Total Productive Maintenance: Strategies and Implementation Guide (Systems Innovation Book Series) [2 ed.] 1032223367, 9781032223360

Citation preview

Total Productive

Maintenance

This new edition emphasizes new techniques and strategies to Total Productive Maintenance (TPM) through the use of innovation and management after the pandemic to show effective communication and implementation of TPM techniques. Total Productive Maintenance: Strategies and Implementation Guide, Second Edition, presents step-by-step approaches to TPM integration with a clear direction from project infancy to completion. It discusses innovation and management through the use of TPM and offers empowerment and encouragement to associates so they feel more comfortable using TPM in everyday settings. The book is completely updated specifically with new case studies of implementing TPM after the pandemic, cultural change, and what that entails. The book is written for manufacturing engineers, reliability engineers, industrial engineers, operations managers, factory managers, project managers, supply chain managers, logistics, and can also be used as additional reading in the classroom.

Systems Innovation Book Series Series Editor: Adedeji B. Badiru Systems Innovation refers to all aspects of developing and deploying new technol­ ogy, methodology, techniques, and best practices in advancing industrial production and economic development. This entails such topics as product design and develop­ ment, entrepreneurship, global trade, environmental consciousness, operations and logistics, introduction and management of technology, collaborative system design, and product commercialization. Industrial innovation suggests breaking away from the traditional approaches to industrial production. It encourages the marriage of systems science, management principles, and technology implementation. Particular focus will be the impact of modern technology on industrial development and indus­ trialization approaches, particularly for developing economics. The series will also cover how emerging technologies and entrepreneurship are essential for economic development and society advancement. Systems Engineering Using the DEJI Systems Model® Evaluation, Justification, Integration with Case Studies and Applications Adedeji B. Badiru Handbook of Scholarly Publications from the Air Force Institute of Technology (AFIT), Volume 1, 2000–2020 Edited by Adedeji B. Badiru, Frank Ciarallo, and Eric Mbonimpa Project Management for Scholarly Researchers Systems, Innovation, and Technologies Adedeji B. Badiru Industrial Engineering in Systems Design Guidelines, Practical Examples, Tools, and Techniques Brian Peacock and Adedeji B. Badiru Leadership Matters An Industrial Engineering Framework for Developing and Sustaining Industry Adedeji B. Badiru and Melinda Tourangeau Systems Engineering Influencing Our Planet and Reengineering Our Actions Adedeji B. Badiru Total Productive Maintenance, Second Edition Strategies and Implementation Guide Tina Agustiady and Elizabeth A. Cudney

Total Productive

Maintenance

Strategies and Implementation Guide Second Edition

Tina Agustiady and Elizabeth A. Cudney

Designed cover image: © Shutterstock Second edition published 2024 by CRC Press 2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2024 Tina Agustiady and Elizabeth A. Cudney First edition published by CRC Press 2021 Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978–750–8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging‑in‑Publication Data Names: Agustiady, Tina, author. | Cudney, Elizabeth A., author. Title: Total productive maintenance : strategies and implementation guide / Tina Agustiady and Elizabeth A. Cudney. Description: Second edition. | Boca Raton, FL : CRC Press, 2024. | Series: Systems innovation book series | Includes bibliographical references and index. Identifiers: LCCN 2023031792 (print) | LCCN 2023031793 (ebook) | ISBN 9781032223360 (hbk) | ISBN 9781032223377 (pbk) | ISBN 9781003272168 (ebk) Subjects: LCSH: Total productive maintenance. | Plant maintenance. | Industrial equipment—Maintenance and repair. Classification: LCC TS192 .A38 2024 (print) | LCC TS192 (ebook) | DDC 658.2/02—dc23/eng/20230808 LC record available at https://lccn.loc.gov/2023031792 LC ebook record available at https://lccn.loc.gov/2023031793 ISBN: 978-1-032-22336-0 (hbk) ISBN: 978-1-032-22337-7 (pbk) ISBN: 978-1-003-27216-8 (ebk) DOI: 10.1201/9781003272168 Typeset in Times by Apex CoVantage, LLC

I would like to dedicate this book to my amazing husband, Brian, and our two wonderful children, Caroline and Josh. Elizabeth A. Cudney

Contents

About the Authors....................................................................................................xv

Chapter 1

Instructional Strategies.........................................................................1

1.1 1.2 1.3 1.4

Chapter 2

Purpose......................................................................................1

TPM Project Backgrounds ........................................................1

TPM Case Study Goals .............................................................1

Learning Design ........................................................................2

Introduction ..........................................................................................3

2.1 TPM...........................................................................................3

2.2 Four-Phase Approach ................................................................4

2.3 Conclusion .................................................................................6

References ............................................................................................7

Chapter 3

Overview of TPM.................................................................................8

3.1 3.2 3.3 3.4

TPM...........................................................................................8

History of TPM .........................................................................8

Goals and Benefits of TPM .......................................................9

TPM Stages ...............................................................................9

3.4.1 Stage 1: Preparation ................................................... 10

3.4.2 Stage 2: Introduction.................................................. 10

3.4.3 Stage 3: Implementation ............................................ 10

3.4.4 Stage 4: Institutionalize ............................................. 10

3.5 Pillars of TPM ......................................................................... 11

3.5.1 Pillar 1: 5S.................................................................. 11

3.5.2 Pillar 2: Autonomous Maintenance ........................... 11

3.5.3 Pillar 3: Kobetsu Kaizen............................................ 12

3.5.4 Pillar 4: Planned Maintenance................................... 13

3.5.5 Pillar 5: Quality Maintenance.................................... 13

3.5.6 Pillar 6: Training........................................................ 14

3.5.7 Pillar 7: Office TPM .................................................. 14

3.5.8 Pillar 8: Safety, Health, and Environment ................. 14

3.6 Conclusion ............................................................................... 15

References ..........................................................................................15

Chapter 4

TPM and Six Sigma ........................................................................... 16

4.1 4.2

Six Sigma................................................................................. 16

Variation .................................................................................. 17

vii

viii

Contents

4.3 4.4 4.5 4.6 4.7 4.8 4.9

Project Charter ........................................................................ 19

Supplier-Input-Process-Output-Customer Diagram................20

Kano Model ............................................................................. 21

Critical-to-Quality Characteristics..........................................22

Affinity Diagram .....................................................................22

Measurement Systems Analysis ..............................................23

Process Capability ...................................................................27

4.9.1 Capable Process .........................................................27

4.9.2 Capability Index.........................................................28

4.9.3 Process Capability Index Applications......................29

4.9.4 Potential Abuse of Cp and Cpk ....................................29

4.10 Graphical Analysis .................................................................. 32

4.11 Cause and Effect Diagram....................................................... 32

4.12 Failure Modes and Effects Analysis........................................34

4.13 Hypothesis Testing ..................................................................36

4.14 Analysis of Variance ............................................................... 37

4.15 Correlation............................................................................... 38

4.16 Simple Linear Regression........................................................ 38

4.17 Theory of Constraints.............................................................. 42

4.18 Single-Minute Exchange of Dies............................................. 43

4.18.1 Stage 1: Separate Internal versus External Setup ...... 45

4.18.2 Stage 2: Convert Internal Setups to External

Setups.........................................................................46

4.18.3 Stage 3: Streamline the Setup Operation................... 47

4.19 Conclusions..............................................................................48

Reference............................................................................................49

Chapter 5

Empowering and Encouraging Associates to Use TPM ....................50

5.1 5.2

Chapter 6

The People Side of TPM..........................................................50

5.1.1 Just-in-Time Pillar...................................................... 52

5.1.2 Respect for People Pillar............................................ 52

Conclusions.............................................................................. 57

Types of Maintenance ........................................................................ 58

6.1 6.2 6.3

Maintenance ............................................................................ 58

Breakdown Maintenance......................................................... 58

Preventive Maintenance .......................................................... 59

6.3.1 Periodic Maintenance ................................................60

6.3.2 Predictive Maintenance ............................................. 62

6.4 Corrective Maintenance .......................................................... 63

6.5 Maintenance Prevention ..........................................................65

6.6 Conclusion ............................................................................... 67

References ..........................................................................................68

ix

Contents

Chapter 7 Overall Equipment Effectiveness.......................................................69

7.1 Overall Equipment Effectiveness ............................................69

7.2 Planned versus Total Maintenance.......................................... 73

7.3 Maintainability ........................................................................ 73

7.4 Reliability ................................................................................ 74

7.5 Equipment FMEA ................................................................... 75

7.6 Conclusion ...............................................................................77

References ..........................................................................................78

Chapter 8 Introducing and Institutionalizing TPM ............................................ 79

8.1 8.2 8.3

Introducing TPM ..................................................................... 79

Deploying TPM .......................................................................80

Institutionalizing TPM ............................................................80

8.3.1 TPM Purpose .............................................................80

8.3.2 Equipment Maintenance ............................................ 82

8.3.3 Operator Involvement ................................................ 83

8.4 Implementing TPM .................................................................85

8.5 Conclusion ...............................................................................86

References ..........................................................................................87

Chapter 9 Preparatory and Introduction Stages..................................................88

9.1 Preparatory Stage ....................................................................88

9.2 Conclusion ...............................................................................95

References ..........................................................................................97

Chapter 10 Organizational Change Management.................................................98

10.1 Change Management and Organizational Culture ..................98

10.2 Resistance to the Status Quo ................................................. 103

10.3 Utilizing Known Leaders to Challenge the Status Quo ........ 103

10.4 Communicating Change........................................................ 104

10.5 Conclusion ............................................................................. 106

References ........................................................................................107

Chapter 11 Incorporating TPM into the Strategic Goals of the

Organization..................................................................................... 108

11.1 11.2 11.3 11.4 11.5

Strategic Lean Implementation ............................................. 108

Two Levels of Policy Deployment......................................... 110

Planning for Policy Deployment ........................................... 110

Daily Management of Policy Deployment ............................ 111

Develop a Hoshin Strategic Plan Summary .......................... 114

11.5.1 How to Create a Hoshin Strategic Plan

Summary for the Organization ................................ 114

x

Contents

11.6 Drive the Strategy Down to the Department

Level—Develop a Hoshin Plan Summary ............................ 116

11.6.1 Develop Implementation Strategies for the

Hoshin Plan Summary............................................. 117

11.6.2 Decide Where to Focus the Improvement

Efforts ...................................................................... 117

11.7 Develop the Hoshin Action Plan ........................................... 118

11.8 Develop the Hoshin Implementation Plan............................. 120

11.9 Conduct a Hoshin Implementation Review ........................... 121

11.10 The Three Main Tools of Policy Deployment....................... 122

11.10.1 Deming’s Plan-Do-Check-Act Cycle ...................... 123

11.10.2 Cross-Functional Management ............................... 123

11.10.3 Catchball .................................................................124

11.11 Conclusion .............................................................................124

Reference..........................................................................................126

Chapter 12 TPM Tools and Best Practices ......................................................... 127

12.1 Autonomous Maintenance..................................................... 127

12.1.1 Specialized Maintenance......................................... 138

12.1.2 Equipment Kaizen ................................................... 139

12.1.3 Early Equipment Management ................................ 140

12.1.4 Organizations and Prioritization.............................. 140

12.2 Conclusions............................................................................ 142

Chapter 13 TPM and Life Cycle Cost................................................................. 143

13.1 TPM....................................................................................... 143

13.2 Conclusion ............................................................................. 150

Chapter 14 TPM Case Study: First Pass Quality................................................ 152

14.1 14.2 14.3 14.4 14.5 14.6 14.7 14.8

Executive Summary .............................................................. 152

Introduction ........................................................................... 153

Define .................................................................................... 153

Measure ................................................................................. 155

Analyze.................................................................................. 159

Improve.................................................................................. 163

Control ................................................................................... 172

Conclusion ............................................................................. 173

Chapter 15 TPM Case Study: Hydraulic Leak Reduction.................................. 174

Chad Olson, Xueyang Chen, Heng Liu, and Elizabeth A. Cudney 15.1 Introduction ........................................................................... 174

Contents

xi

15.2 Define .................................................................................... 175

15.3 Measure ................................................................................. 175

15.4 Analyze.................................................................................. 177

15.5 Design.................................................................................... 179

15.6 Verify..................................................................................... 180

15.7 Improve.................................................................................. 181

15.8 Control ................................................................................... 182

15.9 Conclusions............................................................................ 183

References ........................................................................................183

Chapter 16 TPM Case Study: Pressure Regulator .............................................. 184

Bill Dean, Charlie Barclay, Nanda Dey, and Elizabeth A. Cudney 16.1 Executive Summary .............................................................. 184

16.2 Define .................................................................................... 184

16.2.1 Problem Statement ................................................... 184

16.2.2 Problem Goals.......................................................... 185

16.2.3 Requirements and Expectations............................... 186

16.2.4 Project Boundaries................................................... 186

16.2.5 Process Flow Diagram............................................. 186

16.3 Measure ................................................................................. 187

16.4 Gage Repeatability and Reproducibility ............................... 187

16.4.1 Data Collection ........................................................ 187

16.4.2 Process Baseline ...................................................... 187

16.4.3 Graphical Analysis................................................... 191

16.5 Analyze.................................................................................. 193

16.5.1 Failure Modes and Effects Analysis ........................ 193

16.5.2 Sources of Variation................................................. 196

16.5.3 Prioritization of Improvement Opportunities .......... 196

16.5.4 Hypothesis Testing................................................... 196

16.6 Improve.................................................................................. 197

16.7 Control ...................................................................................200

16.8 Conclusions............................................................................200

References ........................................................................................200

Chapter 17 TPM Case Study: Roller Assembly Redesign.................................. 201

DeVaughan Woodside, Apurva Chinchore, Sujitkumar Dongare, and Elizabeth A. Cudney 17.1 Introduction ........................................................................... 201

17.2 Project Description ................................................................ 201

17.3 Project Goals .........................................................................204

17.4 Requirements and Expectations ............................................204

17.5 Project Boundaries ................................................................205

17.6 Project Management..............................................................205

xii

Contents

17.7 Gantt Chart ............................................................................205

17.8 Invent/Innovate......................................................................209

17.8.1 SWOT Analysis (VOC) ...........................................209

17.9 Critical to Satisfaction ........................................................... 211

17.10 Data Collection Plan.............................................................. 212

17.11 Quality Function Deployment ............................................... 213

17.12 Design.................................................................................... 214

17.13 Concept Generation (3P) ....................................................... 214

17.14 Seven Concepts...................................................................... 215

17.15 Pugh’s Concept Selection ......................................................220

17.16 Prototype Selected/Final Design........................................... 221

17.17 Optimize................................................................................ 221

17.17.1 Design Failure Modes and Effects Analysis............ 221

17.18 Validate..................................................................................224

17.18.1 Process at a Glance ..................................................224

17.19 Trial Runs ..............................................................................226

17.20 Financial Savings................................................................... 227

17.21 Conclusion .............................................................................228

References ........................................................................................228

Chapter 18 TPM Case Study: Sticky Foil........................................................... 229

Adebolaji Jobi‑Taiwo, Adam Miller, Amelia Lopez, and Elizabeth A. Cudney 18.1 Introduction ........................................................................... 229

18.2 Project Charter and Project Management Plan Highlights ... 229

18.2.1 Project Charter......................................................... 229

18.3 Project Management Plan......................................................230

18.4 Define ....................................................................................230

18.5 Problem Statement.................................................................230

18.6 Problem Goals ....................................................................... 232

18.7 Requirements and Expectations ............................................ 232

18.8 Project Boundaries ................................................................ 233

18.9 Process Flow Diagram .......................................................... 233

18.10 Measure ................................................................................. 233

18.11 Data Collection...................................................................... 233

18.12 Process Baseline .................................................................... 237

18.13 Process Data .......................................................................... 237

18.14 Analyze..................................................................................242

18.15 Current Performance and Customer Expectations................242

18.16 Sources of Variation .............................................................. 243

18.17 Gap Analysis .........................................................................245

18.18 Improve..................................................................................245

18.19 Possible Causes Ruled Out ....................................................245

18.20 Potential Causes to Analyze Further.....................................246

18.21 Control ...................................................................................246

Contents

xiii

18.22 Financial Savings...................................................................246

18.23 Conclusions............................................................................248

Chapter 19 Future and Challenges of TPM ........................................................249

19.1 TPM in Service and Healthcare ............................................249

19.1.1 Applying TPM Concepts to the Design and

Development Life Cycle...........................................249

19.2 Engagement and Success Factors .......................................... 251

References ........................................................................................253

Index...................................................................................................................... 255

About the Authors

Tina Agustiady is a certified Six Sigma Master Black Belt and Continuous Improvement Leader. She is currently a vice president of lean training and development at JP Morgan Chase. Agustiady worked previously at MetLife across various deployments as part of leading sustainability efforts for MetLife Way. Agustiady oversaw sustaining Lean transfor­ mations and creating a Kaizen Practitioner Certification Pro­ gram. Agustiady was responsible for the strategic and tactical implementation of a Lean system deployment and furthering the transformation to a Lean culture to support strategic business initiatives and to help drive overall operational excellence within the Continuous Improvement initiative for a $2 billion manufacturer of construction components by focusing on management of crossfunctional teams and improvement of operational metrics and objectives at all levels of the organization. As Director, Operations Master Black Belt, at Philips Healthcare, Agustiady drove all continuous improvement projects in the CT/AMI operations function, resulting in the highest efficiency and effectiveness levels within Philips Healthcare. She was the transformation leader for the two businesses, providing coaching and leadership in the new methodology. She was recently a strategic change agent as a key member of the BASF Site Leadership Team responsible for infusing the use of Lean Six Sigma throughout the organization. Agustiady consistently improves cost, quality, and delivery by applying Lean and Six Sigma tools to achieve improvements through a simplification process. She is an experienced leader who has facilitated many Kaizen, 5S, and Root Cause Analysis events throughout her career in the healthcare, food, and chemical industries. She has conducted Six Sigma training and improvement programs in the baking industry at Dawn Foods and at Nestlé Prepared Foods where she held positions as a Six Sigma product and process design specialist responsible for driving optimum fit of product design and current manufacturing process capability, reducing total manufacturing costs and consumer complaints. Her many activities and responsibilities include serving as a past Lean Division president and technical vice president of Institute of Industrial and Systems Engi­ neers (IISE). She has served as a track chair for the IISE annual conferences and Lean Six Sigma conferences. Agustiady is an instructor who trains and certifies students for Lean Six Sigma for IISE. Her accomplishments as a writer and author are numerous, including serving as an editorial board member for the International Journal of Six Sigma and Com­ petitive Advantage. She has co-authored Statistical Techniques for Project Control, xv

xvi

About the Authors

Sustainability: Utilizing Lean Six Sigma Techniques, and Total Productive Mainte­ nance: Strategies and Implementation Guide. She has also authored Communication for Continuous Improvement Projects and her recently published book, Design for Six Sigma: A Practical Approach through Innovation. She serves as series editor for the CRC Press/Taylor and Francis book series “Continuous Improvement.” Agustiady was a featured author in 2014: www.crcpress.com/authors/i7078-tina­ agustiady She was honored to be a Feigenbaum medal winner for 2016, presented to an indi­ vidual who is 35 years of age or younger (as of October 1 of applying year), who has displayed outstanding characteristics of leadership, professionalism, and potential in the field of quality and also whose work has been, or will become, of distinct benefit to mankind. http://asq.org/about-asq/awards/honors/feigenbaum.html Finally, Agustiady is the 2018 ASQ Crosby Medal winner, presented to an indi­ vidual who has authored a distinguished book contributing significantly to the exten­ sion of the philosophy and application of the principles, methods, or techniques of quality management. https://asq.org/about-asq/asq-awards/honors/crosby Agustiady received her BS in industrial and manufacturing systems engineering from Ohio University. She earned her Black Belt and Master Black Belt certifications at Clemson University. Elizabeth A. Cudney, PhD, is President of Cudney Consult­ ing Group, LLC. She is a professor of data analytics in the John E. Simon School of Business at Maryville University. She received her BS in industrial engineering from North Carolina State University, master of engineering in mechani­ cal engineering and MBA from the University of Hartford, and doctorate in engineering management from the Univer­ sity of Missouri–Rolla. Dr. Cudney received the 2022 Crosby Medal from ASQ for her book on Lean Six Sigma. She also received the 2021 Bernard R. Sarchet Award from ASEE EMD for “lifetime achievement in engineering management education.” She received the 2021 Walter E. Masing Book Prize from the International Academy for Quality for her book on Lean Six Sigma. In 2018, Dr. Cudney received the ASQ Crosby Medal for her book on design for Six Sigma. Dr. Cudney received the 2018 IISE Fellow Award. She also received the 2017 Yoshio Kondo Academic Research Prize from the International Academy for Quality for sustained performance in exceptional published works. In 2014, Dr. Cudney was elected as an ASEM Fellow. In 2013, Dr. Cudney was elected as an ASQ Fellow. In 2010, Dr. Cudney was inducted into the International Academy for Quality. She received the 2008 ASQ A.V. Feigenbaum Medal and the 2006 SME Outstanding Young Manufacturing Engineering Award. She has published 12 books, 14 book chapters, and more than 200 peer-reviewed publications. In addition, she has presented numerous keynote presentations inter­ nationally. Dr. Cudney is a certified Lean Six Sigma Master Black Belt. She holds eight ASQ certifications, which include ASQ Certified Quality Engineer, Manager of Quality/Operational Excellence, and Certified Six Sigma Black Belt, among others.

1 1.1

Instructional Strategies

PURPOSE

This book’s purpose is to guide learners and practitioners of Total Productive Main­ tenance (TPM). TPM is a Lean tool. Lean is a broad term that refers to more than just a set of techniques. Lean is a philosophy and culture that promotes continuous improvement to reduce and, ideally, eliminate waste in an organization. Like Lean, TPM is a culture that focuses on continuous improvement.

1.2

TPM PROJECT BACKGROUNDS

This book provides several real-world case studies and applications of TPM that have shown significant improvement in meeting customer requirements and reducing the downtime of processes. Both industry professionals and academics can use these cases to learn how to apply TPM. The case studies will benefit readers by showing them the TPM methods and how to integrate them for process improvement. The case studies provide a detailed, step-by-step approach to TPM with clear direction from project infancy to completion. The book is designed to engage the reader by enabling hands-on experience with real TPM project cases in a safe environment where experienced Lean and mainte­ nance managers can help mentor the students in the TPM methodologies. Case stud­ ies enable students to work through the exercises and provide sufficient background information to apply the tools as if they collected the data themselves. This approach helps prepare them to see actual data and make decisions when they embark on realworld projects. The TPM case studies provide an overview of each project for the students so that they understand the project’s background, as well as sufficient information regarding the processes that need improvement. The chapters provide relevant data collected during the TPM projects for applying TPM tools and analysis. In addition, the tools are reinforced with thought-provoking homework problems at the end of chapters to offer stimulating work for the student readers.

1.3

TPM CASE STUDY GOALS

To complete the TPM case studies, participants must apply appropriate problem-solving methods and tools from the TPM toolkit to understand the problem, develop poten­ tial process improvements, and develop a plan to implement change.

DOI: 10.1201/9781003272168-1

1

2

Total Productive Maintenance

1.4 LEARNING DESIGN Each case study design enables readers to experience and understand the following: • Team interaction, the definition of team ground rules, brainstorming, and consensus building, as well as the stages of team growth • Choosing how to apply TPM tools and problem-solving methods • Supporting their decisions and application of the tools with data • Reviewing information for relevant and irrelevant information and data and reframing into what is essential to solving the problem • Development of an understanding and application of specific tools and problem-solving methods • Development of written and oral communication through customer interac­ tion and written reports and presentations, as well as the ability to present technical information • Application of project management tools to manage activities and complete tasks in a timely manner • Experience in solving an unstructured problem in a safe learning environ­ ment where mentoring is available The instructor’s role is to facilitate the learning process. The instructor should act as a coach or mentor to the student teams. It can also be helpful to have mentors expe­ rienced in applying TPM tools and methods assigned to each student team to mentor them in using TPM projects. Most TPM programs work on projects in teams. Therefore, the instructor can organize the students into groups of four to six students, depending on the class size. There is great value in having students work together as a team to work on a TPM project. They can learn how to work more effectively as a team, and team members can transfer learning across the team members because students grasp the complex concepts of TPM at different paces. An effective way to organize the teams is to determine the students’ experience and balance the team with a group ranging from no experience to extensive experience.

2

Introduction

2.1 TPM Downtime can bring any organization to a standstill. Traditional manufacturing oper­ ates under the “we fix it” mentality in which the maintenance department performs all maintenance activities. These activities are “firefighting” maintenance that occurs when a machine breaks down. The maintenance department performs preventative maintenance; however, organizations have limited time to perform maintenance around standard machine operating times. In the traditional manufacturing environ­ ment, the manufacturing department functions under the “we operate” and “run it ’til it breaks” mentality (Cudney, 2009). The operators generally do not perform any maintenance activities. Instead, the operators contact maintenance when a machine breakdown occurs. In addition, the operators are inactive during the maintenance activities. When a process operates using continuous flow and pull, a down piece of equipment impacts all preceding steps since the operator can only pull work from the preceding step. The incorrect mentality is to build up products between pro­ cesses “just in case” there is a breakdown. This mentality is why total productive maintenance (TPM) is vital for organizations to become Lean. Organizations should employ TPM as part of the Lean initiative to improve equipment uptime and relia­ bility. The purpose of this book is to provide a guide for learners and practitioners of total productive maintenance. Total productive maintenance is an innovative approach to equipment maintenance involving maintenance personnel and operators working in teams to eliminate equip­ ment breakdowns and equipment-related defects. It is a systematic approach to improv­ ing production and quality systems by including all employees through a moderate investment in maintenance (Cudney et al., 2013). The full support of all employees and top management is necessary for TPM to be successful. TPM is also a vital aspect of a quality management system. TPM strives to increase productivity by investing in appropriate maintenance to reduce losses. There are six preventable losses: 1. 2. 3. 4. 5. 6.

Breakdowns Setup and adjustments Idling Minor stoppages Quality Rework

The first two losses, breakdowns and setup, affect equipment availability. Losses from idling and minor stoppages affect equipment efficiency. The last two losses, quality and rework, result from reduced output quality.

DOI: 10.1201/9781003272168-2

3

4

Total Productive Maintenance

There are three main goals of TPM: 1. Reduce unplanned equipment downtime 2. Eliminate barriers between departments 3. Reduce equipment-related defects In addition, there are three main objectives: 1. Total employee involvement 2. Hands-on approach 3. Improve the organization’s competitiveness Just as with any other Lean technique, implementing TPM takes work. It is essential to have a clear strategy with metrics that are communicated and easy to understand.

2.2

FOUR-PHASE APPROACH

The initial focus of TPM is to return the equipment to like-new condition and pre­ vent further deterioration. Implementing preventive maintenance (PM) schedules is the first step toward eliminating deterioration. Figure 2.1 illustrates the four-phase approach to PM schedules. In the four-phase PM schedule development, the first phase consists of inspect­ ing target equipment, utilizing customized checklists, and tagging and documenting problems. The second phase involves prioritizing identified problems and identifying

FIGURE 2.1

Four-phase preventive maintenance approach.

5

Introduction

causes of the highest-priority problems. Commonly used tools in this phase include brainstorming, data collection, and maintenance/operator experience. In phase 3, the focus is to develop inspection standards and build PM schedules. The inspection standards should be written standards. These are essential for developing accurate PM schedules. They should communicate the procedure necessary to carry out effec­ tive PM. Finally, the fourth phase is to deliver training, implement PM schedules, and monitor and adjust. Practical training should be developed and delivered to tar­ geted operators and maintenance personnel. Training is essential for effective PM implementation. In addition, implementing monitoring measurements provides the vehicle to adjust and change the performance of the equipment. Overall equipment effectiveness is an effective monitoring tool for these reasons. The total productive maintenance methodology consists of four key phases, as outlined in Figure 2.2. The methodology starts by returning equipment to almost new conditions. Next, the focus is on zero breakdowns through proper maintenance. The third phase focuses on consolidating information for future use. The final phase of TPM is zero defects. Complete TPM implementation centers on autonomous maintenance, equipment improvement, maintenance prevention systems, and quality maintenance, as shown in Figure  2.3. Autonomous maintenance involves developing preventative main­ tenance practices. For equipment improvement, the equipment activities should focus on eliminating all breakdowns through physical equipment analysis (PEA)

FIGURE 2.2

Total productive maintenance phases.

6

Total Productive Maintenance

Complete TPM Implementation

Autonomous Maintenance (eliminate accelerated deterioration)

Equipment Improvement

Preventative Maintenance

Equipment activities focusing on eliminating all breakdowns via physical equipment analysis (PEA) techniques

Maintenance Prevention Systems

Information system consisting of all TPM activities used to expand initiative and make equipment related decisions based on maintenance and performance data

Zero breakdowns

FIGURE 2.3

Quality Maintenance

TPM activities focusing on eliminating equipment related defects via physical quality analysis (PQA) techniques

Zero defects

Total productive maintenance implementation.

techniques to target zero breakdowns. The organization should develop an infor­ mation system for maintenance prevention systems that consists of all TPM activ­ ities. Organizations should base equipment-related decisions on maintenance and performance data. Finally, quality maintenance eliminates equipment-related defects through physical quality analysis (PQA). The goal is zero defects. Preventive maintenance is a time- or usage-based method of maintaining equip­ ment. Much like maintenance of oil changes in an automobile, maintenance activities are performed on equipment based on defined time or usage intervals to prevent equipment breakdowns from occurring. Examples of preventive maintenance are PM schedules and team activities. Predictive maintenance is a situation-based method of maintaining equipment. Maintenance activities are performed on equipment based on visible signals or diag­ nostic techniques to prevent equipment breakdowns from occurring. Examples of predictive maintenance include vibration analysis, ultrasound, thermography, laser measuring, generator analysis, and oil analysis.

2.3

CONCLUSION

TPM is an innovative approach that eliminates equipment breakdowns and defects. The next chapter provides an overview of TPM and how TPM fits into the Lean methodology. In addition, Chapter 3 presents the goals, benefits, and pillars of TPM.

QUESTIONS 1. Explain how maintenance works in traditional manufacturing. 2. How does a machine breakdown affect a continuous flow and pull process? 3. What are the differences between TPM and traditional manufacturing?

Introduction

7

4. List and describe the six preventable losses. 5. What are the phases in the four-phase preventive maintenance approach? Describe each phase. 6. Which of the phases in the four-phase preventive maintenance approach focuses on zero breakdowns through proper maintenance? 7. What is autonomous maintenance? 8. What is the difference between predictive and preventive maintenance?

REFERENCES Cudney, E. (2009). Using Hoshin Kanri to Improve the Value Stream. Productivity Press, New York. Cudney, E., Furterer, S., and Dietrich, D. (2013). Lean Systems: Applications and Case Studies in Manufacturing, Service, and Healthcare. CRC Press, New York.

3

Overview of TPM

3.1 TPM Total productive maintenance (TPM) has become a well-known Lean methodology. The goal of TPM is to maximize an organization’s effectiveness by improving its production machinery’s life cycle. TPM is a journey of educating and training the workforce about machinery, parts, processes, efficiencies, losses, and damages while promoting productivity. TPM is critical to Lean manufacturing. If machine reliability or uptime of the machine is not predictable and not able to be sustained, the process must hold addi­ tional inventory to buffer against this uncertainty. Breakdowns or inconsistently performed maintenance result in unreliable uptime. Improving maintenance ena­ bles improvements in uptime and production speed through a given area, allowing a machine to run at its full production capacity. Four different types of maintenance are involved with TPM, including breakdown, preventative, corrective, and maintenance prevention. Chapter 6 describes these in detail.

3.2 HISTORY OF TPM Seiichi Nakajima developed TPM between 1950 and 1970 in Japan as a method­ ology to improve machine availability and throughput by utilizing more efficient maintenance and production resources. Early efforts in the 1950s focused on pre­ ventative maintenance to keep equipment in good operational condition and prevent deterioration. In the 1960s, Nippondenso, a supplier for Toyota, introduced a plant-wide mainte­ nance program based on preventative maintenance for its automated processes. Nip­ pondenso received the distinguished plant prize for developing and implementing TPM from the Japanese Institute of Plant Engineers (JIPE). Automation was increasing in manufacturing organizations. The program at Nippondenso had employees utilizing machines, and dedicated the maintenance department to maintaining those machines. However, the plan required the dedica­ tion of many maintenance personnel to maintain these machines. Instead of hiring more personnel, management decided it was more logical to use existing labor. Management decided to use operators currently working on the machines to main­ tain those machines. Management realized this labor cost would be much lower than hiring skilled engineers. It would also help ensure that the operators thor­ oughly understood the machines they operated throughout the day. These oper­ ators would quickly be able to detect if an issue was occurring, if the machines were performing well, or if the quality of the product was decreasing due to the machinery. This concept now freed up maintenance to focus on more complex 8

DOI: 10.1201/9781003272168-3

Overview of TPM

9

problems and determine long-term upgrades and fixes for machines, focusing on reliability. The communication that stemmed from the operators to maintenance allowed changes to the machines to be made on an ongoing basis, which allowed for the prevention and detection of issues while increasing the quality of the machines. This change increased the quality of the goods sold and reduced levels of scrap and defects. These efforts resulted in autonomous maintenance, which operators performed. The maintenance crew improved equipment reliability by modifying equipment, which led to maintenance prevention. Thus, preventive maintenance combined with maintenance prevention and maintainability improvement resulted in a new concept called productive maintenance. The goal of productive maintenance was to maximize plant and equipment effectiveness to achieve optimum life cycles of production equipment. The involvement of all employees helped make the program a well-established system. Through communication and group effort, the teams worked on preventative maintenance, prevention of maintenance, and maintaina­ bility improvement. The combination of autonomous and preventative maintenance became total productive maintenance.

3.3

GOALS AND BENEFITS OF TPM

TPM aims to increase job satisfaction by reducing breakdowns, quality issues, safety/environmental incidents, and costs. These improvements increase throughout and provide a competitive advantage. In addition, TPM minimizes emergency and unplanned maintenance (Cudney et al., 2013). From a broader perspective, TPM helps avoid waste in quickly changing Lean environments. TPM reduces the costs of manufacturing associated with downtime, defects, and unplanned maintenance. TPM also enables organizations to produce a low batch quantity at the earliest possible time. Organizations utilizing TPM produce products with lower variance and fewer defects (Cudney, 2009). TPM has a few common targets for improving production, quality, cost, delivery, and safety while improving skills. Common goals for production include obtaining a minimum of 80% for overall production effectiveness (OPE) and a minimum of 90% for overall equipment effectiveness (OEE). Organizations should run the machines even during lunch, which is the philosophy that lunch is for operators, not machines. For quality, organizations should strive to operate so that there are no customer com­ plaints. In addition, organizations should implement TPM to reduce manufacturing costs by 30% and achieve 100% on-time delivery of goods as the customer requires. With respect to safety, organizations must maintain an accident-free environment. Finally, organizations should focus on developing multiskilled and flexible machine operators.

3.4

TPM STAGES

Rolling out TPM in an organization consists of four stages: preparation, introduction, implementation, and institutionalization.

10

Total Productive Maintenance

3.4.1

Stage 1: PreParation

The first step in preparing for TPM is announcing the plans to implement TPM. Proper understanding, commitment, and active involvement of the top management are necessary for this step. Senior management should develop awareness programs, after which they should announce the initiative throughout all levels of the organiza­ tion. Publicity and awareness of the program are essential at this stage. The next step is to provide training and education. Training should occur based on the need. Some need intensive training in the main concepts in addition to aware­ ness training. Successful implementation roles will occur where the organization has matured in the areas. The next step to prepare for TPM is to set up TPM and departmental committees. TPM includes improvement, autonomous maintenance, and quality maintenance as part of its core structures. The organization must establish a TPM working system and targets. The organiza­ tion must benchmark each area and develop target key performance indicators (KPIs) to monitor achievements. The final step in preparing for TPM is to institutionalize a master plan. The master plan must focus on the implementation leading to institutionalizing such that TPM becomes part of the organizational culture.

3.4.2

Stage 2: introduction

This stage includes a ceremony to celebrate the start of the TPM efforts. Organ­ izations should invite their suppliers to increase their awareness and ensure they understand the need for quality support. Organizations should also invite related and affiliated companies who can become partners or customers. This stage is where much of the learning will take place. Customers will appreciate the communication efforts as well.

3.4.3

Stage 3: imPlementation

This stage includes eight activities, called the pillars of the TPM activities. Four activities take place to establish the system: 1. 2. 3. 4.

Production efficiency Initial control system of new products and equipment Improvements in efficiency Control of safety

3.4.4 Stage 4: inStitutionalize At this point, the TPM system should be mature. Therefore, the organization should challenge its achievement level to drive improvements further.

11

Overview of TPM

3.5 PILLARS OF TPM TPM is represented as pillars inside a house, as shown in Figure 3.1.

Safety, Health, and Environment

Office TPM

Training

Quality Maintenance

Planned Maintenance

Kobetsu Kaizen

Jishu Hozen (autonomous maintenance)

Pillars of TPM

5S – Foundation of TPM Workplace - Gemba

FIGURE 3.1 Pillars of TPM.

3.5.1

Pillar 1: 5S

TPM starts with 5S (sort, straighten, shine/sweep, standardize, sustain), based on the premise that issues cannot be seen clearly in an unorganized place. Cleaning and organizing will uncover problems. Making problems visible is the first step in improvement. Figure 3.2 summarizes 5S.

3.5.2

Pillar 2: autonomouS maintenance

Autonomous maintenance (AM) is also known as jishu hozen (JH). Autonomous maintenance empowers and develops operators to take care of minor maintenance tasks. It frees up skilled maintenance people to spend time on more value-added activities and technical repairs. The operators are responsible for the upkeep of their equipment to prevent it from deteriorating. There are several targets for autonomous maintenance. The first target is to reduce process time. The second target is to increase AM activities throughout operations. The next target is to reduce equipment interruptions, which enables operators to be flexible and maintain other equipment. Finally, this pillar focuses on eliminating defects at the source through employee participation.

12

Total Productive Maintenance

Sort

Sustain

Eliminate Waste

Standardize

FIGURE 3.2

Straighten

Sweep

5S.

There are eight steps of autonomous maintenance: 1. 2. 3. 4. 5. 6. 7. 8.

3.5.3

Preparing/training employees Conducting an initial cleanup of machines Implementing countermeasures Setting tentative JH standards Performing a general inspection Integrating autonomous inspection Standardizing autonomous maintenance Managing autonomous maintenance

Pillar 3: KobetSu K aizen

Kobetsu kaizen is a Japanese term for targeted improvement or focused improvement. Kai means “change.” Zen means “for the better.” Kaizen means “continuous improve­ ment.” The concept focuses on small incremental improvements that add up over time.

Overview of TPM

13

Kobetsu kaizen targets zero losses sustained with minor stops, measurements, and adjustments. The main focus of this pillar is zero defects and unavoidable downtime. This focus reduces manufacturing costs. There are five steps to kobetsu kaizen: 1. Practice concepts of zero losses in every sphere of activity 2. Relentless pursuit of achieving cost-reduction targets in all sources 3. Relentless pursuit of improving overall plant equipment effectiveness 4. Extensive use of preventive maintenance (PM) analysis to eliminate losses 5. Focus on easy handling of operators

3.5.4 Pillar 4: Planned maintenance Pillar 4 aims to ensure trouble-free machinery and equipment with zero defects for 100% customer satisfaction. The goal is for the organization to become proactive versus reactive while utilizing trained maintenance staff to help train operators to maintain their equipment better. Scheduling and planning maintenance help organizations target zero equipment failures and breakdowns. Organizations should target improving reliability and maintainability by 50% and reducing maintenance costs by 20%. In addition, organ­ izations should ensure the availability of spares at all times. There are six steps for implementing planned maintenance: 1. Evaluate equipment and record present status 2. Restore deterioration and improve weakness 3. Build an information management system 4. Prepare a time-based information system, select equipment, parts, and members, and map out the plan 5. Prepare a predictive maintenance system by introducing equipment diag­ nostic techniques 6. Evaluate planned maintenance

3.5.5

Pillar 5: Quality maintenance

The fifth pillar aims to increase customer delight through the highest quality through defect-free manufacturing. The focus is on systematically eliminating nonconformances. Organizations must understand what parts of the equipment affect product quality and begin to eliminate current quality concerns, then move to potential quality concerns. In this pillar, the organization transitions from reac­ tive to proactive. There are three main targets for quality maintenance. The first is to reduce the number of customer complaints to zero. The next is to reduce in-process defects by 50%. The last target is to reduce the cost of quality by 50%. Quality defects include external defects and internal defects. For the defects received by the customer, the organization must obtain data from the customer and field complaints. Internal data should include data related to products and the process.

14

Total Productive Maintenance

3.5.6

Pillar 6: training

The training pillar aims to have multiskilled and energized employees who have high morale and are eager to come to work to perform all their required functions inde­ pendently and effectively. Organizations must provide education and training to their operators to upgrade their skills. The training pillar aims to achieve and sustain downtime at zero on critical machines. In addition, the focus is on achieving and sustaining zero losses due to a lack of knowledge/skills/techniques. Another aim is for 100% participation in con­ tinuous improvement activities. There are six steps for the training pillar: 1. Set policies and priorities to check the status of education and training 2. Establish a training system for operations and maintenance skills 3. Train employees in operation and maintenance skills 4. Prepare a training calendar 5. Kick off the training 6. Evaluate activities and study future approaches

3.5.7

Pillar 7: office tPm

Office TPM should start after activating the other pillars of TPM (jishu hozen, kobetsu kaizen, planned maintenance, and quality maintenance). Office TPM must improve productivity, efficiency, and flow in the administrative functions while identifying losses. The goal is to analyze processes and procedures to automate office activities. The 12 major losses of office TPM are as follows: 1. Processing loss 2. Cost loss, including in areas such as procurement and accounts marketing, leading to high inventories 3. Communication loss 4. Idle loss 5. Setup loss 6. Accuracy loss 7. Office equipment breakdown 8. Communication channel breakdown 9. Time spent on retrieval of information 10. Unavailability of correct online stock status 11. Customer complaints due to logistics 12. Expenses on emergency dispatches/purchases

3.5.8

Pillar 8: Safety, HealtH, and environment

The eighth pillar focuses on creating a safe workplace and a surrounding area free of damage from processes or procedures. This pillar plays an active role in each of the

Overview of TPM

15

other pillars regularly. The zero mindset mentality encompasses zero accidents, zero health damages, and zero fires.

3.6

CONCLUSION

Organizations use TPM as a Lean methodology to improve organizational perfor­ mance. TPM increases equipment reliability and availability. TPM reduces safety and environmental incidents by improving maintenance. Further, TPM improves production efficiency and workforce utilization. Finally, TPM also increases owner­ ship and personnel satisfaction. The next chapter will discuss innovation and man­ agement through the use of TPM.

QUESTIONS 1. What is the goal of productive maintenance? 2. Discuss the history of preventative maintenance. 3. What was the first company to obtain total productive maintenance? 4. How was maintenance prevention developed? 5. What are the stages of rolling out TPM in an organization? 6. At which TPM stage are the pillars of the TPM activities rolled out? 7. Describe the second pillar of TPM. 8. What does kaizen mean? 9. Describe the major losses covered in office TPM. 10. How does TPM consider production, quality, cost, safety, and cross-functional teams? 11. How does TPM link to Lean?

REFERENCES Cudney, E. (2009). Using Hoshin Kanri to Improve the Value Stream. Productivity Press, New York. Cudney, E., Furterer, S., and Dietrich, D. (2013). Lean Systems: Applications and Case Studies in Manufacturing, Service, and Healthcare. CRC Press, New York.

4

TPM and Six Sigma

4.1 SIX SIGMA Six Sigma is a business process improvement approach that seeks to find and elim­ inate causes of defects and errors, reduce cycle times, reduce operations costs, improve productivity, meet customer expectations, achieve higher asset utilization, and improve return on investment (ROI). Six Sigma aims to produce data-driven results through management support of continuous improvement initiatives. Six Sigma requires actual data to make data-driven decisions. Six Sigma’s basic meth­ odology includes a five-step approach consisting of Define, Measure, Analyze, Improve, and Control, abbreviated with the acronym DMAIC. The Define phase initiates the project, describes the specific problem, identifies the project’s goals and scope, and defines key customers and their critical to quality (CTQ) attributes. The focus of the Measure phase is for Six Sigma teams to under­ stand the data and processes with a view to specifications for meeting customer requirements, develop and evaluate measurement systems, and measure current pro­ cess performance. In the Analyze phase, the team identifies potential causes of prob­ lems; analyzes current processes; identifies relationships between inputs, processes, and outputs; and conducts data analysis. Next, the team generates solutions based on root causes and data-driven analysis in the Improve phase while implementing effec­ tive measures. In the Control phase, the team finalizes control systems and verifies long-term capabilities to ensure long-term success. Six Sigma strives for perfection by reducing variation and meeting or exceed­ ing customer demands. Organizations should set specifications based on customer expectations, also known as the voice of the customer (VOC). Statistically speaking, Six Sigma is a process that produces 3.4 defects per million opportunities. A defect is any event outside the customer’s specifications. A  defect opportunity is how a product or service can fail to meet customer expectations (e.g., a product or service with multiple specifications). The normal distribution underlies the statistical models of the Six Sigma model, as shown in Figure 4.1. The Greek letter σ (sigma) marks the distance on the horizontal axis between the mean, μ, and the curve inflection point. The greater the distance, the greater the spread of values encountered. Figure 4.1 shows a mean of 0 (μ = 0) and a standard deviation of 1 (σ  = 1). The plot also illustrates the areas under the normal curve within different ranges around the mean. The upper and lower specification limits (USL and LSL) are ±3 standard deviations from the mean or within a six-sigma spread. Due to the properties of the normal distribution, values lying as far away as ±6 standard deviations from the mean are rare because most data points (99.73%) are within ±3 standard deviations from the mean except for processes that are out of control. 16

DOI: 10.1201/9781003272168-4

17

TPM and Six Sigma

FIGURE 4.1 Areas under the normal curve.

Sigma Level 1 2 3 4 5 6 FIGURE 4.2

DPMO 697,672 308,770 66,811 6,210 233 3.4

% Defective 69.76721% 30.87702% 6.68106% 0.62097% 0.02326% 0.00034%

% Yield 30.2328% 69.1230% 93.3189% 99.3790% 99.9767% 99.9997%

Defects per sigma level.

Six Sigma allows no more than 3.4 defects per million parts manufactured or 3.4 errors per million activities in a service operation. To appreciate the effect of Six Sigma, consider a process that is 99% perfect (10,000 defects per million parts). Six Sigma requires the process to be 99.99966% perfect to produce only 3.4 defects per million, that is 3.4/1,000,000 = 0.0000034 = 0.00034%. That means the area under the normal curve within ±6 standard deviations is 99.99966% within the specifica­ tion limits and has a defect rate of 0.00034%. The breakdown of defects per sigma level is presented in Figure 4.2 (with 1.5 sigma shift).

4.2 VARIATION Variation is present in all processes; however, the goal is to reduce variation by iden­ tifying and eliminating the root cause. For Six Sigma to be successful, a continuous improvement team must eliminate variation to achieve statistical control of a process

18

Total Productive Maintenance

by reducing variation. The team must analyze the distribution of the measurements to understand the variation and identify outliers or patterns. The study of variation began with Dr. W. Edwards Deming, who was also known as the Father of Statistics. Deming stated that variation happens naturally; however, the purpose is to utilize statistics to show patterns and types of varia­ tions. The two types of variations include special cause variation and common cause variation. Special cause variation refers to out-of-the-ordinary events such as a power outage or broken tool. Those directly involved in the process are typ­ ically responsible for correcting special cause variation (e.g., a machine operator replaces a broken tool). In comparison, common cause variation, such as tool wear, is typical and inherent in all processes. The team must reduce variation so that the processes are predicta­ ble, in statistical control, and have a known process capability. Continuous improve­ ment teams should conduct root cause analysis on special cause variation to prevent a recurrence of the issue. Management is responsible for common cause variation since it requires statistical analysis to determine the root cause and may require a Six Sigma project. Continuous improvement teams should use graphical analyses to understand the variation, followed by capability analyses. The location and spread of the data are important factors as well. Location is a measure of the process centering between the process specifications. Spread is known as the observed values compared to the specifications. Process stability is nec­ essary to predict process output. The process is said to be in statistical control if the distribution of the measurements has the same shape, location, and spread over time. A process is stable after removing all special causes of variation, and only common cause variation is present. An average central tendency of a data set measures the middle or expected value of the data set. Continuous improvement teams can use many different descriptive statistics as measurements of the central tendency of the data items. These include the arithmetic mean, the median, and the mode. Other statistical measures, such as the standard deviation and the range, are called measures of data spread. An average is a single value meant to represent a list of values. The most common measure is the arithmetic mean. However, there are many other measures of central tendency, such as the median, which is more appropriate when the distribution of the values is skewed by small numbers with very high values. It is essential to understand the system variation so that organizations use the best-performing equipment. At the same time, organizations should focus on bring­ ing the least-performing equipment back to its original state of condition and then upgrade or fix it to be capable. The most common Six Sigma tools utilized include project charter, SIPOC, Kano model, CTQ, affinity diagram, measurement system analysis, gage R and R, varia­ tion, graphical analysis, location and spread, process capabilities, cause and effect diagram, FMEA, process mapping, hypothesis testing, ANOVA, correlation, linear regression, theory of constraints, single minute exchange of dies (SMED), total pro­ ductive maintenance (TPM), design for Six Sigma, quality function deployment, design of experiments (DOE), control charts, control, and plan.

TPM and Six Sigma

19

Lean and Six Sigma go hand in hand to complement one other. Lean is a toolset for improving process flow and waste management, whereas Six Sigma is about reducing variation. An integrated Lean Six Sigma approach provides a complete program with many interchangeable tools. Both sets of tools include the management of projects and efficiency gains. Both of the tools focus on continuous improvement. Six Sigma and Lean both deal with predicting processes to reduce waste and variation, where Lean utilizes a more visual approach, and Six Sigma uses a more statistical approach. Understanding project complexity and the business need is critical when deciding what tool is needed. Most organizations start with visual management tools to iden­ tify areas of waste and then turn to statistics to drive process changes. The impor­ tance of the concepts is adapting the philosophies to make lasting improvements.

4.3

PROJECT CHARTER

A project charter provides an overview of the project, including the problem state­ ment, scope, participants, goals, and requirements. In addition, the project charter outlines the roles and responsibilities of the team members. The project charter also authorizes a new project from the team members, project champion, and senior lead­ ership. Once the project charter is approved, it should remain unchanged. A project charter begins with the project name, the department of focus, the focus area, and the product or process. Figure 4.3 provides an example of a project charter template.

Project Name: Provide a descriptive name that will be meaningful to non-team members Project Overview: Provides a general description or background of the project. Problem Statement: Explain what the problem is in quantitative terms. The statement should also include what, when, impact, and consequences. This should be based on facts, for example, 4.7% of steering knuckles fail prior to the end of their warranty of one year, which results in warranty costs of $278K in annual warranty costs in North America. Customer/Stakeholders: This should include both internal and external customers and stakeholders. What is important to these customers: Critical to satisfaction (CTS), critical to quality (CTQ), critical to delivery (CTD) and critical to cost (CTC); examples include customer satisfaction, growth, delivery, reduced costs, waste reduction, and environmental impact. Goal of the Project: Explain what the improvement goal is in terms of percent improvement, improvement in sigma level, reduction in cost, and increase in market share. Scope Statement: Explain what is in and/or out of project scope; for example, only Europe or Central US region. Projected Financial Benefit(s): What do you think the estimated benefits to the business could be, could be cost avoidance, improved revenue, P&L improvement, etc.

FIGURE 4.3

Project charter template.

20

Total Productive Maintenance

A project charter serves as the focal point throughout the project to ensure the pro­ ject is on track and the proper people are participating and being held accountable. A project charter is a living document to educate and govern a new project for the project’s goals and objectives.

4.4 SUPPLIER-INPUT-PROCESS-OUTPUT-CUSTOMER DIAGRAM The supplier-input-process-output-customer (SIPOC) diagram identifies the pro­ cess’s major tasks, activities, boundaries, customers, outputs, suppliers, and inputs. The SIPOC diagram is an essential tool for a continuous improvement team to gain a shared understanding of what the customer requires of the outputs, the process inputs from suppliers, and the process needs of the inputs. The SIPOC team also enables the team to identify the best metrics to measure and where to measure them. Continuous improvement teams must work with their suppliers to help them improve. In addition, the team must strive to continually improve the inputs by try­ ing to do the right thing the first time. The team must also describe the process at a high level to understand the process thoroughly. The team should eliminate mistakes within the process by implementing poka-yoke, or mistake proofing, where appro­ priate. The team must strive to continually improve the process outputs by utilizing metrics. Finally, and most importantly, the team must consider the customer and their requirements using critical-to-quality (CTQ) characteristics. Creating a SIPOC involves several steps. First, the continuous improvement team must gain a top-level view of the process to gain a general understanding. Then the team should identify and map the process in simple terms. The team must include all value-added and non-value-added steps in the process map. Next, the team should identify external inputs such as raw materials and employees. Once the team identifies the inputs, they should identify the customer requirements, also known as the outputs. The outputs should include both process and product output variables. The SIPOC implies that the process is understood and helps easily iden­ tify opportunities for improvement. A SIPOC is vital in continuous improvement because it helps develop a solution for improvement. Usually, the process is mapped out in a well-defined but high level first. Figure 4.4 provides an example SIPOC diagram for assembling a part. The critical part of a SIPOC is to look at the details of the current state to deter­ mine opportunities for improvement for the future state. Adding specifications for

FIGURE 4.4 Example SIPOC diagram.

21

TPM and Six Sigma

any of the inputs can identify gaps in the process. Benchmarking one process to another will also identify gaps.

4.5 KANO MODEL Noriaki Kano developed the Kano model in the 1980s. The Kano model is a graphical tool that categorizes VOC and CTQs into three groups: must-be, one-dimensional, and attractive attributes. The Kano model helps identify CTQs that add incremental value versus requirements, where having more is not necessarily better. The Kano model engages customers by understanding the product attributes most likely crucial to customers. The tool aims to support product specifications made by the customer and promote discussion while engaging team members. The model differentiates features of products rather than customer needs by under­ standing necessities and items that do not add value to the customer. Kano also produced a methodology for mapping consumer responses with questionnaires focused on attractive qualities through reverse qualities. The five categories for customer preferences include attractive, one-dimensional, must-be, indifferent, and reverse. Attractive qualities provide satisfaction when fulfilled; however, they do not result in dissatisfaction if not fulfilled. One-dimensional attributes provide satisfaction when fulfilled, and dissatisfaction if not fulfilled. Must-be qualities do not cause cus­ tomer satisfaction if fulfilled but provide dissatisfaction when not fulfilled. Indiffer­ ent characteristics are neither good nor bad, resulting in neither customer satisfaction nor dissatisfaction. Reverse qualities are those that result in high levels of dissatisfac­ tion when present. Figure 4.5 provides an example of the Kano model.

Customer  SaMsfacMon  Very Satsfied  A ractve 

One dimensional 

Indifferent  Fully 

Not at All  Must­be 

Reverse 

FIGURE 4.5

Kano model.

Degree of   Achievement 

22

Total Productive Maintenance

4.6 CRITICAL-TO-QUALITY CHARACTERISTICS Critical-to-quality (CTQ) characteristics are the features important to the customer. CTQs are measurable and quantifiable metrics that come from the voice of the cus­ tomer (VOC). CTQ is critical to communication because organizations must under­ stand the essential aspects that matter most to the customer. Utilizing an internal VOC for manufacturing is an excellent way to understand processes and what matters to employees. In manufacturing, the production opera­ tors are the customers, and management gathers the VOC from them. A tree format helps with the visualization of CTQs (Figure 4.6).

4.7

AFFINITY DIAGRAM

An affinity diagram is an organizational tool for organizing the VOC. An affinity diagram is a tool that is used to place large amounts of information in an organized manner by grouping the data into characteristics. There are six steps for creating an affinity diagram. The first step is to clearly define the question or focus of the exer­ cise. The team should describe the process or problem on paper or a sticky note. The next step is to record all participant responses on note cards or sticky notes. The third step is to lay out all note cards or post the sticky notes onto a wall. Next, the team should look for and identify general themes. The fifth step is moving the note cards or sticky notes into clusters of similar themes until the team allocates all responses into a theme. Often, the team does this in silence and does not explain the thoughts behind the movement of the notes. Once the team completes grouping cards or notes

FIGURE 4.6

CTQ tree example.

TPM and Six Sigma

FIGURE 4.7

23

Example affinity diagram.

into the clusters, they discuss the groupings and address any problems that are now more visible in the cluster. The team also determines a heading or name that repre­ sents each cluster. Finally, the team must re-evaluate the groupings and themes and make adjustments. The clusters and general themes enable problem-solving. The team can then weigh the pros and cons to make informed decisions. Figure 4.7 provides an example of an affinity diagram for a distribution center.

4.8

MEASUREMENT SYSTEMS ANALYSIS

Gage repeatability and reproducibility (R&R) is a measurement systems analysis (MSA) technique that uses continuous data based on several principles. First, the data must be in a state of statistical control. The variability must be small com­ pared to product specifications. Discrimination should be about one-tenth of product specifications or process variations. Measurement systems reveal possible sources of process variation. Repeatability and reproducibility are primary contributors to measurement errors. The total variation is equal to the actual product variation and the variation due to the measurement system. The measurement system variation is equal to the variation due to repeatability plus the variation due to reproducibility. Finally, the total (observed) variability is an additive between product (actual) and measurement variability.

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Total Productive Maintenance

Discrimination is the number of decimal places that the system can measure. Increments of measure should be about one-tenth of the width of a product specifi­ cation or process variation that provides distinct categories. Accuracy is the average quality near the true value. The true value is the theoretically correct value. Bias is the distance between the average value of the measurement and the true value, the amount by which the measurement instrument is consistently off target, or systematic error. Instrument accuracy is the difference between the observed average value of measurements and the true value. Bias considers instruments and opera­ tors. Operator bias occurs when different operators calculate detectable averages for the same measure. Instrument bias occurs when different instruments have different detectable averages for the same measure. Precision encompasses the total variation in a measurement system. Precision measures the natural variation of repeated measurements, which includes repeatabil­ ity and reproducibility. Repeatability is the inherent variability of a measurement device. It occurs in the short term for repeated measurements of the same variable under identical conditions (i.e., same operators, setups, test units, environmental conditions). Repeatability is an estimate of the pooled standard deviation of repeated measurement distribution. Repeatability is always less than the total variation of the system. Reproducibility is the variation resulting from measurements under differ­ ent conditions. The different conditions may be operators, setups, test units, or environmental conditions in the long term. Reproducibility is an estimate of the standard deviation of the average of measurements from different measurement conditions. The measurement capability index is also known as the precision-to-tolerance (P/T) ratio. The equation is P/T = (5.15 × σMS)/tolerance. The P/T ratio is usually expressed as a percentage and indicates what percent of the tolerance is taken up by the measurement error. It considers both repeatability and reproducibility. The ideal ratio is 8% or less, while an acceptable ratio is 30% or less. The 5.15 standard devia­ tion accounts for 99% of MS variation and is an industry standard. The P/T ratio is the most common estimate of measurement system precision. It is useful for determining how well a measurement system can perform to the specifica­ tions. The specifications, however, may need to be more accurate or need adjustment. The percent R&R (%R&R) addresses the percent of the total variation taken up by measurement error and includes both repeatability and reproducibility, as shown in Equation 4.1. % Gage R&R =

s MS ´100 s Total

(4.1)

Teams can also perform a gage R&R for discrete data, known as binary data. These data are also known as yes/no or defective-/non-defective-type data. The data still require at least 30 data points. The percentages of repeatability, reproducibility, and compliance should be measured. There will also be no reproducibility if the data cannot show repeatability. The matches should be above 90% for the evaluations.

TPM and Six Sigma

25

FIGURE 4.8 Gage R&R example.

A good measurement system will have a 100% match for repeatability, reproducibil­ ity, and compliance. However, if the result is below 90%, the operational definition must be revisited and redefined. The team should investigate coaching, teaching, mentoring, and standard operating procedures to improve the results. Figure 4.8 provides an example of gage R&R. The gage R&R bars should be as small as possible, driving the part-to-part bars to be larger. The averages of each operator are different, meaning reproducibility is an area of concern. The operator needs training to make consistent measurements. The oper­ ator*samples interactions lines should be reasonably parallel to each other. In the example, the operators are not consistent with each other. Further, the measurement by samples graph shows minimal spread for each sample and a slight shift between samples. The measurement by operators also indicates that the operators are not con­ sistent. The measurements from operator 2 usually are lower than the rest. Figure 4.9 provides the results for the gage R&R. The sample times operator of 0.706 shows that the interaction is insignificant, which is what this study wants. The percent contribution from part to part of 10.81 indicates the parts are the same. The total gage R&R percent study variation is 94.44. The percent contribution is 89.19. The tolerance is 143.25, and the number of distinct categories is one. These results indicate this is not a good gage. First, the number of categories is less than two, suggesting the measurement system is of minimal value since it will be difficult to distinguish one part from another. Figure  4.10 further illustrates how the gage is not acceptable. The gage run chart shows that there is no consistency between measurements.

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Total Productive Maintenance

Source Samples Operators Samples * Operators Repeatability Total

Two-Way ANOVA Table with Interaction DF SS MS F 9 282.49 31.388 3.3908 4 611.14 152.785 16.5050 36 333.25 9.257 0.8398 50 551.13 11.023 99 1778.01

P 0.004 0.000 0.706

Alpha to remove interaction term = 0.25 Source Samples Operators Repeatability Total % Contribution Source Total Gage R&R Repeatability Reproducibility Operators Part-to-Part Total Variation

Two-Way ANOVA Table without Interaction DF SS MS F 9 282.49 31.388 3.0523 4 611.14 152.785 14.8573 86 884.38 10.283 99 1778.01 Gage R&R VarComp 17.4086 10.2835 7.1251 7.1251 2.1104 19.5190

(ofVarComp) 89.19 52.68 36.50 36.50 10.81 100.00

%Study Var StdDev(SD) 4.17236 3.20679 2.66929 2.66929 1.45274 4.41803

%Tolerance (5.15 * SD) 21.4876 16.5150 13.7468 13.7468 7.4816 22.7529

Process tolerance = 15 Study Var Source Total Gage R&R Repeatability Reproducibility Operators Part-to-Part Total Variation

Number of Distinct Categories = 1

FIGURE 4.9 Gage R&R results.

FIGURE 4.10 Gage R&R run chart.

(%SV) 94.44 72.58 60.42 60.42 32.88 100.00

(SV/Toler) 143.25 110.10 91.65 91.65 49.88 151.69

P 0.003 0.000

27

TPM and Six Sigma

4.9

PROCESS CAPABILITY

The capability of a process is the spread that contains most of the values of the process distribution. Teams should only establish process capability on a stable process with a dis­ tribution that only has common cause variation. Figure 4.11 illustrates process capability.

4.9.1 caPable ProceSS A process is capable if its natural tolerance lies within the engineering tolerance or specifications. If the natural tolerance is less than the engineering tolerance, the value of Cp is greater than one. The measure of process capability of a stable process is esti­ mated when there is only inherent process variability in the process (i.e., only com­ mon cause variation is present). Organizations generally use a minimum Cp  = 1.33 for an ongoing process. This value ensures a reject rate of 0.007%; therefore, it is an effective strategy for preventing nonconforming items. Cp is defined mathematically, as shown in Equation 4.2. Cp =

FIGURE 4.11

Process capability.

(USL ­ LSL ) 6s

(4.2)

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Total Productive Maintenance

Where, USL = upper specification limit LSL = lower specification limit Cp measures the effect of the inherent variability only. The analyst should use R-bar/d2 to estimate from an R-chart in a state of statistical control, where the R-bar is the average of the subgroup ranges and d2 is a normalizing factor tabulated for different subgroup sizes (n). It is not necessary to verify control before performing a capability study. Teams can conduct the study, then verify process control after calculating process capability using control charts. If the process is in control during the study, then the process capability estimates are correct and valid. However, if the process is not in statistical control, the team gains valuable information and proper insights into the corrective actions to pursue.

4.9.2 caPability index Continuous improvement teams can assess process centering when a two-sided spec­ ification is available. If the capability index (Cpk) is equal to or greater than 1.33, then the process may be adequately centered. Cpk can also be employed when there is only a one-sided specification. Equation 4.3 provides the Cpk calculation for a two-sided specification. é USL ­ X X ­ LSL ù C pk = min ê , ú 3s û ë 3s

(4.3)

Where, X  = overall process average However, for a one-sided specification, the actual Cpk obtained is reported. This Cpk can determine the percentage of observations out of specification. The overall long­ term objective is to make Cp and Cpk as large as possible by continuously improving or reducing process variability, s , for every iteration so that a greater percentage of the product is near the key quality characteristics target value. The goal is to center the process with as little variability as possible. If a process is centered but not capable, one or several courses of action may be necessary. One of the actions may be integrating designed experiments to gain additional knowledge of the process and developing control strategies. If excessive variability exists, one may conduct a nested design to estimate the various sources of variability. The team can then evaluate these sources of variability to determine what strategies to use to reduce or permanently eliminate them. Another action may be changing the specifications or continuing production and sorting the items. Teams can observe three characteristics of a process for capability: 1. The process may be centered and capable. 2. The process may be capable but not centered. 3. The process may be centered but not capable.

TPM and Six Sigma

4.9.3

29

ProceSS caPability index aPPlicationS

The potential applications of the process capability index include communication, continuous improvement, audits, improvement prioritization, and defect prevention. Cp and Cpk have been used in industry to establish a dimensionless common language helpful in assessing the performance of production processes. Engineering, quality, and manufacturing, among other departments, can communicate and understand pro­ cesses with high capabilities. Organizations can also use the indices to monitor continuous improvement by observing the changes in the distribution of process capabilities. For example, sup­ pose an organization has 20% of processes with capabilities between 1 and 1.67 in a month, and some of these improved to 1.33 and 2.0 the next month. In that case, this indicates that improvement has occurred. Various kinds of audits are in use today to assess the performance of quality sys­ tems. A  comparison of in-process capabilities with capabilities determined from audits can help establish problem areas. Analyzing all processes with unacceptable Cp or Cpk values can be extremely powerful in establishing the priority for process improvements. Teams can focus on improving process capabilities for CTQ characteristics to increase customer satis­ faction. The capability indices enable continuous improvement teams to evaluate all indices and then prioritize based on customer impact, ease of improvement, cost of improvements, or other criteria. For process qualification, it is reasonable to establish a benchmark capabil­ ity of Cpk  =  1.33, making nonconforming products unlikely in most cases. By increasing process capabilities, the organization can prevent nonconforming products.

4.9.4 Potential abuSe of Cp and Cpk Despite its several possible applications, the process capability index has some potential sources of abuse. First, Cpk can increase without process improvement, even though repeated testing reduces test variability. In addition, the wider the specifica­ tions, the larger the Cp or Cpk, even if taking action does not improve the process. Third, analysts tend to focus on the number rather than the process. Analysts also tend to determine process capability before establishing statistical control. Analysts should calculate process capability when only common cause var­ iation is present. Common cause variation enables analysts to predict what to expect in the future. Special causes of variation make prediction impossible, and the capa­ bility index needs to be clarified. Analysts must also consider non-normality. Some processes result in non-normal distribution for some characteristics. Since capability indices are very sensitive to departures from normality, analysts may need to use data transformation to achieve approximate normality. Analytical and statistical tools coupled with sound managerial approaches provide organizations with a robust implementation of improvement strategies. One method that has emerged as a sound organizational principle is Lean.

30

Total Productive Maintenance

Teams should only conduct Cp and Cpk capability analyses using normally dis­ tributed data. Using data for capability analyses is very easy, especially on software systems that calculate the data automatically. Therefore, the first step in the capabil­ ity analysis is to check for normality. Only if the data are normal can the team can perform capability studies. If the data are not normal, the team must eliminate special cause variation. In addition, the team should only remove data points if the reasoning is known for the data point that is an outlier (e.g., temperature change, shift change). Once an outlier is for a known reason, the team can remove the outlier. Then the team must check the data for normality again. If there is no root cause for the outlier, the team must collect more data; however, the team should still not conduct a capability analysis until normality is proven. The importance of finding the capable equipment or products in a business through process capabilities will enable the team to identify variation through benchmark­ ing. The team should then apply the best-in-class (BIC) practices on the appropri­ ate equipment, products, and processes by implementing improvements to increase process capability. It is imperative to perform preventative maintenance to ensure equipment performs at the highest possible process capability. The first step when comparing two or more equipment pieces is to perform a nor­ mality test, as previously discussed. The normality of Equipment A and B are shown in Figures 4.12 and 4.13, respectively. The conclusions from the normality tests are that the data from equipment A1 are not normal, and the data from equipment B1 are normal. Since the data from equip­ ment B1 are normal, the next step is to calculate its process capabilities, as shown in Figure 4.14.

FIGURE 4.12 Normality of equipment A.

TPM and Six Sigma

FIGURE 4.13 Normality of equipment B.

FIGURE 4.14 Process capability for equipment B1.

31

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Total Productive Maintenance

Equipment B1 has a short-term capability (Cp of 1.34) approximately equivalent to a short-term Z of 4. The long-term capability needs some improvement (Ppk of 0.94). The team should continue analyzing process capability in a systematic fashion (e.g., monthly or quarterly) to understand if the processes are improving. Further, the team should continuously improve the equipment to enhance its capabilities.

4.10 GRAPHICAL ANALYSIS Graphical analyses are visual representations of tools that show meaningful key aspects of projects. Common graphical tools include dot plots, histograms, normality plots, Pareto diagrams, second-level Paretos (also known as stratification), boxplots, scatter plots, and marginal plots. Plotting the data is a crucial beginning step in any data anal­ ysis because it visually represents the data. A graphical analysis summary can cover sample size, mean, standard deviation, variance, skewness, kurtosis, p-value, and confi­ dence intervals, as shown in Figure 4.15. A Pareto chart visually represents what occurs the most by separating the vital few versus the trivial many, as shown in Figure 4.16.

4.11

CAUSE AND EFFECT DIAGRAM

After mapping a process, teams can develop a cause and effect (C&E) diagram. This process is essential because it enables root cause analysis. The basis behind root cause analysis is to ask “why?” five times to get to the actual root cause. This process is known as the five whys. Often, problems are “band-aided” to fix the top-level prob­ lem rather than addressing the problem itself. Figure 4.17 provides a C&E diagram.

FIGURE 4.15

Graphical analysis summary.

TPM and Six Sigma

FIGURE 4.16

33

Pareto chart example.

FIGURE 4.17 Cause and effect diagram.

The C&E diagram is also called a fishbone diagram because it visually looks like a fish where the bones are the causes and the fish head is the effect or problem statement. The C&E diagram often consists of several categories: measurements, material, personnel, environment, methods, and machines. However, these cate­ gories typically work best in manufacturing environments. Therefore, continuous improvement teams commonly use an affinity diagram approach to determine the bone themes. This process requires a team to do a great deal of brainstorming, focus­ ing on the causes of the problems based on the categories.

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Total Productive Maintenance

4.12

FAILURE MODES AND EFFECTS ANALYSIS

Continuous improvement teams should use the failure modes and effects analysis (FMEA) tool to select action items from the C&E diagram and prioritize the projects. The FMEA identifies the causes, assesses risks, and determines further steps. The steps to an FMEA are the following: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Define process steps Define functions Define potential failure modes Define the potential effects of failure Define the severity of a failure Define the potential mechanisms of failure Define current process controls Define the occurrence of failure Define current process control detection mechanisms Define the ease of detecting a failure Multiply severity, occurrence, and detection to calculate a risk priority number (RPN) 12. Define recommended actions 13. Assign actions with key target dates to responsible personnel 14. Revisit the process after taking action to improve it 15. Recalculate RPNs with the improvements Figure  4.18 provides an FMEA. Figures  4.19 through 4.21 provide the severity, occurrence, and detection criteria. The RPN is used to prioritize improvement activities. It is essential to under­ stand the process’s severity to a customer and increase the capability of the process to, in turn, improve the process. Reducing the RPN will make the entire process more sustainable by being able to deliver the process at its best capabilities through careful project management. It is vital to maintain the FMEA by documenting improvements.

Item: ______________________________ Team: _____________________________ Process Function

Potential Failure Mode

Potential Effect(s) of Failure

Page _____ of _____

Prepared by: __________________

FMEA Date (Orig.): ____________

Process Responsibility: _________________________ Key Date: ____________________________________ S E V

Class

Potential Cause(s)/ Mechanism(s) of Failure

FIGURE 4.18 Process FMEA.

O C C

Current Controls

D E T

R P N

Recommended Actions

Responsibility & Target Completion Date

Action Results Actions S O D Taken E C E V C T

R P N

35

TPM and Six Sigma Effect Hazardous – Without Warning Hazardous – With Warning Very High High Moderate Low Very Low Minor Very Minor None

FIGURE 4.19

Criteria: Severity of the Effect Very high severity ranking when a potential failure mode affects safety and involves non-compliance without warning. Very high severity ranking when a potential failure mode affects safety and involves non-compliance with warning. Process is not operable and has loss of its primary function. Process is operable, but with a reduce functionality and an unhappy customer. Process is operable, but not easy to manufacture. The customer is uncomfortable. Process is operable, but uncomfortable with a reduced level of performance. The customer is dissatisfied. The process is not in 100% compliance. Most customers are able to notice the defect. The process is not in 100% compliance. Some customers are able to notice the defect. The process is not in 100% compliance. Very few customers are able to notice the defect. No effect

Ranking 10 9 8 7 6 5 4 3 2 1

Severity criteria.

Probability of Failure Failure is almost inevitable High: Repeated failures Moderate: Occasional failures Low: Very few failures Remote: Failure in unlikely

Possible Failure Rates >= 1 in 2 1 in 3 1 in 8 1 in 20 1 in 80 1 in 400 1 in 2,000 1 in 15,000 1 in 150,000 U S L 0.00 P P M Total 0.00

42

E xp. Within P erformance P P M < LS L 66.07 P P M > U S L 12.03 P P M Total 78.11

48

54

60

E xp. O v erall P erformance P P M < LS L 2322.11 PPM > USL 881.06 P P M Total 3203.17

FIGURE 14.21 Capability analysis of Match Point.

66

Pp Low er U pper PPL PPU P pk Low er U pper C pm Low er

CL CL

CL CL CL

0.99 0.91 1.07 0.94 1.04 0.94 0.86 1.03 * *

169

TPM Case Study: First Pass Quality

Operator

1

2

3

FIGURE 14.22

Sample 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Gage R&R data.

Trial 1 95.1 96.2 96.3 96.4 96.8 96.8 96.8 96.9 95.1 95.2 95.1 96.2 96.3 96.4 96.9 96.7 96.8 96.8 95.2 95.2 95.1 96.2 96.1 96.2 96.3 96.8 96.8 96.9 95.4 95.4

Trial 2 95.2 96.3 96.3 96.4 96.8 96.8 96.8 96.7 95.2 95.2 95.1 96.2 96.3 96.6 96.8 96.6 96.8 96.6 95.1 95.2 95.4 96.2 96.3 96.4 96.8 96.8 96.8 96.7 95.3 95.3

Trial 3 95.1 96.4 96.3 96.4 96.8 96.8 96.8 96.9 95.1 95.2 95.1 96.2 96.3 96.4 96.8 96.8 96.8 96.9 95.1 95.1 95.2 96.2 96.3 96.4 96.8 96.8 96.8 96.7 95.2 95.2

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Total Productive Maintenance

Gage R&R (ANOVA) for Measurement A Reported by : Tolerance: M isc:

G age name: D ate of study : Components of Variation

Measurement A by Sample

100

% Contribution

97

Percent

% Study Var

96

50

0

95 Gage R&R

Repeat

Reprod

1

Part-to-Part

2

3

4

5 6 Sample

7

8

9

10

Sample Range

R Chart by Operator 1

2

Measurement A by Operator

3

97

0.4

UCL=0.2917 _ R=0.1133 LCL=0

0.2 0.0 1 2

3 4 5 6

7 8 9 10

1 2 3

4 5 6 7

2 3 4 5

8 9 10 1

6 7 8

9 10

96 95

Sample

1

2 Operator

1

97

2

3 _ _ CL=96.275 X=96.159U LCL=96.043

96 95

1 2

3 4 5 6

7 8 9 10

1 2 3 4

5 6 7

8 9 10 1

2 3 4 5

97

6 7 8 9 10

Operator 1 2

96 95

Sample

FIGURE 14.23

3

1

2

3

4

5 6 Sample

7

8

9

10

Gage R&R summary graphs.

Source Samples Operators Samples * Operators Repeatability Total

Two-Way ANOVA Table with Interaction DF SS MS F 9 39.9246 4.43606 308.960 2 0.0082 0.00411 0.286 18 0.2584 0.01436 1.700 60 0.5067 0.00844 89 40.6979

Alpha to remove interaction term = 0.25 Gage R&R % Contribution Source Total Gage R&R Repeatability Reproducibility Operators Operator*Sample Part-to-Part Total Variation

VarComp 0.010416 0.008444 0.001971 0.000000 0.001971 0.491300 0.501716

(ofVarComp) 2.08 1.68 0.39 0.00 0.39 97.92 100.00

Study Var Source Total Gage R&R Repeatability Reproducibility Operators Operator*Sample Part-to-Part Total Variation

%Study Var StdDev(SD) 0.102057 0.091894 0.044398 0.000000 0.044398 0.700928 0.708319

%Tolerance (5.15 * SD) 0.61234 0.55136 0.26639 0.00000 0.26639 4.20557 4.24992

Number of Distinct Categories = 9

FIGURE 14.24

3

Sample * Operator Interaction

Average

Sample Mean

Xbar Chart by Operator

Gage R&R results.

(%SV) 14.41 12.97 6.27 0.00 6.27 98.96 100.00

P 0.000 0.754 0.065

171

TPM Case Study: First Pass Quality

Gage Run Chart of Measurement A by Sample, Operator Reported by : Tolerance: M isc:

G age name: D ate of study :

1

2

3

4

5

96.5

Measurement A

Mean

O perator 1 2 3

96.0 95.5

6

7

8

9

10

96.5 Mean

96.0 95.5

Operator Panel variable: Sample

FIGURE 14.25

Gage run chart.

The team made the following changes in the Improve phase. The first change was to the piping. After several tests and data analysis, the team minimized thin and thick issues after the piping changes. The IMR chart showed that all points were in control after the piping changes, a positive result for root cause analysis. The Normality test results were above 0.05 after the piping changes, which is the goal (0.547 for Match Point). This result further proved that the piping changes were significant. The team performed capability analyses after the piping changes. The Cpk (short-term) value of 1.27 shows that the capability was desirable. The team can still improve the Ppk (long-term) value of 0.97, but it was also desirable (a value of 1.33 is considered acceptable). The PPM total (exp. overall performance) is the number of parts per million (3,203.17) whose characteristic of interest is outside the tolerance limits. This value means that approximately 3,203.17 out of 1 million batches do not meet the specifications. After sustaining the results with the correct recipe, the team will track improvements again. Therefore, the piping changes were significant and were an impact and root cause of the thick/thin product issues. The team also made changes with respect to the specifications and standard devi­ ations for thin or thick products. First, the team checked the interaction for signifi­ cance. The interaction is only significant if the p-value is less than 0.05. In this case, the p-value is 0.065. Therefore, the interaction is not significant, which is the goal of this study. In the actual gage R&R, the reproducibility is 14.97, which is sufficient. Repeat­ ability is 6.27. The team must focus on reducing variation from repeatability and reproducibility. The data gives just one distinct category if the repeatability is too high. The standard deviation of the gage R&R for part-to-part is 0.700. Again, the

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Total Productive Maintenance

team must minimize the noise. In this case, the study variation is 0.61, less than the allowable amount of 30. The P/T ratio is 14.41, which is close to the 15 or less that is ideal. The gage consumes 14% of the total variation in the measurement system. Further, the gage run study showed that operator 3 needed training to keep consist­ ent measurements. The components of the variation chart showed low noise to the high signal. The gage R&R is the noise variable, and the repeatability and reproduc­ ibility make up the gage R&R. The part-to-part is the signal. The number of distinct categories was nine. Therefore, according to the MSA, the standard deviation changes showed a pos­ itive result. The gage needs some improvements; however, the repeatability and reproducibility values were acceptable after changing the standard deviation.

14.7

CONTROL

The team implemented the control plan shown in Figure 14.26 after completing and sustaining the improvements in the previous phases. In addition, the team will con­ tinue to use the audit checklist to ensure the manufacturing groups are on track as illustrated in Figure 14.27. The latest audit shows all items are on track. The team made one main change to minimize variation through piping changes. The water was trapped in a pipe, making the product thin or thick based on when the water purged from the pipe. The team changed the pipe, which immediately fixed the problems. The team also changed the standard deviation to ensure customers received products within specifications. The standard deviation was centered. The temperature seemed an issue but turned out to be a false variable. The team also implemented preventive maintenance and training to ensure the variation is minimized and continues monthly and quarterly. The team analyzed the hypothesis testing results as follows. First, the confidence interval for the mean difference between the two materials does not include zero, which suggests a difference between them. The small p-value (p  =  0.007) further indicates that the data are inconsistent with H0: μ1 − μ2 = 0. In other words, the two

Lean Sigma Corporation Control Plan Process: Customer: Stakeholder: Business:

Preparer: Email: Phone: Owner:

Match Point Various T Agustiady No No

Process

Process Step

CTQ/Metric

Specification/ Requirement

CTQ / Me tric Equation

LSL

Piping Changes

Change pipe so water will not be stuck in 3 way valve

PM's completed Standard Deviation Changes Training Facility A checks solids of the 80% of 100% mix Liquid Sugar, 20% of 100% mix Corn Syrup, and then combined syrups

Ensure PM's are completed on equipment Change standard deviation from 1 to 2 Train all operators

Implement solid readings to be taken in slurry kettle for same commodities

FIGURE 14.26

Control plan.

Change pipes

Flow

Measurement Sample Measure Responsible for Size Frequency Metric Method

USL 25

75

1

Complete PM's monthly Change Train all

4

Every batch

Corrective Action

Responsible for Action T Dogsmit

1x/month

J Bryan

1x/month

J Bryan

PM's monthly

T Dogsmit

J Bryan

Change Standard

T Dogsmit

1x/quarter

1

Link or Report Name

Weight validation to be /

Std. Dev

Check readings Solid Readings

Page: of 1 1 Reference No: 1A Revision Date: 12/1/13 Approval: Complete - M Bell

T Agustiady [email protected] 440-111-1111 T Agustiady

1x/batch

J Bryan

J Bryan

Trained all operators Check and record viscosity and temperature on provided sheets

T Dogsmit

T Dogsmit

173

TPM Case Study: First Pass Quality

Audit Checklist Target Area:

Statement of Audit Objective:

Auditor:

Audit Technique

Auditable Item, Observation, Procedure etc.

Individual Auditor Rating (Circle Rating)

Observation

Have all associates been trained?

YES

Observation

Is training documentation available?

YES

NO

Observation

Is training documentation current?

YES

NO

Observation

Are associates wearing proper safety gear?

YES

NO

Observation

Are SOP's available?

YES

NO

Observation

Are SOP's current?

YES

NO

Observation

Is quality being measured

YES

NO

Observation

Is sampling being conducted in random fashion

YES

NO

Observation

Is sampling meeting it's sample size target?

YES

NO

Observation

Are control charts in control

YES

NO

Observation

Are control charts current?

YES

NO

Observation

Is the process capability index >1.0?

YES

NO

Audit Date:

NO

Number of Out of Compliance Observations Total Observations #DIV/0!

Audit Yield Corrective Actions Required Auditor Comments

FIGURE 14.27

Audit checklist.

factories do not perform equally. Specifically, regarding factory differences, the first set (mean = 79.697) performed better than the next set (mean = 83.623). The conclu­ sion is to reject H0 and state that the difference does not equal the chosen reference value: μ1 – μ2 ≠ 0. The temperature variable seemed to be a major issue but proved it was not. There­ fore, the team did not change the temperature variable. The results indicated that the two factories could have different temperatures that did not affect the product’s specifications.

14.8 CONCLUSION The team investigated three variables to determine if they were the root cause of the thick/thin product issues: piping, specifications and standard deviations for thin or thick products, and temperatures. Piping and specifications were factors in the thin/thick product issues. The temperature was not an issue after all.

15 Hydraulic Leak Reduction TPM Case Study

Chad Olson, Xueyang Chen, Heng Liu, and Elizabeth A. Cudney

15.1 INTRODUCTION Every manufacturing business, large or small, faces competitive pressures to lower costs and reduce variability or defects. Six Sigma is a production philosophy that uses data, processes, and tools to nearly eliminate defects and bring performance close to perfection. Six Sigma means, at most, 3.4 defects occur per one million opportunities. Stand­ ard deviation, denoted by the Greek letter sigma, is the statistical measure for var­ iation (Cudney et  al., 2013). Six Sigma refers to six standard deviations between the mean of a process and the closest specification limit. It is important to note that customers determine the product or service specifications. In practice, Six Sigma relies on a five-phase methodology that follows the define-measure-analyze-improve-control (DMAIC) framework (Cudney, 2009): • Define. The purpose of this phase is to identify, define, and pinpoint where variability might be occurring. The team maps the process, including valueadded and non-value-added steps in the work process. • Measure. The team determines the appropriate metrics in this phase and gathers process data. • Analyze.  The team uses data and other tools to determine variation and analyze its causes in this phase. • Improve.  The team implements mistake-proof devices in the work pro­ cesses with the help of tools and technology. • Control. In the last phase, the team monitors and controls the new process to ensure the organization does not backslide to create variability again. Developed in the 1980s by Motorola, Six Sigma is a measure of quality that strives for near perfection. Six Sigma is a disciplined, data-driven approach and methodology for eliminating defects in any process, from manufacturing to transactional processes and products to services. Companies can achieve an incredibly high level of performance with the rigor of the Six Sigma methodology and data-driven approach to problemsolving and business process improvement since the focus is on driving what is most critical to customers, resulting in increased performance and profitability. Therefore, the team used Six Sigma to improve the machine reliability for this project. 174

DOI: 10.1201/9781003272168-15

TPM Case Study: Hydraulic Leak Reduction

15.2

175

DEFINE

Project Overview: Hydraulic systems are needed to make our product func­ tion. Therefore, the quality of our hydraulic components is critical to the satisfaction of our customers as well as the efficient flow of our assembly line. Due to the quantity and complexity of our hydraulic subassemblies, it is difficult to eliminate all hydraulic leaks using our current assembly pro­ cesses. We regularly have to repair and clean up leaks on the assembly line, resulting in line delays and potential safety risks to operators. Numerous variables exist when torquing the fittings in our hydraulic subassemblies that are not controlled or monitored. Problem Statement: There are several hydraulic leaks a day. Operators typi­ cally find most leaks at the hydraulic fill station when filling the product with oil. However, operators find others later down the assembly line. Customer/Stakeholders: Assembly line, external/final customer. Project Goal: Develop a new mistake-proofed assembly method to eliminate all hydraulic leaks resulting from improper torquing of hydraulic fittings. Scope Statement: Leaks due to supplier quality of parts are out of scope for this project because it is not a part of our manufacturing process, and the quality department should address these. Therefore, the team assumed that all parts received met the specifications and quality standards. Projected Financial Benefit(s): The financial benefits to the business will be opportunity cost savings due to reduced delay times on the assembly line and reduced warranty costs. DISCLAIMER: Due to the proprietary nature of this project and product, the raw data for the analysis is not provided; however, the team thoroughly explains the results.

15.3

MEASURE

The hydraulic subassembly that this project focuses on is the most complex hydrau­ lic component of our product and, therefore, has the most opportunities for failure. Analyzing the last two years internal and external quality data provides a strong business case for the projected human and capital resources required to complete this project and achieve our stated goals. According to the data available, warranty costs (shown in Figure 15.2) exceeded $22,000. Further, opportunity costs resulting from downtime due to repair and rework caused by issues with this subassembly are over $738,000 (Figure 15.3). The organization expects the actual quantity and cost of leaks to be even higher than the data suggests since there are leaks that occur that operators may not document due to time constraints on the assembly line. Therefore, a leak study will be performed on the assembly line to capture current process data. The team performed a leak study at the hydraulic fill station. The operators find most hydraulic leaks immediately after the subassembly installation in this station. The team conducted the study over 29 working days. During this period, 38 hydrau­ lics leaks occurred on 600 subassemblies.

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FIGURE 15.1 Hydraulic system.

Warranty Type Defect Contamination Damaged O-rings Loose Fitting Total FIGURE 15.2 Warranty cost.

Impact # of Claims Claim Cost 21 $13,209 25 $6,406 6 $2,445 52 $22,060

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TPM Case Study: Hydraulic Leak Reduction

Defect

Manufacturing Defect # of Defects

Loose fittings Missing material Orientation of fittings Wrong valve block built Total

37 14 12 1 line delay 64

Location Defect Found In Station Before Final Assembly 15% 10% 20% 0% -

0% 0% 0% 100% -

Cost Final Assembly Opportunity Cost (repairing, not building) 85% $416,250 90% $157,500 80% $135,000 0% $30,000 $738,750

FIGURE 15.3 Repair cost.

Using the data provided by the leak study, the team calculated the Cpk for the pro­ cess as follows: Percent nonconforming = (38/600) = 0.0633 Lookup Z-score in Z-table for 0.0633/2 = 0.03165 Cpk = Z-score/3 = 1.855/3 = 0.61833 The process capability analysis for the current state resulted in a value of Cpk = 0.61833. Therefore, the current process is not capable.

15.4

ANALYZE

The team performed a process failure modes and effects analysis (PFMEA) next to determine the risks in the current process. The engineering team picked an operation for building the subassembly that requires all the tasks required for the assembler to perform. Using this complex task, the team conducted a PFMEA to identify all the possible risks and their potential root causes. The risk priority level for each failure mode identified can be found in the right-hand column, as shown in Figure 15.4. The engineering team used the results of the PFMEA and determined that the current process will never eliminate the risks present when building these subas­ semblies. Therefore, the PFMEA was used to drive the requirements and subsequent design of the new torque control system that would mitigate the risks identified. The risks and the mitigation method are detailed as follows. • Joining parts—torque • Wireless clicker wrench system with Global 8 Controller • Capable of +/– 4% of targeted torque • Current clicker wrenches are only capable of +/– 15%. The tolerance specified is +/– 12.5% for hydraulic fittings. • Monitors torque and batch count • Immediate operator notification of a fault, audio, and visual • Identifies if the wrong tool is selected • Identifies if the fitting is cross threaded • Custom attachments to provide proper tool clearance

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FIGURE 15.4 Process failure mode and effects analysis.

• Interaction between parts • Leaks caused by fitting and O-ring damage • O-ring damage caused by contamination and improper assembly method • O-rings will be assembled to the fittings at the supplier by July 27th • Contamination risk contained by new “clean box” in the work center (benchmarked from another facility) • Fitting damage caused by hoisting fittings to transport • Adopted best practice from the supplier to utilize a lifting lug installed in the center of the subassembly at the supplier, which replaces the large plug in the center of every subassembly. • Prepare parts for assembly • P80 liquid Teflon for surface preparation of threads

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TPM Case Study: Hydraulic Leak Reduction

How can we make the subassembly station more efficient? Station Layout

Station Functionality

Maintenance

Reduce part handling

Reduce number of fixtures

Eliminate mark for torque

Reduce hoisting distance

Eliminate clamping with fixture

Work instruction display

Reduce work center footprint

Part/Tool Presentation Organize parts by usage

Pick lights for parts/tools

Documentation Improve work instructions

Build in 5S

FIGURE 15.5 Affinity diagram.

• Recognize the need for a part/activity • The proximity switch will detect if the fixture has the wrong subassembly. In addition to achieving the quality goal for this project, it was also necessary to increase the capacity of the subassembly station by reducing the cycle time. This change was important so that one operator could satisfy the daily demand, requir­ ing the purchase, maintenance, space, and staffing for only one set of controls. The team used an affinity diagram before the new station’s design to achieve the project’s budget and return on investment (ROI) targets. Figure 15.5 provides the affinity dia­ gram developed by the team.

15.5 DESIGN The team also used Design for Six Sigma (DFSS) principles to implement new mistake-proofed system functions and achieve robust process control. A key focus of the design of this system was to eliminate ways for the operator to bypass the system if an error occurred or they chose to ignore the process. The system needed to achieve level 2 mistake-proofing by removing the ability of the operator to pass a defec­ tive subassembly to the following operation. Benchmarking for this project was not applicable because it is the first of its kind in this facility. The entire system design and process resulted from the team’s highly successful collaboration and effort. The system design involved the following steps: 1. The operator hoists the hydraulic component into the fixture. 2. The operator scans the build tag to begin the build sequence. a. The fixture verifies the operator places the correct component in the fixture and the proper orientation (one of three possible components depending on the model).

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b. The fixture locks the component into place (level 2 mistake-proofing). c. The controller begins the build sequence or “recipe” for that component and the combination of options ordered by the customer (1 of 19 pos­ sible variations). 3. The work instruction for the first step of the sequence displays on the screen. 4. The pick lights select the first torque wrench and the part required. 5. The operator torques the fitting(s) onto the component. a. The controller verifies that the operator uses the right torque tool. b. The controller verifies process achieves a torque within the tolerance. c. The controller notifies the operator if the process reaches the proper torque by both a visual cue with a stack light and an audio signal. d. The controller verifies that the process torques the correct number of parts. 6. The following work instruction displays, and the next tool and part lights up. 7. The operator procures the next tool(s) and part(s) to continue the build sequence. 8. The fixture releases the finished subassembly when the build sequence completes successfully.

15.6 VERIFY The team performed a second process capability study to measure the new system’s performance with a Cpk  = 2.00, a Six Sigma level. Out of 600 subassemblies made, there were zero leaks from risks associated with this work center. In addition to a new process capability study, the team performed a post-PFMEA to evaluate the success of the mitigation plan executed with the new mistake-proofed system. Figure 15.6 illustrates the risk reduction in the work center.

Initial PFMEA

FIGURE 15.6 Before and after PFMEA comparison.

Post PFMEA

TPM Case Study: Hydraulic Leak Reduction

181

15.7 IMPROVE The team implemented several system improvements. In this subassembly station, the primary sources of waste are excessive movement and body travel to procure fittings. The station was designed in a U-shape around the operator so that all 40 fittings and eight torque wrenches are within reach of the operator while standing at the fixture to reduce the time wasted. Before this project, the process required a second fixture for one of the parts. Therefore, to reduce the size of that station, a new fixture was designed to hold all three components. The team designed the new fixture by re-laying out the rest of the work center, including the conveyors for incoming material and large parts, reducing the overall square footage required by 67%. The team also considered the ergonomics and safety of the process. The team designed new racks holding all the fittings based on ergonomic requirements. The new fixture is also on a height-adjustable stand so operators can build the subas­ sembly at the best height according to their height. The Safety and Ergonomic Risk Assessment score also dropped from a medium risk level of 60 to a low-risk level of 12. The team redesigned the process using 5S principles. The new fixture stand, in combination with the new part racks and new holders for each torque wrench, elimi­ nated all flat surfaces in the work center so that the operator has to return all tools to their home location. Before this project, the operator used a large 8-foot-long bench with tools and part locations scattered and spread across it with little organization defined. The operator no longer has to search for parts or tools because of this organ­ ization and the pick-light system. The improvements reduced the average cycle time per component by 43%, from 39 minutes per subassembly to 22 minutes. The team calculated the average cycle time using the work measurement standard derived from the work instructions for the work center and the average amount of work per day based on the production schedule. This schedule considers the model mix and customers’ options for their machines. Takt time for this subassembly is based on a nine-hour day with a demand of 24 per day, resulting in a takt time of 22.5 minutes. Therefore, the new cycle time is within takt time even after adding eight additional subassemblies to the opera­ tor’s workload, which the team achieved through the station’s design and efficiency improvements. The improvements reduced the cycle time, which reduced daily work from 12 to 7 hours. This reduction allowed one operator to build all the subassemblies for the entire production schedule, eliminating the need for an operator at another work center to flex over to help. This change aligned well with the system’s ability to build all three components on one fixture. Figure 15.7 provides a summary of the process improvements.

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FIGURE 15.7 Process improvements summary.

15.8 CONTROL The team built control techniques into the design of the system. The operator must use the prescribed tools because they link to the wireless controller. Therefore, the sequence will not continue if it does not recognize that the correct tool was used and torqued to the correct value. The operator can only start the process if the correct part is loaded into the fixture properly or remove the subassembly from the fixture if the process builds the subassembly precisely to specifications. In the control phase, the team also calculated the financial savings of the project: Average rework time per defect = 32 minutes Reduction in manpower requirements = 6.8 hours/day The cost of rework is $34.00 per hour Number of production days per year = 150 days Warranty = $22,000 per year Project investment = $50,000 I = 15% (minimum) Annual cost savings with zero defects = $22,000 + (32/60) × $34 × 150 + 6.8 × $34 × 150 = $59,400 Five-year ROI = 116% Payback period = 0.97 years

TPM Case Study: Hydraulic Leak Reduction

183

15.9 CONCLUSIONS The results from this project were far above what the team expected, having exceeded all of the goals set in the charter. These results are attributed mainly to the robust and efficient design of the system, which was driven by completing a thorough PFMEA and assessing the previous station’s capabilities. A  complete understanding of the manufacturing process was crucial for writing a well-defined charter, setting realistic goals, and successfully achieving them using Six Sigma and TPM tools.

REFERENCES Cudney, E. (2009). Using Hoshin Kanri to Improve the Value Stream. Productivity Press, New York. Cudney, E., Furterer, S., and Dietrich, D. (2013). Lean Systems: Applications and Case Studies in Manufacturing, Service, and Healthcare. CRC Press, New York.

16 Pressure Regulator

TPM Case Study Bill Dean, Charlie Barclay, Nanda Dey, and Elizabeth A. Cudney

16.1

EXECUTIVE SUMMARY

This chapter details the work performed regarding a pressure regulator to improve its reliability and performance. The team employed the Six Sigma approach to investigate the variation in the valve’s outlet flows. The team followed the Define, Measure, Ana­ lyze, Improve, and Control (DMAIC) methodology throughout the project to analyze the current configuration, develop strategies to improve it, and then test the strategy. The testing results were encouraging as the outlet flow increased and the variation reduced.

16.2 DEFINE 16.2.1 Problem Statement The project under analysis involves the performance of a regulator system. The sys­ tem consists of a regulator installed into a high-pressure cylinder for use in hospital environments to supply medical-grade oxygen to patients. The cylinder is made from an aluminum alloy and has a threaded connection to install the regulator. Depending on size, it stores oxygen at either 2,820 psig (pounds per square inch gage) or 3,102 psig. Using high pressure allows for the maximum use of oxygen before the system requires refilling. When a patient needs oxygen, it is reduced to 70 psig nominal using a pressure regulator. The regulator is called a valve, integrated pressure regulator (VIPR). Figure 16.1 illustrates a cross-section of the regulator. The VIPR has the following components: • Refill connection—interfaces with a gas company’s fill station when the patient returns an empty system. • Pressure relief device (PRD)—bursts during a fire or over-pressurization. • Pressure gage—displays the cylinder pressure. • Flame arrestor—suppresses any internal ignition event before it reaches the low-pressure section. • Pressure regulator—reduces the cylinder pressure to 70 psig. • Flow selector—meters the 70 psig oxygen supply to one of 11 preset flows. A physician prescribes this flow for each patient’s needs. • Connection—supplies a “high” flow of the 70 psig oxygen supply to a hos­ pital ventilator. 184

DOI: 10.1201/9781003272168-16

TPM Case Study: Pressure Regulator

FIGURE 16.1

185

Cross-section of the regulator assembly.

The problem involves the connection. It was not initially required to meet any specific flows. It only had to supply a “high-flow” of 70 psig oxygen. The customer has given a new requirement for a minimum of 200 liters per minute (lpm) of oxygen flow when the cylinder is considered ready for refilling at 700 psig.

16.2.2 Problem goalS The team will investigate the system’s operation to meet the 200 pm minimum requirement. The team should identify anything that may affect the outlet flow. The

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team will ignore factors that are too difficult or not deemed significant. However, the team will examine all remaining factors at various levels. Finally, the resulting flow tolerance range is to be determined.

16.2.3

reQuirementS and exPectationS

The existing design is a proven, field-tested design. Therefore, the team should keep its basic functions for storage, pressure regulation, delivery of oxygen through the flow selector, and refill. The improved design must deliver a minimum of 200 ppm of oxygen from the high-flow connection. The team must achieve this level while regulating the pressure between 67 and 91 psig and a cylinder pressure of 700 psig.

16.2.4

ProJect boundarieS

There are currently dozens of variations of the VIPR design. However, this project is constrained to only the VIPR produced most often.

16.2.5

ProceSS flow diagram

The team created a process flow diagram to understand better the valve and the process of assembling the valve. The diagram is an excellent visual tool to aid in developing ideas to improve the flow (Cudney et al., 2013). Figure 16.2 provides the process flow diagram.

FIGURE 16.2

Process flow diagram for the assembly of the valve.

TPM Case Study: Pressure Regulator

187

16.3 MEASURE The goal of the Measure phase of a Six Sigma DMAIC project is to get as much information as possible on the current process to understand how it works and how well it works. The Measure phase entails three key tasks: creating a detailed pro­ cess map, gathering baseline data, and summarizing and analyzing the data. In some cases, teams conduct the process mapping first so that information gleaned from it can guide the data collection process (Cudney, 2009). Teams already know the gen­ eral data needs in other cases and work on the two pieces simultaneously.

16.4

GAGE REPEATABILITY AND REPRODUCIBILITY

Gage repeatability and reproducibility (gage R&R) is one type of measurement sys­ tem analysis performed to evaluate the performance of a test method or measurement system. Such a study quantifies the capabilities and limitations of a measurement instrument, often estimating its repeatability and reproducibility. It typically involves multiple appraisers measuring a series of items numerous times. This project used three appraisers to conduct the gage R&R on ten different valves and repeated the process three times. Figure 16.3 documents the results. Figure 16.4 provides a complete report based on the data from Figure 16.3. Each of the quantities in the measurement unit analysis column is as follows. Equipment variation (EV) is an estimate of the standard deviation of the variation due to repeat­ ability, and the value is 0.03722. Appraiser variation (AV) is an estimate of the stand­ ard deviation of the variation due to reproducibility, and the value is 0.3086. Gage repeatability and reproducibility (GRR) estimates the part variation’s standard devi­ ation due to the measurement system. The value is 0.311, and the % GRR is 1.845, which is significantly below the limit of 10%; hence, the measurement system is considered acceptable. Part-to-part variation (PV) is an estimate of the standard devi­ ation of the variation due to the part difference, and the value is 16.841. Finally, total variation (TV) is an estimate of the standard deviation of the total variation in the study, and the value is 16.843.

16.4.1 data collection The team collected data from all the experiments conducted during the project. Thirty pieces of VIPR were marked and used during the different stages of the project. The team completed the initial baseline process experiments on the thirty VIPRs and recorded the statistical data documented in Figure 16.5.

16.4.2

ProceSS baSeline

The team obtained the baseline statistical data by allowing high-pressure nitrogen to flow through the fully assembled 30 units with the 100-micron filter, ball, and stake. Figure 16.5 documents the results obtained. The outlet flow from the VIPR was measured and recorded. The team performed statistical analysis on the data to calculate various parameters, as shown in Figure 16.6.

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FIGURE 16.3 Gage R&R data sheet.

TPM Case Study: Pressure Regulator

FIGURE 16.4

Gage R&R report.

189

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Part Number

Flow (lpm)

Outlet Pressure (psig)

Normal

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

68.2 37.9 49.7 47.1 49.8 109.4 109.1 30.6 82.2 91.6 95.6 59.5 72.4 38.4 67.8 71.0 38.7 52.7 84.7 38.3 49.0 70.8 59.7 54.2 78.6 42.3 98.3 47.5 75.7 46.4

76.0 74.5 69.7 71.1 70.3 74.5 73.9 69.7 76.9 80.1 74.5 80.1 68.9 68.6 72.9 80.2 77.1 78.4 73.1 80.1 68.9 71.7 71.0 74.5 77.3 75.9 72.8 71.7 70.1 68.9

0.017616586 0.009064108 0.014620944 0.013490452 0.014691480 0.002207481 0.002279526 0.005846840 0.012795388 0.008258340 0.006485818 0.017600072 0.016695522 0.009278333 0.017677965 0.017056438 0.009433060 0.015795190 0.011574214 0.009258196 0.014321862 0.017097189 0.017625968 0.016319213 0.014416048 0.011193777 0.005412797 0.013669348 0.015593396 0.013140474

FIGURE 16.5 Baseline data.

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TPM Case Study: Pressure Regulator

Mean Median Maximum Minimum Range Skewness Kurtosis Variance Standard Deviation

63.9 59.6 109.4 30.6 78.8 0.543820574 -0.663174579 493.9862669 22.22580183

FIGURE 16.6 Statistical analysis of baseline data.

16.4.3 graPHical analySiS The following graphical analysis helped the team to visualize and understand the baseline data. Figure 16.7 shows a comparative study between the outlet flow and the frequency of occurrences. Figure 16.8 shows a Pareto chart of the outlet flow to that of the frequency. Figure 16.9 provides another frequency distribution of the outlet flow. Figure 16.10 illustrates the distribution of the data. In addition, Figure 16.11 shows the run chart for the outlet flow, which demonstrates the variation currently present in the valve.

Frequency

Histogram 8 6 4 2 0

Frequency 68.6 - 70.52

70.53 ­ 72.45

72.46 ­ 74.38

74.39 ­ 76.31

Flow (Outlet Pressure)

FIGURE 16.7 Histogram of outlet flow.

76.32 ­ 78.24

78.25 - 80.2

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Frequency

Histogram 10

120.00%

8

100.00% 80.00%

6

60.00%

4 2 0

43.73 56.85

69.99 83.11

30.6 43.72

56.86 69.98

96.25 109.4

83.12 96.24

40.00%

Frequency

20.00%

Cumulative %

0.00%

Flow (Outlet Pressure)

FIGURE 16.8

Pareto chart of outlet flow.

Frequency

Histogram 10

120.00%

8

100.00% 80.00%

6

60.00%

4 2 0

43.73 56.85

69.99 83.11

30.6 43.72

56.86 69.98

96.25 109.4

83.12 96.24

40.00%

Frequency

20.00%

Cumulative %

0.00%

Flow (lpm)

FIGURE 16.9

Frequency distribution of the flow.

Data Distribution

Probability

0.02 0.015 0.01

0.005 0

0

20

40

60 Flow (lpm)

FIGURE 16.10

Distribution of data.

80

100

120

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TPM Case Study: Pressure Regulator

Run Chart 120.0

Flow (lpm)

100.0 80.0 60.0 40.0 20.0 0.0

0

5

10

15

20

25

30

Sample

FIGURE 16.11

Run chart of outlet flow.

16.5 ANALYZE The team identified the current gaps between performance and customer expectations in the analyze phase. A quick look at the statistics gathered during the process baseline analysis allows for immediate recognition that there is a considerable gap between the current capabilities of the valve versus what the customer requires. The negative process capability numbers indicate the average flow generated below the current minimum flow requirement.

16.5.1

failure modeS and effectS analySiS

The team performed a failure modes and effects analysis (FMEA) to identify potential causes of failure. Throughout the completion of the FMEA, all potential failures were identified and prioritized. The FMEA allowed the team to look for solutions that would not adversely affect safety while operating the valve. In addition to identification and prioritization, the team also suggested risk control methods for each failure mode and measures for implementing these methods. Figures 16.12 and 16.13 provide the FMEA that the team conducted.

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(S)

Potential Cause

(O)

PCAN

Is Risk Reduction

Step 2

Step 3

Step 3

Step 3

SxO Step 4

Necessary? Step 4

Ignition

10

Adiabatic Compression

2

20

YES

Minimize probability

10

Flame propagation from fill cylinder

3

30

YES

Minimize probability

10

Flame propagation from coupler

3

30

YES

Minimize probability

10

Particle impingement

1

10

NO

10

Particle friction

1

10

NO

10

Static Electricity

1

10

NO

10

Fire burns out poppet seat and spring

3

30

YES

Minimize probability

Reduce amount of fuel by removing oring and backuo rings. Use 360 brass alloy, PCTFE and Inconel X750 for HP oxygen w etted surfaces.

8

Skilled operator

Calibrate torque drivers to 60-75 ftlbs and check setting every 6 months.

3

Calibrate torque drivers to 35 +/-2 inlbs. Check calibration every 6 months. Calibrate torque drivers to 87-90 inlbs. Check calibration every 6 months.

3

Rupture of high pressure section

Rupture of low pressure section

Loss of system function

Risk Control Measure Step 5

Risk Control Measure Im plem entation Step 6 This risk is managed by minimizing pressure changes over time and by absorbing as much energy as possible. Incorporate one 30 micron & one 35 micron sintered bronze filters. Incorporate flame arrestor fitting. Use 360 brass alloy for HP oxygen w etted surfaces. This risk is managed by using brass to absorb heat and using sintered bronze filters to retard flame propagation. Incorporate one 30 micron & one 150 micron sintered bronze filters. Incorporate flame arrestor fitting. Use 360 brass alloy for HP oxygen w etted surfaces. This risk is managed by using brass to absorb heat and using sintered bronze filters to retard flame propagation. Incorporate one 30 micron & one 150 micron sintered bronze filters. Use 360 brass alloy for HP oxygen w etted surfaces.

(E)

CAN

Step 6

Step 7

9

2.222

Filters: 250110-1 & 1531340804-3 Ball: 1539111306-1 Body: 252101-1 ISO 10524 Section 11.8.1

9

3.333

Filters: 250110-1 & N/A 1531340804-3 Ball: 1539111306-1 Body: 252110-1 ISO 10524 Sec 11.8.1 ASTM G175­ 03

9

3.750

Filters: 250110-1 & N/A 1531340804-3 Ball: 1539111306-1 Body: 252110-1 ISO 10524 Sec 11.8.1 ASTM G175­ 03 N/A

10.000

Reference Docum entation

10.000

10

Overtorqued body

2

20

YES

10

Wall thickness is too thin

1

10

NO

10

Fill Fitting threads are stripped out

2

20

YES

Skilled operator

Minimize probability

10

Rupture Disk threads are stripped out

2

20

YES

10

Poppet, Spring and/or Housing is missing or fails

1

10

NO

7

Piston fails

2

14

YES

Minimize probability

7

Failure of Flow Selector Retaining Ring

2

14

YES

7

Build up of pressure due to a damaged seat

2

14

7

Solvent stress cracking of Flow Selector Valve Body

2

7

Failure of torx screw

7

Risk / Benefit Analysis Step 8, if necessary N/A

N/A

10.000

N/A

3.750

N/A Poppet Assy: 4201640207-1 Fill Fitting: 252102-1 Spring: 4201340203­ 1 Spring Guide: 251803-1 N/A

6.667

10.000

N/A

6.667

Fill Fitting Body: 252102-1

N/A

Rupture Disk: 4211340104-1

N/A

3

6.667

10

1.000

N/A

Place controls on the piston assembly machining process. Incorporate relief valve into the regulator

3

4.667

Minimize probability

Incorporate retaining ring having sufficient strength to w ithstand 200 psig of pressure.

10

1.400

YES

Minimize probability

Incorporate relief valve.

9

1.556

14

YES

Minimize probability

Reinforce the plastic compound w ith 20% glass fibers to increase the chemical resistance.

8

1.750

N/A Piston Assy: 15307112B1-2 Spring: 15306370108-1 Guide: 1530670107­ 2 Retaining Ring: A304­ N/A 125 See QTR 1539111300 Guide: 1530670107­ N/A 2 Piston: 15307112B1-2 Spring: 1530670108­ 1 Flow Selector Body: N/A 0359110140-2 Reference Failure Report: FR9510030­ 1

2

14

YES

Skilled operator

4

3.500

Reference Failure N/A Report: FR990706-1.

Missing O-rings on Piston or Flow Selector Valve

2

14

YES

Skilled operator

Calibrate torque drivers so screw is not over-torqued. See assembly instructions for torque value. Check calibration every 6 months. The risk is minimized by adherence to assembly instructions and by the use of a relief valve.

4

3.500

Guide: 1530670107­ N/A 2 Piston: 15307112B1-2 Spring: 251701-1 Reference assembly instructions

7

Check valve failure

3

21

YES

Minimize probability

4

5.250

Body:850701-1 Spring: 850504-1 Poppet: 850702-1

3

Failure of Teflon O-ring at interface of regulator and cylinder Particle contamination

4

16

YES

Minimize probability

2

6

NO

6.000

2

Vent hole blockage

3

6

NO

6.000

2

Vibration and impact effects Shearing off of outlet fitting

1

2

NO

2.000

N/A

5

15

YES

1.875

Flow selector shaft breakage

1

3

NO

Fitting: 1779110201­ N/A 1 Body: 0359110140-2 Reference Tipover Testing QTR ? N/A

3

3

3

FIGURE 16.12

Minimize probability

The risk is removed by designing the poppet to be physically captured by the check valve body. The body is torqued to 15+/- in-lbs. Check calibration every 6 months. Procedures?

Easily replaced aluminum outlet fitting is used and installed into brass insert. Fitting is also protected by hendle.

8

3.000

N/A

N/A N/A

FMEA performed during the completion of this project (image 1 of 2).

195

TPM Case Study: Pressure Regulator

Impact Injury

5

Failure of Handle due to broken or missing screw s

3

15

YES

Minimize probability

This risk is managed by using material of sufficient strength and qualified by Drop Testing.

3

Failure to stake flame arrestor

4

12

YES

Minimize probability

Procedures? Caught during testing?

The risk is managed by using straight threads and moving the seal inside the check valve body. The risk is manages by coining the material around the filter to keep it in place. This risk is managed by using materials of sufficient strength and qualified by Tipover Testing. This risk is managed by using materials of sufficient strength and qualified by Tipover Testing. The risk is managed by including a filtration design that reduces particle activity. Place "Open Shutoff Valve Slow ly" w arning in Operating Instructions.

4

Leakage at check valve

3

12

YES

Minimize probability

5

Blow ing out inlet filter due to fire or filling

3

15

YES

Minimize probability

6

Tipover of unrestrained cylinder resulting in component breakage Inverted drop resulting in a broken cylinder valve or unit Overpressurization of dow nstream components due to heat of compression initiating rapid combustion

10

10

Increased fire risk

10

Leakage due to insufficient tightening of regulator on cylinder

10

Leakage from a cracked or broken unit Normal flow at patient end of cannula

10

2

12

YES

Minimize probability

2

20

YES

Competent medical practice

2

20

YES

Minimize probability

Labeling

3

30

YES

Skilled operator

3

30

YES

Skilled operator

4

40

YES

Competent medical practice Labeling

10

Torx Screw s: 252103-1 Reference Drop Testing: ER040219 Body:850701-1 Spring: 850504-1 Poppet: 850702-1 Filter: 151340804-3

8

1.500

6

2.500

10

1.200

Reference Tipover Testing: QTR

6

3.333

Reference QTR

N/A

8

2.222

Filters: 250110-1 & 1531340804-3

N/A

1

N/A

Reference N/A Operating Instructions. Reference assembly N/A instructions.

3

10.000

3

10.000

5

6.667

N/A

8.333

N/A Reference Operating Instructions. Gauge: 5800450100­ 1 N/A

3.333

Reference N/A Operating Instructions. Gauge: 5800450100­ 1 N/A

2.000

Reference N/A Operating Instructions. Gauge: 5800450100­ 1 N/A

1

QTR 2521

N/A

Using oxygen w ithin immediate vicinity of an open flame or other source of ignition

2

10

Heat of compression initiates kindling chain

2

20

YES

Minimize probability

Labeling

Place "Open Shutoff Valve Slow ly" w arning in Operating Instructions.

1

10

Particle Impact initiates kindling chain

2

20

YES

Minimize probability

Particle Impacts events are minimized by the use of sintered bronze filters.

8

Labeling

Place "Open Shutoff Valve Slow ly" w arning in Operating Instructions.

1

2

Flow control knob is missing Regulator not functioning

2

4

NO

2

8

Flow Selector Valve not functioning (flow plate not aligned) Unable to read flow indication by visually impaired patient Improper flow indication registration Patient does not have a clear understanding of the operation of unit Gauge is torn off of socket due to impact

2

8

3

6

NO

6.000

N/A

2

4

NO

4.000

N/A

3

12

YES

Labeling

2

14

YES

Minimize probability

2

6

NO

4

2

2 4

7

3

Chemical toxicity Particle inhalation

Competent medical practice Labeling

Handle: 1539114902­ 2, Reference Drop Testing: ER040219

5

4

Gauge rupture

YES

1.667

Smoking w ithin immediate vicinity of oxygen flow

10

Incorrect Gas Delivery

50

The risk is managed by proper training of assembly personnel and the use of calibrated torque drivers w hich are checked every 6 months. The valve is designed to w ithstand anticipated abuse. Care providers shall ensure that the outlet flow does not accumulate to dangerous levels. Place "No Smoking" and "No Open Flame" w arnings on cylinder and in Operating Instructions along w ith their corresponding symbols on the gauge dialface. Care providers shall not allow smoking near system. Place "No Smoking" and "No Open Flame" w arnings on cylinder and in Operating Instructions along w ith their corresponding symbols on the gauge dialface. Care providers shall not allow open flames near system. Place "No Smoking" and "No Open Flame" w arnings on cylinder and in Operating Instructions along w ith their corresponding symbols on the gauge dialface. The risk is managed by designing the valve to have a large mass of parts w ith high autoignition temperatures.

9

10 9

Spiral tube inside of gauge ruptures due to fatigue or overpressurization Attachment to w rong gas source Soot from ignition w ithin regulator and/or cylinder

FIGURE 16.13

20

YES

Competent medical practice Labeling

5 1

5 1

9

2.222

Reference Operating Instructions.

N/A

Reference Operating Instructions.

N/A

N/A

4.000

N/A

NO

8.000

N/A

NO

8.000

N/A

1

10

NO

3

27

YES

Develop Operating Instructions to illustrate the features and proper operation of the unit. This risk is managed by installing a gauge protector.

2

6.000

8

1.750

6.000

Minimize probability

This risk is managed by reducing the potential of particles passing through to the low pressure side of the regulator using sintered bronze filters.

9

Reference Operating Instructions. Gauge Protector: 5800450200-3 Reference QTR 2521

N/A

N/A

N/A

10.000

N/A

3.000

Filters: 1539111302­ N/A 1 & 1531340804-3

FMEA performed during the completion of this project (image 2 of 2).

196

16.5.2

Total Productive Maintenance

SourceS of variation

There are several sources of variation within the valve. The first is the size of the filter placed in the valve. The filters can range from 40 microns to 100 microns. The differ­ ence in size could cause more flow through the filter resulting in more flow through the outlet. Another source of variation is the depression of the valve that allows the gas to flow through the outlet. When the valve is attached to a connector on the outlet side, a small valve is depressed in the orifice. Depressed the valve farther or less than nominal can alter the flow.

16.5.3

Prioritization of imProvement oPPortunitieS

After analyzing the statistical data sources of variation, the team concluded that the main focus of our testing should be the filters that can obstruct flow. The team decided that these were likely the best opportunity to improve the outlet flow produced by the valve quickly. The team also decided to test the effect of varying the amount that the outlet valve is depressed to see how it affects the flow.

16.5.4 HyPotHeSiS teSting The team decided that performing a detailed hypothesis test was necessary to under­ stand better how the different factors affect the outlet flow. Also, since the base­ line statistics showed such dramatic variation across the various valves, running a complete set of hypothesis tests was deemed critical for the project. Figures 16.14 through 16.16 provide the two hypothesis tests performed to study the impact of the filters and ball on the outlet flow. The test on the left was conducted without the ball and the 40-micron filter, while the test on the right used a 100-micron filter only. Figure 16.15 shows another hypothesis test the team performed to analyze the effects of the filters. In this test, a 40-micron filter, stake, and ball were all fitted into the valve. Figure 16.16 shows the test the team performed to study the effects of varying the amount that the valve in the outlet orifice was depressed. The team performed this test by inserting washers between the coupling and the orifice valve. The team con­ ducted the test with zero, one, and two washers. The results of the tests were somewhat as expected. The flow significantly improved for the tests without the brass ball and 40-micron filter. Not only did the lack of a brass ball and filter significantly improve the flow, but it also dramati­ cally reduced the variation and nearly cut the range of outlet flows in half. The tests performed with the 40-micron filter, stake, and ball (without the 100-micron filter) also increased flow. However, the increase observed in this test was minimal and not deemed sufficient to warrant further testing. The tests performed with the single washer also showed a slight improvement in flow, while the addition of the second washer caused a decrease over the single washer.

TPM Case Study: Pressure Regulator

FIGURE 16.14

197

Hypothesis tests without ball and filter and with 100-micron filter only.

16.6 IMPROVE In the improve phase, the team focused on identifying solutions. The team deter­ mined the best results from the hypothesis tests were removing the brass ball, stake, and 40-micron filter. This configuration provided the largest outlet flow with the least amount of variation. Removing the 100-micron filter also provided positive results, but not enough to meet the latest requirements.

198

FIGURE 16.15

Total Productive Maintenance

Hypothesis test with 40-micron filter, stake, and ball.

TPM Case Study: Pressure Regulator

FIGURE 16.16

Hypothesis tests performed with one and two washers.

199

200

16.7

Total Productive Maintenance

CONTROL

The outlet flow can be successfully controlled by not installing the ball and 40-micron filter into the valve. The minimum flow is 210 lpm at 700 psi cylinder pressure when not installing the ball and filter. Therefore, the team can control the customer require­ ment of 200 ppm. During the control phase, the team also calculated the financial savings. The finan­ cial savings for each VIPR when not using the ball and 40-micron filter is approxi­ mately $0.20 per valve plus the labor involved in installing them. There is also the marketability of a VIPR with a high flow of 200 lpm that will enhance the ability to sell more valves and increase revenue.

16.8 CONCLUSIONS An analysis of a baseline of 30 units showed that the mean outlet flow was 45.6 lpm with a range of 56.3 lpm and a process capability of –1.98. The team used the Six Sigma methodology to determine the appropriate machine design and settings. The outlet flow can be controlled by not installing the ball and 40-micron filter. With this configuration, the resulting mean outlet flow was 159.0 lpm with a standard deviation of 5.68 and a Cp of 1.11. These findings indicate that the process is now capable of a lower specification limit of 200 ppm. The team also used an FMEA analysis to identify that valve ignition and rupture of the high-pressure sections had the highest preliminary risk (i.e., severity x occurrence). Therefore, the team recommends addi­ tional testing with higher micron-rated filters inside the regulator to help minimize the hazards due to ignition.

REFERENCES Cudney, E. (2009). Using Hoshin Kanri to Improve the Value Stream. Productivity Press, New York. Cudney, E., Furterer, S., and Dietrich, D. (2013). Lean Systems: Applications and Case Studies in Manufacturing, Service, and Healthcare. CRC Press, New York.

17 Roller Assembly Redesign TPM Case Study

DeVaughan Woodside, Apurva Chinchore, Sujitkumar Dongare, and Elizabeth A. Cudney

17.1

INTRODUCTION

Design for Six Sigma (DFSS) is a road map for developing robust products. It seeks to avoid manufacturing and service process problems by using the advanced voice of the customer techniques and proper systems engineering techniques to prevent process problems at the outset (Cudney & Agustiady, 2019). These techniques also include tools and processes to predict, model, and simulate the product delivery sys­ tem and analyze the developing system life cycle to ensure customer satisfaction with the proposed system design solution (Creveling et al., 2003). DFSS addresses the voice of the customer (VOC) and the voice of the product or processes (VOP) (Cudney et  al., 2012). DFSS comprises the five interconnected phases of Define, Measure, Analyze, Design, and Verify.

17.2 PROJECT DESCRIPTION The project aims to utilize the DFSS methodology to improve and modify the top and bottom roller assembly design for passivation cleaning machines one and two at an equipment manufacturer for the chemical industry. The project will analyze two main assemblies: the top and bottom rack assemblies. Figures  17.1 and 17.2 provide the fishbone diagrams for the current component breakdown of each of the two assemblies. The top rack assembly consists of six primary components and five secondary components, which are all subcomponents of the stainless steel shaft pri­ mary component. The bottom rack assembly consists of four primary components and four secondary components, which are all subcomponents of the stainless steel shaft assembly. Over a period of processing time, the components displayed in the fishbone diagrams wear, and, as a result, the passivation machine’s moving parts require removal and replacement. In other words, the organization must rebuild the pas­ sivation machine itself. The replacement time for both roller assembly shafts is approximately one hour per roller assembly with current operating conditions. The replacement requires a 7/8-inch diameter stainless steel round rod stock to be turned down to ½-inch diameter (i.e., approximately 45% of the purchased stock is waste). This project will utilize DFSSS to redesign the passivation units’ assemblies and DOI: 10.1201/9781003272168-17

201

202

Total Productive Maintenance

FIGURE 17.1

Top rack assembly fishbone diagram.

FIGURE 17.2 Bottom rack assembly fishbone diagram.

components. The equipment manufacturer will realize several key benefits once the team meets this project’s goals. As shown in Figure  17.3, the driving mechanisms drive the top rollers. On the other hand, the bottom rollers are free-spinning rollers. Figure 17.4 shows the top roller assembly. Figure 17.5 shows the bottom roller assembly. Top Rack

Bottom Rack

FIGURE 17.3 Passivation machine.

TPM Case Study: Roller Assembly Redesign

203

Top roller assembly. FIGURE 17.4

204

Total Productive Maintenance

Blown-Up Assembly

Bottom Roller Assembly

FIGURE 17.5 Bottom roller assembly.

17.3 PROJECT GOALS The organization selected this project because the facility identified passivation machine rebuilds as a process that, if improved, could reap a certain amount of cost savings. Hence, the goals are to reduce the top and bottom roller assemblies’ rebuild time, machining times, and stock waste. The improvement would reduce spare parts and total labor costs of passivation machine rebuilds. Figure 17.6 provides the pro­ ject goals.

17.4 REQUIREMENTS AND EXPECTATIONS As the team completes this project, we must adhere to specific requirements and meet a particular set of expectations. Through an in-depth study and analysis of the process, the team will gain more knowledge about the process and the passivation machine rebuild process. At the very minimum, the team must understand which components of the top and bottom assemblies offer improvement opportunities. At the same time, the team expects to have a detailed understanding of how to build the components. Through various data measurements, analysis, and DFSS tools and techniques, the team should also expect to understand which subcomponents are worth redesign or modification. Defining the process under study and completing the requirements should allow the team to understand and best eliminate waste in the current design

Item Old Design Goal Rebuild Time 1hr/roller Decrease 75% (45min) Stock Waste ≈160g/Shaft Decrease 75% (120g) Machining Time 1hr/shaft Decrease 75% (45min) FIGURE 17.6 List of goals.

TPM Case Study: Roller Assembly Redesign

205

(Cudney et al., 2012). Any changes made to the process during the project would have to be fully documented and approved. In addition, the team must inform all stakeholders of the status of the project on a continual basis. It is the hope of production stakeholders that there would be mini­ mal interruption to the production lines as the team conducts any experiments, data collection, and general inquiries. Once the project is complete, management expects a full report of all improve­ ments, findings, and takeaways. As a result of the lessons learned from the pro­ ject, the team will transfer the knowledge and learning to other areas of the facility.

17.5 PROJECT BOUNDARIES Redesign or modification of any of the components is a possibility. However, stain­ less steel is the only metal designers can use to produce parts. Further, the team can­ not modify the top and bottom racks themselves. Any changes to the current roller assemblies will require testing for a certain period defined by the management before designers can modify a full set of bottom and top roller assemblies.

17.6

PROJECT MANAGEMENT

The team managed the project using the road map in Figure 17.7 to ensure successful product development and implementation. After establishing the flow of work and timelines, a customer survey was conducted as a focus group approach and arranged to gather the customer’s voice. This team’s road map structure consists of four phases: Invent/Innovate, Develop, Optimize, and Validate. The team used a checklist to identify the tools and best practices required to fulfill a gate deliverable within a phase and the entire product implementation. For example, in column one, row two, the task was to gather client needs using a SWOT analysis tool. There was an assigned start and finish date. Once completed, the team would mark that row with an “X.” Similarly, an “X” in the complete box cell alongside the DFSS activities summary statement of tools and best practices column two, row nine, signifies that all the tools and best practices for that portion of the road map are complete. Completing a phase allowed the team to move through tollgate one and on to the project’s next phase.

17.7

GANTT CHART

The team developed a Gantt chart, as shown in Figure  17.8, to maintain the pace of the project. It shows the phases of the project from start to finish. The team also developed a detailed timeline for the various tasks within each phase. The team used the chart to compare the current tasks to the planned scheduled road map in Figure 17.7. Full implementation in the Gantt chart is limited to this project’s scope. The actual implementation decision at the equipment manufacturer will be as per the “trial runs tracker,” explained later in the chapter.

206

Total Productive Maintenance

Identify Client Needs

Tollgate 1 Design Conceptual Design

DFSS Activities

DFSS Tools

Start Date

Finish Date

Days

Gather needs Develop CTS’s CTS’s to functional requirements

SWOT Analysis Use six-point plan

1/12 1/16

1/15 1/18

4 3

Use six-point plan Data collection plan Gantt Chart Design road map

1/18

1/19

2

1/20 1/25 1/26

1/24 1/25 1/30

5 1 5

House of Quality

1/31

2/5

6

Checklist

2/6

2/7

2

2/10

2/10

1

2/11

2/11

1

2/02

2/12

1

2/13 2/14

2/13 2/17

1 4

2/18

2/21

4

Narrow selection

2/22

2/24

3

Checklist

2/25

2/25

1

Product planning Process Map/ Standardization

2/26

2/29

4

X

3/17

3/19

3

X

Collect data Develop plan Develop plan Quality Function Deployment Summary statement of tools and best practices Identify purpose of design 3P

Tollgate 2 Preliminary Design

Design at a glance 3P/Pugh selection matrix/Select top 3 Summary statement of tools and best practices Quality Function Deployment flow down Mistake proof design

FIGURE 17.7 (Continued)

Key words that describe function Find natural world examples Sketch/post examples Sketch background and conditions Develop 7 ways Devise and sketch methods

Complete X X X X X X X X

X X X X X X X X

207

TPM Case Study: Roller Assembly Redesign

Tollgate 3 Design/ Optimize Detail Design/ Prototype

Tollgate 4 Optimize/ Validate

Pre-Launch

Tollgate 5 Validate

Tollgate 6

Summary statement of tools and best practices

3P Assess risk Summary statement of tools and best practices

Work instructions for installing prototypes into machine

Summary statement of tools and best practices Monitor system capability

Summary statement of tools and best practices

Full Implementation

FIGURE 17.7 Design road map.

Checklist

3/20

3/20

1

X

Construct prototype DFMEA

3/22 3/25

3/24 3/28

3 4

X X

Checklist

3/29

3/31

3

X

How to install Track scrap Track material quality

4/26 4/30

4/30 8/01

5 90

4/30

8/01

90

Checklist

4/3

4/3

Track scrap Track material quality Trueness measurement

4/4

4/5

4/6

4/7

4/8

Scrap Material quality Changeover time

Not complete/ ongoing

90

X

90

X

4/8

ongoi ng

Done with rebuilds

4/9 4/10 4/11

4/9 4/10 4/11

1 1 1

X

4/12

11/20

208 Total Productive Maintenance

Gantt chart. FIGURE 17.8

TPM Case Study: Roller Assembly Redesign

17.8 17.8.1

209

INVENT/INNOVATE Swot analySiS (voc)

To gather the voice of the customer data, the team took a more practical approach by using a focus group and face-to-face approach rather than other traditional approaches. The main reason for this is because, in this case, the customers are all internal cus­ tomers within the facility; therefore, it would make sense to bring all stakeholders together to voice their input regarding the roller design modification. The team used a strengths, weaknesses, opportunities, and threats (SWOT) analysis, a tabular way of summarizing a particular process, product, department, or organization regarding its strengths, weaknesses, opportunities, and threats (Figure 17.9). The focus group team comprised employees, including one toolmaker, a maintenance associate, a machine operator, and the department supervisor. The engineer in the department facilitated the exercise. The first step was to brainstorm the four areas (SWOT). Strengths are strong internal characteristics/aspects of the roller assemblies (i.e., the parts themselves and the rebuilding of the part). Weaknesses are weak internal characteristics/aspects (i.e., the parts themselves and the rebuilding of the part) of performance. Opportunities are those characteristics the team can improve. On the other hand, threats are events, openings, external or internal changes to the roller assembly parts, and the rebuild themselves that could be detrimental to the performance of the assembly and the

FIGURE 17.9

SWOT analysis diagram.

210

Total Productive Maintenance

machine. The team challenged each focus group member to generate at least seven aspects for each area. Focus group members wrote each aspect on a sticky note and placed it in the appropriate quadrant of the diagram displayed (see Figure 17.3). Then each team member assigned a value of zero (not important), one (minor concern), two (concern), three (important), four, or five (most important) to each of the aspects in each of the four areas and added the totals to identify the relative strengths or impor­ tance of the factors listed and determine priorities for action. Figure 17.10 provides a list of the items identified by the focus group. The list con­ tains the items related to the strengths and weaknesses of the current design, the oppor­ tunities available to improve the current design, and the possible dangers in modifying

Item/(Strength) Sturdy Reliable Predictable Resistant to citric acid corrosion Top roller shafts cannot fall out of rollers They have worked well for a long time They are familiar to the team Bottom rollers are easily taken off the bottom rack assembly Item/(Weaknesses) Top roller shafts are difficult to remove and install from the top rollers Top rollers are difficult to remove from the top rack Bottom roller shafts are difficult to remove from the bottom rack assembly Bottom roller shafts are difficult to install in the bottom rack assembly Tool makers are the only personnel capable of removing and/or installing roller assembly shafts If there is any mechanical issue with the assemblies maintenance/engineering has to resolve the issue, i.e. production associates are not able to trouble shoot No consistent spare parts production procedure/standard Item/(Opportunities) Easier/Quicker rebuilds Decrease stock waste Operators can perform some rebuilding tasks Increased “trueness” of top and bottom assemblies Create a rebuild procedure Create a rebuild training procedure Create a troubleshooting document Measure and record “trueness” of current and modified roller assemblies Item/(Threats) Increased scrap Increased maintenance Design Rework Unqualified persons performing rebuilds Inadequate training To many hands in the “pot” Roller assemblies not “true” enough Increased rebuild frequency

FIGURE 17.10

Tool Maker 5 5 5 5 5 2 1 5 Tool Maker 5

VOTE Mechanic Supervisor Operator 5 5 5 5 5 1 3 5

5 5 5 5 5 0 2 5

5 5 5 5 5 3 1 5

Mechanic Supervisor Operator

Total 20 20 20 20 20 6 7 20 Total

5

5

4

19

5 5

5 5

5 5

4 5

19 20

5

5

4

3

17

5

3

3

4

15

3

5

5

5

18

5

2

1

1

9

Tool Maker 5 5 5 5 4 4 4 5

Mechanic Supervisor Operator

Tool Maker 5 5 5 5 5 5 5 5

Mechanic Supervisor Operator

SWOT analysis items and scoring.

5 3 5 5 5 5 5 5

5 5 4 5 5 5 5 5

5 5 5 5 4 5 5 5

5 5 4 3 4 5 4 5

5 3 3 5 4 4 5 5

5 3 4 3 4 4 3 4

Total 20 16 18 20 17 18 19 20 Total 20 18 17 16 18 19 17 19

211

TPM Case Study: Roller Assembly Redesign

what is already in place (i.e., threats). Each item contains the number assigned by each team member and the sum to understand the item’s overall importance to the team. For example, the first item listed and voted on under the strength column was that the current design is sturdy, and every team member assigned it the highest number (five). The votes summed up to 20, highlighting that the team thought it was vital for the new design to be just as sturdy or even sturdier than the current design.

17.9 CRITICAL TO SATISFACTION The team organized and listed the critical to satisfaction elements of the voice of the customer. As previously discussed, the team gathered the voice of the customer via a face-to-face focus group through a SWOT analysis. The team analyzed the more criti­ cal requirements/items (i.e., those elements that scored highest from the votes received) for practicality and how much the team thought or agreed that the item would aid in achieving the three main goals of the project. The team then laid out these items in the SWOT analysis, as shown in Figure 17.11. Column two of the table clarifies the impor­ tance of the customer requirement, and column three lists the number or metric critical

Voice of the Customer

The “Why” after clarification

Too much stock waste when building roller shafts

Current design requires 7/8” stock to be turned down to 1/2” stock because the shoulder portion of the shaft component is 7/8” diameter and the stem of the shaft component is 1/2”. Machine and its components are relatively reliable in its current state. New design must maintain or surpass current reliability conditions in terms of downtime caused by the roller assemblies. Must maintain or surpass current concentricity conditions/ measurements

Machines are relatively reliable under current conditions Top rollers must be as concentric as possible

Bottom rollers must be Must maintain or surpass current concentricity conditions/measurements as concentric as possible Scrap New design must not be the cause for scrap increase in that cell. Top shaft removal and Shaft is welded to the roller as a result it requires them to be drilled out installation to difficult/time when replaced. consuming (rebuild time) Bottom shaft removal Shaft is welded to the bottom rack as a result it and installation to requires them to be cut out when replaced. difficult/time consuming (rebuild time) Too much time to Current design requires 7/8” stock to be turned machine components down to 1/2” stock because the shoulder portion of the shaft component is 7/8” diameter and the stem of the shaft component is 1/2”.

FIGURE 17.11 Critical to satisfaction elements.

Critical Customer Requirement Decrease stock waste by 75% Zero downtime