MEDICAL DEVICE DESIGN FOR SIX SIGMA

Similar documents
Certified Six Sigma - Black Belt VS-1104

APPENDIX A: Process Sigma Table (I)

Certified Six Sigma Professionals International Certification Courses in Six Sigma Green Belt

Reduce the Failure Rate of the Screwing Process with Six Sigma Approach

Visit us at:

Probability and Statistics Curriculum Pacing Guide

Practical Research. Planning and Design. Paul D. Leedy. Jeanne Ellis Ormrod. Upper Saddle River, New Jersey Columbus, Ohio

STA 225: Introductory Statistics (CT)

CHALLENGES FACING DEVELOPMENT OF STRATEGIC PLANS IN PUBLIC SECONDARY SCHOOLS IN MWINGI CENTRAL DISTRICT, KENYA

Problem Solving for Success Handbook. Solve the Problem Sustain the Solution Celebrate Success

Green Belt Curriculum (This workshop can also be conducted on-site, subject to price change and number of participants)

Knowledge management styles and performance: a knowledge space model from both theoretical and empirical perspectives

Analysis of Enzyme Kinetic Data

Python Machine Learning

TABLE OF CONTENTS TABLE OF CONTENTS COVER PAGE HALAMAN PENGESAHAN PERNYATAAN NASKAH SOAL TUGAS AKHIR ACKNOWLEDGEMENT FOREWORD

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Introduction on Lean, six sigma and Lean game. Remco Paulussen, Statistics Netherlands Anne S. Trolie, Statistics Norway

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS

Learning Disability Functional Capacity Evaluation. Dear Doctor,

Algebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview

Minitab Tutorial (Version 17+)

A GENERIC SPLIT PROCESS MODEL FOR ASSET MANAGEMENT DECISION-MAKING

For Portfolio, Programme, Project, Risk and Service Management. Integrating Six Sigma and PRINCE Mike Ward, Outperfom

ECE 480. Six Sigma Overview & Introduction to Design for Six Sigma

2 Lean Six Sigma Green Belt Skill Set

Robot manipulations and development of spatial imagery

Lecture 1: Machine Learning Basics

The Lean And Six Sigma Sinergy

Section I: The Nature of Inquiry

PRODUCT COMPLEXITY: A NEW MODELLING COURSE IN THE INDUSTRIAL DESIGN PROGRAM AT THE UNIVERSITY OF TWENTE

ACTL5103 Stochastic Modelling For Actuaries. Course Outline Semester 2, 2014

STABILISATION AND PROCESS IMPROVEMENT IN NAB

Knowledge-Based - Systems

Lecture 15: Test Procedure in Engineering Design

ENME 605 Advanced Control Systems, Fall 2015 Department of Mechanical Engineering

A Reinforcement Learning Variant for Control Scheduling

AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS

DIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE. Junior Year. Summer (Bridge Quarter) Fall Winter Spring GAME Credits.

GACE Computer Science Assessment Test at a Glance

Major Milestones, Team Activities, and Individual Deliverables

Diagnostic Test. Middle School Mathematics

Module Title: Managing and Leading Change. Lesson 4 THE SIX SIGMA

Expert Reference Series of White Papers. Mastering Problem Management

Probability and Game Theory Course Syllabus

Mathematics subject curriculum

BEST OFFICIAL WORLD SCHOOLS DEBATE RULES

For information only, correct responses are listed in the chart below. Question Number. Correct Response

Guide to Teaching Computer Science

Value Creation Through! Integration Workshop! Value Stream Analysis and Mapping for PD! January 31, 2002!

Utilizing Soft System Methodology to Increase Productivity of Shell Fabrication Sushant Sudheer Takekar 1 Dr. D.N. Raut 2

Introduction to Simulation

Software Maintenance

Availability of Grants Largely Offset Tuition Increases for Low-Income Students, U.S. Report Says

Interactive Whiteboard

1.11 I Know What Do You Know?

Research Design & Analysis Made Easy! Brainstorming Worksheet

Detailed course syllabus

An Introduction to Simio for Beginners

Julia Smith. Effective Classroom Approaches to.

Instructor: Mario D. Garrett, Ph.D. Phone: Office: Hepner Hall (HH) 100

Introducing the New Iowa Assessments Mathematics Levels 12 14

4.0 CAPACITY AND UTILIZATION

M55205-Mastering Microsoft Project 2016

University of Cincinnati College of Medicine. DECISION ANALYSIS AND COST-EFFECTIVENESS BE-7068C: Spring 2016

The CTQ Flowdown as a Conceptual Model of Project Objectives

The Application of Lean Six Sigma in Alleviating Water Shortage in Limpopo Rural Area to Avoid Societal Disaster

3. Improving Weather and Emergency Management Messaging: The Tulsa Weather Message Experiment. Arizona State University

Math-U-See Correlation with the Common Core State Standards for Mathematical Content for Third Grade

Unit 3. Design Activity. Overview. Purpose. Profile

ANNEXURE VII (Part-II) PRACTICAL WORK FIRST YEAR ( )

Faculty Athletics Committee Annual Report to the Faculty Council September 2014

Evolutive Neural Net Fuzzy Filtering: Basic Description

FUZZY EXPERT. Dr. Kasim M. Al-Aubidy. Philadelphia University. Computer Eng. Dept February 2002 University of Damascus-Syria

International Series in Operations Research & Management Science

Ph.D. in Behavior Analysis Ph.d. i atferdsanalyse

Lahore University of Management Sciences. FINN 321 Econometrics Fall Semester 2017

ENVR 205 Engineering Tools for Environmental Problem Solving Spring 2017

School of Innovative Technologies and Engineering

Sociology 521: Social Statistics and Quantitative Methods I Spring 2013 Mondays 2 5pm Kap 305 Computer Lab. Course Website

The Good Judgment Project: A large scale test of different methods of combining expert predictions

EECS 571 PRINCIPLES OF REAL-TIME COMPUTING Fall 10. Instructor: Kang G. Shin, 4605 CSE, ;

Accounting 380K.6 Accounting and Control in Nonprofit Organizations (#02705) Spring 2013 Professors Michael H. Granof and Gretchen Charrier

A Variation-Tolerant Multi-Level Memory Architecture Encoded in Two-state Memristors

Grade 6: Correlated to AGS Basic Math Skills

D Road Maps 6. A Guide to Learning System Dynamics. System Dynamics in Education Project

Objectives. Chapter 2: The Representation of Knowledge. Expert Systems: Principles and Programming, Fourth Edition

Litterature review of Soft Systems Methodology

Mathematics (JUN14MS0401) General Certificate of Education Advanced Level Examination June Unit Statistics TOTAL.

A R "! I,,, !~ii ii! A ow ' r.-ii ' i ' JA' V5, 9. MiN, ;

ECE-492 SENIOR ADVANCED DESIGN PROJECT

CS Machine Learning

Mathematics process categories

ScienceDirect. A Lean Six Sigma (LSS) project management improvement model. Alexandra Tenera a,b *, Luis Carneiro Pintoª. 27 th IPMA World Congress

THE INFLUENCE OF COOPERATIVE WRITING TECHNIQUE TO TEACH WRITING SKILL VIEWED FROM STUDENTS CREATIVITY

Moderator: Gary Weckman Ohio University USA

University of Groningen. Systemen, planning, netwerken Bosman, Aart

Lecture Notes on Mathematical Olympiad Courses

Math 121 Fundamentals of Mathematics I

Generating Test Cases From Use Cases

Mathematics. Mathematics

Transcription:

MEDICAL DEVICE DESIGN FOR SIX SIGMA A Road Map for Safety and Effectiveness BASEM S. EL-HAIK KHALID S. MEKKI WILEY- INTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION

CONTENTS Foreword Preface xvii xix 1 Medical Device Design Quality 1 1.1 Introduction, 1 1.2 The Essence of Quality, 2 1.3 Quality Operating System and the Device Life Cycle, 5 1.3.1 Stage 1: Idea Creation, 6 1.3.2 Stage 2: Voice of the Customer and Business, 7 1.3.3 Stage 3: Concept Development, 8 1.3.4 Stage 4: Preliminary Design, 9 1.3.5 Stage 5: Design Optimization, 9 1.3.6 Stage 6: Verification and Validation, 9 1.3.7 Stage 7: Launch Readiness, 10 1.3.8 Stage 8: Mass Production, 10 1.3.9 Stage 9: Consumption, 11 1.3.10 Stage 10: Disposal or Phaseout, 11 1.4 Evolution of Quality, 11 1.4.1 Statistical Analysis and Control, 12 1.4.2 Root-Cause Analysis, 13 1.4.3 Total Quality Management, 13 1.4.4 Design Quality, 14 1.4.5 Process Simplification, 15 1.4.6 Six Sigma and Design for Six Sigma, 15

viii CONTENTS 1.5 Business Excellence: A Value Proposition, 17 1.5.1 Business Operation Model, 17 1.5.2 Structure of the Medical Device Quality Function, 18 1.5.3 Quality and Cost, 22 1.5.4 Quality and Time to Market, 23 1.6 Summary, 23 2 Design for Six Sigma and Medical Device Regulation 25 2.1 Introduction, 25 2.2 Global Perspective on Medical Device Regulations, 25 2.3 Medical Device Classification, 28 2.4 Medical Device Safety, 29 2.5 Medical Device Quality Management Systems Requirements, 31 2.6 Medical Device Regulation Throughout the Product Development Life Cycle, 34 2.6.1 Design and Development Plan, 36 2.6.2 Design Input, 42 2.6.3 Design Output, 44 2.6.4 Design Review, 46 2.6.5 Design Verification and Validation, 47 2.6.6 Design Transfer, 49 2.6.7 Design Changes, 50 2.6.8 Design History File, 50 2.6.9 QSIT Design Control Inspectional Objectives, 51 2.7 Summary, 52 3 Basic Statistics 53 3.1 Introduction, 53 3.2 Common Probability Distributions, 53 3.3 Methods of Input and Output Analysis, 56 3.4 Descriptive Statistics, 58 3.4.1 Measures of Central Tendency, 59 3.4.2 Measures of Dispersion, 61 3.5 Inferential Statistics, 63 3.5.1 Parameter Estimation, 63 3.5.2 Hypothesis Testing, 65 3.5.3 Experimental Design, 69 3.6 Normal Distribution and Normality Assumption, 70 3.6.1 Violating the Normality Assumption, 72 3.7 Summary, 72

4 The Six Sigma Process 73 4.1 Introduction, 73 4.2 Six Sigma Fundamentals, 73 4.3 Process Modeling, 74 4.3.1 Process Mapping, 74 4.3.2 Value Stream Mapping, 75 4.4 Business Process Management, 76 4.5 Measurement Systems Analysis, 77 4.6 Process Capability and Six Sigma Process Performance, 78 4.6.1 Motorola's Six Sigma Quality, 82 4.7 Overview of Six Sigma Improvement, 84 4.7.1 Phase 1: Define, 84 4.7.2 Phase 2: Measure, 84 4.7.3 Phase 3: Analyze, 85 4.7.4 Phase 4: Improve, 85 4.7.5 Phase 5: Control, 85 4.8 Six Sigma Gose Upstream: Design for Six Sigma, 86 4.9 Summary, 86 Appendix 4A: Cause-and-Effect Tools, 87 5 Medical Device Design for Six Sigma 89 5.1 Introduction, 89 5.2 Value of Designing for Six Sigma, 91 5.3 Medical Device DFSS Fundamentals, 94 5.4 The ICOV Process in Design, 96 5.5 The ICOV Process in Product Development, 98 5.6 Summary, 100 6 Medical Device DFSS Deployment 101 6.1 Introduction, 101 6.2 Medical Device DFSS Deployment Fundamentals, 102 6.3 Predeployment Phase, 103 6.3.1 Predeployment Considerations, 105 6.4 Deployment Phase, 125 6.4.1 Training, 126 6.4.2 Project Financials, 127 6.5 Postdeployment Phase, 128 6.6 DFSS Sustainability Factors, 129 6.7 Black Belts and the DFSS Team: Cultural Change, 132 6.8 Summary, 135 7 Medical Device DFSS Project Road Map 137 7.1 Introduction, 137 7.2 Medical Device DFSS Team, 139 ix

X CONTENTS 7.3 Medical Device DFSS Road Map, 143 7.3.1 Phase 1: Identify Requirements, 144 7.3.2 Phase 2: Characterize Design, 148 7.3.3 Phase 3: Optimize Requirements, 151 7.3.4 Phase 4: Verify/Validate the Design, 152 7.4 Software DFSS ICOV Process, 154 7.5 Summary, 157 8 Quality Function Deployment 159 8.1 Introduction, 159 8.2 History of QFD, 160 8.3 QFD Fundamentals, 161 8.4 QFD Methodology, 161 8.5 HQQ Evaluation, 164 8.6 HQQ 1: The Customer's House, 165 8.6.1 Kano Model, 167 8.7 HQQ 2: Translation House, 170 8.8 HQQ 3: Design House, 171 8.9 HQQ 4: Process House, 171 8.10 Application: Auto 3D, 172 8.11 Summary, 175 9 DFSS Axiomatic Design Method 177 9.1 Introduction, 177 9.2 Axiomatic Method Fundamentals, 179 9.3 Introduction to Axiom 1, 183 9.4 Introduction to Axiom 2, 185 9.5 Axiomatic Design Theorems and Corollaries, 189 9.6 Application: Medication Mixing Machine, 192 9.7 Application: Axiomatic Design Applied to Design Controls, 193 9.8 Summary, 196 Appendix 9A: Matrix Review, 196 10 DFSS Innovation for Medical Devices 198 10.1 Introduction, 198 10.2 History of the Theory of Inventive Problem Solving, 198 10.3 TRIZ Fundamentals, 200 10.3.1 Overview, 200 10.3.2 Analytical Tools, 204 10.3.3 Knowledge-Based Tools, 204 10.4 TRIZ Problem-Solving Process, 209 10.5 Ideal Final Result, 210

CONTENTS 10.5.1 Itself Method, 210 10.5.2 Ideality Checklist, 211 10.5.3 Ideality Equation, 211 10.6 Building Sufficient Functions, 212 10.7 Eliminating Harmful Functions, 212 10.8 Inventive Principles, 213 10.9 Detection and Measurement Concepts, 219 10.10 TRIZ Root Cause Analysis, 220 10.11 Evolution trends in Technological Systems, 221 10.12 TRIZ Functional Analysis and Analogy, 224 10.13 Application: Using Triads to Predict and Conceive Next-Generation Products, 225 10.14 Summary, 234 Appendix 10A: Contradiction Matrix, 234 11 DFSS Risk Management Process 240 11.1 Introduction, 240 11.2 Planning for Risk Management Activities in Design and Development, 241 11.3 Risk Assessment Techniques, 244 11.3.1 Preliminary Hazard Analysis, 245 11.3.2 Hazard and Operability Study, 245 11.3.3 Failure Mode and Effects Analysis, 245 11.3.4 Fault Tree Analysis, 246 11.4 Risk Evaluation, 248 11.5 Risk Control, 250 11.6 Postproduction Control, 250 11.7 Summary, 250 Appendix HA: Robust Design Failure Mode and Effects Analysis, 251 11A.1 Parameter Diagram, 252 11A.2 Robust Design FMEA Elements, 253 12 Medical Device Design for X 259 12.1 Introduction, 259 12.2 Design for Reliability, 262 12.3 Design for Packaging, 265 12.4 Design for Manufacture and Design for Assembly, 269 12.4.1 DFMA Approach, 269 12.4.2 DFMA in the ICOV DFSS Process, 271 12.4.3 DFMA Best Practices, 274 12.4.4 Example, 280 12.5 Design for Maintainability, 281 12.6 Design for Serviceability, 282

XII CONTENTS 12.6.1 DFS Guidelines, 282 12.6.2 Application: Pressure Recorder PCB Replacement, 285 12.7 Summary, 290 13 DFSS Transfer Function and Scorecards 291 13.1 Introduction, 291 13.2 Design Mapping, 292 13.2.1 Functional Mapping, 293 13.2.2 Process Mapping, 294 13.2.3 Design Mapping Steps, 297 13.3 Design Scorecards and the Transfer Function, 297 13.3.1 DFSS Scorecard Development, 299 13.3.2 Transfer Function Life Cycle, 299 13.4 Transfer Function Mathematics, 302 13.5 Transfer Function and Optimization, 306 13.6 Monte Carlo Simulation, 308 13.7 Summary, 309 14 Fundamentals of Experimental Design 311 14.1 Introduction, 311 14.2 Classical Design of Experiments, 314 14.2.1 Study Definition, 314 14.3 Factorial Experiment, 324 14.3.1 Mathematical Transfer Function, 325 14.3.2 Interaction Between Factors, 325 14.4 Analysis of Variance, 327 14.5 2 k Füll Factorial Designs, 332 14.5.1 Design Layout, 333 14.5.2 Data Analysis, 334 14.5.3 DOE Application, 334 14.5.4 The 2 3 Design, 341 14.5.5 The 2 3 Design with Center Points, 342 14.6 Fractional Factorial Designs, 343 14.6.1 The 2" Design, 344 14.6.2 Half-Fractional 2 k Design, 345 14.6.3 Design Resolution, 346 14.6.4 One-Fourth Fractional 2 k Design, 347 14.7 Other Factorial Designs, 349 14.7.1 Three-Level Factorial Design, 349 14.7.2 Box-Behnken Designs, 350 14.8 Summary, 350 Appendix 14A, 351 14A.1 Diagnostic Plots of Residuais, 351 14A.2 Pareto Chart of Effects, 351

CONTENTS xiii 14A.3 Square and Cube Plots, 351 14A.4 Interaction Plots, 352 15 Robust Parameter Design for Medical Devices 353 15.1 Introduction, 353 15.2 Robust Design Fundamentals, 354 15.2.1 Robust Design and DFSS, 355 15.3 Robust Design Concepts, 357 15.3.1 Concept 1: Output Classification, 357 15.3.2 Concept 2: Quality Loss Function, 358 15.3.3 Concept 3: Signal, Noise, and Control Factors, 361 15.3.4 Concept 4: Signal-to-Noise Ratios, 362 15.3.5 Concept 5: Orthogonal Arrays, 363 15.3.6 Concept 6: Parameter Design Analysis, 365 15.4 Application: Dynamic Formulation, 368 15.5 Summary, 376 16 Medical Device Tolerance Design 377 16.1 Introduction, 377 16.2 Tolerance Design and DFSS, 378 16.2.1 Application: Imprecise Measurements, 380 16.3 Worst-Case Tolerance, 382 16.3.1 Application: Internal Pressures in Disposable Tubing, 383 16.4 Statistical Tolerances, 388 16.4.1 Relationship of Tolerance to Process Capabilities, 389 16.4.2 Linear Statistical Tolerance, 389 16.4.3 Nonlinear Statistical Tolerance, 391 16.5 Taguchi's Loss Function and Safety Tolerance Design, 394 16.5.1 Nominal-the-Best Tolerance Design, 394 16.5.2 Smaller-the-Better Tolerance Design, 396 16.5.3 Larger-the-Better Tolerance Design, 397 16.6 High- vs. Low-Level Requirements' Tolerance Relationships, 398 16.6.1 Tolerance Allocation for Multiple Parameters, 399 16.7 Taguchi's Tolerance Design Experiment, 400 16.7.1 Application: Tolerance Design, 402 16.8 Summary, 404 17 Medical Device DFSS Verifikation and Validation 405 17.1 Introduction, 405 17.2 Design Verification Process, 408 17.2.1 Building a Verification Prototype, 416

xiv CONTENTS 17.2.2 Prototype Testing, 417 17.2.3 Confidence Interval of Small-Sample Veriflcation, 418 17.3 Production Process Validation, 419 17.3.1 Device Veriflcation Analysis, 427 17.4 Software Validation, 428 17.5 Design Validation, 429 17.6 Summary, 430 18 DFSS Design Transfer 431 18.1 Introduction, 431 18.2 Design Transfer Planning, 432 18.3 Process Control Plan, 433 18.4 Statistical Process Control, 434 18.4.1 Choosing the Control Chart, 435 18.4.2 Interpreting the Control Chart, 437 18.4.3 Taking Action, 438 18.5 Process Capability, 438 18.6 Advanced Product Quality Planning, 439 18.6.1 APQP Procedure, 440 18.6.2 Product Part Approval Process, 444 18.7 Device Master Record, 446 18.7.1 Document for Intended Employees, 449 18.7.2 Adequate Information, 451 18.7.3 Preparation and Signatures, 452 18.8 Summary, 453 19 Design Change Control, Design Review, and Design History File 454 19.1 Introduction, 454 19.2 Design Change Control Process, 455 19.2.1 Pre- and Postdesign Transfer CCP, 455 19.3 Design Review, 457 19.4 Design History File, 459 19.5 Summary, 460 20 Medical Device DFSS Case Study 462 20.1 Introduction, 462 20.2 DFSS Identify Phase, 462 20.3 DFSS Characterize Phase, 467 20.4 DFSS Optimize Phase, 470 20.4.1 DOE Optimization Analysis, 476 20.4.2 DOE Optimization Conclusions, 476 20.4.3 DOE Confirmation Run, 479

CONTENTS xv 20.5 DFSS Verify/Validate Phase, 480 20.6 Summary, 487 Glossary: DFSS Terminology 488 Appendix: Statistical Tables 497 References 510 Index 523