INVESTIGATION OF EFFECTIVE ADAPTATION

OF LEAN MANUFACTURING SYSTEM IN

APPAREL MANUFACTURING LINES

Report of Industrial Training

I.U.M. Dissanayake

(082168)

Department of Statistics and Mathematical Sciences

Department of Computing and Information Systems

Faculty of Applied Sciences

Wayamba University of Sri Lanka

Kuliyapitiya

December – 2012

INVESTIGATION OF EFFECTIVE ADAPTATION OF

LEAN MANUFACTURING SYSTEM IN

APPERAL MANUFACTURING LINES

This report submitted in a partial fulfillment of the requirements for

the four year Bachelor of Science (Joint Major) Degree

in ‘Statistics’ and ‘Computing and Information Systems’

I.U.M. Dissanayake

(082168)

Principal Supervisor’s Name: Mrs. Bhagya Munasinghe

Program Coordinator’s Name: Dr. K.D.D.N. Dissanayake

Name of the Course Module: INDT 421 Industrial Training

Training Period: 02/05/2012 to 02/11/2012

External Supervisor’s Name: Mrs. Kokila Padmasiri

Ceylon Knit Trend (PVT) LTD.

Maharagama

Department of Statistics and Mathematical Sciences

Department of Computing and Information Systems

Faculty of Applied Sciences

Wayamba University of Sri Lanka

Kuliyapitiya

December – 2012

ii

DECLARATION

I declare that:

a) Except where due acknowledgement has been made, the work is that of the

student’s alone;

b) The work has not been submitted previously, in whole or in part, to qualify for

any other academic award;

c) The content of the report is the result of work which has been carried out since

the official commencement date of the Industrial training program of the faculty;

d) Any editorial work, paid or unpaid, carried out by a third party is acknowledged;

and

e) Procedures and guidelines of the faculty have been followed

Signed:

Signature

………………………………….

(I.U.M. Dissanayake)

Date:

iii

APPROVAL FOR SUBMISSION

Internal Supervisor : (Signature)………………………………………..

: (Title & Name)…………………………………...

: (Date)……………………………………………..

Program Coordinator : (Signature)………………………………………..

: (Title & Name)…………………………………...

: (Date)……………………………………………..

External Supervisor : (Signature)………………………………………..

: (Title & Name)…………………………………...

: (Date)……………………………………………..

Assistant Registrar : (Signature)………………………………………..

: (Title & Name)…………………………………...

: (Date)……………………………………………..

iv

ACKNOWLEDGEMENT

First of all I would like to give my special thanks to my internal supervisor Mrs. Bhagya

Munasinghe for giving me so much guidance, advices, directions and valuable support

to carry out this research from the very beginning.

I also thank to Dr. K.D.D.N. Dissanayake, our Industrial Training Program Coordinator,

for giving me guidance, advices, and directions all over the training period which

helped me a lot in completing a valuable research.

Then I would like to give my special acknowledge to all the lecturers and demonstrators

in the Department of Statistics and Computing and Information Systems, for providing

me enough guidance and support.

Then I want to acknowledge Mrs. Kokila Padmasiri, Human Resource Manager, Mr.

Nandana Prasanna Bandara, Work Study Manager(Knit cluster) for giving me a huge

support by providing details and guidance whenever I needed and for all the

departments heads and members of staff where I was assigned for training sessions.

My thanks also go to my colleagues who always support and motivate me whenever I

needed.

Finally, I give many thanks to my family for their constant and valuable support and

encouragement.

v

ABSTRACT

Lean Manufacturing (herein after referred to as Lean) can be considered as an effective

business strategy for waste elimination through continuous improvement and lead time

reduction in manufacturing processes to achieve competitive advantage over the market

competitors. This technique was originated and developed in Japan. Lean is the latest

technique in today’s Garment Industry in Sri Lanka to face the challenges of the

competitive business world.

For companies to successfully implement Lean it is very much required that they

understand the issues that are associated with value added and non-value added

activities in their manufacturing process. Sri Lankan apparel sector especially have

attempted to implement this. So this little research work is carried out regarding

suitability of Lean in a selected apparel manufacturer in Sri Lanka.

This research is an attempt to identify the effectiveness of two selected manufacturing

lines of Ceylon Knit Trend (PVT) Ltd. (CKT), in which the Lean techniques are being

implemented in its manufacturing process. The evaluation was carried out using one of

the most important tools called “Value Stream Mapping (VSM). This dissertation

presents the finding of a research, analysis of data, discussion of the results and findings

and also a conclusion over the findings. It has identified the possible waste, rationale for

such waste and suggests elimination of them during the manufacturing process.

As the initial stage, a literature review was carried out to study Lean Manufacturing and

Value Stream Mapping (VSM). VSM was applied in a selected garment design (style)

which go through two manufacturing lines during its manufacturing. The attributes for

VSM was selected by matching theoretical VSM attributes into the CKT environment.

Factors affecting the lead time was then identified based on those two manufacturing

lines.

The findings revealed can help in understanding the effectiveness of adopting Lean into

mass production apparel industries in order to derive positive results such as reducing

wastes in inventory and defects. Further, VSM visualization helped the managers of the

company of interest to visualize the different types of wastes generated in their

organization thereby future possibilities of eliminating or reducing them. The findings

can be extended to similar apparel organizations in the future.

vi

List of Abbreviations

CKT- Ceylon Knit Trent

CKTM- Ceylon Knit Trent PVT LTD, maharagama

CSR- Corporate Social Responsibility

C/O – Change Over

FIFO- First In, First Out

JIT- Just In Time

NVA- None Value Added

VA-Value Added

SWS- Standard Work Sheet

TQM- Total Quality Management

TPM-Total Productive Maintain

VSM- Value Stream Map

WIP- Work in Progress

vii

Table of Contents DECLARATION .............................................................................................................. ii

APPROVAL FOR SUBMISSION ................................................................................... iii

ACKNOWLEDGEMENT ............................................................................................... iv

ABSTRACT ...................................................................................................................... v

List of Abbreviations........................................................................................................ vi

1 Introduction ............................................................................................................... 1

1.1 Organization, Structure and History ................................................................... 1

1.1.1 The Hirdaramani philosophy....................................................................... 1

1.1.2 History of the Hirdaramani ......................................................................... 2

1.1.3 Coparate responsibility................................................................................ 2

1.2 Nature of business and operation ....................................................................... 3

1.2.1 Hirdaramani Apparel Production ................................................................ 3

1.2.2 About Ceylon Knit Trend (PVT) LTD........................................................ 3

1.3 Departments, Divisions, and Sections of study .................................................. 4

1.4 Background and Rational for the research ......................................................... 5

1.4.1 Problem Statement ...................................................................................... 6

1.5 Study / Research Objective ................................................................................ 6

1.6 Scope of the Study/ Research ............................................................................. 7

1.7 Outline of the Report .......................................................................................... 7

2 Literature Review and Theoretical Background ....................................................... 9

2.1 Literature related to area of the study ................................................................. 9

2.2 Theories related to area of study ...................................................................... 10

2.2.1 Lean Manufacturing System ..................................................................... 10

3 Research Questions/ Problems ................................................................................ 19

3.1 Research Questions/ Problems ......................................................................... 19

3.2 Rational to select research question ................................................................. 20

3.3 Potential benefits to the organization by solving the question ......................... 20

4 Research Approach and Methodology .................................................................... 21

4.1 Research design with a rational ........................................................................ 21

4.2 Data collection strategy with rationale ............................................................. 21

4.3 Details of Design & Development of Data Collection Tools ........................... 22

4.4 Data Analysis Strategies and Rationale ............................................................ 22

4.5 Statistical Tests and Methods of Applications and Limitations ....................... 22

viii

4.5.1 Limitations ................................................................................................ 23

5 Data Collection and Analysis .................................................................................. 24

5.1 Details of Data Collection ................................................................................ 24

5.2 Details of responses .......................................................................................... 24

5.3 Details of data analysis ..................................................................................... 24

5.3.1 Value Stream Map..................................................................................... 24

5.3.2 Factors affecting to the efficiency ............................................................. 29

5.3.3 Evaluate performances before and after implementing the Lean

manufacturing ......................................................................................................... 30

5.3.4 Hypothesis testing of Factory efficiency................................................... 30

5.3.5 Hypothesis testing of Machine Breakdown Time ..................................... 30

5.3.6 Hypothesis testing for Needle breakages .................................................. 31

5.3.7 Hypothesis testing for Defects .................................................................. 32

5.4 Results .............................................................................................................. 33

5.4.1 Value stream map ...................................................................................... 33

6 Identification of causes and alternative solutions ................................................... 34

6.1 Result Interpretation ......................................................................................... 34

6.1.1 Result on value stream map ...................................................................... 34

6.1.2 Result on multiple regression .................................................................... 35

6.1.3 Result on hypothesis testing ...................................................................... 35

6.2 Causes of the Problem ...................................................................................... 37

6.3 List of Alternative Solutions ............................................................................ 39

6.3.1 Solutions to the issues identified by analyzing section 6.2 ....................... 39

6.4 Implemented Lean activities involved in achieving the solutions given in

section 6.3 ................................................................................................................... 40

6.4.1 Total productive maintenance (TPM) ....................................................... 41

6.4.2 PULL System ............................................................................................ 41

6.4.3 Standard Work .......................................................................................... 42

6.4.4 ANDON .................................................................................................... 42

6.4.5 Just-In-Time (JIT) ..................................................................................... 42

7 Discussion and conclusion ...................................................................................... 43

7.1 Limitation of this Research .............................................................................. 43

7.1.1 Data collection limitations ........................................................................ 43

7.1.2 Time .......................................................................................................... 43

ix

7.1.3 Company Terms and Regulations ............................................................. 43

7.2 Problems Encountered and Alternative Action Taken ..................................... 44

7.3 Further/Future Research Operation .................................................................. 44

7.4 Discussion and Conclusion .............................................................................. 45

8 Details of industrial training ................................................................................... 46

8.1 Introduction to training ..................................................................................... 46

8.2 Details of method & techniques, Tools, and equipment .................................. 47

8.3 Details of operations, process and Procedures Learned ................................... 49

8.4 Detailed of new Learning- theoretically and practically .................................. 49

8.5 Issues and Challenges Encountered and Action Taken to Overcome .............. 50

REFERENCES ................................................................................................................ 51

Appendix 1 ...................................................................................................................... 53

Data sheet of before and after lean implementation .................................................... 53

Data sheet of ANDON tracking Inventory delay .................................................. 53

Appendix 2 ...................................................................................................................... 55

Statistical Analysis Results ......................................................................................... 55

x

List of Tables

Table 2-1. Summary of symbols commonly used in value stream mapping .................. 16

Table 4-1. Model of the work sheet for collecting data .................................................. 22

Table 5-1.Test for efficiency of before and after ............................................................ 30

Table 5-2.Test for machine Breakdown Time ................................................................ 31

Table 5-3 T-Test for Needle -Before & After ................................................................. 31

Table 5-4.Test for defects before and after ..................................................................... 32

List of Figures

Figure 2-1. The relationship between work standardization and other standards .......... 15

Figure 3-1. Fish bone diagram of the production floor ................................................... 19

Figure 5-1 Value stream map before lean implementation ............................................. 25

Figure 5-2 Value stream map after lean implementation ................................................ 28

Figure 6-1 Scatterplot of efficiency vs. machine break down ........................................ 36

Figure 6-2 Scatterplot of efficiency vs. needle breakage ................................................ 36

Figure 6-3 Scatterplot of efficiency vs. inline defects .................................................... 37

1

1 Introduction

1.1 Organization, Structure and History

From the beginning as a single retail store in the heart of Colombo's commercial district,

the Hirdaramani Group has diversified in recent years to encompass the apparel, leisure,

power, information technology and retail industries, Hirdaramani consist of over 30,000

employees spread across six countries and six industries.

Hirdaramani group is spread across Sri Lanka, Bangladesh and Vietnam. They produce

approximately 13 million articles of clothing each monthly. Hirdaramani cater to a

myriad of renowned designer and high street labels including Tommy Hilfiger, Levi’s,

Nike, M&S, Tesco, Ralph Lauren, Abercrombie & Fitch and True Religion.

Among many novelties Hirdaramani has adapted and practices, they have set up the

world’s first custom built ‘green’ factory in Agalawatte, Sri Lanka. The initiatives

reflect the policy of sustainability with the ultimate goal of becoming a completely

carbon neutral organization. Hirdaramani has also been implementing successful energy

saving initiatives across the entire group in order to reiterate their commitment to

sustainability and to being a greener organization.

The Hirdaramani commitment to being a responsible corporate citizen is reflected in

their social responsibility projects currently operating across the country (Hirdaramani

Group, 2012).

1.1.1 The Hirdaramani philosophy

The constant commitment to develop and the inspiration that comes from within have

been the driving force behind the company’s success, giving meaning and light to

Hirdaramani motto “Your Company, Your Future” (Hirdaramani Group, 2012).

1.1.1.1 Mission

To offer quality customer service through innovation, leadership and excellence while

being responsive to change in a competitive global environment. Further, to instill

professionalism by embracing a positive spirit of enterprise within the group, to increase

global market share and do what we do better.

2

1.1.1.2 Vision

Design

To consistently provide meticulous, high quality products that are sought after by brand

conscious customers with originality and consistent innovation

Customer First

Continuing our longstanding tradition of upholding the highest standard of customer

service, we keep our customers at the forefront in all aspects of product design,

production and delivery

Enable

To promote entrepreneurship from within via high quality training and support in order

to enable employees to reach newer heights, maximize potential and be all they can be

Sustainability

To continue to keep a 100+ year business going strong through commitment to our

people and the communities we exist in

Productivity

To engage with our workforce and deliver products with a clear understanding of

market requirements and an adherence to clear and structured process

Commitment

To our people, the environment and to the communities around us

1.1.2 History of the Hirdaramani

The Hirdaramani legacy began in 1890 when, at just 16, Parma and Hirdaramani set up

the first Hirdaramani retail store in Fort, Colombo. He made a name for himself in the

early 1900s by introducing the concept of same-day tailoring to passengers of cruise

liners that docked at the Colombo Harbor. The innovative Hirdaramani spirit took flight,

emerging from these small beginnings to steadily become the one-stop manufacturing

hub and diversified group that it is today (Hirdaramani Group, 2012).

1.1.3 Coparate responsibility

Sustainability is an important goal at the Hirdaramani Group, and for us sustainability is

about corporate responsibility. Responsibility for protecting our environment, assisting

the communities around us and enabling and empowering our employees has always

3

been part of Hirdaramani culture. They believe that this is the foundation for success

and for building a more sustainable industry.

The Hirdaramani Group's eco-friendly factory 'Mihila' has been awarded

CarbonNeutral® certification making it the first Apparel Factory in Asia to achieve this

distinction. The certification is awarded by The CarbonNeutral ® Company, a global

provider of carbon reduction solutions. All Hirdaramani companies are committed to

being a zero carbon company

The Hirdaramani Group has been investing in the future of young Sri Lankans for many

years. Their education program covers a range of CSR initiatives varying from

infrastructure development to the provision of school uniforms and learning materials.

They have always had a focus on nurturing and developing education in the country

(Hirdaramani Group, 2012).

1.2 Nature of business and operation

1.2.1 Hirdaramani Apparel Production

The Hirdaramani Group operates 28 state-of-the-art production facilities in Sri Lanka,

Bangladesh and Vietnam, with the capacity of producing over 13 million pieces of

clothing per month. Coupled with their innovations in design, this makes Hirdaramani

one of the leading apparel industry production hubs in the world (Hirdaramani Group,

2012).

HIRDARAMANI INTERNATIONAL EXPORT (PVT) LTD

HIRDARAMANI INDUSTRIES LTD

HIRDARAMANI MERCURY APPAREL (PVT) LTD.

CEYLON KNIT TREND (PVT) LTD.

HIRDARAMANI GARMENTS LTD

KENPARK BANGLADESH (PVT) LTD

REGENCY GARMENTS LTD. BANGLADESH

FASHION GARMENTS LTD. VIETNA

1.2.2 About Ceylon Knit Trend (PVT) LTD.

Comprised of manufacturing units based in Maharagama, Eheliyagoda, and Agalawatte,

CKT focuses on the production of knitted garments. The Agalawatte factory, more

famously known as “Mihila” holds the distinction of being both the First

4

CarbonNeutral® Apparel Factory in Asia and the First Custom-built Green Apparel

Factory in the World.

Altogether, the cluster operates 75 lines and several leased units with a total capacity of

1 million pieces a month while boasting an in-house Textile Laboratory to ensure color

fastness and washing plants at some of the outlet (Hirdaramani Group, 2012).

1.2.2.1 Product Portfolio

CKT (PVT) LTD Maharagama Specializes in knit garments, including men's, women's

and children's t-shirts, polo shirts, fleece tops, polar fleece, pants and lingerie.

It has diversified brand portfolio. They focused on Global Drive Brands.

Tesco, PVH, Nike, Decathlon, Calvin Klein, Adidas, Colombia,

Tommy Hilfiger, Patagonia, American Eagle, Victoria’s Secret, M&S,

Main suppliers

Ocean Lanka,

Brandix textile LTD.

Technology:

Tuka Tech, Gerber Automatic Spreading System, Microsoft Dynamics ERP, Orax

Automatic laser Cutters

Certifications:

GSV C-TPAT

ISO 14001-2004

OHSAS-18001-2007

LEED Gold, USG BC

Fair Trade Certification

1.3 Departments, Divisions, and Sections of study

The Work Study Department is the major department, at which this study was carried

out. However this study was the combination of Work Study Department, Lean

Manufacturing Department and Production Department.

Nevertheless, many personnel from some other Departments too were consulted in order

to find out relevant information and documents for the research.

5

1.4 Background and Rational for the research

The Garment Industry in Sri Lanka today accounts for more than 43% of Sri Lanka’s

total exports. Although Sri Lanka’s garment industry is reputed as a quality

manufacturer it has many disadvantages such as low labor productivity and excessive

lead times (Pettersen, 2009). In today’s competitive business world, companies require

small lead times, low costs and high customer service levels to survive. Because of to

perform in a global market, short lead times are essential to provide customer

satisfaction.

Organizations that have focused on cycle time as a productivity measure can reduce

delivery time and improve quality, thereby creating more satisfied customer. Cycle time

or lead time is from the time a customer release an order until the time they receive the

finished product.

In this respect garment industry in Sri Lanka has faced problem to reduce their lead time

than their competitors. Because the fabric manufacturing industry in Sri Lanka is not

enough to fulfill Sri Lankan demand. But the competitors of the Sri Lanka such as

Turkey, India, Bangladesh, China, Morocco, Egypt garment industry save the lead time

by producing fabric own country. Therefore Sri Lanka garment industry waste about 30

days than other country to ordering and import fabric. After that organization remain

only 15 days and they should organize their value stream map within 14 days.

Before 1980, customers tolerated long lead times which enabled producers to minimize

product cost by using economical batch sizes. Later, when customers began to demand

shorter lead times, they were able to get them from competitors. This is when the

problem arose and companies started to look for changes to be more competitive. In an

attempt to reduce lead time, businesses and organizations found that in reality 90% of

the existing activities are non-essential and could be eliminated. As soon as

manufacturers focused on processes, they found waste associated with changeovers,

quality defects, process control, factory layout, and machine down time. So they tried to

find ways to reduce or eliminate waste. By eliminating the non-value adding activities

from the processes and streamlining the information flow significant optimization

results can be realized (Hassanzadeh, 2008)

In order to face this global challenge Sri Lanka garment sector have apply different

strategy. The recent adoption is the lean manufacturing tool which is used by Toyota

6

Company to reduce their lead time by reducing non added value of the organization by

optimizing the organization value stream map. It is called as waste eliminating tool

because it focused on seven wastes (transport, inventory, motion, waiting, over

production, over processing, defects) and eliminates them.

The subjective research in this writing was carried out as a six months industrial training

project as a partial requirement of the Bachelor of Science (Joint Major) Degree

Programme of the Applied Sciences Faculty of Wayamba University of Sri Lanka. This

dissertation presents the findings of a research carried out to evaluate the effectiveness

of the adaptation of lean manufacturing in the manufacturing process of Ceylon Knit

Trend Ltd of Hirdaramani Group, one of the leading garment manufacturers in Sri

Lanka.

1.4.1 Problem Statement

Due to higher manufacturing cost in garment production, high variation in product mix.

It is very difficult to sustain in the global market. This paper will focus on customized

implementation of Lean tool for minimizing the Work in progress (WIP), as well

optimizing the value stream map, line setting time in a Knitted T-shirt Production

Industry which in turn reduces the cost of production.

Based on the above explanation a border research problem can be started as “How can

lean manufacturing system used to improve the performance of apparel industry”

1.5 Study / Research Objective

In answering the research problem, the study sought to accomplish the research

objective.

1. To examine the current situation of the lean manufacturing and organization

status.

2. To identify and propose potential avenues for improving lean manufacturing

for better performance of the garment sector.

The Present Study analyzed the lean manufacturing system and its’ value stream map of

existing production facilities of CKT (PVT) LTD Maharagama. It is an attempt to and

understands the root causes which would increase the lead time of the process .The

Study subsequently examined some of the suitable lean tools and techniques for

7

proposing the new system of value stream mapping. Finally the study compared and

evaluated the production performance before establishing Lean system and after.

1.6 Scope of the Study/ Research

Lean manufacturing technology with the standard work can be applied in to any

manufacturing sector. The research focused only on manufacturing process and the data

were gathered only from the production division and the work study department of CKT

(PVT) LTD Maharagama. The research was carried out with in a time frame of six

months as an internal training in the Work Study Department.

1.7 Outline of the Report

Chapter 1. Introduction

This chapter provides the background, objectives and significance of the study. It also

briefs the formation of the remaining chapters.

Chapter 2. Literature Review and Theoretical Background

According to the scope and problems, a relevant literature should be searched and

studied. There are some text books, journals, and past reports about the lean technology.

Chapter 3. Research Questions / Problems

This chapter describes the research problem and its rationale. It also provide the

potential benefits of the findings.

Chapter 4. Research Approach and Methodology

Chapter $ discusses the project rationale along with the data, data collection strategy

and the limitations encountered.

Chapter 5. Data Collection and Analysis

Data collection is discussed in detail in this Chapter. Graphical data representations and

summarizations are given in this Chapter.

Chapter 6. Identification of Causes and Alternative Solutions

Detailed analysis of the data presented in Chapter 5 is given in Chapter 6.

Chapter 7. Discussion and Conclusion

8

Chapter 7 consists of the discussion and conclusions. It also detailes the limitations

encountered during the project and presents the suggestions and avenues for future

developments of a similar project.

Chapter 8. Details of Industrial Training

A briefing of the Industrial Training experience is presented in this Chapter.

9

2 Literature Review and Theoretical Background

2.1 Literature related to area of the study

Lean Manufacturing is defined as systematic approach to identify & eliminate the

process wastages through continuous improvement (Kumar and Sampath, 2012).Lean is

the Pull based Manufacturing approach, also known as the Toyota Production system,

which was established in the year 1970’s by Taichiohno and shigeoshingo at Toyota

Motor Company. This results in an integrated and efficient manufacturing environment

(Abdulmalek and Rajgopal, 2007)

Lean has been developed and defined as elimination of waste (Denis, 2011). In Lean

Philosophy, “value” is determined by customer point of view. It refers what the

customer is willing to pay for and, what creates value for the end product (Hahrukh and

Jin, 2012). Lean philosophy is always thinking on customer point of view. Major

objective of Value stream map is identifying value added and non-value added activity

of manufacturing a product from its raw material. With this understanding one can find

out ways to minimize the non-value added activity towards the value chain instead of

replacing the useful value added activity.

Most popular way in lean manufacturing tool to reduce non added value in production

line is Standardization. Masaki Imai in his seminal work says he learned that there can

be no kaizen (continuous improvement) without standardization. Standardization is

actually the starting point for continuous improvement (Jeffrey and David, 2006).The

establishment of standardized processes and procedures is the greatest key to creating

consistent performance. It is only when the process is stable that you can begin the

creative progression of continuous improvement.

According to Pettersen (2009), Vijitha Ratnayake and Gamini Lanarolles aid that high

Work in Progress (WIP) levels and its fluctuation are inherent characteristics in a non-

lean environment. Further they observed that the hypothesis testing on the WIP of 42

garment manufacturing lines manufacturing various types of garments shows this is a

common problem across the industry.

Abdulmalekand Rajgopal (2007) said that before implanting lean manufacturing process

there were huge amount of waste represented by the excessive inventory and large

production lead time. He accomplished that the link between the current state map (after

10

implementing lean tools) and the unveiling of waste was very clear. The procedure

demonstrated a universally applicable method to view the value stream and identify area

of large inventories long lead time and lack of information coordination. Value stream

mapping is a valuable tool in any lean manufacturing effort and can unveil all the

wastes in the entire value stream and not just portions of it.

2.2 Theories related to area of study

2.2.1 Lean Manufacturing System

Lean Manufacturing is an operational strategy oriented toward achieving the shortest

possible cycle time by eliminating waste (Jeffrey and David, 2006). It is derived from

the Toyota Production System and its key thrust is to increase the value-added work by

eliminating waste and reducing incidental work. The technique often decreases the time

between a customer order and shipment, and it is designed to radically improve

profitability, customer satisfaction, throughput time, and employee morale. Then Lean

manufacturing derive continuous improvement in manufacturing process by eliminating

waste.

2.2.1.1 History of lean production

Lean thinking and lean production became popular in western industry as a means to

improve productivity. One reason for this was that the Japanese industries, during the

last decades, have far exceeded the western industries in productivity and quality

(Womack and Jones, 2003).

After the Second World War, Toyota and other Japanese organizations suffered from

the effects of the war. Resources were strained and Japan needed to rebuild its

manufacturing industry. Many of the Japanese companies turned to western industries to

gain ideas and inspiration on how to build up their industry (Womack and Jones, 2003).

In the United States, the call was for mass production to satisfy the needs of a large

populace that saved and sacrificed during the war. The Japanese market on the other

hand was much smaller and investment capital was scarce. With smaller production

volumes per part and limited resources, there was a need for developing a

manufacturing system that was flexible and used less resource the solution was to

develop a lean production system, and the production genius TaiichiOhno at Toyota is

said to be the man behind the development of lean production (Hassanzadeh, 2008).

11

In the beginning of 1980, the western automotive industry began to realize that the

Japanese way of manufacturing vehicles far exceeded the methods that were used in the

European and American industries. Japanese companies achieved higher productivity

and better quality using fewer resources (Hassanzadeh, 2008).

2.2.1.2 Wastes in Lean Manufacturing

Lean manufacturing system has identified Seven Wastes in manufacturing process.

These wastes are, Called as “TIMWOOD” (LeanMan, 2012)

1. Transportation or conveyance.

2. Inventory

3. Motion

4. Waiting

5. over production

6. Over processing

7. Defects

2.2.1.2.1 Over production

Over production is producing more than the customer demand. Over production is

highly costly to a manufacturing plant because it obstructs the smooth flow of material

and degrades the quality and productivity. It can be defined as producing more, sooner

or faster than what is required by the next process

2.2.1.2.2 Defect waste

The lack of quality is another source of waste. Defects can be either production defects

or service errors. Repairing of rework, replacement of production and inspection means

wasteful handling time and effort.

12

2.2.1.2.3 Unnecessary inventory

Any type of inventory (raw material or in process or finish goods) does not add value to

the product and it should be eliminated or reduced. Excess inventory results in longer

lead times, obsolescence damaged goods, transportation and storage costs, and delay.

The positive points for reducing inventory are listed below:

• Reducing tied up capital

• Shortening through-put time

• Lessening risk of obsolete material

• Smoothing production flow

• Lowering space rental costs

• Decreasing the time needed to detect quality problems

2.2.1.2.4 Unnecessary processing

Incorrectly designed process could also be a source of waste. Activity in an

organizational process can be divided into 3 categories: value adding, non-value adding

but necessary and non-adding value but unnecessary. Lean production emphasizes

reducing this non-adding value but unnecessary process. This is due to poor layout, poor

tools and poor product design, caution unnecessary motion and producing defects.

2.2.1.2.5 Unnecessary transportation between work sites

Transportation waste includes all types of unnecessary transportation of material, work

in process and components, which do not add value to the products. Most unnecessary

transportation is due to the inappropriate layout of a factory.

2.2.1.2.6 Waiting

Waiting may be due to different reasons such as waiting for correct information,

products waiting to be processed, machines waiting for their operators and machines

waiting for material to arrive. Value Stream Mapping is a tool for identifying the

product flow through the factory (Hahrukh and Jin, 2012).Processing time, throughput

times, set-up times, inventory levels, etc., are mapped with standardized symbols.

13

2.2.1.2.7 Unnecessary motion in the work place

Motion consumes time and energy. Due to the poor layout, poor work flow and poor

methods generate unnecessary motion as the non-added value in unnecessary.

2.2.1.3 Fourteen Principles of the Toyota way.

The authors of Toyota Way Field Book (Jeffrey and David, 2006) have provided the

framework for analysis methodology.

1. Base your management decisions on a long term philosophy, even at the expense

of short term financial goals.

2. Create continuous process flow to bring problems to the surface.

3. Use “Pull” systems to avoid overproduction.

4. Level out the workload (Heijunka). (Work like the tortoise not the hare.)

5. Build a culture of stopping to fix problems, to get quality right the first time.

6. Standardized tasks are the foundation for continuous improvement and

employee empowerment.

7. Use Visual control so no problems are hidden.

8. Use only reliable, thoroughly tested technology that serves your people and

processes.

9. Grow leaders who thoroughly understand the work, live the philosophy, and

teach it to others.

10. Develop exceptional people and teams who follow your company’s philosophy.

11. Respect your extended network of partners and suppliers by challenging them

and helping them improve.

12. Go and see for yourself to thoroughly understand the situation.

13. Make decisions slowly by consensus, thoroughly considering all options;

implement decisions rapidly.

14. Become a learning organization through a relentless reflection (Hansei) and

continuous improvement (kaizen)

14

2.2.1.4 Lean Manufacturing Tools and Technique

2.2.1.4.1 Just in time

Closely associated with lean manufacturing is the principle of just in time (JIT), since it

is a management idea that attempt to eliminate source of manufacturing waste by

producing the right part in the right place at the right time (Stratergos International,

2012)

JIT utilize what is known as a pull system. Customer demand, which is the generator of

the order, sends the first signal to production. As a result, the products gets pulled out of

the assemble process. The final assembly line goes to the preceding process and pulls or

withdraws the necessary parts in the necessary quantity at the necessary time

(Abdulmalek and Rajgopal, 2007).

A Kanban is used to manage these shipments. Kanban is a visual information system

that is used to control the number of parts to be produced in every process. By utilizing

Kanban system under JIT, smaller lot sizes and huge inventory reductions can be

achieved. So inventories are kept to a minimum and the lean manufacturing principles

are followed to inventory as source of waste. Therefore overproduction waste also can

be reduced.

2.2.1.4.2 Standardization of work

A precise description of each work activity specifying cycle time, “takt” time, the work

sequence of specific tasks, and the minimum inventory of parts on hand needed to

conduct the activity. Often standardized work is thought to be mainly a set of

instructions for the operator. In reality one of the most powerful uses of standardized

work is for analyzing and understanding waste in the operation. The documented work

procedure will be a visual representation of the waste (opportunity for improvement)

that exists (Jeffrey and David, 2006). This derive more smooth production floor

supporting JIT and effective output. Figure 2.1 shows the relationship between work

standardization and other standards.

15

Relationship between standardized work and other standards

Figure 2-. The relationship between work standardization and other standards (Source Toyota way field book

(Jeffrey and David, 2006))

A tool that is used to standardize work is called “takt” time. Takt (German for rhythm or

beat) time refers to how often a part should be produced in a product family based on

the actual demand.

Takt Time=(𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑤𝑜𝑟𝑘 𝑡𝑖𝑚𝑒 𝑝𝑒𝑟 𝑑𝑎𝑦)

(𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑑𝑒𝑚𝑎𝑛𝑑 𝑝𝑒𝑟 𝑑𝑎𝑦) (Abdulmalekand Rajgopal, 2007).

2.2.1.4.3 Total productive maintain

There are main components of the total productive maintenance program: preventive

maintenance, corrective maintenance and maintenance prevention,

Corrective maintenance deal with decisions such as whether to fix or Purchase machines

that maximize productive potential (Jeffrey and David, 2006). If a machine is always

down and its components are always breaking down then it is better to replace those

parts with newer ones. As a result the machine will last longer and its uptime will be

higher.

2.2.1.5 Theoretical frame work on value stream map

Value stream map is one of the most powerful Lean tools for an organization waiting to

plan, and improve on its lean journey. Value stream improvement, sometimes called

“flow level kaizen,” is the best tool for identifying and planning opportunities for

process kaizen (Silva, 2012).

16

Current state value stream mapping allows an organization to identify waste and sources

of waste. The current state provides a baseline from which people can work to create a

lean future state.

Future state mapping is a process by which organizations identify a lean future

condition. This future condition includes things like continuous flow manufacturing

wherever possible, supermarkets or FIFO lanes (depending on the degree to which the

products are custom) where continuous flow is not possible, and level production (Silva,

2012)

By practicing value stream map, the organization can streamline its business process

and achieve the goal of eliminating wastes remarkably.

There are four stages of implementing the value stream map technique.

1. Identify the product or family of products to be mapped

2. Draw the current stage of the process.(current VSM)

3. Identify where the improvements can be done to eliminate waste.

4. Draw and implement the future value stream map.

2.2.1.5.1 Value stream map symbol

Following table 2.1 summarizes the symbols commonly used in value stream mapping.

Table 2-. Summary of symbols commonly used in value stream mapping

Symbol Description

Outside source

Inventory

Truck Shipment

Supplier

24rall

17

2

(Source: International Journal of Lean Thinking Volume 3, Issue 1 (June 2012))

2.2.1.6 Theoretical frame work on Statics tolls

2.2.1.6.1 Hypothesis testing

A statistical hypothesis is an assumption about a population variable. This assumption

may or may not be true (www.sagepub.com). The best way to determine whether a

statistical hypothesis is true would be to examine the entire population. Since that is

often impractical, researchers typically examine a random sample from the population.

If sample data are consistent with the statistical hypothesis, the hypothesis is accepted;

if not, it is rejected.

There are two types of statistical hypotheses.

Null hypothesis. The null hypothesis, denoted by H0, is usually the hypothesis

that sample observations result purely from chance. H0 is a simple hypothesis

associated with a contradiction to a theory one would like to prove.

Alternative hypothesis. The alternative hypothesis, denoted by H1 or Ha, is the

hypothesis that sample observations are influenced by some non-random cause.

Alternative hypothesis (often composite) associated with a theory one would like

to prove.

p-value

The probability, assuming the null hypothesis is true, of observing a result at

least as extreme as the test statistic

Process name

# of Operator

Cycle Time 1Pc

Batch size

Process Time

Scrap/Rework% -

C/O Time -

Uptime%

First Pass Yield% 100

Manufacturing process data box

1

1=Process lead time

2=Process value added time

18

T-Value

A statistical examination of two population means. A two-sample t-test

examines whether two samples are different and is commonly used when the

variances of two normal distributions are unknown and when an experiment uses

a small sample size.

Size / Significance level of a test (α)

For simple hypotheses, this is the test's probability of incorrectly rejecting the

null hypothesis. The false positive rate for composite hypotheses this is the

upper bound of the probability of rejecting the null hypothesis over all cases

covered by the null hypothesis. The complement of the false positive rate, (1 −

α), is termed specificity in biostatistics.

2.2.1.6.2 Multiple regression

Multiple regression analysis is a powerful technique used for predicting the unknown

value of a variable from the known value of two or more variables- also called the

predictors (Nicola, Richard, and Rosemary)

In general, the multiple regression equation of Y on X1, X2, …,Xk is given by:

𝑌 = 𝑏0 + 𝑏1 𝑋1 + 𝑏2 𝑋2 + … … … … … … … … + 𝑏𝑘 𝑋𝑘

Here b0 is the intercept and b1, b2, b3, …,bk are analogous to the slope in linear

regression equation and are also called regression coefficients. They can be interpreted

the same way as slope

Once a multiple regression equation has been constructed, one can check how good it is

(in terms of predictive ability) by examining the coefficient of determination (R2). R2

always lies between 0 and 1.

R2 - coefficient of determination

All software provides it whenever regression procedure is run. The closer R2 is to 1, the

better is the model and its prediction. When carrying out multiple regression following

assumptions are considered.

Dependent variable is normal

Residual are random

Ro relationship among independent variables

19

3 Research Questions/ Problems

3.1 Research Questions/ Problems

Delivering high quality garments at low cost in shorter lead times are the major

challenges faced by the apparel manufacturers. Most of the apparel manufacturers are

trying to achieve these challenges successfully.

To optimize the lead time company had to go through various ways on finding the

factors affected to the company efficiency.

Below fish bone diagram shows the factors identified in the production flow.

Figure 3-. Fish bone diagram of the production floor

To face globale challenge company must reduce the problems in the company which

affect to the company affanciency. In order to face this global challenge, most of the

local apparel manufacturers have adopted different strategies. The recent adoption is

Lean Manufacturing to achieve low cost, short lead times and improved quality.

Application of lean techniques in the production floor has shown apparent effectiveness

over the production. Nevertheless, no investigation was carried out at CKT to evaluate

the effectiveness of these applications.

20

3.2 Rational to select research question

In this research, tries to investigate the applicability of one of the most important Lean

Manufacturing tool called “Value Stream Mapping” in Sri Lankan apparel sector. Based

on the above explanation a broader research problem can be stated as: “How can VSM

are effectively used to improve the performance of apparel industry?”

Then research went through investigating the applicability of the lean manufacturing by

analyzing the identified factors which affected to the factory efficiency.

Since the research is limit for six months above research problem selected to investigate

only for the production flow.

3.3 Potential benefits to the organization by solving the question

The research went through the value stream map and the testing performance of the

company after lean manufacturing. VSM is an easy to understand tool and also a

graphical presentation, therefore the findings are easily interpreted and effectively

presented. Following section briefs the potential of benefits of VSM.

Value Stream Mapping helps identify waste

One of the greatest benefits of value stream mapping is that you can easily identify

where the waste is in your business process. Anything that does not add value to the

end-customer is waste. The value stream map can help identify the most common types

of waste, also known as the seven deadly wastes. These are Overproduction, Waiting,

Transport, Extra processing, Inventory, Motion and Defects. None of these add value to

the end-customer, and the value stream map helps you see these types of waste clearly.

So waste reduction can be improved more efficiency it will be help to go for a lean lead

time. And identifying the places more inventory handling in the process flow, they can

be reduced as the lean concept so company investment can be reduced.

Then testing performance of the company before and after lean manufacturing, the

company will be able to get an idea of the applicability of lean. Then the factors that

need more consideration can be identified and it will help to improve company

performance.

21

4 Research Approach and Methodology

4.1 Research design with a rational

According to the understandings of the literature review readings the production and

cutting departments were selected as most suitable departments. Further, the selected

data collection strategy was based on judgmental sampling techniques for this study.

Then research path was designed to identify the lean manufacturing using main lean

tool of value stream map and evaluate its progress using statistical tools of multiple

regression and hypothesis testing.

4.2 Data collection strategy with rationale

When data are concerned, they can be obtained in three basic methods. They are,

by accessing data in the company’s ANDON tracking system, a tool provided by

Toyota Ways (Jeffrey K. L. & David M., 2006):

The ANDON tracking system contains data captured by the production line

such as line efficiency per hours, machine brake down, needle break down,

quality issues, cut delay, thread issues etc. This system was launched as a

requirement of lean manufacturing system and it is being monitored by the

lean manufacturing department.

by gathering data from relevant documented records:

During the manufacturing process, the company documented every record

of all operational data every day, machine wise and department wise

separately. Therefore, some data were gathered from the documents.

by gathering data from quality tests:

Sometimes, data are collected by doing quality tests. By these test methods,

very accurate data can be gathered for variables

by collecting data from directly interview:

To have a basic idea of the past situation of the company managers were

interviewed as well as non-executives were interviewed to know how their

jobs become easier through the lean manufacturing.

22

4.3 Details of Design & Development of Data Collection Tools

This study manly focuses only on some major variables of garment production. To

identify factors affecting the production output of the company, a site tour was

conducted in order to get a clear idea about the existing products and the overall process

of the company. And garment style selected was the one which touches most number of

operations and has highest product volume in units to map into the VSM. Following

table 4-1 shows model work sheet for collecting data.

Table 4-. Model of the work sheet for collecting data

band date EFF. #

absent

Machine

break

Needle

break

Defects Thread

delay

Hanger

delay

Other

delay

6 20/8 0.4893 3 0 0 0 0 0 0

3 20/8 1.003 0 0 0 0 0 0 0

4.4 Data Analysis Strategies and Rationale

There are different ways to analyze different variables. Firstly analyzing the production

output against with some identified factors, and then most powerful factors, that

effecting production output were identified. Then, the situation before implementing the

lean manufacturing in to the organization was studied by interviewing the manager of

Stores, Cutting, Production, Work Study and Lean manufacturing departments. Then

considering the flow chart of every department and past data mapped the acceptable

average value stream map before implanting the lean manufacturing system. After

selecting garment style mapped the current state value stream map.

Thereafter the lean manufacturing progress was evaluated by analyzing information

gathered for years 2010and 2012.

MINITAB 14 software was used for the statistical analyzing considering its accuracy of

data analyzing.

4.5 Statistical Tests and Methods of Applications and Limitations

This study is mainly based on lean manufacturing tools and statistical methods. As

described in sections 2.2.1.6.1 and 2.2.1.6.2, the Statistical hypothesis testing and

multiple regression were used evaluate lean manufacturing system.

23

4.5.1 Limitations

As mentioned above, this research focused only on one style of the CKT (PVT) LTD.

But in reality there are many different garment styles. It was not possible to study the

entire collection of styles due to the time limitation and also some short quantity

garment styles had no sufficient historical data for canalization. Therefore the only

acceptable style selected was the one which contained highest production quantity and

went through the maximum number of manufacturing lines. Some past data could not be

collected due to the access limitations to some documents. Furthermore, the data on

days in which no considerable quantity manufactured also had to be excluded from the

study.

24

5 Data Collection and Analysis

5.1 Details of Data Collection

In this section, the data collection consists of data derived from three different sources

as explained in Chapter 04. They are, data from ANDON tracking system, relevant

documents and data gathered from quality tests. When collecting data, long term styles

were mainly considered because of the size of the data samples which affect the

accuracy of the data canalization and results. Some data useful for the VSM could not

be collected due to the reasons like that the selected style not touch the particular line or

the unavailability of a good source to collect data. Further explanation of data is

presented in the remaining sections.

5.2 Details of responses

Company efficiency and affected factors on the efficiency were considered as responses

during the data analysis. To value stream map, the lead time, change over time, batch

size, WIP and first pass yield rate were considered as attributes.

5.3 Details of data analysis

During the data analysis of this study, several major steps were carried out to enhance

the overall efficiency of the study. The steps of the analysis are as follows

Multiple regression was used to identify the factors, affecting to the

production efficiency.

Identified the problem through the fish bone diagram

Mapped the value stream map describing production floor before and

after lean implementation.

Evaluate progress of lean manufacturing for its effecting factors.

5.3.1 Value Stream Map

Value stream map was created according to the data collected by the lean manufacturing

department and floor chart of each department that directly involved to the production

process. The general status of the production department, cutting department, stores and

packing department before Lean implementation were considered first. Also their past

experiences and researchers’ observations were also taken into considerations.

25

Figure 5- value stream map before lean implementation

5.3.1.1 Value stream map before lean

26

Figure 5-1 value stream map before lean implementation

27

Figure 5-2 value stream map after lean implementation

1.1.1.1 Value stream map after lean implementation

28

Figure 5- value stream map after lean implementation

29

Looking at the value stream map common causes before lean implementation were

identified by observing the values each attribute has achieved. The root causes of

gaining such a long lead time and huge WIP will be discussed in next chapter. Then the

value stream map after lean implementation was drawn. Pull inventory control system

concept of Lean is applied here in contrast to the previous system.

5.3.2 Factors affecting to the efficiency

Multiple regression analysis as a powerful technique used for predicting the unknown

value of a variable from the known value of two or more variables was used to find

factors affecting efficiency. Multiple regressions is also a powerful statistical tolls for

identifying the relationship of unknown variables with known variable. Statistical

calculations in deriving the following regression equation are given in Appendix 2.And

it shows how to prove the assumptions consider in section 2.2.1.6.2.

The regression equation is for efficiency per hour

𝑬𝑭𝑭. = 𝟔𝟓. 𝟏 − 𝟏. 𝟎𝟕 (𝑴𝒂𝒄𝒉𝒊𝒏𝒆 𝑩𝒓𝒆𝒂𝒌 𝒅𝒐𝒘𝒏 𝒕𝒊𝒎𝒆)

− 𝟏. 𝟖𝟐 (𝑵𝒆𝒆𝒅𝒍𝒆 𝑩𝒓𝒆𝒂𝒌𝒂𝒈𝒆𝒔 𝒕𝒊𝒎𝒆) + 𝟏. 𝟓𝟖 (𝑫𝒆𝒇𝒆𝒄𝒕𝒔)

S = 34.6585 R-Sq = 61.8% R-Sq (adj) = 59.2%

S = the square root of the mean square error

R-sq = estimated R-square

R-sq (adj) = estimated adjusted R-square

Multiple regressions was calculated using the data of selected band and data was

collected per hour only for 5 days (Appendix 1). Time was measured for the nearest

minute in machine breakdowns and needle breakages and inventory delay. Cut delay,

thread delay, packing inventory (hangers, polybags, and tags) etc. are included in

inventory delay category. Only end line defects were considered as the defects.

According to P value checking of the correlation coefficient and significant checking of

ANOVA table (Appendix 2) absenteeism and inventory delay was removed for the

model. So it was redone excluding those two factors and model was found as above.

30

5.3.3 Evaluate performances before and after implementing the Lean

manufacturing

After identifying the factors affecting the factory efficiency, a situation analysis was

carried out for before and after lean manufacturing implementation based on them here,

some factors such as absentees and inventory delay could not be analyzed due to the

unavailability of past data and strong relationship with the efficiency.

Data were analyzed 95% significant level using paired T test.

H0 :𝜇1 ≤ 𝜇2

H1 :𝜇1 < 𝜇2

5.3.4 Hypothesis testing of Factory efficiency

Table 5.1 shows the paired T test result of efficiency testing performances before and

after Lean. Test was carried out under the 95% confidence level. Sections 5.3.5 to 5.3.7

present the performances of the selected factors concerning the efficiency.

Paired T-Test for efficiency of before and after

𝜇1=mean of the Factory efficiency before implementing the lean manufacturing

𝜇2=mean of the Factory efficiency after implementing the lean manufacturing

Table 5-.Test for efficiency of before and after

Efficiency N Mean StDev SE Mean

Before 15 35.8947 6.0946 1.5736

After 15 38.6960 6.6047 1.7053

Difference 15 -2.80133 4.76943 1.23146

95% upper bound for mean difference: -0.63235

T-Test of mean difference=0(vs. <0): T-Value = -2.27 P-Value = 0.020

P-value=0.020< (0.05)

So reject H0

5.3.5 Hypothesis testing of Machine Breakdown Time

Table 5.2 shows the paired T test result of machine break down testing performances

before and after Lean. This factor was selected since it shows a relationship with the

factory efficiency according to the regression line.

31

Paired T-Test for machine Breakdown Time

. H0 :𝜇1 ≤ 𝜇2

H1 :𝜇1 > 𝜇2

𝜇1=mean of the machine break down before implementing the lean manufacturing

𝜇2=mean of the machine break down after implementing the lean manufacturing

Table 5-.Test for machine Breakdown Time

Machine Break down N Mean StDev SE Mean

Before 16 9.09250 4.29493 1.07373

After 16 5.29875 2.16823 0.54206

Difference 16 3.79375 2.47048 0.61762

95% lower bound for mean difference: 0.44244

T-Test of mean difference = 0 (vs. > 0): T-Value = 2.53 P-Value = 0.012

P-Value = 0.0012 <(0.05)

So reject H0

There is no enough evidence to reject the H1.

5.3.6 Hypothesis testing for Needle breakages

The hypothesis testing results of needle break down are shown in Table 5.3. This test

was analyzed under the 95% significant level.

Paired T-Test for Needle -Before & After

H0 :𝜇1 ≤ 𝜇2

H1 :𝜇1 > 𝜇2

𝜇1=mean of the needle break down before implementing the lean manufacturing

𝜇2=mean of the needle break down after implementing the lean manufacturing

Table 5-T-Test for Needle -Before & After

Needle Break down N Mean StDev SE Mean

Before 16 68.5625 30.9773 7.7443

After 16 54.1250 19.9595 4.9899

Difference 16 14.4375 19.5515 4.8879

32

95% lower bound for mean difference: 5.8688

T-Test of mean difference = 0 (vs.> 0) T-Value = 2.95 P-Value = 0.005

P-Value = 0.005 <(0.05)

So reject H0

There is no enough evidence to reject the H1.

5.3.7 Hypothesis testing for Defects

The hypothesis testing results of defects of selected manufacturing lines are shown in

Table 5.3. This test was analyzed under the 95% significant level.

Paired T-Test for defects before and after

H0 :𝜇1 ≤ 𝜇2

H1 :𝜇1 > 𝜇2

𝜇1=mean of the number of the defects before implementing the lean manufacturing

𝜇2=mean of the number of the defects after implementing the lean manufacturing

Table 5-.Test for defects before and after

Defects N Mean StDev SE Mean

Before 16 9.13750 4.33193 1.08298

After 16 5.29875 2.16823 0.54206

Difference 16 3.83875 2.50375 0.62594

95% lower bound for mean difference: 2.74145

T-Test of mean difference = 0 (vs.> 0): T-Value = 6.13 P-Value = 0.000

P-Value = 0.005 < (0.05)

So reject H0

There is no enough evidence to reject the H1.

Inventory delay time & absentees couldn’t be analyzed because there is no acceptable

source to collect the past data.

33

5.4 Results

5.4.1 Value stream map

Results of value stream map of before implementing the lean manufacturing system are

given below. The data analysis and the results discussion are presented in the next

Chapter 6.

Tackt time - 0.52 min

Lead time - 41997min (30 days)

Tot. Lead time - 60 days

VA time - 27.18min

VA ratio - 0.06415%

Results of value stream map of before implementing the lean manufacturing system

Tackt time - 0.48 min

Lead time - 12052min (8.3 days)

Tot. Lead time - 38.3 days

VA time - 27.21min

VA ratio - 0.2257%

Result of the Hypothesis Testing

According to the hypothesis testing below results were found

Mean of the Factory efficiency before implementing the lean manufacturing is

less than the mean of the Factory efficiency after implementing the lean

manufacturing.

Mean of the machine break down before implementing the lean manufacturing

is more mean of the machine break down after implementing the lean

manufacturing.

Mean of the needle break down before implementing the lean manufacturing is

more than after implementing the lean manufacturing.

Mean of the number of the defects before implementing the lean manufacturing

is higher than that of after the implementation.

Efficiency has a negative relationship with the all the other factors of concern.

34

6 Identification of causes and alternative solutions

6.1 Result Interpretation

In the Chapter 5, the data collection, analysis and results have been discussed. There,

the results have been theoretically discussed and in this Chapter the results will be

interpreted as in each case’s practical scenarios.

6.1.1 Result on value stream map

Looking at the map of the state before the lean manufacturing (figure 5.1), several

common causes were identified:

(a) Large inventories

(b) The difference between the total production lead-time 41997min (30 days)

and the value added time 27.18min which is under 1% of the total time

consumed,

(c) Each process producing to its own schedule.

In order to reduce the waste and improve the value adding portion following main

opportunities were identified.

There were excess inventory between inspections and relaxing, sewing process

contain excess inventory. Also, after the sewing process all the stores contained

excess inventory. So many places in the packing department contain different

types of styles. It create desultorily place in the process.

Then the current state VSM (after Lean implementation) is drawn by progressively

eliminating waste in the processes. It applies pull inventory control system in contrast to

the previous system shown in figure5.2. Here the lead time has been reduced

remarkably from 41997min to 12052minutes. Therefore the value added ratio has

increased from 0.06415%- 0.2257%. Also there is reduction in work-in-progress (WIP)

inventory. WIP has been controlled into 3 pieces switching the sewing line. In fact WIP

could not be controlled in to exactly 3 pieces every time and every place in the process

but continuing low WIP creates an orderly work flow.

35

6.1.2 Result on multiple regression

Multiple regression discussed under the subtopic 5.3.2was used to identify the factors

affecting the efficiency. According to the regression equation R-Sq and R-Sq (adj) are

respectively 61.8% & 59.2%. This is an acceptable for the practical data set.

According to the regression equation in section 5.3.2, there are three factors affecting on

efficiency. A strong negative relationship between efficiency vs. Machine Break down

time and Needle Breakages time is observed. So increase in the values of these causes a

decrease in efficiency of the factory. Furthermore, defects also affected on the

efficiency

6.1.3 Result on hypothesis testing

6.1.3.1 Compare the efficiency before and after lean implementation

Efficiency before and after lean manufacturing system were tested on 95% confidence

level (5.3.3). There were -0.63235 mean differences between before and after

implementing lean manufacturing. This can be interpreted as the Lean implementation

has effected positively over the efficiency of the selected manufacturing lines.

In hypothesis testing P-value=0.020 < (0.05). So reject H0. Its mean is𝜇1 < 𝜇2. So we

can conclude that the mean of the factory efficiency before implementing the lean

manufacturing less than the mean of the factory efficiency after implementing the lean

manufacturing. This statistical analysis justifies the above observation hypothetically.

Therefore we can strongly conclude that the implementation of Lean has positively

affected the two manufacturing processes.

6.1.3.2 Compare the Machine Breakdown Time before and after lean implementation

Comparing the machine break down time before and after lean manufacturing under the

95% confidence level hypothesis result was found as P-Value = 0.012 < (0.05). So

accept H1as its mean after the lean manufacturing machine breakdown has reduced than

the before situation.

Figure 6.1 shows the scatter plot of the efficiency vs. machine break down. A negative

relationship between efficiency vs. machine break down is clearly visible in the graph.

36

Figure 6- Scatterplot of efficiency vs. machine break down

So lean implementing has positively effected on the company by reducing the machine

break down. According to the identification of the multiple regressions above scatter

plot decreasing matching break down cause to the increasing of the factory efficiency.

As well this benefit reduces the cost of the company.

6.1.3.3 Compare the Needle Breakdown Time before and after lean implementation

Needle break down, before and after the lean manufacturing was tested 95% confidence

level (5.3.4). There were 5.8688 mean differences before and after implementing the

lean manufacturing. In the hypothesis testing P-value = 0.005 < (0.05). So H0 is

accepted here. Its mean is𝜇1 < 𝜇2. So we can conclude that the mean of the needle

break down before implementing the lean manufacturing is more than the mean needle

break down after implementing the lean manufacturing.

Figure 6-Scatterplot of efficiency vs. needle breakage

37

Above figure 6.2 is drown for identify the relationship needle break down and

efficiency in graphically.

So according to the regression line and scatter plot, reducing the needle break down lean

manufacturing has positively affected to the company efficiency. Because scatter plot

show the needle break down inversely proportional on factory efficiency

6.1.3.4 Compare the number of defects before and after lean implementation

Testing the defects before and after lean manufacturing under the 95% confidence level

(5.3.5) hypothesis result were found as P value = 0.000. Since P value < (0.05) reject H0

and accept H1. So we can conclude that mean of the number of the defects before

implementing the lean manufacturing is more than mean of the number of the defects

after implementing the lean manufacturing.

Figure 6- Scatterplot of efficiency vs. inline defects

Scatter plot defects vs. efficiency figure 6.5 shows there is strong negative relationship

among them. So after implementing lean manufacturing company has experienced

benefit of it by reducing defects and improving the efficiency.

6.2 Causes of the Problem

This section analyses the major causes experienced by the manufacturing process before

the lean manufacturing implemented by interpreting the results analysis given in the

previous sections of this chapter.

38

Before implementing Lean, there was a huge WIP in the factory working flow and huge

lead time in the factory. Due to the overflow of WIP, there was no continuous work

flow. Some activities of the manufacturing process and their respective work places in

the manufacturing line and also their respective departments had no coordination among

them (i.e.: among Cutting department and Production department, among Production

department and Packing department). Manufacturing lines and departments were having

their own schedules which had no particular relevance to the connecting lines and

departments of the actual manufacturing process. This lack of coordination had resulted

in an increase in the change over time also.

When WIP is increased operators responsible of executing these activities were forced

to do the same work over and over may have caused exhaustion and workers may have

felt fed up of what they do. This could have lessen the productivity level and efficiency

of the human resource (research were not extended to that area due to the time

constraints). The disadvantages of high levels of WIP are numerous, and many of the

disadvantages of high WIP levels that are difficult to economically evaluate are not

being able to respond to demand changes quickly and the potential build a considerable

quantity of poor equality stock before realizing that there is a quality problem. To help

control inventory within production and manufacturing facilities. Further, the high WIP

causes to handle with high inventory thereby the company needing to invest very high

capital to handle large inventory.

There were overall 58 processes for the selected style, out of which only 10 were value

adding processes. All other activities involved either inspection, or stock keeping, or

transportation. As identified by the hypothesis testing of the factors affecting to the

efficiency of the factory, machine breaker down, needle break down and defects can be

considered as the major factors directly effecting the efficiency of production. Before

implementing Lean in to the company, above three factors were in an increase causing a

decrease in factory efficiency while adding additional cost in to the manufacturing

process.

Earlier there were no good method for maintaining the machine and factory. It caused

an increase in machine and needle break down time. Since there was no total productive

maintaining method, nobody could assure to maintain the plant or equipment in good

39

condition without interfering with the daily process. It also was decreasing the

efficiency while adding extra cost to the company.

Defects also were higher before implementing the lean manufacturing. Poor quality

control tools, poor machine condition and low awareness of machine operators may also

have caused to increase the defects. Since there was no good method to correct the

defects, defects had accumulated during the manufacturing process. Indirectly it caused

to absenteeism of the machine operators.

6.3 List of Alternative Solutions

According to the issues presented in the previous section, alternative solutions are listed

below. Lean manufacturing tools were considered in explaining the solutions. Based on

the result given in Chapter 5, lean manufacturing can be identifying as a tool that can

positively effect on factory efficiency.

6.3.1 Solutions to the issues identified by analyzing section 6.2

Minimize Transportation Time

To minimize transportation time it is needed to re-layout the process flow as

respectively manufacturing process is flow.

Minimize Excess inventory

Before Lean was implemented, the outsourced activities were not done regularly.

Outsourcing was done in large lot vise for the entire production batch. Therefore, the

embellishments outsource lot size needed to be reduced. Also, coordination with the

embroidery/printing plants is necessary to receive them as the production lines need it

reducing their inventory up to 2 days. Starting one piece flow manufacturing (single

operator working on a single item at a given time rather than working on a batch) in

Sewing department and arranging shipment weekly basic to reduce to finish good

inventory would help to reduce WIP of manufacturing process. By reducing the fabric

inventory by having proper fabric in date, company can reduce excess inventory in the

stores.

Reduce Waiting

Majority of the waiting time was spent at sewing department and at Cutting department.

So to reduce that issue, Cutting department should coordinate their schedule with the

40

Sewing departments’ schedules to provide garments cut before start sewing at a constant

rate. It should better be just in time (JIT) otherwise it will increase WIP artificially.

Minimize Overproduction

Counting errors cause over production. If stores could supply only the necessary

amounts of fabric and accessories to cutting and sewing departments, synchronization

between the two departments can be easily achieved.

Reduce Over Processing

Over processing at sewing lines refers to the non-value added activities involved in

preparing the fabric before it is sent to sewing operators (i.e.: tagging). This causes the

sewing operators spending extra time and effort removing them. At Quality department

also, such activities could be identified like repacking after inspection as the workers

were not properly trained. These over processing could be reduced by giving proper

training thereby reasonably reducing the unnecessary inspection points at Quality

department.

Reduce Defects

Number of defects of an end product can be reduced by rectifying many activities

involved during the production process.

To reduce the fabric inspection time,

Get testing reports from fabric suppliers

Need batch wise test reports from supplier

Get 100% shrinkage report from supplier

Supply good quality fabric and trims to reduce inspection lead time.

Send a person to the fabric mill to inspect fabric before in-house.

Proper supervision can control the in line defects of the sewing line.

6.4 Implemented Lean activities involved in achieving the solutions given

in section 6.3

After implementing Lean in the factory several lean tools were added in to the

production and manufacturing process. As analyzing results show about the situation

after Lean, company performance has been improved. This section discusses how the

41

factory has achieved the expected performance increase by implementing Lean with

respective to lean manufacturing tools.

6.4.1 Total productive maintenance (TPM)

Preventive maintenance was carried out by all employees. Equipment maintenance was

performed on a company wide basis. TPM has five goals.

1. Maximize equipment effectiveness.

2. Develop a system of productive maintenance for the life of the equipment.

3. Involve all departments that plan, design, use or maintain equipment in

implementing TPM.

4. Actively involve all employees.

5. Promote TPM through motivational management.

By preventive maintenance company has reduced cost of maintenance as well increase

the efficiency of the company.

6.4.2 PULL System

A pull system regulates the flow of resources in a manufacturing process by replacing

only what has been consumed and only what is immediately deliverable. As a result, the

business becomes increasingly lean, eliminating excess inventories of raw materials,

work in process, and finished goods. Customer orders drive production schedules based

on actual demand and consumption rather than forecasting.

There are several benefits for a company that implements a Pull System.

1. It standardizes the amount of inventory in the production process.

2. It uses visual controls to activate the replenishment process.

3. It reduces batch or lot sizes.

A Pull System using Kanban can help a business to transition from a batch and queue

process towards becoming a single piece or continuous flow process. A Pull System

will control the amount of inventory throughout the production system, which helps to

focus on building what the customer wants, when they need it. As a result of better

inventory control, all production resources are focused effectively; this will speed up the

process and reduce lead times.

42

CKT has adopted this Lean tool throughout their manufacturing floor.

6.4.3 Standard Work

Sewing operators at CKT are specially trained to carry out their work to imply with

work standardization recommendations. Therefore a constant WIP is successfully

achieved at the sewing lines of CKT. Work standardization has also provided synergy

over the production floor to a visible level.

6.4.4 ANDON

CKT has implemented the necessary hardware indicators of ANDON tool in their work

floor. Visual indicators for machine break down, quality issues and work-to-do queue

over floor. ANDON indicators (visual and audio) are also implemented between the

Cutting and Sewing departments alarming the Cutting department of the fabrics needed

by the Sewing lines.

6.4.5 Just-In-Time (JIT)

CKT now practices JIT throughout their manufacturing process. Raw materials are

requested to the stores at and when they are needed. Also, the finished products are

shipped to the customers at more regular intervals with smaller shipment sizes than

waiting for the full order to be completed. Therefore the warehouse overflow is

minimized and the cost of store keeping is reduced to a reasonable level.

WIP through the production lines are effectively reduced and successfully kept to a

constant level by successful implementation of the other Lean tools within the

manufacturing floor, as described above.

43

7 Discussion and conclusion

In this Chapter, the discussions, recommendations and conclusions on those interpreted

results are presented.

7.1 Limitation of this Research

This research was done to investigation compatibility of lean manufacturing system in

garment sector. This research has been presented systematically right through as

described in prior Chapters. For that, there have been some assumptions and Limitations

taken into account, which are described in the subsequent points.

7.1.1 Data collection limitations

During this research, only one style of CKT (PVT) LTD Maharagama was considered

due to the difficulty of accessing many styles and collecting data during the given six

months period. This limitation was also affected the value stream mapping process

where the focus had to be given only to one manufacturing process style due to the

difficulty of collecting data during a short period.

When identifying the factors affecting on efficiency only six factors were considered.

Among the factors identified by the regression, inventory delay and absentees could not

be analyzed further due to the difficulty of collecting the past data. Therefore only three

factors were considered for further analyzing.

7.1.2 Time

Time was a limited factor for this research as it involved an extensive data collection.

Some avenues of the research had to be eliminated solely due to the time constraint.

More effective feedback could be given to the Sponsor organization, had there been

more time to collect the necessary data.

7.1.3 Company Terms and Regulations

Company terms and regulations restrict access to sensitive data. Due to this reason some

data sources cannot be accessed. The fore such data could not be considered for the

analysis.

44

7.2 Problems Encountered and Alternative Action Taken

The research was based on the data of efficiency and factors affected by them. So, these

data were gathered using the ANDON tracing system of CKT. However the full data

collection could not be accessed due to the rules and regulations of the company.

Further, some data such as inventory delay ware not sufficient for an effective statistical

analysis. Therefore, certain limitations (given in section 7.1) had to be made as an

alternative solution.

CKT is still at the stage of getting-used-to the Lean system. Though the Factory flow is

reasonably clean, it is still not completely free of unwanted and unorganized inventory

in its operation areas. Some good visual controls signs (hour by hour chart, line

identification) are in place yet they are not efficiently working and effectively

interpreted in the work floor. Targets are not still effectively set by interpreting the

visual controls nor is proper abnormality management using Kaizen newspaper

implemented (as recommended by Lean system). Major improvement in visual

management can be made in order to incorporate abnormality management helping to

fix daily and issues and to start process of continuous improvement. But less awareness

of staff prevent to keep this organization discipline.

7.3 Further/Future Research Operation

The study has been conducted for a selected garment style in an organization in the

CKT (PVT) LTD Maharagama. In future, researchers can deploy VSM for different

styles, for several organizations across the apparel industry. It is also possible to

examine the waste elimination level / improvement level over time during different

periods since present study has taken into observations one single time slot. (E.g.

observing waste elimination over several discrete time periods and variation). And it

can be deploy further for the more tools of lean manufacturing and also more factors

that are affected by lean manufacturing tools. The research could also be extended to

investigate the attributes absenteeism and inventory delay which show a relation to the

efficiency yet a proper investigation could not be made.

Investigations can also be extended to evaluate the efficiency based on employee

feedback. No proper investigation is yet conducted to interview the employees

regarding the Lean adaptation.

45

7.4 Discussion and Conclusion

To carry out this research successfully, a Lean manufacturing system adopted factory

was selected. Then data of before and after lean manufacturing implementing collected

for mapping the value stream map. Factors affecting the manufacturing line efficiency

were then identified using multiple regression analysis. Hypothesis tests were carried

out on selected factors identified in the regression analysis to evaluate their influence

over the performance. A value stream map was drawn based on two selected

manufacturing lines. From the result of statistical analysis and VSM, observation was

that the factors selected in the multiple regression analysis have positively affected on

factory efficiency after adopting Lean system.

Lean manufacturing adopting has effected to improve efficiency by identifying factors

affecting such as machine break down, needle break down and defects and decreasing

their influence over efficiency. After Lean implementation, the company lead time and

WIP has reduced. Pull system and JIT concept are important lean tools of lead time

reduction and contain WIP in acceptable amount. Lean has visually controlled

abnormalities and 7waste defects using ANDON lights, work standardization and other

lean tools. TPM concept in preventive maintenance company has reduced cost of

maintenance as well as increased the efficiency of the company.

Lean implantation within the manufacturing floors has raised some issues. Among them

the foremost is the less awareness of employees and staff. Lot of inconveniences and

stagnation in improvement are obvious throughout the manufacturing process thus the

company need to pay serious attention over staff training and awareness.

Initializing Lean manufacturing system implementation needs higher venture capital

investment. Therefore a stepwise approach is advisable for such implementations in

small manufacturing businesses. JIT production and purchase and Pull system are

suitable for initial stages due to their low cost implementation. However according to

the investigations conducted Lean manufacturing adoption has positively affected on

apparel manufacturing lines in CKT (Pvt) Ltd.

46

8 Details of industrial training

8.1 Introduction to training

This internship is the practical part of the industrial training of the University degree

program. The objective of this kind of internship is to obtain practical experience in a

business organization and to have exposure to the industry at large.

The six month training related to this study was obtained at CKT (PVT) LTD,

(Hirdaramani Group) Maharagama. The training was started on the 2st of May 2012 and

ended on 31th of October 2012.

CKT (PVT) LTD Maharagama is main office of the CKT cluster. Under the top level

management of CKTM manage five factory of the CKT cluster of Hirdaramani group.

Marketing department, costing department, merchandizing department, has been

centralized in the CKTM providing their service for the other factory also in CKT

cluster.

My general training covered understanding the entire garment manufacturing process

and after the general training I was referred Work Study department to be trained as a

work-study trainee.

Covered Departments

Sample Room

o Cad/Cam section

Stores

Cutting department

Production department

Quality department

Work Study department

Maintain department

o Automation section

Finishing Room

47

8.2 Details of method & techniques, Tools, and equipment

During the general training period at CKT (PVT) LTD Maharagama I followed training

at its departments for four weeks studying business process and manufacturing process.

For the remaining training period after the general training I was attached to the Work

Study department under the supervision of Work-study Manager.

Sample room

At the sample room I studied planning process of Sample room. First and

foremost thing of the sample room is to estimate the fabric consumption of

buyers’ order(s) and planning how to complete the order to get a profitable

income. How to develop pattern according to the buyer’s requirements were

then studied. How to decrease consumption using TUKACAD software was

also part of the study. At Sample room I worked with planning officers, pattern

makers CAD CAM officers and Quality checkers under the supervision of

Head of the Sample room. Most importantly I was exposed to a lot of technical

words. And I identified new machines and studied where they are used. And I

Studied develop the pattern by using TUKACAD, TUKAMARK software.

Cutting department

In the cutting department I trained how to create a cut plan and to optimizing it

to reduce fabric consumption. I was also trained to handle some cutting

instruments and studied the technical side of spreader machine, Garber laser

cutting machine etc... This department is the main place controlling the WIP.

So I studied to how to maintain WIP from the cutting department and learned

what causes prevent continuing the lean manufacturing flow and how to solve

them.

Production department

Production department training was a good opportunity for me to work with

different types of people. In the production department I was trained to learn

the employee satisfaction and motivation in achieving the target of the

production line. I studied how it is easier to achieve targets and keep defects

rate at an acceptable level by maintaining. Lean manufacturing production

flow.

48

Quality department

Quality department’s role is very necessary to achieve company targets and

satisfy customer. Statistical quality control tolls are used to control the defects

rate of the production line. Quality requirements are changed buyer wise.

Studding the quality policy of the each buyer identified the quality requirement

of them. However company uses a material system (4points system) when

inspecting fabric Ralls.

Individual roll point=𝑅𝑜𝑙𝑙 𝑝𝑜𝑖𝑛𝑡𝑠 𝑋 100

Inspected meter∗Cut able width(m) point/100m2

o Laboratory

In the laboratory I was trained to test fabric shrinkage, color fastness using

method of rubbing, color fastness to perspiration, phenolic yellowing test,

print durability, and calculate GSM (Grams per square meter).

Maintaining department

In the Maintaining department I was trained to identify the machine, machine

parts and repairing, and safety side of the factory and machine operators.

Work Study department

Main technique I trained in the Work Study department was Work

standardization. Standard work sheets are used as the visual guide line to

improve the employee skills and efficiency continuously. Work study officer

also can control WIP by preparing lay out, operation breakdown, controlling

bottle neck of the line. To create correct lay out use SMV, Takt time, cycle

time, time study, Skill inventory, line balancing, attachment, ergonomics etc.

Lean department

In the lean department I studied all the lean tools that were explained in this

report in detail.

49

8.3 Details of operations, process and Procedures Learned

During this internship of six months, different activities have been performed at

garments manufacturing Company with dedication. As mentioned above, internship had

been really a period of gaining experience. The experience gained during my internship

has been categorized into several areas and described along with their related tasks

performed under different activities.

During studying overall manufacturing and business proses, the sub process such as cut

planning, quality policy, production planning etc. also studied experiencing the practical

situation.

8.4 Detailed of new Learning- theoretically and practically

For the most part of my industrial training was with work study department by studying

having experience of Work Study (George, 1992).

Work Study means the time and motion study: an analysis of a specific job in an effort

to find the most efficient method in terms of time and effort.

Method study, activity sampling, quality control tolls, inventory control methods, SMV,

ergonomics etc. were learned under the work study theory. Factory efficiency also

created using SMV. SMV can be calculate by analyzing pass data, observation time,

using software (sew easy/GSD), rated activity sampling etc.

𝑆𝑀𝑉 = 𝐵𝑎𝑠𝑖𝑐 𝑇𝑖𝑚𝑒 + 𝐴𝑙𝑙𝑜𝑤𝑎𝑛𝑐𝑒

This allowance is changed by company regulations.

Basic Time= 𝑂𝑏𝑠𝑒𝑟𝑣𝑒 𝑡𝑖𝑚𝑒 𝑋 𝑜𝑏𝑠𝑒𝑟𝑣𝑒 𝑟𝑎𝑡𝑖𝑜

standard Time

Line Target=𝑀𝑎𝑐ℎ𝑖𝑛𝑒 𝑜𝑝𝑒𝑟𝑎𝑡𝑜𝑟𝑋 60

𝑆𝑀𝑉

Then I was trained how to apply this theory in real life situation. Especially in real life

situation found some problem getting target, line balancing, WIP maintaining etc.

practically followed form feeding new line until it achieve the targeted efficiency

solving problem and deploying work study theories.

50

8.5 Issues and Challenges Encountered and Action Taken to Overcome

In this training period I worked in various types of department and various types of

people. Some people dislike working with the trainee or they have no any time to

allocate for training us. So trained under the guidance of such a people was very hard.

As well when collecting data some workers do not like to support it or provide some

data of them. Facing such issues of training period my Industrial training and research

were carried out successfully

51

REFERENCES

Abdulmalek, F.A. and Rajgopal, J. (2007). Analyzing the benefits of Lean

Manufacturing and Value Stream Mapping via simulation: a process sector case study,

International Journal of Production Economics, Vol. 107, pp. 223-36.

Alexander C. (2012) What Are the Benefits of Value Stream Mapping:

http://www.ehow.com/list_6624878_benefits-value-stream-

mapping_.html#ixzz2FFoUjPf1, Accessed on 12 December, 2012.

Campbell, R.J,( 1995) “Management Cycle Time”, Management Accounting, Vol.76

No.7,pp.34-35,

Denise R. (2011) Lean production and organization: The link between suply chain and

sustainalbe development: IAE de Lyon, Université, version 1

Darren D. (2007.) Creating a Future State Value Stream

Map:http://www.emsstrategies.com/dd020107article.html , Accessed on 10 December,

2012.

George K. (1992). Introduction to Work Study:4th (revised) Edition, International

Labour Office, Geneva.

Guy L. C. (2004). Manufacturing Systems Modeling and Analysis: New York

Hahrukh A.I. and Jin Z. (2012) Value stream map of complete product: The Ohio State

University Columbus, ISBN. OH 43210

Hassanzadeh M.R. (2008). Lead time Reduction: Case study: BEAB elikett & system

AB,

Hirdaramini Group (2012) history of hirdaramini group of company:

http://www.hirdaramani.com/hirdaramani/www.hirdaramani.com/company/about_us_p

hilosophy.html ,Accessed on 16 December, 2012

Ishikawa K., (1968)Guide to Quality Control (Japanese).JUSE Press, Ltd., Tokyo

Jeffrey K. L. & David M., (2006).The Toyota Way Field Book: McGrew-Hill, New

York

52

Kumar B.S. & sampath V.R (2012). Garment Manufacturing through Lean Initiative-

An Empirical Study on WIP Fluctuation in T-Shirt Production Unit: European Journal

of Scientific Research,Vol.73 No.2

LeanMan(2012) TIMWOOD7 Seven Wastes: http://leanman.hubpages.com/hub/Seven-

Wastes.html ,Accessed on 14 September, 2012

Marshal Instute (2012) The Benefits of TPM / Total Process

Reliability:http://www.marshallinstitute.com/default.asp.Page=Consulting&Area=OurT

PMPhilosphy&Sub=Benefits, Accessed on 16 December, 2012.

Nicola, B., Richard, K. and Rosemary, S. SPSS for Psychologists:chapter 7.An

introduction to multiple regression Performing a multiple regression on SPSS:

http://www.palgrave.com/pdfs/0333734718.pdf, Accessed on 14 May, 2012

Pettersen, J., 2009. Defining lean production: Some conceptual and practical issues. The

TQM Journal, 21(2), 127 - 142.

Rathnayake V. & Lanarolle G. (2006). Cellular Lean Model to Reduce WIP Fluctuation

in Garment Manufacturing: a process sector case study.

Stratergos International A Brief History of (Just-In-Time)

http://www.strategosinc.com/just_in_time.htm , Accessed on 13 December, 2012

Silva S.K.P.N. (2012). Applicability of Value Stream Mapping (VSM) in the Apparel

industry in Sri Lanka, Vol, 3 Issue 1

Womack J.P. and Jones D.T, (2003)“the machine that changed world: The Triumph of

lean production”, Rawson Associates, New York, NY, USA.

Womack, J.P. and Jones, D.T. (2003).Lean Thinking. 1st Free Press ed., New York:

Free Press, pp. 16- 26.

www.sagepub.com Probability and the foundations of inferential statistics: chapter

8.introduction to hypothesis testing: http://www.sagepub.com/upm-

data/40007_Chapter8.pdf, Accessed on 14 May, 2012

53

Appendix 1

Data sheet of before and after lean implementation

Efficiency Needle break Defects Machine break

Before After Before Before After After Before After

35.31 43.00 38 33 5.18 2.10 5.54 2.6

32.32 39.70 24 31 4.83 4.20 6.64 4.1

34.80 35.20 82 60 9.30 5.20 4.37 4.2

39.50 37.40 34 43 3.70 2.70 3.65 2.9

28.80 32.70 56 64 6.90 3.70 7.39 4.9

33.70 37.40 117 87 16.10 9.50 5.92 8.6

31.41 38.00 48 61 12.80 6.90 9.35 6.7

32.16 43.00 59 64 7.50 5.70 10.1 4.7

36.08 31.40 129 82 15.20 8.40 10 7.6

31.23 33.56 109 91 12.40 6.90 7.1 6.58

36.41 36.10 88 51 14.69 7.08 5.8 3.32

30.60 35.95 73 35 7.70 5.20 5.99 5.75

38.50 35.40 55 37 9.60 5.65 4.48 3.05

51.40 58.63 76 41 12.40 5.87 6.43 9.5

46.20 43.00 35 31 5.20 1.98 4.95 1.28

Data was collected for 15 manufacturing line for first two weeks in September by turns

Data sheet of ANDON tracking Inventory delay

band date EFF. # absent

Machine

break Needle

break

Defects Thread

delay

Hanger

delay

Other

delay

6 20/08 0.48939 3 0 0 0 0 0 0

3 20/08 1.00311 0 0 0 0 0 0 0

10 20/08 0.44619 2 0 0 0 0 220 0

10 17/08 0.67913 3 0 0 0 0 0 0

6 17/08 0.83340 1 0 0 0 0 0 0

3 17/08 1.00221 0 0 0 1495 0 0 700

10 16/08 0.59765 0 0 0 0 640 0 0

6 16/08 0.75568 0 0 0 0 0 0 0

3 16/08 1.02384 0 0 0 0 0 0 0

3 15/08 0.80393 1 0 0 0 0 0 0

10 15/08 0.81520 0 0 0 0 0 0 0

6 15/08 0.80100 0 0 0 0 0 0 0

6 14/08 0.67942 0 0 0 0 0 0 0

3 14/08 0.76467 2 0 0 0 0 0 0

10 14/08 0.75191 1 0 0 0 80 0 0

10 13/08 0.70977 1 0 0 0 0 0 0

6 13/08 0.66438 3 0 0 0 0 0 0

3 13/08 0.81691 2 0 0 0 0 0 0

10 10/08 0.80435 2 0 0 0 40 0 0

6 10/08 0.66906 2 285 0 0 100 150 0

3 10/08 0.81258 3 0 0 0 0 0 0

6 09/08 0.65189 6 0 0 0 0 0 580

54

10 09/08 0.80073 3 0 0 0 0 0 0

3 09/08 0.80180 1 0 0 0 0 0 0

10 08/08 0.80724 2 0 0 0 0 0 0

3 08/08 0.86325 4 0 0 0 0 0 0

6 08/08 0.58084 6 360 0 0 0 0 0

3 07/08 0.56509 4 0 0 0 0 0 0

6 07/08 0.46832 7 0 0 0 0 0 0

10 07/08 0.50850 5 2625 0 0 0 0 0

3 06/08 0.55417 6 0 0 0 0 0 470

6 06/08 0.39526 9 0 0 0 0 0 0

10 06/08 0.75082 0 0 0 0 0 0 0

6 03/08 0.36493 10 0 0 0 0 380 0

10 03/08 0.76854 6 1080 0 405 0 0 0

3 03/08 0.39431 6 0 0 0 0 0 0

6 02/08 0.23375 8 255 0 0 0 0 0

3 02/08 0.48398 4 0 0 0 0 0 0

10 02/08 0.40922 4 0 0 0 0 0 0

3 31/07 0.39205 4 0 0 0 0 0 0

6 31/07 0.47509 0 0 240 400 0 0 0

10 31/07 0.77892 2 0 0 0 0 0 0

3 30/07 0.90211 0 0 0 0 0 0 0

10 30/07 0.43616 1 0 0 0 0 0 0

6 30/07 0.55770 2 0 0 0 0 0 0

10 27/07 0.48338 2 0 0 0 0 0 0

3 27/07 0.90127 0 0 0 0 0 0 0

6 27/07 0.67958 0 0 0 0 0 0 0

3 26/07 0.80970 0 0 621 0 0 0 120

6 26/07 0.68075 0 0 0 0 0 350 0

10 26/07 0.51215 3 0 0 1500 0 0 0

6 25/07 0.67900 1 0 0 0 0 0 0

10 25/07 0.58228 2 0 0 0 0 0 0

3 25/07 0.85368 2 0 0 460 0 0 0

3 24/07 0.82412 2 0 0 0 0 0 0

10 24/07 0.55502 3 0 0 0 0 0 0

6 24/07 0.42344 2 570 0 0 0 0 0

3 23/07 0.71557 2 0 0 0 0 0 0

10 23/07 0.59745 0 0 0 0 0 0 0

6 23/07 0.46519 5 570 450 0 480 0 0

10 20/07 0.47643 3 0 0 650 0 0 0

6 20/07 0.46324 4 0 0 0 0 0 0

3 20/07 0.87693 0 0 0 0 0 0 0

3 19/07 0.69400 5 0 330 0 0 0 0

10 19/07 0.55400 2 0 220 0 0 0 0

6 19/07 0.39500 3 0 0 0 0 0 0

10 18/07 0.56451 0 0 0 1125 0 0 0

6 18/07 0.02283 10 0 160 0 0 0 0

3 18/07 0.78951 5 345 0 0 0 0 0

10 17/07 0.68283 4 0 0 1440 0 0 0

6 17/07 0.35082 5 0 0 0 0 0 210

55

3 17/07 0.86017 0 0 690 0 0 0 0

10 16/07 0.73690 0 0 0 0 0 0 0

6 16/07 0.80355 0 0 0 0 0 0 0

3 16/07 0.62872 0 0 0 0 0 0 0

6 13/07 1.02262 0 0 0 0 0 0 0

10 13/07 0.45291 7 0 0 0 0 0 0

3 13/07 1.05817 0 0 0 0 0 0 0

10 12/07 0.44437 4 210 0 1365 0 0 0

6 12/07 1.04265 0 270 0 0 0 0 0

3 12/07 0.49833 5 0 0 0 0 0 0

10 11/07 0.32912 6 0 0 0 0 0 0

6 11/07 1.01286 0 0 0 0 0 0 0

3 11/07 1.02560 0 0 0 0 0 0 0

10 10/07 0.49227 3 0 0 0 0 0 0

6 10/07 0.66033 3 0 0 0 0 0 0

3 10/07 0.49105 3 0 0 0 0 0 330

10 09/07 0.45291 3 0 0 0 0 0 0

6 09/07 0.82698 1 0 0 0 0 0 0

3 09/07 0.82206 0 0 0 0 0 0 0

6 07/07 0.74281 0 0 220 0 0 0 0

3 07/07 1.05530 0 0 0 0 0 0 0

10 07/07 0.75771 2 0 0 0 0 0 0

6 06/07 0.93500 0 0 230 0 90 0 0

3 06/07 0.64200 2 0 0 0 250 0 0

10 06/07 0.60600 3 0 0 0 0 0 0

10 05/07 0.72055 0 0 0 0 0 0 0

6 05/07 0.56173 2 0 0 0 150 0 0

3 05/07 0.94028 0 0 0 0 0 320 0

10 04/07 0.71853 3 0 0 0 0 0 0

6 04/07 0.66571 1 0 880 0 0 0 0

3 04/07 1.07966 0 0 0 0 0 0 0

10 02/07 0.56213 2 0 0 0 0 0 0

6 02/07 0.25991 9 0 0 0 0 0 120

3 02/07 0.59749 3 0 0 0 0 0 0

Data was collected in seconds

Appendix 2

Statistical Analysis Results

The assumptions of multiple regression seem to be valid for interpretation of the model.

Residuals lie in the line in probability plot.so residuals are normally distributed. There is

no any systematic pattern in residuals plot. So residuals are random.

56

Standardized Residual

Pe

rce

nt

420-2-4

99.9

99

90

50

10

1

0.1

Fitted Value

Sta

nd

ard

ize

d R

esi

du

al

1401201008060

2

0

-2

-4

Standardized Residual

Fre

qu

en

cy

210-1-2-3

24

18

12

6

0

Observation Order

Sta

nda

rdiz

ed

Re

sid

ua

l

1201101009080706050403020101

2

0

-2

-4

Normal Probability Plot of the Residuals Residuals Versus the Fitted Values

Histogram of the Residuals Residuals Versus the Order of the Data

Residual Plots for EFF

H0: All coefficients are zero Vs H1: There is at least 1 significant coefficient

Analysis of Variance

Source DF SS MS F P

Regression 5 38482.9 7696.6 14.94 0.000

Residual Error 118 60801.6 515.3

Total 123 99284.6

Since the P value is less than we can reject H0. Therefore we can conclude there

are significant factors affecting efficiency of the band within the model.

H0: coefficient is significant Vs. H1 Coefficient is not significant

Predictor Coef SE Coef T P

Constant 67.100 8.290 7.91 0.000

Total Time(Machine Break downs) -1.0802 0.4316 -2.49 0.014

Total Time(Needle Breakages) -1.815 1.027 -1.77 0.001

Defects 1.581 1.112 1.42 0.040

No abs 1.886 2.427 0.78 0.439

Inventory Delay 1.941 0.2392 7.00 0.070

S = 22.6995 R-Sq = 54.8% R-Sq (adj) = 52.9%

From the above P values we can conclude that Machine Breakdown time, Needle

Breakage turns and the Defects are significant factors that affect the efficiency of the

band. Since P value of No. of absenteeism and Inventory delay are more than significant

level (0.05) further those factors will not be considered for multiple regression

analysis.

57

Cheeking Multicollinearity

Correlations: Efficiency. Absenteeism, Machine break down, Needle break down,

defects and delay

EFF. absent Mb Nb defects

Absent -0.496

0.730

Mb -0.697 0.202

0.030 0.039

Nb 0.615 -0.081 0.688

0.002 0.886 0.053

defects -0.330 -0.004 0.498 -0.055

0.042 0.972 0.049 0.581

delay -0.270 -0.040 0.075 0.038 -0.058

0.072 0.688 0.550 0.601 0.559

Cell Contents: Pearson correlation

P-Value

H0 there is relationship between variables

H1 there is no relationship between variables

By considering the p-value it can be conclude that there are relationship between

dependent variable and machine break down, needle break down. & defects.

When consider independent variables p-values except absenteeism and machine break

down time other factors’ P-values are greater than 0.05 significant levels. So

Multicollinearity problem was not occurred. For other factors. When consider Pearson

correlation absent has low coefficient than machine break down. As well ANOVA also

reject “Absent” from the model. So for the multiple regression analysis “Absent” was

not considered as a factor affecting on efficiency.

So model was redone including Machine break down, needle breakage, defects.

Revise Multicollinearity checking for modified model

Correlations: Efficiency. Machine break down, Needle break down, defects

EFF. Mb Nb

Mb -0.702

0.029

Nb 0.615 0.688

0.003 0.053

Defects -0.330 0.498 -0.055

0.042 0.051 0.581

H0: there is relationship between variables

H1: there is no relationship between variables

There are no relationship between independent variables.