everything you wanted to know about six-sigma but were afraid to ask! dave stewardson - isru ronald...

Everything you wanted to know about

Six-Sigma but were afraid to ask!

Dave Stewardson - ISRU

Ronald Does – The Netherlands

Soren Bisgaard - USA

Bo Bergman – Sweden

Ron Kennet – Israel

Oystein Evandt – Norway

Xavier Tort-Martorell - Spain

Pro-EnbisAll joint authors - presenters- are members of:

Pro-Enbis and ENBIS.

This presentation is supported by Pro-Enbis a Thematic Network funded under the ‘Growth’ programme of the European Commission’s 5th Framework research programme - contract number G6RT-CT-2001-05059

ENBIS

European Network for Business and Industrial Statistics

www.enbis.org

Overview

Brief resume of Six Sigma- Key concepts- Training- Execution

The Scientific Method Project selection “Quotes” Barriers – Overcoming these Critique of ISO 9000 Change programs Real reasons why six-sigma works Simple case study

Hoovering 30 m2 on 6-level means only 1 cm2 missed. 1/3.4 million part of the day equals 0.29 second 1/3.4 million part of the equator of the earth equals about 140

meter.

is the symbol for the standard deviation.

“6” is equivalent with 3.4 defects per million opportunities.

6 : new world

A new way of doing business?

Statistical background

Target =

Some Key measure


Target =

‘Control’ limits

LSL USL


Required Tolerance

Target =

LSL USL


Tolerance

Target =

Six-Sigma

LSL USL

ppm1350

ppm1350


Tolerance

Target =

LSL USL

ppm0.001

ppm1350

ppm1350

ppm0.001


Tolerance

Target =


But Six-Sigma allows for un-forseen ‘problems’ and longer term issues

when calculating

failure

error or

re-work rates

Assumes a process ‘shift’

LSL

0 ppm ppm3.4

USL

ppm3.4ppm

66803


Tolerance

Performance Standards

23456

30853766807

62102333.4

PPM

69.1%93.3%99.38%99.977%99.9997%

Yield

Processperformance

Processperformance

Defects permillion

Defects permillion

Long term yield

Long term yield

Current standardCurrent standard

World ClassWorld Class

Number of processesNumber of processes 3σ3σ 4σ4σ 5σ5σ 6σ6σ

110

100500100020002955

110

100500100020002955

93.3250.090.10000

93.3250.090.10000

99.37993.9653.644.440.200

99.37993.9653.644.440.200

99.976799.7797.7089.0279.2462.7550.27

99.976799.7797.7089.0279.2462.7550.27

99.9996699.996699.96699.8399.6699.3299.0

99.9996699.996699.96699.8399.6699.3299.0

First Time Yield in multiple stage process

Performance standards

Benefits of 6approach w.r.t. financials

-level Defect rate (ppm)

Costs of poor quality Status of the company

6 3.4 < 10% of turnover World class 5 233 10-15% of turnover 4 6210 15-20% of turnover Current standard 3 66807 20-30% of turnover 2 308537 30-40% of turnover Bankruptcy

Financial Aspects

Simple– Eliminate defects– Eliminate the opportunity to have defects

Complex– Vision– Metric (Standard measuring method)– Benchmark– Philosophy– Method– Tool for:

Customer satisfaction ‘Breakthrough’ improvements Continuous improvement

Employee involvement – Agressive goals

What is Six Sigma as a Concept?

A scientific and practical method to achieve improvements in a company

Scientific:• Structured approach.• Assuming quantitative data.

Practical:• Emphasis on financial result.• Start with the voice of the customer.

“Show me the data”

”Show me the money”

Six Sigma

Six Sigma Methods Production

DesignService

Purchase

HRM

Administration

QualityDepart.

Management

M & S

IT

Where can Six Sigma be applied?

GE “Service company”Examples

Approving a credit card application Installing a turbine Lending money Servicing an aircraft engine Answering a service call for an appliance Underwriting an insurance policy Developing software for a new CAT product Overhauling a locomotive

DMAIC

Define Select a project

Measure Prepare for assimilating information

Analyze Characterise the current situation

Improve Optimise the process

Control Assure the improvements

Six-Sigma - A “Roadmap” for improvement

• Belongs to the middle management• Is well-educated• Project is related to his daily activities• May prioritise his work• Well motivated• Willing to change• Has good social skills

Black BeltBlack Belt

Improvement potential: € 50 000

Execution

Training (1 week)Training (1 week)

Work on project(3 weeks)

Work on project(3 weeks)

ReviewReview

Define

Measure

Analyze

Improve

Control

Throughput time projectThroughput time project

4 months (full time)4 months (full time)

Classic Training strategy

In Spain5 x three day sessions

Includes weekend

More ‘Homework’

Heaver individual support

Fewer advanced methods

It is their view that some training is not assimilated by delegates and that some items do not fit the need of some delegates

Training

In Poland

5 x 5 day sessions

But with 5 weeks between training sessions not 3 weeks

Extra Support via on-line materials

Individual support stepped up

Training

In Sweden

4 x 5 day day sessions

3 week gaps as in America

Less emphasis on top-down

Perceived to be more need for buy-in by staff than in America

Training

Black BeltBlack Belt

TrainingTraining

ApplicationApplication

ReviewReview

MBBMBB

MBB,Champion

MBB,Champion

MBB,Champion

MBB,Champion

Project execution

Traditional Six Sigma

-Project leader is obliged to make an effort.

-Set of tools .

-Focus on technical knowledge.

-Project leader is left to his own devices.

-Results are fuzzy.

-Safe targets.

-Projects conducted “on the side”.

-Black Belt is obliged to achieve financial results.

-Well-structured method.

-Focus on experimentation.

-Black Belt is coached by champion.

-Results are quantified.

-Stretched targets.

-Projects are top priority.

Conducting projects

Black Belt is given the required resources

-Training in statistical methods.

-Time to conduct his project!

-Software to facilitate data analysis.

-Permissions to make required changes!!

-Coaching by a champion – or external support.

Project support

In other words the Black Belt is

-Empowered.

-In the sense that it was always meant!

-As the theroists have been saying for years!

Project support

7. Screen potential causes.8. Discover variable relationships.9. Establish operating tolerances.

10. Validate measurement system.11. Determine process capability.12. Implement process controls.

DMAIC procedure

4. Establish product capability.5. Define performance objectives.6. Identify variation sources.

1. Select CTQ characteristic.2. Define performance standards.3. Validate measurement system.

Measure

Analyze

Improve

Control

Define

Roadmap to improvement

Statistics

Methods for the collection, presentation and analysis of data.

Based on mathematics and mathematical modelling.

Major role is played by uncertainty / variation.

Statistical approach to quality improvement:1. Explain predict control.2. All ideas are empirically tested before they are accepted.

1. Y = f(X1, X2, … , Xn).

2. “Show me the data”.

1. Y = f(X1, X2, … , Xn).

2. “Show me the data”.

Basic approach

Data, measurements, observations

Hypotheses (potential leverage variables)

Cre

ativ

eth

inki

ng

Critical

thinking

TestingExploratory

study

Learning by scientific method:

Scientific method

Scientific method (after Box)

INDUCTION INDUCTION

DEDUCTION DEDUCTION

DataFacts

TheoryHypothesisConjectureIdeaModel

Check

Plan

DoAct

Plan

DoCheck/Study

Act

Deming Cycle

The Scientific Process

Key elements:– Formulation of the problem– Collection of data– Experimentation– Generation of ideas from patterns in data–

hypothesis generation– Making predictions from hypothesis– Comparing predictions with real data– Making inferences from the data

Exploratory study:At first we search -- like a detective -- in the data for traces of potential leverage variables. We must not be critical. It is more important to find all leverage variables.

Testing:Then we determine -- like a judge -- which of the potential leverage variables are indeed important. We do this by conducting an experiment.

How to discover potential leverage variables:

Exploit available knowledge:• FMEA• Cause and effect diagram• Technical literature

Collection and analysis of data:• Control chart• Boxplot• Scatter diagram

The search for root causes

Practical solution Statistical solution

Statistical problemPractical problem

Y = f(X1, X2, …, Xn)

Approach to improve

Problem fixing vs. explanation

Define

Select:- the project - the process- the Black Belt- the potential savings- time schedule- team

Project selection

Is management’s responsibility.

Projects may be selected according to:

1. A complete list of requirements of customers.

2. A complete list of costs of poor quality.

3. A complete list of existing problems or targets.

Project selection

1. Requirements,2. Costs,3. Problems.

1.Collect data 2.Arrange the information

3.Give priority-Financial benefits-Expected throughput time of the project-Severity of the problem

321

Project prioritization

Before a simple case study a few quotes - some important

issues - then some why’s?

“the most important initiative GE has ever undertaken”.

Jack WelchChief Executive OfficerGeneral Electric

• In 1995 mandated each GE employee to work towards achieving 6 sigma• The average process at GE was 3 sigma in 1995• In 1997 the average reached 3.5 sigma • GE’s goal is to reach 6 sigma by 2001• Investments in 6 sigma training and projects reached 45MUS$ in 1998, profits increased by 1.2BUS$

• In 1995 mandated each GE employee to work towards achieving 6 sigma• The average process at GE was 3 sigma in 1995• In 1997 the average reached 3.5 sigma • GE’s goal is to reach 6 sigma by 2001• Investments in 6 sigma training and projects reached 45MUS$ in 1998, profits increased by 1.2BUS$

General ElectricGeneral Electric

“At Motorola we use statistical methods daily throughout all of our disciplines to synthesize an abundance of data to derive concrete actions….How has the use of statistical methods within Motorola Six Sigma initiative, across disciplines, contributed to our growth? Over the past decade we have reduced in-process defects by over 300 fold, which has resulted in a cumulative manufacturing cost savings of over 11 billion dollars”*.

Robert W. GalvinChairman of the Executive CommitteeMotorola, Inc.

MOTOROLMOTOROLAA

*From the forward to MODERN INDUSTRIAL STATISTICS by Kenett and Zacks, Duxbury, 1998*From the forward to MODERN INDUSTRIAL STATISTICS by Kenett and Zacks, Duxbury, 1998*From the forward to MODERN INDUSTRIAL STATISTICS by Kenett and Zacks, Duxbury, 1998*From the forward to MODERN INDUSTRIAL STATISTICS by Kenett and Zacks, Duxbury, 1998

“Six Sigma is making war on defects”

Bill Smith, Motorola

“If an employee is not enthusiastic about Six Sigma, GE is simply not the right company for that person”

Jack Welch, General Electric

“If all we have is spirit, we will lose to the US”

President Idei, Sony

Some more Quotes

Even more Quotes

“Six-Sigma is remarkable – it has made managers start to adopt those simple and efficient methods that they have all needed desperately ever since they were developed back in the 1920s”

Translated from Oystein Evandt (Norway)

“Six-sigma’s focus on the bottom line provides the missing ingredient in Deming’s philosophy”

KnowledgeKnowledgeManagementManagement

The Six Sigma InitiativeThe Six Sigma Initiativeintegrates these effortsintegrates these efforts

Black Belt training programs may includeBlack Belt training programs may includeBlack Belt training programs may includeBlack Belt training programs may include

• 6 sigma principles• Quality Improvement• Quality by Design• Quality Control• Teamwork• Effective presentations• QFD/VOC • Statistical thinking• Process mapping• Barriers to breakthroughs• JMP, MINITAB…..

• Gage R&R• SPC• SPC Strategy• Risk Management• FMEA• Statistical Inference• Design Of Experiments• DOE Strategy• Bootstrapping• Robust Designs• System Thinking

Barrier #1: Engineers and managers are not interested in mathematical statistics

Barrier #2: Statisticians have problems communicating with managers and engineers

Barrier #3: Non-statisticians experience “statistical anxiety” which has to be minimized before learning can take place

Barrier # 4: Statistical methods need to be matched to management style and organizational culture

Barrier #1: Engineers and managers are not interested in mathematical statistics

Barrier #2: Statisticians have problems communicating with managers and engineers

Barrier #3: Non-statisticians experience “statistical anxiety” which has to be minimized before learning can take place

Barrier # 4: Statistical methods need to be matched to management style and organizational culture

Barriers to implementation

Technical Technical SkillsSkills

Soft SkillsSoft Skills

StatisticiansStatisticiansMaster Master

Black BeltsBlack BeltsBlack BeltsBlack Belts

Quality Improvement Quality Improvement FacilitatorsFacilitators

BBBBBBBBMBBMBB

Leadership Group

Processes, internal and external customers

Team 1 Team 2 Team 3

BBBBBBBB BBBBBBBB BBBBBBBB

MBBMBB

The The 6 Sigma6 Sigma Project Structure Project Structure

KPAISRUIBISENBISCAMT

Comparing three recent developments in

“Quality Management”

ISO 9000 (-2000) EFQM Model Quality Improvement and Six

Sigma Programs

ISO 9000

Proponents claim that ISO 9000 is a general system for Quality Management

The de facto applications seem to be – an excessive emphasis on Quality Assurance, and – standardization of already existing systems with

little attention to Quality Improvement It would have been better if improvement

efforts had preceded standardization

Critique of ISO 9000

Bureaucratic, large scale Focus on satisfying auditors, not customers Certification is the goal; the job is done when certified Little emphasis on improvement The return on investment is not transparent Main driver is:

– We need ISO 9000 to become a certified supplier, – Not “we need to be the best and most cost effective supplier to

win our customer’s business” Corrupting influence on the quality profession

EFQM Model

A tool for assessment: Can measure where we are and how well we are doing

Assessment is a small piece of the bigger scheme of Quality Management:– Planning – Control – Improvement

EFQM provides a tool for assessment, but no tools, training, concepts and managerial approaches for improvement and planning

The “Success” of Change Programs?

“Performance improvement efforts … have as much impact on

operational and financial results as a ceremonial rain dance has on the weather”

Schaffer and Thomson,Harvard Business Review (1992)

Change Management:Two Alternative Approaches

Activity Based Programs

Result Oriented Programs

ChangeManagement

Reference: Schaffer and Thomson, HBR, Jan-Feb. 1992

Activity Centered Programs Activity Centered Programs: The pursuit of

activities that sound good, but contribute little to the bottom line

Assumption: If we carry out enough of the “right” activities, performance improvements will follow– This many people have been trained– This many companies have been certified

Bias Towards Orthodoxy: Weak or no empirical evidence to assess the relationship between efforts and results

No Checking with Empirical Evidence, No Learning Process

ISO 9000

Data

Hypothesis

Deduction Induction

An Alternative: Result-Driven Improvement Programs

Result-Driven Programs: Focus on achieving specific, measurable, operational improvements within a few months

Examples of specific measurable goals:– Increase yield

– Reduce delivery time

– Increase inventory turns

– Improved customer satisfaction

– Reduce product development time

Result Oriented Programs: Project based Experimental Guided by empirical evidence Measurable results Easier to assess cause and effect Cascading strategy

Why Transformation Efforts Fail! John Kotter, Professor, Harvard Business

School Leading scholar on Change Management Lists 8 common errors in managing

change, two of which are: 1. Not establishing a sense of urgency2. Not systematically planning for and

creating short term wins

Six Sigma Demystified*

Six Sigma is TQM in disguise, but this time the focus is:– Alignment of customers, strategy,

process and people– Significant measurable business results– Large scale deployment of advanced

quality and statistical tools– Data based, quantitative

*Adapted from Zinkgraf (1999), Sigma Breakthrough Technologies Inc., Austin, TX.

Keys to Success*

Set clear expectations for results Measure the progress (metrics) Manage for results


Six Sigma

The precise definition of Six Sigma is not important; the content of the program is

A disciplined quantitative approach for improvement of defined metrics

Can be applied to all business processes, manufacturing, finance and services

Focus of Six Sigma*

Accelerating fast breakthrough performance Significant financial results in 4-8 months Ensuring Six Sigma is an extension of the

Corporate culture, not the program of the month

Results first, then culture change!


Six Sigma: Reasons for Success

The Success at Motorola, GE and AlliedSignal has been attributed to:

– Strong leadership (Jack Welch, Larry Bossidy and Bob Galvin personally involved)

– Initial focus on operations – Aggressive project selection (potential savings in

cost of poor quality > $50,000/year)– Training the right people

The right way!

Plan for “quick wins”– Find good initial projects - fast wins

Establish resource structure– Make sure you know where it is

Publicise success– Often and continually - blow that trumpet

Embed the skills– Everyone owns successes

RoastRoast

CoolCool

GrindGrind

PackPack

Coffeebeans

Sealed coffee

Moisture content

Moisture content

Savings:-Savings on rework and scrap-Water costs less than coffee

Potential savings:500 000 Euros

Case study: project selection

Measure

1. Select the CTQ characteristic

2. Define performance standards

3. Validate measurement system

Case study: Measure

Measure

Moisture contents of roasted coffee

1. CTQ

- Unit: one batch- Defect: Moisture% > 12.6%

2. Standards

Case study: Measure

Gauge R&R studyGauge R&R study

3. Measurement reliability

Measurement system too unreliable!

Case study: Measure

So fix it!!

Analyze

4. Establish product capability

5. Define performance objectives

6. Identify influence factors

Case study: Analyze

USLUSL

USLUSL

Improvement opportunities

CT

Q

CT

Q

CT

Q

CT

Q

Diagnosis of problem

-Brainstorming-Exploratory data analysis

6. Identify factorsMaterialMachineMan

Method Measure-ment

MotherNature

Amount ofadded water

Roastingmachines

Batchsize

Reliabilityof Quadra Beam

Weatherconditions

Moisture%

Discovery of causes

Control chart for moisture%

Discovery of causes

- Roasting machines (Nuisance variable)

- Weather conditions (Nuisance variable)

- Stagnations in the transport system (Disturbance)

- Batch size (Nuisance variable)

- Amount of added water (Control variable)

Potential influence factors

A case study

Improve

7. Screen potential causes

8. Discover variable relationships

9. Establish operating tolerances

Case study: Improve

- Relation between humidity and moisture% not established

- Effect of stagnations confirmed

- Machine differences confirmed

7. Screen potential causes

Design of Experiments (DoE)

8. Discover variable relationships

Case study: Improve

Experiments are run based on: IntuitionKnowledgeExperiencePowerEmotions

Possible settings for X1

Po

ssible se

ttings fo

r X2

X: Settings with which an experiment is run.

X

X

XX

X

X

X

Actually:• we’re just trying • unsystematical• no design/plan

How do we often conduct experiments?How do we often conduct experiments?

Experimentation

A systematical experiment: Organized / disciplineOne factor at a timeOther factors kept constant

Procedure:

XX XX OX X X X X

X: First vary X1; X2 is kept constant

O: Optimal value for X1.

X: Vary X2; X1 is kept constant.

: Optimal value (???)

X

X

X

X

X

X

X

Possible settings for X1

Po

ssible se

ttings fo

r X2

Experimentation

One factor (X)

low high

X1 21

Two factors (X’s)

low

high

high

X2

X1

22

high

Three factors (X’s)

low highX1

X3

X2

23

Design of Experiments (DoE)

Experiment:

Y: moisture%

X1: Water (liters)X2: Batch size (kg)

A case study: Experiment

Feedback adjustments for influence of weather conditions

A case study

9. Establish operating tolerances

A case study: feedback adjustments

Moisture% without adjustments

A case study: feedback adjustments

Moisture% with adjustments

Control

10. Validate measurement system (X’s)

11. Determine process capability

12. Implement process controls

Case study: Control

long-term < 0.280

Objective

long-term = 0.532

Before

long-term < 0.100

Result

Results

Benefits of this project

long-term < 0.100

Ppk = 1.5This enables us to increase the mean to 12.1%

Per 0.1% coffee: 100 000 Euros saving

Benefits of this project:

1 100 000 Euros per year

Benefits

Approved by controller

- SPC control loop- Mistake proofing- Control plan- Audit schedule

12. Implement process controls

Case study: control

- Documentation of the results and data.

- Results are reported to involved persons.

- The follow-up is determined

Project closure

- Step-by-step approach.

- Constant testing and double checking.

- No problem fixing, but: explanation control.

- Interaction of technical knowledge and experimentation methodology.

- Good research enables intelligent decision making.

- Knowing the financial impact made it easy to find priority for this project.

Six Sigma approach to this project

Re-cap I!

Structured approach – roadmap Systematic project-based improvement Plan for “quick wins”

– Find good initial projects - fast wins Publicise success

– Often and continually - blow that trumpet Use modern tools and methods Empirical evidence based improvement

Re-cap II!

DMAIC is a basic ‘training’ structure Establish your resource structure

– Make sure you know where external help is

Key ingredient is the support for projects

- It’s the project that ‘wins’ not the training itself

Fit the training programme around the company needs

– not the company around the training

Embed the skills– Everyone owns the successes

everything you wanted to know about six-sigma but were afraid to ask! dave stewardson - isru ronald...

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