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TOWARDS IMPROVING PRODUCTIVITY OF

SOLAPUR BASED TEXTILE SMEs

A thesis

Submitted to

Solapur University, Solapur

For the Degree of Doctor of Philosophy

in

Mechanical Engineering

Under the Faculty of Engineering

By

PRADIPKUMAR R. KULKARNI

Under the Guidance of

Prof. (Dr.) S. P. KALLURKAR

Principal,

Atharva College Of Engineering, Malad, Mumbai

Research Center

Walchand Institute of Technology, Solapur

June - 2015

DECLARATION

I hereby declare that the thesis entitled ‘Towards improving productivity of

Solapur based textile SMEs’ completed and written by me has not previously

formed the basis for the award of any Degree or Diploma or other similar title of

this or any other University or examining body.

Place : Solapur PRADIPKUMAR R. KULKARNI

Date :

CERTIFICATE

This is to certify that the thesis entitled ‘Towards improving productivity of

Solapur based textile SMEs ’which is being submitted herewith for the award of

the degree of Doctor of Philosophy in Mechanical Engineering, under the Faculty

of Engineering of Solapur University, Solapur is the result of original research

work completed by Shri. PRADIPKUMAR R. KULKARNI under my supervision and

guidence and to the best of my knowledge and belief the work embodied in this

has not formed earlier the basis for the award of any Degree or similar title of this

or any other University or examining body.

Prof. (Dr.) S. P. KALLURKAR

Principal,

Atharva College Of Engineering, Malad, Mumbai.

Place : Solapur

Date :

CONTENTS

Acknowledgement i

Abstract iii

Thesis at a glance vi

Abbreviations vii

List of tables ix

List of figures xi

Chapter

No.

Title Page

No.

1 Introduction 01-15

1.1 Importance of textile industry 02

1.1.1 Indian textile industry 04

1.1.2 Classification of textiles 07

1.1.3 Terry towel industry 09

1.2 Need of studying the productivity improvement of Solapur

based textile SMEs

14

2 Literature Review 16-74

2.1 Studies related to textile industries 16

2.2 Studies related to manufacturing industries 31

2.3 Studies related to apparel industries 38

2.4 Studies related to clothing industries 43

2.5 Studies related to garment industries 46

2.6 Summary of literature review 47

2.7 Frequency analysis of variables 64

2.8 Identification of research gaps 73

2.9 Research problem 74

2.10 Objectives of research work 74

2.11 Scope of research work 74

3 Research Methodology 75-84

3.1 Methodology adopted for identification of variables 76

3.2 Methodology for experience survey 77

3.3 Methodology adopted for questionnaire design 77

3.3.1 Selection of type of questionnaire 77

3.3.2 Sequence and number of questions 78

3.3.3 Question formulation and wording 78

3.3.4 Selection of measurement scale and guidelines for

respondents

79

3.3.5 Stages of questionnaire design 80

3.4 Methodology adopted for data collection 80

3.4.1 Sample size determination 80

3.4.2 Selection of industries 81

3.4.3 Selection of respondents 81

3.4.4 Instructions to respondents 81

3.5 Methodology adopted for contacting and collecting

questionnaire from respondents

82

3.6 Testing of data for suitability 82

3.7 Methodology adopted for analysis of data 82

4 Data Collection by Experience Survey 85-97

4.1 Expert panel 85

4.2 Work carried out 87

4.2.1 Dependent variables 88

4.3 Experience survey 89

4.3.1 Structured questionnaire development 89

4.3.2 Collection of list of textile SMEs in Solapur 89

4.3.3 Data collection 89

4.4 Testing of data for suitability 90

4.4.1 Data validity 90

4.4.2 Data reliability 90

4.5 Data analysis 91

4.5.1 Classification of textile SMEs 91

4.5.2 Factor analysis of variables 92

4.5.3 Regression analysis 94

4.5.4 Results and discussion 94

4.6 Findings of experience survey 96

5 Developing and Implementing Methodology for

Productivity Improvement

98-131

5.1 Methodology adopted for improving productivity 98

5.2 Procedure for applying TOC to textiles 100

5.3 Case study 1 102

5.3.1 Objectives of case study 102


5.3.3 Identifying system constraints 103

5.3.4 Cause and effect diagram 104

5.3.5 Pareto analysis 105

5.3.6 Exploit the system constraints 106

5.3.7 Experimentation 106

5.3.8 Subordinate 108

5.3.9 Conclusions 109





5.4.4 Exploit the system constraints 111


5.4.5 Conclusion 112


5.1.1 Objective of case study 112



5.5.4 Exploit the system constraint 114






5.6.3 Identification of system constraint 120














5.8.3 Experimentation 127

5.8.4 Results and discussion 127


5.9 Summary of case studies 128

5.10 Module for skill development 129

5.10.1 Skill development program 130

6 Research Conclusions and Recommendations 132-138

6.1 Conclusion related to identification of variables 132

6.2 Conclusion related to factor analysis 132

6.3 Conclusion related to model development 133

6.4 Conclusions related to developing methodology for

improving productivity.

134

6.5 Conclusion related to module for skill development for

improving productivity

134

6.6 Research objectives and research conclusions at a glance 135

6.7 Contributions of current research 136

6.8 Recommendations 136

6.8.1 To manufacturers of textile SMEs 136

6.8.2 To ministry of textiles 137

6.9 Limitations of current research 138

6.10 Scope for future work 138

Appendix No. Appendix title 139-165

I Publications based on current research work 139

II Award received for current research work 140

III Questionnaire for data collection (experience survey) 142

IV List of respondent companies for survey questionnaire 151

V Certificates issued by the companies 160

References 166-175

i

ACKNOWLEDGEMENT

I would like to take this opportunity to express my deep sense of gratitude to my guide

Prof. Dr. S. P. Kallurkar, Principal Atharva college of Engineering, Malad, Mumbai. He has

continuously and consistently encouraged me for working on this research. It would not have

been possible for me to complete this research work without his painstaking efforts in guiding

the project. I sincerely thank him, for his affection and enthusiasm, constant guidance and help at

every stage of preparation of this thesis.

I would like to express my sincere gratitude to Solapur University, Solapur. I would like

to express my sincere gratitude to Principal Dr. S. A. Halkude, Walchand Institute of

Technology, Dean- Faculty of Engineering Solapur University, Solapur, for his valuable

guidance, help and constant encouragement. I am also grateful to Principal, Dr. B. P. Ronge,

Chairman BOS- Mechanical Engineering, Solapur University, Solapur. I also thank all the

committee members of DRC, Solapur University, Solapur for their valuable guidance. I am also

grateful to Prof. A. B. Ankulkar, HOD Mechanical Department, WIT, Solapur; I am very much

grateful to Prof. (Dr.) M. S. Pawar, Principal, B.M.I.T., Solapur, for his continuous guidance

throughout the research work. I am thankful to Mr. S. P. Patil, MD, Laxmi Oil Pumps and

Systems (P) Ltd. Solapur. I am also grateful to Principal, Dr. S. V. Deshpande, Vice –Principal-

Prof. S. N. Kulkarni.

I am grateful to Mr. K. D. Utpat, TOC consultant, Pune, for his guidance, continuos

encouragement and whole hearted support at all the times. Mr. Satyram Myakal, Chairman,

Myakal Texile, President, TDF, Solapur, Mr. Pentappa Gaddam, President, Solapur Yantra Mag

Dharak Sanghatana, Solapur, Mr. Srinivas Bura, Vice-President, TDF and Partner, Bura Texile,

Mr. Govind Zanwar, Director, TDF, Partner-Balaji Weaving Mill, Solapur, Mr. Nagesh

Dhayafule, Partner Dhayafule textiles, Mr. Venugopal Divate, Director, Divate Textiles, Pvt.

Ltd. Solapur, Mr. Amar Samleti, Manager, TDF, Mr. S.S. Yajurvedi, Textile Consultant,

Solapur, Prof. Vilas Bet, Principal (retired), M. S. W. College, Solapur, Mr. Ramesh Patil,

Statistician I am very much thankful to all the textile SMEs, especially to those who have given

all the data and information from time to time.

ii

I would like to thank specially to IBM-SPSS for providing software support without

which the research project could have not been completed. And also I would like to thank to Prof

(Dr.) B. B. Deshmukh, Prof. P. P. Mitragotri, Prof. (Dr.) A. K. Bewoor, Prof. S. B. Tuljapure,

Mr. A. S. Vidap, Mr. Laxmikant Virpe, system analyst, Mr. Suresh Athani and all staff members

of Laxmi Oil pumps and Systems, Pvt. Ltd, Solapur, Prof. Vikrant Malwadkar and Mr.

Shivaprasad Pogul.

I would like to take this opportunity to thank all those who have helped me, directly or

indirectly, in completing this project.

Pradipkumar R. Kulkarni

iii

ABSTRACT

Indian Textile Sector contributes to our economy as follows (CITI- 2014):

4% of GDP (at factor cost)

11% Industrial Production

8% Excise and Customs revenue collections

12% of total manufacturing exports

Second largest provider of employment after agriculture

Considering the importance of this sector, Government of India has prepared a

strategic plan for textiles for the period: 2011-12 to 2015-16. The vision, mission and

objectives as stated in the strategic plan clearly focus on productivity improvement. One of

the objectives as stated in strategic plan is

“To improve productivity across the entire textile value chain.”

It highlights the need to improve the productivity of entire textile sector.

The textiles can be classified into yarn and powerloom, handloom, woolen, jute,

sericulture and silk, handicraft, clothing and apparel, technical textile, etc. One of the

products of powerloom is terry towels (and allied products such as napkins).

Solapur is the home of powerloom industry (mainly for manufacturing terry towels

and allied products) which provides direct employment approximately to 1,00,000 persons.

There are around 3000 power looms operational in this area. The products like chadders,

bedsheets, terry towels, napkins etc. are produced on jacquard power looms. Out of the

total industries, 85% are producing terry towels and napkins. Solapur has a significant

(almost like monopoly) share of business in the international market for “Yarn dyed terry

towels on jacquard power looms”. It caters to about 80% of total international demand of

this category. In terms of financial figures it amounts to approximately Rs. 1100 crores of

annual turnover (as of prices on 2013). The financial analysis shows that only few

powerloom industries are making satisfactory profits (SOZIYA- 2013).

iv

The above data indicates the importance for an in-depth study of this sector.

Therefore a research work is undertaken which is titled as,

“Towards improving productivity of Solapur based textile SMEs”.

The chapter wise summary of this research work is presented below:

Chapter one introduces the background, importance and need of the research work.

Chapter two describes the in-depth literature study and review. The literature

review is done to know the variables used by earlier researchers and methodologies used to

improve productivity. It then identifies the research gaps. Based on the identified research

gaps, the objectives of the present research works are formulated.

Chapter three reports methodology adopted for this research work. It covers

methodologies adopted for identification of variables, questionnaire design, data collection

(by experience survey method), analysis of data using the suitable statistical techniques,

and software.

Chapter four describes data collection using experience survey method. It includes

designing the structured questionnaire using identified 38 variables affecting productivity,

collecting the data from 167 textile manufacturing SMEs. Testing of data for suitability is

studied by crombach’s alpha (0.74). Then analysis of variables is done using SPSS (17)

software, which resulted into grouping of the 38 variables into the 9 factors. Further

relation between productivity and these factors is established by using multiple regression

analysis. A methodology for improving productivity, using these factors based on Theory

of Constraints (TOC) (Goldratt 1984) is developed which is described in chapter 5.

Chapter five describes the TOC based methodology for improving productivity of

Solapur based textile SMEs. It gives introduction about TOC and five focusing steps used

in it. The applicability of these steps is presented with various case studies. All the case

studies have reported improvement in productivity. Based on the case studies, a module for

skill development is prepared.

v

Chapter six gives research conclusions, contribution to knowledge and

recommendations. Various conclusions related to identification of variables, factor

analysis, regression analysis, methodology for improving productivity, etc. are presented.

Contribution of the current research to knowledge is highlighted. Finally,

recommendations at various levels are made.

These chapters are supported by number of tables, explanatory appendices and

references.

This study is important, as it identifies factors affecting productivity of textile

SMEs. The applicability of TOC to improve productivity of textiles is validated. Therefore

textile manufacturing organizations may improve their productivity (profitability) by using

TOC based approach.

vi

Importance of

textiles

Classification

Terry towel

industry

Need for study

Review of

literature

Research

objectives

Identified

research gaps

Literature

review of

various

textile

sectors such

as

Clothing

Apparel

Garment

Knitting

Mfg,

Very few

studies on-

Jacquard

Powerlooms

Terry towel mfg

Solapur based

textile SMEs

Linking

profitability with

productivity

Identify variables

affecting

productivity

Grouping

variables into

factors.(factor

analysis)

Establish

relation

between

productivity and

factors

Identify

methodology

for improving

productivity

To contribute to

the knowledge

in the field of

productivity

Identification

of variables

Develop

structured

questionnaire

Data

Collection

Analysis of

data

Derive

conclusions

Develop

methodology

for

improvement

of

productivity

(TOC).

Validation by

case studies

Expert panel

Develop

structured

questionnaire

Data

Collection

(167 firms-

primary data)

Analysis by a

Crombach’s

alpha (0.74)

KMO and

Barlett’s

test(0.00)

Factor

analysis- 38

variables

grouped into

9 factors

Carried out

multiple

regression of

9 factors

Factor

analysis -38

Variables

grouped

into 9

factors

(Constraints)

Module

developed on

skill

development

for–

a) policy

makers

b) executives

c) Implementers

conducted

skill

development

programs to

textile SMEs

feedback/

results

showed

positive

change

Thesis at a Glance- Towards Improving Productivity of Solapur based Textile SMEs

Identified 38

variables affected

productivity

Grouped into 9

factors /constraint

(factor analysis)

Developed relation

between

productivity and

factors

Developed TOC

based methodology

to improve

productivity

Developed module

on skill

development

The manufacturing units

may establish a QA Dept..

Center for productivity

improvement may be

established by BTRA/

TDF/SOZIYA

A skill development

center may be established

to conduct programs

jointly by Textile

Department, TDF,

SOZIYA and a local

Institute.

A center for guidance and

implementation of

systems like ISO 9001,

BSCI, etc. may be

established with

TDF/SOZIYA

Use of non-conventional

energy may be promoted

by various nodal agencies.

Cluster approach may be

used to increase the

utilization of the capacity

of resources.

Study and implementation

of different central and

state Govt. schemes.

Chapter III Chapter II Chapter I Chapter IV Chapter V Chapter VI

Introduction Literature Review Research

Methodology Data Collection

Analysis of Data and

Development of

Methodology Research conclusions and recommendations

To undertake

research

study on

improving

productivity

of Solapur

based textile

SMEs

Identified

variables

and

classified

into

Input

variable

Process

variable

Output

variable

Identify

variables

for current

research

based on

literature

review

A few studies

define

methodology

for

productivity

improvement

Applicability of

variables of

other sector

Solapur textile

not studied

Hence a need to

undertake

research

Develop

methodology

for

productivity

improvement

Develop

module for

skill

development

Developed a

methodology

based on TOC

for

productivity

improvement

Validated the

findings by

5 case

studies

Productivity

improveme

nt recorded

in all cases.

Conclusion Recommendations

Contribution to

knowledge:-

Identified factors

affecting

productivity

Developed

relation between

productivity and

factors

TOC has proved

effective tool for

improving

productivity

Analysis

of data

Analysis by a

Crombach’s

alpha(0.74)

KMO and

Barlett’s

test(0.00)

Carried out

multiple

regression

of 9 factors

and

productivity

vii

ABBREVIATIONS

Abbreviations Full form/description

BSCI Business Social Compliance Initiative

CA corrective action

CFC Common Facility Centre

CSP Count strength product

CV Coefficient of Variation

CWS Common Work Shed

EMS Environmental Management System

F1 Factor 1- synchronization of management processes

F2 Factor 2- TPM for weaving and dyeing

F3 Factor 3- input and process quality

F4 Factor 4- HR policies for textile SMEs

F5 Factor 5- Process technology

F6 Factor 6- labor behavior

F7 Factor 7- use of scientific tools for improvements

F8 Factor 8- use of renewable energy for processes

F9 Factor 9- system deployment

GDP Gross Domestic Product

GPL Grams per liter

GSM Grams per square meter

HR Human Resource

HRM Human Resource Management

ISO International Organization for Standardization

I.V. Input Variables

KMO Kaiser-Meyer-Olkin

MEDA Maharashtra Energy Development Agency

MLR Multiple Logistic Regression

OHSAS Occupation Health And Safety Assessment Series

O.V. Output Variables

viii

Abbreviations Full form/description

PA Preventive Action

PP Partial Productivity

P.V. Process Variables

RPM Revolution Per Minute

SME Small and Medium Enterprise

SMED Single Minute Exchange of Die

SOZIYA Solapur Zilla Yantramag Dharak Sangh

SPC Statistical Process Control

TDF Textile Development Foundation

TFP Total Factor Productivity

TOC Theory Of Constraints

TPM Total Productive Maintenance

V Variable

ix

LIST OF TABLES

Table No. Title Page No.

1.1 Growth rates of (combined) textiles and apparel exports (to the world) from

Selected Asian Countries (2004-2009) (ICRIER, 2010)

3

1.2 Input Cost Ranking in Five Countries (ICRIER, 2011) 4

1.3 Trends in Segmental share of Cloth Production (TCR- 2014) 5

2.1 Summary of literature review with identified research gaps 48

2.2 Frequency analysis of input variables 64

2.3 Frequency analysis of process variables 68

2.4 Frequency analysis of output variables 70

3.1 Ratio scale values and effect level 79

3.2 Table of R2

(adjusted) 83

4.1 Expert panel 86

4.2 List of variables 87

4.3 Responses received by type and size of company 91

4.4 KMO and Bartlett's Test 92

4.5 Factor analysis of variables 93

4.6 Identified factors 94

4.7 Logistic regression 95

5.1 Details of the manufacturing unit (case study 1) 103

5.2 Details of the machinery and capacities (case study 1) 103

5.3 Effect of temperature on yarn strength 106

5.4 Effect of humidity on yarn strength 107



5.7 Preventive maintenance schedule for power loom 111





x

Table No. Title Page No.



5.14 Details of dyeing process 126

5.15 Readings of temperature of water and quantity of dyestuff 127

5.16 Summary of case studies 128

6.1 Research objectives and conclusions 135

xi

LIST OF FIGURES

Fig. No. Name of figure Page No.

1.1 Flow diagram of manufacturing of terry towel 9

1.2 Doubling 9

1.3 Dyeing 10

1.4 Winding 10

1.5 Warping 11

1.6 Powerloom 11

1.7 Stitching 12

1.8 Cross-section of a towel through the warp 12

2.1 Graph of input variables 67

2.2 Graph of process variables 70

2.3 Graph of output variables 72

5.1 Representation of terry towel manufacturing as a chain (case study 1) 104

5.2 Cause and effect diagram (case study 1) 105

5.3 Pareto analysis 105

5.4 Graph of temperature Vs Yarn strength 107

5.5 Humidifier 108



5.8 Pulley of bobbin winding machine 114

5.9 Bobbin winding machine 115


5.11 Bush bearings 121

5.12 Ball bearings 121

5.13 Ball bearing at U bracket 122


5.15 Dyeing machine before improvement 125

5.16 Dyeing machine after improvement 125

5.17 Graph of water temperature vs. quantity of dyestuff 127

1

Chapter 1

INTRODUCTION

Textile products play a vital role in meeting human basic needs. We often only

consider textiles to be the clothes we wear. Obviously, the clothing industry is where

the majority of textiles are produced and used. However, textiles are also important in

all aspects of our lives from birth to death. The textile sector includes yarn and power

loom, cotton, hand loom, wool, jute, sericulture, handicraft etc. The various textile

products are cloth, suiting-shirting, garments, apparels, chadders, bedsheets, terry

towels, napkins etc. The use of textiles has been traced back over 8500 years.

The name “terry” comes from the French word “tirer” which means to pull out,

referring to the pile loops which were pulled out by hand to make absorbent traditional

Turkish toweling. In research conducted on terry weaving by the Manchester Textile

Institute, it was concluded that original terry weaving was likely the result of defective

weaving. The research indicates that this development occurred in Turkey, probably in

Bursa city, one of the major traditional textile centers in Turkey. Terry weaving

construction is considered a later development in the evolution of woven fabrics. Terry

toweling is still known as „Turk Fabric‟, „‟Turkish Toweling‟ or „Turkish Terry‟

(Humpries M.- 2004).

India is a traditional textile -producing country. It is amongst the world‟s top

producers of yarns and fabrics, and the export quality of its products is ever increasing.

Textile industry is one of the largest and oldest industries in India. Textile Industry in

India is a self-reliant and independent industry and has great diversification and

versatility. The textile industry can be broadly classified into two categories, the

organized sector and the unorganized decentralized sector. The organized sector of the

textile industry represents the mills. It could be a spinning mill or a composite mill.

Composite mill is one where the spinning, weaving and processing facilities are carried

out under one roof. The decentralized sector is engaged mainly in the weaving activity,

which makes it heavily dependent on the organized sector for their yarn requirements.

This decentralized sector is comprised of the three major segments viz., powerloom,

handloom and hosiery. In addition to the above, there are readymade garments, khadi as

well as carpet manufacturing units in the decentralized sector. The Indian textile

2

industry has an overwhelming presence in the economic life of the country. It is the

second largest textile industry in the world after China.

Textile sectors contribution to the Indian economy (CITI- 2014)

4% of GDP

11% Industrial production

8% Excise and customs revenue collections

12% of total manufacturing exports.

Employs about 35 million people

Second largest provider of employment after agriculture

The Governments, both Central and State play a major role in the development

of the textile sector. Separate ministry has been formed at central and state level, which

highlights its importance in the economy. The Government‟s role extends to a range of

activities such as price support to cotton and jute, incentives for investments in

technology up-gradation and modernization, setting up of world class integrated textile

parks, implementation of technology mission on cotton, jute and technical textiles,

development of mega clusters for power looms, handlooms and handicrafts,

development of handlooms, handicrafts, sericulture and wool sub-sectors by

implementing a number of schemes, implementation of welfare schemes for handloom

weavers and handicrafts artisans and promoting skill development of textile workers in

collaboration with the industry. The Government is also providing a number of

incentives for export of textile products. A large network of Government Offices,

public sector enterprises, textile research associations, textile design and education

institutions such as National Institute of Fashion Technology (NIFT), Sardar Vallabhai

Patel International Institute of Textile Management, various textile industry

associations, Export Promotion Councils etc. provide a robust institutional framework

for the development of the textile sector.

1.1 Importance of textile industry

As per the study by U.N. Contrade and DGFT, there are top five countries in

Asia which are producing textiles namely Bangladesh, India, China, Pakistan and

Vietnam. The growth rate of these countries is presented in table 1.1.

3

Table 1.1 Growth rates of (combined) textiles and apparel exports (to the world)

from selected Asian countries (2004-2009) (ICRIER, 2010)

Countries 2005 2006 2007 2008 2009

India 20% 8% 6% 9% -13%

Bangladesh 15% 66% -11% -16% -19%

China 21% 14% 6% 10% -11%

Pakistan 18% 17% 10% 19% -10%

Vietnam 12% 33% 29% 18% 25%

The standard cost of production is one of the major factors in determining

international competitiveness in global textile and apparel industries. This include key

cost categories: the price of land, price of labor, hours worked, electricity and energy

costs, building costs (or rent), transport and taxation. Along with this equally important

are delivery times and the cost of inventories held in the factory, in transit or at the

warehouse. The table 1.2 indicates input costs ranking in five countries. India has

strong competition with Pakistan, Bangladesh and China with respect to apparel and

garment manufacturing industry.

4

Table 1.2 Input cost ranking in five countries (ICRIER, 2011)

Cost/Ranking 1 2 3 4 5

Labor Cost

(US$/hour)

Bangladesh

(0.32)

Cambodia

(0.53)

Pakistan

(0.55)

India

(0.83)

China

(1.44)

Hours Worked Bangladesh

(2336)

China

(2328)

Pakistan

(2324)

India

(2280)

Cambodia

(1960)

Power Cost

(US$/KWH)

Bangladesh

(0.053)

China

(0.065)

Pakistan

(0.071)

India

(0.086)

Cambodia

(0.14)

Ocean Transport

(US$/20

container)

China

(1800)

Bang./

Camb.

(1900)

Pakistan

(2000)

India

(2100) --

Land Transport

(US$/20

container)

Bangladesh

(250)

Pakistan

(300)

India

(400)

China

(470)

Cambodia

(600)

Building Cost

(US$/Sq .m)

China

(97)

Bangladesh

(120)

Cambodia

(130)

India

(140)

Pakistan

(150)

VAT for Textile

and Apparel

Export (%)

Bangladesh,

Pakistan,

Cambodia(0)

-- -- --

India

(12.5%)

and (0) in

SEZ

Corporate Tax

( % of profits)

Cambodia

(20)

China

(25)

India

(33.6)

Bang./

Pakistan

(35)

--

The study indicates that India has high input cost for labor, power, building etc.

and need to be more productive for facing competition.

5

1.1.1 Indian textile industry

The trend in Indian textile industry is presented in table 1.3.

Table 1.3 Trends in Segmental share of Cloth Production (Tex. commission report 2014)

Item

2001

-02

2009

-10

% G

row

th o

ver

2008

-09

CA

GR

% (

2001

-02 t

o 2

009

-10)

Pro

ject

ion

for

2015

-16 a

s p

er

curr

ent

CA

GR

Targ

eted

CA

GR

for

the n

ext

5

yea

rs (

per

cen

t)

Pro

ject

ion

for

2015

-16 a

s p

er t

he

targ

eted

CA

GR

Mill Sector 1546 2016 12.25% 3.37% 2379 6 2860

Handloom

sector 7585 6806 1.93% 1.35% 6359 3 8127

Powerloom

Sector 25192 36997 9.95% 4.92% 47039 10 6554

Hosiery

Sector 7067 13702 13.46% 8.63% 20727 12 27045

Others

(Khadi,Wool,

Silk

714 812 5.73% 3.22% 880 4 1027

Total Cloth

Production 41390 60333 9.76% 4.51% 77384 9.6 104601

It is seen that all the sectors are having a good growth potential. Considering

this aspect, Government of India, Ministry of Textiles has prepared the strategic plan

(2011-12 – 2015-16) with a view to achieving a number of strategic development goals

and objectives for the textile sector in consultation with the stakeholders.

6

The vision, mission and objectives as stated in strategic plan (2011-12 to 2015-16)

are as follows:

a) Vision as stated in Strategic Plan (2011-12 to 2015-16)

To build state of the art production capacities and achieve a pre-eminent global

standing by 2020 in manufacture and export of all types of textiles including technical

textiles, jute, silk and wool and develop a vibrant handloom and handicraft sector for

sustainable economic development and promoting and preserving the age old cultural

heritage in these sectors.

b) Mission as stated in Strategic Plan (2011-12 to 2015-16)

1. To promote planned and harmonious growth of textiles by making available

adequate fibres to all sectors.

2. To promote technological up-gradation for all types of textiles including technical

textiles, jute, silk and wool.

3. To promote skills of all textile workers, handloom weavers and handcrafts artisans,

creation of new employment opportunities and development of new designs to

make these sectors economically sustainable.

4. To ensure proper working environment and easy access to health care facilities and

insurance cover to weavers and artisans to achieve better quality of life.

5. To promote exports of all types of textiles and handicrafts and increase India‟s

share of world exports in these sectors.

c) Objectives as stated in Strategic Plan (2011-12 to 2015-16)

1. To have sustainable growth and development of textiles Sector in the

country

2. To improve productivity across the entire textiles value chain

3. To achieve inclusive growth by improving productivity in handlooms,

handicrafts and sericulture and by ensuring welfare of weavers and

handicrafts artisans

4. To develop Sericulture & Silk Sector

5. To promote growth and development of technical textiles in India

6. To develop Wool & Woollen Textiles Sector

7. To develop and modernize the decentralized Powerlooms Sector.

Powerloom cloth production targeted to grow at 10% per year

7

8. To develop handloom sector and ensure welfare of weavers. Handloom

cloth production projected to grow at an annual average rate of 5%

9. To develop Handicrafts Sector and ensure welfare of artisans

10. To improve the functioning of PSUs and to make all PSUS profitable by

2015-16

11. To ensure efficient functioning of the RFD System

12. To improve internal efficiency/responsiveness/service delivery of Ministry

It is to be noted that, one of the objectives has been set as

“To improve productivity across the entire textiles value chain.” This indicates the

importance of topic.

To focus on a particular area for improving the productivity, the classification of

textiles is studied and the area for improvement is selected. The classification is given

below.

1.1.2 Classification of textiles

The textile sector can be broadly classified into following categories:

Yarn and Power loom: This part of industry includes fiber and filament yarn

manufacturing units. The powerlooms sector is decentralized and plays a vital role in

Indian textiles industry. It produces large variety of cloths, including terry towels and

napkins to fulfill different needs of the market. It is the largest manufacturer of fabric

and produces a wide variety of cloth. The sector contributes around 62% of the total

cloth production in the country and provides ample employment opportunities to 4.86

million people.

Cotton: Cotton is one of the major sources of employment and contributes in

export in promising manner. This sector provides huge employment opportunities to

around 50 million people related to activities like cultivation, trade, and processing.

India‟s cotton sector is second largest producer of cotton products in the world.

Handloom: The handloom sector plays a very important role in the country‟s

economy. This sector accounts for about 13% of the total cloth produced in the country

(excluding wool, silk and Khadi). The sector is highly labor intensive.

8

Woolen: The woolen textile sector is an organized and decentralized Sector.

The major part of the industry is rural based. India is the 7th

largest producer of wool,

and has 1.8% share in total world production. The share of apparel grade is 5%, carpet

grade is 85%, and coarse grade is 10% of the total production of raw wool. The

Industry is highly dependent on import of raw wool material, due to inadequate

production.

Jute: Jute is called Golden fiber and after cotton it is the cheapest fiber

available. Indian Jute Industry is the largest producer of raw jute and jute products in

the world. India is the second largest exporter of jute goods in world.

Sericulture and Silk: The Silk industry has a unique position in India. India is

the 2nd

largest producer of silk in world and contributes 18% of the total world raw silk

production. In India silk is available with varieties such as, Mulberry, Eri, Tasar, and

Muga. Sericulture plays vital role in cottage industry in the country. It is the most labor-

intensive sector that combines both agriculture and industry.

Handicraft: The Indian handicrafts industry is highly labor intensive, cottage

based and decentralized industry. It provides employment to a vast segment of craft

persons in rural & semi urban areas and generates substantial foreign exchange for the

country, while preserving its cultural heritage.

One of the major products of powerloom is terry towel. The introduction to

terry towel industry is presented herewith.

1.1.3 Terry towel industry

Terry or Turkish towels were originally woven in handloom and originated in

Constantinople of Turkey. Now, it is produced either by weaving or by knitting,

wherein woven terry towels are much more popular. Methods of chemical processing

have also a significant role in determining the quality, besides the role of different

fibres and yarns mainly for manufacturing bathrobes with soft and cooling effect.

9

Yarn Doubling Bleaching

Dyeing Winding Warping

Power loom Stitching / Cutting Trimming/finishing

/inspection

The flow diagram of manufacturing of terry towel is shown in figure 1.1.

Figure 1.1 Flow diagram of manufacturing of terry towel

Figures 1.2 to 1.7 show various process of terry towel manufacturing.

Figure 1.2 Doubling

Inspection Packing Dispatching

10

Figure 1.3 Dyeing

Figure 1.4 Winding

11

Figure 1.5 Warping

Figure 1.6 Powerloom

12

Figure 1.7 Stitching

Terry fabrics basically belong to the group of pile fabrics, wherein additional

loose (with lesser tension) yarn is introduced to form loops called as piles to give a

distinct appearance and effect. In the present age, pile formation is microprocessor

controlled with high level of accuracy and distinct features.

Terry towel consist of three types of yarns which are Ground warp, Pile warp

and Weft. The meshing of these yarns is as shown in figure 1.8.

Figure 1.8 Cross-section of a towel through the warp

These yarns are woven on a Jacquard powerloom. The photographic view is

shown in figure 1.6.

Till last decade, Indian terry towel industry was dominated by decentralized

Handloom and Powerloom sectors of Panipat, Karur, Erode, Mumbai, Solapur,

13

Ahmedabad and Delhi, constituting the share of over 80% of the total production of

Towel Industry. But, for the last 10 years, many of the organized sectors have entered

in this segment.

Organized Sectors are mainly moving from mid low end to mid high end market

whereas decentralized Sholapur, Panipat are concentrating more on low end and

domestic market. Some of the high quality power loom fabrics from decentralized

sectors are being slowly accepted in leading markets of USA and EU. In the recent

past, many of them installed shuttle less rapier looms with modern processing facilities

for high end solid, dobby and jacquard velour beach towel.

USA is the world‟s single largest buyer for Made-ups and Terry Towels. India,

China and Pakistan together supply 65% towels, 81% of sheets and 79% of comforters

imported by USA. While India has a dominant position in America‟s terry towel

import with a share of around 26%, India‟s home textile contributes around 22% i.e. US

$ 4.1 billion to India‟s textile export of US $ 19 billion. However, the share of terry

towel is just 5.8% of total home textile export i.e. US $ 255 million in 2005-06 and US

$ 239 million in 2006-07, and there is a room to grow. Till recent time, marketing

effort was concentrated in USA, but many are looking for other markets of the EU and

other parts of the world.

Small players are concentrating for value addition by providing decorative

aspects like design, embroidery, etc. whereas bigger players are bringing various

structural innovation, with better absorbency, eco-friendly inputs, fragrance, etc. Most

of them are in the combined business of bed and bath terry towel products.

India still has cost advantage on availability of raw material and cheap labor for

manufacturing terry towel. Looking to the growing economy and vast middle class

population, domestic market is also expected to grow significantly. Many of the Indian

companies are also expected to enter in the World Market predominantly through

acquisition and branding with this segment in the years to come. Even some of the

smaller players are moving towards export market prominently. Towels are subject to

changing fashion and demand new designs with different fabric finish, loop pile and

flat structures. Major functional proportion such as moisture absorbency, water

retention, drying ability, resistance to abrasion, softness and feel are predominantly

going to influence the consumer all the time. This should be an ongoing exercise by

14

using different quality of yarn, fiber, proportion of water-soluble fiber component,

piles‟ length, fabric design and structure.

1.2 Need of studying the productivity improvement of Solapur based textile SMEs

Solapur (Maharashtra, India) is known as a city of textiles because of its

manufacturing capacity and capabilities especially for terry towels, napkins and allied

products. Terry towels and allied products can be manufactured either by yarn dyeing

or fabric dyeing. The looms can be either power looms, rapier looms or hand looms. A

mechanism called as “Jacquard” is used to produce a colorful design on the terry towel.

Solapur is the home of power loom industry (mainly to manufacture terry

towels and allied products) which provides direct employment approximately to

1,00,000 persons. There are around 3000 power looms operational in this area. The

products like chadders, bedsheets, terry towels, napkins etc. are produced on jacquard

power looms. Out of the total industries, 85% are producing terry towels and napkins.

Solapur has a significant (almost like monopoly) share of business in the international

market for “yarn dyed terry towels on jacquard power looms”. It caters to about 70% of

total international demand of this category. In terms of financial figures it amounts to

approximately Rs. 1100 crores of annual turnover (as of prices on Oct. 2013). The

financial analysis shows that only 25% of power loom industries are making

satisfactory profits (7.5% or more) (SOZIYA- 2013).

The above data indicates the need and importance for an in depth study of this

sector. Therefore it is proposed to carry out the research in the field of productivity of

Solapur based terry towel manufacturing industries (SMEs). The proposed research will

be helpful to improve the productivity and thereby profitability of the same.

15

The title of the proposed research work is

“Towards improving productivity of Solapur based textile SMEs”.

After completing literature reviews, literature gaps are identified (presented in

chapter two). Based on identified literature gaps, following research objectives are

formulated.

1. Identification of different variables affecting productivity of Solapur based terry

towel manufacturing industries (SMEs)

2. To carry out factor analysis of the variables studied, by using suitable software

3. To develop a model representing the relationship between identified variables/factors

and the productivity

4. To develop a methodology for improving existing level of productivity

5. To develop a suitable module for skill development to improve the productivity

After deciding the topic, research work is undertaken. It starts with in depth

literature study and review. This is reported in next chapter.

16

Chapter 2

LITERATURE REVIEW

The textile sector in India has undergone a significant change after multi fibre

agreement in 2005. The quota system was abolished. As a result of this, domestic textile

firms are facing a challenge of improving productivity so as to remain competitive. The

trend has become reducing the price simultaneously improving the quality.

Researchers and academicians have taken a note of this changed phenomenon.

Numbers of researchers have done significant work in the area of productivity

improvement of textile sector. During current research work, number of recent publications

from different research database (viz: Sciencedirect, Emerald library, Springer link, Taylor

and Francis, DOAJ, etc.) related to productivity of textile are extensively reviewed. The

literature review is presented herewith.

2.1 Studies related to textiles industries

S. Karthi et al. (2013) have reported the case study of implementing Lean Six

Sigma Quality Management System -2008 model in a textile mill. They have suggested

that L6QMS-2008 model was successfully implemented in a spinning mill located in south

India. Though Lean Six Sigma concepts were never tried in the textile unit, two L6QMS-

2008 projects could be implemented without any difficulty with the full cooperation of the

shop floor team and top management involvement. Sliver waste reduction project

(LSS0001) and training lead time reduction project (LSS0002) were carried out within the

ambit of ISO 9001:2008 standard-based QMS maintained in the spinning mill (Unit A).

They yielded an annual cost reduction in around two million rupees for the company.

These steps enabled the team members to understand the integrated concepts easily and

achieve the targeted results in both the projects without any hassles within the given time

frame. The authors suggested hypothetical steps to implement the techniques.

Mohammed A. Ahmed Al-Dujaili (2012) has studied the relation between cost of

quality and productivity for textile sector in Iraq. The paper seeks to measure the impact of

quality improvement on productivity and costs, hence creating a practical opportunity for

17

improvements for organizations. The study was done by collecting data from a textile

company in Iraq. The analysis of result shows that improving quality plays a fundamental

role in increasing operations productivity in any organization and improved quality is

related to productivity. In addition, human aspects (senior management and employees),

are significant for the construction of the relationship among quality, productivity and

costs. Additionally, based on the study, it is inferred that TQM has a positive effect on

TQC and productivity. This is evident in the operational and business performances,

employee relationship and customer satisfaction.

Baskaran, V. et al. (2012) have carried out Indian textile suppliers‘ sustainability

evaluation using the grey approach. Using a sample of sixty-three suppliers and six

sustainability criteria such as discrimination, abuse of human rights, child labor, long

working hours, unfair competition, and pollution, the authors have categorized the

suppliers into three categories: ‗good performer‘, moderate performer‘, and ‗performance

not up to expectation‘. Since all the chosen criteria are subjective, the Grey approach is

chosen for analysis. The results of this study indicate that the criterion of long working

hours plays an important role in evaluating suppliers in both categories (garment

manufacturers and ancillary suppliers). In the case of garment manufacturers, it was

observed that pollution and unfair competition were also important criteria. Employing

child labor is found to be a critical criterion in the case of ancillary suppliers. Policies

derived from the findings of this study, and properly implemented, will foster smoother

relationships between garment suppliers and multinational garment retailers. This has the

potential to make the Indian textile and clothing industry more competitive globally.

Ali Hasanbeigi, Lynn Price (2012) has conducted a review of energy use and

energy efficiency technologies for the textile industry in China. They have concluded that

there are various energy-efficiency opportunities that exist in every textile plant. However,

even cost-effective options often are not implemented in textile plants, mostly because of

limited information on how to implement energy-efficiency measures. Know-how on

energy-efficiency technologies and practices should, therefore, be prepared and

disseminated to textile plants. This paper provides information on the energy use and

energy-efficiency technologies and measures applicable to the textile industry. The paper

18

includes case studies from textile plants around the world and includes energy savings and

cost information when available. A total of 184 energy efficiency measures applicable to

the textile industry are suggested in this paper. Also, the paper gives a brief overview of

the textile industry around the world. An analysis of the type and the share of energy used

in different textile processes are also included in the paper. Subsequently, energy-

efficiency improvement opportunities available within some of the major textile sub-

sectors are given with a brief explanation of each measure. This paper shows that a large

number of energy efficiency measures exist for the textile industry and most of them have

a low simple payback period.

Mason, G., Leary, B. O., & Vecchi, M. (2012) has analyzed the relationship

between human capital and productivity growth using five-country multi-industry dataset.

The analysis makes use of a cross-country industry-level dataset which contains annual

series for output, capital, labor input and workforce skills for 26 manufacturing industries

in five countries (UK, US, France, Germany and the Netherlands) over the period 1979–

2000. They have found that the evidence of positive human capital effects on growth in

average labor productivity. They concluded that multi-factor productivity (MFP) growth is

positively related to the use of high-skilled labor.

Lin, H., et al.. (2011) have examined the relationship between industrial

agglomeration and firm-level productivity in China‘s textile industry. Estimates obtained

from various specifications confirmed the common finding that industrial agglomeration

has a significantly positive impact on firm-level labor productivity. Industrial

agglomeration and productivity were found to be nonlinear in those highly-concentrated

areas. The productivity-enhancing effect brought about by industrial agglomeration was

observed to be stronger for small firms; In addition, they found that ownership matters to

productivity. (Foreign-owned enterprises experienced higher productivity). They

concluded that state-owned enterprises have lower productivity than other types of

enterprises. Moreover the smaller firms have higher productivity than the larger firms.

With positive externalities that will further enhance the high- tech firms' productivity,

small firms established in more clustered regions could obtain more external economic

benefits of agglomeration than large firms.

19

Boothby, D., Dufour, A., & Tang, J. (2010) have studied the combinations of

technologies and types of training that are commonly undertaken by firms, presumably as

part of their strategies to effectively utilize the adopted technologies and to improve their

economic performance. The paper estimates the relationship between these common

technology-training combinations and productivity performance. They showed that these

combinations are associated with higher productivity. The study confirms that there are

important complementarities between new technology adoption and organizational change

(specifically training in this study). Appropriate combinations of new technologies and

training lead to higher productivity than adoption of new technologies alone. The data set

helped to identify with a high degree of specificity (the types of technology adopted and

the types of training provided) and to investigate the types of training that are

complementary to a given technology.

Gruber, H. (2010) has studied the diffusion of technology with reference to shuttle

less loom. He argued that, in the weaving process the technological progress has been

achieved through the introduction of the shuttle less loom. A shuttle less looms is about

three times more productive than a shuttle loom. However, innovation is not limited to the

weaving process, but affects also upstream industries such as the production of yarns.

Shuttle less looms require good and constant quality yarns for proper operation. As a

result, the combination of new technology and good quality yarns improves the quality of

the woven fabric. The determinants of the diffusion of innovations are market size, cost of

innovation, market structure and uncertainty about future innovations. The diffusion of the

shuttle less loom, a major innovation in the textile industry, has been relatively slow in

spite of the undisputed improvement with respect to the conventional shuttle loom used for

weaving textiles. The diffusion of shuttle less looms in industrialized countries is analyzed

in the framework of an epidemic diffusion model where the diffusion speed parameter is

allowed to vary. Of the various factors affecting the speed of diffusion, trade liberalization

seems to have the most potent impact in accelerating diffusion.

Lu, X., Liu, L., Liu, R., & Chen, J. (2010) has done research in reuse of water

discharge. They have used the technology of combined treatment system of bio aerobic

treatment and membrane technology. The treated effluent quality satisfied the requirement

20

of water quality for dyeing and finishing process excluding light coloration. The study

concluded that it can both conserve or supplement the available water resource and reduce

or eliminate the environmental pollution in China which results in reduction in input cost

also.

Pardo Martínez, C. I.(2010) analyzed energy efficiency in the German and

Colombian textile industries. Their results also showed that the German and Colombian

textile industries have achieved meaningful improvements in energy efficiency. The energy

consumption of each textile manufacturing activity corresponded with its production levels

in both countries, indicating a direct relation between output and energy use. The

production function reveals the following for the German textile industry: (1) No

significant influence of company size or plant capacity utilization can be identified (the

coefficients are statistically insignificant); and (2) Capital and energy price variables have

an enhancing influence on the efficiency of the gross production-energy ratio. For the

Colombian case, it can be concluded that: (1) No significant influence of the capital energy

ratio can be identified (the coefficient is statistically insignificant); (2) Labor, materials and

plant capacity utilization have an enhancing influence on the efficiency of the gross

production-energy ratio; (3) There is evidence for energy augmenting technological

progress; and (4) A negative effect of company size exists. These results they showed the

importance of technology, economies of scale, and energy efficiency-oriented policies and

management strategies in improving energy efficiency within the textile industry.

Puig, F., et al. (2009) have analyzed the impact of globalization on the

manufacturing operations of textile industries and industrial districts and how it influences

the specialization and diversification of manufacturing decisions. They concluded that

globalization tends to diminish the district and sub sector effects over time, but they have

also showed the positive impact of specialization on productivity and of diversification on

business growth of this sector.

Vankar, P. S., & Shanker, R.(2009) have tried to improve productivity of textile by

using partial productivity. They have studied dying process for improvement and evaluated

the efficiency of dyeing on cotton wool and silk fabrics with natural dye obtained from

21

kitchen waste of dry skin extract of Allium cepa. They found that the preference of using

easily and cheaply available material for dyeing by conventional dyeing lowers the cost of

natural dyeing and enhances resource productivity and as a result, reduces waste. In this

study onion scales have been used as natural dye source which has been developed

scientifically for generating shades of light brown and dark brown for cotton, silk and wool

dyed samples. The method developed for natural dyeing of cotton, silk and wool fabrics

using skin extract of allium in conjunction with metal mordanting have showed marked

improvement in terms of dye adherence and fastness properties and can thus be

recommended for industrial application.

M. Ilangkumaran, S. Kumanan (2008) have focused on the use of analytic hierarchy

process (AHP) under fuzzy environment and technique for order preference by similarity

to ideal solution (TOPSIS) to select an optimum maintenance strategy for a textile

industry. The maintenance strategy selection involves multifaceted factors; it needs multi

criteria decision-making to evaluate the strategies. An optimal maintenance policy mix can

improve availability levels of plant equipment and also avoid unnecessary investment in

maintenance. Considering the imprecise ranking of AHP, TOPSIS were used to obtain

ranking of different maintenance strategies. This study services to scrutinize the critical

equipments and give the most accurate decision when choosing a maintenance policy and

also the case study shows that the AHP combination with TOPSIS is applicable as an

evaluation technique for maintenance strategies selection problem. In a textile industry

tremendous adoption of equipments due to proliferation of advance machines in the market

needs optimal maintenance policy. The total operating budget of the firm directly is

influenced by the maintenance policy. The new maintenance policy is considered, when

the maintenance characterization factors are changed. The maintenance strategy selection

involves multifaceted factors; it needs multi criteria decision-making to evaluate the

strategies. An optimal maintenance policy mix can improve availability levels of plant

equipment and also avoid unnecessary investment in maintenance. Considering the

imprecise ranking of AHP, TOPSIS is used to obtain ranking of different maintenance

strategies. This study services to scrutinize the critical equipments and give the most

accurate decision when choosing a maintenance policy and also the case study shows that

22

the AHP combination with TOPSIS is applicable as an evaluation technique for

maintenance strategies. In a textile industry tremendous adoption of equipments due to

proliferation of advance machines in the market needs optional maintenance policy. The

total operating budget of the firm is directly influenced by the maintenance

characterization factors are changed.

L.C.R. Carpinetti and O.T. Oiko (2008) focused on the development and

application of a benchmarking information system designed for use within a textile cluster.

They suggested that the information system for collaborative benchmarking and

performance management developed in is in line with benchmarking trends reviewed in the

literature. However, the applications have shown that it takes quite a long time to build a

database that can be really meaningful for benchmarking purposes and that it requires

management maturity, an organizational culture of performance management and finally

systematic procedures to collect and input data. However, despite the difficulties pointed

out and the lack of maturity for benchmarking and performance management to be

overcome, the governing institutions and most of the companies have realized that

implementation of this system in itself represents a step towards managing the

improvement of this cluster.

Brun, A., et al. (2008) have studied air-jet looms to improve setting time for the

same. Setup of looms is traditionally performed by very experienced operators and, in case

of retirement, their knowledge can be hardly transferred to newly employees. Hence they

have suggested a procedure for setting the loom. They claimed that the development of a

procedure would be helpful in making the setting up less dependent on single operators. It

has made the training of new employees easier than before. The cost of each setup is

reduced. Expected savings are not only in terms of time but also in terms of money since

usually some scraps are produced during the setting up. This benefit is even more

important considering that the annual number of setups is increasing due to the reduction

of volume of single orders. This study is based on air-jet loom; however, it is reasonable to

think that such a methodology and its relative benefits might be successfully applied to

other types of loom.

23

Kumar, S., & Gangopadhyay, S. (2007) have used plant-level data from two Indian

industries, namely, electrical machinery and textiles, to examine the empirical relationship

between structural reforms like abandonment of entry restrictions to the product market,

competition and firm-level productivity and efficiency. Their results suggest that both the

industries have improved their efficiency and scales of operation by the turn of the century.

Gains in labor productivity were much more evident in states that either have a strong

history of industrial activity or those that have experienced significant improvements in

business environment since 1991. (e.g., Tamil Nadu). Local factors continued to play an

important role in determining gains in labor productivity.

Bilalis, N.et al. (2007) have presented a methodological path for assessing the

competitiveness of a textile sector with the use of the Industrial Excellence Award (IEA)

model. The paper introduces the concepts evaluated by the IEA model and addresses the

ways with which varied management data may be analyzed in order to provide useful

insights for improvement in industrial processes such as new product and process

development, supply chain management, strategy formulation and deployment. The

analysis shows that European textile companies substantially lag in performance when

compared to the best-in-class industry sectors. There are big improvement opportunities

and many can most certainly be identified by thoroughly benchmarking the best-in-class

IEA companies. Key elements to success are adaptability, the use of modern technology

and differentiation. Proper focusing strategies also play an integral part in the companies‘

success. The development of proprietary technology ensures the advantageous first-tier

supplier position, while the continuous improvement of standardized products empowers

the companies to better utilize their resources in order to achieve healthy profit margins.

High performing textile companies boast production flexibility and proper employee

motivation as the foundations of their success.

U. Subadar, et al. (2007) have provided a cross-sectional analysis of the firms

operating in the Mauritian Textile and Apparel sector in the period 2004.they argued that

as more and more industries experience the globalization of business activities, measuring

productivity performance has become an area of concern for policy makers all around the

world. They have compared the productivity of Chinese and Mauritian workers in this

24

particular sector. Their results suggest that Chinese workers are in general more productive

than Mauritian ones.

Mahdi H. Al-Salman (2007) has measured the technological change and

productivity in textile industries in Kuwait during year 1992–2002. He analyzed the

mechanism of structural change in the Kuwaiti manufacturing sector using the input–

output framework combined with factor-productivity analysis for selected sectors with

special reference to high technology industries. The proposed methodology integrates

factor productivity and relative price analysis with input–output model by using V-RAS

method. The model developed was then used for simulation analysis. He used cost, profit

margin, import price, investment as input variables and measured the output in total factor

productivity (TFP). Two main results were derived from the analysis. First, the

acceleration in technical progress gives rise to a higher rate of investment and industrial

growth with more imports and lower trade surplus. Second, the demand for primary

imports in accelerated scenario tends to fall, offsetting its saving effect by its higher

income effect.

Margono, H.(2006) estimated the technical efficiencies and total factor productivity

(TFP) growths in food, textile, chemical and metal products industries from 1993 to 2000

in Indonesia by using the stochastic frontier model. Furthermore, the determinants of

inefficiency were also analyzed and TFP growth was decomposed into technological

progress, a scale component, and efficiency growth. The results reveal that the food, textile

sector is an on average 50.79%, 47.89% technically efficient. It was noted that location and

size contributed to technical inefficiencies in the textile sector. It is noted that productivity

in textile sector decreased at the rate of 0.26%. The decomposition of TFP growth indicates

that the growths are driven positively by technical efficiency changes and negatively by

technological progress in all four sectors. In general, private firms are more efficient than

the public firms but the age of a firm had almost no effect on the efficiencies. This

indicates that output growths in textiles, chemicals and metal products sector are driven by

capital rather than by material or labor.

N. Towers & J. McLoughlin (2005) have examined how widespread TQM has been

implemented within the UK textile manufacturing sector that is characterized by a high

25

proportion of Small and Medium sized Enterprises (SMEs) managing unpredictable and

volatile demand. The survey investigates the effects of quality management systems on

business performance and highlights a number of difficulties including cost constraints,

and lack of training and productivity improvements. Reported benefits in team working,

quality awareness and customer satisfaction were noted. TQM was seen as a method of

removing waste by involving everyone in improving the way things are done. TQM

approach was about changing attitudes and skills so that the processes become one of

prevention rather than detection.

Ozturk, H. K. (2005) has studied energy usage and costing in textile industry in

Turkey. He recorded that energy takes about 10% of total cost of production in Turkish

textile industry. The relationship between energy consumption, energy cost and production

has been presented. It has been found that the total energy consumption, electricity

consumption and heat energy consumption increases linearly with production. He has

concluded that the results can be useful not only in estimating the cost of energy for any

given production levels but also in estimating the reduction in production costs for any

energy saving. Finally conservation measures have been proposed.

Simelane, X. (2005) have studied about the Textiles and employee relations in

Swaziland. He used the case study approach. The case study is based on interviews and

some observation of employees. He has used various factors such as skill, literacy, job

security, working hours, health and safety, HR practices, etc. for study. The effect of these

factors on output and productivity were studied and conclusions are presented. Infusion of

capital leads to technology up gradation leading to improvement in productivity but worker

generally oppose technology up gradation due to fear of losing the job.

Moore, S. B., & Ausley, L. W.(2004) have presented the case of productivity

improvement through ―green production‖ in U.S. textile industry. They have used a case

study based approach. They have developed relatively low cost process of waste water

treatment and used the same water for the process. Finally, they have highlighted the

benefits of this technology. They concluded that textile industry is leading the movement

towards global manufacturing and hence such efforts towards productivity improvement

are important.

26

Lindner, S. H. (2002) has studied about the technology and textile globalization. He

took labor cost, energy cost, maintenance cost, cost of space; capital costs for machines,

production costs (Total), fiber consumption, running time of machine, etc. as variables. He

concluded that the textile centers suffered stagnation and decline not because of a lack of

innovations, but because of investments in the most modern technology. In Asia, new

machines were regularly installed to meet the growing demands of the market. This led to

the reduction of competitive capacity of the old textile centers, since the newcomers had

long working-hours, cheap labor and (more) modern machines.

Ren, X. (2000) has developed environmental performance indicators (EPIs) for

textile process and product. The increasing demand for environmental performance

evaluation of industry requires development of sector-specific environmental performance

indicators (EPIs). For the consumer product manufacturing industry, in this case the textile

industry, the need to evaluate environmental performance both from process and product

life cycle perspectives leads to development of EPIs of process and product dimensions.

Such types of EPIs have been developed, with best achievable values being identified, by

this study for cotton woven products and wet processing. An in-depth discussion has been

presented concerning problems in developing and applying EPIs, while areas for further

research are also recommended. Development of EPIs for textile industry reveals that:1)

Environmental performance of industry should always be assessed both from process and

product perspectives, especially for the consumer product manufacturing industry. 2)

There are conflicts among different kinds of environmental and/or health and safety

objectives in processes, different companies, and the interests of stakeholders. Therefore,

priorities must be set up at each level. 3) Criteria can then be identified for the

development and application of EPIs. Such a framework, established at the process, plant,

local, national, regional and global level will ensure the consistency and usability of the

outcomes by different users. 4) It is very difficult to identify quantitative values for product

dimension EPIs due to the greater variation of products than processes. The application of

product EPIs will be based on comparison of value achievable by CT with that of TCP.A

shift from qualitative to semi-quantitative and quantitative EPE and CT assessment will

require the further development of EPIs.

27

Char, P.et al. (1998) have evaluated in the performance of 29 Canadian textile

companies in 1994 using Data Envelopment Analysis (DEA). Using the Chames, Cooper

and Rodes (CCR) model in DEA, they have first obtained the results of efficiency scores

and returns to scale of 29 Canadian textile companies. They have recognized that the

returns to scale are the key factor that helps companies to better utilize their inputs

(resources). So, they focused on returns to scale to explore the alternatives to reducing

inefficient inputs. For the DMUs in increasing returns, they considered the trade-off

between an increase or non-increase in inputs by evaluating the amount of the output that

can be increased. They developed a mathematical model to find the best expansion plan in

terms of increments in outputs and inputs. The data of the 29 Canadian textile companies

in 1994 show that most Canadian textile companies did not perform well, with a few being

DEA efficient and the rest very poor performers. They have suggested that for improving

performance significant changes in structure, strategy and capacity plans are needed.

Singletary, E. P. et al. (1998) have studied U.S. textile industry for developing

competitiveness. They have observed that all the textile manufacturers studied are

transforming their traditional, mass-production systems into smaller-scale, flexible systems

and processes designed to provide superior quality, responsiveness, and customer value.

Overall, the observed textile- manufacturing transformation amounts to a paradigm shift

from mass production toward agile, intelligent production that enables companies to thrive

in the environment of continuous and unpredictable change. They proposed a framework

for strategic planning of systematic organizational change, including: (i) a model for

strategic transformation consisting of potential production states and transition paths that

allow for sustainable shift from mass production to agility; and (ii) a profiling tool that

maps the alignment between key internal and external organizational sub-systems focused

on the development of congruent system-wide relationships for comprehensive, sustainable

change. The following general conclusions were reached. First, companies that operate in

rapidly changing, uncertain markets need to adopt the concepts of agility in order to master

their competitive environment and thrive on change. Second, the path for effective

transformation includes a phased sequence of changes in organizational scope and

capabilities, gradually expanding from internal- to external-change focus and from

28

incremental- to radical-change rate. Third, effective organizational transformation requires

alignment between key internal and external sub- systems, such as company strategy,

structure, management system, employee system, technical system, information system,

and market environment.

Karacapilidis, N. I., & Pappis, C. P.(1996) have presented an interactive model

based system for the management of production in textile production systems focusing on

the Master Production Scheduling problem. Because of the special characteristics of the

industry, that is mainly the multi-phase process with multiple units per phase, different

planning horizons and different production requirements for each phase, the scheduling of

these systems becomes quite complex. Apart from a comprehensive presentation of the set

of the modules the system is composed of, together with their interrelationships, the above

characteristics are analyzed, and their impact on the production control system is

explained. The system is also related to two well-known production control systems,

namely MRP-II and Optimized Production Technology. A new system (called as YFADI)

has been developed aiming at inventory reduction, increased -productivity, improved

customer service and control of the business in a textile industrial unit. The phases covered

(weaving, starching and warp making) are the most difficult ones in terms of scheduling.

The system has been integrated in a structured form, oriented by the textile manufacturing

process phases. Two particular features of YFADI are that: - a production order can be

split up into a set of jobs which is then assigned to multiple parallel machines; all customer

orders are accepted and the available capacity is adapted accordingly, basically due to the

ease of subcontracting.

Susan Christoffersen (1993) has studied on the topic of textile R&D. his study aims

to find out whether R&D in textile industry delivers success? He has concluded about

R&D expenditure and success (competitiveness) that the investment in R&D may not be

the road to success in the textile industry. Each of the financial indicators shows that many

firms are faltering, while a few are prospering. Many process innovations in the textile

industry are embodied in high tech equipment. Innovation may not show up on the balance

sheet as R&D investment but rather expenditure on plant and equipment. Entrenched

protection has trained the industry to seek new protections, not innovation. To assess this,

29

he measured firm profitability using Tobin's q', the ratio of the stock market valuation of

the firm compared to the book value of the firm's assets. Q values are compared to other

financial ratios, and then used to assess the impact of research and development (R&D)

spending. A Mann-Whitney rank test indicated firms that conduct R&D are not more

profitable, as measured by q, than those that do not conduct R&D.

Chakrabarti, K. (1990) has studied and explained the relationship between

innovation and productivity growth in the textile industries. It seems that innovation is

related with productivity growth. Although the textile mills themselves spent very little

money on research and development, innovations introduced by its suppliers helped

increase productivity. Innovations in weaving looms and other equipment helped the

productivity grow significantly. Innovations in other areas such as dye, finish, etc. helped

increase the productivity. Industries related to the textile industry experienced new

opportunities for innovation as major changes in weaving and spinning were introduced.

ANTONELLI et al. (1990) have focused on the interdependence among technical

changes in different stages of production in the textile industry. A model is developed to

present the different linkages induced by price and quality effects of technical change and

technological complementarities. The paper studied the diffusion of technological change

in two consecutive production stages in the textile industry. Over the last decades

technological change in the spinning and weaving industries involved three major

innovations. One is the product innovation of synthetic fibers, the other two are process

innovations in spinning (open-end rotors) and in weaving (shuttle-less looms),

respectively. These radical innovations were introduced at quite different dates: synthetic

fibers in the 194Os, shuttle-less looms in the 196Os, and open-end rotors in the 1970s.

They were all preceded and followed by incremental innovations which constantly

improved techniques and products. These changes helped to modernize the production

facility of textile industry.

Noweir, M. H.(1984) has studied the effect of noise exposure as related to

productivity of textile workers. He has studied a sample of workers exposed to average

noise levels ranging from 80 to 99 decibels in different operations of three textile mills

with respect to their productivity, work rule violations, absenteeism, and accidents. Noise

30

exposure levels were measured in individual departments of the mills, and workers were

interviewed to ascertain socioeconomic background, work history data, and health status.

And he concluded that the results of this study present suggestive evidence of an

association between excessive exposure to workplace noise and problems in productivity,

discipline, absenteeism, and safety. The study suggests that controlling noise exposure in

the textile industry may have benefits in ameliorating these problems and consequently,

could be an economically sound investment. Noise appeared to affect the quality of work

as reflected by disciplinary actions for material damage, and this effect was higher in

weaving and spinning operations which involved vigilance tasks. Certain personal and

socioeconomic factors affected high vs. low noise exposure differences found among

workers for the investigated variables. These effects were most apparent for absenteeism

and, to a lesser extent, productivity. Disciplinary actions did not appear to be influenced by

any such individual factors. It was concluded that noise abatement in the textile industry

could be beneficial to worker productivity and well-being and contribute to more

economically effective operation.

The report of 12th

Shirley International Seminar (1981) discusses some of the

opportunities for waste heat recovery within the textile industry. These opportunities were

identified in 19 papers presented to delegates at the 12th Shirley International Seminar,

held near Manchester, U.K., in September 1980. The activities include detailed

investigations of energy consumption and conservation measures in textile processes.

Energy is particularly important to the textile industry when one studies the energy content

of textiles when compared with other common manufactured products which are normally

associated with 'energy intensive' industries.

Pickett, J., & Robson, R. (1977) have done a comparative study of operating

conditions and technology in African textile production and European countries. They have

discussed the data on machine and labor requirements to produce cotton cloth in six

African textile factories. The findings confirm the view that more use has to be made of

machines and labor in Africa than in Europe to obtain the same output. They also reveal

marked differences between the two countries, and - in one country ~ among factories.

They analyzed the problem with two operations, first is spinning. In this they used normal

31

spindle speed, machine price per spindle, direct labor required, value of equipment

required to produce 1lb of yarn per hour these as variables. Second one is weaving, in this

basic loom speed, capacity output, machinery price per loom, efficiency (percent yards per

hours), wages per hours, etc were used as variables and analyzed. The analysis showed that

European textile firms are using higher loom speeds.

2.2 Studies related to manufacturing industries

Charoenrat, T., & Harvie, C. (2014) have studied the technical efficiency of Thai

manufacturing (Including textiles) SMEs and their firm-specific determinants utilising

firm-level industrial census data. Results from a stochastic frontier production function and

technical inefficiency effects model reveal that Thai SMEs are overwhelmingly labor

intensive with low average technical efficiency. Results also indicate that firm size, firm

age, skilled labor, location, type of firm, ownership, government assistance, foreign

investment and export activity are important firm-specific factors contributing to the

technical efficiency of SMEs. They have suggested that specific policies are warranted to

improve Thai SMEs. These policy measures include: easier access to financial services,

access to skilled labor, training of the workforce and entrepreneurs, addressing location

and regional capacity inequities, encouraging foreign investment for operational synergies

and export incentives for penetration in the world market. Skilled labor had a significant

and positive correlation with the technical efficiency of all categories of manufacturing

SMEs. This shows the importance of continually upgrading the knowledge and skills of the

workforce in manufacturing SMEs through the provision of appropriate educational and

training opportunities. Without access to a skilled workforce improvement in the technical

efficiency of Thai SMEs will be difficult to achieve, making it difficult to engage in higher

knowledge, innovative and higher value adding activities.

Oh, Donghyu et.al. (2014) have presented the parametric estimation of the rates of

technical change and total factor productivity (TFP) growth of 7462 Korean manufacturing

firms over the period 1987–2007. In addition to making estimates of the TFP growth and

its decomposition, the paper compares the parametric TFP growth measure with the non-

parametric data. Three variables are used in the empirical examination of the production

32

function and computation of TFP growth. The value- added of each firm is used as a

measure of output (Y). Capital stock and labor (K and L) are used as input variables.

Hypotheses related to technology level, firm sizes, industrial sectors, skill biased

technological change and macroeconomic and industrial policies are tested to explain the

growth patterns and heterogeneity in technical change, input biases and TFP growth rates.

Using second regression analysis, the paper explores the determinants of TFP growth and

their policy implications. They concluded that, (i) large firms and high technology

industries show a higher rate of TFP growth in the Korean manufacturing industry. (ii) The

capital intensity growth and the competitive market condition are negatively related to the

rate of TFP growth, (iii) the age and patenting activities of the firm positively affect its

TFP growth.

Lin, S., & Ma, A. C. (2012) have done investigation of the productivity effect of

outsourcing by using the Korean manufacturing (including textiles) industry data. They

have found that there are positive productivity gains from material outsourcing. The gains

may be due to firms outsourcing their inefficient production stages overseas while

continuing to focus on the process where they have a comparative advantage. The results

also suggest that during sample period Korea‘s experiment with service outsourcing did

not lead to an increase in its productivity. The reason could be that it initially experienced

misalignments between domestic firms and international providers in service outsourcing.

M.I. Shahidul and S.T. Syed Shazali (2011) have examined the impact of favorable

working environment (FWE) and R&D on manufacturing productivity of labor intensive

industries. More specifically, the paper intends to generate quantitative evidence of the

effect of FWE and R&D-based manufacturing process on outputs and productivity.

Convenience sampling method has been used to conduct this study. This method provides

the opportunity for selecting those manufacturing industries that are convenient to get

access for collecting relevant information. Three categories of labor intensive

manufacturing industries such as category A, B and C have been chosen to perform this

research. Industrial category A represents the manufacturing operations which are based on

skill of labor. Category B is a group of industries which provides the FWE the ability to

utilize the potential of skill in the manufacturing process. However, category C is a

33

specialized group of industries and its manufacturing process is dependent on R&D. The

operating data of inputs cost and the revenue of corresponding outputs have been gathered

from audited documents of the relevant sample industries and the data have been analyzed

by using standard statistical techniques in order to establish the relationship between

dependent and independent variables. The result has shown that the expenditure on FWE is

positively associated with productivity. The expenditure on R&D is strongly correlated

with productivity. The study concludes that FWE as proxy of job satisfaction of workforce.

John Van Reenen (2011) has studied issue related to productivity and competition.

They have argued that competition does increase productivity and a main mechanism is

through improved management practices. Their view is that management should be seen

partly as a transferable technology and that competition fosters the adoption of better

management practices through both selecting out the badly managed firms (reallocation)

and giving incumbent firms stronger incentives to improve their management practices.

Rajesh K. Singh, et al. (2009) have analyzed different challenges for small and

medium enterprises (SMEs) in India and China following globalization. The paper aims to

describe the status of these enterprises and examine the roles of government policies and

strategy development for competitiveness. A questionnaire-based survey was conducted.

They found that the governments of China and India have launched various promotional

schemes for SMEs. Various challenges for SMEs in these countries are similar; however,

the rate of growth is different. Indian SMEs give more attention to supplier development,

total productive maintenance and the organization‘s culture. Chinese SMEs pay more

attention to relationship management and cost reduction. Human resource development and

quality improvement are also highly correlated with competitiveness. They recognized that

SMEs should focus on developing their human resources and improving product quality.

This effort will help SMEs retain human capital as well as increase the demand for their

products.

Ghosh, S. (2009) has examined the association between productivity, ownership

and employment growth, using data on Indian state-owned enterprises. After accounting

for various firm level controls, the evidence indicates that firm growth improves primarily

through passive learning, whereas higher levels of active learning appear to slow down

34

firm growth, although the magnitude of these effects is economically small. Besides, he

suggested that ownership is significantly and non-linearly related to firm growth. Using

unique firm-level data covering virtually the entire population of public enterprises in

India, the importance of size and age, the role of productivity and ownership in driving

firm growth were examined. Accordingly, firm- level productivity measures were obtained

using advanced econometric techniques. The evidence indicates that firm growth is

negatively related to firm size and in a non-linear way, following an inverted U-pattern. In

addition, he also found that firms facing higher financial pressure exhibit lower growth,

although no association could be observed between firm innovativeness and its growth.

Finally he suggested that increases in total factor productivity improve firm growth.

Raj Kumar, et al. (2009) analyzed the various factors which are important for total

quality management implementation in various manufacturing organizations (including

textiles) and to assess their relevance for Indian manufacturing organizations. They have

proposed a model to implement TQM. It provides a direct approach to top management to

implement TQM program through customer satisfaction as main focus. A clear focus on

defining and managing the customer side, process emphasis, and creating knowledge

through innovation will create a new business environment. Under this new environment,

TQM systems will shift towards a philosophy of quality based strategic management

systems. They have further recommended that the Indian industry must make all efforts to

implement TQM, may be in a phased manner. This will help in making industries

competitive on global level.

Mavannoor Parameswaran (2009) has examined the effect of trade facilitated R&D

spillovers on the productivity of manufacturing firms in India. Output, capital stock, labor

hours, raw materials, energy, share of recent investments in capital, goods purchased, total

capital stock, technology imports, R&D were used as variables. He concluded that

imported machinery have a significant effect on productivity in technology-intensive

industries. The effect of trade-facilitated knowledge spillovers is significant in all cases

with a greater effect on productivity in technology-intensive industries. The study also

shows that investments in plant and machinery, both imported and domestically produced,

enhance the effect of knowledge spillovers on productivity. Thus, this study provides

35

detailed micro-level evidence on the argument that trade openness promotes technological

progress and economic growth in developing countries.

Ã, L. L., Markowski, et al. (2008) have examined the relationship among TQM,

ERP implementation, operations management, customer satisfaction, and a firm‘s

performance. In this study, the researchers have provided three substantive findings to

advance the literature on TQM and ERP implementation: (i) ERP implementation can be

successful if it is preceded by a TQM focus; (ii) there is a causal relationship between

TQM focus and customer satisfaction, as well as between ERP implementation and

operations performance; and (iii) ERP implementation positively contributes to operations

performance, which has significant effects on customer satisfaction performance.

Furthermore, better customer satisfaction performance contributes to better performance.

Consequently, manufacturing firms would be well advised to place a focus on TQM before

implementing ERP systems to achieve the expected successful results.

Pinho, C.(2008) has analyzed the importance of developing a quality management

approach as a way to enhance the bottom line results of small and medium sized

enterprises (SMEs). The main goal was to examine the synergistic relationships between

TQM, performance, consumer orientation and innovation. He concluded that the most

relevant TQM components impacting on SME performance and consumer orientation are

measuring results, quality assurance systems, top-manager training programmes and

leadership initiatives. Results also confirm both the impact of innovation on performance

as well as that of consumer orientation on innovation. Furthermore, no statistical evidence

was found to either confirm the effect of TQM on innovation, or that of consumer

orientation on performance.

Singh, R. K., & Garg, S. K. (2008) have studied various problem (including textile)

faced by SME‘s for their growth as engine for economic growth all over the world. After

the globalization of market, SMEs have got many opportunities to work in integration with

large-scale organizations. The units cannot exploit these opportunities and sustain their

competitiveness if they focus only on certain aspects of their functioning and work in

isolation. They tried to identify the major areas of strategy development by SMEs for

improving competitiveness of SMEs in globalized market. They concluded that all over the

36

world, SMEs are considered as major source for economic growth. SMEs have not given

due attention for developing their effective strategies in the past. But they revealed that

most of the strategies have been formulated for short-term goals as most of them are

localized in their functioning. On the export front, they are facing many constraints due to

their limited resources and lack of innovation in capability development. Major problems

are related with knowledge loss, product design and development capability, training

infrastructure and networking. SMEs are also not following any comprehensive framework

for developing their strategies and quantifying their competitiveness productivity of same.

Cesar, L., et al. (2008) have discussed a conceptual model for performance

measurement and management of an industrial cluster. Cost, wages of works, labor force,

supply chain management, performance management were taken as variables. The result

showed that applicability of continuous improvement cycle gives improved performance of

the cluster. Defining objectives of performance, deploying actions and evaluating results

and feedback are important steps to implement the same.

M. Jerzmanowski (2008) has studied about total factor productivity differences

between appropriate technologies vs. efficiency. He used capital per worker, growth rate,

technology, total factor productivity (TFP), output per worker as variables. And his paper

attempts to use a empirical approach to shed light on two issues. First, he examined how

sensitive the findings of the development accounting literature are to the assumption of

Cobb–Douglas production function. Second, within the Cobb–Douglas framework, he

looked for evidence of the two alternative explanations of total factor productivity

differences: The inefficiency view and the appropriate technology view. Overall, he has

suggested that although there are differences in technologies that are available to rich and

poor countries, inefficiency is more important than technology for understanding the vast

income disparities across countries.

Pattnayak, S. S., & Thangavelu, S. M. (2005) have analyzed the effects of

liberalization on the Indian manufacturing industries initiated by the 1991 economic

reforms. Their results suggest that the key industries have experienced technological

change and increase in total factor productivity growth. The study also suggests that the

37

industries have experienced economies of scale, and the scale effects have been exploited

more intensively since the 1991 economic reforms. The results suggest that the total factor

productivity growth has improved after the 1991 economic reform for most of the

industries. However, they do not expect this result to hold in the future if the demand for

capital investment increases substantially. As the cost of capital is reduced it will increase

total factor productivity growth in the manufacturing sector. As the economy liberalizes

and permits greater inflow of capital into the economy, the usage of foreign capital could

make important productive contribution to the industrial structure.

Atack, J.et al. (2003) have studied the productivity in manufacturing and the length

of working day. They found that, elasticity of output with respect to daily hours was

positive but less than one, implying diminishing returns to a lengthening of the working

day. They have found that diminishing returns to days per month and months per year, but

the degree was much smaller than for daily hours. They also explore the relationships

between operating times and daily wages. The results were less conclusive but suggest a

small positive relationship between daily hours and daily wages, at least for certain subsets

of establishments and certain measures of daily wages. These results have important

implications for understanding changes in output in manufacturing over time as well as for

the factors influencing the long-term decline in hours worked per day. They have

concluded that rapid growth in the demand for labor due to World War I created an

especially tight labor market in which workers were less willing to work long daily hours,

even at higher wages.

TARLOK SINGH (2003) has analyzed the effects of exports on productivity (level

of output per capita) and growth in India. He carried out an analysis of ten industries in the

manufacturing sector in India. Capital, labors, rate of growth, output per capita, total factor

productivity (TFP) were used as variables. He obtained the two sets of results; one based

on the model estimated with exports and the other based on the model estimated without

exports. Both these sets of results do not provide any evidence of convergence, and instead

support the contrary evidence of divergence among industries. These results suggest that

the industries with low output per capita tend to lag behind the industries with relatively

38

higher output per capita and there is a tendency for divergence in the growth process of the

sample manufacturing sector.

Wakelin, K.(2001) has done a study in productivity growth and R&D expenditure

in U.K. manufacturing firms. He has presented in the paper that the role of R&D

expenditure in productivity growth in the UK is similar to that found for other countries

such as the US, France and Japan. He concluded that the innovative firms spent more on

R&D expenditure relative to sales than non-innovating firms (2.3% against 0.8% in the

period 1988–1992); this R&D expenditure also appears to have a higher rate of return than

the R&D expenditure of non-innovating firms. The rate of return is particularly high when

firms are located in sectors that are net users of innovations. Both the innovation history of

the firm and the sector appear to be important influences on the rate of return to R&D:

innovative firms and firms located in ‗innovation using‘ sectors both have higher rates of

return than other firms.

2.3 Studies related to apparel industries

William E. James et al. (2010) have reviewed the textile and apparel industries.

They have presented a comparative study of Textile in China, Vietnam, Taiwan,

Colombian, Pakistan, Sri Lanka and India. Further they have recommended features for

Indonesia‘s textiles sector to be competitive. Some of the measures are: Improving supply

chain, Reducing tariffs and taxes on high quality yarn, increasing capacity and promoting

quality culture, etc. They concluded that:- these measure will help to improve exports of

Indonesian‘s textile and apparel sector.

Venu Varukolu and Haesun Park-Poaps (2009) have studied the status of

technology adoption of Indian apparel manufacturing firms and the organizational factors

that affect the level of technology adoption. Fourteen technologies applicable to apparel

manufacturing were examined. A survey with an online questionnaire to apparel

manufacturers in India was conducted to collect the data. The TQM factors developed in

the questionnaire were - leadership, training, employee management, information and

analysis, supplier management, process management, customer focus, and continuous

improvement, and the performance measures were employee performance, innovation

39

performance, and firm performance. Results of the study reveal that employee performance

and innovation partially mediate the relationship between TQM practices and firm

performance. The study suggests that continuous improvement and process management

can be combined with breakthrough innovation. The study recommends that firms should

focus and satisfy employees‘ needs to improve performance, market share, and

competitiveness. The study also finds that firms should improve innovativeness to become

competitive in a changing marketplace.

Anbanandam, R., et al. (2009) have proposed a methodology to measure the extent

of collaboration between apparel retailers and manufacturers in the apparel retail industry

in India. They confirmed the validity of the proposed collaboration index for measuring

collaboration. The findings also show that the collaboration index is positively associated

with operational performance. They have derived a collaboration index using a graph

theoretic approach by considering all the variables in totality. A methodology was

developed to measure supply chain collaboration by using five dimensions, namely top

management commitment, information sharing, trust among supply chain partners, long-

term relationship and risk and reward sharing. Several apparel retail companies in India

were chosen to test the proposed methodology. A total of 35 companies participated in the

research. Their survey results proved that the proposed methodology to quantify

collaboration was highly reliable and adequately valid. Their research also showed the

positive effect of the collaboration index on operational performance.

The study by Kapuge, A. M., & Smith, M. (2007) aims to focus on the

implementation of, total quality management (TQM), among apparel companies in Sri

Lanka, to determine the impact on business strategy, management practices and

performance reporting. The results demonstrate a significant difference in the business

strategy implemented by the two groups, with those companies adopting TQM regarding

quality as more important than cost efficiencies. Significant differences in both quality

management practices and performance reporting systems were observed, except in the

area of employee empowerment. The competitive strength of the Sri Lankan garment

industry has historically been based on cheap labor, high-labor standards, a literate labor

force, investment-friendly government policies and strategic shipping lanes. On the other

40

hand, competitive disadvantages are readily apparent: long lead times, lack of product

development, weak marketing and low labor productivity partly due to outdated

technology. Emerging low-labor cost East-Asian countries (e.g. Cambodia and Vietnam)

mean that Sri Lanka cannot continue to compete on the basis of low-cost labor, meaning

that measures are necessary to secure improvements in the productivity and quality of the

sector. Management practices like TQM, to assist the survival of the industry, have thus

received renewed attention.

N. B. Powell & N. L. Cassill (2006) have analyzed new product development

(NPD) processes as a competitive tool to develop and launch textile products. They

concluded that, NPD is imperative for the global textile and apparel industry, but requires a

disciplined process that flows from a well-organized and well-communicated cross-

functional team. This team should have strong leadership and be creative in approach,

and seek consideration of varied new products. NPD requires an integrative approach to

meet global marketplace demands, including the elements of marketing, design, materials,

and technology. A critical thinking/team approach is imperative within companies as well

as across companies within an industry (e.g., strategic partnerships) in order to realize

creative new product concepts. The interaction between the marketing function and the

research and development responsibilities allow for more efficient and effective product

development. The ability to organize research and competitive information in the market

into matrices that influence decision makers from each segment of the organization is

important in that it encourages an objective consensus. The procedural steps and

checkpoints in an NPD process are considered before the product enters the market.

Jimmy K.C. Lam, R. Postle, (2006) have studied textile and apparel supply chain

management in Hong Kong. The typical problems facing with textile and apparel supply

chain are, short product cycle for fashion articles, long production lead-time, forecasting

errors for fashion items, long production lead-times and minimum batch sizes for

production, all of which force to improve efficiency and enhance competitiveness through

supply chain management. The differentiation of product demands into functional and

innovative products helps the supply chain company to employ different supply chain

strategies for different products, namely responsive supply chain strategy for innovative

41

products and efficiency supply chain strategy for functional products. These two supply

chain strategies are focused on the downstream supply chain aiming at shortening the time

to research the market and also to reduce the stock levels in the retailing industry. They

conclude that the supply chain in Hong Kong, instead of focusing on logistics,

transportation, time to market and forecast demands, should focus on product design,

material control, and production co-ordination. The Hong Kong supply chain activities

should streamline the whole production process from fibers to yarn, knitting, weaving,

dyeing and finishing, through to the garment manufacturing process.

Teng, S. G. et al. (2006) have provided an illustration of collaboration in South

American small to medium-sized companies in the textile/apparel industry concerning

quality, logistics, forecasting techniques, lead time, inventory management, and integration

of supply chain. The results provide recommendations based on the evaluation of strengths

and weaknesses that may be used as references for these small companies to increase their

potential of being active partners in the US supply chain. Continuous improvement in the

different SCM processes is essential in the implementation as a part of strategy, especially

in areas such as customer service management, procurement, commercialization and

manufacturing flow management. Integration with customers in foreign markets is another

key driver that these organizations must establish as priority organizations. If the

companies cooperate, make strategic alliances and act as partners, instead of competitors,

the perception in the US industry will then improve, creating trust and ultimately more

business with more stability.

Erin Dodd Parrish, et al. (2004) have studied textile sector in U.S. They have

identified opportunities in the international textile and apparel market place for niche

markets. The study points out that U.S. textile companies are starting to focus on products

that are more capital and technologically intensive versus those products which are

historically labor intensive. Companies are also searching for products in which they could

have a large and profitable market share, particularly those that are protected from

competitors. One way in which US textile companies can utilize this idea of specialization

is by the development of niche markets. It has been proven that product differentiation, i.e.

42

niche markets, is related to profitability. Based on the theories, specialization and in turn,

niche markets, could prove to be the ―saving grace‖ of the US textile industry.

Ramcharran, H.(2001) has studied the productivity in U.S. apparel industry. He has

indicated through study that over the period of 1976-93 the apparel industry made

adjustment in moderate downsizing in employment that contributed to an increase in

productivity and profits, although profits declined slowly from 1992-95. The results

indicate industry adjustment by increasing labor productivity and maintaining fairly stable

profits despite job losses. He has suggested that to revive the industry, policy makers will

have to seriously consider options in technological improvements, industrial and trade

strategies. However, due to evidence of rapidly decreasing capital productivity, substantial

technological improvements will be necessary. Further study by author revealed that the

large apparel firms increased research and development expenditures for quality

improvement and utilized engineering advances in Computer Aided Design (CAD)

systems for pattern design, marking, grading and cutting, sewing, the most important

activity, has been difficult to mechanize. Automation of the apparel industry has been

extremely costly due to the soft and varied nature of fabrics, the complexity of assembly

process, and the frequent modifications required by changing clothing fashions.

Toni, A. De, & Meneghetti, A.(2000) have investigated that how the decision

variables of the production planning process for a network of firms in the textile-apparel

industry, i.e. planning period length, material availability, the link between production

orders and customer orders as regards colour mix, can affect the system's time

performance. To adhere to reality, they have studied and collected actual data from one of

the most important Italian companies and using these observations as a basis, a simulation

model was built. Only the production planning period compression has been recognized as

yielding a significant improvement in the external time performance. A relation between

the external time performance and the internal time performance of the network is

recognized. Their conclusion show how even from a systemic as well as from a single firm

point of view, achieving a favorable internal time performance is a means of gaining an

external time performance, recognizable by customers. The production planning process

was found to be an important area of improvement for a network in a time-based logic;

43

shortening the production planning period, in fact, significantly affects the weighted

average delivery anticipation.

Sara Umberger Douglas, Arathi Narayan (1993) has made comparative analysis of

the textile and apparel industries in India and the United States. And they concluded that

there is potential for continued growth for textile and apparel industries in India and

respondents displayed realistic perceptions of industry problems. Technology, markets, and

foreign competition may be more serious problems than they are perceived to be, but if

managers are unavoidably distracted with policy and raw materials issues, the latter may

need to be addressed first. More horizontal and vertical co-operation is needed in order to

achieve a better working relationship with the government as well as to guarantee a steady

supply of competitively priced domestic materials. Technology enhancement and product

upgrading (including attention to high quality fabrics and creative designs) should result in

production of higher value-added items, which in turn would capture new international

markets and yield higher unit values within quota restrictions. Indian producers need to

build on such existing strengths as their cotton and silk production, as well as other

products such as hand-knotted and other wool carpets.

Lin, S. H. et al. (1993) have studied on productivity and production in the apparel

industry. He has studied sewing systems and their effects on productivity of apparels. He

has used technology, system of production, product life cycle, product type, lead time,

style, fashion and output etc. as variables. He concluded that consumer‘ demands have

been increasingly diversified and individualized, creating the need for apparel producers to

be responsive to the rapidly growing individualization of consumers‘ needs. These new

demands for consumer responsiveness call for a shortened product life cycle and increased

diversification of fashion. This increased responsiveness requires that successful apparel

producers have the capability to produce many different types of products in small

quantities in a shorter lead time.

2.4 Studies related to clothing industries

Pal, R., Hakan Torstensson (2011) have studied to synthesize critical success

factors for Swedish textile and clothing firms using Three Dimensional Concurrent

Engineering. Product quality, Lead time, Cost, Production Flexibility, Coordination and

44

trust, brand value, Service level, Information Sharing, Innovation, Sustainability,

Organization culture were used as variables. A semi-structured survey of 42 Swedish

companies was carried out. They inferred that product quality was considered to be the

most important success factor for organizations. There was no firm which rated product

quality below ―high-priority‖ in the scale, closely followed by high service level as another

key performance driver. The surveyed firms also prioritized high flexibility in product

designing, supply chain; high supply chain coordination; and brand value as critical factors

in driving success. Low lead times and high degrees of innovation were also considered as

potential CSFs for success. On the other hand, price level benefits were considered less

imperative for business success. Results showed that most of the key success factors are

synthesized and sustained through (3-DCE) three-dimensional concurrent engineering

designing. The paper also highlights the necessity of incorporating intangible value

propositions of culture, leadership and governance, knowledge, image and relationship into

the 3-DCE (three-dimensional concurrent engineering) model to generate an ―extended 3-

DCE‖ framework for mediating operational performance and hence organizational success.

Taplin, I. M., & Winston-Salem(2006), have examined how the textile and clothing

industries, which retain a significant employment presence in the EU, have responded

differently to heightened overseas competition and changes in buyer-supplier relations.

They concluded that clothing proves more robust in retaining an employment presence

than the more capital-intensive textile sector. This is surprising since labor-intensive

industries are expected to suffer more from intensified global competition than capital

intensive ones. Job losses continue in both sectors but firms are innovating in restructuring

practices to remain competitive and responsive to buyer pressures. Technological

innovation, the pursuit of niche markets and increased outsourcing are key responses.

While employment has dropped, productivity has increased, and firms that remain, manage

to exploit market niches where fast turnaround, quality and small batch production provide

competitive advantage. Conversely, the capital-intensive textile sector has been less able to

adapt to the shift to overseas production. Changes continue to reshape industries in high-

wage economies such as the EU.

45

Andrew Hughes (2005) has studied the Textile SMEs in U K. He claims that, there

is potential to improve the competitive performance of small to medium-sized companies

(SMEs) particularly in the UK clothing and textile industry. He showed that there are

opportunities to improve the profitability of SMEs if the findings were transposed to other

similar businesses willing to invest the time and effort into setting up an ABC/ABM

system. Activity Based Costing/ Activity Based Management (ABC/ABM) enables firms

to focus on its activities and products; it traces cost-to-cost drivers, for example, the

number of machinists needed to produce trousers. The business then understands; its

business processes in detail; the cost of process failures; the relationship of processes to

customers; the profitability of customer segments; and the affordable amount that can be

spent on influencing the preferred customer groups. He recommends that management

must institute a conscious process of organizational change and implementation if the

organisation is to receive benefits from the improved insights resulting from an ABC

analysis‖.

V. N. Balasubramanyam, et al. (2005) have made a comparative analysis of

Textiles and Clothing Exports from India and China. Total exports, percentage share,

similarity index were used as variables. They have used Kreinin-Finger similarity index as

methodology. By using this they had a benchmarking of India and China‘s exports, shares,

etc. Their results indicate that China has much higher shares in world exports of both

textiles and clothing, while India has a comparative advantage in women‘s clothing of

various sorts and men‘s shirts. India would have to improve her competitive strengths in

export markets vis-à-vis China, especially so in high value design oriented products in the

EU and the US markets.

Tony Hines (1993) has studied about the competitive nature of the clothing industry

in the European Union. He provided a statistical summary of trends and competitive

structures within the European Union (EU), concerning employment, trade policy,

production, imports, exports, retail structures, consumer expenditure and labor costs across

the Member States. He concluded that the lowest labor costs in the EU are in Portugal and

the highest labor costs are in Denmark.

46

2.5 Studies related to garment industries

Joshi, R. N., & Singh, S. P (2010) argued that the Indian garment industry has

witnessed a significant change since the inception of the New Textile Policy 2000 that

suggests removing the industry from the list of small-scale industries with a view to

improving its competitiveness in the global market. As productivity is the driving factor in

enhancing the competitiveness of any decision-making entity (firm), a study of total factor

productivity (TFP) and its sources can provide vital inputs to a firm for improving its

competitiveness. Keeping this as a backdrop, the paper has attempted to measure the TFP

in the Indian garment-manufacturing firms. They have identified sources of the TFP; and

suggested measures for the firms to enhance their productivity. They concluded that the

Indian garment industry has achieved a moderate average TFP growth rate of 1.7 per cent

per annum during the study period. The small-scale firms are found to be more productive

than the medium- and large-scale firms. The decomposition of TFP growth into technical

efficiency change (catch-up effect) and technological change (frontier shift) reveals that the

productivity growth is contributed largely by technical efficiency change rather than by

technological change.

Gunesoglu, S., & Meric, B. (2007) have studied the operator activities in garment

industry in Turkey to find productive time and decide allowances. The percentage

distribution of operations was analyzed for personal and delay allowances by observing the

operations and deriving the ratios within a manufacturing period. A work sampling

technique was used. In accordance with work sampling technique, the operations to be

observed in a sewing room were defined, the number of observations and observers

required for each day and the procedure for making observations were determined and the

distributions of work flows were calculated. It was found that 72.7 per cent of working

time in a general sewing room was spent for productive activities and 23.2 per cent for

personal and unavoidable delay allowances. Distribution of operations within non-

productive activities was also determined. They found that personal based operations or

intervals have the greatest amount of non-productive activities. Controlling and checking

the work, cutting action before sewing and waiting the pieces have also remarkable

percentages. They have suggested measures to increase the efficiency of a sewing room,

47

distribution of actions of operators should be reduced since wrongly determined production

line cause delays during the execution of a work. All materials should be in required place

at correct time to prevent delays. For this purpose, standard time should be determined by

time measurement studies and work flow should be organized.

Hurreeram, D. K. (2007) has illustrated the development and use of a

manufacturing strategy audit tool for both assessing the current manufacturing strategy and

for selecting appropriate alternative strategies with a view to implement benchmarks,

specifically in garment making companies in Mauritius. The research demonstrates that a

sine qua none condition for a manufacturing organization to stay in business is to achieve

the benchmarks in any one or a combination of the functional areas such as sales and

marketing, product design and development, production planning and scheduling,

operations and quality management, purchasing and inventory management, human

resource management, and finance/accounting: achieving excellence in all being equivalent

to world-class manufacturing. The Mauritian garment making companies, which have been

the focus of this research, were found to be far from world-class companies. The use of the

audit tool in the selected ―successful‖ companies clearly showed that the companies

excelled mainly in the production function with heavy emphasis on quality standards and

labor productivity. Areas of poor performance and practice, for each of the functional

areas, in comparison with industry benchmarks were clearly identified and the courses of

action for achieving enhanced competitiveness were worked out for implementation

through the case study method. The use of the manufacturing system model together with

the strategy audit tool has proved to be a vital instrument for guiding companies in their

quest for continuous improvement and meeting benchmarks in the sector.

2.6 Summary of literature review

Form the study of above referred literature, a summary is prepared consisting of

author, year and country, key findings, variables used is prepared. The table helps to

identify the research gaps at a glance. The last column represents the identified research

gaps. This will further help to formulate the objectives of current research.

The details are presented in table 2.1.

48

Table 2.1 Summary of literature review with identified research gaps

Sr

no.

Author, Year

& Country Key findings

Variables

used

Identified

research gaps

1 Charoenrat,

T., & Harvie,

C. (2014)

Thai

Skilled labor had a significant

and positive correlation with the

technical efficiency of all

categories of manufacturing

SMEs.

Size, firm age,

labor, type of

firm,

ownership,

government

assistance,

training.

Little studies are

available on:-

1. Productivity

of powerlooms.

2. Manufacturing

of yarn dyed

terry towel and

allied products

with jacquard

mechanism.

3. Textile SMEs

in Solapur.

2 Oh, Donghyu,

et.al.. (2014)

Korea

Large firms and high

technology industries show a

higher rate of TFP growth in the

Korean manufacturing

industry.(i) the capital intensity

growth and the competitive

market condition are negatively

related to the rate of TFP

growth, (ii) the age and

patenting activities of the firm

positively affect its TFP growth.

Output (Y),

capital stock

and labor (K

and L), firm

size, skill.

3 S. Karthi ,.et

al.(2013)

India

Successfully implemented Lean

six sigma QMS-2008 (L6QMS)

model in a textile mill and

thereby achieving annual

savings of 2 million INR, they

have suggested 20 hypothetical

steps to implement this model.

Quality,

training.

4 Mohammed

A., Ahmed

Al-Dujaili

(2012) Iraq

Improving quality plays a

fundamental role in increasing

operations productivity in

textile units and improved

quality is related to

productivity. TQM has a

positive effect on TQCs and

productivity.

Cost of quality,

profit and

profitability,

quality

planning,

quality control,

human

resource (HR),

customer

satisfaction.

49

Sr

no.

Author, Year

& Country Key findings Variables used

Identified

research gaps

5 Baskaran, V.

et al.(2012)

India

Criterion of long working hours

plays an important role in

suppliers‘ sustainability

evaluation in garment

manufacturers.

Discrimination,

abuse of human

rights, child

labor, long

working hours,

unfair

competition and

pollution.

Little studies

are available

on:-

1. All the

variables from

yarn to finished

product (terry

towel).

2. Effect of

productivity on

profitability.

3. Applicability

of findings of

other sectors

(such as

garments,

clothing) to

terry towel

manufacturing.

6 Ali

Hasanbeigi,

Lynn Price

(2012) China

A large number of energy

efficiency measures exist for

the textile industry and most of

them have a low simple

payback period.

Energy, energy

technologies,

returns on

investment.

7 Lin, S., & Ma,

A. C. (2012)

Koria

Korea‘s experiment in

manufacturing industry proved

that material outsourcing has a

positive effect on productivity

and service outsourcing has a

negative effect on productivity.

Material

outsourcing,

service

outsourcing,

output (sales).

8 Mason, G.,

Leary, B. O.,

& Vecchi, M.

(2012) UK,

US, France,

Germany and

Netherland

Human capital affects positively

on growth of labor productivity

and multi-factor productivity

(MFP) growth is positively

related to the use of high-skilled

labor in manufacturing

industries of European

countries.

Output (sales),

capital, labor

input and

workforce skills.

9 Lin, H., Li,

H., & Yang,

C. (2011)

China

Ownership matters to

productivity (Foreign-owned

enterprises experienced higher

productivity), state-owned

textile enterprises have lower

productivity than other types of

enterprises, and moreover the

smaller firms have higher

productivity than the larger

firms.

Ownership, size

of firms,

Industrial

agglomeration

and labor

productivity

(value added per

labor).

50

Sr

no.

Author, Year


Identified

research gaps

10 Pal, R., Hakan

Torstensson

(2011)

Sweden

Product quality, flexibility in

product design, supply chain

and brand value are critical

factors for organizational

success of Swedish textile and

clothing industries.

Product quality,

lead time, cost,

flexibility in

design,

coordination and

trust, brand

value, service

level,

information

sharing,

innovation,

sustainability,

organization

culture.

Little studies

are available

on:-

1. Productivity

improvement

of Solapur

based textile

SMEs.

.

2. Co-relating

productivity

gains in terms

of profitability.

3. Yarn dyed

terry towel

manufacturing.

11 M.I. Shahidul

and S.T. Syed

Shazali

(2011)

Malaysia

Favorable Work Environment,

job satisfaction of workforce

and R&D on manufacturing

process is value-added inputs

for labor intensive industries

and it is positively associated

with manufacturing

productivity.

Throughput,

rejection, output

variability,

efficiency, R&D

expenditure,

revenue, share

value of firms,

favorable work

environment.

12 John Van

Reenen

(2011) UK

Competition does increase

productivity and a main

mechanism is through improved

management practices.

Management

quality, per

capita total

factor

productivity,

percentage sales

per worker, total

sales.

13 Boothby, D.,

Dufour, A., &

Tang, J.

(2010)

Canada

Combination of technologies

and types of training that are

commonly undertaken by firms

are studied. Appropriate

combinations of new

technologies and training lead

to higher productivity than

adoption of new technologies

alone.

Training, skill,

firm size,

technology and

value-added per

worker.

51

Sr

no.

Author, Year


Identified

research gaps

14 Gruber, H.

(2010)

Luxembourg

A shuttle less loom is about

three times more productive

than a shuttle loom and

combination of new technology

and good quality yarns

improves the quality of the

woven fabric.

Technology,

quality, market

size, cost of

innovation,

market structure,

output in kg,

diffusion speed.

Little studies

are available

on:-

1. Directly co-

relating

productivity

gains in terms

of profitability.

2. Textile

manufacturing

units, having

all the facilities

(processes)

under one roof

3. Technical

parameters and

their effects on

productivity.

15 William E.

James et. al

(2010)

Indonesia

Improving supply chain,

Reducing tariffs and taxes on

high quality yarn, increasing

capacity and promoting quality

culture, etc. These will help to

improve exports of Indonesian‘s

textile and apparel sector.

Supply chain

capacity, quality

and exports.

16 Joshi, R. N.,

& Singh, S. P.

(2010) India

As productivity is the driving

factor in enhancing the

competitiveness of any

decision-making entity (firm), a

study of total factor

productivity (TFP) and its

sources can provide vital inputs

to a firm for improving its

competitiveness.

Total factor

productivity

(TFP) - total

output, technical

efficiency,

technology, size

of the firm.

17 Lu, X., Liu,

L., Liu, R., &

Chen, J.,

(2010) China

Knitting dyeing and finishing

wastewater was treated using

the combined processes for

reuse, which was an attractive

alternative.

Dyeing process,

manufacturing

cost.

18 Pardo

Martínez, C.

I. (2010)

German and

Colombia

The results showed that the

German and Colombian textile

industries have achieved

meaningful improvements in

energy efficiency leading to

improvement in productivity.

Labor, materials,

and plant

capacity

utilization,

capital and

energy price.

52

Sr

no.

Author, Year


Identified

research gaps

19 Rajesh K.

Singh and

Suresh K.

Garg, S.G.

Deshmukh

(2009) India

and China

Indian SMEs give more

attention to supplier

development, total productive

maintenance and the

organization‘s culture while

Chinese SMEs pay more

attention to relationship

management and cost reduction

for strategy development and

competitiveness.

Supplier

development,

total productive

maintenance,

organization‘s

culture, human

relationship

management

(HRM), cost,

strategy

development and

competitiveness,

output (sales).

Little studies

are available

on:-

1. Productivity

of

power looms.

2.

Manufacturing

of yarn dyed

terry towel and

allied products

with jacquard

mechanism.

3.Textile SMEs

in Solapur.

20 Venu

Varukolu,

Haesun Park-

Poaps (2009)

India

Employee performance and

innovation performance

partially mediate the

relationship between TQM

practices and firm performance.

Leadership,

training,

employee

management and

performance,

information and

analysis, supplier

management,

process

management,

customer focus,

continuous

improvement,

innovation

performance,

and firm

performance.

21 Anbanandam,

R., Banwet,

D. K., &

Shankar, R.

(2009) India

The proposed methodology to

quantify collaboration was

highly reliable and adequately

valid shows the positive effect

of the collaboration index on

operational performance.

Top management

commitment,

information

sharing, trust

among supply

chain partners,

long-term

relationship and

risk and reward

sharing

53

Sr

no.

Author, Year


Identified

research gaps

22 Ghosh, S.

(2009) India

Ownership is significantly and

non-linearly related to firm

growth. Firm growth is

negatively related to firm size

and in a non-linear way,

following an inverted U-pattern.

Size, age, firm

growth

(percentage

increase in

output).

Little studies

are available

on:-

1. All the

variables from

yarn to finished

product (terry

towel).

2. Effect of

productivity on

profitability.

3. Applicability

of findings of

other sectors

(such as

garments,

clothing) to

terry towel

manufacturing.

23 Raj Kumar,

Dixit Garg,

T.K. Garg,

(2009) India

The Indian industry must make

all efforts to implement TQM,

will help in making industries

competitive on global level.

Competitiveness,

quality, customer

satisfaction,

manufacturing

process, top

management

commitment,

innovation.

24 Puig, F.,

Marques, H.,

& Ghauri, P.

N. (2009)

Globalization tends to diminish

the district and sub sector

effects over time, and positive

impact of specialization on

productivity and of

diversification on business

growth.

Specialization,

diversification,

business growth,

location.

25 Vankar, P. S.,

& Shanker, R.

(2009) Sri

Lanka

Preference of using easily and

cheaply available material (dye

obtained from kitchen waste of

dry skin extract of Allium cepa)

for dyeing by conventional

dyeing lowers the cost of

natural dyeing and enhances

resource productivity and as a

result, reduces waste.

Resource

productivity

(material),

dyeing

wastage/rejectio

n, cost.

26 M.

Ilangkumaran

(2008) India

Optimal maintenance policy

mix can improve availability

levels of plant equipment and

also avoid unnecessary

investment in maintenance.

Maintenance,

investment.

54

Sr

no.

Author, Year


Identified

research gaps

27 L.C.R.

Carpinetti and

O.T. Oiko

(2008) Brazil

Despite the difficulties and a

lack of maturity for

benchmarking and performance

management to be overcome,

the governing institutions and

implementation of the system in

itself represents a step towards

managing improvement of the

clusters.

Benchmarking,

performance.

Little studies

are available

on:-

1. Directly

co-relating

productivity

gains in terms

of profitability.

.

2. Textile

manufacturing

units, having

all the facilities

(processes)

under one roof

3. Technical

parameters and

their effects on

productivity.

28 Brun, A.,

Corti, D.,

Pozzetti, A.,

& Milano, P.

(2008) Italy

A procedure for setting the

loom is developed which has

resulted into reduction of setup

time and reduced the scrap.

Input material

quality, set up

time, output in

kg, training,

rejection/rework.

29 Pinho, C.

(2008)

Portugal

Most relevant TQM

components impacting on SME

performance and consumer

orientation are measuring

results, quality assurance

systems, top-manager training

programmes and leadership

initiatives.

Performance,

consumer

orientation and

innovation.

30 Singh, R. K.,

& Garg, S. K.

(2008) India

SMEs are considered as major

source for economic growth, on

the export front, they are facing

many constraints due to their

limited resources and lack of

innovation in capability

development.

Economic

growth, product

design, training,

development

capability,

31 Gunesoglu,

S., & Meric,

B. (2007)

Turkey

To increase the efficiency of a

sewing room, distribution of

these activities should be

reduced since wrongly

determined production line

cause delays during the

execution of a work.

Productive and

non-productive

activities time,

unavoidable

delay

allowances,

workers skill.

55

Sr

no.

Author, Year


Identified

research gaps

32 Hurreeram, D.

K. (2007)

Mauritius

The use of the manufacturing

system model together with the

strategy audit tool has proved to

be a vital instrument for guiding

companies in their quest for

continuous improvement and

meeting benchmarks in the

sector.

Sales, product

design,

operations,

purchasing,

inventory,

quality and HR

management,

Little studies

are available

on:-

1. Productivity

of power

looms.

2.

Manufacturing

of yarn dyed

terry towel and

allied products

with jacquard

mechanism.

3. Textile

SMEs in

Solapur.

33 Kapuge, A.

M., & Smith,

M. (2007) Sri

Lanka

The competitive strength of the

Sri Lankan garment industry

has historically been based on

cheap labor, high-labor

standards, a literate labor force,

investment-friendly government

policies and strategic shipping

lanes.

Labor, total

quality

management

(TQM).

34 Kumar, S., &

Gangopadhya

y, S. (2007)

India

Electrical machinery and

textiles, both the industries have

improved their efficiency and

scales of operation by the turn

of the century.

Types of firm,

sales, operations.

35 Bilalis, N.et

al. (2007)

France

European textile companies

substantially lag in performance

when compared to the best-in-

class industry sectors.

New product and

process

development,

supply chain

management,

strategy

formulation and

deployment.

36 U. Subadar, et

al. China and

Mauritia

(2007)

Mauritius

Chinese workers are in general

more productive than Mauritian

ones.

Workers

productivity,

capital labor

inputs, workers,

level of

education and

experience.

56

Sr

no.

Author, Year


Identified

research gaps

37 N. B. Powell

& N. L.

Cassill (2006)

US

Companies were utilizing new

product development (NPD)

processes as a competitive tool,

but are using a combination of

NPD strategies to develop and

launch products in the global

marketplace.

Design and

development,

sourcing,

merchandising,

marketing and

sales, supply

chain

management,

operations, and

engineering, new

product

development

(NPD)

38 Jimmy K.C.

Lam, R.

Postle, (2006)

Hong Kong

The supply chain in Hong

Kong, should focus on product

design, material control, and

production co-ordination

instead of focusing on logistics,

transportation, time to market

and forecast demands to

improve efficiency and enhance

competitiveness.

Logistics,

transportation,

time to market,

forecast

demands,

product design,

material control,

and production

co-ordination.

Little studies

are available

on:-

1. All the

variables from

yarn to finished

product (terry

towel).

2. Effect of

productivity on

profitability.

3. Applicability

of findings of

other sectors

(such as

garments,

clothing) to

terry towel

manufacturing.

39 Margono, H.

(2006)

Indonesia

Total Factor Productivity

growth indicates that the

growths are driven positively by

technical efficiency changes

and negatively by technological

progress in food, textile, and

chemical and metal products

sectors.

Technical

inefficiency,

technological

progress, types,

age and size of

firms, capital.

40 Taplin, I. M.,

& Carolina,

N. (2006)

France

Clothing proves more robust in

retaining an employment

presence than the more capital-

intensive textile sector.

Industry type,

employment,

quantity of firm,

Turnover and

Investment

current prices.

57

Sr

no.

Author, Year


Identified

research gaps

41 Teng, S. G . et

al. (2006)

South

American

Recommendations based on the

evaluation of strengths and

weaknesses that may be used as

references for these small

companies to increase their

potential of being active

partners in the US supply chain.

Quality,

logistics,

forecasting

techniques, lead

time, inventory

management,

integration of

supply chain.

Little studies

are available

on:-

1. Productivity

of power

looms.

2.

Manufacturing

of yarn dyed

terry towel and

allied products

with jacquard

mechanism.

3. Textile

SMEs in

Solapur.

42 N. Towers &

J.McLoughlin

(2005) UK

Effects of quality management

systems on business

performance and highlights a

number of difficulties including

cost constraints, and lack of

training and productivity

improvements.

Cost constraints,

training, team

working, quality

awareness and

customer

satisfaction.

43 Pattnayak, S.

S., &

Thangavelu,

S. M. (2005)

Singapore

As the economy liberalizes and

permits greater inflow of capital

into the economy, the usage of

foreign capital could make

important productive

contribution to the industrial

structure.

Capital

investment,

types of capital,

economy.

44 Andrew

Hughes,

(2005) U K

There is potential to improve

the competitive performance of

small to medium-sized

companies (UK clothing and

textile industry), a sector of the

economy that has had little

exposure to activity-based

costing and activity-based

management (ABC/ABM).

Wages, direct

materials,

Indirect

overheads, Units

produced,

Selling price per

unit, Revenue

Cost, Profits.

45 Ozturk, H. K.

(2005) Turkey

The total energy consumption,

electricity consumption and

heat energy consumption

increases linearly with

production.

Annual heat

energy and

electricity

consumption,

electricity usage

per year,

Monthly fuel-oil

usage per year.

58

Sr

no.

Author, Year


Identified

research gaps

46 Moore, S. B.,

& Ausley, L.

W. (2004) US

How to increase productivity

through greener

(environmentally conservative)

production induced by

cooperative stakeholder actions,

an example.

Gross Domestic

Production,

effluent toxicity,

types of fibers,

types of process

of dying and

chemical

treatment.

Little studies

are available

on:-

1. Productivity

improvement

of Solapur

based textile

SMEs.

.

2. Co-relating

productivity

gains in terms

of profitability.

3. Yarn dyed

terry towel

manufacturing.

47 Erin Dodd

Parrish, .et al.

(2004) USA

One way in which US textile

companies can utilize this idea

of specialization is by the

development of niche markets.

It has been proven that product

differentiation, i.e. niche

markets, is related to

profitability.

Labor input of

good, output of

good, input of

good, capital and

labor of

countries.

48 Atack, J.et al.

(2003) USA

Holding labor and capital inputs

constant and controlling for

days of operation per month and

months per year, this elasticity

was positive but less than one,

indicating diminishing returns.

Flow of output,

capital, labor,

shortest possible

period of

production,

hours per day,

working days per

year, working

months per year.

49 Ramcharran,

H. (2001)

USA

Apparel industry in US made

adjustment in moderate

downsizing in employment that

contributed to an increase in

productivity and profits and

Industry adjustment by

increasing labor productivity

and maintaining fairly stable

profits despite job losses.

Elasticity of

substitution, real

value added of

the textile

industry, real

gross fixed

capital

formation,

number of

workers

employed, trend

factor.

59

Sr

no.

Author, Year


Identified

research gaps

50 Wakelin, K.

(2001) UK

The relationship between

productivity growth and R&D

intensity it is found to be very

sensitive to the inclusion of

sector dummy variables,

indicating an important role for

different sector conditions in

explaining variations in

productivity growth. Separating

the firms according to their

innovation histories, the rate of

return to R&D is much higher

for innovative than non-

innovative firms.

Sales, R&D,

benchmarkin

g, innovation.

51 Ren, X.

(2000) China

Environmental performance of

industry should always be

assessed both from process and

product perspectives, especially

for the consumer product

manufacturing industry.

Life time of the

product,

technology

currently

practiced

(TCP),toxicity of

dyes and

chemicals in

receiving water,

BOD, different

die machines and

water used for

them,

52 Toni, A. De,

& Meneghetti,

A. (2000)

Italy

The production planning period

compression has been

recognized as yielding a

significant improvement in the

external time performance.

While the production planning

process is shown to be an

important area for improvement

in a time-based logic, its results

can be amplified by involving

the other processes performed

in the network.

Material

availability,

capacity loading,

ordered quantity,

amount of yarn,

set up time of

product, process

rate(unit/h),retur

ns transport and

set-up costs,

60

Sr

no.

Author, Year


Identified

research gaps

53 Char, P.et

al.(1998)

Canada

Returns to scale are the key

factor that helps companies to

better utilize their inputs

(resources).

Data Envelope

Analysis (DEA)

ROI, efficiency.

54 Singletary, E.

P.et al.(1998)

US

Companies that operate in

rapidly changing, uncertain

markets need to adopt the

concepts of agility in order to

master their competitive

environment and thrive on

change and the path for

effective transformation

includes a phased sequence of

changes in organizational scope

and capabilities, gradually

expanding from internal- to

external-change focus and from

incremental- to radical-change

rate.

Competitiveness,

Agility

55 Karacapilidis,

N. I., &

Pappis, C. P.

(1996)

Germany

Productivity is affected by MRP

and technological process.

Productivity is positively

correlated with technological

process.

MRP- II,

technological

process

(weaving,

starching and

warping)

Productivity,

customer

satisfaction.

56 Sara

Umberger

Douglas,

Arathi

Narayan,

(1993) US

and India

First, while Indian Textile and

Apparel Industries cannot

afford to disregard the

importance of production

efficiencies, they place greater

emphasis on the external

environment — including better

knowledge of competition,

consumers, and government

policy. The second is a

reiteration of old advice: the US

must increase its exports.

Company size

(number of

employees),

Degree of

unionization,

Company

ownership,

Product

produces, Textile

and Apparel

companies.

61

Sr

no.

Author, Year


Identified

research gaps

57 Susan

Christoffersen

(1993)

Warwick

R&D expenditure in the textile

industry may not have the

expected impact on success.

sales, product,

number of

shares, preferred

stock, liabilities

and total assets

of the firm.

58 Chakrabarti,

K. (1990) US

The textile mills themselves

spent very little money on

research and development,

innovations introduced by its

suppliers helped increase

productivity. Innovations in

weaving looms and other

equipment helped the

productivity grow significantly.

Productivity

growth rates,

technical change,

improvement

and limitations

of material,

equipment,

process

instruments, etc.

59 Antonelli et

al.(1990)

USA

The diffusion of technological

change is in three consecutive

production stages: the surge of

synthetic fibers, the

development of shuttle-less

looms, and the development of

open-end rotors.

Working speed

(knots), stage of

production, kind

of material,

capital cost,

effective level of

diffusion,

scraping rate.

60 Noweir, M.

H. (1984)

USA

Noise abatement in the textile

industry could be beneficial to

worker productivity and well

being and contribute to more

economically effective

operation.

Production

efficiency,

production

incentives,

disciplinary

actions,

absenteeism,

accident

frequency rate

and severity rate,

workers, noise

level.

62

Sr

no.

Author, Year


Identified

research gaps

61 12th

Shirley

International

Seminar

(1981) Britain

Energy is particularly important

to the textile industry when one

studies the energy content of

textiles when compared with

other common manufactured

products which are normally

associated with 'energy

intensive' industries.

Energy

consumption,

conservation

62 Pickett, J., et

al. (1977)

Britain

More use has to be made of

machines and labor in Africa

than in Europe to obtain the

same output (production of

cotton cloth).

Labor,

productivity,

machinery and

technology

63 Lindner, S. H.

(2002)

Germany

Textile centers suffered

stagnation and decline not

because of a lack of

innovations, but because of

investments in the most modern

technology.

cost, energy,

fiber

consumption,

working time of

machine

64 Mavannoor

Parameswaran

(2009) India

Imported machinery has a

significant effect on

productivity in technology-

intensive industries.

Capital stock,

labor hours, raw

material, energy,

capital.

65 Mahdi H. Al-

Salman

(2007)

Kuwait

The acceleration in technical

progress gives rise to a higher

rate of investment and industrial

growth with more imports and

lower trade surplus and the

demand for primary imports in

accelerated scenario tends to

fall, offsetting its saving effect

by its higher income effect.

Investment,

import price,

profit margin,

cost.

66 Cesar, L., et

al. (2008)

Brazil

Applicability of continuous

improvement cycle gives

improved performance of the

cluster.

cost, wages,

labor force

63

Sr

no.

Author, Year


Identified

research gaps

67 M.

Jerzmanowski

(2008) USA

There are differences in

technologies that are available

to rich and poor countries,

inefficiency are more important

than technology for

understanding the vast income

disparities across countries.

capital per

worker, growth

rate

68 V. N.

Balasubraman

yam, et al.

(2005) India

China has much higher shares

in world exports of both textiles

and clothing, while India has a

comparative advantage in

women‘s clothing of various

sorts and men‘s shirts.

total export,

percentage share,

labor cost

69 Simelane, X.

(2005) South

Africa

Infusion of capital leads to

technology up gradation leading

to improvement in productivity

but worker generally oppose

technology up gradation due to

fear of losing the job.

worker, labor

market,

management

power, working

hours

70 Tarlok Singh

(2003)

Australia

The industries with low output

per capita tend to lag behind the

industries with relatively higher

output per capita and there is a

tendency for divergence in the

growth process of the sample

manufacturing sector.

capital, labor,

rate of growth

71 Lin, S. H. et

al. (1993)

USA

Consumer‘ demands have been

increasingly diversified and

individualized, creating the

need for apparel producers to be

responsive to the rapidly

growing individualization of

consumers‘ needs.

types of

production,

products,

production

volume, no. of

workers

72 Tony Hines

(1993)

Warwick

The lowest labour costs in the

EU are in Portugal and the

highest labour costs are in

Denmark.

employment,

import, export,

customer

expenditure

64

2.7 Frequency analysis of variables

From literature review, the variables are grouped into input variables, process

variables and output variables. Further the frequency analysis of these variables is done

which are presented in tables 2.2, 2.3 and 2.4.

Table 2.2 Frequency analysis of input variables

Sr.

No

Variables

considered

by previous

researchers

Source Frequency

1 Labor Charoenrat, T., & Harvie, C. (2014), Baskaran, V. et

al.(2012), Mason, G., Leary, B. O., & Vecchi, M.

(2012), Lin, H., Li, H., & Yang, C. (2011), John Van

Reenen (2011), Pardo Martínez, C. I. (2010), Rajesh K.

Singh and Suresh K. Garg, S.G. Deshmukh (2009),

Venu Varukolu, Haesun Park-Poaps (2009),

Gunesoglu, S., & Meric, B. (2007), Hurreeram, D. K.

(2007), Kapuge, A. M., & Smith, M. (2007), U.

Subadar, et al.(2007), Taplin, I. M., & Carolina, N.

(2006), Erin Dodd Parrish, .et al. (2004), Atack, J.et al.

(2003), Ramcharran, H. (2001), Sara Umberger

Douglas, Arathi Narayan (1993), Noweir, M. H.

(1984).

18

2 Training

Charoenrat, T., & Harvie, C. (2014), S. Karthi ,.et

al.(2013),Boothby, D., Dufour, A., & Tang, J. (2010),

Venu Varukolu, Haesun Park-Poaps (2009), Brun, A.,

Corti, D., Pozzetti, A., & Milano, P. (2008), Pinho, C.

(2008), Singh, R. K., & Garg, S. K. (2008), N. Towers

& J. McLoughlin (2005).

08

65

3 Quality S. Karthi ,.et al.(2013), Mohammed A., Ahmed Al-

Dujaili (2012), Pal, R., Hakan Torstensson (2011),

John Van Reenen (2011), Gruber, H. (2010), William

E. James et. al (2010), Raj Kumar, Dixit Garg, T.K.

Garg, (2009), Brun, A., Corti, D., Pozzetti, A., &

Milano, P. (2008), Pinho, C. (2008), Hurreeram, D. K.

(2007), Kapuge, A. M., & Smith, M. (2007), Teng, S.

G . et al. (2006), N. Towers & J. McLoughlin UK

(2005).

13

4 Product mix /

type

Mohammed A., Ahmed Al-Dujaili (2012), Pal, R.,

Hakan Torstensson (2011), Gruber, H. (2010), Lu, X.,

Liu, L., Liu, R., & Chen, J., (2010), Rajesh K. Singh

and Suresh K. Garg, S.G. Deshmukh (2009), Vankar,

P. S., & Shanker, R. (2009), N. Towers & J.

McLoughlin UK (2005), Andrew Hughes, (2005),

Toni, A. De, & Meneghetti, A. (2000), Chakrabarti, K.

(1990). Pal, R., Hakan Torstensson (2011), Boothby,

D., Dufour, A., & Tang, J. (2010), William E. James et.

al (2010), Singh, R. K., & Garg, S. K. (2008), Jimmy

K.C. Lam, R. Postle, (2006), N. B. Powell & N. L.

Cassill . US (2006), Andrew Hughes, (2005).

17

5 Management S. Karthi ,.et al (2013), Anbanandam, R., Banwet, D.

K., & Shankar, R. (2009), Raj Kumar, Dixit Garg, T.K.

Garg, (2009), Hurreeram, D. K. (2007), N. Towers & J.

McLoughlin (2005).

05

6 H R

Management

Mohammed A., Ahmed Al-Dujaili (2012), Pal, R.,

Hakan Torstensson (2011), William E. James et. al

(2010), Rajesh K. Singh and Suresh K. Garg, S.G.

07

66

Deshmukh (2009), Venu Varukolu, Haesun Park-Poaps

(2009), Ghosh, S. (2009), Hurreeram, D. K. (2007) .

7 Production

planning

Hurreeram, D. K. (2007), Ozturk, H. K. (2005). 02

8 Rejection /

Rework

M.I. Shahidul and S.T. Syed Shazali (2011), Vankar,

P. S., & Shanker, R. (2009), Brun, A., Corti, D.,

Pozzetti, A., & Milano, P. (2008).

03

9 Market Oh, Donghyu, et.al.. (2014), Gruber, H. (2010),

Hurreeram, D. K. (2007), N. B. Powell & N. L. Cassill

. US (2006), Jimmy K.C. Lam, R. Postle, (2006), Erin

Dodd Parrish, .et al. (2004), Singletary, E. P.et

al.(1998).

07

10 Size of the

firm

Charoenrat, T., & Harvie, C. (2014), Oh, Donghyu,

et.al.. (2014), Lin, H., Li, H., & Yang, C. (2011),

Boothby, D., Dufour, A., & Tang, J. (2010), Joshi, R.

N., & Singh, S. P. (2010), Ghosh, S. (2009), Sara

Umberger Douglas, Arathi Narayan (1993), Andrew

Hughes, (2005), Margono, H. (2006).

09

11 Energy Ali Hasanbeigi, Lynn Price (2012), Pardo Martínez, C.

I. (2010), Ozturk, H. K. (2005), 12th

Shirley

International Seminar (1981).

04

12 Age of firm Charoenrat, T., & Harvie, C. (2014), Oh, Donghyu,

et.al.. (2014), Ghosh, S. (2009), Margono, H. (2006).

04

13 Finance and

capital

Oh, Donghyu, et.al.. (2014), Mason, G., Leary, B. O.,

& Vecchi, M. (2012), Pardo Martínez, C. I. (2010), U.

Subadar, et al. China and Mauritia (2007), Margono, H.

11

67

(2006), Taplin, I. M., & Carolina, N. (2006), Pattnayak,

S. S., & Thangavelu, S. M. (2005), Erin Dodd Parrish,

.et al. (2004), Atack, J.et al. (2003), Ramcharran, H.

(2001), Antonelli et al.(1990).

14 Supply chain Pal, R., Hakan Torstensson (2011), William E. James

et. al (2010), Anbanandam, R., Banwet, D. K., &

Shankar, R. (2009), Bilalis, N.et al. (2007), N. B.

Powell & N. L. Cassill . US (2006), Jimmy K.C. Lam,

R. Postle, (2006), Teng, S. G . et al. (2006).

07

15 Ownership Charoenrat, T., & Harvie, C. (2014), Lin, H., Li, H., &

Yang, C. (2011), Ghosh, S. (2009), Sara Umberger

Douglas, Arathi Narayan (1884).

04

16 R&D M.I. Shahidul and S.T. Syed Shazali (2011), Wakelin,

K. (2001), Susan Christoffersen, (1993), Chakrabarti,

K. (1990).

04

Figure 2.1 Graph of input variables

68

Table 2.3 Frequency analysis of process variables

Sr.

No.

Variables

considered by

previous

researchers

Source Frequency

1 Technology

Oh, Donghyu, et.al.. (2014), Ali Hasanbeigi, Lynn

Price (2012), Lin, H., Li, H., & Yang, C. (2011),

Boothby, D., Dufour, A., & Tang, J. (2010), Gruber,

H. (2010), Joshi, R. N., & Singh, S. P. (2010), Venu

Varukolu, Haesun Park-Poaps (2009), Kapuge, A. M.,

& Smith, M. (2007), N. B. Powell & N. L. Cassill .

US (200), Moore, S. B., & Ausley, L. W. (2004), Sara

Umberger Douglas, Arathi Narayan, (), ANTONELLI

et al.(1990), Pickett, J., et al. (1977).

13

2 Skill Charoenrat, T., & Harvie, C. (2014), Oh, Donghyu,

et.al.. (2014), Mason, G., Leary, B. O., & Vecchi, M.

(2012), Boothby, D., Dufour, A., & Tang, J. (2010),

N. Towers & J. McLoughlin UK (2005).

05

3 Benchmarking L.C.R. Carpinetti and O.T. Oiko (2008), Hurreeram,

D. K. (2007), Kumar, S., & Gangopadhyay, S. (2007),

Bilalis, N.et al. (2007), U. Subadar, et al. China and

Mauritia (2007), Wakelin, K. (2001), Sara Umberger

Douglas, Arathi Narayan, (1993), Pickett, J., et al.

(1977).

08

4 Training Charoenrat, T., & Harvie, C. (2014), Boothby, D.,

Dufour, A., & Tang, J. (2010), Ghosh, S. (2009),

Brun, A., Corti, D., Pozzetti, A., & Milano, P. (2008),

Pinho, C. (2008), Singh, R. K., & Garg, S. K. (2008).

06

69

5 Maintenance Rajesh K. Singh and Suresh K. Garg, S.G. Deshmukh

(2009), M. Ilangkumaran (2008).

02

6 Process Ali Hasanbeigi, Lynn Price (2012), M.I. Shahidul and

S.T. Syed Shazali (2011), John Van Reenen (2011),

Lu, X., Liu, L., Liu, R., & Chen, J., (2010), Venu

Varukolu, Haesun Park-Poaps (2009), Anbanandam,

R., Banwet, D. K., & Shankar, R. (2009), Puig, F.,

Marques, H., & Ghauri, P. N. (2009), Vankar, P. S., &

Shanker, R. (2009), Bilalis, N.et al. (2007), Jimmy

K.C. Lam, R. Postle, (2006), Ramcharran, H. (2001),

Toni, A. De, & Meneghetti, A. (2000), Karacapilidis,

N. I., & Pappis, C. P. (1996), Chakrabarti, K. (1990).

Mohammed A., Ahmed Al-Dujaili (2012), A, L.L.,

Markowski, et al.(2008), Gunesoglu, S., & Meric, B.

(2007).

17

7 Total Quality

Management

Mohammed A., Ahmed Al-Dujaili (2012), Venu

Varukolu, Haesun Park-Poaps (2009), Raj Kumar,

Dixit Garg, T.K. Garg, (2009), Pinho, C. (2008),

Kapuge, A. M., & Smith, M. (2007), N. Towers & J.

McLoughlin UK (2005).

06

8 Capacity Charoenrat, T., & Harvie, C. (2014), William E.

James et. al (2010), Pardo Martínez, C. I. (2010)

03

70

Figure 2.2 Graph of process variables

Table 2.4 Frequency analysis of output variables

Sr

No.

Variables

considered by

previous

researchers

Source Frequency

1 Competitiveness William E. James et. al (2010), Rajesh K. Singh and

Suresh K. Garg, S.G. Deshmukh (2009), Raj Kumar,

Dixit Garg, T.K. Garg, (2009), Singh, R. K., &

Garg, S. K. (2008), Bilalis, N.et al. (2007),

Hurreeram, D. K. (2007), Jimmy K.C. Lam, R.

Postle, (2006), Taplin, I. M., & Carolina, N. (2006),

Andrew Hughes, (2005), Singletary, E. P.et

al.(1998), Susan Christoffersen, (1993).

11

2 Productivity

(output per unit

of measurement)

Oh, Donghyu, et.al.. (2014), Mohammed A.,

Ahmed Al-Dujaili (2012), Lin, S., & Ma, A. C.

(2012), Mason, G., Leary, B. O., & Vecchi, M.

25

71

(2012), Lin, H., Li, H., & Yang, C. (2011), M.I.

Shahidul and S.T. Syed Shazali (2011), John Van

Reenen (2011), Boothby, D., Dufour, A., & Tang, J.

(2010), Joshi, R. N., & Singh, S. P. (2010), Ghosh,

S. (2009), Puig, F., Marques, H., & Ghauri, P. N.

(2009), Vankar, P. S., & Shanker, R. (2009), Singh,

R. K., & Garg, S. K. (2008), Hurreeram, D. K.

(2007), Kapuge, A. M., & Smith, M. (2007), Kumar,

S., & Gangopadhyay, S. (2007), U. Subadar, et al.

China and Mauritia (2007), Erin Dodd Parrish, .et al.

(2004), Atack, J.et al. (2003), Ramcharran, H.

(2001), Wakelin, K. (2001), Karacapilidis, N. I., &

Pappis, C. P. (1996), Chakrabarti, K. (1990),

Noweir, M. H. (1984), 12th

Shirley International

Seminar (1981).

3 Efficiency Charoenrat, T., & Harvie, C. (2014), Ali Hasanbeigi,

Lynn Price (2012), Pardo Martínez, C. I. (2010),

Gunesoglu, S., & Meric, B. (2007), Kumar, S., &

Gangopadhyay, S. (2007), Jimmy K.C. Lam, R.

Postle, (2006), Margono, H. (2006), Char, P.et

al.(1998).

08

4 Performance

Baskaran, V. et al.(2012), Pal, R., Hakan

Torstensson (2011), Boothby, D., Dufour, A., &

Tang, J. (2010), Venu Varukolu, Haesun Park-Poaps

(2009), Anbanandam, R., Banwet, D. K., &

Shankar, R. (2009), L.C.R. Carpinetti and O.T. Oiko

(2008), Pinho, C. (2008), Kapuge, A. M., & Smith,

M. (2007), Bilalis, N.et al. (2007), N. Towers & J.

McLoughlin UK (2005), Andrew Hughes, (2005),

13

72

Ren, X. (2000), Toni, A. De, & Meneghetti, A.

(2000).

5 Total Factor

Productivity

Oh, Donghyu, et.al.. (2014), Joshi, R. N., & Singh,

S. P. (2010), Ghosh, S. (2009), Margono, H. (2006),

Pattnayak, S. S., & Thangavelu, S. M. (2005).

05

6 Cost Pal, R., Hakan Torstensson (2011), Lu, X., Liu, L.,

Liu, R., & Chen, J., (2010), Rajesh K. Singh and

Suresh K. Garg, S.G. Deshmukh (2009), Brun, A.,

Corti, D., Pozzetti, A., & Milano, P. (2008), Ozturk,

H. K. (2005).

05

7 ROI Ali Hasanbeigi, Lynn Price (2012), Ozturk, H. K.

(2005), Wakelin, K. (2001), Char, P.et al.(1998).

04

8 Value Oh, Donghyu, et.al.. (2014), Hurreeram, D. K.

(2007).

02

9 Profitability Andrew Hughes, (2005), Erin Dodd Parrish, .et al.

(2004).

02

Figure 2.3 Graph of output variables

73

2.8 Identification of research gaps

After in-depth study and review of literature, the following research gaps are

identified:

1. There are few studies reported on productivity improvement of Solapur based textile

SMEs.

2. There are few studies specifically on jacquard powerlooms.

3. There are few research studies on yarn dyed terry towel manufacturing.

4. Many studies are not directly co-relating productivity gains in terms of profitability.

5. There are few studies for textile manufacturing units, having all the facilities under

one roof (i.e. yarn doubling, dyeing, preparatory warping, stitching, finishing and

packing). Rather the studies are carried out for any one section/process in isolation.

6. Variables/factors related to entire operations from yarn to terry towel (finish product)

manufacturing are reported in few studies.

7. Few studies on technical parameters (such as quality of yarn, dyeing parameters,

weaving parameters) and their effects on productivity are observed.

8. There are few studies on the use of industrial engineering techniques such as work

measurement, method study, theory of constraints (TOC), design of experiments

(DOE), etc. to improve productivity.

9. There are little studies on textile having majority of operations carried out manually

(highly labor intensive units, almost without any automation)- a typical feature of most

of the textile SMEs.

10. The Solapur terry towels and allied products have a market share of around 60% in

global demand for this particular sector, still few studies are reported on improving

productivity of this sector.

11. Few studies are reported about the applicability of clothing, garments, apparel sectors,

etc. for terry towel manufacturing units.

74

2.9 Research problem

All the identified research gaps from review literature are clearly pointing out need

and the importance for further research. It is also seen from the literature review that not

much productivity studies for Solapur textile SMEs are reported, in spite of its significant

contribution in Indian economy. Hence, the research problem undertaken is titled as,

”Towards improving Productivity of Solapur based textile SMEs.”

2.10 Objectives of research work

The objectives of the research work are as follows:

1. Identification of different variables affecting productivity of Solapur based textile

SMEs

2. To carry out factor analysis of the variables studied, by using suitable software

3. To develop a model representing the relationship between identified factors and

productivity

4. To develop a methodology for improving existing level of productivity

5. To develop a suitable module for skill development to improve the productivity

2.11 Scope of research work

It is proposed to carry out the studies in and around Solapur city for ―Yarn dyed

terry towels and allied products on Jacquard powerlooms‖. The scope of the research work

is limited to SME sector only.

The research methodology is discussed in the next chapter.

75

Chapter 3

RESEARCH METHODOLOGY

Research methodology provides guidelines to carry out the research work. It deals

with the decision about selection of method and chronological order to carry out the work.

Dane (1990) enforces that, the researcher should make an informed choice of the

approach to be used by studying the advantages and disadvantages of each approach as it is

applied to the research questions. Wilson, (1996) reinforces this by reminding that, aim of

the method is to collect valid and reliable data. A number of researchers have described the

various research methodologies separately. These research methodologies are identified on

the basis of type of research such as based on: experiment, survey, case study, grounded

theory approach, action research, cross-sectional and longitudinal studies, descriptive and

exploratory studies. These are not mutually exclusive methods. Experts opine that, no

single method can be considered the best. Hence, selection of research method is an

important decision. The selection of research method at various stages is reported below.

Numbers of researchers advocate the use of surveys to determine the characteristics

of a large population in an inexpensive and reliable way. They contend that properly

constructed questionnaires containing open or closed questions provide a powerful tool for

researchers providing standardized data that is authoritative and can be compared with

other sources of data. The success of the research depends on the way in which primary

data is collected, analyzed and produced (Churchill, 1995; Easterby-Smith, et al, 1996;

Ghauri, et al, 1995). It also allows the researcher to control the research and not have to

rely on other sources of data (Babbie, 1998; Easterby-Smith, et al, 1996; Wilson, 1996).

In case of present research, two (main) research methods are used, viz. the research

into the population (of Solapur based textile SMEs) using experience survey method and

case study based research method. Each of these methods requires a different strategy.

Fink (1995) defines a survey as: “a system for collecting information to describe,

compare or explain experience/knowledge and attitudes". The survey method is selected

for the present study contains number of variables, which makes an experimental study as

76

not feasible option. Similarly, for the problems requiring in-depth diagnosis/observations,

case study method is selected. Earlier number of research scholars have used this method,

such studies are Miguel and Dias (2009), Lee-Mortimer (2007), Aggelogiannopoulos et al.

(2007), McAdam and Lafferty (2004) etc. The various steps in present work are reported in

the following research framework.

Methodology adopted for carrying out this research work is followed as per the

requirement of logical steps. These steps are as follows.

Identification of variables from common functional areas (key technical as well as

other areas) essential for the study of improving productivity of Solapur based textile

SMEs.

Data collection by experience survey.

Analysis of data

Developing methodology for improving productivity based on TOC.

Validation by using case study based research work.

Conclusions and recommendations.

At different stages of this research work, in-depth discussions with expert panel are

made. Expert panel include: industry experts, consultants, researchers and academicians

working in the same/similar area.

The methodology selected and details of the procedures followed at each stage are

reported below.

3.1 Methodology adopted for identification of variables

Productivity of textile is affected by internal and external factors. Many variables

from all the functional area have an impact on productivity. Considering this aspect, the

expert panel is requested to confirm the common functional areas and variables affecting

productivity. Then related published literature is reviewed critically (reported in chapter 2)

77

to identify the variables affecting productivity of textile SMEs. Almost all the departments

and processes are covered while deciding the list of variables.

3.2 Methodology for experience survey

Experience survey means the survey of people who have practical experience with

the (research) problem to be studied. The experience survey is selected to obtain insight

into the relationships between identified variables and the main objectives related to the

research problem (Kothari, 2004). Experience survey based research methodology adopted

for current research is in-line with the earlier researchers (Arauz and Suziki 2004;

Mahadevappa and Kotreshwar (2004), Barua, and Dhat (2006), Koc (2007), Zaramdini,

(2007), Padma et al. (2008) etc.) in the same area.

Experience survey methodology includes important stages viz. (i) Design of

structured questionnaire, (ii) Data collection, and (iii) Data Analysis.

Methodology adopted at all these stages is discussed next.

3.3 Methodology adopted for questionnaire design

Much of the literature on questionnaire design contains more advice on what not to

put in to a questionnaire than advice on what to put in (Rummel and Ballaine (1963),

Sheatsley (1983), Bell (1993), Churchill (1995), Fink (1995), Alreck and Settle (1995)).

This advice and similar generic advice from Kothari (2004) is taken into consideration in

designing the questionnaire for this research work to ensure that it followed a format that is

logical, simple to understand, avoid possible misinterpretation, and facilitate statistical

analysis of the results.

In the current research work, the procedure followed for questionnaire design is

discussed below.

3.3.1 Selection of type of questionnaire

Questionnaire is measuring instrument, which is considered as the heart of a survey

based research. It can either be structured or unstructured questionnaire. In current research

‘structured questionnaire’ is used because, it includes definite, concrete and pre-determined

78

questions. Structured questionnaire may also have fixed alternative answers in which

responses of the informants are limited to the stated alternatives. All above concepts are

applicable for the current work. Hence, structured questionnaire is selected for experience

survey.

3.3.2 Sequence and number of questions

Number of questions must be just sufficient to get the desired data. The logical

sequence must be followed so the respondent is comfortable to answer these questions.

With these views the questionnaire is designed and it contains forty five questions.

To collect data related to productivity, all the functional and process areas are

identified (such as yarn doubling, dyeing, preparatory, weaving, stitching and finishing).

This has formed the base for developing questions. Numbers of questions are limited to the

identified processes of respective functional areas. Some sub-questions related to processes

are also asked with intension to help the respondent to recall the related information

correctly and quickly. Each question presents a variable in a more helpful and logical

order. Hence, the order expressed in the questionnaire is suitable for respondents of the

survey.

3.3.3 Question formulation and wording

All questions are formulated considering the following requirements.

(a) Question should be easily understood

(b) Question should convey only one thought at a time

(c) Question should be concrete and should conform as much as possible to the

respondent’s way of thinking

(d) Question should avoid the data which respondent think it is confidential

(e) Question should use minimum time required to answer

The care has been taken, so that each question is very clear to avoid any sort of

misunderstanding (as misunderstanding can do irreparable harm to a survey results).

79

Questions are impartial to get unbiased picture of variables and their effect on productivity

of textile SMEs.

3.3.4 Selection of measurement scale and guidelines for respondents

Scales selected is on the basis of its widespread use and general acceptability by

respondents and can be evaluated through standard statistical techniques of data analysis.

Earlier researchers have experienced mixed results of productivity

(increase/decrease) in SMEs around the world. They have reported, positive, negative and

no impact on productivity. Keeping this in mind, for measuring effect on productivity, ratio

scale is used. As in this type of measurement scale, a certain distance along the scale

means the variation in the variable/parameter value under consideration and zero on the

scale represents the absence of the change being measured. Therefore, all questions in

measurement instrument for conducting survey; ratio scale (-3 to +3) is used to quantify

the net effect on productivity (profitability). In this scale, (-3) representing the lowest or

most negative effect and (+3) representing the highest or most positive effect and zero

value indicate no effect. The ratio scale values with their effects are as shown in table 3.1.

Table 3.1 Ratio scale values and effect level

Value Effect Level

+3 Highly positive (15.1 % and above)

+2 Moderately positive (7.1 % to 15%)

+1 Marginally positive (1% to 7%)

0 No change

-1 Marginally negative (loss) (-1% to -7%)

-2 Moderately negative (-7.1 % to -15%)

-3 Highly negative (-15.1 % and below)

80

3.3.5 Stages of questionnaire design

First step: With the comprehensive literature review and discussion with few industrial

experts, the first draft of the questionnaire is prepared.

Second step: A draft questionnaire is given to expert panel for their comments. After

including their comments, a second draft is submitted again to the expert panel to

confirm the proper inclusions of their previous comments.

Third Step: A pilot test of questionnaire is carried out. All the researchers emphasize

the need to pilot test (any survey instrument) to simply ensure that, it does what is

intended to do. The purpose of the pilot test is to refine the questionnaire so that

respondents will have no problems in answering the questions and there will be no

difficulty in recording the data. It also makes an assessment of the questions’ validity

and the likely reliability of the data that would result. For the pilot test, third draft is

sent to ten industries for their feedback. According to the observations/suggestions

further changes are made. The fourth and final version is the one that is subsequently

used and is attached as appendix III.

3.4 Methodology adopted for data collection

The data collection is done by using structured questionnaire. For data collection,

first the sample size is decided. Then the data of industries is collected by contacting

Textile Development Foundation, Solapur and Yantra Mag Dharak Sangh, Solapur. The

respondents are selected randomly from these units and the respondents were contacted

personally. All the details about the data collection are reported next.

3.4.1 Sample size determination

Sample size determination for experience survey is a plan for obtaining samples

from the population of existing textile SMEs in Solapur. As a general rule followed, the

number of observations should be about four times the number of variables. However, in

some cases, the numbers of observations are about two times the number of variables. For

factor analysis it is also recommended that, the sample size must be more than fifty,

preferably, it should be hundred or larger (Hair et al., 1990). In present study, 38

81

independent variables have identified. The sample size determined is 152 (minimum). The

statistician when consulted confirmed the sample size taking into the objectives of this

research work and analysis part of the study. From available valid source of information

for deciding the sample size, random sampling method is used.

3.4.2 Selection of industries

There are two registered associations of textile manufacturing industries in Solapur

viz.:

1. Textile Development Foundation (TDF)

2. Solapur Zilla Yantra Mag Dharak Sangh (SOZIYA)

These associations are involved by local, state government bodies while policy

making and their implementation. Almost all the textile SMEs are registered as members

with these associations. Hence a list of all textile SMEs is collected from these associations

which can be taken as a reliable source for data collection. The respondents were selected

randomly form this list.

3.4.3 Selection of respondents

The information compiled from the perceptions of key participants is often better

than limited collection of incomplete objective data gathered independently by researchers

themselves (Meredith, 1995). Keeping this view in mind, the key informant approach is

used, according to which the persons in charge of the respective functional

areas/departments/owner in an organization (e.g. quality engg., production engg., purchase,

HR etc.) are requested to respond to the questionnaire, because these persons are best able

to provide information related to variables affecting productivity. Hence, the respondents

selected in this study are the owners/partners of the textile manufacturing units.

3.4.4 Instructions to the respondents

Separate guidelines/instructions (for how to respond to the questions) are provided in

the questionnaire. It is attached in the appendix III.

82

3.5 Methodology adopted for contacting and collecting questionnaire from

respondents

Earlier researchers have highlighted the degree to which response rates can be

influenced by the methods adopted to contact respondent for questionnaire distribution and

data collection. In current research, the researcher has personally contacted the respondents

with prior permission and appointment. During discussion in person with respondents, the

researcher has clarified the queries asked.

3.6 Testing of data for suitability

The sophistication of the statistical techniques that are applied to any data set, the

results of any data analysis will only be as good as the consistency of the data upon which

it is based. Issues of reliability and validity are very important aspects of survey design to

ensure that the research instrument achieves the set objectives. Babbie, (1998) contends

that, a research instrument would be valid if it could measure what it is supposed to

measure and it will be reliable when it yields the same responses over time when

administered to the same subjects. In present study, testing of data for suitability is done by

data validation and data reliability analysis (by calculating Cronbach - value).

3.7 Methodology adopted for analysis of data

The data, after collection, has to be processed and analyzed in accordance with the

outline laid down for the purpose at the time of developing the research plan. For data

analysis statistical techniques are used. There are two major areas of statistics viz. (i)

descriptive statistics is concerned with the development of certain indices from the raw

data, (ii) inferential statistics is concerned with the process of generalization.

The collected data is analyzed for calculating mean and standard deviation values,

as these values are the indicators of the impact on productivity.

To model mathematically the relation between productivity and variables, factor

analysis and regression analysis is carried out. As, factor analysis is statistical technique

that uses correlation between variables to underling dimensions. Repeated attempts of

factor analysis are used to all different methods of extraction and rotation. It is observed

83

that combination of principal component analysis method of extraction and Varimax

method of rotation results into comparatively more meaningful results. Hence, it is used as

the appropriate method for factor analysis.

According to Hair et al. (1998), a variable may be considered important for

interpretation, if its factor loading is 0.4 and above, with the representative factor, the

variable may be considered as significant. The same criterion is adopted in this study.

These are the factors without name or label. It would be difficult to interpret and

communicate without any name assigned to the factors. Therefore, suitable names are

given based on the importance of the variables covered under the respective factors.

Using Simple Logistic Regression (SLR) , the factor scores are correlated to the

corresponding ‘Y’ values is carried out to understand which factors contribute to ‘Y’

maximally with respect to the multiple co-efficient (R2 in %). Therefore, factors with R

2

more than 50% can be considered for building equation/model.

At this stage, there are four possibilities, which are represented in table 3.2

Table 3.2 R2

(adjusted)

Condition (%) Possibilities

R2 lies between 50 to 80 Influence of corresponding

factor is high on Y


factor is moderate on Y


factor is weak on Y

R2 lies less than 20 Influence of corresponding

factor is very weak on Y

Therefore, factors with R2 more than 50% can be considered for building

equation/model using multiple logistic regression (MLR). MLR analysis generates an

equation or a model to describe the statistical relationship between two or more predictor

variables (independent variables) and the response (dependent variable) by fitting a linear

84

equation to observed data. This equation is an algebraic representation of the regression

line and is used to describe the relationship between the response and predictor variables.

The regression equation / model take the form of:

Response = constant + coefficient (predictor) + … + coefficient (predictor)

or

Y= bo + b1X1 + b2X2 + … + bkXk

Where, Y is the value of the response. Constant (bo) is the value of the response variable

when the predictor variable(s) is zero. The constant is also called the intercept because it

determines where the regression line intercepts (meets) the Y-axis. Predictor(s) (X) is the

value of the independent variable(s). Coefficients (b1, b2… bk) represent the estimated

change in mean response for each unit change in the predictor value. In other words, it is

the change in Y that occurs when X increases by one unit.

The analysis is performed by using SPSS V17 software. This analysis also

estimates the coefficient ‘p-value’ for the predictors. The coefficient ‘p-value’ helps us to

understand, whether or not the association between the response and predictor(s) is

statistically significant. A commonly used cut-off value for the ‘p-value’ is 0.05 (Draper

and Smith, 1981).

The data collection using experience survey methodology and related findings are

discussed in chapter 4.

85

Chapter 4

DATA COLLECTION BY EXPERIENCE SURVEY

The work carried is reported in chapters 4 and 5. This chapter reports the

experience survey work. It includes the report about survey details such as (a) data

collection, (b) data analysis, (c) findings of experience survey. The detail report of these

steps including survey results and statistical analysis are discussed. Discussions about

inferences derived/findings are also included in the chapter. The work carried out at each

step is presented in various sections below.

4.1 Expert panel

The identification of variables for collection of data in the survey is done by

literature study and expert opinion. At different stages of current research work, in-depth

discussions with expert panel are carried out to take decisions. Expert panel includes:

industry experts, experienced persons, consultants, researchers and academicians working

in the same/similar area. The selected experts with their details are presented in table 4.1

86

Table 4.1 Expert panel

Sr. No. Experts Category Details

1. Prof. (Dr.) S. P.

Kallulkar

Academician

(Subject

Expert)

Principal, Atharva College of Engineering,

Malad, Mumbai. [Area of research: Ind.

Engg, Productivity, QMS, Six Sigma etc.]

2. Prof. (Dr.) M. S.

Pawar

Academician

(Subject

Expert)

Principal, B.M.I.T., Solapur. [Area of

research: Ind. Engg, Productivity, QMS, Six

Sigma etc.]

3. Mr. S. P. Patil

Researcher,

Industrial

expert, CII

committee

member

Managing Director, Laxmi Oïl Pumps and

Systems (P) Ltd. Solapur.

[Area of research – Productivity, Statistics,

Theory of Constraints]

4. Mr. Satyram

Myakal

Industry

Expert

Chairman, Myakal Texile, President,

Textile Development Foundation, Solapur.

5. Mr. Srinivas

Bura

Industry

Expert

Vice-President, Textile Development

Foundation, Solapur.

Partner, Bura Texile.

6. Mr. Govind

Zanwar

Academician

and Industry

Expert

Director, Textile Development Foundation,

Solapur, Partner, Balaji Weaving Mill,

Solapur.

7. Mr. Venugopal

Divate

Industry

Expert

Director, Divate Textiles, Pvt. Ltd. Solapur.

8. Mr. S.S.

Yajurvedi

Textile

Consultant

Textile Consultant, Solapur. [Expert in

dyeing and weaving.]

9. Prof. Vilas Bet

H. R.

Consultant,

Researcher

and

Academician

Principal (retired), M. S. W. College, Ashok

Chowk, Solapur.

10. Mr. Ramesh Patil Statistician

Statistician, Dr. V. M. Medical College,

Solapur. [Expert in Statistics and SPSS

Software.]

87

4.2 Work carried out

After in-depth literature review, discussion with expert panel, academicians and

researchers, the variables are identified. About 50% variables are identified through

literature review. It is ensured that variables from all section and departments are covered

in the list. There are some variables which are specific to Solapur based textile SMEs.

These are also added in the list. The list of variables is shown in table 4.2.

Table 4.2 List of variables

Sr. No. Abbreviations Variables

1 V1 Top management commitment

2 V2 Well defined organization structure

3 V3 Defined productivity targets and plans

4 V4 Review of productivity related issues/targets

5 V5 Use of scientific tools such as 6 sigma, Lean, TOC etc.

6 V6 Production planning

7 V7 Availability of work instructions for workers

8 V8 Preventive maintenance

9 V9 Breakdown maintenance

10 V10 Yarn quality

11 V11 Dye quality

12 V12 Water quality

13 V13 Warp quality (Beam)

14 V14 Weft quality (Shuttle)

15 V15 Stitching quality

16 V16 Final inspection

17 V17 Use of SPC (Statistical Process Control) tool

18 V18 Well defined authority and responsibility

19 V19 Training to employees

20 V20 Policy for motivation (reward/award scheme)

21 V21 Performance appraisal system

88

Sr. No. Abbreviations Variables

22 V22 Occupation health and safety practices

23 V23 Complaints and grievance handling system

24 V24 Involvement of employees in productivity related decisions

25 V25 Salary Structure (Daily/Weekly Monthly-Fixed/Pc. Rate

26 V26 Labor Absenteeism

27 V27 Carelessness of labors

28 V28 young generation of labors not ready to join this sector

29 V29 Well defined system of records

30 V30 Presence of systems like ISO 9000

31 V31 Corrective actions

32 V32 Preventive actions

33 V33 System for continual improvement

34 V34 Manufacturing process (Power loom/Shuttleless/Rapier)

35 V35 Dyeing process (Manual/Semiautomatic/Automatic)

36 V36 Beam lifting method (Manual/Semiautomatic/Automatic)

37 V37 Stitching process (Manual/Semiautomatic/Automatic)

38 V38

Use of renewable energy such as solar/wind energy for

various processes (Y/N)

39 V39 Profitability (Productivity Measurement)

After the identification of variables, dependent and independent variables are

decided and the structured questionnaire is formulated.

4.2.1 Dependent variable

The dependent variable (Y) in this study considered is profitability, which is taken

as a measure of productivity. Profitability is defined as gross profit / total sales.

Productivity is defined as output / input. Gross profit depends upon sales, cost of raw

material, cost of processing, cost of employees, other overhead costs, etc. The changes in

any one of these costs are directly reflected into change in profitability. Some of the earlier

researchers have also used “profitability” as a dependent variable. Therefore this parameter

89

is selected as a dependent variable (Y) for study. All other variables become independent

variables.

4.3 Experience survey

Experience survey method involves development of structured questionnaire,

selection of list of respondents, data collection and analysis of data.

4.3.1 Structured questionnaire development

Methodology adopted for developing „structured questionnaire for experience

survey‟ is explained in chapter 3. The questionnaire finalized by using the methodology

selected is reported in appendix III.

4.3.2 Collection of list of textile SMEs in Solapur

There are two registered associations of textile manufacturing industries in Solapur

viz.:

1. Textile Development Foundation (TDF)

2. Solapur Zilla Yantra Mag Dharak Sangh (SOZIYA)

These associations are involved by local, state government bodies while policy

making and their implementation. Almost all the textile SMEs are registered as members

with these associations. Hence a list of all textile SMEs is collected from these associations

which can be taken as a reliable source for data collection.

4.3.3 Data collection

In this research, the collection of data is done through a questionnaire survey. For

collecting data of questionnaire, the top management people (viz. CEO/

Owner/Partner/Director) of the companies surveyed are requested to give response, as it is

an important recommendation from the expert panel.

The information compiled from the perceptions of key participants is often better

than limited collection of incomplete objective data gathered independently by researchers

themselves (Meredith, 1995). Keeping this view in mind, the key informant approach is

90

used, according to which, the top management people (viz. CEO/ Owner/Partner/Director,

the person in-charge of the respective functional areas/departments) in the organization are

requested, as these persons are the best source of information related to productivity and

variables affecting the same. In case of the current research, impact on productivity is

measured as change in profitability for last two years.

Out of 194 companies contacted, 172 companies responded. Out of these 5

questionnaires are eliminated for subsequent analysis as, they had incomplete responses.

Thus, the research analysis and conclusions are based on the data provided by 167, which

leads to 86.08% response rate.

4.4 Testing of data for suitability

In present study testing of data for suitability is done by data validation, testing the

normality for distributions of collected data and data reliability analysis. The data

suitability is confirmed / tested.

4.4.1 Data validity

According to Hair et al. (1998) validity is the degree to which a measure accurately

represents what it is supposed to. The research instrument developed and used in this study

is subjected to validation for its design, evolution and analysis. It followed a

comprehensive literature review that examined similar research instruments and it is

subjected to extensive review by research guide and expert panel. It then passed through a

rigorous pilot process before the final version is approved. In the researcher's opinion, the

applied tests of validity are the most reasonable in the circumstances. In addition, factor

analysis has also served to test data construct validity.

4.4.2 Data reliability

Reliability is related to internal consistency of group of variables. In this study, the

internal consistency is estimated by calculating the Cronbach‟s alpha reliability coefficient.

The Cronbach‟s alpha value for all variables has resulted as 0.74, which is higher than 0.6,

which suggests a satisfactory reliability (Malhotra, 2004). The confidence level is set at

95%. After testing the data for suitability, in depth analysis is carried out and is reported.

91

4.5 Data analysis

The data, after collection, has to be processed and analyzed in accordance with the

outline laid down for the purpose at the time of developing the research plan. In current

study, for data analysis, descriptive (which concern the development of certain indices

from the raw data) as well as inferential (which concern with the process of generalization)

statistical techniques are used. SPSS V17 software is used for the data analysis.

Data analysis consists of:

i. Factor analysis of variables.

ii. Regression analysis.

4.5.1 Classification of textile SMEs

A descriptive statistical analysis of the companies‟ demographic information is

presented in table 4.3.

Table 4.3 Responses received by type and size of company

Type/Size No. of response Percentage

Common Type of Manufacturing environment

(a) Domestic market 123 73.67

(b) Export market 33 19.76

(c) Both Domestic & Export 11 6.57

Total 167 100

*Size of companies

(a) Medium 29 17.36

(b) Small 138 82.64

Total 167 100

*Categorization is done as per guidelines given by „Micro, Small, Medium and Large

scale Industrial act: 2006‟

92

4.5.2 Factor analysis of variables

To test suitability of the data set for factor analysis, Kaiser-Meyer-Olkin (KMO)

test and Barlett‟s test of sphericity have been conducted as shown in table 4.4. The value of

the overall KMO measure of sampling adequacy for the factor analysis is equal to 0.782

(greater than 0.5) and significance level of Bartlett's test is equal to 0.000 (less than 0.05),

which indicate the suitability of data for further factor analysis (Malhotra, 2004).

Table 4.4 KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy 0.782

Bartlett's Test of Sphericity Approx. Chi-Square 3478.864

Df 703

Sig. 0.000

The factor analysis is used to reduce the multiple relationships that may exist

among variable statements. It uncovers the common dimensions that link together the

seemingly unrelated variables, and provides insight into the underlying structure of the

data. The principal component extraction method is chosen to analyze the correlation

matrix, and to extract the Eigen-values over one. For interpretation of the data set, the

Varimax rotation is applied. Only the factor loadings, that had values greater than 0.4, are

considered (Malhotra, 2004). The factor analysis of 38 variables (using SPSS V17) has

resulted into 9 factors. The output of factor analysis is presented in table 4.5

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Table 4.5 Factor analysis of variables

Sr.

No. Variables

F-1 F-2 F-3 F-4 F-5 F-6 F-7 F-8 F-9

Varimax rotated loadings

1 V1 0.594

2 V2 0.801

3 V3 0.853

4 V4 0.829

5 V8 0.495

6 V9 0.542

7 V10 0.844

8 V11 0.813

9 V12 0.772

10 V13 0.800

11 V14 0.655

12 V34 0.837

13 V36 0.535

14 V37 0.838

15 V18 0.432

16 V19 0.662

17 V20 0.785

18 V21 0.488

19 V22 0.794

20 V23 0.708

21 V24 0.450

22 V26 0.887

23 V27 0.862

24 V28 0.767

25 V5 0.754

26 V17 0.885

27 V38 0.668

28 V35 0.839

29 V30 0.667

30 V31 0.810

31 V32 0.854

32 V33 0.749

33 Eigen

Value 8.096 4.469 3.085 2.262 2.019 1.846 1.485 1.397 1.120

34 Cumulative

% 10.80 20.74 30.20 39.49 47.84 54.01 59.02 63.55 69.84

94

The factor analysis has helped to identify 9 factors from significant variables,

having factor loading equal to more than 0.4. These factors are tabulated in table 4.6.

Table 4.6 Identified factors

Factor no. Name of the factor

F1 Synchronization of management processes

F2 TPM for weaving and dyeing

F3 Input and process quality

F4 Process technology

F5 HR policies for textile SMEs

F6 Labor behavior

F7 Use of scientific tools for improvements

F8 Use of renewable energy for processes

F9 System deployment

4.5.3 Regression analysis

The logistic regression model (similar to a linear regression) is a specific

calculation tool which is used for a description of relations among the output variables, i.e.

the dependent variable „Y‟ and one or more input variables i.e. independent variables. In

the case of a linear regression model, the explained variable is continuous. However, if the

analyzed categorical variable „Y‟ contains only a limited number of values, it is necessary

to choose the logistic regression model (Miriam Andrejiova et al. 2014). The same model

is used for analysis of the data.

4.5.4 Results and discussion

The logistic regression analysis is carried out for the above referred 9 factors. MLR

analysis predictor variables are the scores of 9 factors, which represent all process

variables. The response (dependent variable „Y‟) is a score related to improved

profitability. The analysis is done by SPSS V17 software.

95

This analysis has revealed the significance (p value) of these factors which is given

in table 4.7.

Table 4.7 Logistic regression

Sr. No. Name of the factor Estimate Sig. (p Value)

1 Synchronization of management processes 0.234 0.002

2 TPM for weaving and dyeing 0.415 0.000

3 Input and process quality 0.760 0.000

4 Process technology 0.242 0.000

5 HR policies for textile SMEs 0.159 0.035

6 Labor behavior -0.006 0.966

7 Use of scientific tools and techniques 0.239 0.289

8 User of renewable energy for processes 0.251 0.062

9 Systems deployment 0.306 0.045

The regression is found to be highly significant (p value less than 0.05) for the

factors 1 to 4 and factor no. 9. The pseudo R2

adjusted value is found to 0.89 which is

above 0.8.

The regression analysis can be written as:

Y= 0.189 + 0.760 F3 + 0.415 F2 + 0.242 F4 + 0.234 F1+ 0.159 F5 + 0.306 F9 + 0.251 F8

+ 0.239 F7 – 0.006 F6 Eq. (4.1)

Where, „Y‟ is profitability.

The same equation is written with their nomenclature,

Improved Profitability = 0.189 + 0.760 Input and process quality

+0.415 TPM for weaving and dyeing

+ 0.242 Process technology

+ 0.234 Synchronization of management processes

+ 0.159 HR policies for textile SMEs

+ 0.306 System deployment

+ 0.251 Use of renewable energy for processes

+ 0.239 Use of scientific tools for improvements

- 0.006 labor behavior

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In this regression model, the association between the response and predictor(s) is

statistically significant (refer p-values in table 4.6) and 9 factors together explain 69.84%

of variability. Hence, this model can be used for prediction purpose.

4.6 Findings of experience survey

The result shows that input process quality has the highest weightage with a

coefficient of 0.76 and p value (sign) of 0.000. This factor includes variables such as yarn

quality, dye quality, water quality, warp quality and weft quality. Out of these, yarn quality

is most important as it will have effect on all other further processes such as dyeing, warp,

weft and weaving. The strength of yarn is a dominant parameter in the quality of yarn.

The second important factor is maintenance for weaving and dyeing, which also has

a p value of 0.000. This factor is the combination of preventive and breakdown

maintenance. It has a coefficient of 0.415 which indicates that TPM will improve the

productive capacity.

The third factor is Process technology for textile SMEs, which has a p value of

0.000 (indicating highest significance) and with a coefficient of 0.242. The technologies

used for various processes such as weaving, dyeing, beam lifting and stitching are

considered here. The technology has been classified as manual, semi-automatic and

automatic. It is observed that high level of technology (i.e. automatic) will help to improve

productivity.

The fourth factor is synchronization of management processes with a p value of

0.002 (less than 0.005) and coefficient of 0.234. The variables considered in this factor are

top management commitment, well defined organization structure, defining productivity

targets and their review.

The fifth significant factor is HR policies having a p value of 0.035 (less than 0.05).

This factor involves the aspects such as training, performance appraisal, system

involvement of employees in productivity related issues, policy for motivation.

97

The sixth factor is system deployment having p value as 0.045 (less than 0.05)

which has a coefficient of 0.306. Systems like ISO 9001 (QMS) are considered in this

factors. Implementation of ISO 9001 QMS will have a positive impact on productivity.

The seventh factor is use of renewable energy for various processes such as dyeing,

sewing has started in textiles. So to know the impact of such use on productivity, the factor

has been included in survey questionnaire. It has a marginal significance with p value of

0.062.

The eighth factor is use of scientific tools / techniques such as six sigma, lean, SPC,

etc. which is not significant as its p value is 0.289 (greater than 0.005). It indicates that the

improvement in this factor will have marginal impact on productivity.

The ninth factor is labor behavior, which has p value 0.966 (which is very much

above the limit of p>=0.05). Therefore its impact on productivity will be very low. The

negative sign for this factor indicates that the labor absenteeism will decrease productivity.

From the findings, it is observed that, first three factors (input and process quality,

TPM for weaving and dyeing, process technology) are of highest significance as the p

values for these three factors are 0.000. Therefore initially it is important to focus on these

three factors for their improvements. An appropriate methodology for making

improvement in these three factors is necessary. The methodology and its implementation

are reported in next chapter.

98

Chapter 5

DEVELOPING AND IMPLEMENTING METHODOLOGY FOR PRODUCTIVITY

IMPROVEMENT

Experience survey and consequent statistical analysis clearly reveal that there are

nine factors affecting productivity of Solapur based textile SMEs. These factors are as

follows:

1. Input and process quality

2. TPM for weaving and dyeing

3. Process technology

4. Synchronization of management processes

5. HR policies for textile SMEs

6. System deployment

7. Use of renewable energy for processes

8. Use of scientific tools for improvements

9. Labor behaviour

The First three factors (input and process quality, TPM for weaving and dyeing,

process technology) are of highest significance as the p values for these three factors are

0.000. If these three factors are improved, then it will have highest positive impact on

productivity. The validation and productivity improvement of these three factors are done

through the case studies. A methodology based on theory of constraints (TOC) is used for

this purpose.

5.1 Methodology adopted for improving productivity

A methodology is developed to improve productivity of Solapur based textile

SMEs. It is based on Theory of Constraints (TOC).

Theory of Constraints (TOC) is a way to look at business processes to make them

more productive according to their goals (Goldratt, 1984). It looks at the business by

looking at its constraints. Every system has at least one constraint, which limits the profits

of business. To improve the profit, one has to exploit these constraints.

99

Business is complex phenomenon. TOC says “behind every complexity, their lies

inherent simplicity”, identify that simplicity so as to manage the business effectively and

efficiently.

The Theory of Constraints is based on the following five-step model

(Goldratt, 1984):

Step 1 Identify the system‟s constraint or bottleneck

Step 2 Decide how to exploit the system‟s constraint or bottleneck

Step 3 Subordinate everything else to the above decision

Step 4 Elevate the system‟s constraint or bottleneck

Step 5 If in a previous step a bottleneck has been broken go back to step 1.

Smith (2000) made the following base line assumptions on the usage of these steps:

a. There are a few bottlenecks (or key leverage points) in any interdependent system.

They determine the overall performance of any organization. These bottlenecks can be

identified.

b. Maximizing the contribution margin (sales minus truly variable costs) per unit of

the constraining resource will maximize the system‟s profit. Truly variable cost is

identified as a cost with a direct linear relationship with volume. Besides the obvious raw

materials, other truly variable costs can include sales commissions, packaging material

and shipping costs, but not direct labour, with the exception of labour payment based

on piece-rate production.

c. The reality is that constraints or bottlenecks exist. Either manages them or they

will manage the organization and result in constant fire fighting.

A constraint/ bottleneck is defined as any resource whose capacity is less than the

demand placed on it. A bottleneck can be for example a machine, scarce or highly skilled

labor, or a specialized tool. A non-bottleneck is any resource whose capacity is greater

than the demand placed on it. A non-bottleneck, therefore should not be working

constantly because it can produce more than is needed.

100

The concept of productivity as per TOC is that every organization has a definite

goal. Productivity is defined as any action/decision taken to achieve the goal. Only these

actions are productive, rest all unproductive. Assuming throughput is quantified, TOC

uses the following equation for productivity.

Productivity = Throughput/Operating Expense

Where, Throughput: the rate at which the system generates money through sales.

Investment: all the money the system invests in purchasing items the system intends to sell.

Operating Expense: all the money the system spends in turning investment into throughput.

This is based on the fact that the operational goal of a firm is to increase throughput

while reducing inventory and operating expense. Treating these three simultaneously and

continually achieves the goal of making money.

5.2 Procedure for applying TOC to textiles

By applying the five steps of TOC to textile SMEs following specific procedure

can be suggested.

Step 0: Decide the goal of the system i.e. to increase the productivity of manufacturing unit

which should result into improving profitability.

Step 1: Identify the system constraint

The following guidelines are suggested to identify the system constraint.

a) Draw a process flow diagram of terry towel manufacturing (from yarn to terry towels).

b) Indicate the output per shift of each process. This helps to visualize the actual capacity.

c) Identify the process having lowest output. This becomes the system constraint and will

decide the output of the system (i.e. no. of towels in kg per shift.)

d) Alternately the constraint can be decided by observing bottleneck in the manufacturing

setup.

101

Step 2: Exploit the system constraint

Exploiting in TOC means, to get maximum of the constraint resource, without any

substantial increase in the input. Exploiting can be done by utilizing constraint capacity

resources (CCR) to the maximum possible extent. Exploiting of the constraint can be done

in following ways:

a) No time should be wasted on CCR

b) Process parameters should be optimized

c) Competency of the human resource (skill, knowledge, experience and qualification)

may be ensured.

d) Waiting time at CCR should be as minimum as possible.

e) Set up time, number of batches may be as minimum as possible.

A cause and effect diagram can be helpful to identify the causes of lower output

from CCR. Further A-B-C (Pareto) analysis may be used to decide “A” category

cause/causes.

Once “A” category cause is identified, possible solution to address the cause can be

developed. Various techniques such as Brainstorming, DOE, Six sigma, Lean, FMEA,

MSA, SPC etc. can be useful to develop the solution. The factors revealed by experience

survey will help to provide the solution for the causes identified for low productivity.

(Generally those will be related to input and process quality and TPM in the most of the

cases).

Step 3: Subordinate

The third step of TOC says that- “Subordinate everything else to CCR”. It implies

that everybody in the organization will give highest priority to CCR. In case of dilemma,

it will be the responsibility of everybody to see that CCR is giving its required output. It is

necessary that, all other processes may be given secondary importance as compared to

CCR.

102

Step 4: Elevate the constraint

In spite of all the possible exploitation and subordination, still the output is not

matching to the demand (i.e. production capacity is lower than market demand), then it is

suggested that, the CCR should be elevated. Elevation means adding new capacity by

addition of resources. It may be technological up-gradation like adding shuttleless kit to

power loom or replacing the powerloom itself, by shuttle less loom or addition of

manpower, addition of another resource for constraint capacity, etc. It is the last

alternative to increase the capacity of the entire unit. This may be equivalent to

productivity improvement by upgrading technology.

Step 5: Go back to step 1

After elevating the constraint, now the constraint will shift to some other process.

All the steps from 1 to 3 are to be repeated for newly identified constraint. In case of

textiles, if shuttleless looms are installed in place of powerloom, the constraint may shift to

back process like bobbin winding, cone winding, beaming, pern winding etc.

For analysing applicability and field validation of proposed methodology, it is

implemented in SMEs at Solapur. These case studies are discussed in detail here with.

5.3 Case study 1

5.3.1 Objectives of case study

a) To study the effect of input quality on productivity.

b) To improve productivity by TOC methodology.


The details of the manufacturing unit and present capacities are given in table 5.1

and 5.2 respectively

.

103

Table 5.1 Details of the manufacturing unit

1. Factory Located at MIDC, Solapur (small scale)

2. No. Of workers 32

3. Shift Single shift of twelve hours (8 am to8 pm)

4. Products Yarn dyed terry towel

5. Size 30” x 60”

6. Loom Jacquard power loom

7. No. of looms 16

8. Yarn 100% cotton

Table 5.2 Details of the machinery and capacities

Sr. No. Machine Specifications Capacity (kg/shift)

01 Doubling 400 Spindles 450

02 Hank dyeing 48 Arms 400

03 Winding 24 Spindles 480

04 Warping 2 Machines 400

05 Pern filling 8 Spindles 1000

06 Loom Jacquard power loom

(16 no.)

240

07 Stitching Juki (10 no.) 750

5.3.3 Identification of system constraint

As per TOC methodology the system constraint is the one, which is having lowest

output (or bottleneck). The capacities are presented in the form of a chain (as defined in

TOC) as shown in figure 5.1. Numbers in figure represent corresponding machines from

table 5.2.

104

Figure 5.1 Representation of terry towel manufacturing as a chain

It is observed from the data that all the processes, except the powerloom have

excess capacity. Therefore any productivity improvement on these non-constraint

resources will not improve the productivity of the total plant. Either it may increase

idleness of the capacity or increase the inventory. To improve the output of the plant, the

productivity of power looms has to be increased. Hence powerloom is obviously the

constraint of the system.

The present average output of the powerloom is 15 kg/loom/shift. To increase the

output we have to go to step 2 of TOC i.e. exploit the system constraint. Exploiting the

constraint involves getting maximum output from the constraint without adding any

significant resources. Therefore the various causes of lower output of the power loom are

studied which are presented with the help of cause and effect diagram.

5.3.4 Cause and effect diagram

This diagram helps us to identify the various causes for an effect in a systematic

way. The causes are broadly classified as 6M, namely- Man. Machine, Material, Method,

Management and Miscellaneous. The various causes for lower output of the power loom

are categorized under these headings and are as shown in figure 5.2.

System Constraint (Power Loom)

105

Figure 5.2 Cause and effect diagram

The data related to occurrence of these causes is collected and their frequency

analysis is done. This is presented in the form of Pareto analysis.

5.3.5 Pareto analysis

This tool helps us to identify a few vital causes whose impact is significant on the

end result. The frequency distribution is converted into percentage basis and is shown in

figure 5.3.

Figure 5.3 Pareto analysis

Lower output of

power loom

Material Method Miscellaneous

Management Man Machine

Tem

p

Humidity

Pulp

Remover

Occupational

Hazards

Knotting

Set up

Changes

Beam

Loading

Speed (RPM)

Design

Jacquard

Dye Quality

Yarn Quality

Yarn

Breakage

PPC

Safety

HR Policies

Spare part

management

Invt

.

Knotting

Breakdown

Maintenance

Preventive

Maintenance

Stand by

stand

Yarn

Breakage

Performance

Appraisal Skill

Absenteeism

Negligence

Motivation

Main

t

106

It is revealed from Pareto analysis, that „A‟ category cause was „breakage of yarn‟

during production. Therefore it was decided to find out the possible solution for this

problem.

5.3.6 Exploit the system constraint

It was observed that yarn breakage was taking place frequently during weaving.

Therefore experimentation was undertaken to decide the relation between breakage of yarn

and parameters such as temperature and humidity.

5.3.7 Experimentation

Trials were conducted by varying temperature of yarn and its effects on yarn

breakage were recorded.

Following parameters were maintained during experimentation

1. Yarn count : 16 single

2. Type of yarn : 100 % cotton

3. Humidity : 30 % RH

The readings are given in table 5.3.

Table 5.3 Effect of temperature on yarn strength

Sr. No. Temperature of yarn ( °C) Yarn strength (CSP)

01 12 to 15 2764.8

02 18 to 20 2519.4

03 24 to 28 2431.3

04 36 to 38 2317.6

107

The graphical representation of the above data is given in figure 5.4.

Figure 5.4 Graph of temperature vs. yarn strength

From the graph it is observed that as the temperature increases the yarn strength

decreases. The drop in strength is more in temperature range of 40 to 20.

The same set of experiments were repeated by varying relative humidity from 25%

RH to 65 % RH by keeping temperature constant at 30° C to establish the relation between

humidity and yarn strength. The readings are given in table 5.4.

Table 5.4 Effect of humidity on yarn strength

Sr. No. Humidity (% RH) Yarn strength (CSP)

1 25 2070.8

2 35 2191.4

3 45 2308.7

4 55 2421.3

5 65 2557.2

108

It is observed that the yarn strength is maximum at 65 % RH and then goes on

decreasing as RH decreases. The RH level at room temperature (30° C to 36° C) is 21% to

25%. Therefore a humidifier is installed. It increased RH value up to 35%.

Figure 5.5 Humidifier

At the same time, temperature also dropped because of humidification by 3° C.

Additionally heat insulation was provided which further reduced temperature by 2° C. As

result of these changes, yarn breakage reduced by 50%. Another important parameter

responsible for yarn breakage is coefficient of variation (CV). CV was reduced from 9% to

3-4% by undertaking maintenance of spinning machine. As a result, yarn breakage

decreased by 90%.

5.3.8 Subordinate

To facilitate “subordinate”, the following changes were made in Quality

Management System:

Purchase of yarn- The suppliers of yarn were informed about the requirement of

CV (max. 4%) and CSP. The purchase orders were amended accordingly (without change

109

in price). Verification of purchase product- The incoming material (yarn) quality checks

list is modified to add the parameter of CV and CSP.

The procedures and work instructions were changed accordingly. Jobbers and

workers were trained about these changes. After making these changes a system is

stabilized for an output of average 20kg/loom/shift.

5.3.9 Conclusions

a) By improving quality of yarn (reducing CV) and applying TOC, the productivity

improved by 20%.

b) Quality of yarn is affected by temperature and humidity. As temperature increases,

yarn strength (CSP) decreases and as humidity increases (up 65% RH), yarn strength

increases.

5.4 Case study 2


a) To study the effect of preventive maintenance of powerloom on productivity.



The data of the textile manufacturing unit is given in table 5.5 and 5.6.


1. Factory Located at MIDC, Solapur (small scale)

2. No. Of workers 40


4. Products Yarn dyed terry towel

5. Size 30”x60”


7. No. of looms 20

8. Yarn 100% cotton

110


Sr. No. Process/machine Specifications Capacity (kg/shift)

1 Doubling 440 Spindles 470

2 Hank dyeing Hank dyeing single unit 400

3 Winding 40 Spindles 480

4 Warping 2 Machines 400

5 Pern filling 12 Spindles 1000

6 Loom Jacquard power loom (20 no.) 280


The terry towel manufacturing system can be represented (by TOC way) in figure 5.4.


After collecting the data the system constraint is identified.


Out of these processes, weaving process (powerloom) was obviously the system

constraint. A cause and effect diagram was used to find out the root cause/s for lower

output of the powerloom. Further ABC analysis was done to determine the major factors

contributing to lower output. It was noticed that, maintenance of the powerloom was the

major cause.

System Constraint (Power Loom)

111


After exploiting the possible solutions on the powerloom, preventive maintenance

system was established. The check sheet of the preventive maintenance of powerloom is

shown in table 5.7.

Table 5.7 Preventive maintenance schedule for powerloom

Preventive maintenance was followed as per schedule. After carrying out

preventive maintenance of powerloom, the productivity increased approximately from 14

kg/loom/shift to 17 kg/ loom/shift.

Lubrication

Sr. no. Parameters Frequency

1 Crank shaft & connecting rod (L-R side bush) Daily

2 Bottom shaft ( L-R side bush) Daily

3 Bottom shaft cam ( L-R ) Daily

4 Gear assembly on Bottom Daily

5 Bushing via binder to 3rd

shaft Daily

6 Tappets on 3rd shaft Daily

7 Tappet arm Daily

8 Picking shaft assembly Daily

9 Weights of lower & upper beam checking Daily

10 Slay shaft (LR) Daily

11 Eccentric mechanism Daily

12 Connecting rod Daily

13 U- bracket Daily

14 Top- bracket Daily

15 Knife rod pin Daily

16 Knife rod bracket Daily

17 Gear on crank Weekly

18 Take up motion (ratchet wheel & gear) Weekly

19 Jacquard gear assembly Weekly

20 Crank shaft sprocket Monthly

Replacement Parts

Sr. no. Parts Frequency

1 Picker belt 6 months

2 Picking stick 11 months

3 Picker and buffer 3 months

112

5.4.5 Subordinate

The above referred changes were made as a part of the system by following

modifications in the quality management system (QMS).

a) A system of maintenance along form, formats, etc. was established.

b) Spare part management system in the stores was introduced.

5.4.6 Conclusion

Preventive maintenance and TOC methodology improved the productivity of

powerloom by 21%.

5.5 Case study 3


a) To study the effect of process technology on productivity.





1 Factory Located at MIDC, Solapur (Small scale)

2 No. of workers 52

3 Shift Single shift of twelve hours (8 am to8 pm)

4 Products Yarn dyed terry towels, napkins

5 Size 30”x60”, 27”x54”, 24”x48”, 20”x34”, 14”x21”


7 No. of looms 42

8 Yarn 100% cotton

113


Sr. No. Machine Specifications Capacity

(kg/shift)

1 Two for one (TFO)

twister (Doubling)

144 Spindles

(6 machines) 1500

2 Hank dyeing 40 Arms 960

3 Bobbin Winding 40 Spindles

(3 machines) 600

4 Sectional Warping and

Beaming 4 Machines 1200

5 Pern winding 12 Spindles

(4 machines) 1500

6 Loom

Jacquard

powerloom

(42 No.)

630



As per TOC methodology the system constraints is the one, which is having lowest


TOC).


System Constraint (Bobbin winding)

114

It is observed from the data that all the processes, except the bobbin winding have

excess capacities. Therefore any productivity improvement on these non-constraint

resources will not improve the productivity of the total plant. Either it may increase

idleness of the capacity or increase the inventory. To improve the output of the plant, the

productivity of bobbin winding has to be increased. Hence bobbin winding is obviously the

constraint of the system.

Average output of the bobbin winding was 600 kg / shift. To increase the output, it

was necessary to go to step 2 of TOC i.e. exploit the system constraint.


The various causes of lower output of the bobbin winding machines were studied.

A cause and effect diagram was drawn for the same. Pareto analysis was done of the

causes. It was noted from the Pareto analysis that lower rpm was the major cause which is

reducing the output of the machine. A modification of pulley diameter for increasing speed

of the bobbin winding machine was done. The details are as follows.

Figure 5.8 Pulley of bobbin winding machine

115

Figure 5.9 Bobbin winding machine

Following data refers to existing situation.

d1 = diameter of smaller pulley = 90 mm

d2 = diameter of larger pulley = 60 mm

n1 = speed of smaller pulley = 960 rpm

n2 = speed of larger pulley = 216 rpm

Eq. (5.1)

α = 13.27°

116

Angle of lap on smaller pulley = θ =180 - 2α Eq. (5.2)

= 180-2 × 13.27

= 153.46°

= 2.67 radian

Since, B type belt is used,

Mass of belt = 0.189 kg/m

Centrifugal tension = Tc = m × v2

Eq. (5.3)

Where,

Eq. (5.4)

= 4.52 m/s

Hence, Tc = 0.189 × 4.522

= 3.86 N

T = max. tension in belt

= allowable tensile stress × cross section area

= 2 N/mm2 × 150 mm

2

=300 N

Tension on tight side of belt = T1 = T - Tc Eq. (5.5)

=300 – 3.86

= 296.14 N

Let, T2 = tension on slack side of belt

Eq. (5.6)

Where, β = half of the groove angle of pulley =17°

μ = coefficient of friction between belt & side of groove

= 0.12

By solving Eq. (5.6),

T2 = 99 N

Power transmission capacity of one belt = (T1 – T2) × v Eq. (5.7)

= (296.14 – 99) × 4.5

= 887.17 W

117

Modified design:

The speed of winding can be increased either by increasing diameter of driver

(small) pulley or decreasing diameter of driven (larger) pulley. As driven (smaller) pulley

diameter was fixed, diameter of driver (smaller) pulley is increased to 110 mm.

d1 = 110 mm

d2 = 400 mm

Eq. (5.8)

= 264 rpm

As same belt is to be used, center distance will not change.

Now, center distance = 659 mm

Using equation (5.1),

α = 12.71°

Using equation (5.2),

Angle of lap on smaller pulley = θ = 180- 2α

= 154.28°

= 2.69 radian

Mass of belt is 0.189 kg/m

Centrifugal tension = Tc = m × v2

But, from equation (5.1),

= 5.529 m/s

Tc = 0.189 × 5.5292

=5.77 N

Max. allowable tension, T = 300 N

Tension on tight side of belt = T1 = T - Tc

=300 – 5.77

= 294.23 N

118

Let, T2 = tension on slack side of belt

Where, β = half of the groove angle of pulley =17°

μ = coefficient of friction between belt & side of

groove = 0.12

By solving equation (5.6),

T2 = 99 N

Power transmission capacity of one belt = (T1 – T2) × v

= (294.23 – 98) × 5.529

= 1084.95 W

Hence, the same belt can be used for new arrangement. As the existing motor (0.75 hp) can

sustain the new load, the same was used.

It was decided to increase the rpm of the machine by modifying the pulley

diameters. It was observed that, yarn breakage was more at higher rpm. Therefore the

quality of the yarn was studied. The yarn quality was improved by reducing C.V. 2-3%.

During processing good washing of agents were used such as Dekol FBSN, Prodet C. It

was ensured that minimum number of washes should be 2. All these changes reduced the

yarn breakage during bobbin winding and the output of the bobbin winding machine

increased from 600 kg/shift to 660 kg/shift.

5.5.5 Subordinate


Management System:

The procedures and work instructions were changed for dyeing process. Jobbers,

dyers and workers were trained about these changes. After making these changes a system

is stabilized for an output of average from 600 kg/shift to 660 kg/shift. The conclusion of

the case study is as follows:

5.5.6 Conclusion

By modifying the bobbin winding machine (process technology), the productivity

increased by 10%.

119

5.6 Case study 4


a) To study the effect of corrective maintenance of powerloom on productivity.





1. Factory Located at MIDC, Solapur (Small scale)

2. No. of workers 45


4. Products Yarn dyed terry towels, napkins

5. Size 30”x60”, 27”x54”, 24”x48”, 20”x34”,

14”x21”


7. No. of looms 28

8. Yarn 100% cotton, blended.


Sr.No. Machine Specifications Capacity

(kg/shift)

1 Doubling 144 Spindles (2 machines) 2000

2 Hank dyeing 40 Arms (2 machines) 960

3 Bobbin Winding 40 Spindles (3 machines) 600

4 Sectional Warping

and Beaming

2 Machines 600

5 Pern winding 12 Spindles (4 machines) 1500

6 Loom Jacquard power loom (28no.) 420


120




TOC).


It is observed from the data that all the processes, except the power loom have

excess capacity. To improve the output of the plant, the productivity of powerloom has to

be increased. Hence powerloom is obviously the constraint of the system.

Average output of the powerloom was 15 kg/loom /shift. To increase the output, it



A check sheet for preventive maintenance developed during second case study was

also used in this unit. In addition, a corrective maintenance was developed for the

mechanism used for power and motion transmission from powerloom to jacquard. This

mechanism involves gear mounted on shaft and U- bracket which was located at top of the

powerloom (approximate height of 14 feet). At both these points, sliding contact (bush)

bearings were used which required frequent lubrication (daily) as shown in figure 5.11.

Lubrication had to be done by operator by standing on platform (height 10 feet). This was

time consuming which reduced productive time of powerloom. Oil slippage from these

System Constraint (Power loom)

121

bearings was observed on terry towel during production. It reduced quality of terry towel.

Wear and tear was also more which involved frequent replacement of bush bearings (6-7

months).

The solution to problem was to replace bush bearings by ball bearings. Ball

bearings 6307Z and 62032RSC3 were used for U- bracket and connecting rod respectively

as shown in figure 5.12 and 5.13. This completely avoided lubrication problem. The life of

ball bearing is 5-6 years as recommended by manufacturer.

Before

Figure 5.11 Bush bearings at connecting rod

After

Figure 5.12 Ball bearings at connecting rod

122

Figure 5.13 Ball bearing at U- bracket

The various causes of lower output of the powerloom are studied. A cause and

effect diagram is drawn for the same. Pareto analysis is done of the causes. It is noted from

the Pareto analysis that breakdown maintenance is the major cause which is reducing the

output of the powerloom. As result of this, productivity increased from 15 kg/loom/shift to

approximately 17 kg/loom/shift.

5.6.5 Subordinate


Management System:

The quality manual was amended. The procedures and work instructions are added.

A checklist for preventive maintenance was made compulsory for all powerlooms. Timely

record keeping is done.

5.6.6 Conclusion

By implementing corrective maintenance and TOC methodology for powerloom,

the productivity increased by 9.86%.

123

5.7 Case study 5


a) To study the effect of dyeing process (process technology) of powerloom on

productivity.





1 Factory Located at MIDC, Solapur (Small scale)

2 No. of workers 38

3 Shift Single shift of twelve hours (8 am to8 pm)

4 Products Yarn dyed terry towels, napkins

5 Size 30”x60”, 27”x54”, 24”x48”, 20”x34”, 14”x21”


7 No. of looms 36

8 Yarn 100% cotton, blended.


Sr. No. Machine Specifications Capacity

(kg/shift)

1 Doubling 144 Spindles (2 machines) 2000

2 Hank dyeing 40 Arms (1 machines) 400

3 Bobbin Winding 40 Spindles (3 machines) 600

4 Sectional Warping and

Beaming

2 Machines 600

5 Pern winding 12 Spindles (4 machines) 1500

6 Loom Jacquard power loom (36

no.)

540


124




TOC).


It is observed from the data that all the processes, except the dyeing process have

excess capacity. To improve the output of the plant, the productivity of dyeing process has

to be increased. Hence dyeing process is obviously the constraint of the system.

Average output of the dyeing machine was 400 kg /shift. To increase the output it



The various causes of lower output of the dyeing process are studied. After

studying the cause and effect relationship it is observed that arm dyeing machine capacity

was the limiting factor.

After design analysis, numbers of arms were increased from 40 to 54 and length of

each arm was increased from 32 inches to 36 inches. This design improved the

productivity of dyeing process by 28 % (approx.).

5.7.5 Subordinate


Management System:

The dyer and workers were trained for the modified design. The procedure and

work instructions were changed accordingly.

System Constraint (Dyeing)

125

Figure 5.15 Dyeing machine (Before improvement)

Figure 5.16 Dyeing machine (After improvement)

126

5.7.6 Conclusion

By modifying dyeing machine and TOC methodology, the productivity increased

approximately by 28%.

5.8 Case study 6

5.8.1 Objective of case study

To study the effect of dyeing process on productivity ( hank dyeing machine)

Dyeing is of two types namely- a) Hot dyeing b) Cold dyeing. The scope of the

case study was limited to cold dyeing process only.

Though it is recommended that cold dyeing should be ideally carried out at 400C,

most of the textile manufacturing industries at Solapur carry out dyeing at room

temperature. In fact, the temperature of water is never measured and recorded. The dye

stuff quantity is fixed throughout the year (irrespective of the variations in the water

temperature). It means that dye stuff quantity may have been set considering the lowest

temperature of the water.

The experimentation was carried out to study the relationship between temperature

of water and quantity of dye stuff to achieve the same colour shade. Following were the

conditions of experimentation:


The data collected is presented in the table 5.14

Table 5.14 Data of dyeing process

Yarn 100% Cotton Yarn

Type of Yarn 2/20 (Double Yarn with 20 count)

Type of Dye stuff Reactive dyes

Brand M brand

Liquor ratio ( yarn : water ) 1:9

Soaking time 45 minutes for each stage

PH value of water 7

Variation of water temperature 150 to 40

0C

127

5.8.3 Experimentation

Trials of yarn dyeing at different temperatures were conducted varying the quantity

of dye stuff to achieve the M brand – light shade. The readings of experimentation are

presented in table 5.15 and graphically represented in figure 5.17.

Table 5.15 Readings of temperature of water and quantity of dyestuff

Sr. No. Temperature of Water (°C) Quantity of Dyestuff (gm)

1 15 1100

2 20 1000

3 30 850

4 40 750

Figure 5.17 Graph of water temperature vs quantity of dyestuff

5.8.4 Results and Discussion

It is observed that, as water temperature is increased, the dye stuff quantity

decreases to maintain the same shade. The dye stuff quantity was 750 grams at 400C

against 1kg at 200C. The fastness to washing and rubbing fastness also goes on improving

as the temperature increases. When the dye stuff quantity is reduced, gpl (grams per litre)

of sodium chloride and soda ash is proportionately reduced. This resulted into reduction in

the input cost of dyes, sodium chloride, soda ash and water. Hence the productivity

improved by 15% (the output remained same but input cost is reduced).

128

5.8.5 Conclusion

The productivity of cold dyeing process for reactive dyes was improved by 15%.

5.9 Summary of case studies

Summary of case studies is given in table 5.16

Table 5.16 Summary of case studies

Case

study

No.

Constraint

identified Improvement done

Present

productivity

Improved

productivity

1 Powerloom

Controlled conditions of

temperature and humidity

were maintained

CV of yarn improved

from 9% to 4%

15

kg/loom/shift

18

kg/loom/shift

2 Powerloom

(weaving process)

A system for preventive

maintenance (for

powerloms) was

established

14

kg/day/loom

17

kg/day/loom

3 Bobbin winding

machine

Increased the rpm of the

machine by modifying the

pulley diameters and use

of good washing agents

during dyeing process.

600 kg /shift 660 kg /shift

4

Powerloom A system for corrective

maintenance was

established

15

kg/loom/shift

17

kg/loom/shift

(approx.)

5

Dyeing process

(Arm dyeing)

Length of the arm is

increased from 32 inches

to 36 inches

400 kg /shift 512 kg /shift

6 Dyeing process

(Hank dyeing)

Reduction in dye quantity

for dyeing process

250 kg per

batch

290 kg per

batch

After implementing successfully the TOC methodology for above referred case

studies, it is felt that, there is a need for developing a module for disseminating the

knowledge of TOC and outcomes of the research experimentation. Therefore a module for

129

skill development is developed which should help the industries to implement the

productivity improvement techniques.

5.10 Module for Skill Development

Skills development is central to improving productivity. In turn, productivity is an

important source of improved living standards and growth. Other critical factors include

macroeconomic policies to maximize opportunities for pro-poor employment growth, an

enabling environment for sustainable enterprise development, social dialogue and

fundamental investments in basic education, health and physical infrastructure (ILO).

Training and skills development are important factors in improving the conditions

of employment for the vast majority of employees and workers. Furthermore, for

enterprises in the informal economy, training and increased productivity are important

strategies for making the transition to the formal economy (Vandenberg, 2004).

Building effective management and supervisory skills are important for textile

sector, especially for SMEs. After in depth study and analysis of factors affecting

productivity, the relationship between the same (factors and productivity) is established. A

methodology based on TOC is developed to improve the productivity using these factors.

This methodology is validated by using case studies. All the case studies have shown

improvement in productivity. As a result of research outcome, a skill development

program is developed which is presented herewith:

The skill development program is developed for Solapur based textile SMEs, since

majority of SMEs are either proprietary or partnership firms, most of the business activities

are looked after by a single person. The data collection also shows that the owners (top

management cadre) are not having higher educational qualifications (illiterate to SSC in

most of the cases). Almost all the organizations have employed supervisors/jobbers/dyers

to look after the various management and technical functions in their respective areas.

There is no formal organizational structure present in most of the cases. Often most of the

decisions are taken jointly by owners and the supervisor/jobber/dyers. Therefore while

developing a skill developing program a single module is developed, which will be

applicable and useful to both owners (top management cadre) and supervisors.

130

5.10.1 Skill Development Program

1. Objectives:

a) To learn various concepts of productivity along with its importance

b) To know about various techniques/methodology for improving productivity

2. Outcomes:

After completion of the program participants will be able to:

(a) Apply concept of productivity improvements in their organization

(b) Select a proper technique/tool to manage the constraint

3. Contents:

4. 3.1 Productivity– (04)

Definition, meaning, importance, objectives, productivity and profits, profitability,

various concepts like dyeing productivity loom productivity, stitching products,

process productivity etc.

3.2 Theory of Constraints – (06)

Introduction, basics, 5 steps of TOC, types of constraints like yarn and process

constraints, loom constraints, maintenance constraint, market constraint, labour

constraint, jobber constraint etc. Methods of exploiting the constraints

3.3 Tools/Techniques– (06)

7 tools of quality control, diagram, Cause effect diagram, Pareto analysis, graphs, bar

chart, etc.

3.4 Introduction to system like ISO 9001, ISO 14001, BSCI (04)

3.5 Documentation and record keeping, creating various forms and formats, (02)

Methods of record keeping and their importance, analysis of data

3.6 Continuous improvement- PDCA cycle (03)

131

Skill development program (referred above) was conducted on pilot basis to the

employees of 6 textile manufacturing SMEs. The feedback was obtained from all the

participants. Almost all of the participants expressed satisfaction about the program and

communicated that they will be implementing the contents of the program in their

organizations.

Research conclusion and recommendations are presented in next chapter.

132

Chapter 6

RESEARCH CONCLUSIONS AND RECOMMENDATIONS

The methodology adopted for current research is discussed in chapter 3. The key

results of the data analysis with their findings are reported in chapter 4. On the basis of

findings of experience survey, the new methodology based on “Theory of Constraints

(TOC)” is developed. The findings related to implementation of this methodology are

discussed in chapter 5. In this chapter, contributions of current research are discussed.

Conclusions from results of different stages of current research are summarised. The

recommendations to manufacturers, practitioners and consultants are suggested. The scope

for future work is also indicated.

In this study five objectives are set. The current study has achieved all of them. The

objectives and findings from the corresponding work done along with conclusions are

discussed below.

6.1 Conclusion related to identification of variables

The first objective is to identify the different variables affecting productivity of

Solapur based textile SMEs. Based on the pilot study and discussion with expert panel,

variables have been identified. While selecting variables extensive and in depth literature

review is done. The variables in literature review are classified as input variables, process

variables and output variables. The frequency analysis of these variables is done (Chapter

2). Out of these variables which have, lower frequency has been selected for the current

research work.

Form this analysis the key variables and their impact on productivity is understood.

The analysis concluded that 38 variables have an impact on productivity of textiles. The

last (39th

) variable is profitability, which is taken as a measure of productivity which is

most appropriate to current research work.

6.2 Conclusion related to factor analysis

The second objective is to decide the factors affecting productivity by analysing the

variables.

After identifying 38 variables, a structured questionnaire is prepared. An

experience survey of 167 manufacturing textile SMEs is carried out. The responses of this

133

survey are recorded. Analysis of this data is done by using (SPSS 16) software. It related

total nine factors. All the nine factors are having a loading of 0.4 or more; and Eigen value

greater than 1. The value of R2 adjusted is 69.84% which is sufficient to take these factors

for further analysis. The factors are named considering the grouping of the variables, viz.-

1) Synchronization of management processes

2) TPM for weaving and dyeing

3) Input and process quality

4) Process technology

5) HR policies for textile SMEs

6) Labor behavior

7) Use of scientific tools for improvements

8) Use of renewable energy for processes

9) System deployment

Hence it can be concluded that, the factor analysis which has led to 9 factors, are

significant, reliable (from statistical study) and can be used for further analysis.

6.3 Conclusion related to model development

The third objective is to develop a model representing the relationship between

factors and the productivity of textile SMEs.

The multiple regression analysis is done to model mathematically the relation

between 9 factors and profitability. The model is represented as follows (regression

equation):

Improved Profitability = 0.189 + 0.760 Input and process quality



+ 0.234 Synchronization of management processes






134

6.4 Conclusions related to developing methodology for improving productivity

The fourth objective is to develop a methodology for improving productivity of

textile SMEs in Solapur.

The result of multiple regression analysis indicates three most significant factors

namely input and process quality, TPM for weaving and dyeing, process technology are

affecting productivity. To improve productivity using these factors, a methodology based

on TOC is developed.

Six case studies based on this methodology are conducted. After successful

implementation, the following conclusions are drawn.

1. TOC methodology is useful for improving productivity of textile SMEs.

2. TOC methodology pinpoints the area/process for improvement which will have an

impact on productivity of the entire organization.

3. Improvement on non-constraint resource may not lead to increase in productivity of

organization.

6.5 Conclusion related to module for Skill Development for improving

productivity

The fifth objective is to develop a suitable skill development module for improving

productivity.

A module for skill development is developed for policy makers and executers. A

skill development program is conducted on pilot basis for six textile SMEs. Almost all

participant express satisfaction about the program and are interested in implementation.

Hence it can be concluded that, the module for skill development has served its

objective. It will be really helpful to all textile SMEs to improve their productivity.

135

6.6 Research objectives and conclusions at a glance

The research objectives and conclusions are shown in table 6.1

Table 6.1 Research objectives and conclusions

Research objectives Conclusions

1. To identify the variables

affecting productivity of

Solapur based textile SMEs

Total 38 variables have been identified having an

impact on productivity of textiles, and the 39th

variable is profitability, which is taken as measure

of productivity.

2. To identify the factors

affecting productivity based

on the identified variables

The factor analysis has led to following 9 factors:

1) Synchronization of management processes

2) TPM for weaving and dyeing

3) Input and process quality

4) HR policies for textile SMEs

5) Process technology

6) Labour behaviour

7) Use of scientific tools for improvements

8) Use of renewable energy for processes

9) System deployment

3. To develop a model

representing the relationship

between factors and

productivity

A model based on multiple regression analysis is

developed, which is as follows:

Improved Profitability =

0.189 + 0.760 Input and process quality



+0.234 Synchronization of management processes






136

4. To develop a methodology

for improving productivity

A methodology based on TOC is developed and

implemented successfully through six case studies.

5. To develop a suitable

module for skill

development for improving

productivity

A module for skill development is developed for

policy makers and executers.

6.7 Contributions of current research

The contributions of current research work are as follows:

It has validated the applicability of variables/factors used by earlier researchers to

other textile segments such as garments, clothing, apparel, etc.

It has identified 38 variables which have been reduced to 9 factors affecting

productivity of Solapur based textile SMEs.

It has developed a model establishing the relationship between various factors and

productivity.

It has developed a methodology of application of Theory of Constraints (TOC) to

textile SMEs. It is argued that it may be a first TOC application to Solapur based

textile SMEs.

6.8 Recommendations

Following recommendations are made based on the findings and conclusions of the

current research:

6.8.1 To manufacturers of textile SMEs

Quality of yarn (CSP, CV, Imperfections, elongation at break, etc.), dyes, chemicals

and other input material are most important for productivity. Hence it is

recommended that a quality assurance department may be established to ensure

required input and process quality.

A Productivity cell may be established in the organization. Setting the targets for

productivity, implementation and reviews should be a part of the process.

Quality circles, Kaizens may be started across all levels of organizations.

137

Scientific tools and techniques such as Theory of Constraints (TOC), six sigma, lean

manufacturing, SMED, SPC, problem solving methods, cause and effect diagram,

Pareto analysis etc., may be used to improve the effectiveness and efficiency of the

existing processes.

Most of the organizations have little knowledge about systems and standards like ISO

9001, ISO 14001, OHSAS 18000, BSCI, etc., and hence have a myth that such

systems may not have any value addition and correlation with productivity. Therefore

top management cadre may undergo a training and get and exposure to such systems

(some customers are specifying the above referred certifications as a mandatory

requirement to do the business with them).

6.8.2 To ministry of textiles (Government of Maharashtra)

A skill development centre may be established for designing and conducting various

skill development programs at all levels of the employees (including top management

and owners) jointly by TDF/SOZIYA/BTRA and a local engineering/management

institute. A module for skill development, as suggested in this research may be used

as a reference. For skill up-gradation of workmen and supervisors practical training

programs may be conducted in association with local institutes like ITI.

A guidance centre may be established to implement systems like ISO 9001, ISO

14001, OHSAS 18000, BSCI, etc., to create awareness regarding changing needs of

the customers. It is proved by the researchers that such systems have a positive impact

on productivity.

Up-gradation of the existing textile laboratories may be undertaken (which are

operated by Bombay Textile Research Association, Govt. Of India) to conduct test

such as Elongation at break, imperfections etc., (for yarn) and AZOFREE test for dyes

and chemicals.

To promote use of non-conventional energy for various applications such as dyeing,

through nodal agencies like MEDA.

To establish a centre for study and implementation of different central and state

Government schemes for modernization of existing as well as new machinery units,

like textile parks, CFC, CWS, etc.

138

6.9 Limitations of current research

1) The research findings are limited to Solapur based textile SMEs.

2) These findings are applicable to yarn dyed terry towels and allied products produced

on jacquard powerlooms.

6.10 Scope for future work

Research studies can be undertaken on:

Issues related to use of modern technology such as cone dyeing and rapier looms

Applicability of various tool and techniques such as six sigma, lean manufacturing,

SMED, etc. to improve the competitiveness of textile manufacturing units

Use of non-conventional energy, study of energy efficiencies and conservation

measures for various textile processes

Layout and material handling in various textile sections

Ergonomics at workplace such as final inspection, packing, bobbin winding

Application of ISO 14001 (EMS), OHSAS 18000, BSCI, etc. to find the effects of

various textile processes on environment change, occupational health and safety,

social compliance, productivity and performance etc.

Impact of ISO 9001 (QMS) on performance and productivity of textiles

139

APPENDIX I

Publications based on current research work

Papers published in National/International Journal

Sr. no. Description of paper

01. Critical review of improving productivity of Solapur based textile

SMEs. International journal of business, management and social

sciences (IJBMSS), Vol. I, Issue 3 (III), 2011, pp. 89-92. ISNN: 2249-

7463.

02 The critical review of studies on productivity analysis of textile SMEs.

International journal of multidisciplinary research and advances in

engineering (IJMRAE), Vol. 5, Issue III, 2013, pp. 57-68. ISSN: 0975-

7074.

03 An empirical study of factors affecting productivity of Solapur based

terry towel manufacturing textile industries (SMEs). International

journal of industrial engineering research and development (IJIERD),

Vol. 5, Issue I, 2014, pp. 31-38, ISSN: 0976-6987.

04 Improvement in dyeing process parameters – a case study of Solapur

based textile SME. Journal of Solapur university, Avishkar- 2013

(Accepted, yet to be published).

Paper presented in International Conference

01 Study of variables of textile manufacturing industries and their effects

on productivity of Solapur based SMEs, 2nd

International Conference

on Industrial Engineering (ICIE), S. V. National Institute of

Technology, Surat (Gujarat), India. And Indian Institution of Industrial

Engineering (IIIE), Mumbai. [20-22 Nov. 2013].

Manuscript sent for publication

01 To improve the productivity by applying Theory Of Constraints

(TOC) – A Case Study of Solapur based textile SME, 3rd International

Conference (2015) on Industrial Engineering (ICIE) at S. V. National

Institute of Technology, Surat (Gujarat), India.

140

APPENDIX II

Award received based on the current research work

1. First prize at Avishkar- 2014 in Ph.D. category: University Level Research

Festival, Solapur University, Solapur.

2. Participated and shortlisted in top five projects at 9th

Maharashtra State University

Research Convention – Avishkar- 2014, at Nagpur.

To identify factors for improving productivity of Solapur based textile SMEs

142

APPENDIX III

Questionnaire for data collection (experience survey)

Guidelines for filling questionnaire

1. Concept of productivity

Productivity is the ratio of output to input.

Productivity of unit =

As it is the ratio of output to input, output and input must be in the same units. The output

must be acceptable to the customer. Productivity can be improved by

a) Increasing output and keeping the input same.

b) Keeping the output same and reducing the input

c) Increasing the output and reducing the input simultaneously

2. Impact of productivity

The variables have been identified by conducting a preliminary survey, literature

survey, expert opinion. The impact of these variables on productivity is to be studied

through this survey.

The direct types of questions are asked and the respondents are requested give the

impact of the variable on productivity in their unit. The impact can be negative, positive or

neutral. The positive impact is quantified as +3, +1, +2; negative impact is quantified as -3,

-2,-1, neutral impact is quantified as 0. If a variable is not applicable it may be indicated as

NA.

The following scale (weightages) may be used:

+3 Impact is highly positive -1 Impact is marginally negative

+2 Impact is moderately positive -2 Impact is moderately negative

+1 Impact is marginally positive -3 Impact is highly negative

0 No change/do not know NA Not applicable

Please consider the impact of productivity for last 2-3 years.


143

3. Measurement of impact on profitability

The impact of variables on productivity is reflected in the level of profitability. In

this study, the profitability is defined as ratio of gross profit to total sales (as a percentage).

While measuring the change in the level of profitability, following assumptions are made.

a) Investment pattern does not change.

b) Price fluctuations (raw materials, sales price, and dollar/rupee) are not considered.

Respondents are requested to give the level of profitability of average of last 2 years on the

following scale.

+3 High profitability (15.1 % and above) -1 Marginal negative profitability (loss)

+2 Moderate profitability (7.1 % to 15%) -2 Moderate negative profitability

+1 Marginal profitability (1% to 7%) -3 Highly negative profitability

0 Breakeven (no profit, no loss)


144

INFORMATION ABOUT THE INDUSTRY

1. Name of the Industry : ……………………………………………………………

2. Address : ……………………………………………………………

……………………………………………………………

3. Phone Number: ………………………Email ID:.…..………………………………………

4. Type of Ownership : Proprietorship / Partnership / Pvt. Ltd./Ltd./Co-Op/Govt

5. Name & Qualifications of

Proprietor/Partner(s)/ Director(s) :

Name Designation Qualifications Experience

In Years Nature of Work

6. Year of Establishment : ……………………….

7. Product(S) : ……………………….

8. Type of Market : Domestic / Export / Both

9. Type of Industry : Small / Medium

10. No. of Employees : ………………….

11. No. of looms : ………………………

12. Profitability (Please tick (√) on a scale of +3 to -3):

+3 High

profitability

+2 Moderate

profitability

+1 Marginal

profitability

0 Break Even

(No profit,

No loss)

-1

Marginal

negative

profitability

(loss)

-2

Moderate

negative

profitability

-3

Highly

negative

profitability

Date: Name & sign of authorized person


145

Please indicate the level of impact of following variables affecting productivity

Variables for Study

(Quality)

Impact on productivity

Increase Neutral Decline

+3

Hig

hly

po

siti

ve

+2

Mo

der

atel

y p

osi

tiv

e

+1

Mar

gin

ally

po

siti

ve

(0)

-1 M

arg

inal

ly n

egat

ive

-2 M

od

erat

ely n

egat

ive

-3 H

igh

ly n

egat

ive

Not

app

lica

ble

No

ch

ang

e

Do

n’t

know

1 Yarn quality

2 Dye quality

3 Water quality

4 Warp quality (Beam)

5 Weft quality (Shuttle)

6 Stitching quality

7 Final inspection

8 Use of SPC (Statistical

Process Control) tools

for quality

improvement

9 Any others – Please

specify


146


Variables for Study

(Top Management)



+3

Hig

hly

po

siti

ve

+2

Mo

der

atel

y p

osi

tiv

e

+1

Mar

gin

ally

po

siti

ve

(0)

-1 M

arg

inal

ly n

egat

ive

-2 M

od

erat

ely n

egat

ive

-3 H

igh

ly n

egat

ive

Not

app

lica

ble

No

ch

ang

e

Don’t

know

1 Top management

commitment

2 Well defined

organization

Structure

3 Defined Productivity

targets and plans

4 Review of

productivity

related issues/ targets

5

Use of scientific tools

such as 6 sigma,

Lean, TOC etc.


specify


147


Variables for Study

(HR)



+3

Hig

hly

po

siti

ve

+2

Mo

der

atel

y

po

siti

ve

+1

Mar

gin

ally

po

siti

ve

(0)

-1 M

arg

inal

ly

neg

ativ

e

-2 M

od

erat

ely

neg

ativ

e

-3 H

igh

ly

neg

ativ

e

Not

appli

cab

le

No c

han

ge

Don’t

know

1

Well defined

authority

and responsibility

2 Training to employees

3

Policy for motivation

(reward/award

scheme)

4 Performance appraisal

system

5

Occupational health

and

safety practices

6

Complaints and

grievance handling

system

7

Involvement of

employees in

productivity related

decisions

8

Salary Structure

(Daily/Weekly/Month

ly- Fixed/Piece Rate)

9 Labour Absenteeism

10 Carelessness of

labours

11

Young generation of

labors not ready to

join this sector


specify


148


Variables for Study

(Systems)



+3

Hig

hly

po

siti

ve

+2

Mo

der

atel

y p

osi

tiv

e

+1

Mar

gin

ally

po

siti

ve

(0)

-1 M

arg

inal

ly n

egat

ive

-2 M

od

erat

ely n

egat

ive

-3 H

igh

ly n

egat

ive

Not

app

lica

ble

No

ch

ang

e

Don’t

know

1 Well defined system of

records

2

Presence of systems

like

ISO 9000

3

Corrective action in

case

of rejection/wastage

4

Preventive actions to

prevent

occurrence of

potential problems

5 System for continual

improvement

6 Any others- please

specify


149

Please indicate the level of impact of following variables affecting productivity.

Variables for Study

(Operations)

IMPACT ON PRODUCTIVITY

INCREASE NEUTRAL DECLINE

+3

Hig

hly

po

siti

ve

+2

Mo

der

atel

y p

osi

tiv

e

+1

Mar

gin

ally

po

siti

ve (0)

-1 M

arg

inal

ly n

egat

ive

-2 M

od

erat

ely n

egat

ive

-3 H

igh

ly n

egat

ive

Not

appli

cab

le

No c

han

ge

Don’t

know

1

Production planning

(Effect of more

number of batches on

productivity)

2

Availability of Work

instructions for

workers

3 Preventive

Maintenance

4 Break down

Maintenance


specify


150

Please indicate the level of impact of following variables affecting productivity.

Variables for Study

(Technologies)



+3

Hig

hly

po

siti

ve

+2

Mo

der

atel

y

po

siti

ve

+1

Mar

gin

ally

po

siti

ve

(0)

-1 M

arg

inal

ly

neg

ativ

e

-2 M

od

erat

ely

neg

ativ

e

-3 H

igh

ly

neg

ativ

e

Not

app

lica

ble

No

chan

ge

Don’t

know

1

Manufacturing process

(Power

loom/Shuttleless/Rapier)

2

Method of Dyeing

process

(Manual/Semiautomatic/

Automatic)

3

Method used for beam

lifting


Automatic)

4

Method of stitching

process


Automatic)

5

Use of renewable

energy such as solar

energy for processes

(Y/N)

151

APPENDIX IV

List of respondent companies for survey questionnaire

Sr.

No. Company Name Address Phone

1 Dhanlaxmi

Textiles Vijay R. Madur

Plot No. 31, M.I.D.C.,

Akkalkot Road, Solapur-

413006

0217-

2748461

2 Marta Udyog Ravi Prakash Marta

27, M.I.D.C, Akkalkot

Road,

Solapur-413006

970088111

3 Nagnath Dasari

Textiles Nagnath Dasari

D37, M.I.D.C, Akkalkot

Road,

Solapur - 413006

0217-

2392029

4 Kamurti Textiles Balej R. Kamurti 14/94, Gandhinagar,

Solapur

0217-

3293848

5 Balaji Weaving

Mills

Mr. Govind Zanwar

Mr. Rajgopal

Zanwar

E-73, M.I.D.C, Akkalkot

Road,

Solapur-413006

0217-

2741231

6 Sri Diwate

Textiles P. Ltd.

Dattatray Diwate

Dnyaneshwar

Diwate

Vinod Diwate

Plot No. 171, M.I.D.C,


413006

7709854171

7 Jamuna Textiles Pravin Kote

Nishikant Kote

E-17, M.I.D.C.,


413006

0217-

2392030

8 Renuka

Enterprise

Rajendra S

Dyarkonda

254/55, M.I.D.C.


413006

0217-

2745130

9 Marda Textiles Gokul Marda

252, M.I.D.C.


413006

0217-

2653535

10 Tulsaidas

Yanganti Tulsidas Yanganti

202, M.I.D.C.


413006

9420663266

11 Himalaya

Textiles

Satyaram Myakal

Shridhar Myakal

E-25, M.I.D.C.


413006

0217-

2651178

12 Srinivas Balaji

Kyatam Srinivas Kyatam

Plot No. 15, M.I.D.C.

Akkalkot Road, Soslapur-

0217-

2743545

13 Devasni Textiles Ramchandra K

Devsani

34/A/24, New Paccha

Peth,

Near WIT, Solapur

0217-

2745120

152

Sr.


14 Chandrasheker R.

Alli Textile Chandrakant

13/14, M.I.D.C


413006

0217-

2745220

15 Birru Udyog Dattatraya Birru

9, M.I.D.C


413006

0217-

2747201

16 Patel Textiles Amit Patel

Kiritbhai Patel

A-15, M.I.D.C


413006

0217-

2651054

17 Venkatraman

Textile Vijay Bave

B2/1/12, M.I.D.C


413006

0217-

3297003

18 Marda Textiles

Industries Sagar Marda

205, M.I.D.C


413006

0217-

2391858

19 Bhoopati Textiles Bhoopati Samleti

Balaji Samleti

A-16, M.I.D.C


413006

0217-

2651761

20 Chilka Weaving

Mills

Venktesh Chilka

Gopal Chilka

64, M.I.D.C


413006

0217-

2747683

21 Jeevanjyoti

Textiles S.I. Gaddam

34/3, New Paccha Peth

Solapur

0217-

2745072

22 Prakash Textiles Upendra Devsani

E-52, M.I.D.C


413006

0217-

2651567

23 Dattatraya

Devsani Textiles Dattatraya Devsani

E-48, M.I.D.C


413006

0217-

2651567

24 G Laxmipati

Industries

L.L. Gaddam

Nanda Gaddam

E-91/1, M.I.D.C


413006

0217-

2745071

25 Pitamaha Textiles Govind H. Bure 1375, Badravati Peth

Solapur

0217-

3297004

26 Shreenath

Industry

J.C. Khandelwal

L.B. Chandak

E2, M.I.D.C.

Akkalkot Road, Solapur

0217-

2743218

27 Rajashree

Industries

Nagnath Bura

Srinivas Bura

E1/1, M.I.D.C.


0217-

2651882

153

Sr.


28 Sudarshan

Textiles

Nagesh Dhayafule

Sudarshan

Dhayafule

E-11, M.I.D.C.


0217-

2748741

29 N.K Dhayafule

Industries

Arun Dhayafule

Sanjay Dhayafule

34/5, B, New Paccha Peth

70 Ft. Road, Solapur

0217-

2748746

30 Dhayafule

Textiles

Devidas Dhayafule

Manohar Dhayafule

E-4, M.I.D.C


0217-

2748740

31 Gaddam Udyog Irappa Gaddam E-1/2, M.I.D.C.,


0217-

2651504

32 Madhukar L

Yemul Madhukar Yemul

12, Channabasaveshwar

Nagar, Near Sunil Nagar,

Solapur

0217-

745022

33 Laxmikant A.

Yemul Laxmikant Yemul

91, M.I.D.C.,


0217-

2745020

34 M/s Kalpana

Industries

Sattyanarayan

Singam

Gagadhar Singam

B-7, M.I.D.C.,


0217-

2744942

35 Ambadas M.

Sargam Ambadas Sargam

208, M.I.D.C.,

Akkalkot Road, Solapur 9881236002

36 Madhusudan

Industries Madhukar Yemul

92, M.I.D.C.,


0217-

2745020

37 Siddheshwar

Textiles Dhananjay S. Ali

190, M.I.D.C.,


0217-

2745651

38 Madhusudan

Textiles Laxmikant Yemul

116, New Pachha Peth,

Solapur

0217-

2745021

39 Vivek T.

Company

Vivek

Siddheshwar Ali

142, M.I.D.C.,


0217-

2745652

40 Rajhans

Industries

Sattanarayan

Singam

E-92/3, M.I.D.C


0217-

2744943

41 Suresh Textiles Suresh Totad E-59, M.I.D.C


0217-

2652074

42 Prestige Textiles Gaddam 143 Markendaya Nagar

Solapur

0217-

3299880

43 S.B. Kodam S.B. Kodam D45/1/2, M.I.D.C,

Akkalkot Road,Solapur 9370388999

44 Subhash S.

Mudgundi

Subhash S.

Mudgundi

W14, M.I.D.C.,


154

Sr.


45 Ashok S.

Mudgundi Ashok S. Mudgundi

717/1/3, N.S.Bazar

Sant Tukaram

Chowk,Solapur

0217-

2310947

46 Surana Textiles Padamchand Surana E-36, M.I.D.C


0217-

2656104

47 B.B.Kodam

Textile Group B.B.Kodam

E-40, M.I.D.C


0217-

2652266

48 Vasudeo S.

Channa Vasudeo Shanna

C-26/23, Vinkar Vasahat,

Sah. Sanstha, M.I.D.C.


932607992

49 Yuvraj Textiles Narayan V. Adaki 83,84 M.I.D.C.


50 Venugopal

Martha Venugopal Martha

139, M.I.D.C


413006

9422459092

51 N Gali Textile Srinivas Gali 1187, New Paccha Peth,

Solapur

0217-

2740030

52 Naval Textile

Corporation Ramkrishna Udgir

C-10/2/9, M.I.D.C.


0217-

2652881

53 Sou Sunita V.

Channa Sunita Channa

C-26/22, Vinkar Sah.

Sanstha, Solapur

0217-

2651929

54 Prestige Textiles

(Chilka Unit) Ambadas Gaddam

E-9, M.I.D.C.,


0217-

3299882

55 Rajendraprasad

Martha

Rajendraprasad

Martha

C-9, M.I.D.C.,


56 Srinivas Shankar

Aken

Srinivas Shankar

Aken

Plot No. 12-18, Ganesh

Nagar, Solapur

0217-

2745912

57 Jalandhar S.

Channa Jalandhar Channa

C-20, M.I.D.C.,


58 M/s Bhagyashri

Textiles Bhagyashri N. Gali

C-26, Plot No. 41,

M.I.D.C.,


0217-

2740032

59 Shankar R.

Jagilam Shankar R. Jagilam

34/5B/24/29, New

Paccha Peth, Solapur 9422066256

60 Sreenavas R.

Kanda Sreenavas R. Kanda

18/29, Madhav Nagar,

Solapur 9420263383

61 Dashrath

Narsayya Penti Dashrath N. Penti


Solapur

0217-

2392323

155

Sr.


62 Shankar Uplayya

Gundla Shankar U. Gundla

43, Shrikrishna Nagar,

Swagat Nagar Road,

Solapur

0217-

2605958

63 Purshottam J.

Vidap Purshottam Vidap

1586, Kuchan Nagar,

Solapur

0217-

2621279

64 Laxminarayan

Textiles Loknath Gundenti

24/25, Navanath Nagar,

Solapur

9422647872

9545320997

65 Vinayak Textiles Raymally B.

Kamtam

201/1/2, Sub P. 22,

Kamtam Vasahat,Solapur

0217-

2654510

66 Shreenavas N.

Nalla Shreenavas N. Nalla

C-10/2/30, M.I.D.C.,


67 Narsingdas S.

Aken Narsingdas S. Aken

33/3/103, New Paccha

Peth, Solapur

0217-

2745911

68 Prestige Textile

(Margam Unit) A. Gaddam

D-5, M.I.D.C.,


0217-

2652321

69 Sarojini N. Gali Sarojini N. Gali 201/1/15, Kamtam

Vasahat, Solapur

0217-

2740031

70 Tirupati N. Penti Tirupati N. Penti 18/68, Madhav Nagar,

Solapur 9422645081

71 Ramanjam C.

Konda

Ramanjam C.

Konda

18/5, Madhav Nagar,

Near Akashwani, Solapur

0217-

2749341

72 Ashok J Vidap Ashok J Vidap 104/105, Shanti Nagar,

Solapur

0217-

2605692

73 Gundeti Udyog Ramesh R. Gundeti 2/8/7, Adarsh Nagar,

Solapur 9595320999

74 Aditya Textiles &

Dye House

Bhagyalaxmi N.

Udgiri

C-10/217, M.I.D.C.,


75 Laxminarayan N.

Nalle

Laxminarayan N.

Nalle

C-9/4, M.I.D.C.,


76 Goski Terry

Towels Rajesh Goski

E-99/B, M.I.D.C.,


77 Surya Weaving

Mill

Yaddamma M.

Gaddam

34/10, New Paccha Peth,

Solapur

0217-

2745010

78 Bolli Textiles Laxminarayan Bolli

B-2/1, M.I.D.C.,


0217-

2651259

79 Shankar L.

Yemul Shankar L. Yemul

201/1/5, Kamtam

Vasahat, Solapur

0217-

2745170

80 V.J. Vidap Venktesh J. Vidap 26A, Adarsh Nagar,

Solapur

0217-

2652050

156

Sr.


81 Sagar Dying

Works

Jugalkishor R.

Udgire

C10/218, M.I.D.C.,


82 Raimallu Narayan

Nalla Raimallu N. Nalla

33/34 A, New pachha

Peth, Solapur 9822842705

83 Vidap Textile

Mill

Ramgopal Laxman

Vidap

9/1, M.I.D.C.,


0217-

2653317

84 Nasir Textiles Nasir Sardar 16,Sunil Nagar, Near

Aakashwani, Solapur

85 Goski Home

Textiles Savita R. Goski

47, M.I.D.C., Akkalkot

Road, Solapur 9422459001

86 Sujata Yantramag Bhaskar N. Pitta 16B, Kumbhari,


87 Anand Mineayya

Gaddam Anand M. Gaddam

195, New Sunil Nagar,

Solapur

0217-

2845011

88 Rajendra Shankar

Yemul Rajendra S. Yemul

33/3/78, New Paccha

Peth, Solapur

0217-

2745172

89 Bhumesh M.

Kamtam

Bhumesh M.

Kamtam

34A 54, New Paccha

Peth, Solapur

0217-

2744532

90 Govardhan R.

Kamtam

Govardhan R.

Kamtam

201/1 / 21, New Paccha

Peth, Solapur

0217-

2744530

91 Vijay Textiles Madhusudan

Kamtam


Solapur

0217-

2745470

92 Kamtam Fabrics Vijaynarayan

Kamtam

34A52, New Pachha

Peth, Solapur. 9422457595

93 Ramesh

Industries Ramesh N. Kamtam

201/5/13/20,

Kamtam Vasahat,

Solapur

0217-

2745472

94 Raju Nagnath

Ittam Raju N. Ittam

139/2, Mallikarjun Nagar,

Solapur

0217-

2745211

95 Janardhan

Mineaya Gaddam

Janardhan M.

Gaddam

191, Mehtre Nagar,

Solapur

0217-

2655518

96 Shreeniwas

Shankar Yemul Rajendra S. Yemul


Road, Solapur.

0217-

2745173

97 Mallikarjun R.

Kamtam

Mallikarjun R.

Kamtam


Solapur

0217-

2744533

98 Mrs. Geeta G.

Kamtam

Mrs. Geeta G.

Kamtam

201/1/ 12, New Pachha

Peth, Solapur

99 Yemul Textiles Sattyanarayan

B.Yemul


Road,Solapur

0217-

2391010

157

Sr.


100 Banda Industries Basavraj Banda

Shivkumar Banda

E-95, M.I.D.C., Akkalkot

Road, Solapur.

0217-

2748830

101 Mineayya V.

Gaddam

Mineayya V.

Gaddam

E-104, M.I.D.C,


0217-

2651939

102 Rakeshkumar R.

Goal

Rakeshkumar R.

Goal

E-108, M.I.D.C.


103 Venktesh

Rajmogli Arkal Venktesh R. Arkal

178,179, M.I.D.C.,


104 Srinivas Nagnath

Ittam Srinivas N. Ittam

139/1, Mallikarjun Nagar,

Bhedari Vasti, Solapur

0217-

2745212

105 Yemul Udyog Murlidhar R. Yemul 244, M.I.D.C.,


0217-

2391524

106 Banda's Shobha B Banda

Pawan B. Banda

Survey No. 155/3,


0217-

2749985

107 Balaji Lingayya

Bolli Balaji L. Bolli

18/18, Madhavnagar,

Solapur 9422458628

108 Vitthal Irayai

Annaldas Vitthal I. Annaldas

46, Markendaya Nagar,

Solapur

0217-

2600312

109 Sou Madhavi

Raju Adagatla

Madhavi R.

Adagatla

155/3, M.I.D.C.,


0217-

3297750

110 Ravindra

Rajmogli Arkal Ravindra R. Arkal

E-100/10, M.I.D.C.,


111 Yemul Industries

Rajesham B. Yemul

Sattyanaran B.

Yemul

E-14, M.I.D.C.,


0217-

2651843

112 Vinay R. Goal Vinay R. Goal E-107, M.I.D.C.,


113 Bhupati Annaldas Bhupati Annaldas 9, Siddhanath Nagar,

(Swagat Nagar), Solapur

0217-

2603801

114 Rajesham

Lingayya Bolli Rajesham L. Bolli

E-60, M.I.D.C.,


115 Sattyanarayan

Rajmogli Arkal

Sattyanarayan R.

Arkal

155/2B, Gandhinagar,


116 Yemul Weaving

Mills

Bhummaya M.

Yemul

E-14, M.I.D.C.,


0217-

2652160

117 Raju Siddram

Adagatla Raju S. Adagatla

155/3, Akkalkot Road,

Gandhinagar, Solapur 9595109696

158

Sr.


118 Narayan

Lingayya Bolli Narayan L Bolli

860, New Paccha Peth,

Solapur 9420659801

119 Mrs. Bharatibai

R. Arkal Bharatibai R. Arkal

155/1/B, Gandhinagar,

Solapur

0217-

2744740

120 Annaldas Udyog Devidas Annaldas P-21, M.I.D.C.,


0217-

2391961

121 Kankayya

Annaldas Kankayya Annaldas 2, Siddhanagar, Solapur 9422457092

122 Srinivas Annaldas Srinivas Annaldas E-94, M.I.D.C, Akkalkot

Road, Solapur

123 Damodar V.

Annaldas

Damodar V.

Annaldas

Plot No. 8, Yashwant

Nagar, Solapur 9021212327

124 Sou Vijaylaxmi

S. Adagetla

Mrs. Vijayalaxmi

Adagetla

C-5,6 Vinkar Society,


0217-

3297752

125 Sattyanarayan S.

Adagantla

Sattyanarayan S.

Adagantla

A-16,B-10, Padmashali

Nagar, Akkalkot Road,

Solapur

9370460406

126 Rangayya

Narsayya Guntuk

Rangayya N.

Guntuk

Survey No. 192/18,

Ganesh Nagar, Solapur 9325669292

127 Ganesh Y.

Boddal Ganesh Y. Boddal

8, Kanda Nagar,

Near Akkalkot Naka,

Solapur

0217-

2626933

128 Kandikatla

Textile Group

Raghuramlu

Kandikatla

2/1, Ganesh Nagar,

M.I.D.C., Solapur 9326162687

129 Dhulam

Industries

Hirachand D.

Dhulam

280, M.I.D.C.,


0217-

2749550

130 Suprabha Textiles Pramod Keshav

Jatla

243, M.I.D.C.,


131 Kotex Mills Shirish Kolhapure Plot No. B-2/1/4,

M.I.D.C., Solapur. 9423591200

132 Srinivas R.

Boddul Srinivas R. Boddul

7, Konda Nagar, Near

Akkalkot Naka, Solapur

0217-

2376032

133 Kongari Textiles Suresh S. Kongari

1520, Daji Peth(Office)

164,MIDC, Akkalkot

Road, Solapur

9552631550

134 Narendra R.

Guntuk Narendra R. Guntuk

18/4/2, Madhav Nagar,

Near MIDC, Solapur 9325470659

135 Sattaya R. Boddul Sattya R. Boddul E-100, M.I.D.C.,


0217-

2651017

136 Sudhakar

Venktesh Singam

Sudhakar V.

Singam

34A68 New Paccha Peth

Solapur 9370229619

159

Sr.


137 Sou Sundarabai

A. Dasari

Sundarabai A.

Dasari


Road, Solapur

0217-

2744541

138 Rajayya

Durgayya Dasari Rajayya D. Dasari

34/3, New Paccha Peth

Solapur

0217-

2744540

139 Mallaya Narsayya

Bhairi Mallaya N. Bhairi


Road, Solapur

0217-

2734722

140 Deccan Textile

Mills Venktesh M. Ittam

Plot No. 34/3/41,

New Paccha Peth,

Solapur

141 R.M. Bhairi Ramesh M. Bhairi

R. M. Bhairi

W-21, M.I.D.C.,


0217-

2734723

142 M.N. Bhairi

Textiles

Vijaylaxmi M.

Bhairi

Plot No. 34/3/60,

New Pachha Peth,

Solapur

0217-

2734721

143 Sou Sabita D.

Vidap Sou Sabita D. Vidap


Solapur 9422492206

144 Venktesh R.

Boddul Venktesh R. Boddul


Akkalkot Naka, Solapur -

6

0217-

2326061

145 Bhagyashri N.

Myana

Bhagyashri N.

Myana

C-29/33, Nagnath

Society, Akkalkot Road

MIDC, Solapur

146 Sou Aruna Srihari

Vidap

Sou Aruna Srihari

Vidap


Solapur

0217-

2656565

147 Ashok Industries Ambadas M. Yemul 34/A/63, New Paccha

Peth,Solapur.

0217-

2653284

148 D.D. Mill Veerswami R.

Dasari

311, M.I.D.C.,


0217-

2744543

149 Venktesh Pitta Venktesh Pitta D8/2, M.I.D.C.,


150 Yadgiri Pitta Yadgiri Pitta D8/1,5, M.I.D.C.,


151 Mrs. Sushila S.

Boddul

Mr.s Sushila S.

Boddul


Akkalkot Naka, Solapur 9422460721

152 Narayan Pitta Mr. Narayan Pitta D8/3,4, M.I.D.C.,


153 Sattayanarayan R.

Gurram Gurram S.R.

279, M.I.D.C.,


154 Srinivas Ramayya

Gurram

Mr. Srinivas R.

Gurram

W2, M.I.D.C.,


0217-

2745711

160

Sr.


155 Laxminarayan R.

Gurram

Laxminarayan R.

Gurram

24A43, New Pachha

Peth, Solapur.

0217-

2745710

156 Venktesh Textiles Venktesh M.

Shrigandhi

D-5/3, M.I.D.C.,


157 Akude Textiles Ravi M. Akude 34/5, B23/1 C, New

Pachha Peth, Solapur

0217-

2651437

158 Gajul Weaving

Mill Balaji N. Gajul

14,19, Gandhinagar,

Solapur 9422686049

159 Shubhlaxmi

Textiles

Mr. Srinivas A

Dudam

14/11, Gandhinagar,

Solapur 9175262128

160 Narayan S.

Myana

Mr. Narayan S.

Myana

C-29/40, Nagnath

Society,

Akkalkot Road MIDC,

Solapur

0217-

2652243

161 Sriraj Textiles Mrs. Shivlaxmi R.

Vidap

D-100/11, M.I.D.C.,


162 Gangaji Weaving

Mill Sandip S. Gangaji

5/3, Ravivar Peth,

Solapur

0217-

2747392

163 Shrigandhi

Fabrics

Mohan P.

Shrigandhi

140, M.I.D.C.,


0217-

2656019

164 Prashant Textiles Srinivas Kandle 155/5, Akkalkot Road,

Solapur

0217-

2377197

165 Kumarswami

Ramayya Nakka

Kumarswami Nakka

Laxminarayan

Nakka

29/31, Nagnath Society,

M.I.D.C., Akkalkot Road

Solapur

9421075716

166 Pulgam Textiles Dnayaneshwar R.

Pulgam

P-18/2, M.I.D.C.,


0217-

2654046

167 M/s Vishwa

Traders / mfg. Naresh V. Chatla

A/21, M.I.D.C.,


0217-

2392992

166

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