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ProblemSolving

ProblemSolving

ControlControlProcess

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Copyright 2015. All Rights Reserved.

Structured

Problem Solving

Structured Structured

Problem SolvingProblem Solving

Today’s problems are often

born from yesterday’s solutions.

Presenter:

Ken Myers, CQE, CSSBB, CMBB, PBSL

President, Ascendant Consulting Service, Inc.

Overview:

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Presentation Coverage

Presentation Coverage

� Introduction

� The Basics

� Problem Solving Roadmap

� Case Study

Structured Problem Solving 3

About Me

� Ken has 25+ years industrial experience in problem solving, root-cause analysis, CAPA methodology, and problem prevention.

� Within this period he has 17 years combined experience in the medical device, biotech, vaccines and biopharmaceutical industries.

� His remainder experience is in the microelectronics, materials science applications, and military electronics manufacturing areas.

� Ken’s client companies include Bayer Healthcare, Genentech, BP Americas, Novartis, and Sanofi-Pasteur.

� His lifetime supported multi-company benefits are ~US$450M.

� He holds degrees in both Physics and General Engineering.


Popular Problem Solving Methods

� Shewhart/Deming: Plan-Do-Study-Act, (AKA. PDCA)

� Kepner-Trego Problem Analysis

� Ford Motor Corporation’s 8D Problem Solving

� Toyota’s A3 Problem Analysis

� Ed DeBono’s Six Thinking Hats

� Genrich Altshuler’s TRIZ and Innovative Algorithm

� Goldratt’s Theory of Constraints and Thinking Tools


� MIL-STD-1520 is the basis of most problem solving systems used in industry.

� In the automotive industry, the 8D system is a composite of MIL-STD 1520 and PDCA.

� In the medical device, pharma, and vaccines industries CAPA (Corrective and Preventive

Action) is based on MIL-STD 1520.

Basis of Many P-S Systems


Causal Analysis Guidance?

P-Sol’vg Derived from MIL-STD 1520

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The Basics


Within Industrial Settings:

How do You Define a Problem?

A change in the performance of a process…

Time


Reference:

Classes of Industrial Problems

~50%

� Analytical Problem Solving

� DOE Methodology

� Process Control

� Change in Process Yield

� Change in Production Quality

� Change in Market Needs

Performance Problems

~20%

� Cause & Effect Analysis

� Conflict Resolution Diagrams

� System Dynamics

� Quality vs. Production Cost

� Labor Cost vs. Revenue

� Change in Component Safety or Quality

Conflict or Contradiction Problems

~30%

� Operations Research

� Factory Physics

� Lean Methods

� Lack of/Too Much Inventory

� Lack of Production Throughput

� Lack of raw materials

Sufficiency Problems

Frequency of Occurrence

Typical Tools & Methods

Example(s)Class ofProblem

Focus of work in Quality…


IPO Framework:

Anatomy of a Performance Problem

Problem Process:Problem Process:

Producing undesirable Producing undesirable

results through a results through a

combination of inputscombination of inputs

Material

Inputs Outputs

Equipment

Policies

Procedures

Environment

People

Methods

Problem SymptomsObserved Testing

Problem Symptoms Observed in Product Use

Problem SymptomsObserved in Manufacturing

Process

Language can get in the way of understanding!


Problem Solving Challenge:

Solving Symptoms Instead of Problems

“Symptomatic”Solution

ProblemSymptom

“Fundamental”Solution

DelayDelay

SideSide

EffectsEffects

Balancing

Balancing

Reinforcing

Symptomatic

Intervention

(Quick Fixes)

Systems-Based

Solution

Side effects due to symptomatic

interventions inhibiting fundamental solution

Causal Loop Diagram

Archetype: “Shifting the Burden”

Dr. J. Forrester, MIT, 1960


A Paradigm Change:

All Problems are Systems Based…

Problem Process:Problem Process:

Producing undesirable Producing undesirable

results through a results through a

combination of inputscombination of inputs

Material

KeyInputs

Outputs

Policies

Procedures

Problem SymptomsObserved in Test

Problem Symptoms Observed in Use

Problem SymptomsObserved in Manufacturing

Process

Everything in this box is considered the “Problem”


People are the primary cause!

Problem Solving Challenge:

Focus of Many Problem Investigations


A Common Mistake in Problem Solving

Allocate the blame!


What Sherlock Holmes Says…

� “Always approach a case with an absolutely blank mind—form no theories before one has the data.”

� “In solving a problem, the grand thing is to be able to reason backwards. “

� “It is of the highest importance in the art of detection to be able to recognize out of a number of facts which are incidental and which are vital.”

� “Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth…”

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Problem Solving Roadmap

Structured Problem Solving Process

6. Validate, Implement, and

Standardize Solution:

Implementation Plan

TASK SCHEDULE

4

123

5

2a. Identify Potential Causes:

3. Compare Causes to the Facts

1. Describe the Problem

5. Determine Corrective Actions:4. Collect Additional Data to Identify Root Cause(s):

2b. Collect, Organize, an Analyze Existing Data:

Man

(People)

Mot

her

Nat

ure

Mea

sure

s

EFFECTEFFECT

Materials

Machine

Met

hods

Cause & Effect Diagram (6M)

Distinctions Changes

Color of product

More impurity

Timing of loss

Occurring shift

Problem location

Reagent supplier

1st shift process

1st shift process

1st shift line

Reagent supplier

Distinctions & Changes

Problem Definition

Final yield of processchanged from 96% to83% during the periodfrom March 1999 toApril 1999.

Definition

SamplingPlan

O-C CurvePa

% Defectives

ScatterDiagram

Time Line

p=

0.1

%

Pro

cess

Adju

ste

d

p=

8.6

%

Equip

ment

Change

2/96 7/98 8/98 4/99

Component Swapping Study

UDL

LDL

LDL

UDLError

Proofing

- Elimination

- Facilitation

- Mitigation

- Flagging

E

V

A

L

U

A

T

E

C

O

N

T

R

O

L

Is/Is Not Diagram

WHAT

WHEN

WHERE

EXTENT

ISIS

NOT

Analysis of Means

UDL

LDL

CapabilityStudy

USLLSL

Multi-VariChart

CapabilityStudy

USLLSL

S

O

L

V

E

Box Plots

- ANOVA

- Contingency

Tables

- ComparisonTests

StatisticalComparisons

D

E

S

C

R

IB

E

I

D

E

N

T

I

F

Y

CorrectiveAction Plan

Contradiction Matrix

Cau

se

s f

rom

Ste

p 2

Facts From Step 1

Failure Modes &Effects Analysis

Failure Modes & Effects Analysis

Item Function Failure Mode Causes Effects O S D RPN

Response Surfaces

Va

ria

ble

x2

Variable x1

Change Point Analysis

CUSUM Chart of Defect Rate18

8.5

-1

CU

SU

M

3 6 9 12 15 18 21 24

Row

Process Control Chart

UCL

LCL

Input/OutputDiagram

OutputsInputs

Pro

ce

ss


UCL

LCL

Thigh

TLow

Low High

Output Performance

Variable x1

CharacterizationExperiments

Process orUsage Flow

END

START

Y

N

Pareto Analysis

Cu

m P

erc

en

t

Fre

qu

en

cy

0%

100%

Defect Type Checksheet

M T W Th F

XX X X

X XX X

AB

CD



� Implementation Plan/Gantt Chart

� Process Control Charts

� Process Control Plan

Implement controls, verify solution, standardize controls

Control

� Variation Transmission Analysis

� Solution Selection MatrixImplement corrective actionsSolve

� Contradiction Matrix

� Analysis of Means

� Design of Experiments

� Component Swapping Analysis

Evaluate the causes against the facts to eliminate unlikely causes

Evaluate

� Change Point Analysis


� Pareto Analysis

� Check sheets & Concentration Diagrams

Use existing data to characterize the extent and timing of the problem

Identify(P2)

� Cause and Effect Diagram

� Distinction & Changes Matrix

� Current Reality Tree

Identify the potential causes of the problem

Identify(P1)

� IS/IS NOT Matrix

� Timeline of Changes

� Process or Usage FlowDescribe the problemDescribe

Key Activity FocusDetailsStep


P-S Activity Classification

� Implementation Plan/Gantt Chart


� Process Control Plan

Implement controls, verify solution, standardize controls

Control

� Variation Transmission Analysis

� Solution Selection MatrixImplement corrective actionsSolve

� Contradiction Matrix

� Analysis of Means

� Design of Experiments

� Component Swapping Analysis

Evaluate the causes against the facts to eliminate unlikely causes

Evaluate

� Change Point Analysis


� Pareto Analysis

� Check sheets & Concentration Diagrams

Use existing data to characterize the extent and timing of the problem

Identify(P2)

� Cause and Effect Diagram

� Distinction & Changes Matrix

� Current Reality Tree

Identify the potential causes of the problem

Identify(P1)

� IS/IS NOT Matrix

� Timeline of Changes

� Process or Usage FlowDescribe the problemDescribe

Key Activity FocusDetailsStep

CorrectiveAction Steps

Cause Determination

Steps


A Good Problem Solving Process …

Uses facts to narrow the search

Show me the data!

CAUSESCAUSES

CAUSESCAUSES

CAUSESCAUSES


6. Validate, Implement, and

Standardize Solution:

Implementation Plan

TASK SCHEDULE

4

123

5

2a. Identify Potential Causes:

3. Compare Causes to the Facts

1. Describe the Problem

5. Determine Corrective Actions:4. Collect Additional Data to Identify Root Cause(s):

2b. Collect, Organize, an Analyze Existing Data:

Man

(People)

Mot

her

Nat

ure

Mea

sure

s

EFFECTEFFECT

Materials

Machine

Met

hods

Cause & Effect Diagram (6M)

Distinctions Changes

Color of product

More impurity

Timing of loss

Occurring shift

Problem location

Reagent supplier

1st shift process

1st shift process

1st shift line

Reagent supplier

Distinctions & Changes

Problem Definition

Final yield of processchanged from 96% to83% during the periodfrom March 1999 toApril 1999.

Definition

SamplingPlan

O-C CurvePa

% Defectives

ScatterDiagram

Time Line

p=

0.1

%

Pro

cess

Adju

ste

d

p=

8.6

%

Equip

ment

Change

2/96 7/98 8/98 4/99

Component Swapping Study

UDL

LDL

LDL

UDLError

Proofing

- Elimination

- Facilitation

- Mitigation

- Flagging

E

V

A

L

U

A

T

E

C

O

N

T

R

O

L

Is/Is Not Diagram

WHAT

WHEN

WHERE

EXTENT

ISIS

NOT

Analysis of Means

UDL

LDL

CapabilityStudy

USLLSL

Multi-VariChart

CapabilityStudy

USLLSL

S

O

L

V

E

Box Plots

- ANOVA

- Contingency

Tables

- ComparisonTests

StatisticalComparisons

D

E

S

C

R

IB

E

I

D

E

N

T

I

F

Y

CorrectiveAction Plan

Contradiction Matrix

Cau

se

s f

rom

Ste

p 2

Facts From Step 1

Failure Modes &Effects Analysis

Failure Modes & Effects Analysis

Item Function Failure Mode Causes Effects O S D RPN

Response Surfaces

Va

ria

ble

x2

Variable x1

Change Point Analysis

CUSUM Chart of Defect Rate18

8.5

-1

CU

SU

M

3 6 9 12 15 18 21 24

Row


UCL

LCL

Input/OutputDiagram

OutputsInputs

Pro

ce

ss


UCL

LCL

Thigh

TLow

Low High

Output Performance

Variable x1

CharacterizationExperiments

Process orUsage Flow

END

START

Y

N

Pareto Analysis

Cu

m P

erc

en

t

Fre

qu

en

cy

0%

100%

Defect Type Checksheet

M T W Th F

XX X X

X XX X

AB

CD


The Contradiction Matrix, (or C-Matrix):

Core Tool in Problem Solving

Cause #5

Cause #4

Cause #3

Cause #2

Cause #1

Causes

Facts

Fact

#1

Fact

#2

Fact

#3

Fact

#4

Fact

#5

Fact

#6

Fact

#7

X - FACT contradicts CAUSE A - Assumptions made; need data to verify

O - FACT supports CAUSE BLANK- Need more data


Reference:

Classifying Contradictions

� Is there a location fact that contradicts the suggested cause, e.g., two locations should be affected equally, but are not?

� Is there a timing fact that contradicts the suggested cause, e.g., the symptoms occurred long before you observed the proposed cause?

� Is there a scientific fact that contradicts the suggested cause, e.g., you would have to violate known physical or chemical principles for the cause to occur?

AnalyzeAnalyzeAnalyze


Illustration:

Using the Contradiction Matrix

XOOCause #5

OOAOOACause #4

XAOOOCause #3

XCause #2

OOOAOOCause #1

Causes

Facts

Fact

#1

Fact

#2

Fact

#3

Fact

#4

Fact

#5

Fact

#6

Fact

#7

X - FACT contradicts CAUSE A - Assumptions made; need data to verify

O - FACT supports CAUSE BLANK- Need more data


C-Matrix Example:

Reducing Causal Complexity


Roadmap for Problem Resolution

� Describe the problem dimension with IS/IS Not Matrix.

� Develop an extensive list of potential causes.

� Organize and analyze the existing data.

� Use the Contradiction Matrix to rule out unlikely causes.

� Identify unknowns and assumptions – follow-up.

� Construct an interim action plan based on unknowns and assumptions from the C-Matrix.

� Determine the causal mechanism for the problem.

� Prepare an action plan to eliminate the problem.

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Case StudyCase Study

� Description of the Process

� Problem Definition

� Root Cause Identification

� Solution Development

� Corrective Action Implementation

“Causes and effects are often not

closely related in time and space.”


The Manufacturing Process:

Human Blood Plasma Fractionation

SeparationProcess

Blood Plasma

Sealer Proteins

Coagulation Proteins

Inhibitors, etc

Immunoglobulins

Albumin

Illustration of the Plasma Fractionation Process

Problem Area

No red blood cells


The Observed Problem

Albumin

QC TestSample

Heat Stability Testing @ QC

57deg-C / 50hrs

Particulates in Solution

After QC Testing

Reqm’t: No Observable Particulates are Allowed in Solution


Describe the Problem


Problem Statement

In the last month, 3 consecutive batches of

Human Albumin show excessive levels of an

unknown precipitate after heat stability testing

in QC causing them to be placed into a

quarantined state. Loss of all three batches is

expected based on past experience.


Business Case

The total production cost of these 3

batches is $185,000, and the expected

sales value is $350,000. This problem has

occurred three times in the past without

adequate solution rendering a total

business loss to date of $1.4M


Goal Statement

Identify the cause of excessive product

precipitation at heat stability testing within 2

weeks. Determine if the quarantined lots

can be recovered without adverse risks.

Develop a corrective action plan within 3

weeks, and implement a solution plan within

4 weeks.

Establish agreement with Management Team upfront…


Is vs. Is Not Matrix: (a required tool)

Specifying the Problem Dimensions

HOW MANY OBJECTS COULD HAVE THE

DEFECT, BUT DO NOT?

WHAT OTHER SIZE COULD IT BE, BUT IS NOT?

HOW MANY OBJECTS HAVE THE

DEFECT?

WHAT IS THE SIZE OF THE DEFECT?EXTENT

WHEN ELSE COULD THE DEFECTIVE OBJECT

BEEN OBSERVED, BUT WAS NOT?

WHAT OTHER TIMES COULD IT HAVE BEEN OBSERVED, BUT WAS NOT?

WHEN ELSE COULD IT HAVE BEEN SEEN IN ITS LIFECYCLE, BUT WAS NOT?

WHEN WAS THE DEFECTIVE OBJECT

FIRST OBSERVED?

WHEN SINCE THAT TIME HAS IT BEEN OBSERVED? TIME PATTERN?

WHEN IN THE OBJECTS LIFE CYCLE WAS IT FIRST OBSERVED?

WHEN

WHERE ELSE COULD IT BE OBSERVED, BUT IS NOT?

WHERE ELSE COULD IT BE SEEN ON THE

OBJECT, BUT IS NOT?

WHERE IS THE DEFECTIVE OBJECT OR ISSUE FOUND?

WHERE IS THE DEFECT LOCATED ON

THE OBJECT?

WHERE

WHAT SIMILAR OBJECT COULD HAVE THE

DEFECT, BUT DOES NOT?

WHAT OTHER DEFECTS COULD BE OBSERVED, BUT ARE NOT?

WHAT SPECIFIC ITEM OR OBJECT HAS

THE PROBLEM?

WHAT IS THE SPECFIC DEFECT? WHAT

IS NOTIS


Example:

Is vs. Is Not Matrix


Step 2a. Indentify:

Identify Potential Causes


Example:

The Possible Causes

Man (Personnel)

MaterialsMachines

Environment

Methods

Measurements

pH Measuring

Equipment

Sample Transfer Tubing

Stir

rer

Stoppers

Silicone

Emulsi

on

Quality of

Albumin Bulk

Change in

transp

ort pers

onnel

Change in personnel in

production area

Bulk storage

Sample collection

Tra

nsp

ort

Fill volume

of samples

Dilu

tion

Sample preparation Dilutio

n

FiltrationPro

tein

conc

ent

ratio

n

Sam

ple

stab

iliza

tion

pH

adju

stm

ent

Transport

Zeichnung4/17.05.00/C. Gelich

Weather conditionsStorage of

intermediates

Quality of stabilizersPrecipitates ofAlbumin in the

QC SampleContainers

Wat

er b

ath

Glass bottle washing machine

inner wall

coating

Supplier

Glass bottles

Stability

Change in la

boratory

personnel

Quality o

f bottle

rinsin

g wate

r

People

Temperature

water bath

Test area

Box

Cooling blocks

Sample storage

Bottle

pre

-treatm

ent

fixed pH in Fr. IV-1,4Testprocedure


Example:

Distinction and Changes

A siliconized container has a thin film of silicone deposited on the

inner walls of the glass bottle to keep the contents from contacting the glass walls. Why is this needed? And, why are some sample bottles siliconized while others are not?


Step 2b. Indentify:

Analyze Existing Data


Plot of Number OoS Test Samples7

3.5

0

Nu

m O

oS

Jan 1998 Jun 1998 Nov 1998 Apr 1999 Sep 1999 Feb 2000 Jul 2000 Dec 2000 May 2001

Period

Analyze Existing Data:

Limited Understanding from Data

• This industry focuses on measuring what is “Out of Spec.”

• Which may or may not be of interest to us in identifying Cause(s).

UCL

USL


Illustration:

Optical Density Measurement

� A sample free of coagulated particulates has a transmitted light ~100%.

� With coagulated particulates, scattering reduces transmitted light to <100%.

� OD test method is highly variable, but can be use as a rough guide.

Detector

Source

SourceLight

TransmittedLight

Sample


Plot of %Optical Density100

95

90

85

80

%O

D

Jan 1998 Jun 1998 Jan 1999 Jun 1999 Dec 1999 Jun 2000 Nov 2000 May 2001

Date Period

• Evaluated base %Optical Density measures for clues.

• Plotted data on an Individuals SPC chart—a pattern emerged!


Working with Base Measures

UCL

%Relative OD

LCL


• Using a method called Change Point Analysis we were able to

determine the confidence bounds for the timing changes…


Evaluating Base Measures

Plot of %Optical Density100

95

90

85

80

%O

D

Jan 1998 Jun 1998 Jan 1999 Jun 1999 Dec 1999 Jun 2000 Nov 2000 May 2001

Date Period

USL%Relative OD

UCL

LCL


What did Sherlock Say?

� “Always approach a case with an absolutely blank mind—form no theories before one has data.”

� “In solving a problem of this sort, the grand thing is to be able to reason backwards. “

� “It is of the highest importance in the art of detection to be able to recognize out of a number of facts which are incidental and which are vital.”

� “Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth.”



The Pattern of Changes

Very Strong

Strong

Strong

Strong

Somewhat Strong

Somewhat Weak

Somewhat Weak

Weak


Step 3. Evaluate the Data:

Comparing Causes to the Facts


C-Matrix Example:

Narrowing the Causal Possibilities



Limiting the Possibilities

X

X

X

X

X

F A C T S

Sam

ple

s t

ran

sp

ort

ed

at

7am

daily

Sample preparationSample preparationSample preparationSample preparation

Quality of bottle from supplierQuality of bottle from supplierQuality of bottle from supplierQuality of bottle from supplier

Albumin stabilizersAlbumin stabilizersAlbumin stabilizersAlbumin stabilizers

Quality of bulk materialQuality of bulk materialQuality of bulk materialQuality of bulk material

Bottle pretreatment processBottle pretreatment processBottle pretreatment processBottle pretreatment process

Sample transport to QC LabSample transport to QC LabSample transport to QC LabSample transport to QC Lab

QC test procedureQC test procedureQC test procedureQC test procedure

Pre

cip

itate

s in

sam

ple

co

nta

iners

Co

ncen

trati

on

>20

%

Alb

um

in

Co

mp

lyin

g c

on

secu

tiv

e b

atc

hes

Larg

e p

recip

itate

s n

ot

in p

rod

uct

Old

sam

ple

bo

ttle

s w

ere

cle

ar

Sam

ple

& p

rod

uct

bo

ttle

s s

am

e

Sm

all p

recip

itate

s in

lo

t 3000

No

n-c

om

ply

ing

sam

ple

s in

QC

lab

Re-t

esti

ng

in

PIA

Lab

was g

oo

d

Pre

cip

itati

on

on

2n

d h

eat

treat

C A

U S

E S

X - FACT contradicts CAUSE

O - FACT supports CAUSE

Blank - Need more data

NR - Fact NOT Connected to Cause

A - Assumptions made, need data

O

O O O

O O O A O

O O

O O O O O O O O O A

O O O O O O O O O

O O O

O O

O O O O O

Fa

cts

fro

m I

s/Is

No

t M

atr

ix

Causes from Step 2

O

O

O



Initial Conclusions

� The Contradiction Matrix identified two areas that could effect particle generation at heat QC stability testing:

• Bottle Pre-treatment process

• Quality of the Bottle from Supplier

� Six other areas, which contained 50-60 possibilities, were excluded as possible causes of the observed problem.



Collect Additional Data


Step 4. Collect New Data:

Verifying the Potential Causes

What factors within the bottle pre-treatment process could contribute to the problem?

Particle CountAlbumin Concentration

Bottle Cleaning Quality

Silicone Application

Sterilization Temperature

Particle Size

Bottle InnerSurface Color

INPUT OUTPUT

Bottle

Pre-Treatment

?

?

?

?

Experiment #1

Cleaning Process

Albumin in Solution (Y/N)

Pasteurization

Particle Size

Bottle InnerSurface Color

INPUT OUTPUT

Bottle

Pre-Treatment

?

?

?

Particle Count

Experiment #2


Evaluate New Data:

Achieving Understanding

HC=23 deg-C/10hrs

HC=60 deg-C/10hrs

PS - Avg1.67

1.335

1

PS

0 percent 20 percent

AS

PS - CV0.691

0.350

0.01

PS

0 percent 20 percentAS

Effects Plots for Particle Count

Term Log[Average]

0.22204 **-0.045081

0.1995

0.24458 *0.1995

-0.24458 *

CV

0.26245 ***0.047859

0.27638 ***

0.22852 **0.27638 ***-0.22852 **

* = Significant (* = 0.1, ** = 0.05, *** = 0.01)

Average, AS^2, HC^2, AS*HCCPAS

HCCP*AS, AS*HCCP*HC, AS*HC

Effects Table for Particle Count, Categorical (ea)


Observations and Results

� Initial experiments identified the likely inputs affecting particle generation during heat stability testing to be:

• Protein Concentration (trivial factor)

• Bottle Siliconization Process (a key factor)

• Foam and Bubbles in Test Sample (observation)

• Environmental controls in filling and test areas (a key factor)


Albumin

Causal Mechanism:

Evaluating Container Passivation

SiliconizedLayer

ContainerWall

NucleationSite

AgglomeratedAlbumin

Open areas in silicone passivation can cause Albumin to nucleate

Entire process was pushed forward by changes in area temp and humidity…


Determine Corrective Actions


Step 5. Corrective Actions:

Required Action Items

� Observed the bottle siliconization process.

� Reviewed the process validation.

� Significant areas in the process validation were incomplete.

� Increased coating thickness, key cause.

� Reworked siliconization process validation.

� Improved process consistency.


Establish New Controls ControlControlProcess

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Step 6. Control:

Establish Process Controls

� Developed a test for passivating coating viscosity.

� Included viscosity test into process control and monitoring schema.

� Established requirements for viscosity, bake temperature, and time.

� Encoded requirements into automated equipment operation.

� Particle generation at heat stability testing no longer exists.


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What was the Process?

Roadmap for Problem Resolution

� Describe the problem dimension with IS/IS Not Matrix.

� Develop an extensive list of potential causes.

� Organize and analyze the existing data.

� Use the Contradiction Matrix to rule out unlikely causes.

� Identify unknowns and assumptions – follow-up.

� Construct an interim action plan based on unknowns and assumptions from the C-Matrix.

� Determine causal mechanism for the problem.

� Prepare an action plan to eliminate the problem.


Some Closing Comments:

Learning from Structured Methods

� Focuses on Facts and data instead of

Perceptions.

� Managed by eliminating the unlikely causes

first—what remains is likely…

� Allows everyone to participate.

� Uses time and limited resources efficiently.

� Method is a Natural Team Building Activity.

Visit our site at: www.ascendantconsulting.net

To Obtain a Presentation Copy…

Select the Articles link in upper

right corner

ProblemSolving

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Thank You!

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