Mixed-load machine utilization improvement and transfer batch size optimization using hybrid simulation approach.

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Faculty of Manufacturing Engineering

MIXED-LOAD MACHINE UTILIZATION IMPROVEMENT AND

TRANSFER BATCH SIZE OPTIMIZATION USING

HYBRID SIMULATION APPROACH

Hayati Mukti Asih

Master of Science in Manufacturing Engineering

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MIXED-LOAD MACHINE UTILIZATION IMPROVEMENT AND TRANSFER BATCH SIZE OPTIMIZATION USING HYBRID SIMULATION APPROACH

HAYATI MUKTI ASIH

A thesis submitted

in fulfillment of the requirements for the degree of Master of Science in Manufacturing Engineering

Faculty of Manufacturing Engineering

UNIVERSITI TEKNIKAL MALAYSIA MELAKA

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DECLARATION

I declare that this thesis entitle “Mixed-Load Machine Utilization Improvement and Transfer

Batch Size Optimization using Hybrid Simulation Approach” is the result of my own

research except as cited in the references. The thesis has not been accepted for any degree and is not currently submitted in candidature of any other degree.

Signature : ...

Name : Hayati Mukti Asih

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APPROVAL

I hereby declare that I have read this thesis and in my opinion this thesis is sufficient in terms of scope and quality for the award of Master of Science in Manufacturing Engineering.

Signature : ...

Name : Assoc. Prof. Dr. Chong Kuan Eng

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DEDICATION

To my beloved families:

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i ABSTRACT

Current technological development has increased the competitiveness in the manufacturing system, especially for the electronic industry. This research is based on case company in the automatic testing and label printing processes of a multinational hard disk drive (HDD) manufacturing system with the objective of improving the tester utilization while achieving the production target. The problem is complex as the testers are employed to simultaneously load multiple product families. Each product family has several models with different testing durations. In addition, apart from the high product mixes for each product family undergoes different process flow making the problem more complicated. The company has difficulty to achieve the targeted tester utilization of 96%, as the current utilization is 71.14%. As the problem is too complicated to be solved by an analytical method, a hybrid simulation approach was employed to solve the operation machine allocation and the transfer batch size problem. Firstly, the problem of mixed-load tester was formulated through a mathematical model. Then, a simulation model was designed and developed to evaluate the scenarios of the mixed-load tester configurations. After that, the multi criteria decision making techniques were employed to determine the best scenario. Finally, the transfer batch size was optimized to improve system WIP. The final proposed configuration successfully increased the tester utilization by 24.89% and reduced the number of testers by 37.77% for Tester A and by 27.27% for Tester B while improving the throughput by 6.88% compared to the current system. In addition, the transfer batch size was reduced from 120 units to 86 units and system WIP was successfully reduced by 6.43%.

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ii ABSTRAK

Perkembangan teknologi semasa telah meningkatkan daya saing dalam sistem pembuatan, terutamanya bagi industri elektronik. Penyelidikan ini berasaskan kepada kes di syarikat yang melaksanakan pengujian automatik dan proses pencetakan label bagi sistem pembuatan multinasional cakera keras (HDD) dengan matlamat untuk memantapkan lagi penggunaan penguji sekaligus mencapai sasaran pengeluaran. Masalahnya kompleks kerana penguji digunakan untuk memuatkan jenis produk berganda pada masa yang sama. Setiap jenis produk mempunyai beberapa model dengan jangka masa ujian yang berbeza. Sebagai tambahan, selain daripada campuran-campuran produk yang tinggi setiap jenis produk menjalani aliran proses yang berbeza menyebabkan masalah yang menjadi lebih rumit. Oleh itu, syarikat itu sukar untuk mencapai penggunaan penguji itu kepada 96%, manakala sistem sedia ada adalah 71.14%.Kerana masalah itu terlalu rumit untuk diselesaikan dengan kaedah analitikal, pendekatan simulasi hibrid telah diambil untuk menangani peruntukan operasi ini pada mesin dan masalah pemindahan saiz kelompok. Pertama sekali, masalah penguji yang bercampur beban dirumuskan melalui model matematik. Kemudian, model simulasi direkabentuk dan dibangunkan bagi menilai senario konfigurasi penguji yang bercampur beban itu. Kemudian, pelbagai kriteria teknik membuat keputusan telah digunakan untuk menentukan senario yang terbaik. Akhir sekali, pemindahan saiz kelompok telah dioptimumkan untuk memperbaiki sistem WIP. Konfigurasi akhir yang dicadangkan berjaya meningkatkan penggunaan penguji sebanyak 24.89% dan mengurangkan bilangan penguji sebanyak 37.77% untuk Penguji A dan sekitar 27.27% untuk Penguji B masa yang sama memperbaiki daya pemprosesan sebanyak 6.88% berbanding dengan sistem semasa. Di samping itu, saiz kelompok pemindahan telah dikurangkan dari 120 unit kepada 86 unit dan sistem WIP telah berjaya dikurangkan sebanyak 6.43%.

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ACKNOWLEDGEMENT

In the name of Allah, The Beneficent, The Merciful.

Alhamdulillahi rabbil `alamin. All praise belongs to Allah, who by his blessing and mercy all righteous deeds are being perfected, and this thesis would not have been completed without his divine guidance.

Firstly, I would like to express my sincere thanks to my supervisor, Assoc. Prof. Chong Kuan Eng and Prof. Adi Saptari for their patience, guidance, advice, and motivation to complete this thesis since the very beginning.

Muhammad Faishal who is always beside me, believing in me, and encouraging me in the good things. The understanding, support, and patience of him is ark for me to stare the future wiser.

I will always be grateful to my beloved parents, Achmad Kartubi and Qurrotu Aini for their prayers, love, and patience. The spirit of their prayers and smiles is always adorned the peace of this heart to pursue a dream. Also, perfect thing cannot be achieved without their sincerity.

I also want to thank my lovely sister, Hayati Puji Astuti, who always giving me a support and cheerfulness.

Lastly, friendship and togetherness have been always there for all my Indonesian and Malaysian friends. A beautiful moment and word of thanks always decorate for laughter and discussions with you all.

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TABLE OF CONTENTS

DECLARATION APPROVAL DEDICATION

ABSTRACT i

ABSTRAK ii

ACKNOWLEDGEMENT iii

TABLE OF CONTENTS iv

LIST OF TABLES viii

LIST OF FIGURES x

LIST OF APPENDICES xiii

LIST OF ABBREVIATIONS xiv

LIST OF PUBLICATION xv

CHAPTER

1. INTRODUCTION 1

1.1. Introduction 1

1.2. Company Background 2

1.3. Problem Statement 4

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1.5. Research Objectives 5

1.6. Research Scopes 6

1.7. Organization of the Thesis 6

2. LITERATURE REVIEW 8

2.1. Operation Management 8

2.1.1. Production Planning and Control 9

2.1.2. Loading 12

2.1.3. Transfer Batch Size (TBS) 24

2.2. Hard Disc Drive Manufacturing System 27

2.2.1. Hard Disc Drive Manufacturing System Problems 28

2.3. Simulation 29

2.3.1. Why Simulation 31

2.3.2. Simulation in Manufacturing 32

2.3.3. Simulation Optimization 39

2.4. Multi Criteria Decision Making (MCDM) analysis 41

2.4.1. Analytic Hierarchical Process (AHP) 42

2.4.2. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) 44

2.5. K-Chart 45

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3. RESEARCH METHODOLOGY 50

3.1. Introduction 50

3.2. Detail Company`s Background 50

3.3. Research Design 54

3.3.1. Phase 1: Preliminary Study 56

3.3.2. Phase 2: Experimental Design 1 58

3.3.3. Phase 3: Experimental Design 2 60

3.3.4. Documenting and Reporting 60

4. DEVELOPMENT OF MODELS AND EXPERIMENTS 62

4.1. Introduction 62

4.2. Process Flow in Case Company 62

4.3. Conceptual Modeling 64

4.3.1. Scope of the Model and Level of Details 65

4.3.2. Assumptions 67

4.4. Model Data 67

4.5. Model Translation in ProModel® 7.5 Simulation Software 72

4.6. Verification and Validation 90

4.7. Warm-up Determination 94

4.8. Number of Replications 97

4.9. Experimentations 98

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vii

4.9.2. Experimental Design 2 113

5. RESULTS AND DISCUSSIONS 118

5.1. Introduction 118

5.2. Results for Experimental Design 1 118

5.2.1. Result of Mathematical Model for Mixed-load Tester 118

5.2.2. Results of Simulation Modeling 126

5.2.3. Results of Identification of the Best Scenario 130

5.3. Results for Experimental Design 2 136

5.4. Summary of Experimental Results and Discussion 138

5.5. Research Contributions 140

6. COCLUSIONS AND RECOMMENDATIONS 142

6.1. Conclusion 142

6.2. Recommendations 144

REFERENCES 145

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viii

LIST OF TABLES

TABLE TITLE PAGE

2.1 A Summary of Related Literature on Machine Loading Problem in Various

Applications 18

2.2 Applications and categorization of simulation technique 35

3.1 The Historical Data Throughput 52

4.1 Scope of the Model 65

4.2 Level of Detail 66

4.3 Testing Time and Processing Time of Product T (Standard Time) 68

4.4 Current Mixed-load Tester Model 70

4.5 Testing Time Distribution of the Automatic Testing Process and 85

4.6 Input Quantity Distribution 89

4.7 Validation: Student T-Test for Throughput 91

4.8 Validation: Student T-Test for Tester Utilization 92

4.9 Results from Five Replications of Throughput Data 94

4.10 Scenarios of the Mixed-load Tester 104

4.11 Inputs for Calculating the Allocation Problem 105

4.12 Pairwise Comparison of Each Grade (adopted by Chen et al., 2009) 110

5.1 The Mixed Loading of Tester A for Scenario 1 120

5.2 The Hourly Loading of Tester A for Scenario 1 121

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TABLE TITLE PAGE

5.4 The Hourly Loading of Tester B for Scenario 1 124

5.5 The Scenarios of Mixed-load Tester with the Number of Testers 125

5.6 The Hourly Loading for All Product Families in Tester A and Tester B 126

5.7 Confidence Interval of the Six Scenarios 128

5.8 The AHP Results for Six Selected Scenarios 130

5.9 Normalized Decision Matrix 131

5.10 The Weighted Normalized Decision Matrix 132

5.11 Positive Ideal Solution 133

5.12 Negative Ideal Solution 133

5.13 Positive Ideal Separation 134

5.14 Negative Ideal Separation 134

5.15 The Relative Closeness to the Ideal Solution 135

5.16 The Comparison Results of AHP and TOPSIS 135

5.17 The Differences of Scenario 1 and Scenario 2 136

5.18 A Summary Confidence Interval and Prediction Interval for Throughput, System

WIP, and Tester Utilization for the Transfer Batch Size of 86 units 136

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LIST OF FIGURES

FIGURE TITLE PAGE

1.1 General Process Flow Diagram 3

1.2 Layout of Automatic Testing and Label Printing Processes 3

1.3 Average Tester Utilization: Current System versus Target 4

2.1 The General Process Flow of the HDD Manufacturing System 27

2.2 Research K-Chart 47

3.1 Back-end Process 51

3.2 The Operation Process 54

3.3 Research Design 55

3.4 The Details of the Hybrid Simulation Approach for Mixed-load Tester 61

4.1 Layout of the Automatic Testing and Label Printing Processes 63

4.2 Operation Process Flow Diagram 64

4.3 The Model Translation of the Base Model using the ProModel 7.5 Simulation

Software 73

4.4 The Entities using the ProModel 7.5® Simulation Software 74

4.5 The Locations in ProModel 7.5® Simulation Software 75

4.6 The Locations of Sample Tester Group 76

4.7 The Resources in ProModel 7.5® Simulation Software 77

4.8 The Operator and Robot Paths for the Automatic Testing Process 78

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FIGURE TITLE PAGE

4.10 The Routings of the Automatic Testing Process 79

4.11 The Routings using the ProModel® 7.5 Simulation Software 81

4.12 The Routings of Product T 82

4.13 The Routings of Product A, B, and S 83

4.14 The Entity Operation 84

4.15 The External File 85

4.16 Generate Testing Time of Product S 86

4.17 Export Data: Testing Time of Product S 86

4.18 The Entity Arrival using the ProModel 7.5® Simulation Software 87

4.19 The Auto::Fit of Distribution: Input Quantity of Product S 87

4.20 Summary of Goodness of Fit: Input Quantity of Product S 88

4.21 Goodness of Fit: Input Quantity of Product S 88

4.22 Export Data: Input Quantity of Product S 89

4.23 The Warm – Up Determination 97

4.24 The Number of Replications Determination 98

4.25 Model Input, Process, and Output 99

4.26 The Input, Process, and Output of Simulation Modeling 105

4.27 The Simulation Model of Scenario 1 for Experiment 1 108

4.28 AHP Model for Scenarios of Mixed-load Tester 110

4.29 Simulation Optimization Model 113

4.30 The Pallet Run 114

4.31 Macros Tool Box in the ProModel® 7.5 Simulation Software 115

4.32 Decision Variable Determination 115

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xii

FIGURE TITLE PAGE

4.34 Simulation Parameter 117

5.1 The Proportion of the Mixed Loading in Tester A for Scenario 1 121

5.2 The Proportion of the Mixed Loading in Tester B for Scenario 1 124

5.3 The Average of Simulation Results for All Scenarios 127

5.4 The Improvement for system WIP 137

5.5 The System WIP in the SimRunner® Optimization Software 138

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xiii

LIST OF APPENDICES

APPENDIX TITLE PAGE

A Historical Data of Throughput 163

B Verification Form 164

C Data of Testing Time 166

D Historical Data of Throughput for Each Model 168

E Historical Data of Input Quantity 169

F The Model Elements of Base Model 171

G Historical Data of Tester Utilization 174

H Simulation Results for All Scenarios with Three Replication Runs 175

I The Simulation Results of Scenario 1 177

J The Optimization Results 185

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xiv

LIST OF ABBREVIATIONS

AHP Analytic Hierarchy Process

FIFO First-in-First-out

HDA Hard Disk Assembly

HDD Hard Disk Drive

MCDM Multi Criteria Decision Making

OM Operation Management

PPC Production Planning and Control

TBS Transfer Batch Size

TH Throughput

TOPSIS Technique for Order Preference by Similarity to Ideal Solution

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LIST OF PUBLICATION

1. International Journal

Hayati Mukti Asih and Chong Kuan Eng (2014). Throughput and Tester Utilization Improvement in the Hard Disk Drive Assembly Line using Hybrid Simulation Approach.

Advanced Science Letters, Vol. 20, No. 02.

2. Proceeding

Hayati Mukti Asih and Chong Kuan Eng. Throughput and Tester Utilization Improvement

in the Hard Disk Drive Assembly Line using Hybrid Simulation Approach. The 2013 Annual

International Conference on Advances Technology in Telecommunication, Broadcasting, and Satellite (TelSaTech). 02 – 03 August 2013, Jakarta, Indonesia.

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1 CHAPTER 1

INTRODUCTION

1.1. Introduction

Current technological development has increased the competitiveness in the manufacturing system, especially for hard disk drive (HDD) industry. In addition, the companies are required to focus on the job allocation of every machine and transfer batch size issues due to the high production volumes and variety of products. In the real world, many uncertainties influence the performance of a production system. According to Ho (1989), these uncertainties are caused by operating variables (i.e. the uncertainty of production yield, lead time, quality, product development, etc.) and environmental factors (i.e. the uncertainty of demand and supply).

One of the essential performances of the production system is machine utilization. Improving the tester utilization will result in achieving the production target based on customer order. In addition, another way to achieve good production performance measure is the work-in-process (WIP). The system WIP is the amount of product waiting in the production system. It could be the raw material, in-progress product and finished product. Reducing the WIP level yields increase the throughput, reduces cycle time and reduces cost as well.

Allocating jobs with stochastic input parameters, high product mixes and long production process time make it difficult to investigate the system performance through analytical methodology. This research, which is based on a case study of an HDD company, uses the hybrid simulation approach to solve the mixed-load tester and transfer batch size

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problems. The mixed-load tester problem is the ability of a tester to simultaneously load multiple product families.

1.2. Company Background

This research is based on a case study of a back-end process of an HDD manufacturing system located in Petaling Jaya, Malaysia. The Backend process is a testing and inspection process after the completion of a one piece flow of Hard Disc Assembly (HDA) at the Cleanroom. The process flow in the assembly line is presented in Figure 1.1.

In these processes, there are two types of product produced in this company are 2.5” HDD and 3.5” HDD. There are more than twenty product families that are produced for both product types whereas each product family has different process flows, different production volumes and different testing process. Hence, this makes the HDD company more complex.

This research focuses on the automatic testing and label printing processes. The layout is shown in Figure 1.2. In the automatic testing process, there are two stages, i.e. Tester A and Tester B. It consists of seven lines for Tester A and three lines for Tester B. The feeder is the one who distributes the amount of drives from the buffer before automatic testing process to the small buffer which is close to each tester, then takes the tested drive to the next stage. A feeder is responsible for handling a line. Then, an operator loads and unloads drives to testers. On the other hand, in label printing process, the duty of the operator is to print the label on each drive and the feeder is to distribute the drives to each small buffer close to the label printing machine.

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Clean Room

Clean Room PCBA AssemblyPCBA Assembly Helium Charge and Seal Installation

Helium Charge and

Seal Installation Filler TestFiller Test Seal RemovalSeal Removal

Helium Discharged Helium Discharged Label Printing Label Printing Sorting Sorting Packaging Packaging Customer Customer Automatic Testing Process Automatic Testing Process

Figure 1.1 General Process Flow Diagram

Tester A Line 0 No of testers: 3 No of operators: 1

Tester B Line 0 No of testers: 5 No of operators: 2

Tester B Line 0 No of testers: 5 No of operators: 2 Buffer before Automatic Testing Process

No of feeders : 9

Tester B Line 7 – Line 8 No of testers: 6 No of operators: 3

Tester B

Line 7 – Line 8

No of testers: 6 No of operators: 3

Buffer before Label Printing No of feeders : 1

Label Printing No of machines: 30 No of operators: 30

Tester A Line 1 – Line 6 No of testers: 42 No of operators: 18

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4 1.3. Problem Statement

The observations of the production process and from the discussions with the managers, IE planner, plant supervisors and production line associates found that the characteristics of the shop floor are complex. The automatic testers are employed have almost three thousands slots that able to load multiple product families, simultaneously. Moreover, there are more than fifteen models in all product families with different testing durations. In addition, to the high product varieties, each product family undergoes different production process flow making the problem more complicated.

Therefore, the company is difficult to achieve the target of average tester utilization as 96%. While, the current system is 71.14% for 35 days. Figure 1.3 presents the gaps of the average tester utilization between the current system and the company`s target.

Figure 1.3 Average Tester Utilization: Current System versus Target

Because of the problem considering the uncertainties in production system, the analytical method is hard solving the problem alone (Robinson, 2004). Hence, the

96% 71.14% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Target Current System

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xiv

LIST OF ABBREVIATIONS

AHP Analytic Hierarchy Process FIFO First-in-First-out

HDA Hard Disk Assembly

HDD Hard Disk Drive

MCDM Multi Criteria Decision Making

OM Operation Management

PPC Production Planning and Control

TBS Transfer Batch Size

TH Throughput

TOPSIS Technique for Order Preference by Similarity to Ideal Solution

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LIST OF PUBLICATION

1. International Journal

Hayati Mukti Asih and Chong Kuan Eng (2014). Throughput and Tester Utilization Improvement in the Hard Disk Drive Assembly Line using Hybrid Simulation Approach.

Advanced Science Letters, Vol. 20, No. 02.

2. Proceeding

Hayati Mukti Asih and Chong Kuan Eng. Throughput and Tester Utilization Improvement in the Hard Disk Drive Assembly Line using Hybrid Simulation Approach. The 2013 Annual International Conference on Advances Technology in Telecommunication, Broadcasting, and Satellite (TelSaTech). 02 – 03 August 2013, Jakarta, Indonesia.

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1 CHAPTER 1

INTRODUCTION

1.1. Introduction

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problems. The mixed-load tester problem is the ability of a tester to simultaneously load multiple product families.

1.2. Company Background

In these processes, there are two types of product produced in this company are 2.5” HDD and 3.5” HDD. There are more than twenty product families that are produced for both

product types whereas each product family has different process flows, different production volumes and different testing process. Hence, this makes the HDD company more complex.

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3 Clean Room

Clean Room PCBA AssemblyPCBA Assembly Helium Charge and Seal Installation Helium Charge and

Seal Installation Filler TestFiller Test Seal RemovalSeal Removal Helium Discharged Helium Discharged Label Printing Label Printing Sorting Sorting Packaging Packaging Customer Customer Automatic Testing Process Automatic Testing Process

Figure 1.1 General Process Flow Diagram

Tester A

Line 0 No of testers: 3 No of operators: 1

Tester A

Line 0 No of testers: 3 No of operators: 1

Tester B

Line 0 No of testers: 5 No of operators: 2

Tester B

Line 0 No of testers: 5 No of operators: 2

Buffer before Automatic Testing Process

No of feeders : 9

Tester B

Line 7 – Line 8 No of testers: 6 No of operators: 3

Tester B

Line 7 – Line 8 No of testers: 6 No of operators: 3

Buffer before Label Printing

No of feeders : 1

Label Printing

No of machines: 30 No of operators: 30

Label Printing

No of machines: 30 No of operators: 30

Tester A

Line 1 – Line 6 No of testers: 42 No of operators: 18

Tester A

Line 1 – Line 6 No of testers: 42 No of operators: 18

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4 1.3. Problem Statement

Figure 1.3 Average Tester Utilization: Current System versus Target

Because of the problem considering the uncertainties in production system, the analytical method is hard solving the problem alone (Robinson, 2004). Hence, the

96%

71.14%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Target Current System

Mixed-load machine utilization improvement and transfer batch size optimization using hybrid simulation approach.

Faculty of Manufacturing Engineering