IMPLEMENTATION OF SIX SIGMA IN INJECTION

PROCESS STAGE TO REDUCE BURRY DEFECT OF

GEAR OIL PUMP PRODUCT IN PT. ABC

By

Dean Nanda Putra

ID No. 004201400015

A Thesis presented to the Faculty of Engineering President

University in partial fulfillment of the requirements of Bachelor

Degree in Engineering Major in Industrial Engineering

2019

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ABSTRACT

In this modern manufacturing era, each manufacture based company would tried

their best to provide their loyal customers with a product that has added value. PT.

ABC as a leading multinational company in polymer product manufacturing, with

a focus in the motorcycle parts manufacture. A good six sigma implementation

demands a continuous improvements, with aiming in the DPMO value to be as

minimum as possible, so that a quality target could be achieved and could be

considered zero defect, also to be assessed later along with DMAIC (Define,

Measure, Analyze, Improve, Control). The desirable results have been obtained

after an improvements phase, with a sigma value of 3.4 in the period of January to

March 2018, then achieved 3.9 sigma in the period of April to June 2018, with a

focus in minimizing burry defects. Those boosts of sigma value were also caused

by defect occurrences that were decreased for 75%. Indeed, the effect of the

improvements implementation could prevent the further loss suffered by the

company for IDR 135,023,000 loss in pre-improvement period into a decrease of

only IDR 33,883,000 loss in after-improvement period.

Keywords: Six Sigma, Polymer Manufacturing, DMAIC, DPMO, Burry Defects

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ACKNOWLEDGEMENT

Firstly, I cannot finish and prepare this thesis in time without the help of Allah

Subhanahuwata’ala, so I thanked Him, by giving me a chance to complete it

without any obstacles. I would like to express my gratitude to:

1. Sir Johan Krisnanto Runtuk, as my thesis advisor, for continuous support

already given to me regarding this thesis progress.

2. Mam Andira Taslim as the Head of Industrial Engineering Study Program,

for the guidance that has been given since I began my study in President

University.

3. Mr. Martua Sianipar as my internship supervisor, for the shared

knowledge regarding plastic injection industry, and the guidance

throughout my internship period.

4. All lecturers in President University, which cannot be mentioned one by

one, for the guidance, supports, and shared knowledge which are

invaluable for me.

5. My family, for giving invaluable supports since I was born until what I

become now.

6. Engineering Students batch 2014, kost squad, which consists of Rhyan,

Digo, Ari, Andre, Alvon, Riano, Wawan, and the others which I cannot

mention one by one, for the good memories throughout my university life.

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TABLE OF CONTENT

THESIS ADVISOR ............................................. Error! Bookmark not defined.

RECOMMENDATION LETTER ........................ Error! Bookmark not defined.

DECLARATION OF ORIGINALITY ................. Error! Bookmark not defined.

ACKNOWLEDGEMENT ................................................................................... v

TABLE OF CONTENT ...................................................................................... vi

LIST OF FIGURES ............................................................................................ ix

LIST OF TERMINOLOGIES .............................................................................. x

CHAPTER I ........................................................................................................ 1

INTRODUCTION ............................................................................................... 1

1.1. Problem Background ............................................................................. 1

1.2. Problem Statement ................................................................................. 3

1.3. Research Objectives ............................................................................... 3

1.4. Scope ..................................................................................................... 3

1.5. Assumptions .......................................................................................... 3

1.6. Research Outline.................................................................................... 3

CHAPTER II ....................................................................................................... 5

LITERATURE STUDY ....................................................................................... 5

2.1. Six Sigma .............................................................................................. 5

2.2 DMAIC ...................................................................................................... 7

2.3.1. Define ................................................................................................. 8

2.3.2. Measure .............................................................................................. 9

2.3.3. Analyze ............................................................................................. 10

2.3.4. Improve............................................................................................. 11

2.3.5 Control ............................................................................................... 12

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CHAPTER III .................................................................................................... 13

RESEARCH METHODOLOGY ....................................................................... 13

3.1. Initial Observation ................................................................................... 14

3.1.1. Problem Identification ....................................................................... 14

3.1.2. Literature Study ................................................................................ 15

3.1.3. Data Collection ................................................................................. 15

3.1.4. Data Analysis .................................................................................... 16

3.1.5. Conclusions & Recommendations ..................................................... 17

3.2. Research Framework ............................................................................... 18

CHAPTER IV .................................................................................................... 19

DATA COLLECTION & ANALYSIS............................................................... 19

4.1 Overview .................................................................................................. 19

4.2 Define ...................................................................................................... 21

4.3. Measure ................................................................................................... 26

4.4. Analyze ................................................................................................... 31

4.5. Improve ................................................................................................... 35

4.6. Control .................................................................................................... 44

4.6.1 Result of Implementation ................................................................... 46

CHAPTER V ..................................................................................................... 50

CONCLUSIONS AND RECOMMENDATIONS .............................................. 50

6.1. Conclusion .............................................................................................. 50

6.2 Recommendations .................................................................................... 51

REFERENCES .................................................................................................. 52

APPENDIX ....................................................................................................... 54

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LIST OF TABLES

Table 2.1 Sigma Value ......................................................................................... 7

Table 4.1 Defect Measurements ......................................................................... 22

Table 4.1 Defect Measurements (continued) ...................................................... 23

Table 4.2 Amount of Defects of January – March 2018 Period ........................... 25

Table 4.3 Defect Data January-March 2018 ....................................................... 26

Table 4.4 CL, UCL, LCL Calculation (Before Improvement) ............................. 27

Table 4.5 Calculation of DPU and DPMO Values (Before Improvement) .......... 30

Table 4.6 Why’s Analysis of Man Failure .......................................................... 32

Table 4.7 Why’s Analysis of Material Failure .................................................... 33

Table 4.8 Why’s Analysis of Machines Failure .................................................. 34

Table 4.8 Why’s Analysis of Machines Failure (continued) ............................... 35

Table 4.9 Action Planning for Root Causes Table .............................................. 36

Table 4.10 Defect Quantity After Improvement ................................................. 46

Table 4.11 Calculation of DPU and DPMO Values (After Improvement) ........... 47

Table 4.12 Before & After Improvements Comparison ...................................... 48

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LIST OF FIGURES

Figure 2.1 SIPOC Diagram .................................................................................. 9

Figure 2.2 Cause and Effect Diagram ................................................................. 11

Figure 3.1 Theoretical Framework ..................................................................... 13

Figure 3.2 Research Framework ......................................................................... 18

Figure 4.1 Production Process Flow for Gear Oil Pump ..................................... 19

Figure 4.2 Inspection Sequence for Gear Oil Pump ............................................ 21

Figure 4.3 SIPOC Diagram ................................................................................ 21

Figure 4.4 Pareto Chart of Types of Inspections Defect ...................................... 25

Figure 4.5 P Chart of Pre-Improvement.............................................................. 28

Figure 4.6 P Chart Diagnostic of Pre-Improvement ............................................ 28

Figure 4.7 Laney P Chart Diagnostic of Pre-Improvement ................................. 29

Figure 4.8 Cause &Effect Diagram of Man ........................................................ 32

Figure 4.9 Cause & Effect Diagram of Materials ................................................ 33

Figure 4.10 Cause & Effect Diagram of Machines ............................................. 34

Figure 4.11 Improvement Plan of Noise Defect Problem .................................... 36

Figure 4.12 Machine Operators Monthly Evaluation Forms ............................... 38

Figure 4.13 Material Operators Monthly Evaluation Forms ................................ 39

Figure 4.14 Material Insertion Check Sheets ...................................................... 40

Figure 4.15 Mold Maintenance Check Sheet ...................................................... 41

Figure 4.16 Machine Parameter Settings (Current) ............................................. 42

Figure 4.17 Machine Parameter Settings (Corrected Version) ............................ 43

Figure 4.18 Daily Maintenance Check Sheet of Injection Machine ..................... 45

Figure 4.19 P Chart of After Improvement ......................................................... 46

Figure 4.20 Before & After Improvement Comparison Graphic ......................... 48

Figure 4.21 Before & After Improvement Cost Loss Comparison Graphic ......... 49

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LIST OF TERMINOLOGIES

Six Sigma : The set of tools for improvements activity in a chain

of process with a goal of removing causes of defects

DMAIC : Define, Measure, Analyze, Improve and Control

(DMAIC) is a method in six sigma, used as a

standalone quality improvement procedures

DPMO : Defect per Million Opportunity is the number of

defect’s forecast with an aim to calculate the

possibility of defect in million.

DPU : Defect per Unit is an average number of defects

population in sampling activity

Upper Control Limit

(UCL)

: Limit value as an indication of highest level of

acceptable and tolerable level of quality

Lower Control Limit

(LCL)

: Limit value as an indication of lowest level of

acceptable and tolerable level of quality

Polypropelene (PP) : The core materials of gear oil pump products,

responsible for chemical resistance and strength

Nylon (PA) : The mixture materials of gear oil pump products,

responsible for product hardness

High Density

Polyethylene (HDPE)

: The mixture materials of gear oil pump products,

responsible for product flexibility

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Acrylonitrile Butadiene

Strylene (ABS)

: The mixture materials of gear oil pump products,

responsible for product dimensional stability

Carburizing Surface

Chemical Heat Treatment

(CSCHT)

: One of the treatment for mold aiming for increasing

mold hardness against corrosive materials

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CHAPTER I

INTRODUCTION

1.1. Problem Background

The journey of a company to finally reach six sigma or known as zero defect state

was definitely not easy. In every big improvement, come along a big amount of

costs also. A thorough research must been done, or even a separate team consists

of Quality & Production Department personnel may be formed to focus only in

reaching the objectives. And also, the price competition of a particular product,

have become an obstacle in reaching Six Sigma state. But, with respect to the role

of Six Sigma in reducing the defects, it has been demonstrated in several studies

that the defect rate per unit is reduced after its implementation in manufacturing

systems (Kumar et al., 2006).

From the customer’s perspective, a good supplier is the one who always do a

maximum internal check to continuously minimize the amount of defect product

that found in the manufacturing process. In polymer manufacturing, there are lots

of defect types that could occur, but if a certain type of defect has high chance of

occurrence, the company should start to pay attention to it. In this modern era

where advanced technology of manufacturing is vastly used, Six Sigma is a truly

important aspect in determining a manufacturing process quality in a certain

manufacturers. Therefore, Six Sigma is also defined as a multifaceted, customer-

oriented, structured, systematic, proactive and quantitative philosophical approach

for business improvement to increase quality, speed the deliveries up and reduce

costs (Mahanti and Antony, 2005).

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PT. ABC is a national polymer injection company, which focuses on the

manufactures of plastics material of motorcycle spare parts and accessories. PT.

ABC already known as an everlasting supplier of the biggest motorcycle

manufacturers in Indonesia ever since. PT. ABC only focused on producing

motorcycle spare parts by using plastic injection method. The products are ranged

from; motorcycle step floors, center cover, headlamps cover, side body cover, oil

pump gears, chain cover, and many more.

Indeed, every manufacturing company is aiming to have an efficient, reliable, less

defect occurrence in their whole production chain. That is why a strict monitoring

in the production activities and a continuous improvement is always needed in

order to maintain production cost efficiency.

In PT.ABC noise defect were only found in one type of their products, which is

the gear oil pump, but, In January to March, PT. ABC suffered a loss of

approximately IDR 135.023.000, from the occurrence of it. In that period of time,

the noise defect was in an amount of 21.778 occurrences. The calculation was

based on the current price of gear oil pump products with IDR 6,200. Indeed, the

company has the potential to suffer a bigger loss of approximately IDR

540.094.400 in a whole year if no further improvements actions.

The occurrence of noise defect is initially discovered by the customers of gear oil

pump products, within the very first batch of production. A gear oil pumps must

be assembled into the motorcycle engine to identify the noise defect occurrence.

With a decibel meter attached directly into the front end of the motorcycle, the

noise measurements tolerances were set at a maximum of 78db. The improvement

will be later focusing on one of the top contributors of noise defects, indeed, with

an analysis of monthly production and defect proportion data, it would be easy to

identify it later.

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1.2.Problem Statement

The background of the problem leads into these statements:

How to reduce the burry defect occurrence during injection process stage?

How much the cost loss that could be reduced through the burry defect

reduction?

1.3. Research Objectives

The research aims to follow statements below.

To reduce the burry defect occurrence during injection process stage by

implementing Six Sigma: DMAIC.

To calculate the cost loss regarding burry defect occurrence on before and

after improvement phase by implementing Six Sigma: DMAIC.

1.4. Scope

The scope in this research:

The initial observation data were collected from January to March 2018

The observations were done in the injection process stage

The improvement actions were implemented on April to June 2018

The improvement actions were focusing on burry type of defects

1.5.Assumptions

In this research, some assumptions were made:

The production machines were always in the same state during the

observations activity

The working environments is always the same on each month of

observations

1.6. Research Outline

Chapter I Introduction

This chapter is focusing on the general explanations regarding this

research, consists of introduction, problem statement, objectives,

and scope.

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Chapter II Literature Study

This chapter contains a theoretical basic of Six Sigma & DMAIC

method, from previous research and journal. The formula of

calculations that would further be used in this research were also

stated in this chapter.

Chapter III Research Methodology

This chapter explained the observation in a more detailed way, also

provides the problem identification from the data that has already

obtained and the analysis of the data.

Chapter IV Data Collection and Analysis

This chapter explained the analysis result of the data, by using the

six sigma and DMAIC method, to be later provides a solution for

the problem.

Chapter V Conclusion and Recommendation

This chapter gives conclusion of the research and also

recommendation for future research.

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CHAPTER II

LITERATURE STUDY

2.1. Six Sigma

According to Pyzdek (2003), Six Sigma is the application of the scientific

methods to the design and operation of management systems and business

processes which enable employees to deliver the greatest value to customers and

owners. Persico (1992) states Six Sigma as a direct extension of total quality

management which, in turn, is based on the principles and teachings of W.

Edwards Deming, the legendary quality guru. Therefore, Six Sigma is a

disciplined, quantitative approach for improvement-based on defined metrics-in

manufacturing, service, or financial processes, (Hahn, Hill, Hoerl, & Zinkgraf,

1999).

The adoption of Six Sigma has improved both the efficiency of the line and the

production capability, including minimizing waste such as reduced need for

inspection, removed useless components and excessive movements and decreased

time for repair (Oke, 2007). For this reason, Six Sigma can be used to build

predictive models based on experiences gathered from earlier uncorrected

measures to ensure a continuous improvement of the process (Johnston et al.,

2008). In recent years, knowledge management has contributed to facilitate the

implementation of Six Sigma and has emerged as a source of competitive

advantage within the businesses (Gowen et al., 2008). Six Sigma is also

recognized as a strategy that drives the cultural change to improve profitability of

the company increasing the benefits from savings generated when the defect is

detected at a very early stage (Antony et al., 2005a).

Reduced costs, reduced project time, improved results and improved data integrity

are some of the benefits of Six Sigma suggested by Ferrin et al. (2005).

In addition, the literature tends to analyze the techniques used to optimize the

process performance.

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The approach taken in many cases, e.g. by Lin et al. (2008) and Antony et al.

(2005), is to give the solutions and the methods built by Six Sigma to achieve

sensible improvements, providing a learning process for managers in order to take

a wide view of the system and change the business effectively

(Thawesaengskulthai and Tannock, 2008).

Besides, there was organizational impact by implementing Six Sigma. Indeed, Six

Sigma methodologies provide guidelines which could help the workers

understand how to carry out the job and train them to solve potential problems. As

a consequence, they become more aware of the production process thereby

improving their morale and reducing the human-related defects (Hong et al.,

2007).

The objective is to enhance the Six Sigma level of performance measures referred

to as the critical to quality (CTQ) which reflects the customer requirements

through a group of tools for the analysis of the data. Statistical tools identify the

main quality indicator which is the parts per million (PPM) of non-conforming

products (Mitra, 2004). Achieving a Six Sigma level means having a process that

generates output with 3.4 defective PPM (Coleman, 2008; Anand et al., 2007).

Mikel Harry and Richard Schroeder (2000) classified company that has sigma

value equals to 2 is a non-competitive company and company with sigma value

with range 3-4 as an industry average company. Although company with the

sigma value in between 3 and 4 can be classified as quite good company, the

company still has to spend 25% to 40% of its revenue for quality cost. It will be

one of waste then marginal profit will be decreased. Indeed, the higher the sigma

values achieved, the better the performance of industrial processes. The Table 2.1

shows the sigma value that is measured by Cost of Poor Quality (COPQ).

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Table 2.1 Sigma Value

2.2 DMAIC

Breyfogle (2003) defined Six Sigma as implementing methods including DMAIC,

define, measure, analyze, improve, control, and DMADV; define, measure,

analyze, design, verify. Chen et al. (2006) and Lucas (2002) used the Six Sigma

method to emphasize the effect of quality improvement.

Existing literature also traditionally categorize these Six Sigma tools under

DMAIC but classification of tools under other alternative approaches such as

DFSS, DCOV or DMADV is lacking. Possible explanation of this is that all these

DFSS tools are custom selected for a particular R&D process, industry and use, so

a fixed formulation is not possible beyond a broad categorization (Watson, 2005).

While DMAIC is a problem-solving method which aims at process improvement,

DFSS is defined by Watson and deYong (2010) as “a process to define, design

and deliver innovative products, provide competitively attractive value to

customers in a manner that achieves the critical-to-quality characteristics for all

the significant functions”. To this end, Mader (2006) believed that companies

with strong market growth and competitive position will be better off with DFSS

(focusing on product development and innovation), whereas for companies with

stagnant market or relatively less competitive, DMAIC is generally a more

favorable choice focusing on cost reduction & retrenchment.

COPQ (Cost of Poor Quality)

Sigma

Value DPMO

COPQ as a Percentage of

Sales Value

1-sigma 691,462 (very uncompetitive) Cannot be calculated

2-sigma 308,538 (average Indonesia's industries) Cannot be calculated

3-sigma 66,807 25-40% from sales

4-sigma 6210 (average USA's industries) 15-25& from sales

5-sigma 233 (average Japan's industries) 5-15% from sales

6-sigma 3.4 (world class industries) <1% from sales

Source:.https://www.researchgate.net/publication/314510260_Using_Six

_Sigma_Tools_to_Improve_Strategic_Cost_Management_Management

_Accounting_Perspective

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The DMAIC is a process improvement cycle of Six Sigma program as well as an

effective problem solving methodology (Hung and Sung, 2011). The five steps

involved in the DMAIC methodology are described as follows.

2.3.1. Define

The first stage of the Six Sigma and DMAIC's methodology is “define”. This

stage aims at defining the project's scope and boundary, identifying the voice of

the customer (VOC) (i.e. customer requirements) and goals of the project (Gijo et

al., 2011).

Stating the project's scope was the next step within the “define” stage of DMAIC.

Nonthaleerak and Henry (2008) suggest that a Six Sigma should be selected based

on company issues related to not achieving customers' expectations.

The chosen projects should be focused on having a significant and positive impact

on customers as well as obtaining monetary savings (Nonthaleerak and Henry,

2008; Murugappan and Kenny, 2000; Banuelas and Antony, 2002).

According to Pande and Cavanagh (2003), three core activities that related

directly into defining the core processes and the customers are:

a. Defining the major core of the current business.

b. Determining the key output of the core processes, and the key customers

that being served.

c. Creating a scheme according to the core process and strategic process.

In this define phase, also includes in determining the target of six sigma quality

improvements. In the top management levels, the goals that have been determined

will become the strategic goals of the organization, as well as: increasing the

Return of Investment (ROI) and the market shares. In operational level, the aim is

to increase production output and productivity level, along with decreasing flaws

on product, and operational cost.

After a Six Sigma technique is selected, the first step to do was define the

problem. The problem should be described specifically to support further

analysis. The purpose of this phase is to set the customer critical to quality (CTQ)

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set the process that needs to be improved, and to set boundaries of the business

process by using SIPOC analysis.

The SIPOC diagram mapping (Supplier-Input-Process-Output-Customer)

Supplier, is the association which provide the information, materials, or

resources that would be used in the process chain.

Input, is the information or materials which would be transformed within

the process stage.

Output, is the product that is utilized by the customer.

Customer, is the person, organization, or association that will receive the

outputs of the process.

Figure 2.1 shows the example of SIPOC analysis.

Figure 2.1 SIPOC Diagram

2.3.2. Measure

This phase focusing on how to know the internal processes that has a large impact

to CTQs. Particularly, the “measure” phase meant the definition and selection of

effective metrics in order to clarify the major defects which needed to be reduced

(Omachonu and Ross, 2004).

http://memo.me/wp-content/uploads/2018/12/it-root-cause-

analysis-template-rca-fishbone-diagram-example.jpg

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In this stage, a measurement of UCL/LCL value, center line, and defect

proportions are very important.

To further proceeds to the measurements of the current process performance,

some calculations are needed with the following formula:

1.) UCL / LCL = p̂ ±3√p̂(1−p̂)

𝑑𝑖 (2-1)

2.) Center line = p̂ = Sum of Defect Qty.

Sum of Product Qty. (2-2)

3.) di = Defect Quantity (2-3)

4.) Defect proportion (p) = Defect Quantity

Produced Quantity (2-4)

The measurement of the DPU and DPMO are also important. By using Gasperz

step, the formula becomes:

1.) Defect Per Unit (DPU) Calculation

DPU = Total Defect Quantity

Total Quantity Produced (2-5)

2.) Defect Per Million Opportunities (DPMO) Calculation

DPMO = (Total Defect Quantity

Total Quantity Produced) x 1,000,000 (2-6)

2.3.3. Analyze

Ishikawa (1982) comments that the identification and solution of root causes of

quality problems is driven out by freedom thinking and participation. In order to

illustrate and categorize the possible causes of the problem, a cause-and-effect

diagram was constructed.

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The cause-and-effect diagram, also known as Ishikawa or fishbone diagram, is a

systematic questioning technique for seeking root causes of problems (Slack et

al., 2010) by providing a relationship between an effect and all possible causes of

such effect (Omachonu and Ross, 2004).

Once completed, the diagram helps to uncover the root causes and provide ideas

for further improvement (Dale et al., 2007).

There are five main categories normally used in a cause-and-effect diagram,

namely: machinery, manpower, method, material and measurement (5 M) (Dale et

al., 2007) plus an additional parameter: environment. Figure 2.2 shows the

example of cause & effect diagram.

Figure 2.2 Cause and Effect Diagram

2.3.4. Improve

Once the root cause of a problem has been found, the next step should be

generated to solve the problem and then improve the whole process of

manufacturing to satisfy customer criteria. Once a good capability process has

been reached, it could become a sign of a good improvement results in the future.

http://memo.me/wp-content/uploads/2018/12/it-root-cause-

analysis-template-rca-fishbone-diagram-example.jpg

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2.3.5 Control

This phase is focusing to ensure that the improvements are sustained (Omachonu

and Ross, 2004) and that ongoing performance is monitored. Process

improvements are also documented and institutionalized (Stamatis, 2004).

Peter S. Pande (2000) stated that control chart is suitable to be applied on control

stage of DMAIC to establish a continuous method which controls process

performance. Control chart will help to identify the existence of special cause

variations which have to be eliminated. Controlling or monitoring is needed in

order to have continuous improvements.

According to Hambleton (2011), this phase controls the improved process or

performance of the product in order to ensure the targets are reached. If the

problem successfully resolved by the solutions, then the improvements must be

standardized and sustained over the period. Standard Operating Procedures (SOP)

is a standardization of the process which useful for documentation and may

require revision in the future so that a control plan should be created to

continuously monitor the company’s performance.

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CHAPTER III

RESEARCH METHODOLOGY

Figure 3.1 Theoretical Framework

Initial Observation

• Observation of current injection process at PT. ABC

• Identification of factors that could be improved

• Obtaining data for problem investigation of injection activity in injection stage process

Problem Identification

• Identifying the main problem and current background.

Based on observation, the problem is high defect of noise

type of defects, with burry defect as top contributor

• Record and analyze the quality report data of injection

process from January to March, 2018

• Define the problem statements, objective, scope and

assumptions of research.

Literature Study

• Six Sigma

• DMAIC

Data Collection

• Collecting Quality Performance and Production Data

• Collecting the initial data of noise defects occurrence

during January to March, 2018 in injection stage process

Data Analysis

Six Sigma

• Define the problem statement.

• Measure the data using process using process capability

test, p chart, and data calculations

• Analyze the root cause.

• Improve the process by implementing potential solution.

• Controlling & monitoring current implementation

Conclusion and Recommendation

• Conclude the whole analysis & improvements

implementation

• Giving recommendation for further research.

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Figure 3.1 shows the theoretical framework of this research. In this chapter, there

will be an explanation of how this research was conducted.

3.1. Initial Observation

The initial observation done by observing the whole production processes, with

the focus in gear oil pumps noise problem, within January to March, 2018. The

observations were done directly within the production machines in PT. ABC

production area that produces or injects gear oil pump products, and also the

collection of the supporting data were done within the Quality Assurance Lab. A

thorough analysis is also done regarding the factors that most likely leads to the

occurrence of noise defects. The factors are ranged from Man, Method, Machine,

and Materials.

3.1.1. Problem Identification

After doing an initial observation, then, an identification of which factor

contributes highly to noise defects should be done. From a brief survey, the

factors can be ranked from most reliable to least reliable. A historical data

regarding noise defects occurrence, machines breakdowns & failures, and defects

preventive actions in the past time were very helpful for thorough analysis of this

research. The supporting data were obtained from the Quality Assurance Lab and

also Production Department archives.

The problem statements are,

How to reduce the burry defect occurrence during injection process stage?

How much the cost loss that could be reduced through the burry defect

reduction?

The research aims to follow the statements below,

To reduce the burry defect occurrence during injection process stage and

by implementing Six Sigma: DMAIC.

To calculate the cost loss regarding burry defect occurrence on before and

after improvement phase by implementing Six Sigma: DMAIC.

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3.1.2. Literature Study

The research was conducted based on researchers literatures, journals, and e-

books referral. The study literature content is mainly about:

Six Sigma

DMAIC

3.1.3. Data Collection

The data for the research were taken from the observation at PT. ABC in January

to March 2018, with an aim to do thorough analysis regarding high defect rate

problem. The data that were used for the observation is Quality Performance Data

provided by Quality Assurance division that provides complete statistics and data

regarding the problem. Also, a weekly and monthly production data from

Production Division becomes plenty helpful to support the analysis.

To minimize a mistake in doing problem analysis, a question and answer session

is being made with the experienced Material Leader and Quality Assurance

operators that currently in charge specifically for gear oil pump products. On top

of that, a discussion also made with the Production Division Section Head, whom

handled the gear oil pump injection machines within his authority.

In this research, will be focusing on the data of the defect occurrence. The data

obtained will be used in the measure phase in six sigma method. Also, the

supporting data were taken directly from the observations of gear oil pumps

injections machine owned by PT. ABC. PT. ABC owned four gear product

injections machine, the first two has 100 tonnage capabilities, and the other two

has 140 tonnages.

From the observations in PT. ABC, regarding noise defects, there are 7

measurements that need to be within standards, so that a product will be

considered good. If any failure occurs on one of those measurements, could

directly lead to noise defect occurrence. Those measurements are; Overbowl, Run

Outs, Diameter E (Inner), Diameter A (Outer), Burry, Base Tangenth, and

Bending.

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3.1.4. Data Analysis

To do further analysis, the Quality Performance data has been obtained, to be later

analyzed & improved with Six Sigma & DMAIC approach. The steps were:

Define

At this stage was started by defining the production process flow of PT.

ABC, and the product checking sequences of gear oil pump product. Then,

a SIPOC diagram is created, so that the boundaries of business process

could be set. The measurements sequences regarding the noise problem

will also be explained thoroughly. A pareto chart and defect proportion

analysis will be done to know which defect contributors that were most

significant.

Measure

In this stage, is the beginning to search for the root causes with all data

that already provided, along with further analysis to provide potential

solution for the current problem. This includes the Process Capability

Analysis phase, along with calculation of UCL, LCL, DPU and DPMO

values before the solutions were implemented, by using formulas and also

Minitab Statistical Software.

Analyze

In this stage, a cause & effect or fishbone diagram will be created to assist

in further analysis to determine the root causes that leads to potential

solutions. Also, a 5 Why’s analysis were included in this stage to help in

the findings of potential solutions and corrective actions.

Improve

At Improve stage, contains the improvement actions regarding the

occurred problems, along with the results, after the improvement has been

implemented in the production chain.

17

By creating an Action Planning for Root Causes table, could lead to early

defects detections or even preventions. Further, the improvements actions

that were implemented by the company would be stated also in this stage.

Control

This stage contains control or monitoring activity that aims to

continuously evaluate the implementation result from improvement stage.

Besides, control activity also helped in keeping the performance level in a

good state.

3.1.5. Conclusions & Recommendations

After doing data analysis and improvements according to Six Sigma & DMAIC

approach, a solution to the problem statements could finally reached. The

recommendation for future research will also be stated.

18

3.2. Research Framework

Figure 3.2 Research Framework

In Figure 3.2 is the detailed framework of the research that aims to portrait the

correct track from the beginning of the research until it’s finished. Also it could

give better visualization regarding the corresponding steps.

19

CHAPTER IV

DATA COLLECTION & ANALYSIS

4.1 Overview

The data collection is based on information and data that has been collected

throughout the observation activity. The data collected were including the data

from many divisions of PT. ABC, mainly from production and quality assurance

division. Those data were important to conduct a further research and

understanding regarding noise problem defect that has been occurred.

4.1.1 Production Process Flow in PT. ABC

Figure 4.1 shows the process flow of the production in PT. ABC for their gear oil

pump products. Indeed, PT. ABC has a lot of polymer based products on their

production line up, but this research is only focusing on the gear oil pump case

analysis.

Figure 4.1 Production Process Flow for Gear Oil Pump

Materials

Insertion

Process

Machine

Injection

Process

Gate

Cutting

Process

Product

Inspection

Process

Materials

Mixing

Process

OK Product

Defect

20

The detailed information regarding each process can be described as the major

points below.

Materials Mixing Process: Is a process where the materials become mixed

before being inserted into the machines, the common material mixture for

gear oil pump products were PP, PA, HDPE and ABS.

Materials Insertion Process: A process where polymer materials were

inserted into the injection machines. One material operator is needed to do

the insertion job. The operator is also responsible for the tight monitoring

of the correct material proportion, and must be ready to do the material

refill activity when needed.

Machine Injection Process: This process occurs when the injection

materials were ready to be injected, and the machine also ready to do the

injection phase. Injection materials were injected into the molding to

create the desired product.

Gate Cutting Process: After the gear oil pump product already injected, it

would fall into the machine containers, then the machine operator will take

it. The freshly injected gear oil pump product always has a “gate” that still

attached on it. That gate needs to be cut by the operators by using a cutting

plier. The gate that was already cut considered as a scrap and not to be

used later in the production process.

Product Inspection Process: The product that already passed the injection

process, will be thoroughly inspected. All the inspection activity should be

done according to the standard operating procedure, and always referring

to the determined standard. The product inspections were done by quality

assurance operator, and also, the quality assurance division is the only one

who owned the measurements tools to do inspections activity.

4.1.2 Product Checking Sequence

PT. ABC already implemented a product checking sequence on their gear oil

pump products. The sequences were made within careful considerations based on

previous inspection records made by their Quality Assurance department. In

Figure 4.2 below are the checking sequences for the gear oil pump products.

21

1 • Burry Inspection

2 • Run Out Inspection

3 • Diameter E (Inner) Inspection

4 • Diameter A (Outer) Inspection

5 • Overbowl Inspection

6 • Base Tangenth Inspection

7 • Bending Inspection

Figure 4.2 Inspection Sequence for Gear Oil Pump

Can be seen in Figure 4.2, right after the gear oil pump exits the production phase,

it will undergo a burry inspection first, then a bending inspection in the end. A

gear oil pump product that able to pass those seven inspections sequence, by any

means, within the determined standard, are considered as an OK product. But if a

product in some point failed one of the inspection sequences above, it will be

considered an NG or defective product.

4.2 Define

In define phase, includes the initial problem identifications to further find the

proper solutions to be implemented by the company. In Figure 4.3, is the SIPOC

diagram.

Figure 4.3 SIPOC Diagram

Supplier Input Process Output Customer

Material

Suppliers

Machine

Vendors

Injection

Materials

Assembly

Part

(ASSY)

Machine

Operators

Plastic

Injection

Process

Plastic

Injection

Product

Scraps

Motorcycle

Manufacturing

Company

22

SIPOC is a visual tool used to document business processes from beginning to

end and serves to identify relevant elements of the improvement project to be

undertaken. Can be seen in the SIPOC diagram, the business process of PT. ABC

was starting from suppliers, then finally in the end could reach the customers.

According to the initial problem identifications, the problem was the noise defects

that occurred on one of PT. ABC product which is the gear oil pumps. Noise

defect could be identified while the gear oil pump products were already

assembled inside the motorcycle. The defect will likely to occur when the

manufactured gear oil pump product cannot pass the determined standard of

quality, that were already set before. Those occurrences of defects in the injection

stage were constantly incur loss to the company. A proper corrective action was

expected to reduce the occurrences of the noise defects.

Table 4.1 shows the detailed information regarding those noise defect

measurements.

Table 4.1 Defect Measurements

No. Defect

Measurements

Details Standards Measurement

Tools

Photos

1 Burry

There should be no

burry spot on each

gear.

No burry

spotted on

the surfaces

Visual

2 Run Outs

The curve between each gear sections

must be within

measurement

standards.

0.03 mm –

0.05 mm

Run Outs Tools

23

Table 4.1 Defect Measurements (continued)

At first, an analysis of defect occurrences among seven inspections sequences

needed to be done. An inspection and defect data from Quality Assurance

department of PT. ABC in period of January to March 2018 will be analyzed.

3 Diameter E

(Inner)

Inner diameter measurements must

be within standards.

51 mm – 53

mm Vernier Caliper

4 Diameter A

(Outer)

Outer diameter measurements must

be within standards.

64.3 mm -

64.5 mm Vernier Caliper

5 Overbowl

A perpendicular

gap between gears needs to be within

measurement

standards.

68.26 mm – 68.67 mm

Micrometer

6 Base Tangenth

The gap between

individual gear

must be within measurements

standards.

19.32 mm –

19.50 mm Micrometer

7 Bending

There should be no bending in each

gear.

0.05 mm (maximum)

Feeler Gauge

24

The data were processed to identify the defect proportion of each checking

sequence by using the formula of:

%Defect = 𝐷𝑒𝑓𝑒𝑐𝑡 𝐴𝑚𝑜𝑢𝑛𝑡

𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑓𝑒𝑐𝑡 𝐴𝑚𝑜𝑢𝑛𝑡

1.) Burry Inspection

Calculation : 5,233

21,778 x 100% : 24,02%

2.) Run Outs Inspection

Calculation : 4,627

21,778 x 100% : 21,24%

3.) Diameter E (Inner) Inspection

Calculation : 4,814

21,778 x 100% : 22,1%

4.) Diameter A (Outer) Inspection

Calculation : 3,107

21,778 x 100% : 14,26%

5.) Overbowl Inspection

Calculation : 2,390

21,778 x 100% : 10,97%

6.) Base Tangenth Inspection

Calculation : 1,306

21,778 x 100% : 5,99%

7.) Bending Inspection

Calculation : 301

21,778 x 100% : 1,38%

25

In Table 4.2 is the amount of defects of January to March 2018, with it’s

percentage amount. The total of defect products was 21,778 pieces among the

seven sequences of product checking.

Table 4.2 Amount of Defects of January – March 2018 Period

No. Inspection Type Defect

Amount

Defect

Percentage

Cumulative

Percentage

1. Burry 5,233 24,02% 24%

2. Diameter E 4,814 22,1% 46,1%

3. Run Outs 4,627 21,24% 67,4%

4. Diameter A 3,107 14,26% 81,6%

5. Overbowl 2,390 10,97% 92,6%

6. Base Tangenth 1,306 5,99% 98,6%

7. Bending 3,01 1,38% 100%

Total 21,778 100% 100%

Figure 4.4 Pareto Chart of Types of Inspections Defect

In Figure 4.4, is the pareto chart of types of the inspection defects. It gives a clear

portrait regarding the amount of product defective in each of the inspection

sequences.

From the analysis that has been done before, during the observations on injection

process stage period of January to March 2018, the top defect contributor is

Burry, with 5,233 occurrences or equal to 24,02% amount of contribution to noise

26

defects. Further analysis will be focusing on the defect reducing actions on burry

defects. The improvement implementations regarding burry defect will be done

during the period of April to June 2018.

4.3. Measure

Table 4.3 Defect Data January-March 2018

Period

Defect

Quantity

(di)

Produced

Quantity

(ni)

Defect

Proportion

(p)

DPMO

I/Jan 1,685 67,800 0.024852507 24852.51

II/Jan 2,347 73,500 0.031931973 31931.97

III/Jan 1,883 70,170 0.02683483 26834.83

IV/Jan 1,857 76,905 0.24146674 24146.67

V/Jan 1,671 64,000 0.026109375 26109.38

I/Feb 1,842 67,985 0.027094212 27094.21

II/Feb 1,914 65,443 0.029246825 29246.83

III/Feb 1,623 53,145 0.030539091 30539.09

IV/Feb 1,558 55,780 0.027931158 27931.16

I/Mar 1,565 63,661 0.02458334 24583.34

II/Mar 1,311 59,056 0.022199268 22199.27

III/Mar 1,444 58,202 0.024810144 24810.14

IV/Mar 1,078 51,450 0.020952381 20952.38

Then, UCL and LCL value should be calculated by using “p” chart with the

following formula:

1.) UCL / LCL = p̂ ±3√p̂(1−p̂)

𝑑𝑖 (2-1)

2.) Center line = p̂ = Sum of Defect Qty.

Sum of Product Qty. (2-2)

3.) di = Defect Quantity (2-3)

4.) Defect proportion (p) = Defect Quantity

Produced Quantity (2-4)

Below is the example of the calculation of CL, UCL & LCL:

27

CL calculation p̂ = 21,178 / 827,097 = 0.026330648

1.) Upper Control Limit (UCL) calculation

UCL = (2-1)

= 0.026330648 + 3√0.026330648(1−0.026330648)

1,685

= 0.038033

2.) Lower Control Limit (LCL) calculation

LCL = (2-1)

= 0.026330648 - 3√0.026330648(1−0.026330648)

1685

= 0.014629

In the table 4.4 below, shows the detailed calculation of the CL, UCL, LCL of

each week respectively.

Table 4.4 CL, UCL, LCL Calculation (Before Improvement)

Period

Defect

Quantity

(di)

Produced

Quantity

(ni)

CL UCL LCL

I/Jan 1,685 67,800 0.026330648 0.038033 0.014629

II/Jan 2,347 73,500 0.026330648 0.036246 0.016415

III/Jan 1,883 70,170 0.026330648 0.0374 0.015261

IV/Jan 1,857 76,905 0.026330648 0.037477 0.015184

V/Jan 1,671 64,000 0.026330648 0.038082 0.01458

I/Feb 1,842 67,985 0.026330648 0.037523 0.015139

II/Feb 1,914 65,443 0.026330648 0.03731 0.015351

III/Feb 1,623 53,145 0.026330648 0.038254 0.014407

IV/Feb 1,558 55,780 0.026330648 0.0385 0.014161

I/Mar 1,565 63,661 0.026330648 0.038473 0.014188

II/Mar 1,311 59,056 0.026330648 0.039597 0.013064

III/Mar 1,444 58,202 0.026330648 0.038971 0.01369

IV/Mar 1,078 51,450 0.026330648 0.040961 0.011701

28

1 31 21 11 0987654321

0,032

0,030

0,028

0,026

0,024

0,022

0,020

Sample

Pro

po

rtio

n

_P=0,02633

UCL=0,02845

LCL=0,02421

1

1

1

1

1

1

Tests performed with unequal sample sizes

P Chart of Pre-Improvement

Figure 4.5 P Chart of Pre-Improvement

In the P Chart analysis in Figure 4.5 by using Minitab software, a lot of outliers

happen in the charts. Which means the designated point was out of the UCL and

UCL proper range.

Figure 4.6 P Chart Diagnostic of Pre-Improvement

29

1 31 21 11 0987654321

0,035

0,030

0,025

0,020

0,01 5

Sample

Pro

po

rtio

n

_P=0,02633

UCL=0,03519

LCL=0,01747

Sigma Z = 4,18391

Tests performed with unequal sample sizes

Laney P′ Chart Pre-Improvement

In Figure 4.6 is the P Chart Diagnostic of pre improvements. In the Figure 4.9

there were found many outliers, so a Laney P Chart were considered to be used,

so the next step is should be using a Laney P 'Chart.

Figure 4.7 Laney P Chart Diagnostic of Pre-Improvement

Figure 4.7 shows the Laney P' chart of pre-improvement. From the chart, no data

which seen outliers or outside the LCL UCL range, because of the UCL and LCL

determination of Laney P’ chart were not as tight as regular P chart. For further

analysis, would be in the next section that could determine the cause of the data

outliers above.

The next step is to measure the level of current Sigma value and Defect Per

Million Opportunities (DPMO). By using formula (2-5) and (2-6) the calculation

of sigma value becomes:

1.) Defect Per Unit (DPU) Calculation

DPU = Total Defect Quantity

Total Quantity Produced

2.) Defect Per Million Opportunities (DPMO) Calculation

DPMO = (Total Defect Quantity

Total Quantity Produced) x 1,000,000

30

Below is the example of the calculation of DPU and DPMO :

1.) Defect Per Unit (DPU) Calculation

DPU = 1685

67,800 = 0.024853

2.) Defect Per One Million Opportunities (DPMO) Calculation

DPMO = 1685

67,800 * 1,000,000 = 24,853

3.) Sigma Value Calculation

By using interpolation the calculation becomes:

Current DPMO : 26,331

Estimated DPMO Range : 28,700 – 22,700

Estimated Sigma Range : 3.4 – 3.5

28,700−22,700

3.4−3.5 =

28,700−26,331

3.4−𝑥

Current Sigma = 3.4

Table 4.5 Calculation of DPU and DPMO Values (Before Improvement)

Period

Defect

Quantity

(di)

Produced

Quantity

(ni)

CL UCL LCL DPU DPMO Sigma

Value

I/Jan 1,685 67,800 0.026330648 0.038033 0.014629 0.024853 24,852.51

II/Jan 2,347 73,500 0.026330648 0.036246 0.016415 0.031932 31,931.97

III/Jan 1,883 70,170 0.026330648 0.0374 0.015261 0.026835 26,834.83

IV/Jan 1,857 76,905 0.026330648 0.037477 0.015184 0.024147 24,146.67

V/Jan 1,671 64,000 0.026330648 0.038082 0.01458 0.026109 26,109.38

I/Feb 1,842 67,985 0.026330648 0.037523 0.015139 0.027094 27,094.21

II/Feb 1,914 65,443 0.026330648 0.03731 0.015351 0.029247 29,246.83

III/Feb 1,623 53,145 0.026330648 0.038254 0.014407 0.030539 30,539.09

IV/Feb 1,558 55,780 0.026330648 0.0385 0.014161 0.027931 27,931.16

I/Mar 1,565 63,661 0.026330648 0.038473 0.014188 0.024583 24,583.34

II/Mar 1,311 59,056 0.026330648 0.039597 0.013064 0.022199 22,199.27

III/Mar 1,444 58,202 0.026330648 0.038971 0.01369 0.02481 24,810.14

IV/Mar 1,078 51,450 0.026330648 0.040961 0.011701 0.020952 20,952.38

Total 21,778 827,097 0.026330648 0.029586 0.023076 0.026331 26,330.65

Average 1,675.23 63,622.841 0.026330648 0.038067 0.014595 0.026331 26,330.65 3.4

31

Table 4.5 shows the complete table containing the DPU and DPMO for each

subgroup. Currently, the sigma value through calculation was in an amount of 3.4,

with the Defect per Million Opportunity (DPMO) of 26,331.

From the Sigma Value in Table 2.1 on the previous chapter, an amount of 3.4

sigma was already in above of average Indonesia’s Industry of 2 sigma. Indeed,

an improvement must be made to achieve the higher sigma level with further

decreasing number of defects. By decreasing defects ratio, automatically will

decrease the production, labor, and material costs in a brief. A thorough analysis

and improvement are needed to achieve six sigma goals.

4.4. Analyze

In Analyze section, contains a series of root cause analysis regarding the

problems. First, a cause & effect analysis using a Cause & Effect Diagram is

chosen. It contains a thorough review of Man, Machine, & Materials analysis, to

be further give a solution to the burry defect problems. By creating a Cause &

Effect Diagram, could help in listing & in classification of each of the possible

causes. Then, will be followed by Why’s analysis to portrait or interpret the cause

& effect diagram.

4.4.1 Cause & Effect Diagram

The root causes of noise defects of gear oil pump products were analyzed by

identifying the three major categories stated in the cause & effect diagram. To get

the correct root causes, and minimizing a mistake in analysis, a question and

answer session is being made with the experienced Material Leader and Quality

Assurance operators that currently in charge specifically for gear oil pump

products. On top of that, a discussion also made with the Production Division

Section Head, whom handled the gear oil pump injection machines within his

authority.

32

Figure 4.8 Cause &Effect Diagram of Man

Figure 4.8 is the cause & effect diagram which shows two causes of burry defect

occurrence caused by man. Those are; mistake occurs in material mixing activity

and machines temperature did not monitored regularly. Those two causes will be

developed and analyzed again to form another sub-causes.

Table 4.6 Why’s Analysis of Man Failure

Table 4.6 is the Why’s analysis for burry defect occurrence caused by man.

Man

Why Answer

Why did gear oil pump burry defect occur due to human error?

Because of mistake occurs in material

mixing activity

Why mistake occurs in material mixing

activity?

Because found too much HDPE that causes

over-flexibility

Why did the HDPE become too much? Because lack of concern regarding proper material compositions

Why did inspectors lacking of concern

regarding proper material compositions?

Because of low working performance among

material operators

Why Answer

Why did gear oil pump burry defect occur due

to human error?

Because of machines didn’t monitored

regularly

Why did the machines not monitored regularly?

Because of the operators did not do continuous monitoring

Why the operators did not do continuous

monitoring?

Because of lack of awareness regarding

machine temperature monitoring

33

Figure 4.9 Cause & Effect Diagram of Materials

Figure 4.9 is the cause & effect diagram which shows two causes of burry defect

occurrence caused by materials. Those are; injected product tends to change

profile and nylon materials causes mold corrosion. Those two causes will be

developed and analyzed again to form another possible sub-cause.

Table 4.7 Why’s Analysis of Material Failure

Table 4.7 is the Why’s analysis for burry defect occurrence caused by materials.

Materials

Why Answer

Why did gear oil pump burry defect occur due

to materials failure?

Because of injected product tend to change

profile

Why did the injected product tend to change

profile?

Because HDPE tend to make the injection

materials leaked out

Why did HDPE tend to make the injection

materials leaked out?

Because too much HDPE in the material

composition

Why Answer

Why did gear oil pump burry defect occur due

to materials failure?

Because of nylon materials causes mold

corrosion

Why did the nylon materials causes mold corossion?

Because current mold cannot withstand nylon materials

34

Figure 4.10 Cause & Effect Diagram of Machines

Figure 4.10 is the cause & effect diagram which shows four causes of burry defect

occurrence caused by machine. Those are; injection pressure instability, injected

materials got out of the mold profile, injection pressure was too high during

injection, and hot runner control and barrel was too high during injection. Those

four causes will be developed and analyzed again to form another possible sub-

cause. The Table 4.8 below is the Why’s analysis for burry defect occurrence

caused by machines.

Table 4.8 Why’s Analysis of Machines Failure

Machines

Why Answer

Why did gear oil pump burry defect occur due

to machine failure?

Because there was injection pressure

instability

Why did injection pressure instability occur? Because injection nozzle clogged up often

happening

Why did injection nozzle clogged up often

happening?

Because the current machine more likely to

clogged up

Why did the current machine more likely to

clog up?

Because 100 tonnage machine cannot provide

proper injection

35

Table 4.8 Why’s Analysis of Machines Failure (continued)

Why Answer

Why did gear oil pump burry defect occur due

to machine failure?

Because injected materials got out of the

mold profile

Why did injected materials got out of the mold

profile?

Because lack of density between the mold

plates

Why did lack of density between the mold

plates occurs?

Because current base mold profile has changed

Why Answer

Why did gear oil pump burry defect occur due

to machine failure?

Because injection pressure was too high

during injection

Why did the injection pressure was too high

during injection?

Because of current machine injection pressure

was set too high

Why did the current machine injection

pressure was set too high?

Because the current machine setting were not

relevant enough

Why Answer

Why did gear oil pump burry defect occur due

to machine failure?

Because hot runner control and barrel

temperature was too high during injection

Why did the hot runner control and barrel

temperature was too high during injection?

Because of current machine temperature

setting was set too high

Why did the current machine temperature

setting was set too high?

Because of current machine setting were not

relevant enough

4.5. Improve

In Improvement, contains the corrective actions regarding the occurred problems,

along with the results that would be seen after the improvements have been

implemented. Even the slightest improvements result could assists in the better

improvement activity in the future.

First, an Improvement Plan is needed to be created first to plan the improvements

activity correctly. An improvement planning basically is a work collaboration

between many divisions in PT. ABC. In Figure 4.11 is the Improvement Plan for

gear oil pump burry problem.

36

Figure 4.11 Improvement Plan of Noise Defect Problem

One of the considerations in determining the root cause, is the ability of the

company to undergo or to take any action of that specific causes, because not all

of the causes were able to be taken actions by the company because of some

particular reasons or factors. In Table 4.9 is the action planning table that contains

all of the company’s actions to fix the root causes of burry defects.

Table 4.9 Action Planning for Root Causes Table

No. Categories Main Causes Root Causes Solutions Corrective Actions

1. Inter Division Meeting to Discuss High Defect Problem

•Inter division meeting is conducted to do problem breakdown, with theinsights and suggestion from each of the corresponding division in PT. ABC,also with an aim to discuss the corrective actions of the company.

2. Corrective Actions Determination

•An actions should be quickly determined by the company. After somethorough analysis, at the end the company must choose which actions that willbe implemented for the burry defect problem.

3. Implementation of the Corrective Actions

•Implementation were done by implementing the corrective actions that alreadydetermined before. The implementation must be fully supported andconducted by all corresponding personnel.

4. Control and Review the Improvements

• After an improvements already seen, then the next task is to control andreview it to assess it's effectivity and reliability.

37

Those corrective actions stated on Table 4.9 were expected to contribute in the

decreasing of defect occurrence of the gear oil pump products. An implementation

1 Man

A mistake occurs

in material mixing

activity

Lack of concern

regarding proper material

compositions

An operator re-

evaluation activity

were needed every month to further

measure operators

performance

A new operator re-

evaluation activity were implemented

every month

2 Man

Machines

temperature didn’t monitored

regularly

Lack of

awareness

regarding machine

temperature

monitoring

An operator re-

evaluation activity

were needed every month to measure

operators

performance

A new operator re-

evaluation activity were implemented

every month

3 Materials

Injected product

tend to change

profile

Too much HDPE in the

material

composition

A material insertion check sheet were

needed to prevent

the same mistake

A new material

insertion check sheet

were obliged to be

filled by the material operators

4 Materials

Nylon materials

causes mold corrosion

Current mold cannot

withstand nylon

materials

An effective mold

surface hardening

activity is needed to prevent mold to

worn out

A new maintenance

activity were implemented, mainly

focusing on mould

repair, coating and

carburizing

5 Machines Instability on

injection pressure

Machine with

100 Tonnage

cannot provide reliable injection

process

A machine with

bigger tonnage is

needed to ensure a proper injection

process

Gear oil pump production activity

were moved into 140

Tonnage machines

6 Machines Injection Pressure

was too high

during injection

Current machine setting were not

relevant enough

A new machine parameter settings

were needed

A new machine

parameter settings were made, with the

proper setting to

prevent burry defects

7 Machines Injected materials

got out of the mold

profile

Lack of density between mold

plates

A new regular

maintenance activity were

needed to refresh

the mold plates

A new maintenance activity were

implemented,

focusing on mould repair, coating and

carburizing

8 Machines

Hot runner and

barrel temperature

was too high during injection

Current machine setting were not

relevant enough

A new machine parameter settings

were needed

A new machine

parameter settings were made, with the

proper setting to

prevent burry defects

38

of the corrective actions must be consistently supported by all corresponding

personnel, to obtain a good result in the future.

An improvement actions were going to be implemented in the period of April to

June 2018, and an improvement in the production result of gear oil pump product

were expected to be seen quickly.

Figure 4.12 Machine Operators Monthly Evaluation Forms

In Figure 4.12 is the new re-evaluation forms that were going to be used in the

machine operators re-evaluation activity that were done in the beginning of each

39

month. A production leader will do the re-evaluation activity within his operator

line-up.

Figure 4.13 Material Operators Monthly Evaluation Forms

Figure 4.13 also shows the new re-evaluation forms, this one is for the material

operators. A material leader will do the re-evaluation activity within his operator

line-up.

Those routine evaluation activities were expected to measure the work

performance and some other important aspects regarding operator’s job

obligation.

40

Figure 4.14 Material Insertion Check Sheets

Figure 4.14 shows the new material insertion check sheets that should be filled by

material operators. This check sheet was made specifically for gear oil pumps

material insertion only. With this check sheet, an improvement was expected to be

seen, because the material composition now is ensured to be accurate all the time,

also with the direct supervision of the material leaders.

Also, starting from the first production week in April 2019, the production activity

of gear oil pump product in PT. ABC were moved into 140 Tonnage machines,

from the previous machines that was 100 Tonnage machines. It is a corrective

action against the root causes that already found before, which is the machine with

100 Tonnage cannot provide reliable injection process. The previous machine was

the Toshiba IS 100 G-5A, and the current machine is the Toshiba IS 140 G-5A.

With this action, an improvement is expected to be seen.

Material Insertion Check Sheet

Gear Oil Pump Machines

Operator Name :

Insertion Time :

Shift :

Machine Number :

1. Material Compositions

PP …..% Standard of 78% Note : Material

compositions could

change according to current conditions

PA …..% Standard of 10%

HDPE …..% Standard of 6%

ABS …..% Standard of 6%

2. Hopper Cleanliness Good Fair Poor

Note : -

3. Injection Site Cleanliness Good Fair Poor

Note : -

Operator Signature: Material Leader Signature:

Mold Maintenance Check Sheet

Gear Oil Pump Mold

41

Figure 4.15 Mold Maintenance Check Sheet

In Figure 4.15 is the new mold maintenance check sheet. This check sheet was

specifically made to overcome the noise defect problem that was caused by any

flaws in the mold, so it is different than the regular mold check sheet. PT. ABC

already prepared a new mold maintenance program, that will be implemented in

the period of April to June 2018. This program was expected to effectively

increase the mold strength against nylon materials, and also along with the

maintenance of the mold cooling system. But at first, the mold must enter

repairment process before undergo further maintenance or coating process.

Figure 4.16 shows the current machine parameter settings that were not relevant

enough to prevent the occurrence of burry defects. The changes of settings will be

made for barrel and hot runner temperature settings, and also an adjustment in

injection pressure settings.

Mold Number :

Machine Number :

Maintenance Date :

Operator :

No. Actions Condition

Note Done Not Done

1 Mold Surface Repairment

2 Mold Cooling System

Repairment

3 Mold Coating

4 Mold Carburizing (CSCHT)

5 Mold Release Agent

Application

6 Mold Cleaning

Maintenance Operator Signature :

Maintenance Leader Signature :

42

Figure 4.16 Machine Parameter Settings (Current)

Machine Parameter Setting Check Sheet

Date :

No. Work Center :

Machine Type :

Product Type :

Material/Colour :

Cavity Number :

Cycle Time : T

emp

erat

ure

Components

Standard

Shift

1 2 3

Set Act Set Act Set Act

Barrel (°C) H1 250-270

H2

H3

H4

H5

Mold Core & Cavity (°C) H1 200-225

H2

Hot Runner Control (°C) H1 275-295

H2

H3

H4

H5

Hopper Dryer (°C) H1 200

H2

H3

H4

H5

Nozzle (°C) H1 210

H2

H3

H4

H5

Inje

ctio

n

Injection Pressure (Kg/cm²)

P1 2275 P2 P3

Plasticizing Speed (mm/s,%)

V1 60-70 V2 V3

Plasticizing Pressure (Bar) P1 2 P2 P3

Date :

Mc. Operators :

Sign :

43

Figure 4.17 Machine Parameter Settings (Corrected Version)

Machine Parameter Setting Check Sheet

Date :

No. Work Center : Machine Type :

Product Type :

Material/Colour :

Cavity Number : Cycle Time :

Tem

per

atu

re

Components

Standard

Shift

1 2 3

Set Act Set Act Set Act

Barrel (°C) H1 220-240

H2

H3

H4

H5

Mold Core & Cavity (°C) H1 200-225

H2

Hot Runner Control (°C) H1 255-275

H2

H3

H4

H5

Hopper Dryer (°C) H1 200

H2

H3

H4

H5

Nozzle (°C) H1 210

H2

H3

H4

H5

Inje

ctio

n

Injection Pressure (Kg/cm²) P1 2025 P2 P3

Plasticizing Speed (mm/s,%) V1 60-70 V2 V3

Plasticizing Pressure (Bar) P1 2 P2 P3

Date :

Mc. Operators :

Sign :

44

In Figure 4.17 shows us the machine parameter settings that were already

corrected to prevent further occurrence of burry defects. The changes were

focusing on the temperature settings of barrel and hot runner, and the injection

pressure settings.

From those all improvement actions, the target of improvement result were

determined to be 70% reduce of defect occurrence. By a decrease of 70% of

defect occurrence, automatically the defect proportion and cost loss will become

smaller, and sigma value would become higher.

4.6. Control

The last phase is a control activity. Control or monitoring aims to continuously

evaluate the implementation result from improvement stage. By doing control

activity, the improvements will be ensured to be sustained and well-implemented

in a long term. Besides, control activity also helped in keeping the performance

level in a good state.

Peter S. Pande (2000) stated that control chart is suitable to be applied on control

stage of DMAIC to establish a continuous method which controls process

performance. Control chart will help to identify the existence of special cause

variations which have to be eliminated. Controlling or monitoring is needed in

order to have continuous improvements.

By using a control chart, will surely help to ensure whether the implementation of

the corrective actions were going continuously. The most suitable control chart for

this case is p chart, concerning the data taken were variable type data.

In Figure 4.18, shows the daily maintenance check sheet of injection machine.

The check sheets were made to sustain the implementations of improvements,

with a focus on regular machine maintenance program. There are six inspection

points that were expected to be done in each production day. The inspection will

be done by the maintenance operator, referring to the Standard Operational

Procedure (SOP) of maintenance.

45

Figure 4.18 Daily Maintenance Check Sheet of Injection Machine

Daily Maintenance Checksheet of Injection Machine

Month : Product :

Machine Number : Maintenance Operator :

Operator Name :

Date A B C D E F Sign

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

31

Check Points :

A : Check whether the “emergency stop button” is functioning properly

B : Check whether the electric safety seal is functioning properly

C : Check whether the mechanic safety seal is functioning properly D : Check the servo motors (noise, vibration, temperature)

E : Check if there is abnormality in the Machine Temperature Control (MTC)

F : Check if there is abnormality in the timing belt

46

1 31 21 11 0987654321

0,0085

0,0080

0,0075

0,0070

0,0065

0,0060

0,0055

0,0050

Sample

Pro

po

rtio

n

_P=0,006657

UCL=0,007578

LCL=0,005737

Tests performed with unequal sample sizes

P Chart of Post-Improvement

4.6.1 Result of Implementation

Table 4.10 shows the data of weekly defect quantity after improvements, showing

CL, UCL, and LCL value with the similar calculation that showed in previous

section. The data were taken during every production week in the period of April

– June 2018.

Table 4.10 Defect Quantity After Improvement

Period

Defect

Quantity

(di)

Produced

Quantity

(ni)

CL UCL LCL

I/Apr 576 79,667 0.006657408 0.016823 -0.00351

II/Apr 404 59,008 0.006657408 0.018795 -0.00548

III/Apr 482 73,411 0.006657408 0.01777 -0.00445

IV/Apr 495 69,015 0.006657408 0.017623 -0.00431

I/May 392 64,889 0.006657408 0.018979 -0.00566

II/May 368 60,320 0.006657408 0.019375 -0.00606

III/May 515 76,572 0.006657408 0.017408 -0.00409

IV/May 524 75,274 0.006657408 0.017315 -0.004

V/May 426 68,550 0.006657408 0.018477 -0.00516

I/Jun 417 66,712 0.006657408 0.018604 -0.00529

II/Jun 191 29,056 0.006657408 0.02431 -0.011

III/Jun 187 28,200 0.006657408 0.024498 -0.01118

IV/Jun 488 70,216 0.006657408 0.017701 -0.00439

.

Figure 4.19 P Chart of After Improvement

47

Figure 4.19 shows the p-chart for after improvement from data taken within all

production weeks in April to June 2018. The p-chart shows that from those data,

none of it was outlier (above UCL or below LCL) which means the data is

statistically controlled. Then, the capability analysis of the attribute data can be

made by using Minitab software.

The average of DPMO value before the improvements were implemented was

26,331, then, the current DPMO within the improvements period is 6,657. To

calculate the post-improvement sigma value, an interpolation should be used

again in order to determine its value.

8,190−6,210

3.9−4.0 =

8,190−6,657

3.9−𝑥

After-Improvement Sigma = 3.9

The calculation regarding the DPU and DPMO was just the same with the one that

has been done in previous section. In Table 4.11 below is the calculation of DPU

& DPMO values after improvements has been implemented.

Table 4.11 Calculation of DPU and DPMO Values (After Improvement)

Period

Defect

Quantity

(di)

Produced

Quantity

(ni)

CL UCL LCL DPU DPMO Sigma

Value

I/Apr 576 79,667 0.006657408 0.016823 -0.00351 0.00723 7,230.095

II/Apr 404 59,008 0.006657408 0.018795 -0.00548 0.006847 6,846.529

III/Apr 482 73,411 0.006657408 0.01777 -0.00445 0.006566 6,565.774

IV/Apr 495 69,015 0.006657408 0.017623 -0.00431 0.007172 7,172.354

I/May 392 64,889 0.006657408 0.018979 -0.00566 0.006041 6,041.086

II/May 368 60,320 0.006657408 0.019375 -0.00606 0.006101 6,100.796

III/May 515 76,572 0.006657408 0.017408 -0.00409 0.006726 6,725.696

IV/May 524 75,274 0.006657408 0.017315 -0.004 0.006961 6,961.235

V/May 426 68,550 0.006657408 0.018477 -0.00516 0.006214 6,214.442

I/Jun 417 66,712 0.006657408 0.018604 -0.00529 0.006251 6,250.749

II/Jun 191 29,056 0.006657408 0.02431 -0.011 0.006574 6,573.513

III/Jun 187 28,200 0.006657408 0.024498 -0.01118 0.006631 6,631.206

IV/Jun 488 70,216 0.006657408 0.017701 -0.00439 0.00695 6,949.983

Total 5,465 820,890 0.006657408 0.009958 0.003357 0.006657 6,657.408

Average 420.38 63,145.38 0.006657408 0.018556 -0.00524 0.006657 6,657.408 3.9

48

Can be seen from Table 4.11 the DPMO values shows an improvement with a

value of 6.657,4 which has the sigma value of 3.9. Based on this result, the value

of the sigma nearly reaches an average standard of USA’s industry which the

average DPMO is 6210.

Table 4.12 shows the detailed comparison before improvement & after

improvement from three important parameters.

Table 4.12 Before & After Improvements Comparison

No Parameter Before

Improvement

After

Improvement Percentage

1 Defect Proportion 0.026249 0,006636 75% decrease

2 Defect Quantity 21,778 5,465 75% decrease

3 Sigma Value 3.4 3.9 17% increase

From Table 4.12, the parameters used in the comparison were, defect proportion,

defect quantity, and sigma value.

Figure 4.20 Before & After Improvement Comparison Graphic

Figure 4.20 shows the comparison graphics of before improvements and after

improvements.

49

It can be seen that the defect quantity is decreasing from the value of 21,778 into

5,465, which means there is 75% defect occurrence decrease. Defect proportion

were also decreasing from a value of 0.026248 to the value of 0.006635. Because

of the decrease in defect proportion, the sigma value also got a boost from the

value of 3.4 into 3.9.

Figure 4.21 Before & After Improvement Cost Loss Comparison Graphic

Figure 4.21 shows the comparison of cost loss on before and after improvements.

For the cost reductions that could be made after improvements were implemented,

the company initially suffered a loss of approximately IDR 135,023,000,

considering the retail price of gear oil pump product were IDR 6,200 per piece.

Loss Sales Before Improvement : 21,778 x IDR 6,200 = IDR 135,023,000

Loss Sales After Improvement : 5,465 x IDR 6,200 = IDR 33,883,000

Then a cost loss reduce were calculated during the improvement implementation

period, and only IDR 33,883,000 cost loss were occurred. It is an approximately a

75% cost loss reduce during the improvement implementation period.

50

CHAPTER V

CONCLUSIONS AND RECOMMENDATIONS

6.1.Conclusion

From the data that were taken during the pre-improvement period in January

to March 2019, found a total noise defect was 21,778 pieces of gear oil

pump. This defect amount was spread among seven inspection sequences,

which are; burry, run outs, diameter inner, diameter outer, overbowl, base

tangenth, and bending. From the analysis by using pareto chart and defect

proportion analysis, found a top defect contributor for noise defect, which is

the burry defects.

The implementation of improvements regarding high defects rates case

seems to go smoothly, by some immediate corrective actions taken by the

company. After doing an internal analysis, PT. ABC implemented the

corrective actions in the period April to June 2018. By adding new

production and materials check sheets, implementing new mold maintenance

program, implementation of monthly operator re-evaluation, the production

machine migration from 100 Tonnage into 140 Tonnage machines and new

machine parameter settings, the defect numbers could be minimized.

After implementing the improvements actions, the defect proportion was

decreased from the value of 0.026249 in pre-improvement period to the

value of 0,006636 in post-improvement period. Those decrease in the defect

proportion value resulting in a boost of sigma value from 3.4 into 3.9, also

because of the defect quantity that decreases for an amount of 75%,

exceeding the improvement target of 70%. From the economic side, PT.

ABC has suffered a loss of approximately IDR 135,023,000, from the defect

occurrence. But in this post-improvement period, they can prevent a further

loss for up to IDR 101,140,000, considering the retail price of one gear oil

pump product as IDR 6,200 per pieces.

51

6.2 Recommendations

For further research, the recommendations are made as follows:

To develop a set of counter-measure planning as a reference of actions

when a same defect still occur in a long period of time in the production

process, so the corrective actions could be planned correctly and

effectively, because of the reference of actions were already exists in the

previous time.

To develop the research by using another methodology, for example by

using Failure Mode and Effect Analysis (FMEA) or the adoption of Poka

Yoke method.

52

REFERENCES

Antony, J. 2007. Is Six Sigma a Management Fad Or Fact?. Assembly

Automation. Vol. 27 (1),

Borror, Connie M. 2009. The Certified Quality Engineer Handbook, Third

Edition, ASQ Quality Press, Wisconsin,

Daneshgari, Perry and Michelle Wilson. 2008. Lean Operations in Wholesale

Distribution., NAW Institute for Distribution Excellence, Washington DC,

Ganguly, Kunal. 2013. Improvement Process for Rolling Mill through DMAIC

Six Sigma Approach. International Journal for Quality Research, India,

George, Michael L. 2003. Lean Six Sigma for Service: How to Use Lean Speed &

Six Sigma Quality to Improve Service and Transactions, McGraw-Hill, New

York,

Harry, M. J., Schroeder R. 2000. Six Sigma: The Breakthrough Management

Strategy Revolutionizing The World’s Top Corporations. Doubleday, Random

House Inc., New York,

Rajashekhariah, Jagadeesh. 2016. Six Sigma Benchmarking of Process Capability

Analysis and Mapping of Process Parameters, Journal of Operations, and Supply

Chain Management, Vol. 09, Sao Paulo,

Pande, Peter S, Neuman, Robert P. and Cavanagh, Roland R. 2000. The Six

Sigma Way How GE, Motorola and Other Top Companies are Honing Their

Performance, McGraw-Hill, New York,

Rimantho, Dino, and Desak Made Mariani. 2017, Penerapan Metode Six Sigma

Pada Pengendalian Kualitas Air Baku Pada Produksi Makanan, Jurnal Ilmiah

Teknik Industri, Vol. 16, Jakarta,

Russell, Roberta S., and Bernard W. Taylor. 2006. Operations Management:

Creating Value Along the Supply Chain, John Wiley & Sons, New Jersey,

53

Shankar, Rama. 2009. Process Improvement Using Six Sigma: a DMAIC Guide,

ASQ Quality Press, Wisconsin,

Varun, S. Appaiah, and Chethan Kumar C.S. 2015. Enhancing the Operational

Effectiveness of Sewing Segment in Garment Industry by DMAIC Approach.,

International Research Journal of Engineering and Technology, India,

54

APPENDIX

Gear Oil Pump Product

Injection Mould

55

Injection Machine

Hopper Part of the Machine