Improving DuPont Businesses Through Quality · Quality & Business Excellence Conference 26 – 27 November 2012 DuPont case studies will be presented on •systematic approaches to

Quality & Business Excellence Conference 26 – 27 November 2012


Improving DuPont Businesses Through Quality

Steven P. Bailey, Ph.D. DuPont Engineering Research and Technology

Applied Statistics Group [email protected]


DuPont case studies will be presented on

• systematic approaches to deploying experimental strategies

• developing and deploying product quality management systems

• engaging employees in executing lean six sigma improvement projects


Learning objectives

• How the tools of experimental design were assembles into a Strategy of Experimentation (SOE) paradigm that has been deployed and refined within DuPont for nearly 50 years, leading to many new and improved products and processes.

• How a strong prevalent business need, combined with collaboration among marketing, manufacturing, and technology, led to rapid development and broad deployment of a Product Quality Management (PQM) methodology within DuPont that combines quality philosophy, management systems and unique quality technology.

• How DuPont’s deployment of lean and six sigma has simultaneously delivered bottom-line business results and improved people capability.


Agenda

• DuPont’s Applied Statistics Group (ASG)

• Strategy of Experimentation (SOE)

• Product Quality Management (PQM)

• DuPont Six Sigma

• Wrap-up and Acknowledgements


DuPont’s 13 Businesses

• Protection Technologies

• Building Innovations

• Safety Resources

• Pioneer Hi-Bred

• Crop Protection

• Nutrition & Health

• Performance Polymers

• Packaging & Industrial Polymers

• Titanium Technologies

• Chemicals & Fluoro

• Performance Coatings

• Industrial BioSciences

• Electronics & Communications

5


1964 -- Applied Statistics Group (ASG)

Applied Statistics


1973 -- Product Quality Management (PQM)

Applied Statistics Quality Technology


1989 -- Quality Management and Technology (QM&T) ISO 9000, PQM, Malcolm Baldrige (DCIC)

Quality Management

Applied Statistics Quality Technology


1999 -- DuPont Six Sigma

Quality Management Six Sigma

Applied Statistics

Note: No attempt was made at depicting the correct sizes or overlap of the above circles.

Quality Technology


2007 (Back to the Future)

Quality Management Six Sigma

Applied Statistics

Note: No attempt was made at depicting the correct sizes or overlap of the above circles.

Quality Technology

Mgmt

Tech

Quality Statistics


Strategy of Experimentation (SOE) History • Late 1950’s – Computing (mainframe) arrives at DuPont

• Early 1960’s – First Response Surface DOEs done in plants and labs

• Mid 1960’s – First SOE course (concurrent with ASG formation)

• Late 1960’s – Internal software developed for DOE design and analysis

• Mid 1970’s – Revised SOE text and created external business

• Late 1970’s – Strategy of Formulations Development (SFD) course

• Late 1980’s – Last SOE text revision

• Late 1980’s – Experimentation for Robust Product Design (ERPD)

• Late 1980’s – Software integrated into SOE and SFD courses

– RS/Discover, EChip, JMP®, Minitab®, etc

Over 40,000 internally and externally trained in DuPont’s SOE!


DOE Theory

1920s Agriculture split plot

experiments (Fisher &

Yates) 1930s

1940s Plackett-Burman designs

1950s Response surface

methods (Box et al.)

1960s Mixture designs

(Scheffe)

1970s Design optimality and

computer-aided designs

Conjoint analysis

1980s Robust parameter

design

1990s Industrial split plot

designs

2000s Computer experiments


DOE Theory DuPont DOE Use

Area Software



Yates)

1930s



methods (Box et al.) R&D

Hand calculations

Univac 1960s

Mixture designs

(Scheffe) R&D

MFG 1970s

Design optimality and


Conjoint analysis

Univac programs

developed internally

1980s Robust parameter

design

R&D, MFG

Agriculture

RS/Discover (VAX)

ECHIP (PC)

Minitab® (VAX)


designs

R&D, MFG, Agriculture

Tech. Sales/Marketing JMP®, Minitab® (PC)


R&D, MFG, Agriculture,

Tech. Sales/Marketing

Marketing

Minitab®, JMP®, SAS®


DOE Theory DuPont DOE Use DuPont DOE Training

Area Software Course Audience



Yates)

1930s



methods (Box et al.) R&D

Hand calculations

Univac

1960s Mixture designs

(Scheffe) R&D

MFG

SOE (1964) Internal offering

1970s Design optimality and


Conjoint analysis

Univac programs

developed internally

SOE

SOFD added Internal & External

offering

(Began selling course

externally in 1974) 1980s Robust parameter

design

R&D, MFG

Agriculture

RS/Discover (VAX)

ECHIP (PC)

Minitab® (VAX)

Last major content

update


designs

R&D, MFG, Agriculture

Tech. Sales/Marketing JMP®, Minitab® (PC) Software updates

Internal & DuPont

Customers


R&D, MFG, Agriculture,

Tech. Sales/Marketing

Marketing

Minitab®, JMP®, SAS®

SOE & SOEFD course

DOE embedded in Six

Sigma training


Strategy of Experimentation (SOE) (from Forward of 1975 SOE Text)

• Every experimental program embodies an experimental strategy that may either be good or bad.

• The strategy selection can catalyze technical progress or cause stagnation.

• “Strategy of Experimentation” teaches the information needed to apply modern experimental designs effectively.

• The course emcompasses the philosophic and practical elements of experimental programs as well as the methodology of statistical experimental design.

• The material represents, both by inclusion and exclusion, a distillation of what is most important for the working scientist (and their supervision) both to understand and be able to do.


Evolution of the Experimental Environment Full Factorials as Building Blocks for Screening and Response Surface Experiments

X3

X2 X1

X3

X2 X1

X3

X2 X1

Full Factorial Experiments

Response Surface Experiments

Screening Experiments


Comparison of Experimental Environments Characteristic Screening Characterization Optimization

No. of Factors More than 6 3-6 2-5

Desired

Information

Critical Factors Understand how

System Works

Prediction

Equation,

Optimization,

Design Space

Model Form Linear or

Main Effects

Linear and

Interaction

Effects

Linear, Interaction

and Curvilinear

Effects

Experiment

Design

Plackett-Burman

Fractional-

Factorials

Full and

Fractional

Factorials

Response Surface


Input Variables (Xs)

Outputs (Ys)

Process Variables (Xs)

– Cause-effect relationships

– Mathematical models

– Optimum X settings for performance of Ys

– Powerful and cost-effective

– Generally minimizes the amount of data required to get the information you need


Portable Power: Number Of Runs Vs. Sensitivity

Rule of Thumb: for balanced 2-level factorial designs (Plackett-Burman, Fractional Factorial, Full Factorial)

= smallest size effect worth detecting =standard deviation of experimental error (your best guess) = “signal-to-noise ratio”

2.0 1.5 1.0 0.5 n 12-16 22-28 49-64 196-256

n = 7 or 8

( )

2


Y = f(Xs) + e Output equals Function of Inputs plus Variation

Continuous vs.

Discrete

“As Is” vs. Transformed vs. “Link Function”

Theoretical vs.

Empirical

Simple vs.

Complex

Continuous vs.

Discrete

Screening vs. Optimization

Blocking and

Randomization

Overt (Pure) and Hidden Replication

vs.

Number of rows of data

(n)

Number of parameters to estimate (p)

Number of additional treatment

combinations for checking lack

of fit (l)

Amount of pure replication

(r)

Historical Data Mining (HDM) vs Designed Experiment (DOE)

Many vs. Few

Bias Error

Random Error

= +

Output

+

Function Inputs Variation

Equation

Words

Considerations

Degrees of

Freedom

Question!


Product Quality Management (PQM)

In response to a quality crisis in a large DuPont business (with 8 manufacturing

sites) it was recognized that

- Procedures were needed for using the tools to meet the business need

- Approaches were needed for managing the connections between

technology and business functions

The systems concept and PQM were born.


What business needs were addressed?

• setting specifications

• product characterization and release

• improving process and product performance

• communicating with customers

• managing and improving test methods

• measuring and monitoring progress

• keeping the system up to date

While statistical tools were used to address most of these needs the

paradigm had been reversed. We were now starting with the business

need and not the technology. Within a year the quality crisis was over

and a new era had begun.


Product Quality Management (PQM)

• Framework for managing the quality of a product or service.

• Operational system the enables Marketing, R&D, Production

and support personnel to work together to meet

increasingly stringent customer requirements

“Within two years product quality had improved to the point of

commanding a marketplace advantage and more than $30 million had

been gained in operating cost improvements. The statistically based

Product Quality Management system developed for “Dacron” was

expanded to other products with further contributions in earnings.”

Richard E. Heckert, Chairman and CEO, DuPont Company, 1986


The PQM manual was first published in 1976 (300 copies). The tenth

and final edition was published in 1991. Well over 10,000 copies were

distributed over that time.

The 1988 edition (shown here) had over 800 pages. The Table of

Contents is included in the next two charts.

During the development and

continued use of PQM regular

meetings were held among the

consultants to share experiences

with the implementing businesses

and to upgrade PQM processes.


• Part 1 - Introduction to Product Quality Management

1) The Values of PQM for Customer and Producer

2) Quality and Quality Management

3) PQM Strategy for Continual Improvement Using the Quality Loop

4) Some Basic PQM Concepts

5) Meeting Customer Requirements

• Part 2 - Managerial Aspects

6) Quality Economics

7) The Role of National and International Standards

8) Report Card Issues

9) The Dynamic Quality Improvement Model

• Part 3 - Simple Diagnostic Techniques

10) Simple Techniques for Quality Problem Diagnostics

• Part 4 - Process Control

11) Process Control Concepts and Introduction to CUSUM

12) Design of CUSUM Control Schemes

13) Control of the Measurement Process

• Part 5 - Characterizing Variability

14) Estimating and Maintaining Variance Components

15) Using Routine and Maintenance Data for Continual Improvement


• Part 6 - Assuring Shipped Product Conforms to Specifications

16) Product Characterization and Release Concepts

17) CUSUM-matched Product Specifications

• Part 7 - Supplier / Producer / Customer Partnerships

18) Classes of Product Properties

19) Measures of Continual Improvement

20) Procedures for Determining Candidate Specification Limits

21) Information Sharing and Acceptance Testing

• Part 8 - System Evaluation, Tuning, and Improvement

22) Product Quality Reviews

23) Applications of Experimental Data in PQM

24) Multi-variable Process Adjustment

25) Interlab Comparisons

• Part 9 - Generalization to Other Product Units

26) Other Product Unit Definitions

27) Sheet Structures

28) Counted Data and Attribute Properties

• Part 10 - Implementation Guidelines

29) Managing Quality in the Quality System Itself

30) PQM System Audits

31) Implementing PQM to Attain Quality as a Business Strategy


The Manual – Looking at the Big Picture

Continuous Improvement Cycle

•Shared view of quality

•Focus of quality of design and

quality of conformance


PQM Systems Technology


CUSUM Status Chart


Product Release Efficiency


Product Release Efficiency


PQM Property Classification

32 Consulting Through Statistical Engineering

Class

SPC

Control?

True- Value

Specifications?

Maintenance

Sampling?

Lot Release to

Achieve Goal

Conformance?

I Yes Yes Yes Yes

II Yes Yes Yes No

III Often No Rarely No

8/02/2011




Leadership GB “graduation day” in Nov 2000 Left to Right: COO Richard Goodmanson MBBs Dave Flattery and Steve Bailey CEO Chad Holliday



For more on DuPont’s Six Sigma

deployment, “read the book” (by Mikel

Harry and Don Linsenmann)


Quality & Business Excellence Conference 26 – 27 November 2012 MBB Roles

Idea Merchant

Pro

ject

Exe

cuto

r

Ch

ange

Age

nt

Perf

orm

ance

Man

ager

Ad

ult

Ed

uca

tor/

Trai

ner

Co

ach

/Men

tor

Too

l Mas

ter

Brand Manager


Improve an Existing Product/Service or Process

Characterize Requirements & Performance

Identify & Characterize Key Elements in the Solution

Define the Problem

Phase 1 Phase 2 Phase 3

Define

Determine the Best Solution

Phase 4

Validate & Implement the Solution

Phase 5

Measure Analyze Improve Control

Design a New Product/Service or Process

Define Measure Analyze Design Verify


Six Sigma Tool Linkage (from Ron Snee)

Customer

Process Process Maps

SPC*

Control Plan*

C&E

Capability*

DOE

FMEA*

Multi- vari

MSA*

* indicates prescriptive QS-9000 / TS16949 requirements)


Select a measure

(Project Y or Critical

X)

Develop Operational Definition

Verify inputs and track thru meas. system to

ensure accuracy. Obtain MBB sign-off

Document Results and Proceed

Run each component thru flow-diagram

MSA on Calculated Variable

Conduct an Attribute Gage

R&R Study

Improve method or develop a new one


Operational definition?

A simple MS with no

judgment or technique?

Continuous measure?

Calculated measure? A

No

No

No No

No Yes Yes

Yes

Yes Is accuracy and R&R

adequate?

A

Resolution adequate?

Improve Resolution

Standard available?

Is R&R study

feasible?

Parallel gages or systems?

Conduct a Long-term vs. Short-

term MSA

Routinely measured?

Analyze Std. Data for Bias, R&R and

Stability

Assess all MSA Results

Measurement adequate?

Contain, Improve, Develop Method


Collect Data to Assess Bias and

Total R&R

Conduct an Inline MSA

Institute Best Practices to

Maintain the MS

No

No

No

No

No

Yes

Yes

Yes Yes

Yes

Yes

No

MSA Flow Chart


Select a measure

(Project Y or Critical

X)

Develop Operational Defintion

Verify inputs and track thru meas. system to

ensure accuracy. Obtain MBB sign-off


Run each component thru flow-diagram

MSA on Calculated Variable

Conduct an Attribute Gage

R&R Study

Improve method or develop a new one


Operational definition?

A simple MS with no

judgment or technique?

Continuous measure?

Calculated measure? A

No

No

No No

No Yes Yes

Yes

Yes Is accuracy and R&R

adequate?

A

Resolution adequate?

Improve Resolution

Standard available?

Is R&R study

feasible?

Parallel gages or systems?

Conduct a Long-term vs. Short-

term MSA

Routinely measured?

Analyze Std. Data for Bias, R&R and

Stability Assess all MSA Results

Measurement adequate?

Contain, Improve, Develop Method


Collect Data to Assess Bias and

Total R&R Conduct an Inline

MSA

Institute Best Practices to

Maintain the MS

No

No

No

No

No

Yes

Yes

Yes Yes

Yes

Yes

No

DuPont Confidential

MSA Flow Chart

43

Identify Measure

“Simple MSA”?

Attribute MSA

Lab Standard

MSA

Standard Gage R&R

MSA

Parallel Systems (In-Line)

MSA

Long-Term vs.

Short-Term MSA

Calculated Variable

MSA

Check Resolution

Assess and Fix MS


30 minutes

• A project has been undertaken to improve yield of a chemical process.

• Yield is calculated based on 2 flow meters using the following formula:

Cl2 Yield = (Chlorine/PCE)*2.339

• Chlorine and PCE flows are measured via on-line instruments,each with parallel flow meters

• Samples are taken once per day at the same time from each instrument.

• Both chlorine instruments and PCE instruments are expected to be close to equal in precision.

• Work individually or in teams to answer the following questions. • You may use either the math or simulation approach for the 2ND question

1. What is the measurement %study var. for Chlorine and PCE?

2. What is the measurement %study var. and %tolerance for C12 Yield? (determined at PCE=20.7 and Cl2=8.85) (Yield tolerance 0.85 to 1.15)

3. Is this measurement system adequate for process improvement?

Activity: In-line and Calculated MSA


%StudyVariation

= 100*smsmt/stotal

= 35%

Voice of the Measurement (VOM)

Voice of the Process

(VOP) Voice of the Customer

(VOC)

%Tolerance

= 100*(6*smsmt)/Spec Range

= 10%

Cp = Spec Range/(6*stotal)

= 3.5

%StudyVariation

= Cp * %Tolerance

or

35% = (3.5) * (10%)


46

Dem

and

Rev

iew

Pro

du

ct/p

roje

ct

Rev

iew

Business Strategy

Sale

s St

rate

gy

Mar

keti

ng

Stra

tegy

Tech

no

logy

St

rate

gy Supply Chain Strategy

Plan – Systems/Pr

ocesses

Buy – Sourcing

Make – Asset & Mfg

Tech

Deliver – SND &

Logistics

Required

Business

Outcomes

Current & future

requirements for

capability and

performance

Production Systems

Manufacturing Locations,

S&L, Engineering,

Across Supply Chains

Sup

ply

Rev

iew

Curr

ent

Capabilit

ies

& P

erf

orm

ance

Impro

vem

ent

Opport

unit

ies/

alt

ern

ati

ves

DIBM 0-24 months

Business Results vs. Objectives

Safety Environmental

People/Ethics

Customer Service Quality

Cash/Asset Productivity

Cost Productivity C

on

text

an

d P

ers

pe

cti

ve


Wrap-up and Acknowledgements

• SOE, PQM and Six Sigma are “systems” that have delivered solid business benefit within DuPont

• ASG could have been “located” anywhere in DuPont

– Corporate Marketing and Sales (CMS)

– Central Research and Development (CR&D)

– Operations and Engineering

• But ASG has been in Engineering ever since it’s “birth”

• So it is not surprising that the “systems” ASG designed and deployed have a lot of the characteristics of what Roger Hoerl and Ron Snee have recently described as Statistical Engineering!

• Thanks to ASGers Steve Larson, Stephanie DeHart and Pat DeFeo


Dr. Steven P. Bailey is Principal Consultant with DuPont's corporate Applied Statistics Group. With over 33 years at DuPont, Steve also leads DuPont's corporate Master Black Belt Network. Steve received his BS, MS and PhD in Statistics from the University of Wisconsin in the 1970's. Steve is a Past President and Chair of ASQ. He holds ASQ certifications as a Six Sigma Black Belt and a Master Black Belt.


Thank You! … Any Questions?

Documents

Improving DuPont Businesses Through Quality · Quality & Business Excellence Conference 26 – 27 November 2012 DuPont case studies will be presented on •systematic approaches to