QUALITY IMPROVEMENT BY ADDING INSPECTION CHECKPOINT(S) WITHIN PARTIAL AUTOMATED FOOTWEAR ASSEMBLY LINE: A TQM STUDY VIA DMAIC AND SIMULATION APPROACH AT PT PANARUB INDUSTRY

(1)

STUDY VIA DMAIC AND SIMULATION APPROACH AT PT PANARUB INDUSTRY

by

INTEN WIDYASTITI 11211069

BACHELOR’S DEGREE in

INDUSTRIAL ENGINEERING

FACULTY OF ENGINEERING AND INFORMATION TECHNOLOGY

SWISS GERMAN UNIVERSITY EduTown BSD City

Tangerang 15339 Indonesia

AUGUST 2015

Revision After Thesis Defense on August 5^th 2015

(2)

STATEMENT BY THE AUTHOR

I hereby declare that this submission is my own work and to the best of my knowledge, it contains neither material previously published or written by another person, nor material which to a substantial extent has been accepted for the award of any other degree or diploma at any educational institution, except where due acknowledgement is made in the thesis.

Inten Widyastiti Student

Approved by:

Dr. Ir. Yuki Indrayadi, MME.

Thesis Advisor

Dr. Ir. Prianggada Indra Tanaya, MME.

Thesis Co-Advisor

Dr. Ir. Gembong Baskoro, M.Sc.

Dean

Date

(3)

ABSTRACT

QUALITY IMPROVEMENT BY ADDING INSPECTION CHECKPOINT(S) WITHIN PARTIAL AUTOMATED FOOTWEAR ASSEMBLY LINE: A TQM

STUDY VIA DMAIC AND SIMULATION APPROACH AT PT PANARUB INDUSTRY

by Inten Widyastiti

Dr. Ir. Yuki Indrayadi, MME. - Advisor Dr. Ir. Prianggada Indra Tanaya, MME. - Co-Advisor

SWISS GERMAN UNIVERSITY

The primary objective of this thesis is to implement Total Quality Management via DMAIC (Define-Measure-Analyse-Control) methodology and simulation approach in PT. Panarub Industry as a part of quality improvement within the company. The company is in progress to integrate automation into their assembly line through ALPA (Assembly Line Project Automation). PT Panarub Industry introduced a performance indicator of good quality product at the first time it is made, which is called RFT (Right First Time) whenever the product does not need to be reworked nor repaired.

Unfortunately, RFT (Right First Time) in ALPA shows inconsistent performance and needs to be improved. So that, this work purposes TQM as quality framework which is presented in DMAIC methodology structure, and integrated with several tools and methods like SPC (Statistical Process Control) and simulation to find out problematic operation in the process. Simulation use Anylogic software which provides logic, 2D, and 3D simulations to compare alternative solutions. The solutions given to PT Panarub Industry is by adding inspection checkpoint(s) and distribute them after critical production process.

Keywords: TQM, DMAIC, Simulation, Quality Control, Partial automated assembly line, Footwear Company, Statistical Process Control

(4)

(5)

DEDICATION

To my beloved family who are always supporting and caring in every situation, To my fellow friends for helping and companion,

And to my lecturers for their critics and suggestion.

(6)

ACKNOWLEDGEMENTS

The author would like to express gratitude to Allah S.W.T. for his constant blessing and guidance throughout the creation and completion of this work.

Thanks to Mr. Ir. Sukiswanto, MBA and Mrs. Netraningtyas Budirahayu as my parents for their unconditional love, support and motivation.

The author is thankful for the advice and support given by Mr. Dr. Ir. Yuki Indrayadi, MME who has patiently supervises the author as advisor to accomplish this work.

Thank you to Mr. Dr. Ir. Prianggada Indra Tanaya, MME as co-advisor for his constant helping and motivation towards the completion of this work.

The author also want to express her sincere gratefulness to Mr. Reyhan Adiel and Mr.

Arya from PT Panarub Industry for their constant help and guide by providing the author the important information, discussion, and suggestion which leads to the result of this work.

Last but not least, the author would like to give her regards and deep appreciation to beloved friends who give cheerfulness and stress-relieved during tough times, Maria Helena Lado, Yuki Masfirah, Firmansyah, Destiara Putri, Anisa Aqlia, Sartika Putri, Gifta Rani, Dyah Indraswari, Rifki Marchianto, Putri Juwita, Kenanga Wungu, Mudalifah, Orlando Firdaus, Joedith Monica, and Dwi Putri. Thanks to all Industrial Engineering 2011 students for their inspiration and motivation, thanks to fellow problem-solvers and critical thinkers, Raezita Dinar, Devi Surliyanti, and Rahadian Pradana. The author would like to give special thanks and blessing to Ekri Bilal for his constant support and help during the completion of this work, and to everyone who has helped the author and cannot be mentioned one-by-one.

(7)

TABLE OF CONTENTS

Page

STATEMENT BY THE AUTHOR ... 2

DEDICATION ... 5

ACKNOWLEDGEMENTS ... 6

TABLE OF CONTENTS ... 7

LIST OF FIGURES ... 12

LIST OF TABLES ... 15

CHAPTER 1 – INTRODUCTION ... 16

1.1. Background ... 16

1.2. Problem Identification ... 17

1.3. Thesis Purpose ... 18

1.4. Thesis Description ... 18

1.5. Thesis Scope ... 19

1.6. Thesis Limitation ... 19

1.7. Hypothesis ... 19

1.8. Thesis Organization ... 20

CHAPTER 2 – LITERATURE REVIEW ... 21

2.1. Introduction ... 21

2.2. Japanese Total Quality control, TQM, Deming’s system of profound knowledge, BPR. Lean, and Six Sigma Comparison and Discussion (Chiarini, 2011) 21 2.3. TQM (Total Quality Management) ... 24

2.3.1. A Working Definition for TQM Researchers (Miller, 1996)... 24

2.3.2. Control Process for Total Quality Management and Quality Assurance (Jabnoun, 2015) ... 25

2.3.3. Investing in automation: Total Quality Management unlocks the Dollars (Horst, 1992) ... 26

2.4. Integrating TQM and Six Sigma ... 27

(8)

for Measuring Medication Errors (Revere & Black, 2003) ... 27

2.4.2. Implementing Six Sigma via TQM Improvement: an empirical study in Taiwan (Cheng, 2008) ... 29

2.5. DMAIC for Quality Improvement ... 32

2.5.1. A Six Sigma and DMAIC application for the reduction of defects in a rubber gloves manufacturing process (Jirasukpraset, et al., 2013) ... 32

2.5.2. Using DMAIC Six Sigma to Systematically Improve Shopfloor Production Quality and Costs (Kumar & Sosnoski, 2009) ... 33

2.5.3. A DMAIC approach for process capability improvement an engine crankshaft manufacturing process (Sharma & Rao, 2014) ... 34

2.5.4. Framework for Continuous Improvement of Production Processes and Product Throughput (Jevgeni, et al., 2014) ... 36

2.6. Concluding Remark ... 37

CHAPTER 3 – METHODOLOGY ... 38

3.2. Overview of PT Panarub Industry ... 38

3.2.1. Company Profile ... 38

3.2.2. ALPA (Assembly Line Project Automation) ... 38

3.3. Quality ... 39

3.3.1. Quality Control ... 41

3.3.2. Inspection ... 42

3.3.3. Quality Assurance ... 44

3.3.4. Control Chart for Attributes ... 44

3.4. Total Quality Management as Quality Framework ... 50

3.5. DMAIC as Problem Solving Methodology ... 54

3.6. Automation ... 56

3.6.1. Benefit of Automation ... 56

3.6.2. Automation Migration Strategy ... 56

3.6.3. Types of Automation ... 59

3.7. Discrete-Event Simulation System ... 60

(9)

3.7.1. Advantages and Disadvantages ... 61

3.7.2. Area of Application ... 62

3.7.3. Model of System ... 62

3.7.4. System and System Environment ... 63

3.7.5. Step in Simulation ... 64

3.7.6. Anylogic Simulation Tool ... 67

3.8. Changeover Planning ... 67

3.9. Footwear ... 68

3.10. Thesis Framework embed to “DMAIC” ... 70

3.10.1. Initiatory Study Phase as “Define” ... 71

3.10.2. Data Gathering & Treatment Phase as “Measure” ... 72

3.10.3. Data Analysis Phase as “Analyse” ... 73

3.10.4. Data Simulation & Modelling Phase as “Improve” ... 74

3.10.5. Data Evaluation Phase as “Control” ... 75

3.11. Overview of Applicant Tools ... 76

3.12. Concluding Remark ... 76

CHAPTER 4 – RESULTS AND DISCUSSIONS ... 77

4.2. Initiatory Study Phase ... 77

4.2.1. Project Charter ... 77

4.2.2. SIPOC Diagram ... 81

4.2.3. Operation Process Chart (OPC) ... 82

4.2.4. Flow Process Chart (FPC) ... 84

4.3. Data Gathering & Treatment Phase... 88

4.3.1. Pareto Diagram ... 88

4.3.2. User’s Expectation ... 89

4.3.3. Control Chart for Attribute ... 89

4.3.4. GAP Analysis ... 98

4.4. Data Analysis Phase ... 92

4.4.1. Process Analysis ... 92

4.4.2. Root-Caused Analysis ... 96

(10)

4.5. Data Simulation & Modelling Phase ... 105

4.5.1. Introduction ... 105

4.5.2. Model Conceptualization ... 105

4.5.3. Parameter Measures ... 107

4.5.4. Assumptions ... 107

4.5.5. Model Translation ... 108

4.5.6. Logic Simulation of Current Condition ... 108

4.5.7. 2D & 3D Simulation of Current Condition ... 110

4.5.8. Model Verification ... 112

4.5.9. Model Validation ... 112

4.5.10. Simulation run lengh ... 113

4.5.11. Analysis of Alternative Different Scenarios ... 113

4.5.12. Concluding Remark ... 135

4.6. Data Evaluation Phase ... 137

4.6.1. Strategic Phase ... 137

4.6.2. Preparation Phase ... 141

4.6.3. Implementation Phase ... 147

CHAPTER 5 – CONCLUSIONS AND RECOMMENDATION ... 148

5.1. Conclusions ... 148

5.2. Recommendations ... 149

GLOSSARY ... 150

REFERENCES ... 151

APPENDICES ... 153

APPENDIX A – Data gathering & treatment phase ... 153

A.1. Pareto Table ... 154

A.2. Defective List and Proportion ... 155

APPENDIX B – Control Chart for Attribute ... 156

B.1. Table p-Chart of current condition ... 157

B.2. Table u-Chart of current condition ... 158

B.3. Table u-chart of ALPA 8 ... 159

(11)

CURRICULUM VITAE ... 168