Lean six sigma in scrap cost improvement an application in electronics manufacturing system

VIET NAM NATIONAL UNIVERSITY HO CHI MINH CITY HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY TRAN NGOC QUYNH LEAN – SIX SIGMA IN SCRAP COST IMPROVEMENT: AN APPLICATION IN ELECTRONICS MANUF

VIET NAM NATIONAL UNIVERSITY HO CHI MINH CITY

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY

TRAN NGOC QUYNH

LEAN – SIX SIGMA IN SCRAP COST IMPROVEMENT:

AN APPLICATION IN ELECTRONICS MANUFACTURING

THIS RESEARCH IS COMPLETED AT:

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY – VNU HCM

Instructor: Assoc Prof PhD Do Ngoc Hien ………

Examiner 1: PhD Nguyen Vang Phuc Nguyen………

Examiner 2: PhD Nguyen Van Thanh………

Master’s Thesis is defended at HCMC University of Technology, VNU-HCM on January 08, 2023 The Board of The Master’s Thesis Defense Council includes: 1 Chairman: PhD Do Thanh Luu ………

2 Secretary: PhD Le Song Thanh Quynh………

3 Counter-Argument Member: PhD Nguyen Vang Phuc Nguyen………

4 Counter-Argument Member: PhD Nguyen Van Thanh………

5 Council Member: Assoc Prof PhD Do Ngoc Hien ………

Verification of the Chairman of the Master’s Thesis Defense Council and the Dean of the Faculty of Mechanical Engineering after the thesis is corrected (if any)

CHAIRMAN OF THE COUNCIL

(Full name and signature)

DEAN OF FACULTY OF MECHANICAL

ENGINEERING

(Full name and signature)

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VNUHCM UNIVERSITY OF TECHNOLOGY

Independence – Liberty – Happiness

MASTER’S THESIS ASSIGNMENTS

Full name: TRAN NGOC QUYNH Learner ID: 2070328 Date of birth: 29thOctober, 1998 Place of birth: Ca Mau Major: Industrial Engineering Major ID: 8520117

I – TITLE: LEAN – SIX SIGMA IN SCRAP COST IMPROVEMENT: AN APPLICATION

IN ELECTRONICS MANUFACTURING SYSTEM/ NGHIÊN CỨU ỨNG DỤNG LEAN SIX - SIGMA VÀO GIẢM CHI PHÍ PHẾ PHẨM: TRƯỜNG HỢP ĐIỂN CỨU TẠI NHÀ MÁY LẮP RÁP HÀNG ĐIỆN TỬ

ASSIGNMENTS AND CONTENT:

Assignments:

 Investigate a production system to identify the quality and productivity-related problems and their root causes

 Collect the information and data to prove the problem

 Propose a Lean Six Sigma framework to reduce the scrap cost rate in an EMS factory

 Reduce the scrap cost rate to meet the company target

Content:

 Chapter 1 gives a general introduction to the research object and problem The research objectives, limitations and scope of the study, the content to be performed, and layout of the study will also be briefly presented in this chapter

 Chapter 2 presents the theories used in the research will be presented, including the Lean concept, Six Sigma methodology, in-process kanban (IPK), Maynard operation sequence technique (MOST) and related studies

 Chapter 3 introduces a research methodology with step to step to carry out this study

 Chapter 4 presents an overview of the research object, such as process, layout, traceability systems, etc The Define, Measure and Analyze phases will be discussed

in this chapter All data collected is analyzed to identify the improvement opportunity and form a research problem As a result, this session will also conduct improvement actions to solve the problem's root cause Moreover, the control plan will also be defined to maintain the improvement actions and their results

 Chapter 5 reports the conclusion of the thesis The remaining shortcomings will be pointed out, and possible developing directions for the future will be suggested

IV – INSTRUCTOR: Assoc Prof PhD Do Ngoc Hien

Ho Chi Minh City, December 23, 2022

INSTRUCTOR

(Full name and signature)

HEAD OF DEPARTMENT

(Full name and signature)

DEAN OF FACULTY OF MECHANICAL ENGINEERING

(Full name and signature)

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ACKNOWLEDGEMENT

"Lean – Six Sigma in scrap cost improvement: an application in Electronics Manufacturing

System" is a result of whale time at the Department of Industrial Systems Engineering, HCMC

University of Technology This thesis would not have been feasible without the untold amount of

encouragement from numerous people who enabled me to complete the study

First and foremost, I would like to express my heartfelt appreciation and deepest gratitude to

my primary advisor – A/Prof Đỗ Ngọc Hiền for his guidance over the course of this thesis study

I am grateful beyond words that he is ready to discuss issues with vast information I have had no

chance to be shared before I could not have asked for a better advisor for my thesis study

Besides, thanks for the enthusiastic support from colleagues at Jabil company In the process

of implementing as well as collecting data, I have received very enthusiastic support from both

teachers and brothers and sisters in the company I would like to especially thank Mr Danh Võ –

IE Functional Manager and Ms Nhi Trần – IE Engineer who facilitated and supported me directly

in this project I would also like to thank the IE support team members who have always

enthusiastically supported me in my work and searching for orientation for graduation thesis topics

in the company

In addition, I would like to thank the friends who have helped me through the difficulties and

also the joys Thank you for making my life more colourful

Last but not least, I would like to express my sincerest thanks to my greatest source of

motivation and inspiration: thank you to my parents for always supporting me in every way Thank

you, my brother – Trần Quốc Công, PhD for advice and guidance I would not have gotten to where

I am today without your support

In the process of working and reporting, mistakes will inevitably be made, and I would like

to ask for your comments and corrections so that I can improve my knowledge as well as future

orientation

Trần Ngọc Quỳnh

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ABSTRACT

This research presents six sigma implementation conducted in an EMS enterprise to reduce scrap cost rate and thereby increase its sigma level The study has gone through the problem that the factory is facing: the percentage of scrap cost is higher than the target (actual 0.03% vs goal 0.02%) Using the DMAIC methodology, the root cause was defined as product handling, tool-using and IPK identification between stations Then, three improvement actions were taken to address the three identified causes The solutions are to change the board handling trays, add a mandatory scan step when using a screw jig, and determine the number of IPKs between stations

After applying improvement solutions, the line performance is significantly improved The sigma level improved from 2.91 to 3.43 for the download station and from 2.6 to 3.19 for the wintest station Since then, the rate of products passing the first time (FPY) has also improved, from 98.31% to 98.55%, compared to the factory's target is 98.54% Furthermore, the research's target was also achieved when improving the scrap cost ratio from 0.03% to 0.014% This result proves the effectiveness of the study, meeting the research objectives and the company's expectations

Keywords: Lean Six – Sigma, scrap cost reduction, MOST, FlexSim simulation, IPK

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TÓM TẮT LUẬN VĂN

Nghiên cứu này trình bày việc triển khai Six - Sigma trong một doanh nghiệp EMS để giảm

tỷ lệ chi phí phế phẩm và từ đó tăng mức sigma của hệ thống Nghiên cứu đã đi sâu vào vấn đề mà nhà máy đang gặp phải: tỷ lệ chi phí phế phẩm cao hơn so với mục tiêu (0.03% thực tế so với mục tiêu 0.02%) Bằng việc sử dụng chu trình DMAIC, nguyên nhân gốc rễ được xác định là do thao tác di chuyển sản phẩm, thiếu công cụ hỗ trợ khi thao tác và IPK giữa các trạm chưa được xác định

rõ ràng Sau đó, ba giải pháp cải tiến đã được thực hiện để giải quyết ba nguyên nhân được xác định Các giải pháp là thay đổi các khay dùng dể vận chuyển bo mạch, thêm bước quét bắt buộc khi sử dụng đồ gá và xác định số lượng IPK giữa các trạm

Sau khi áp dụng các giải pháp cải tiến, hiệu suất dây chuyền được cải thiện rõ rệt Mức sigma được cải thiện từ 2.91 lên 3.43 cho trạm Download và từ 2.6 lên 3.19 cho trạm Wintest Từ đó, tỷ

lệ sản phẩm đạt lần đầu tiên (FPY) cũng được cải thiện, từ 98.31% lên 98.55%, so với mục tiêu của nhà máy là 98.54% Ngoài ra, mục tiêu của nghiên cứu cũng đã đạt được khi cải thiện tỷ lệ chi phí phế liệu từ 0.03% lên 0.014% Kết quả này chứng tỏ tính hiệu quả của nghiên cứu, đáp ứng mục tiêu nghiên cứu và kỳ vọng của công ty

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DECLARATION

I hereby declare that this is my own research All the data and the results used in this research are honest and have not been published in other studies I will be totally responsible for my research

if it is incorrect as mentioned above

TRAN NGOC QUYNH

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CONTENTS MASTER’S THESIS ASSIGNMENTS I ACKNOWLEDGEMENT III ABSTRACT IV TÓM TẮT LUẬN VĂN V DECLARATION VI CONTENTS VII LIST OF FIGURES VIII LIST OF TABLES X LIST OF ABBREVIATIONS XI

CHAPTER 1 INTRODUCTION 1

1.1 B ACKGROUND 1

1.2 R ESEARCH OBJECTIVE AND SCOPE 2

1.4 T HESIS STRUCTURE 2

CHAPTER 2 LITERATURE REVIEW 3

2.1 L EAN S IX S IGMA 3

2.2 I N - PROCESS KANBAN (IPK) 3

2.3 M AYNARD O PERATION S EQUENCE T ECHNIQUE (MOST) 4

CHAPTER 3 THE DMAIC METHODOLOGY 6

CHAPTER 4 CASE STUDY 9

4.1 R ESEARCH OBJECT 9

4.1.1 Overall process 9

4.1.2 Scrap definition, FPY dashboard and EDMR system 9

4.2 DMAIC RESULTS 11

4.2.1 Define 11

4.2.2 Measure 14

4.2.3 Analyze 17

4.2.4 Improve 26

4.2.5 Control 36

CHAPTER 5 CONCLUSION AND DISCUSSION 39

5.1 C ONCLUSION 39

5.2 D ISCUSSION 40

LIST OF PUBLICATIONS 41

REFERENCES 42

APPENDIX 44

PROFILE 58

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LIST OF FIGURES

FIGURE 1 EXAMPLE OF IPK 3

FIGURE 2 MOST DATA CARD 5

FIGURE 3 DMAIC METHODOLOGY FRAMEWORK 6

FIGURE 4 RESEARCH METHODOLOGY 7

FIGURE 5 OVERALL PROCESS 9

FIGURE 6 THE LAYOUT OF THE ASSEMBLY LINE 9

FIGURE 7 FPY DASHBOARD INTERFACE 11

FIGURE 8 EMDR SYSTEM INTERFACE 11

FIGURE 9 BB SCRAP COST IN 10 WEEKS 13

FIGURE 10 %CONTRIBUTION OF SCRAP ISSUES 13

FIGURE 11 %SCRAP COST TREND CHART 14

FIGURE 12 SIPOC OF THE PROJECT 14

FIGURE 13 BB SCRAP BY DEFECTS 15

FIGURE 14 DEFECTS BREAKDOWN BY PROCESS 16

FIGURE 15 BB FPY AND SCRAP TREND CHART 16

FIGURE 16 CTQS OF THE PROBLEM 17

FIGURE 17 DESCRIPTIVE REPORT FOR THE SCRATCH DEFECT RATE 18

FIGURE 18 DESCRIPTIVE REPORT FOR SCRATCH DEFECT RATE (REMOVED OUTLIERS) 18

FIGURE 19 PROCESS CAPABILITY ANALYSIS FOR DOWNLOAD STATION 19

FIGURE 20 DESCRIPTIVE REPORT FOR THE DEFORMED CABLE DEFECT RATE 20

FIGURE 21 DESCRIPTIVE REPORT FOR DEFORMED CABLE DEFECT RATE (REMOVED OUTLIERS) 20

FIGURE 22 PROCESS CAPABILITY ANALYSIS FOR THE WINTEST STATION 21

FIGURE 23 AUTO QUALITY MATRIX 21

FIGURE 24 PROCESS MAPPING 22

FIGURE 25 CHI-SQUARE TEST FOR DEFECT RATE OF 12 OPERATORS 23

FIGURE 26 FISHBONE DIAGRAM FOR SCRATCHES AND DEFORMED CABLE DEFECTS 24

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FIGURE 27 SCRATCHES ON CASING 26

FIGURE 28 THE TRAY BEFORE AND AFTER REPLACEMENT 26

FIGURE 29 DAMAGED NEAR SCREW HOLES 27

FIGURE 30 ASSEMBLY 5 MOST ANALYSIS – BEFORE 27

FIGURE 31 ASSEMBLY 10 MOST ANALYSIS - BEFORE 27

FIGURE 32 JIG VERIFICATION 28

FIGURE 33 ASSEMBLY 5 MOST ANALYSIS – AFTER 28

FIGURE 34 ASSEMBLY 10 MOST ANALYSIS - AFTER 29

FIGURE 35 ASSEMBLY LINE BALANCING CHART BEFORE AND AFTER 29

FIGURE 36 SIMULATED MODEL AND CYCLE TIME GLOBAL TABLE 30

FIGURE 37 PARETO OF PRODUCT DEMAND 31

FIGURE 38 T-TEST OF OUTPUT FROM THE SIMULATION MODEL AND ACTUAL OUTPUT 32

FIGURE 39 MODEL'S THROUGHPUT BY TIME 33

FIGURE 40 WIP BETWEEN PROCESSES 34

FIGURE 41 SIMULATED MODELS WITH A) IPK = 4, B) IPK = 3, C) IPK = 2 AND D) IPK = 1 35

FIGURE 42 DEFINE THE LOCATION FOR WIP BETWEEN WORKSTATIONS 35

FIGURE 43 CHI-SQUARE TEST FOR DEFECT RATES BEFORE AND AFTER IMPROVEMENT 36

FIGURE 44 PROCESS CAPABILITY OF DOWNLOAD AND WINTEST STATION 36

FIGURE 45 BB FPY AND %SCRAP COST RATE TREND BEFORE AND AFTER IMPROVEMENT 37

FIGURE 46 PFMEA WITH SCAN PLV REQUIREMENT 37

FIGURE 47 THE HANDLING CONTAINER WITH CAVITIES REQUIREMENT 37

FIGURE 48 SCRAP COST DASHBOARD AND WEEKLY REPORT 38

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LIST OF TABLES

TABLE 1 SCRAP DEFINITION 10

TABLE 2 PROJECT CHARTER 12

TABLE 3 % SCRAP COST CONTRIBUTION BY CUSTOMER 15

TABLE 4 DATA COLLECTION PLAN 17

TABLE 5 CAUSES AND COUNTERMEASURES 24

TABLE 6 COUNTERMEASURE SELECTION 25

TABLE 7 SUMMARY RESULT OF 4 SCENARIOS 35

TABLE 8 SUMMARY OF IMPROVEMENT ACTIONS 39

TABLE 9 SUMMARY OF RESULTS 40

TABLE A1 SUMMARY OF SCRAP COST IN 10 WEEKS 44

TABLE A2 DEFECT BY OPERATORS 45

TABLE A3 PRODUCTION PLAN FOR FLEXSIM MODEL INPUT 45

TABLE A4 THROUGHPUT FROM SIMULATION MODEL AND FROM MES SYSTEM FOR TOP 5 PRODUCTS 51

TABLE A5 SCRATCHES DEFECT RATE BEFORE AND AFTER IMPROVEMENT 52

TABLE A6 DEFORMED CABLE DEFECT RATE BEFORE AND AFTER IMPROVEMENT 54

PFMEA Process failure modes and effects analysis

as a key performance indicator (KPI) in manufacturing Especially in the electronics manufacturing services (EMS) industry, the scrap rate is an essential KPI due to the competition in this growing market The scrap in EMS usually comes along with high prices and waste in manufacturing since the customers are not willing to pay for it

An EMS company specializing in making-to-order receives customer orders and completes them on time at a quoted cost Therefore, it is essential to ensure output and keep production at a feasible cost That requires minimizing failures in the production process to save on rework or scrap costs On the other hand, the long process, including board manufacturing (PCBA process) and finished product assembly (box build process), can easily lead to missing failures if not well controlled Thus, the division should be made Each area will be assigned primarily responsible for different departments, called process owners Process owners will be responsible for designing and controlling the process, identifying issues, and making the necessary improvements to ensure performance, output, and quality

In the box build area, which is mainly taken responsible by IE, the data in 3 months from February to April shows that the average scrap cost rate is 0.03%, higher than the target of 0.02% The scrap cost rate is calculated as the ratio between the scrap cost and the product cost by materials only (BOM cost) That means the scrap from the box build area accounts for 0.03% of the BOM product cost In other words, when a product is produced in a box build area, 0.03% of the value

of that product is lost when sold to customers Therefore, investigating the cause and making improvements are necessary to reduce the scrap rate in the box build area, improve efficiency, and reduce waste

Lean Six Sigma (LSS) is an effective and disciplined business transformation strategy and problem-solving tool that has evolved through a combination of Lean and Six Sigma The main aim of LSS is to improve a system by eliminating defects and non-value-added activities for the customer because they are costs and money for a company [1] LSS offers a smart set of techniques and tools to facilitate methodology implementation, guaranteeing the utilization of often unneeded resources One of the LSS's distinctive process and quality improvement approaches is the DMAIC (Define, Measure, Analyse, Improve and Control) method DMAIC can help manufacturing organizations achieve quality improvements in their processes, thus contributing to their search for process excellence [2]

This research presents six sigma implementation conducted in an EMS enterprise to reduce scrap cost rate, thereby increasing its sigma level The study will step by step present the application

by using the DMAIC process Various statistical techniques were applied to analyze the data and

to identify solutions Various related literature were studied and cases were discussed for performing this research work

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1.2 Research objective and scope

This research aims to:

 Investigate a production system to identify the quality and productivity-related problems and their root causes

 Collect the information and data to prove the problem

 Propose a Lean Six Sigma framework to reduce the scrap cost rate in an EMS factory

 Reduce the scrap cost rate from 0.03% to 0.02% to meet the target

However, this study is only focus on the Boxbuild area in the factory, where IE is a process owner and can take action easily All data was collected in 3 months from Feb – 2022 to Apr –

2022

1.4 Thesis structure

The study will go through 5 chapters Chapter 1 gives a general introduction to the research object and problem The research objectives, limitations and scope of the study, the content to be performed, and layout of the study will also be briefly presented in this chapter In Chapter 2, the theories used in the research will be presented, including the Lean concept, Six Sigma methodology, in-process kanban (IPK), Maynard operation sequence technique (MOST) and related studies Furthermore, a research methodology with step to step to carry out this study is presented in Chapter 3 Chapter 4 presents an overview of the research object, such as process, layout, traceability systems, etc The Define, Measure and Analyze phases will be discussed in this chapter All data collected is analyzed to identify the improvement opportunity and form a research problem

As a result, this session will also conduct improvement actions to solve the problem's root cause Moreover, the control plan will also be defined to maintain the improvement actions and their results Finally, in Chapter 5, the conclusion of the thesis will be presented, the remaining shortcomings will be pointed out, and possible developing directions for the future will be suggested

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CHAPTER 2 LITERATURE REVIEW

2.1 Lean Six Sigma

Lean Six Sigma (LSS) is a combination of two popular process improvement methods, Lean and Six Sigma, which pave the way for operational excellence Lean focuses on waste elimination (non-value-added processes and procedures) and promotes work standardization and flow, whereas Six Sigma focuses on reducing process variation and enhancing process control A lean initiative approaches doing the right task at the right time and quantity to achieve relentless process flow while eliminating waste, minimizing lead time, work-in-process inventory and improving productivity The concept of Lean is minimizing the wastes (defects, overproduction, transportation, waiting, inventory, motion, over-processing), relentlessly striving to maintain harmony in the flow of materials and information, and continually attempting to reach affirmative results [3] The six Sigma approach elevates performance, uses data-driven technique to improve quality and reduces problem variation of business process activities Typically, it focuses on outcomes that indicate customer expectations [4] Lean, along with Six Sigma, has become a powerful improvement tool LSS is a business strategy and technique that enhance process efficiency, resulting in high customer satisfaction [5]

Many studies have applied LSS in manufacturing to improve the quality of the product, customer satisfaction and financial enhancement Shokri et al [6] have applied the LSS methodology in EMS manufacturing, which has improved the process from 3.65 to 3.85 in the Sigma scoring term, reduced the level of scrap rate and led to the saving of £98,000 per annum Besides, Yadav, Amit, and V K Sukhwani [7] have presented the implementation of the LSS methodology for reducing the rejection of automobile parts in the industry The implementation of DMAIC reduced rejection and reduced the Defect per Million Output (DPMO) from 68181 to 9090.9 and increased the sigma level from 2.99 to 3.86

2.2 In-process kanban (IPK)

In-process kanbans (IPKs) are fixed numbers of buffers between every stage of production lines This is also one of the powerful lean tools to limit WIP Production processes with good IPKs designed into them will have good effective line management because there will be good visual control to help prevent WIP overload IPKs help alleviate bottlenecks in our workflow in real-time They keep bottleneck stations running continuously by alleviating process imbalances and natural human variation between stations and alleviating the impact of line interruptions IPKs also ensure minimal operator downtime due to no WIP

Figure 1 Example of IPK

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The number of IPKs defines the performance of a pull system In other words, how well we design control of WIP entering and exiting our manufacturing lines If we design too few IPKs, we will have constant problems with interruptions, missed deliveries or idle operators and processes Otherwise, if the IPKs are too many, then we will waste physical space, either on our floor or workstations, and also money on inventory Amit Pingle et al [8] introduced an IPK optimization model to minimize the total IPKs with the limit of capacity

John Deere Dubuque [9] has defined the standard IPK in the John Deere Dubuque Works manufacturing As a result of the lean manufacturing implementation, WIP has been cut by over 40% of the baseline inventory levels, cycle times have dropped by 60%, and overtime labour costs have decreased by over 15% Similarly, Cabrita et al [10] have proposed a Kanban supply system driven by two cards in a Portuguese bolts manufacturer The application of this system resulted in

a reduction of 50% in the average stock level between two workstations, increases 10% in the level

of availability of the stamping machine by eliminating the possibility of a lack of material

2.3 Maynard Operation Sequence Technique (MOST)

MOST (Maynard Operation Sequence Technique) is a work measurement technique developed by H B Maynard and Company, Inc in the United States in 1960 MOST has been introduced into various industries, such as aerospace, automotive, electronics, etc., in the EU, US, and Asia Compared with MTM, another popular work measurement technique, MOST, presents

an alleviation of analysis and reduces the workload of handling a large amount of data [11], [12] Belokar et al [13] implemented MOST to increase the work's efficiency and cost-effectiveness and reduce worker fatigue through the identification and minimization of the Non-value-added (NVA) activities As a result of their study, the authors managed to save 18% of the working time and define a new set of reduced standard time Similarly, Gupta and Chandrawat [14]applied the basic MOST in a small Indian industry Their work also shows a possible and significant improvement

in productivity MOST can also be used in combination with some other techniques for a particular purpose Jamil et al [15] integrated MOST with the Ergonomics study to standardize the manufacturing process inside two companies This integration helps simultaneously optimize the standard times of the process activities and reduce the workers' fatigue As a result, the workforce gained better conditions while performing their activities, and the rate of productivity also increased In addition, the capability of proceeding with MOST becomes easier, faster and more reliable through the computerization of data collection and the analysis procedure

MOST is a work measurement technique introduced to compile the standard work time and maximize resource utilization by improving the working method Though Maynard first introduced the concept of MOST in 1960, its industrial application started in 1967 as Basic MOST To perform manual work, the Basic MOST defines a sequence of three actions: General Move, Control Move and Tool Use Some sub-activities are arranged in each sequence model, consisting of a series of parameters organized logically The sequence model defines the events or actions that always occur

in a prescribed order when an object is being moved from one location to another The time value for a sequence model in basic MOST is obtained by simply adding the index numbers for individual sub-activity and multiplying the sum by 10

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For instance, General Move Sequence (GMS) is used for the spatial movement of an object freely through the air Controlled Move Sequence (CMS) is used for the movement of an object when it remains in contact with a surface or is attached to another object during the movement And Tool Use Sequence (TUS) is used for the use of common hand tools All sequences are described by letters (A, B, G, P, M, X, I, and T) and assigned indexes based on the activities they described The indexes are usually defined based on the MOST data card as below:

Figure 2 MOST data card 1

1 Zandin, Kjell B MOST work measurement systems CRC press, 2002

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CHAPTER 3 THE DMAIC METHODOLOGY

The research deals with applying LSS DMAIC (Define – Measure – Analyze – Improve – Control) methodology After identifying the problem, the data and performance are collected and calculated in the measure phase The root causes of the problem are found in the analysis phase Solutions to solve the problem are implemented in improve phase Improvement is maintained and assured in the control phase Chaurasia et al [16]have proposed a framework of the LSS approach

as in figure 3

Figure 3 DMAIC methodology framework 2

Based on that theory, the methodology has been discussed and applied in this study However,

in each step, some specific tools are applied for this research The detail as in figure 4:

2 Chaurasia, Basant, Dixit Garg, and Ashish Agarwal "Framework to improve performance through implementing Lean Six Sigma strategies to oil exporting countries during recession or depression." International Journal of Productivity and Performance Management (2016)

Measure phase

First of all, a SIPOC diagram (Supplier – Input – Process – Output – Customer) is defined to see clearly the process where the problem is found It is important to measure the activities in the production in order to understand the variation of data, form patterns and identify All the data related to the problem statement is collected to show the detailed issue In this stage, the factors which affect the outcome of the critical to quality (CTQ) Y will be defined and collected related data The data collection plan, determining sample size, frequency and method of measurement will also be shown

Analyze phase

The data gathered in the measure phase is now analyzed to increase the understanding of the data In this phase, the process capability analysis is performed to see the current status of the

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process After that, a detailed process mapping and a brainstorming session is held to identify where and what the potential causes are The potential causes are then classified in the fishbone diagram, and the researchers will be the ones to choose the root cause

Improve phase

The goal of the improvement phase is to identify a solution to the problem that the project aims to address This involves brainstorming potential solutions, selecting solutions to test and evaluating the results of the implemented solutions Some preventive actions, such as IPK calculation, standard work, Poka-yoke, etc., will be considered to apply in this step After that, the solutions should be made sure to be effective through the new process capability, the DPMO and process sigma

Control phase

After the improvement and new routines have been implemented, it is important to secure the standardization of the implementation and ensure that the result keeps being as intended The risk of not following up and controlling is that the improvements are ignored, and the root cause emerges again The standardized process and work procedures will be developed in this phase to secure the implementation

Figure 5 Overall process

In this research, we focus on the Boxbuild area, where IE is the process owner In this area, the printed circuit board assembly (PCBA) will be assembled with other materials like cases, cables, caps, etc., to form a final product After that, some functional tests and visualization inspection steps are also performed in this area to ensure the quality of the products before transferring them

to customers For more detail, it includes five small areas, as in figure 4 The PCBA and materials are prepared and checked in the offline area before coming to the assembly line to perform the main assembly process Then, they come to the functional test area, which will test the function of the products After that, the pack-out area will pack the products, and the Out-of-box (OBA) area will be responsible for sampling and checking the products before shipping them to customers

Figure 6 The layout of the assembly line

4.1.2 Scrap definition, FPY dashboard and EDMR system

Currently, the company is facing a lot of quality issues, and some of them cannot be reworked, which causes the increasing the scrap rate It is important to have a list of defect definitions for anyone who may not be familiar with it to serve as the baseline for how products should be

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approved or scrapped in manufacturing The below table shows some scrapped defect names which usually occur in production:

Table 1 Scrap definition

products that are caused by rubbing or cutting the surface of a product They are visibly detectable at arm's distance from the quality control inspector

Deformed

cable

This is a cable deformation error due to physical impact, resulting in failure to function when tested

LCD screen

defect

This issue is detected when there is a black or white spot on the screen or a scratch on the screen and cannot be reworked

When an operator detects a defect, they will scan and key – in the defect onto the manufacturing execution system (MES) The data on MES then become the data source for some dashboards, and one of them is the first pass yield (FPY) dashboard FPY is defined as the number

of units coming out of a process divided by the number of units going into that process over a specified period of time It means the more defects are detected, the lower FPY is In the production line, only some stations are set up as the inspections/ testers that can capture data on MES as well

as the FPY dashboard

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Figure 7 FPY dashboard interface

When a defect cannot be reworked, it will be recorded as scrap The operator will key the scrap product into the system called Electronic Materials Disposition Report (eMDR) with the scrap cost That ticket will need to be approved before turning into a real scrap

Figure 8 eMDR system interface

4.2 DMAIC results

4.2.1 Define

In this phase, a project charter has been created to define the problem statement The company is facing a high % of scrap cost in the box build area, which is not meet the company's target

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Table 2 Project charter

0.03% to 0.02%

Business case

The BB scrap has 2 main categories related Process issue (62.11%) and other issue (37.89% including clear WIP, non - physical scrap…)  %BB Scrap cost related to process issue is a potential for improvement

The %Box Build Scrap had unstable trend from W6 to W15, with the average of 0.03%, didn't meet the target  Need to improve to meet target 0.02%

Problem

statement

What: Box Build Scrap Cost Where: Box Build Area When: Q2 & Q3 – FY22 Why: %Box Build Scrap cost need to be controlled to meet target Who: IE

How many: 0.01% Box Build scrap cost based on BOM cost How: Review KPI weekly

Project scope

Box Build area Q2&3 of FY22 Box Build scrap improve from 0.03% to 0.02% in Q4-FY22 Objectives

%Box Build scrap cost/BOM cost (related

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compared to BOM cost, takes about $3.8003

Figure 10 %contribution of scrap issues

Due to the scope of work, this project only focuses on the process issue scrap, which the engineers can easily investigate and take improvement actions The following trend chart shows that the process issue scrap did not meet the target within ten weeks

Process issue Scrap cost Other issue (Clear Aging WIP…) Scrap cost

Figure 9 BB scrap cost in 10 weeks

Figure 12 SIPOC of the project

Based on historical data, the %contribution of each customer to the scrap cost is as table 3

%BB Scrap due to Process Issue trend chart

%BB Scrap/ BOM cost due to Process Issue % BB scrap goal

Figure 13 BB Scrap by defects

Take a deep dive into the scratches and deformed cable defects in ING, the data shows that most issues are captured at Download and Wintest stations They are the testing processes, where the operators check both functions and visuals of the product For more detail, the Pareto of the processes that contributed to the scratches and deformed cable defects is as below:

62.7%

80.1% 84.9% 88.1%

90.7% 92.9% 94.3% 95.7% 97.2%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%

BB SCRAP PARETO CHART BREAKDOWN BY DEFECTS

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Figure 14 Defects breakdown by process

On the other hand, scrap rate and FPY have a close correlation When a process captures a failure, the NG (not good) product will be transferred to Debug station to be diagnosed the defect symptoms When it is concluded that the NG product cannot be reworked, it will be moved to scrap Therefore, we can see the inverse proportion between %FPY and %Scrap cost When the %scrap increases, the %FPY will fall and vice versa

Figure 15 BB FPY and Scrap Trend chart

Therefore, in this project, the FPY of Download and Wintest stations are chosen to be CTQ The relationship between our problem (big Y) and 2 CTQ (small y) is as below:

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Figure 16 CTQs of the problem

A data collection plan is built based on these 2 CTQs to collect the needed information and see the current status of the process

Table 4 Data collection plan

Performance

measure

Operational Definition

Data source &

Location

Sample size

How will the data

be collected

Other data should be collected

How the data be displayed

Model family

Time series plot Pareto

Model family

Time series plot Pareto

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Figure 17 Descriptive report for the scratch defect rate

The summary report shows that the data collected is not in the normal distribution ( p-value

< 0.05) Moreover, there are two outliers in the population Remove those two outliers and run the descriptive summary again:

Figure 18 Descriptive report for scratch defect rate (removed outliers)

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The data is now in the normal distribution with a p-value > 0.05, and no outlier is left, which means it can be used for further analysis

The defect rate and customer's target for the Download station's FPY, which is 99.73%, are

converted into the parts per million (ppm) defective to use as a measure of process capability [17]

As a result, the Cpk of the process is 0.72, and the Z bench value is 1.41, resulting in a process sigma of 2.91

Figure 19 Process capability analysis for download station

Similarly, the summary of the deformed cable defect rate at the wintest test is as below:

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Figure 20 Descriptive report for the deformed cable defect rate

The data also has two outliers and does not follow a normal distribution (p-value < 0.05) After removing 2 outliers, as shown, the adjusted descriptive is as below:

Figure 21 Descriptive report for deformed cable defect rate (removed outliers)

The data now is in the normal distribution with a p-value > 0.05, and there is no outlier left, which means it can be used for further analysis Then the capability analysis is conducted to see

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the current status of the process The Cpk of the process is 0.62, and the Z bench value is 1.1, resulting in a process sigma of 2.6

Figure 22 Process capability analysis for the wintest station

A survey was sent to line leaders to track and investigate the defects at download and wintest stations Direct operators will check the products and record in the form of having any scratches or deformed cable defects After that, the line leader will investigate to see where the defects come from and record in the survey also The result collected as below:

Figure 23 Auto quality matrix

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As a result, the defects are usually captured at offline, assemblies and debug processes A process mapping is created, which shows briefly describes each step to see potential causes for those defects

Figure 24 Process mapping

Besides, the defect rate was collected operator by operator4 and was conducted the hypothesis testing to see whether there is no difference among defect rate of 12 operators:

• H0: p1 = p2 =…= p12: there is no difference among defect rate of 12 operators

• H1: the defect rate of 12 operators are significantly different

The result shows that the difference of defect rate among different operators is significant with p-value < 0.05

4 Appendix 2