investigation of effective adaptation of lean manufacturing system in apparel manufacturing lines
DESCRIPTION
This report submitted in a partial fulfillment of the requirements forthe four year Bachelor of Science (Joint Major) Degreein ‘Statistics’ and ‘Computing and Information Systems’TRANSCRIPT
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)…………………………………...
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External Supervisor : (Signature)………………………………………..
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Assistant Registrar : (Signature)………………………………………..
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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.
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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
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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
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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)
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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
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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
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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.
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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.
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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
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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.