Performance

(Direct costs)

Cost of Poor Quality

Supplier Cost, Delivery,

Service, Quality

-May result in a reduction in

WIP inventory.

Cost

Productivity

- Hard

Cost

Productivity

- Hard

Cost

Productivity

- Hard


GB121-10


Cost Productivity - Soft

Category Area Discussion Comments How to Measure-To generate savings, the

floor space that is “freed up”

must be reallocated for

production.

-If the production space is

reallocated, the savings will be

“hard”.

-Utilize purchased cost

avoidance.

-The cost savings will be “soft”

until the production space is

utilized.

-Cost per square foot is

considered overhead.

-Creates the ability to run

more product, but the savings

is “soft” until there is

demand.

-Determine the increase in

billings / revenue or reduction in

backlog.

-Provides opportunities for

insourcing.

-Reduction in overtime.

-There is no savings when

you create downtime more

quickly.

-Reduction in variance over time.

-Opportunity to reduce the

equipment base.

-A cost must be eliminated.

“Hard” savings if there are

purchasing savings or if the

useful life is extended resulting

in reduced depreciation

“Soft” savings if internal

capacity is freed up – becomes a

“hard” savings if there is a

resource reduction or additive

work.

Equipment Utilization *

Cost

Productivity

-Soft

Floor Space

Tooling -Initiatives to improve /

increase the life of the tooling.

Cost

Productivity

- Soft

Cost

Productivity

- Soft




Page 6402-108, Rev. C

GB121-11


Working Capital / Cash Flow


-Projects will need to be able

to determine the WIP.

-Hard savings are the result of a

decrease in Work-in-Process

Inventory

-This could have a negative

impact on EBIT – Finance to

stay close to the project to

ensure the correct targets are

established.

- The carrying cost of inventory

should not be included as this is

a financing activity and it does

not impact EBIT or Free Cash

Flow.

-Inventory reduction frees up

cash.

-Essentially the same

discussion as WIP inventory.

-Soft savings could include

savings due to reduction in

obsolete inventory.

-Generally we have data at the

product line level.

-Savings can be determined from

the accrual process or from

actual write off data.

Working

Capital /

Cash Flow -

Hard

Reduction in Receivables or

Increase in Payables terms

- Frees up cash - Hard savings are recognizable

when benefits can be sustained

over time

Finished Goods and

Component Inventory

Work-in-Process InventoryWorking

Capital /

Cash Flow -

Hard

Working

Capital /

Cash Flow -

Hard / Soft


GB121-12

Six Sigma Project

Financial Impact

Examples



Page 7402-108, Rev. C

GB121-13

Financial Impact Examples:

Revenue Growth

Cost Productivity

Working Capital (Cash)

GB121-14

Example 1: Growth Project

A production step in a one of Tyco’s plants is currently running at full capacity (24 hours x 7 days per week) and limiting the total potential sales because the demand for our product is greater than what we can produce. The next capacity limitation for our plant is over 1300 pounds per hour. There are more customers that want to buy this product but our plant is unable to meet their demand due to the capacity constraint. The prior year financials were:

Sales 12.0 MM$

Factory Cost 6.0 MM$ (2.0 Fixed and 4.0 Variable)

O-heads & Chgs 3.0 MM$ (1.5 Fixed and 1.5 Variable)

Operating Income 3.0 MM$

The project Y for the Six Sigma project is to increase the output of this limiting production step from 1200 pounds per hour to 1300 pounds per hour. If this is achieved, the revenue is expected to increase from 12.0 MM$ to 13.0 MM$ / year. In addition, the variable factory cost per unit is expected to then drop from 33.33% of sales to 32.00% of sales.



Page 8402-108, Rev. C

GB121-15

Example 1: Growth Project

The financial benefit of this project is two-fold:

First, the variable manufacturing cost is cut from 33.33% to sales to 32.00% of sales. At last years sales volume, this will result in an operating income impact of $159,600 (12.0 MM$ x (.3333 - .3200)).

Second, since the sales were constrained by the plant capacity, there is a financial benefit of the added sales volume that occurred as the capacity increased. In this example, the sales increased by 1 MM$. The operating income/EBIT impact of this added sales is $555,000 (1.0 MM$ x (1 - .32 - .125)). The .32 is the variable factory cost % and the .125 is the variable other.

The total project benefits were:

$159,600 from reduced variable manufacturing cost

+ $555,000 from income from added sales

$714,600 Total Operating Income impact

If the plant were not operating at full capacity, the project savings would only have been the $159,600 from the reduced variable manufacturing cost.

GB121-16

Example 2: Cost / Productivity ProjectThe final manufacturing step for a product has had poor yields. For the last year, the yield has

been 80%. The Production Reporting information for the last 4 quarters is as follows:

Prior production information

Q1 Q2 Q3 Q4

Output (MM yds) 1.000 1.000 1.000 1.000

Labor (hrs) 1000 1000 1000 1000

Input (MM yds) 1.250 1.250 1.250 1.250

The Input material has a fully burdened cost of $2.00 / sq yd. The variable cost is $1.50 / sq yd. The fully burdened labor cost is $60 / hour. The variable labor cost is $35 / hour.

After the Six Sigma project is complete, the yield improved to 90% and the production reporting information for the next 4 quarters is as follows:

New production information

Q1 Q2 Q3 Q4

Output (MM yds) 1.000 1.000 1.000 1.000

Labor (hrs) 900 900 900 900

Input (MM yds) 1.111 1.111 1.111 1.111

The Operating Income impact is $208,500 / quarter.

(1,250,000 – 1,111,000) x $1.50 per sq yard + (100 x $35) = $212,000

The savings is based on the reduced input material required multiplied by the variable cost of the input material and the variable labor savings (Note that the fully burdened amount is not used since benefit costs for labor are not reduced due to the project).



Page 9402-108, Rev. C

GB121-17

Example 3: Cash ProjectA business currently has sales of 45 MM$ per quarter and a DSO (Days Sales Outstanding of 40 days. Several of these days are the result of collection delays due to discrepancies. The Six Sigma Project is

to try to reduce the days of DSO due to discrepancies from 5 days to 1 day. If everything else stays equal, this will mean that the DSO will drop by 4 days.

Sales & A/R in MM$.

Last 4 quarters 4 Quarters after Project

Q1 Q2 Q3 Q4 | Q1 Q2 Q3 Q4

Sales 45 45 45 45 | 47.25 47.25 47.25 47.25

A/R 20 20 20 20 | 18.9 18.9 18.9 18.9

DSO 40 40 40 40 | 36 36 36 36

If DSO had stayed at 40, the AR would have been: 21.0 21.0 21.0 21.0

The Cash Benefit from this project is 2.1 MM$ (21.0 – 18.9). We would record the cash flow benefit in the first quarter of the project as 2.1 MM$. If the DSO stays at 36 days for the remaining 3 quarters and the sales remain at 47.25 MM$ / quarter the benefit in Q2, Q3, and Q4 would be 0. If either the sales grow

or the DSO drops further, then there would be additional benefit recorded in Q2, Q3, or Q4.

Some notes on this example:

- It is recommended to use the average DSO figures from the prior 4 quarters in establishing the baseline for comparison. If that will distort the real benefit, another method is to use the DSO figure from the prior quarter (or 3 months). It is not considered appropriate to use a projected DSO number as the baseline for comparison.

- Sometimes a DSO project will result in operating income impacts as well. For example, if by reducing discrepancies the business were able to reduce the employee count handling discrepancies from 3 people to 1 person, a cost savings equivalent to two people should be included as an operating income benefit.

GB121-18

There is a Monthly and Quarterly roll up of savings for your entire segment that is reported for all of Tyco. Therefore, youshould update estimates (costs and savings) regularly.

The Appendix shows the timeline of this reporting.

The examples used in this module are very basic. You will be involved in projects that have a much greater complexity. Some more scenarios are enclosed in the appendix.

Involve Finance from the beginning. You must be aligned with them in measuring costs and savings.

Tracking and Roll Up of Savings

As noted before, the tracking tool to be used for the entire company for measurement will be part of a separate training, however, some important things to be aware of are as follows:



Page 10402-108, Rev. C

GB121(Appendix)-19

APPENDIXAPPENDIX

(Refer to Excel file)(Refer to Excel file)

GB121-20

All MBBs/BBs/GBs enter their Actual

impacts for the completed period.

Financial Leaders review Actual numbers for each

project and set Ready for Rollup to ‘Yes’.

Financial System Manager resets all

audit flags (Ready for Rollup) to No for Financial Benefit

metric.

MBB/BB/GB

Financial Leaders

Executives

Financial Manager

Audited Reporting Period

Executives and Financial Leaders can

run Enterprise Financial Benefit rollup reports for audited and un-audited projects

Casual Reporting Period

Financial Benefit reports can be run,

but numbers reported will be un-audited or

may be rough estimates.

5th 10th 11th 15th 15th – 5th

Monthly / Quarterly Update CycleMonthly / Quarterly Update Cycle(To be covered in specific Tracking training separately)(To be covered in specific Tracking training separately)

Quarterly

Quarterly

Quarterly

COST SAVINGS GUIDELINES APPENDIX

tyco

SUMMARY

1 As part of measuring the financial impact of Six Sigma projects, Tyco International and its

subsidiaries has adopted common categorization and measurement standards to determine

savings related to each project.

2 The financial impact of every project is versus the prior year or current year established baseline.

Also, the financial impact needs to be reviewed and approved by the financial individual responsible

for the department and/or division Operating Plan (Budget).

3 All project specific costs must be netted against the increased revenue or reduction of costs for each project.

General costs (e.g. Six Sigma resources and training, external consultants, license fees, etc…) will be

netted against the aggregate net savings for reporting purposes.

Prior Year or Current Year Baseline Impact

Hard Soft

Category (Impact or change vs. (No Impact or change

Baseline) vs. Baseline)

Revenue Growth * Increase Revenue Avoid Loss of Revenue

Cost Productivity Reduce Costs Avoid Additional Cost

Working Capital/ Cash

FlowIncrease Cash (Sustainable) Avoid Additional Investment

* The Hard Budget impact of revenue growth will be the margin associated with the

increase in revenue

Six Sigma Financial Impact Categories

Six Sigma Training for Green Belts - Week 1Tyco, Six Sigma Operational Excellence 402-108, Rev. C


tyco

Six Sigma Financial Impact Examples


RevenueGrowth - Hard

Increased Mfg. Flexibility * -Translates into lead time reduction which can be tied to revenue growth and fall through.

-“Hard” savings is revenue growth due to additive business without additional capacity.

-Look for revenue growth due to ability to meet customer requested dates.-Delivery performance improvement does not equate to increasing inventory.

RevenueGrowth - Soft

Customer Returns / Customer Complaints

-Project the potential loss of business.

-Savings associated with lost business due to poor quality

-Purchased price reduction is “hard” savings.-Reduction in receiving inspection is a “hard” savings – only if the person / position is eliminated.

-Monitor the people component. -Redeployment of people may result in “soft” savings.

-Report the equivalent people’s work that we don’t need to pay.

-Reduction in overtime results in “hard” savings.-Reduction in use of agency results in “hard” savings.-Elimination of outside trucking costs.

-Reduction in premium freight is a “hard” savings.-Elimination of direct labor, e.g. material handler.

-Possible savings from: -“Hard” savings if less raw material is purchased.

-Not needing to buy a certain amount of raw material.

-Calculate the savings using the standard manufacturing costs.

-Calculate the direct costs associated with the replenishment process

- There could be a Revenue impact if the scrap is being sold.

-Must be cautious of the use of variable vs. fixed costs.-To generate savings, the floor space that is “freed up” must be reallocated for production.

-If the production space is reallocated, the savings will be “hard”.

-Utilize purchased cost avoidance.

-The cost savings will be “soft” until the production space is utilized.

-Cost per square foot is considered overhead.

-Creates the ability to run more product, but the savings is “soft” until there is demand.

-Determine the increase in billings / revenue or reduction in backlog.

-Provides opportunities for insourcing.

-Reduction in overtime.

-There is no savings when you create downtime more quickly.

-Reduction in variance over time.

-Opportunity to reduce the equipment base.-A cost must be eliminated.

Equipment Utilization *

Floor Space

Administrative Errors -Reduction in customer returns and complaints.

-Increases revenue due to elimination of errors.

Productivity Performance (Direct costs)

-“Hard” savings is revenue growth due to additive business without additional capacity.

Transportation, parts movement

Cost of Poor Quality

Delivery Performance

Supplier Cost, Delivery, Service, Quality

-May result in a reduction in WIP inventory.


CostProductivity - Hard





CostProductivity - Soft




tyco

Six Sigma Financial Impact Examples


-Savings associated with decreased rework, inspection, production orders to replenish inventory.

“Soft” savings if internal capacity is freed up – becomes a “hard” savings if there is a resource reduction or additive work.

“Hard” savings if there are purchasing savings or if the useful life is extended resulting in reduced depreciation“Soft” savings if internal capacity is freed up – becomes a “hard” savings if there is a resource reduction or additive work.

-CAUTION: this is an area that frequently results in “double dipping”.-Reduction in overhead costs usually won’t occur during the short life of a Six Sigma project.-Projects will need to be able to determine the WIP.

-Hard savings are the result of a decrease in Work-in-Process Inventory

-This could have a negative impact on EBIT – Finance to stay close to the project to ensure the correct targets are established.

- The carrying cost of inventory should not be included as this is a financing activity and it does not impact EBIT or Free Cash Flow.

-Inventory reduction frees up cash.-Essentially the same discussion as WIP inventory.

-Soft savings could include savings due to reduction in obsolete inventory.

-Generally we have data at the product line level.

-Savings can be determined from the accrual process or from actual write off data.

WorkingCapital / Cash Flow - Hard

Reduction in Receivables or Increase in Payables terms

- Frees up cash - Hard savings are recognizable when benefits can be sustained over time

-Some inventory write off is tied to the business environment.

-Avoiding the write off provides “hard” savings by making a contribution to cash flow.

-Can be characterized as “per unit of demand”.

-Reduction in provision balance at the PL or PC level.

Finished Goods and Component Inventory


Customer Returns / Customer Complaints

Overhead Costs

CostProductivity -Soft

Work-in-Process Inventory

-Costs associated with processing CRM.

Inventory Write-off

Indirect Manufacturing Costs -All these savings are “soft”.

Tooling -Initiatives to improve / increase the life of the tooling.

WorkingCapital / Cash Flow



WorkingCapital / Cash Flow - Hard

WorkingCapital / Cash Flow - Hard / Soft


SUMMARY


Page 1402-108, Rev. C


GB121-1

SummaryWeek 1




GB121-2

PurposePurpose

You have been exposed to a number of tools and techniques this week

You now must plan to put those tools to use on your project

This module will provide some insight as to what the expectations are for your project for next week’s presentation

Take the next few hours to develop your action plan for your project efforts over the next few weeks

SUMMARY


Page 2402-108, Rev. C


GB121-3

Getting Ready for “Week” 2 TrainingGetting Ready for “Week” 2 Training

A project team is in place and functioning with the Project Leader effectively facilitating

The team understands the business impact of the project

Definition of the business and project metrics are complete and documented in the Charter

A detailed level 1 process map is developed and reflects key customer requirements and all process variables

The initial Value Stream Map is developed


GB121-4

Getting Ready for “Week” 2 TrainingGetting Ready for “Week” 2 Training

The relationship between process inputs and customer requirements has been investigated to provide direction for variable investigation (C&E Matrix)

Risk assessment and reduction efforts are documented and implemented as a result of the FMEA

The measurement system is in place and the measurement system capability studies are completed for both variables and attributes

Initial Capability Studies have been completed

SUMMARY


Page 3402-108, Rev. C


GB121-5

MeasureMeasure

AnalyzeAnalyze

ImproveImprove

ControlControl

ProjectDescription

ProcessMap

C & EMatrix

PreliminaryFMEA

MSA 1st Capability Study & SPC

Multi-Vari

DOE (or other improvement activities)

ControlPlan

Hand OffTraining

Final Capability

Study

OwnerSign-Off

Final ProjectReport

These are typically doneBefore the second class.

These are typically doneBefore the second class.

Project TrackingProject Tracking


GB121-6

Level 0 Process Map / Initial Value Stream MapLevel 0 Process Map / Initial Value Stream Map

Importance to Improvement Process

ALL processes need to be mapped

Identifies ALL potential Key Process Input Variables (Input Variables) affecting the process

If not done correctly and in detail, further actions may not be effective(don’t miss the noise !)

All other tools in the DMAIC process rely on a well-defined, well-documented process map

The results of this effort will be the input for Input Variable selection later on

IT MUST BE DONE THOROUGHLY

SUMMARY


Page 4402-108, Rev. C


GB121-7


Application Methods

A cross functional team is utilized to document the process steps and Input Variables through a series of focused meetings facilitated by the Project Leader

All pertinent process experts need to be involved

Outputs

A documented process

A complete list of Input Variables by step

Visual representation of the process

Each Input Variable classified as Controlled or Uncontrolled

Initial Control Plans for Output Variables


GB121-8


Questions to Ask

Who was on your team?

How were the Customer Requirements identified?

What does the team feel are the most important Uncontrolled Input Variables in the process?

Does this map reflect the current process or the one we want?

Where are the rework loops identified?

What are the non-value added processes?

Where are the measurement points in the process?

What does your Control Plan include at this time?

How many Input Variables did you identify per step (on average)?

What is the team going to do next?

SUMMARY


Page 5402-108, Rev. C


GB121-9

C&E MatrixC&E Matrix

Importance to Process

Aids in the prioritization of process steps and Input Variables to evaluate further

Provides a method to reduce the efforts in FMEA by only addressing Input Variables correlated highly to Customer Requirements

Application Methods

The Project Leader will develop the matrix and use the Team members to determine the correlation scores

In processes with many steps (over 10), process steps are first prioritized using a C&E Matrix

Then, all Input Variables from the key process steps are prioritized for further action

Marketing, Sales, QA/QC, and others should be utilized to determine customer ranking scores for Customer Requirements


GB121-10


Outputs

A prioritized list of Input Variables to investigate further using FMEA, Multi-vari, etc.

Updated Control Plan focused on variables related to Customer Requirements

Prioritization of potential Measurement Systems to be analyzed

SUMMARY


Page 6402-108, Rev. C


GB121-11


Questions to Ask


Who determined the Customer Requirements weighting?

Did you use the C&E Matrix to prioritize Process Steps to address first?

How did the team determine the correlation values?

Does the prioritization make sense?

How many Uncontrolled Variables rated highly in the sorted matrix?

How did the team determine the cut-off for variables to investigate further?

Did your team include internal customer requirements?

What are your next steps?


GB121-12

C&E Matrix C&E Matrix -- ExampleExampleRating of

Importance to

Customer

5 10 7 8 10 8 5

1 2 3 1 2 3 4

On

-tim

e

De

live

ry

Qu

alit

y to

me

et

Exp

ecta

tion

s

Ca

pa

city

Total Up

tim

e

Sa

fety

1st P

ass

Effic

ien

cy

Sp

ee

d

Total


14 Pre-Heat Plan Limits 9 9 9 198 9 3 9 9 219

16 Pre-Heat

Roll Cleaning

(frequency and

procedure)

3 9 3 126 3 9 9 1 191

7 Pre-HeatRoll Surface

Temperature3 9 3 126 9 3 9 3 189

9 Pre-Heat Valve Positions 3 9 3 126 9 3 9 3 189

17 Pre-HeatAir Pressure to

Control Valves3 9 3 126 9 3 9 3 189

19 Pre-HeatSteam Pressure

from Boiler3 9 3 126 9 3 9 3 189

2 Pre-HeatSheet Gauge

(Variation)3 9 3 126 9 1 9 3 169

8 Pre-HeatRoll Surface

Quality9 9 3 156 3 3 9 1 131

5 Pre-Heat Sheet Dryness 3 9 1 112 3 3 9 1 131

26 Pre-Heat

Airborne

Contaminant

Level

1 9 1 102 3 3 9 1 131

10 Pre-Heat Roll Speeds 3 9 3 126 3 1 9 3 121

28 Pre-Heat Resin Properties 3 9 3 126 1 0 9 3 95

6 Pre-HeatInlet Sheet

Temperature3 3 3 66 3 0 3 9 93

11 Pre-HeatDancer Load Cell

Calibration3 1 1 32 3 3 1 3 77

21 Pre-HeatBearing

Lubrication1 1 1 22 3 3 1 1 67

SUMMARY


Page 7402-108, Rev. C


GB121-13

FMEAFMEA


Identifies potential risks to the process performance as relatedto the Customer Requirements

Identifies prioritized actions to reduce risk

Provides a history of actions taken to improve the process

Provides a basis for a Troubleshooting Guide

Application Methods

The Project Leader will develop the initial FMEA inputs and use the Team members to complete the inputs and rate Severity, Occurrence, and Detection values

Each prioritized Input Variable from the C&E Matrix should be evaluated with the FMEA

Members of the Team determine top ranked Input Variables (highest RPNs) and assign actions to reduce the risk


GB121-14

FMEAFMEA

Outputs

A prioritized list of actions to take to reduce risk

Actions identify Who, What, and When

Re-evaluated impact of actions taken (new RPN value)

Identification of variables to be investigated further using:

Measurement Systems Analysis

Multi-vari studies

DOE methods

Control methods (Poka-yoke, SOPs, etc.)

Updated Control Plan

Initial Troubleshooting Guide / Reaction Plan

SUMMARY


Page 8402-108, Rev. C


GB121-15

FMEAFMEA

Questions to Ask


How were the Severity, Occurrence and Detection values determined by the team?

What was the source of the rating scales for Severity, Occurrence and Detection?

What are the top Input Variables?

What actions are being taken and by whom?

What barriers exist to completion of the top ranked items?

Are there any ‘quick fixes’ to be completed?

Were Safety items included?

How did the team decide what items to investigate further?

How did the actions affect the process performance?


GB121-16

FMEA FMEA -- ExampleExample

Process StepKey Process

InputPotential Failure Mode Potential Failure Effects

S

E

V

Potential Causes

O

C

C

Current Controls

D

E

T

R

P

N

What is the

process step

What is the Key

Process Input?

In what ways does the Key

Input go wrong?

What is the impact on the Key



requirements?

Ho

w S

eve

re is th

e e

ffe

ct

to th

e c

uso

tme

r? What causes the Key Input to

go wrong?

Ho

w o

fte

n d

oe

s c

au

se

or

FM

occu

r? What are the existing controls and

procedures (inspection and test)

that prevent eith the cause or the

Failure Mode? Should include an

SOP number.

Ho

w w

ell

ca

n y

ou

de

tect

ca

use

or

FM

?

MDO Preheat Roll Surface

Temperature

Too high Pick-off, scuffs, increased

cleaning, increased scrap,

decreased uptime

10

Control valve sticking

3

Valve positioner and valve PM

3 90

10Control valve sticking

3Alarm / operator response

3 90

10Failed pressure transducer

4Calibration PM

5 200

10Incorrect set-point

6Plan limits

9 540

10Control hardware failure

3Unknown

10 300

10Control power failure

2None

10 200

10Control software error

2Unknown

10 200

10Temperature sensor incorrect

4Calibration PM

5 200

10Alarm setting incorrect

3None

10 300

10Alarm annuciator failure

1None

10 100

SUMMARY


Page 9402-108, Rev. C


GB121-17

Studying Measurement SystemsStudying Measurement Systems

Once we have the right data we must determine our ability to measure it

Information to be attained



Is the measurement system stable over time?

Is it capable for this study?



GB121-18

Measurement Systems AnalysisMeasurement Systems AnalysisImportance to Process

Measurement System error must be understood to determine the impact to ANY measurement made

Measurement error can have a negative impact on a variety of future actions:

Capability assessment

Multi-vari studies

Statistical Process Control methods

Testing for improvements (any statistical evaluation)

Design of Experiments

Tolerancing of designs

Supplied material assessment

Final product assessment

etc.

SUMMARY


Page 10402-108, Rev. C


GB121-19

Measurement Systems AnalysisMeasurement Systems Analysis

Application Methods

The Project Leader will conduct Measurement Systems Analysis (MSA) for appropriate process measures

Team members and support personnel will be involved in MSA activities and improvements

Initially, each measurement system used for measuring Output Variables and project metrics must be evaluated

Eventually, measurement systems for Critical Input Variables must be evaluated and tracked

This is completed after the verification that the Input Variable has a need to be controlled through DOE


GB121-20


Outputs

Assessment of measurement system capability to measure:

Process changes (%R&R)

Compliance to customer requirements (% P/T)

Actions required to make the measurement system effective in measuring process change and compliance to customer requirements

Updated Control Plans and FMEA

SUMMARY


Page 11402-108, Rev. C


GB121-21


Questions to Ask

Who performed the MSA?

What are the %P/T and %R&R values?

What was the main source of measurement error?

What actions are necessary for improvement, and what impact willthey have?

How were the samples selected for the MSA?

What is the measurement system acceptable for use in assessing -Process Shift or Acceptance to Spec?

What variable(s) have you completed MSA on - Output Variables or Input Variables?

What effect will this measurement system have on Capability assessments, Multi-vari studies, and DOEs?

How is the measurement capability going to be monitored into thefuture?


GB121-22

MSAMSA -- ExampleExample

SUMMARY


Page 12402-108, Rev. C


GB121-23

Attribute Data MSAAttribute Data MSA

If many people are evaluating the same thing they need to agree:

With each other

With themselves

Attribute data contains less information than variables data, but often it is all that’s available

Therefore, we must be diligent about the integrity of attribute measurement systems

The issue is

Can I rely on the data coming from my measurement system?


GB121-24

Attribute MSA GoalsAttribute MSA Goals

Determine:

% overall agreement

% agreement within appraisers (Repeatability)

% agreement between appraisers (Reproducibility)

% agreement with known standard (Accuracy)

Kappa (how much better the measurement system is than random chance)

SUMMARY


Page 13402-108, Rev. C


GB121-25

Kappa Value Suggested Interpretation

-1 to 0.0 Random agreement

> 0.60 Marginal - Significant effort required

> 0.70 Good - Improvement warranted

> 0.90 Excellent

Kappa will be between -1 and +1Kappa will be between -1 and +1

A Kappa value of +1 means perfect agreement

General Rule: If K<0.70, the measurement system needs attention!



GB121-26

Measurement Systems Measurement Systems -- Questions to AskQuestions to Ask

Have you picked the right measurement system? Is this measurement system associated with either critical inputs or outputs?

What do the precision, accuracy, and stability look like?

What are the sources of variation and what is the measurement error?

What needs to be done to improve this system?

Have we informed the right people of our results?

Who owns this measurement system?

Who owns trouble shooting?

Does this system have a control plan in place?

What’s the training frequency? Is that frequent enough?

Do identical systems match?

SUMMARY


Page 14402-108, Rev. C


GB121-27



Initial capability (baseline) of the process to perform to customer requirements (Output Variables) needs to be determined before changes are made

Assessment after improvements are made are used to establish the level of improvement and long-term performance to specification

This is the basis for Statistical Tolerancing (used in designing new products) to provide more designs aligned better with manufacturability

Provides an estimate of the percentage of product that will be non-conforming

Provides first look into the sources of variation in the process through data-mining techniques


GB121-28


Application Methods

The Project Leader will conduct Capability Studies on appropriate project measures

Output Variables

Business Metrics

Team members and support personnel will be involved in data collection

Baseline performance, project goals and process entitlement values are established based on these studies

Outputs

Baseline, Goal and Entitlement values for Business Metrics and

Updated Charter and FMEA reflecting new data

Initial data analysis to identify potential sources of variation

SUMMARY


Page 15402-108, Rev. C


GB121-29


Questions to Ask

What variables have you conducted capability studies on?

What is the expected Short Term and Long Term performance of that variable?

What are the suspected contributing factors to the variation in the process?

How does the measurement system performance impact the process capability?

What should the goals for the process capability (Cp / Cpk) be based on the baseline data?

Over what time frame were the data taken?

How many data points / sub-groups were used to determine the process capability?

Was the data normally distributed?

How do we handle non-normal data?


GB121-30

Capability Study Capability Study -- ExampleExample

SUMMARY


Page 16402-108, Rev. C


GB121-31

Project PresentationProject Presentation

<Project Name>

<Belt>

<Project Review Date>

<Business Unit>

<Champion>


GB121-32

Project PresentationProject Presentation

<Brief Statement of Project Scope>

Belt Candidate: <name> <email>Team Members: <name> <email>…

<Projected Financial Results>

<Name of Project><Name of Project>

SUMMARY


Page 17402-108, Rev. C


GB121-33

Project Accomplishments To DateProject Accomplishments To Date

Key Learning:

Tools Used:


GB121-34

Project Accomplishments To DateProject Accomplishments To Date

Obstacles Overcome:

Benefits to Date:

Financial Results to Date:

SUMMARY


Page 18402-108, Rev. C


GB121-35

Tools OverviewTools Overview

<List tools utilized since the last project review>


GB121-36

Specific Tools ReviewSpecific Tools Review

<What tool was used, where, how>

<Output from Minitab>

<Impact>

<Benefit gained>

SUMMARY


Page 19402-108, Rev. C


GB121-37

Project PlansProject Plans

<Upcoming planned activities and tools to be used to achieve the project deliverables>


GB121-38

Project IssuesProject Issues

<Obstacles that need to be overcome>

SUMMARY


Page 20402-108, Rev. C


GB121-39

Example Process MapExample Process MapQuestions

Who helped develop the map and what organizations do they represent?

Does it reflect current state or the desired process?

Are all non value-added steps included?

What quick-hits did you find from this effort?

What process steps does the team feel can be eliminated or combined to reduce opportunities for scrap and increase rate?

What characterizes an Uncontrolled and a Controlled variable?

How do we measure the product for defects within the process?

PAPERWORK TURN STEAM ON

TO DICY TANKLOAD DMF LOAD DICY LOAD 2MI

BILL OF MATERIALS

ISO PROCEDURES

REWORK

SCALE ACCURACY

PREHEATING

LOAD ACCURACY

CLEANLINESS

RAW MATERIAL

LOAD ACCURACY

ENVIRONMENT

(HUMIDITY)

RAW MATERIAL

MIXER SPEED

LOAD ACCURACY

ENVIRONMENT

(HUMIDITY)

RAW MATERIAL

MIXER SPEED

GET PORTABLE CLEAN IF

NECESSARY

LOAD SOLVENT

TO PORTABLE

LOAD DRUM

STOCK TO

PORTABLE

2

PROPER SIZE

REWORK

CLEANLINESS

CROSS

CONTAMINATION

LOAD ACCURACY

ENVIRONMENT

RAW MATERIAL

LOAD ACCURACY

ENVIRONMENT

RAW MATERIAL

MIXER SPEED

LOAD BULK

MATERIAL TO

PORTABLE

2PUMP DICY

SOLUTION TO

PORTABLE

DIGEST STAGE BATCH TO

TREATER

LOAD ACCURACY

ENVIRONMENT

RAW MATERIAL

MIXER SPEED

PUMP RATE

MIXER SPEED

FILTERING

TEMPERATURE

COMPLETE

SOLUTION

TIME

MIX SPEED

VENTING

ENVIRONMENT

TIME

MIX SPEED

VENTING

ENVIRONMENT

COMPOUNDINGINPUTS

Techniques

Procedures

Raw Materials

Rework

Equipment

Environment

Cleanliness

Dicy Solution Temp

OUTPUTS

Reactivity (Gel)

Viscosity

Cleanliness

Color

Homogeneity

Consistency Batch to Batch

Digestion Time

Temperature


GB121-40

Example Cause and Effects MatrixExample Cause and Effects Matrix


Customer7 9 4 10 8 10

1 2 3 4 5 6

Pain

t

thic

kness

Pain

t

hard

ness

Rate

Covera

ge

Surf

ace

quality

Pro

per

schem

e

Process Step Process InputTotal

1 Paint Nozzle type 9 0 9 9 9 9 351

2 Paint Paint viscosity 9 9 9 9 3 3 324

3 Paint Air pressure 9 3 9 9 9 3 318

4 Prime Primer age 9 9 9 3 9 0 282

5 Paint Paint age 9 9 9 3 9 0 282

6 Paint Surface contamination 1 9 3 9 9 1 272

7 Prime Air pressure 9 1 9 9 9 0 270

8 Prime Nozzle type 9 0 3 9 9 0 237

9 Prime Surface contamination 1 3 3 9 9 0 208

10 Prime Relative humidity 1 9 9 1 9 0 206

11 Paint Ambient temp 1 9 9 0 9 0 196

12 Paint Surface roughness 3 1 3 3 9 3 174

13 Paint Lot number 0 1 0 3 1 9 137

14 Prime Surface roughness 0 1 3 3 9 0 123

15 Prime Lot number 1 3 0 3 1 3 102

16 Prime Ambient temp 1 3 9 0 3 0 94

Total 504

630

384

820

928

310

Lower Spec

Target

Upper Spec

Cause and Effect

Matrix

This table provides the initial input to the FMEA. When each of the

output variables (requirements) are not correct, that represents

potential "EFFECTS". When each input variable is not correct, that

represents "Failure Modes".

1. List the Key Process Output Variables

2. Rate each variable on a 1-to-10 scale to importantance to the

customer

3. List Key Process Input Variables

4. Rate each variables relationship to each output variable on a 1-

to-10 scale

Questions

Who provided inputs to the Customer Requirements for this matrix?

What groups determined the relationship ratings for the Inputs and Outputs?

What method was used to determine the final relationship score?

What actions are being taken on the top ranked Input Variables?

Are there any quick hits that can be assigned to lower ranking Input Variables?

Do the current Control Plans reflect the need to monitor these top Input Variables?

SUMMARY


Page 21402-108, Rev. C


GB121-41

Process


S

E

V

Potential Causes

O

C

C

Current Controls

D

E

T

R

P

N

Load DMF/DMF

Load Accuracy Mischarge of DMF Viscosity out of spec 7 SOP not Followed 5Operator Certification/ Process

Audit5 175

Steam to

DICY/Scale

Accuracy

Scale Not Zeroed Mischarge DMF 3 Faulty Scale 2 None 9 54

Load DMF/DMF

Load Accuracy Mischarge of DMF Viscosity out of spec 7 Equipment Failure 2Maintenance Procedure (SOP

5821)/Visual Check3 42

Steam to

DICY/Scale

Accuracy

Scale > 0 Low DMF Charge 3 Water in Jacket 2Visual Check of Jacket (SOP

5681)4 24

Steam to

DICY/Scale

Accuracy

Scale Inaccurate High DMF Charge 3 Tank Hanging Up 2 Visual Check (SOP 5681) 4 24

Example FMEAExample FMEAQuestions

Who helped develop the FMEA and what organizations do they represent?

Does the tool reflect current state or the desired process?

What quick-hits did you find from this effort?

What type of ranking system did you use ?

Did you complete the actions recommended section of the tool ?


GB121-42

Example Baseline Capability DataExample Baseline Capability DataQuestions

What variables were evaluated?

Are they stable?

Are there explanations for out of control points?

How long was the process monitored to determine stability?

What are the largest types of variation over time - shift to shift, week to week, etc.?

Registration ScrapRegistration Scrap

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

Fiscal Week

SUMMARY


Page 22402-108, Rev. C


GB121-43

Example MSA ResultsExample MSA ResultsQuestions

What are the major sources of measurement error?

How much measurement error exists in comparison to the process variation? In comparison to the customer requirements?

Is the measurement system acceptable for the process improvement efforts?

If not, what actions do you suggest?

How were the samples chosen?

Was the team sensitive to sub-grouping for sample selection?

Gage R&R

%Contribution

Source VarComp (of VarComp)

Total Gage R&R 0.0044375 10.67

Repeatability 0.0012917 3.10

Reproducibility 0.0031458 7.56

Operator 0.0009120 2.19

Operator*Sample 0.0022338 5.37

Part-To-Part 0.0371644 89.33

Total Variation 0.0416019 100.00

Study Var %Study Var %Tolerance

Source StdDev (SD) (5.15 * SD) (%SV) (SV/Toler)

Total Gage R&R 0.066615 0.34306 32.66 68.61

Repeatability 0.035940 0.18509 17.62 37.02

Reproducibility 0.056088 0.28885 27.50 57.77

Operator 0.030200 0.15553 14.81 31.11

Operator*Sample 0.047263 0.24340 23.17 48.68

Part-To-Part 0.192781 0.99282 94.52 198.56

Total Variation 0.203965 1.05042 100.00 210.08

Number of Distinct Categories = 4


GB121-44

Example Capability SummaryExample Capability SummaryQuestions

What are the long and short term process capability values?

How does this compare to the customer’s perspective of performance?

Is there a significant opportunity to improve beyond current levels (i.e., how large is the gap between Cp and Ppk)?

What are the definitions of defects and opportunities?

Are the capabilities established for Input Variables and Output Variables from the C&E?

Customer

Requirement

(Output Variable)

Measurement

Technique

%R&R or

P/T Ratio

Upper

Spec

Limit

Target

Lower

Spec

Limit

Cp CpkSample

SizeDate Status

Fire RetardencyUL 700 25% 3 1.5 na

No Data

Available

Selvage Edge

Consistency

On-line Physical

Measurement15% 4.5 4 3.5 1.15 0.85 50 Sep-95

Improvement

Plan in Place

Membrane

Stability

Oven Test None 0.75 0.5 na 1.1 0.65 25 Aug-95

Measurement

Study

Scheduled

SUMMARY


Page 23402-108, Rev. C


GB121-45

Summary of “Week” 1 TrainingSummary of “Week” 1 Training

Effective team in place with clear goals and plans to achieve success

Good understanding of the current process and it’s capabilities

Measurement systems established and their capabilities understood

Initial Control Plans in place and gaps identified

Linkage between project goals and business impact is internalized by all

Visible project plans at all levels

Documents

sixsigma+wk1