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“AN IN-DEPTH COMPARATIVE STUDY OF SUPPLY
CHAIN MANAGEMENT PRACTICES AT
SELECTED AGRICULTURE PRODUCE MARKETING
COMMITTEES OF NORTH GUJARAT”
A Thesis Submitted for the Partial Fulfillment of the Requirement
for the Degree of
Doctor of Philosophy
IN
MANAGEMENT
Submitted to
GANPAT UNIVERSITY
Submitted by
PATEL AMITKUMAR AMRUTLAL
REGISTRATION No.: MM/01/02/07
Under the Guidance of
PROF. (DR.) MAHENDRA SHARMA
Dean-Faculty of Management Studies
Ganpat University
December 2010
ii
PREFACE
In the last decade world has witnessed the number of fundamental changes in the
business environment, especially in agri-food chains. Consumers across the world have
become more demanding and place new demands on attributes of agriculture products
such as quality (guarantees), integrity, safety, diversity and associated information
(services). Demand and supply are no longer restricted to nations or regions but have
become international processes. An increasing concentration in agribusiness sectors, an
enormous increase in cross-border flows of livestock and agri products and the creation
of international forms of cooperation is observed in the recent time.
The trend towards vertical coordination of agricultural supply chains (ASC), integration
of processes from farm to plates, reduction of government support (subsidies) for
agriculture, globalization and competition among producers, processors and suppliers,
explosion in technological progress applicable to the agri-food industry, changing
consumer demand and consumption patterns, etc, are some of the factors related to the
concentration and industrialization of agriculture.
The agribusiness sector is becoming an interconnected system with a large variety of
complex relationships, reflected in the market place by the formation of Agri Supply
Chain Networks (ASCNs) via alliances, horizontal and vertical cooperation, forward and
backward integration in the supply chain and continuous innovation. The latter
encompass the development and implementation of enhanced quality, logistics and
information systems that enable more efficient execution of business processes and more
frequent exchange of huge amounts of information for coordination purposes. All these
developments necessitate a reorientation of all the players in Indian agriculture sector and
food industry on their roles, activities and strategies.
In India, agricultural produce marketing activities are regulated by Agricultural Produce
Market Committees (APMC), restricting trade within the notified area of APMCs. The
iii
monopoly of government regulated wholesale markets has prevented development of a
competitive marketing system in the country, providing no help to farmers in direct
marketing, organizing retailing, a smooth raw material supply to agro-processing
industries and adoption of innovative marketing system and technologies. There is, in the
process, an enormous increase in the cost of marketing and farmers end up getting a low
price for their produce. Monopolistic practices and modalities of the state-controlled
markets have prevented private investment in the sector. On other hand, various studies
on the impact of regulated markets have highlighted several positive features of the
regulation program. These include a visibly open process of price discovery, more
accurate and reliable weighing, standardized market charges, payment of cash to farmers
without undue deductions, dispute settlement mechanism, timing and sequencing of
auctions, reduction in physical losses of produce, and availability of several amenities in
market yards.
In the recent time, however, the relevance of the market regulation program seems to
have declined and hence the central government has formulated and circulated a Model
Agricultural Produce Marketing (Development and Regulations) Act, 2003 in place of
existing State APMC Acts to remove the bottlenecks of the regulated markets and
facilitates the farming community to benefit from new market opportunities, through
integrating and strengthening the internal agriculture marketing system. The new act
permits the farmers, local authorities and others to establish new markets, setting up of
purchase center, farmer/consumer markets for direct sale in any area and promote public
– private partnership in management and development of agricultural markets as well as
contract farming too.
Agricultural marketing has assumed increased importance after launching of new
economic policy and consequent opening up of India’s market to world market. Hence, it
is necessary to remove various constraints and deficiencies in the existing domestic
markets and marketing practices. It is believed that poor linkages in the marketing
channels and poor marketing infrastructure are leading to high and fluctuating consumer
iv
prices, and to only a small proportion of the consumer rupee reaching the farmers. There
is also substantial wastage, deterioration in quality, and frequent mis-match between
demand and supply spatially and over time.
Agricultural markets of Gujarat and North Gujarat in particular are reputed for their
transparency and neutrality. Government of Gujarat, Department of Agriculture and
Cooperation has stated that Gujarat has a clear competitive advantage in many
commodities on the basis of production and productivity. Higher productivity, however,
will not be translated into a proportionate increase in the level of real income in an
economy in which the distributive system is inefficient. Hence, the economic need for an
efficient operation set up is imperative. This in turn necessitates capitalizing on
developing efficient integrated network by identifying and removing the bottlenecks of
existing system, developing common user infrastructure for value added activities,
optimization of resource use, output management, widening of markets, creating the
opportunities for the growth of agro-based industry, promoting exports and increasing
market intelligence. This, in turn, helps existing APMCs to cope up with upcoming
challenges pose because of amendment of APMC Acts with Model APMC Act.
An efficient movement of farm produces to consumer raises the income level of farmers
and promotes the economic development of the study area. Therefore, it is necessary to
identify and quantify the important factors affecting agriculture supply chain practices at
APMCs. So, the improvements can be directed towards those factors which are crucial in
managing supply chain effectively.
However, a study of the existing agriculture supply chain is necessary to understand the
complexities involved and identification of bottlenecks with a view to providing efficient
services in the transfer of farm produce to consumers. From this study, potential supply
chain players can be identified and their functions, role and relationships in the trade
system can be delineated. Researcher has also studied and identified the key important
variables affecting the system. This helps the intermediaries to understand the important
v
factors affecting their business practices and hence, they can delineate the strategies
accordingly. Comparison of importance given to these variables helps the chain
intermediaries to understand the behaviour of the other chain intermediaries while
purchasing or selling the commodities and policy makers to sincerely initiate the
developmental activities by incorporating the important factors into their action plan to
build competitive advantage. It also helps the management of APMCs to understand the
significance of value adding infrastructure and support services for the long term growth
of the market-yards.
Wholesaler (Pacca Arhatiyas) is the powerful and well developed entity of the chain. He
can play as a chain leader for integrating the supply chain activities with the other chain
partners in the chain. The study attempted to through the light on the integrated supply
chain management practices pursued by the wholesalers at the selected APMCs of North
Gujarat region for trading the commodities cumin, fennel and isabgul. It was found that
the wholesaler who has practiced supply chain management, there was some effort to
coordinate only three processes out of nine processes with other firms. This did not mean
that each of these three process elements was jointly managed to a great degree.
Researcher has tried to delineate the barriers to the supply chain integration.
This study of agriculture supply chain management practices at APMCs in geography
deals with micro-level spatial inquiry of agriculture markets. It takes into consideration
physical, socio-culture and legal etc. which affect markets and their different aspect. The
study helps the policy makers and management of APMCs to understand the key results
areas of their existing system and also facilitate in reviving and reconceptualising their
business model.
vi
ACKNOWLEDGEMENT
Studying supply chain management practices at APMCs would be challenging and not
easy for me. But the support, guidance and encouragement of all who helped me made
this research adventure a Wow! Experience. I wish to thank all for their contribution and
support.
I would like to begin by sincerely thanking Prof. (Dr.) Mahendra Sharma for being my
guide and a mentor during the entire process of my research work. I consider myself
extremely fortunate to have Dr. Sharma’s professional and personal support, and
unfaltering encouragement. I can’t even begin to articulate how much I have learned from
him. Without his connoisseur guidance, support and encouragement, I would not be able
to complete my work successfully.
I owe a debt of gratitude to Dr. L. N. Patel, Vice-Chancellor, Ganpat University, Prof. P.
I. Patel, Hon. Director, M.D.E.F., Ganpat Vidyanagar, Dr. D. V. Patel, Founder, V. M.
Patel Institute of Management, Ganpat University and Management of the Ganpat
University for their moral support and motivation.
I am grateful to Prof. (Dr.) H. J. Jani to review my research work continuously and to
provide valuable suggestions to improve my work.
I am especially grateful to Dr. B. A. Prajapati, Dr. Parimal Vyas, Dr. S. O. Junare, Dr.
Sudhir Yadav, Dr. V. K. Sapovadia, Dr. Tejas Dave, Dr. J. S. Panwar, Dr. Pestonjee, Dr.
A. C. Brambhat, and Prof. Nishit Bhatt for direction and feedback they have provided.
I am also grateful to Shri Narayanbhai L. Patel, Chairman, APMC, Unjha, Shri
Shivambhai Raval and Shri Rajnibhai, Officers, APMC Unjha for making available all
information crucial for my research work and contact details from where I can get the
relevant information. I am thankful to chairmen, secretaries and officers of all APMCs in
vii
North Gujarat region, officers of Department of Agriculture, Government of Gujarat and
officers of District Panchayat Offices for their timely support to get the relevant
information and for providing relevant contacts and making themselves available for
interviews.
I wish to thank Dr. Hiren J. Patel for their direction at a very critical time of writing this
thesis. I am equally thankful to all my friends Dr. Maurvi Pandya, CA. Ujal Mehta, Rajen
Purohit, Kalpesh Ganotra, Mayur Shah, Vitthal Patel, Vineet Prabhakar, Romy Sebastian,
Prodo, Dr. Vipul Patel, Haresh Barot, Jayesh Patel, Nirav Halvadia and administrative
staff of V. M. Patel Institute of Management for support and encouragement. I wish to
acknowledge the moral support of Dr. Akash Patel.
Thanks to my family for providing endless encouragement and support. Heartfelt thanks
to my mother Mrs. Kamlaben for our weekly discussions on life and spirituality. Thanks
to my son Om for providing those warm hugs and kisses which brought such cheer and
joy in life.
Finally, I wish to thank my wife Hiral for her unconditional support and encouragement.
Her companionship and love was like a beacon that carried me through some of the very
difficult times during all these years.
At last, I thank the one and all, for the divine blessings.
Amit Patel
viii
DECLARATION BY CANDIDATE
This thesis titled “AN IN-DEPTH COMPARATIVE STUDY OF
SUPPLY CHAIN MANAGEMENT PRACTICES AT SELECTED
AGRICULTURE PRODUCE MARKETING COMMITTEES OF
NORTH GUJARAT” is submitted in fulfillment of the requirements for the
award of the degree of Doctor of Philosophy (Ph.D.) in Management to
Ganpat University, Mehsana. I declare that this thesis is based on my
original work except for quotations and citations which have been duly
acknowledged. I also declare that this thesis has not been previously or
concurrently submitted either in whole or in part, for any other qualification
to Ganpat University or other institutions.
Date:
Place: Ganpat University (Patel Amitkumar A.)
ix
CERTIFICATE OF GUIDE
This is to certify that this thesis titled “AN IN-DEPTH COMPARATIVE
STUDY OF SUPPLY CHAIN MANAGEMENT PRACTICES AT
SELECTED AGRICULTURE PRODUCE MARKETING
COMMITTEES OF NORTH GUJARAT” submitted by Patel Amitkumar
Amrutlal, at Faculty of Management Studies, Ganpat University, Mehsana is
the bonafide work completed under my supervision and guidance for the
fulfillment of the requirement for the award of the degree of Doctor of
Philosophy (Ph.D.) in Management.
Date:
Place: Ganpat University (Prof. (Dr.) Mahendra Sharma)
x
CONTENTS
Preface ii
Acknowledgement vi
Declaration by Ph. D Student viii
Certificate by Research Guide ix
List of Tables xi
List of Figures xvi
Chapter 1 Introduction 01
Chapter 2 Literature Review 40
Chapter 3 Research Methodology 106
Chapter 4 Data Analysis and Interpretation 115
Chapter 5 Findings and Conclusion 245
BIBLIOGRAPHY 273
Annexure 291
xi
LIST OF TABLES
CHAPTER 1
Table 1.1 Geographical Clusters of Agriculture Crops 08
Table 1.2 Reforms in Agricultural Markets (APMC Act) as on 30.11.2009 27
Table 1.3 Composition of Market Committee 29
Table 1.4 District wise details of Market Committees, Principal 32
Market-Yards and Sub-Yards
Table 1.5 Total Market Arrivals and its Value in all APMCs of Gujarat 33
Table 1.6 District wise details of Total Area, Total Arable Land, Number of 33
Talukas and Villages.
Table 1.7 District wise details of Market Committees, Market Yards and 34
Commodity Traded.
CHAPTER 2
Table 2.1 Supply Chain Management Activities 51
Table 2.2 Representative processes Being Integrated Across Supply Chains 58
Table 2.3 Dimensions of Supply Chain Integration 64
Table 2.4 Intermediaries in the agricultural supply chain and their 100
margins and value additions
Table 2.5 Silent features of Model Act 104
CHAPTER 3
Table 3.1 Value of Three Commodities of Various APMCs of North Gujarat 111
Table 3.2 Respondent wise Sample Size 113
CHAPTER 4
Table 4.1 Entity-Wise Profile of Respondents 118
Table 4.2 Place * Entity Cross tabulation 118
Table 4.3 City (APMC)-Wise Profile of Respondents 119
Table 4.4 Commodity Trade-Wise Profile of Respondents 120
xii
Table 4.5 Commodity Trade * Entity Name Cross-tabulation 120
Table 4.6 Variable under Section-2 for Selecting the Intermediaries to
Sell the Commodities with their Coded Name 122
Table 4.7 Variable under Section-2 for purchasing/processing
the commodities with their coded name 125
Table 4.8 (a) Variables related to Services / Facilities available within the
city/town of the APMC 127
Table 4.8 (b) Variable under Section-III, affecting the selection of
intermediaries in particular Market-Yard 129
Table 4.9 Reliability Statistics For factors considered for selecting the
intermediaries to Sell the products 133
Table 4.10 Reliability Statistics for factors considered for selecting the
intermediaries to Purchase the products 133
Table 4.11 Reliability Statistics For factors considered for selecting the
intermediaries into Particular Market-yard 133
Table 4.12 KMO and Bartlett's Test for Sales related variables 134
Table 4.13 Anti Image Correlation Matrix 135
Table 4.14 Communalities 136
Table 4.15 Revised Anti-Image Matrix 137
Table 4.16 Revised Communalities 138
Table 4.17 Eigenvalues and Total Variance Explained 139
Table 4.18 Component Matrix (a) 141
Table 4.19 Guideline for identifying significant factor loadings based
on sample size 142
Table 4.20 Rotated Component Matrix (a) 145
Table 4.21 Revised Rotated Factor Loading Matrix 146
Table 4.22 Revised Rotated Factor Loading Matrix 147
Table 4.23 Eigenvalues and Total Variance Explained 148
Table 4.24 Revised Communalities 149
Table 4.25 Revised Communalities 150
xiii
Table 4.26 Eigenvalues and Total Variance Explained 150
Table 4.27 Final Rotated Component Matrix (a) 151
Table 4.28 KMO and Bartlett's Test for Purchase related variables 153
Table 4.29 Anti Image Correlation Matrix 154
Table 4.30 Communalities 155
Table 4.31 Revised Communalities 156
Table 4.32 Revised Communalities 157
Table 4.33 Eigenvalues and Total Variance Explained 158
Table 4.34 Rotated Component Matrix (a) 159
Table 4.35 Final Rotated Component Matrix(a) 160
Table 4.36 Eigenvalues and Total Variance Explained 160
Table 4.37 Final Rotated Factor Loading Matrix 161
Table 4.38 KMO and Bartlett's Test for Purchase related variables 163
Table 4.39 Anti Image Correlation Matrix 164
Table 4.40 Communalities 165
Table 4.41 Revised Anti Image Correlation Matrix 166
Table 4.42 Revised Communalities 166
Table 4.43 Revised Anti Image Correlation Matrix 167
Table 4.44 Revised Communalities 168
Table 4.45 Revised Anti Image Correlation Matrix 169
Table 4.46 Revised Communalities 169
Table 4.47 Revised Eigenvalues and Total Variance Explained 170
Table 4.48 Rotated Component Matrix (a) 171
Table 4.49 Revised Rotated component Matrix 172
Table 4.50 Revised Rotated component Matrix and communalities 173
Table 4.51 Revised Eigenvalues and Total Variance Explained 174
Table 4.52 Revised Communalities 175
Table 4.53 Revised Communalities 176
Table 4.54 Eigenvalues and Total Variance Explained 176
Table 4.55 Revised Rotated Component Matrix(a) 177
xiv
Table 4.56 Final Rotated Component Matrix 177
Table 4.57 ANOVA for importance given to the key important
variables by the farmers to sell the product related variable 184
Table 4.58 ANOVA for importance given to the key important variables by
the commission agent to sell the products 187
Table 4.59 ANOVA for importance given to the key important variables
by the stockiest to sell the products 189
Table 4.60 ANOVA for importance given to the key important variables
by the wholesaler to sell the products 191
Table 4.61 ANOVA for importance given to the key important variables
by the exporter to sell the products 193
Table 4.62 ANOVA for importance given to the key important variables
by the Processor to sell the products 195
Table 4.63 Mean and Standard Deviation for importance given to the
key important variables by the different intermediaries to
sell the product 198
Table 4.64 ANOVA for importance given to the key variables by the
commission agent to purchase the product 202
Table 4.65 ANOVA for importance given to the key important variables
by the stockiest to purchase the product 204
Table 4.66 ANOVA for importance given to the key important variables
by the wholesaler to purchase the product 206
Table 4.67 ANOVA for importance given to the key important variables
by the exporter to purchase the product 208
Table 4.68 ANOVA for importance given to the key important variables
by the processor to purchase the product 210
Table 4.69 Mean and Standard Deviation for Importance given to the key
variables by the different intermediaries to purchase the products 213
Table 4.70 ANOVA for importance given to the key variables by the farmers
to select the intermediaries into particular APMC 216
xv
Table 4.71 ANOVA for importance given to the key variables by the
commission agent to select the intermediaries into
particular APMC 219
Table 4.72 ANOVA for importance given to the key variables by the
stockiest to select the intermediaries into particular APMC 222
Table 4.73 ANOVA for importance given to the key variables by the
wholesalers to select the intermediaries into particular APMC 225
Table 4.74 ANOVA for importance given to the key variables by the
exporter to select the intermediaries into particular APMC 227
Table 4.75 ANOVA for importance given to the key variables by the
processor to select the intermediaries into particular APMC 229
Table 4.76 Mean and Standard Deviation for importance given to the Key
Important Variables by the different intermediaries to select the
intermediaries into particular APMC 232
Table 4.77 Proportion of Respondents Jointly Manage one or more business
Processes 236
Table 4.78 Degree to Which Process Elements are Jointly Managed 237
Table 4.79 Distribution of Number of Process Elements Not Jointly
Managed At All 238
Table 4.80 T-test to know Process Elements are jointly managed 239
Table 4.81 T-test for Barriers to Supply Chain Integration 240
Table 4.82 T-test for functional Integration 241
Table 4.83 Intermediaries with whom sample firm manages supply
chain activities 242
Table 4.84 Horizontal Span Length and Span Radius of Sample firms
Practicing Supply Chain Management 243
xvi
LIST OF FIGURES
Figure 2.1 Supply Chain Management: Integrating and Managing Business
Processes across the Supply Chain 57
Figure 2.2 A Model of Supply Chain Management 61
Figure 2.3 Continuum of integration from cooperation to collaboration 65
Figure 2.4 Supply Chain Management Framework: Elements and
Key Decisions 70
Figure 2.5 Mapping of Functions within the organisation with Supply
Chain Processes 75
1
CHAPTER 1 INTRODUCTION
1.1 Background of the research
1.2 Need of the research
1.3 Agriculture Marketing in India: An Inner View
1.3.1 Introduction
1.3.2 Agriculture Marketing in India
1.3.3 Agricultural Marketing through Regulated Market in India
1.3.3.1 Historical Perspective
1.3.3.2 Regulated Marketing System and the Five Year Plans
1.3.3.3 New Thinking on Regulated Markets
1.3.4 APMCs in Gujarat
1.4 Outline of the thesis
2
1.1 Background of the research
Despite the success on many strategic fronts since liberalization, the 1990s have, for all
practical purpose, represented a non-happening decade for the agriculture sector, growth
decelerating from 2.9 per cent in the pre-reform period to just about 2 per cent in post-
reform era1. This failure has introduced a deep sense of disquiet into ongoing reform
effort and has promoted a quest for an appropriate agriculture strategy, which would be
conductive for growth with redistribution.
Agriculture forms the backbone of the Indian economy and despite concerted
industrialization in the last five decades agriculture occupies a special pride.
• It provides employment to almost 62 percent of the total workforce in the country.
• Contributes a major share of national income in India.
• Feed more than a billion of population and demand will increase with increase in
population.
• Agriculture is critical for facing the challenges of rural poverty, food insecurity,
unemployment, and sustainability of natural resources in India.
• One per cent incremental growth in agriculture sector leads to an additional income
generation of INR 10,000 crores in the hands of the farmers, thereby increasing their
disposable income and hence purchasing power2. The increase in demand gives a push to
the industry as well, which in turn raises the overall GDP growth.
This indicates any change in agriculture sector weather positive or negative has multiplier
effect on entire economy. All these signify the fact that the development of national
economy requires rapid agricultural development. Despite of this fact, the sector is
plagued by multitude of problems which hinder its efficient operation.
1 P. Balakrishnan, R. Golait, and P. Kumar, Agricultural Growth in India since 1991, (Mumbai:Reserve
Bank of India, 2008). 2 Kalyan Chakravarthy, et al., Agribusiness in Gujarat: Unleashing the potential, (Mumbai: Confederation
of Indian Industry-Yes Bank Knowledge Initiative, 2007), p.1
3
According to the Indian Ministry of Trade and Industry, approximately 20 percent of
farm produced in India is wasted. Various research studies by the Economic Times
Intelligence Group (ETIG, 20033) and the Investment Information and Credit Rating
Agency (ICRA, 20014) has detailed the weaknesses and problems present in the Indian
agriculture supply chain.
First, tones of products are wasted due to improper handling and storage, pest infestation,
poor logistics, inadequate storage and transportation infrastructure.
Second, intermediaries take large portion of the earnings which should go to farmers.
Third, post-harvest losses are about 25-30 percent in India. Even marginal reductions in
these losses are bound to bring great relief on the food security front as well as improve
the income level of the farmers.
Fourth, Indian consumers pay three to four times the farm gate price, as compared to
developed countries where the consumer pays one and a half to two times the farm gate
price. Also, 60-80 percent of the price that consumers pay goes to traders, commission
agents, wholesalers and retailers. These intermediaries lead to poor coordination and
collaboration in the supply chain, which in turn leads to inefficient information flow.
According to the document stated in The National Agriculture Policy 2000, the Indian
agriculture sector facing the problems of capital inadequacy, lack of infrastructural
support and demand side constraints such as controls on movement, storage and sale of
agricultural products etc, have continued to affect the economic viability of agricultural
sector5.
3 Changing Gears: Retailing in India, (Economic Times Knowledge Series, Mumbai: Economic Times
Intelligence Group, 2003). 4 Report on FMCG, (New Delhi: Investment Information and Credit Rating Agency, March 2001). 5 Ruddar Datt and K. P. M. Sundharam, Indian Economy (Revised 54th edn.), (New Delhi: S. Chand & Co
Ltd, 2006), p. 584
4
To overcome these bottlenecks and strengthen the existing system the National
Agriculture Policy 2000 aims to attain the growth rate of over 4% per annum in
agriculture sector with equity that is based on efficient use of resources and demand
driven. It also aims to cater the demand of domestic markets and maximizes benefits
from exports of agricultural products in the face of challenges from economic
liberalization and globalization.6
Agriculture is the science and practice of activities relating to production, processing,
marketing, distribution, utilization, and trade of food, feed and fiber7. This definition
implies that agricultural development strategy must address not only farmers but also all
the stack-holders of the agriculture supply chain. In this context, efficient supply chain
systems assume added importance. This agriculture supply chain system is the critical
link between farm production sector on the one hand and nonfarm sector, industry, and
urban economy on the other. Besides the physical and facilitating functions of
transferring the goods from producers to consumers, it also performs the function of
discovering the prices at different stages of marketing and transmitting the price signals
in the marketing chain. The issues and concerns relate mainly to the performance
(efficiency) of the supply chain system, which depends on the structure and conduct of
the market.
Market structure is the size and design of the market and refers to those organizational
characteristics which affect the conduct and performance of the market (Acharya and
Agarwal, 2004)8. Important structural characteristics of agricultural produce markets
include concentration of market power, conditions of entry or exit of firms, flow of
market information, degree of product differentiation, and degree of integration - both
vertical and horizontal.
6 Ministry of Agriculture, Government of India, National Agriculture Policy, (New Delhi, 2000), p. 2.
7 S. S. Acharya, Agriculture Marketing and Rural Credit for Strengthening Indian Agriculture. (India
Resident Mission Policy Brief Series, New Delhi: Asian Development Bank, 2006), P.1. 8
S.S. Acharya, and N.L. Agarwal. Agricultural Marketing in India, (Fourth Edition), (New Delhi: Oxford
and IBH).
5
In India, agricultural produce marketing activities are regulated by Agricultural produce
Market Committees (APMC), which are established and regulated under the State
Agricultural produce Market Committee Acts (State APMC Acts) restricting trade within
the notified area of APMCs. The monopoly of Government regulated wholesale markets
has prevented development of a competitive marketing system in the country, providing
no help to farmers in direct marketing, organizing retailing, a smooth raw material supply
to agro-processing industries and adoption of innovative marketing system and
technologies. There is, in the process, an enormous increase in the cost of marketing and
farmers end up getting a low price for their produce. Monopolistic practices and
modalities of the state-controlled markets have prevented private investment in the sector.
A comprehensive study of the agricultural marketing system during the last fifty years by
Acharya (20049) identifies several problems associated with regulated markets. For
example, since the agricultural produce marketing committees do not allow the traders to
buy from the farmers outside the specified market yards or sub-yards, the cost of
marketing increases.
On other hand, various studies on the impact of regulated markets (Acharya, 198510
,
198811
; Suryawanshi et al., 199512
; and Agarwal and Meena, 199713
) have highlighted
several positive features of the regulation program. These include a visibly open process
of price discovery, more accurate and reliable weighing, standardized market charges,
payment of cash to farmers without undue deductions, dispute settlement mechanism,
9 S. S. Acharya, Agricultural Marketing in India: Millennium Study of Indian Farmers, Volume 17, (New
Delhi: Government of India, Academic Foundation, 2004). 10
S. S, Acharya, “Regulation of Agricultural Produce Markets: Some Observations on its Impact”,
Development Policy and Administration Review, Vol. 11, No. 2, (July-December,1985). 11
S. S. Acharya, Agricultural Production, Marketing and Price Policy in India, (New Delhi: Mittal
Publications), p. 317. 12
R. R Suryawanshi, B. N. Pawar and P.D. Deshmukh, “Marketable Surplus and Marketing Cost of
Oilseeds and Pulses in Western Maharashtra”, Bihar Journal of Agricultural Marketing, Vol. 3, No. 2,
(April-June, 1995) pp. 201-4. 13
N. L. Agarwal and B. L. Meena, “Agricultural Marketing in India: Performance of Cumin Marketing in
Rajasthan”, Bihar Journal of Agricultural Marketing, Vol. 5, No. 3, (September-December, 1997) pp. 319-
28.
6
timing and sequencing of auctions, reduction in physical losses of produce, and
availability of several amenities in market yards.
In the recent time, however, the relevance of the market regulation program seems to
have declined and hence there has been a new thinking is required on the part of central a
well as state government to remove the bottlenecks of the regulated markets and
facilitates the farming community to benefit from new market opportunities, through
integrating and strengthening the internal agriculture marketing system. The central
government has formulated and circulated a Model Agricultural Produce Marketing
(Development and Regulations) Act, 2003 in place of existing State APMC Acts14
which
permit the farmers, local authorities and others to establish new markets, setting up of
purchase center, farmer/consumer markets for direct sale in any area and promote public
– private partnership in management and development of agricultural markets as well as
contract farming too.
The proposed Acts encourage: (a) development of competitive agriculture marketing; (b)
deregulate the marketing system; and (c) promote private investment in management and
development of agricultural markets in India. This promote alternative marketing system-
(i) Direct marketing,
(ii) Marketing through farmers interest group,
(iii) Setting up of terminal markets,
(iv) Forward and future market,
(v) E-commerce,
(vi) Setting up of mega markets, and
(vii) Negotiable warehouse receipt system etc. and that may operate parallel to and in
addition to present marketing system.
14
Ruddar Datt and K. P. M. Sundharam, Indian Economy (Revised 54th edn.), (New Delhi: S. Chand &
Co Ltd, 2006), p. 584
7
1.2 Need of the research
In the recent past, a mountain of food was piled up in government godowns. At the same
time, there was an alarming destitution in many parts of the country. The Supreme Court
has been so moved that it had ordered six states to remedy this anomaly of hunger in the
presence of abundance. That is easier said than done. There is practically no country in
the world that is able to balance agricultural production against demand or regulate
agriculture incomes to the satisfaction of farmers. Evidently, India has solved the
problem of production but not that of distribution. Famines arise from faulty distribution
and not for want of goods distribute. The market is powerful tool for promoting
production. Unfortunately, it is not good for ensuring equitable distribution.15
Agricultural marketing is viewed as a process encompassing all the steps involved from
the producers to the consumers including pre and post harvest operations. Such operation
adds value to the produce in terms of time, place and farm utilities.
Agricultural marketing has assumed increased importance after launching of new
economic policy and consequent opening up of India’s market to world market. Hence, it
is necessary to remove various constraints and deficiencies in the existing domestic
markets and marketing practices. It is believed that poor linkages in the marketing
channels and poor marketing infrastructure are leading to high and fluctuating consumer
prices, and to only a small proportion of the consumer rupee reaching the farmers16
.
There is also substantial wastage, deterioration in quality, and frequent mis-match
between demand and supply spatially and over time17
.
Gujarat, a major industrial state, also has great potential to develop a vibrant agrarian
economy through agro-industrialization by deriving competitive advantages from its
15
P. V. Indiresan, Vision 2020: What India can be, and How to make it happen”, (First Edition),
(Hyderabad: ICFAI Press, 2003), p. 125 16
B. M. Asturker and C. D. Deole, “Producers’ Share in Consumers Rupee”, Indian Journal of Agriculture
Economics, Vol. 40, No. 3, (1985). 17
N. Subbanarasaiah, Marketing of Horticulture Crops in India, (Delhi: Anmol Publishing Co.,1991), p.
102
8
unique position in the world for many commodities viz; castor, fennel, cotton, tobacco,
groundnut sesame, chikus, onion, bananas, isabgul, guarseed and cumin.18
Moreover,
these agricultural products are produced in certain geographical clusters (see Table 1.1)
which makes these clusters prima facie suitable for setting up of common user
infrastructure facilities.
Table 1.1 Geographical Clusters of Agriculture Crops
CROP CLUSTER
Fruit crops South Gujarat region, Charotar area and part of Saurashtra region
Vegetable Middle Gujarat, Part of Saurashtra region
Oilseed Saurashtra, part of North Gujarat
Spices North Gujarat, Part of Saurashtra region
Medicinal Herbs South Gujarat, North Gujarat and Middle Gujarat
Source: Reading material for “Training Program on Agri-clinic and Agri-Business Centers”, EDI
Gandhinagar/MANAGE, August 2004, p. 8
Despite many strength and well-developed infrastructure facilities, there is enough scope
for developing and upgrading agriculture infrastructure in the area of specialized storage
facilities, primary and secondary transportation, mechanization, grading standards, export
promotion, processing industry support and market intelligence etc.19
The most of the state governments in India including Government of Gujarat has started
amending State APMC Acts with the Model APMC Act to deregulate the existing
wholesaler trading practices of agricultural produce through APMCs. The shift from
regulated to deregulated agriculture marketing initiatives – promotion of alternate,
parallel channels to existing channel – gives the wake up call by challenging the existing
system; marketing through APMCS; in terms of efficiency and transparency across the
chain. It also gives opportunities to adopt market lead innovative supply chain model to
generate as well as sustain the competitive advantage of APMCs in the world market in
the new economic regime of LPG (Liberalization, Privatization and Globalization). This
18
Gujarat Agriculture-A Synoptic View, Reading material for “Training Program on Agri-clinic and Agri-
Business Centers”, (EDI Gandhinagar/MANAGE, August 2004), p. 8 19
Gujarat Agriculture-A Synoptic View, op.cit, p. 28
9
market-led business model can enhance the competitiveness and trigger a virtuous cycle
of higher productivity, higher income, enlarged capacity for farmer risk management,
larger investments and higher quality and productivity throughout the supply chain. This
promotes the modern trade practices, which in turn pave way for transparency and
efficiency in Indian agriculture marketing system.
Agriculture markets of Gujarat are reputed for their transparency and neutrality. Auction
centers are balancing the interests of farmers and traders/processors ensuring complete
fairness. Infrastructure is also good at major APMCs. Government of Gujarat,
Department of Agriculture and Cooperation has stated that Gujarat has a clear
competitive advantage in many commodities on the basis of production and
productivity.20
Higher productivity, however, will not be translated into a proportionate
increase in the level of real income in an economy in which the distributive system is
inefficient. Hence, the economic need for an efficient operation set up is imperative. This
in turn necessitates capitalizing on developing efficient integrated network by identifying
and removing the bottlenecks of existing system, developing common user infrastructure
for value added activities, optimization of resource use, output management, widening of
markets, creating the opportunities for the growth of agro-based industry, promoting
exports and increasing market intelligence. This, in turn, helps existing APMCs to cope
with upcoming challenges pose because of amendment of APMC Acts with Model
APMC Act.
An efficient movement of farm products to consumer raises the income level of farmers
and promotes the economic development of the study area. Therefore, it is necessary to
identify and quantify the important factors affecting agriculture supply chain practices at
APMCs. So, the improvements can be directed towards those factors which are crucial in
managing supply chain effectively.
20
Department of Agriculture and Cooperation, Government of Gujarat, Gujarat Agro vision 2010: Action
Plan ,Gandhinagar, p. 8
10
However, a study of the existing agriculture supply chain is necessary to understand the
complexities involved and identification of bottlenecks with a view to providing efficient
services in the transfer of farm produce to consumers. From this study, potential supply
chain players can be identified and their functions, role and relationships in the trade
system can be delineated.
It is also imperative to study existing system to identify the key important variables
affecting the system. Researcher has taken this opportunity to study the existing system to
quantify the key important variables of the system.
Researcher has also made a noble attempt to throw the light on the integrated supply
chain management practices pursued by the wholesalers (Pacca Arhatiyas) at the selected
APMCs of North Gujarat region. Researcher has tries to delineate the barriers to the
supply chain process integration. Level of functional integration within the organisation
is also studies.
Researcher has also identified that the study of the agriculture supply chain management
practices of APMCs as a whole in India in general and in Gujarat; North Gujarat; in
particular, remains a neglected area, about which a concrete and rational understanding
has not been developed.
Past studies have given only a general description of prevailing marketing systems for
transaction of different commodities in distinct area, the price distribution at different
level of a marketing channel etc. No doubt, some sporadic attempts were made to discuss
and analyse the role of regulated market in transaction process of the farm products,
availability of infrastructure and so on (Joshi V.R, 197121
, Takur D.S.197422
, Ranade et
al, 198223
, Charan, A. S. et al., 198324
, Patel. S.K., 198325
, Bapan and Rao, 198726
,
21
V.R Joshi, Regulated Markets in Gujarat, (Vallabh Vidyanagar: Sardar patel University, Gujarat, 1971) 22
D. S. Thakur, “Foodgrain Marketing Efficiency: A Case Study of Gujarat”, Indian Journal of Agriculture
Economics, Vol. 29, No. 4, (Ocober-December, 1974). 23
C. G. Ranade, K. H. Rao and D. C. Shah, Groundnut Marketing: A study of Cooperative and Private
Trade, (CMA Monograph No. 92, Ahmedabad: Indian Institute of Management, 1982)
11
Acharya S. S., 198827
, Madaliya V. K., 198828
, Bhatt et al., 198829
, Arya A., 199330
,
Agarwal and Meena, 199731
, Guru S., 200232
).
All these study covers the following aspects:
• The studies are crop specific and area specific,
• Trends in market arrivals and price fluctuations in terms of seasonal and cyclical
variations,
• Focuses the attention on price spreads and marketing margins,
• Discusses the benefits of alternative marketing channels to the farmers,
• Describes composition of market committees, Market Regulation Act and the rules and
regulations of regulated markets and development into it,
• Examines the impact of marketing on cropping pattern and marketable surplus etc.
But all these efforts at academic as well as government levels are very much influenced
by an economist’s macro-level economic understanding of the problem. Thus most of
these studies have economic overtones and emphasis.
24
A.S. Charan., S. P. Seetharaman, S. L. Bapna, “Agriculture Marketing System in Gujarat: A
perspective”, (A paper read at Fifteenth Gujarat Economic Conference, Surat, October-November 1983),
(Photocopy). 25
S. K. Patel, “Problems of Tobacco Marketing in Kheda District”, (A paper read at the Fifteenth Gujarat
Economic Conference, Surat, October-November 1983), (Photocopy). 26
S. S. Acharya, Agricultural produceion, Marketing and Price Policy in India: A study of Pulses, (New
Delhi: Mittal Publishers, 1988). 27
S. L. Bapna and K. R. Rao, Supply and Price outlook of Crops: A study based on Pre-harvest Market
Information in Gujarat, (New Delhi: Oxford and IBH, 1987). 28
V. K. Madaliya, “Functioning of Regulated Markets at Surat and Its Impact”, Indian Journal of
Agricultural Marketing, Vol 2, No. 1, (June 1988). 29
B. D. Bhatt, K. L. Antaniasd, and R. L.Shiyani, “An Analysis of Arrivals and Prices of Important
Vegetable Crops in Ahmedabad Regulated Market in Gujarat State”, Indian Journal of Agricultural
Marketing, Vol. 1, No. 1, (June 1988), p.73. 30
Anita Arya, Agriculture Marketing in Gujarat, (New Delhi: Concept Publishing, 1993), pp.156 31
N. L. Agarwal and B. L. Meena, “Agricultural Marketing in India - Performance of Cumin Marketing in
Rajasthan”, Bihar Journal of Agricultural Marketing, Vol. 5, No. 3, (September - December, 1997), pp.
319-28. 32
Department of Agriculture & Cooperation, Ministry of Agriculture, Government of India, Shankarlal
Guru Report on Agricultural Marketing Reforms, (New Delhi, June 2002). Available on
www.indiabudget.nic.in.
12
This study of agriculture supply chain management practices at APMCs in geography
deals with micro-level spatial inquiry of agriculture markets. It takes into consideration
physical, socio-culture and legal etc. which affect markets and their different aspect. The
study helps the policy makers and management of APMCs to understand the key results
areas of their existing system and also facilitate in reviving and reconceptualising their
business model.
1.3 Agriculture Marketing in India: An Inner View
1.3.1 Introduction
Production and marketing of products are interdependent in the sense that products in the
field have no value unless they are converted into a consumable form and reach the
ultimate consumer at his convenience. Since the greater part of farm output in many
countries is not consumed by the people, who produce this, it must, like industrial
products, be sold to satisfy the consumers’ demand33
. There is an increasing awareness
that it is not enough to produce a crop; it must also be marketed.
Agricultural marketing involves in its simplest form the buying and selling of agricultural
product. This definition of agricultural marketing may be accepted in olden days, when
the village economy was more or less self-sufficient, when the marketing of agricultural
produce presented no difficulty, as the farmer sold his produce directly to the consumer
on a cash or barter basis. But, in modem times, marketing of agricultural produce is
different from that of olden days. In modem marketing, agricultural produce has to
undergo a series of transfers or exchanges from one hand to another before it finally
reaches the consumer.
33
R. Cohen, The Economics of Agriculture, (Cambridge Economic Handbook, Cambridge University
Press, 1965), p. 75.
13
Agricultural marketing includes the farmers’ transaction both buying and selling, but it is
generally confines to the selling side of his business and used to cover all activities
involved from the time when products leave the farm, till it reaches the consumer.
The term ‘agriculture marketing’ has varied connotations as understood by different
scholars.
According to Cohen (196534
), “In its essence, the marketing process is the mechanism for
fixing prices, just as a market is a place where buyers and sellers together arrive, by
bargaining, at the current price”.
Khols (196735
) has defined marketing as “the performance of all business activities
involved in the flow of goods and services from the point of initial agricultural
production until they are in the hands of ultimate consumer”.
According to Rajagopal (198936
), “marketing of agricultural produces is a process, which
starts with a decision to produce saleable farm commodities. It involves an integrated
market system, both functional and institutional based on techno-economic
considerations”.
Saxena (200337
) stated, “Marketing is concerned with the channels of distribution through
which goods move from producers to consumers. The entire process is performed at
places known as market centers which, like organism, rather active with functions,
behavioral pattern and growth process, contributing to a geometric pattern”.
34
R. Cohen, op. cit., p. 77. 35
R. L. Khols, Marketing of Agricultural produces, (3rd
ed.), (New York: The MacMillan Company), 1967,
p.1. 36
Rajagopal, State and Agriculture Trade, (Delhi: Renaissance Publishing House, 1989), p. 23. 37
P. Saxena, Marketing and Sustainable Development, (New Delhi: Rawat Publication, 2003), p. 10
14
According to the National Commission on Agriculture (1976 38
), “Agriculture Marketing
includes all aspects of market structure and system, both functional and institutional, pre
and post-harvest operations, assembling, grading, storage, transport and distribution”
The Indian council of Agricultural Research39
has defined it as involvement of three
important functions, namely (a) assembling (concentration) (b) preparation for
consumption (processing) and (c) distribution.
Subbarao (198940
) , is characterized the agriculture marketing as “task of assembling the
produce from widely scattered area from producers moving them to ultimate consumers,
performed by a chain of intermediaries through which the various agriculture
commodities pass and in the process, gain in the value due to change in time, pace and
ownership”.
It is apparent from the above definitions that the system of agriculture marketing is very
complex. It is a process whereby prices of agriculture commodities are determined, either
by the forces of demand and supply or by some other mechanism over space and time. To
the large extent the determination of these prices depends upon the availability of
marketing infrastructure between the point of production and point of consumption. The
process mostly involves assembling, grading, storage, transportation and distribution
activities apart from the pre and post-harvest operations. Such operations add value to the
produce in terms of time, place and farm utilities. It is also characterized as spatio-
temporal integration of numerous activities from production to consumption in a single
institutional network.
38
Department of Agriculture, Ministry of Agriculture and Irrigation, Government of India, National
Commission on Agriculture-Abridge Report, (New Delhi, 1976), p. 577 39
http://www.world-agriculture.com/agricultural_marketing/agricultural-marketing.php 40
K. Subbarao, Agriculture Marketing and Credit, Monograph 2, Research in Economics, Secondary
Survey, (New Delhi: Indian Council of Social Research, 1989), p. 1.
15
1.3.2 Agriculture Marketing in India
Agriculture marketing today exists in various stages in different parts of the word. In
developed countries production and marketing system has developed greatly, because of
availability of technology and well developed support services form sourcing to
destination. While in developing countries like India, the means of production is quasi-
mechanical and mode of production is for domestic sustenance. In addition to this, lack of
proper marketing services, un-graded and non-standardized commodities, poor and
unscientific packaging and method of transport, absence of public markets and
warehouses, unbalanced production, lack of market information, unfair practices of
middlemen and bad credit facilities are the most important causes of inefficient
marketing. The problem of in-adequate transport network storage and grading facilities
appear to be great hurdles to reach the product from surplus area and season of
production to ultimate consumers at right place and time within adequate quantity and
quality at reasonable price.
Although under a system of individual enterprise and freedom of individual choice as it
operates in agriculture, the marketing services should have been performed at a cheap
price, yet it seems that competition, in fact, is far from perfect. Marketing services bring
in imperfection in the market. Marketing agencies, do not affect consumer’s demand and
supply the produce, rather it is determined by the activities of farmers and agro-climatic
conditions. Therefore, middlemen do not determine retail prices for most kind of agro
products. But the margins taken by middlemen determine the net income of the farmers
which subsequently affects farm production and marketing41
. Consumer preferences are
not communicated to the producer in time and marketing system do not co-ordinate itself
with the fluctuations in supply42
.
With the development, marketing gradually becomes more complex operation than a
simple producer-consumer relationship. Various intermediaries come in between these
41
R. Cohen, op. cit., p. 88 42
K. R. Kulkarni, Agriculture Marketing in India, Vol. 1, 1956, pp. 2-12.
16
two extremes to facilitate operations. This complex system operates and functions in an
orderly society, which sets the rules and norms for the system43
. The effectiveness and
efficiency of the system operations depend on how these rules and norms are obeyed.
Producers are the people who feed into the chain and as such they are the people most
affected by its inefficiency.
In India, the view that agricultural markets are imperfect, exploitative and unhelpful to
the development of the agriculture has been gradually changing over the years. However,
the general belief that traders in these markets manipulate the process through
malpractices and reap excessive profits has not changed much. Contrary to such common
belief, research has found that most agricultural market are benefiting farmers and
contributes substantially to the economic development process (Jasdanwala, 196644
; Lele,
196845
; Bhinde et al., 198146
; Patnaik and Shankar, 198547
; Rajagopal, 198548
, Chatterjee
and Bhaattacharya, 198649
) However, it has been observed that agricultural commodity
market; though, it appears to be competitive, is however restrained by recurring
uncertainties. These uncertainties are related to the supply and demand of agricultural
commodities, especially in seasonal periods when supplies are not sufficiently available
to carryout necessary adjustments after demand changes are recognized. As a result, the
short run prices may be above or below the expected levels, which can’t be achieved
under pure competition. It is imperative that arrangement should exist for efficient
movement of the farmer’s produce to the consumers.
43
R. L. Kohl and N. U. Joseph, Marketing of Agricultural produces. (6th
Ed), (New York: Macmillan, 1985) 44
Z.Y. Jasdanwalla, Market Efficiency in Indian Agriculture, (New Delhi: Allied Publishers Pvt. Ltd, 1985) 45
U. J. Lele, Working of Grain Markets in Selected States of India 1955-56 to 1965-66, Occasional Paper
No. 12, (Ithaca: Cornell University, Department of Agriculture Economics, 1968). 46
S. Bhinde, A. Chowdhary, E.A.O Heady and M.A. Muralidharan, “Structural Changes in an Agricultural
Assembling Market: A case study of Arecanut Market in Mangalore, Karnataka State”, Indian Journal of
Agriculture Economics, Vol. 36, No. 2, (1981), pp. 25-34. 47
K. Patnaik and U. Shankar, “Economioc Performance of Groundnut Marketing Channels: A case study of
Rayalaseema Region of Andhra Pradesh”, Indian Journal of Agriculture Economics; Vol. 40, No. 1,
(1985), pp.26-35. 48
Rajagopal, “Economics of Linseed Marketing in Madhya Pradesh: A Case Study”, Agriculture Situation
in India, Vol. 20, (1985), pp. 50-60. 49
D. R. Chatterjee and K. Bhattacharya, “A note on Marketing of Rice in Burwada District of Best Bengal:
An enquiry of its Spatial and Seasonal Pricing Efficiency”, Indian Journal of Economics; Vol. 46, No. 2,
(1986), pp. 125-135.
17
Agricultural marketing in small and large towns as well as at village markets is in the
form of mandi and haats where the brokers or dalals helps the farmers to dispose of their
product to the wholesalers known as arhatiyas. But in most cases, the dalal is often in
collusion with arhatiyas and therefore the price which is settled is generally to the
advantage of the arhatiyas and not to the farmer. Even number of intermediaries and
middlemen between the farmer and final consumer are too many and margin going to
them is too large. In fact, there was a large chain of middlemen in the agriculture
marketing system like village traders, kutcha arhatiyas, pacca arhatiyas, brokers,
wholesaler, retailers, moneylenders etc. As a result, the share of farmers in the price of
agricultural produce was reduced substantially. A study by D.S. Sidhu (197450
) revealed
that farmers obtained only about 53 per cent of the price of their product, 31 per cent
being the share of middlemen and the remaining 16 per cent being the marketing cost.
Also farmers do not ordinarily get the information about the ruling prices in the big
markets. As a result the farmer have to accept whatever price quoted to them and have to
believe whatever the traders tell them. Adding to this he shouldn’t have the holding
capacity, in the sense, he is unable to wait for times when he could get better prices for
his product and not dispose of the stocks immediately after the harvest when prices are
very low due to the pressure of reducing his debt and also due to the lack of support
infrastructure.
In terms of farmers’ economic benefits from the operations of the marketing system, it is
essential that an efficient marketing system brought about by regulation through rules and
norms formulated by society. Where society itself is unable to ensure the operations of
these norms, government has to play a vital role51
. Government attention has been
focused on agricultural marketing reform since 189752
. However, the creation of an
orderly and efficient marketing system has received particular attention from various
50
Quoted in A.S. Kahlon and M.V. George, Agriculture and Price Policies (New Delhi, 1985), Table 4.1, p.
39 51
J. C. Abbott; “Marketing an accelerator of Economic Growth” in proceedings, Agriculture Marketing
Conference (MFA); (Nepal: Ministry of Food and Agriculture, Government of Nepal, June, 1972), pp. 15-
28. 52
V. R. Joshi, Regulated Markets in Gujarat, (Nadiad Kaira District Cooperative Union, 1971), p. 7.
18
state governments as well as central government since the inception of formal national
planning in 1951.
1.3.3 Agricultural Marketing through Regulated Market in India
1.3.3.1 Historical Perspective
Under ancient economy, characterized by isolation and self-sufficiency of the village, the
marketing of agricultural produce occupied an insignificant position. After the
improvement of irrigation facilities and farming techniques along with growing needs of
the village population, the commercialization of agriculture took place. This
commercialization has opened the scope for private trading channels, cooperatives and
regulated markets. But due to the ignorance, illiteracy and lack of enterprising ability, a
large group of peasants, owning small and marginal size farms, cannot strike a profitable
bargain in dealing with their farm products.
It was generally alleged that private trade, through its speculative activities, create
imperfections in the market and exploit the producers on one hand and consumer on
other.53
On this account, a plea for state intervention was usually made. It was for
regulating agriculture markets in particular.
The first attempt to regulate agricultural markets in India was made in 1897 (Joshi,
197154
). An act was passed called as ‘Berar Act (1897)’ which authorized the British
government, in Hyderabad, to declare any place within his jurisdiction a market for sale
and purchase of agricultural produce and constitute a committee to supervise and regulate
the markets55
.
53
U. Lele, Working of Grain Markets in States of India, USAID Research Project, Cornel University,
(1968). 54
V. R. Joshi, op.cit, p. 7 55
Cotton and Grain markets Act of Hyderabad assigned District, 1897 or so called Berar Law.
19
The law helped to improve buying and selling of cotton. But it suffered from one major
limitation, viz., the market committee consisted solely of traders and this tended to defeat
the declared objective of benefiting the producer-sellers. In practice, the law was applied
only to cotton, the main crop of region and did not include other commodities. Any net
income derived from the market was explicitly stipulated and would go to the local
municipal authority, instead of being spent back in the market for further development.
Thirty years later, with the passage of the Cotton Market Act, 1927 in Bombay, once
again, the law was concerned only with a single crop. However, an important departure
from the Berar Law occurred in Bombay (1927) by giving the due representation to
growers in market committee of the concerned markets.
The Royal Commission on Agriculture (1928), reporting a year later, urged that all
provinces should establish regulated markets to help orderly marketing of all agricultural
produce. It deprecated the practice of treating regulated markets as a source of municipal
revenue and instead on that the revenues and any surplus income generated through the
regulated markets must be used solely to develop and improve the facilities and services
for the benefits of the producers in the markets. Hyderabad Central Province and Madras
promptly acted on the Royal Commission’s recommendations and passed appropriate
legislation in 193056
. Other followed after a long interval: The Bombay Agriculture
Produce market Act was passed in 1939, Punjab and Mysore introduced the act in 1939,
though this act was not operative until 1948. The outbreak of second world-war checked
the progress of Market regulation activities in India. At the end of 1940, there were 135
Regulated Markets in India, which increases to 286 by 195057
. Madhya Pradesh
implemented this act in 1953, Gujarat in 1954 and Orrissa in 1957. At the beginning of
the Third Plan (1961) the act was introduced and implemented in nine states. Four more
states enacted the Agricultural Produce Markets Acts by 1968, and remaining states, viz.,
56
Roul Chhabilendra, Bitter to Better Harvest: Post Green Revolution Agriculture and marketing Strategy
for India, (New Delhi: Northern Bloc Centre, 2001), p.135. 57
N. Sakthivel and A. Selvaraj, “Farmers’ Perception towards Regulated Markets: A Case Analysis”,
Financing Agriculture-Journal of Agriculture & Rural Development, Vol.41, (2009), p.7
20
Assam, Nagaland, Keral and Jammu & Kashmir did so during the Fourth Pan Period
(1969)58
.
The regulated markets established in different states seem much similar today, both in
law and in actual practice. This is largely due to the fact that all state laws of regulated
markets are on same model bill prepared by the central government in 1938, so called
‘Bombay Agriculture Act, 1939’. But actual growth of regulated markets and their
geographical distribution are highly uneven. They are well developed in Maharashtra and
Gujarat followed by Punjab and Madhya Pradesh. Another significant fact about
regulated markets is their heavy concentration in cotton growing states. Eighty per cent of
a total of 1,000 regulated markets in India were located in the five western states,
although together they accounted for only thirty per cent population of India before
196459
. Thus, despite the expostulation of Royal Commission of 1928, the progress made
with regulated markets in the intervening decades had been slow. They are still fully
inadequate in coverage. They are largely confines to cotton and do not embrace other
agricultural produce. Three decades back very few regulated markets were seen in Uttar
Pradesh, west Bengal and Assam60
.
It was realized that the market regulation was not enough to attract the traders and
farmers to take up full advantage of regulated markets. It was because of lack of
sufficient infrastructure. A central sector scheme was initiated for the development of
selected regulated markets in the year 1972-73. The provision during the year 1985-86
was INR 403 lacs for assisting 30 selected regulated markets and 10 terminal markets.61
In India, organized marketing of agricultural commodities has been promoted through a
network of regulated markets. Most state governments and Union Territories (UT)
58
Government of India, Ministry of Agriculture and Rural Development, “Activities of Directorate of
Marketing and Inspection”, 1985, pp. 2-3. 59
Rajagopal, “Development of Agricultural Marketing in India”, In Jagdish Prasad (Ed.), Encyclopedia of
Agricultural Marketing: Concept, Issue, Problems & Prospects, (New Delhi: Mittal Publications, 1999), I,
p. 68. 60
M. Hoda, Agriculture Marketing in Backward Regions, (New Delhi: Rajat Publication, 2006) P. 65. 61
Ibid
21
administrations have enacted legislations to provide for the regulation of agricultural
product markets. While by the end of 1950, there were 286 regulated markets in the
country, their number as on today stood at 7566. In addition, India has 21780 rural
periodical markets, about 15 per cent of which function under the ambit of regulation62
.
The advent of regulated markets has helped in mitigating the market handicaps of
producers/sellers at the wholesale assembling level. But the rural periodic markets in
general and the tribal markets in particular, remained out of its developmental ambit.
1.3.3.2 Regulated Marketing System and the Five Year Plans
During the First Five Year plan (1951-56) period, the regulated markets were established
in Maharashtra, Madras, Punjab, Hyderabad, Mysore and Madhya Pradesh. The
management of these markets was vested in the hand of committees in which there was
participation of growers as well. Apart from the regulations of agricultural produce
markets, the main thrust was laid on the development of cooperative marketing linked
with production, finance and cooperative ownership of processing industries. It will be a
useful instrument for increasing productions, costs, and introducing a system of crop
planning63
.
The primary consideration for the development of agricultural marketing in Second Five
Year Plan (1956-61) was to recognize the existing system so as to protect the farmers’
due shares of consumers’ price of different agricultural commodities. The plan also
stressed on the enactment of Agricultural Produce Marketing Act in the states not
covered during First Plan period, including grading and standardizing of farm products64
.
The total number of agricultural produce markets in the country at the end of Second Plan
was about 2500; out of these 725 were regulated markets as compared to 425 in the First
Plan. The Third Five Year Plan (1961-66) proposed to bring the remaining markets under
62
Department of Agriculture and Cooperation, Ministry of Agriculture, Government of India, Annual
Report, 2006-07[Agricultural Marketing] (New Delhi: The Manager of Publications, 2007), p.1. Available
on http://agricoop.nic.in/AnnualReport06-07/AGRICULTURAL%20MARKETING.pdf 63
Government of India, Planning Commission, “First Five Year Plan”, pp. 243-244. 64
Government of India, Planning Commission, “Second Five Year Plan”, pp. 276-281.
22
regulation and to expand the programme for grading the commodities. Third Plan was
also devoted towards the cooperative marketing65
.
By and large, the Fourth Five Year Plan (1969-74) aimed at improving the agriculture
marketing system in the interest of producers. The objective was to see the imperfections
in the marketing system and to overcome the constraints. During this plan period, 1,300
additional markets were proposed to be covered. The development of the infrastructure
was identified as one of the major task to be carried and the roads, market yards, grading
units including other common amenities were stratified for immediate attention66
.
During the Fifth Five Year Plan (1974-79), the development of agricultural marketing
was planned through the ways and means of co-operatives. The Plan was focused on the
setting-up of various cooperative marketing unions for the commodities and also of
boards to regulate the trading system of the cash crops67
.
The main thrust of the Sixth Five Year Plan (1980-85) was therefore on (a) further
expansion of regulated marketing system in terms of increasing number of markets and
commodities to be brought within the scope of regulation, (b) strengthening and
streamlining the arrangements of enforcement/inspections to ensure a regulated system of
open auction, trading practices and intermediaries, and (c) development of rural and
periodical markets68
.During this plan period, the progress and development of markets
was intensified with the emphasis on survey research and grading of notified
commodities.
The main emphasis during the Seventh Five Year Plan period (1985-90) was towards
further expansion of regulated markets, both in terms of area and coverage. Provision of
certain facilities was also acknowledged like grading centers at the producers level,
intensive surveys to asses the marketable surplus and post-harvest losses and
65
Government of India, Planning Commission, “Third Five Year Plan”, p. 321. 66
Government of India, Planning Commission, “Fourth Five Year Plan”, pp. pp. 142-143. 67
Government of India, Planning Commission, “Fifth Five Year Plan, part II”, pp.83-91. 68
Government of India, Planning Commission, “Sixth Five Year Plan”, p.112.
23
strengthening of various organizations in the states as well as centers for meeting the
rising requirements of training of market functionaries69
.
The document of Eighth Five Year Plan (1992-97) was focused on strengthening of
market infrastructure with special reference to perishable commodities. It was one of the
major pre-requisites for the success of diversified efforts and enabling primary producers
realize a fair share of price in consumer’ rupees. The plan document endorses the need of
developing marketing linkages within and outside the country to promote diversification.
The role of cooperatives in setting of new horizons for domestic marketing was also
argued in the Eight Plan. The commercialization of farming system and new seed policy
for promoting horticultural commodities had made considerable impact on agri-business
in the recent past70
.
Ninth Plan (1997-2002) evaluated that the infrastructure had not kept pace with
accelerated growth of agricultural production in the country. This resulted in significant
post-harvest losses of agricultural produce. The central government provided assistance
for creation of infrastructural facilities for marketing and for setting up rural godowns.
During this period emphasis had been given to develop marketing infrastructure at
panchayat level71
.
During the Tenth Plan (2002-2007) it has been found that marketing system is dominated
by traders. Appropriate and effective linkages between the producers and sellers continue
to be weak. The absence of rural connectivity and other infrastructure, combined with
improper management, lack of market intelligence and inadequate credit support has
resulted in a system that is unfavorable to the framers.
The basic objective of setting up a network of markets is to ensure reasonable profits to
the farmers by creating a conductive environment for the free and fair play of supply and
demand forces to regulate market practices and ensure transparency into the system.
69
Government of India, Planning Commission, “Seventh Five Year Plan, Part II”, p. 20. 70
Government of India, Planning Commission, “Eighth Five Year Plan, Part II”, pp. 11-12. 71
Government of India, Planning Commission, “Ninth Five Year Plan, Part II”, p. 450.
24
Apart from dealing with current imperfection and shortcomings the government has
recognized the importance of liberalizing agriculture markets in the wake of inception of
World Trade Organization (WTO)72
.
1.3.3.3 New Thinking on Regulated Markets
To protect and promote the interest of the farmers by eliminating unhealthy market
practices, the Government of India, as well as State Governments, in line with the
Bombay Agriculture Act, 1939 had passed the legislation known as State Agriculture
Produce Marketing (Regulation) Act to promote organized marketing of agriculture
commodities through a network of regulated markets.
Under this act, only agriculture production was largely free from controls, the same was
not true of marketing and processing of agricultural commodities. Hence, over a period of
time, these markets have acquired the status of restrictive and monopolistic markets,
providing no help in direct and free marketing, organised retailing and smooth raw
material supplies to agro-industries. Addition to this, the State Governments alone was
empowered to initiate the process of setting up of markets for agricultural commodities in
notified areas. Exporters, processors and retail chain operators cannot procure directly
from the farmers as the produce is required to be channelised through regulated markets
and licensed traders. There is, in the process, an enormous increase in the cost of
marketing and farmers end up getting a low price for their produce. Post-harvest losses
are estimated to be of the order of 5-7 per cent in food grains and 25-30 per cent in the
case of fruits and vegetables73
. Processed foods derived from agricultural commodities
suffer from multiple taxes at various stages starting from the harvest to the sale of final
processed products. There were stringent controls on the storage and movement of
several agricultural commodities. Monopolistic practices and modalities of the state-
controlled markets have prevented private investment in the sector. In the present
72
Government of India, Planning Commission, “Tenth Five Year Plan, Part II”, pp. 550-551. 73
Department of Agriculture and Cooperation, Ministry of Agriculture, Government of India, Annual
Report, 2006-07[Agricultural Marketing] (New Delhi: The Manager of Publications, 2007), p.1. Available
on http://agricoop.nic.in/AnnualReport06-07/AGRICULTURAL%20MARKETING.pdf
25
situation these restrictions are acting a disincentive to farmers, trade and industries. It
also restricts the private sector initiative in setting up markets equipped with best
facilities.
To make the present marketing system more effective and efficient by removing
unnecessary restrictions and by establishing a sound framework to reduce uncertainty of
the markets, legal reforms were a need of the hour. In this context, Expert Committee on
Agriculture Marketing (Guru Committee, 2001)74
, Ministry of Agriculture, Government
of India has review the present system of agricultural marketing in the country and
recommended measures to make the system more efficient and competitive. The
Committee in its report has suggested various legislative reforms as well as the
reorientation of the policies and programs for development and strengthening of
agricultural marketing in the country.
The Inter-Ministerial Task force on Marketing Reforms (2002)75
, Ministry of Agriculture,
Government of India; with a view to examine the findings and recommendations of the
Expert Committee and to suggest measures to implement them, has thereupon identified
nine priority areas to work out a road map for strengthening the agricultural marketing
system in the country. The areas identified are a) Legal reforms; b) Direct marketing; c)
Market infrastructure; d) Pledge financing; e) Warehousing receipt system; f) Forward
and futures markets; g) Price support policy; h) IT in agricultural marketing and i)
Marketing extension, Training and Research.
It is not easy to bring major changes in the traditional marketing system. The only way to
modernize marketing is to promote alternative marketing systems by encouraging the
private investment and that may operate parallel to and in addition to present marketing
system. Massive investments required for development of alternative marketing
infrastructure and supporting services, provisions of the APMC Act would need
74
Government of India, Ministry of Agriculture, Report of Task Force on Agriculture Marketing Reforms,
(2001), p. 2 75
Government of India, Ministry of Agriculture, op.cit, pp. 3-32
26
modification to create a lawful role for the private sector in market development.76
Government’s role should be that of a facilitator rather than that of having control over
the management of markets.
The purpose of the proposed alternative marketing is to promote modern trade practices,
which in turn will pave way for transparency and efficiency in market. Even though, the
various forms of alternate marketing like (a) direct marketing, (b) marketing through
farmers interest group, (c) setting up of terminal markets, (d) forward and future market,
(e) e-commerce, (f) setting up of mega markets, (g) negotiable warehouse receipt system
etc. have been suggested by Expert Committee on Agricultural Marketing.
State Agricultural Product Marketing Regulations Act (APMC Act) and the Essential
Commodities Act (EC Act) are the two important legislations that have to be amended to
remove restrictive provisions coming in the way of an efficient and competitive
marketing system.77
Alongside, there is a need to introduce through appropriate legal
change, a ‘negotiable warehousing receipt system’ in the country for agricultural
commodities to enhance institutional lending to the agricultural marketing sector and to
improve price-risk management.
With the same regard, the Department of Agriculture and Cooperation, Ministry of
Agriculture, Government of India has formulated a model Act on agricultural marketing
in order to assist the States in drafting a suitable law for the removal of barriers, whether
legal or policy induced, which introduce inefficiencies and monopoly trends in the
functioning of agricultural markets. The State Governments and Union Territories (UTs)
are being persuaded to change the restrictive provisions of the law dealing with
agricultural markets in line with the provisions of this model Act in order to facilitate
private sector investments for the development of post-harvest and cold-chain
infrastructure close to the farmers’ fields and to establish effective linkage between farm
production and the retail chains including the food processing industry. The progress of
76
Government of India, Ministry of Agriculture, op.cit, p. 4 77
Ibid
27
reforms in agricultural markets as on 31 November, 2009 is briefly indicated above in
table 1.2.
Table 1.2 Reforms in Agricultural Markets (APMC Act) as on 30.11.2009
Sr.
No.
Stage of Reforms
Name of States/ Union Territories
1. States/ UTs where reforms to APMC
Act has been done for Direct
Marketing; Contract Farming and
Markets in Private/ Coop Sectors
Andhra Pradesh, Arunachal Pradesh,
Assam, Chhattisgarh, Goa, Gujarat,
Himachal Pradesh, Jharkhand,
Karnataka, Madhya Pradesh,
Maharashtra, Nagaland, Orissa,
Rajasthan, Sikkim and Tripura.
2. States/ UTs where reforms to APMC
Act has been done partially
a) Direct Marketing:
NCT of Delhi.
b) Contract Farming:
Haryana, Punjab and Chandigarh.
c) Private markets
Punjab and Chandigarh
3. States/ UTs where there is no APMC
Act and hence not requiring reforms
Bihar*, Kerala, Manipur, Andaman &
Nicobar Islands, Dadra & Nagar
Haveli, Daman & Diu, and
Lakshadweep.
4. States/ UTs where APMC Act already
provides for the reforms
Tamil Nadu
5. States/ UTs where administrative
action is initiated for the reforms
Mizoram, Meghalaya, Haryana, J&K,
Uttrakhand, West Bengal, Pondicherry,
NCT of Delhi and Uttar Pradesh. Source: http://agmarknet.nic.in/amrscheme/apmcstatus08.htm
* APMC Act is repealed w.e.f. 1.9.2006.
1.3.4 APMCs in Gujarat
Gujarat, a major industrial state, also has great potential to develop a vibrant agrarian
economy through agro-industrialization by deriving competitive advantages from its
unique position in the world for many commodities e.g. castor, fennel, cotton, tobacco,
groundnut, sesame, chikus, onion, bananas, isabgul (psyllium), guarseed and cumin78
.
Moreover, these agricultural products are produce in certain geographical clusters which
78
Gujarat Agriculture-A Synoptic View, Reading material for “Training Program on Agri-clinic and Agri-
Business Centers”, (EDI Gandhinagar/MANAGE, August 2004), p. 8.
28
makes these clusters prima facie suitable for setting up of common user infrastructure
facilities. Despite many strength and well-developed infrastructure facilities, there is
enough scope for developing and upgrading agriculture infrastructure in the area of
specialized storage facilities, primary and secondary transportation, mechanization,
grading standards, export promotion, processing industry support and market intelligence
etc79
.
The Gujarat State was carved out as a separate state on May 1st, 1960 under the Gujarat
Organisation Act 1960 with a total area of 195,024 sq. kms. Its population is about 5.10
crores of which percentage of rural population is about 70 percent. Nearly 65 lacs farmers
and farm workers are engaged in agricultural production which forms about 55 percent of
total work force in the state. The state is divided in to 25 districts with 225 talukas,
having 18,539 villages and 242 towns.
When Gujarat State came into existence, Bombay Agricultural produce Act of 1939 and
Saurashtra Agriculture Produce Market Act, 1955 were in force in respective areas. No
regulation was in force in Kutch. State Government experienced difficulties in
implementing two different Acts and there was no Act for Kutch Area. Moreover, some
of the provisions of the Act were challenged in the court. Therefore, it was necessary on
the part of the state government to remove these difficulties and to cover the whole state
of Gujarat under one act. As a result, Gujarat government appointed a committee, under
the chairmanship of Jaswantlal Shah to make a comprehensive study of the problem. On
the basis of the recommendations of the committee, present Agricultural produce
Marketing Act came into force from June 1st, 1964. Under this Act the rules and
regulations for the Agricultural Produce Markets were came into the force w.e.f
September 2nd
, 196580
.
79
Gujarat Agriculture-A Synoptic View, op.cit, p. 28 80
Department of Agriculture and Cooperation, Government of Gujarat, Regulation of Agricultural Produce
Market and Market Committee in Gujarat State: Annual Report, (Gandhinagar: Agriculture Marketing
Board, 2009), p.3.
29
The market area consists of a taluka and the market proper consists of villages within a
radius of 10 to 15 km of the market yard covering average 486.34 sq.kms of area by each
market yard in Gujarat.
In market proper all notified commodities are legally required to be brought to the market
yard and could be sold there only. License fees for traders, commission agents and other
market functionaries such as brokers, carting agents, weigh men; hamals etc. are
determined by the market committees under the bye-laws subject to minimum and
maximum prescribed limit under the Gujarat Agricultural Produce Markets Rules 1965.
The market committee has producers, representatives of traders holding general license
situated in market area, representatives of cooperatives marketing societies holding
general license situated in market area, government nominees, and nominee of local
authority in its management. The composition of Market Committee in Gujarat State for
each market is as under:81
Table 1.3 Composition of Market Committee
Constituency Number of members
1. Growers/Producers
2. Representative of traders
3. Local Bodies
4. Nominated from Cooperative bodies
5. Appointed or Nominated by Government
8
4
1
2
2
Total 17
Committee of 17 members is headed by the Chairman and the Vice Chairman. The term
of the committee is three years. The Secretary appointed by the committee executes the
market and responsible for the smooth functioning of the market yard.
81
Department of Agriculture and Cooperation, Government of Gujarat, op. cit. p.2
30
The marketing committee is supposed to maintain and manage market, prevent
adulteration, promote grading and standardization and implement the provisions of the
act, rules, by-laws and the conditions of licences. The market committee has the status of
local authority within the meaning of Bombay General Clauses Act 1904 and is given
power to sanction its budget, appoint its staff except secretary, issue licenses and levy and
collect market fees within prescribed limit.
Functions of Market Committee
• Grant, renew, refuse, suspend or cancel license.
• Provide necessary facilities for marketing of agricultural produce within the market.
• Regulate and supervise the auctions of notified agricultural produce in accordance
with the rules and the bye-laws.
• Regulate the entry of persons and of vehicular traffic into the market.
• Supervise the behavior of those who enter the market for transacting business.
• Maintain and manage the markets, including admissions to, and conditions for the use
of markets.
• Regulate the making, carrying out and enforcement or cancellation of sales,
weighment, delivery and payment to be made thereof.
• Promote and organize grading and standardization of the agricultural produce.
• Take measures for the prevention of purchases and sales below the minimum support
prices as fixed by the government from time to time.
• Collect, maintain, disseminate and supply information in respect of production, sale,
storage, processing, prices and movement of notified agricultural produce including
information relating to crops statistics and marketing intelligence.
• Carry out publicity about the benefits of regulation, system of transactions, facilities
provided in the market area etc.
• Facilitate for settling of disputes arising out of any kind of transactions connected
with the marketing of agricultural produce.
• Receive charges, fees, rates and other sum or money to which the Market Committee
is entitled.
31
• Inspect and verify the books of accounts and other documents maintained by the
licensees.
• Provide storage and warehousing facilities in the market area.
Gujarat stands at the top level in terms of strength and development of APMCs in India
with 401 (Principal and Submarket Yards) successfully managed mandis at the end of the
financial year 2009. There are 207 market committees, 195 Principal Market-Yards and
206 Submarket Yards located at most of the taluka places (225 talukas in Gujarat State)
and other large trading centers. Out of which 40 market committees are in the backward
tribal areas of the state. Trading of notified commodities takes place at the respective
centers under regulation of the Agriculture Produce Marketing Act, 196382
. The average
coverage of regulated markets in Gujarat is 47 villages per market yard. District wise
number of market committees, principal market-yards and sub-yards in Gujarat state is
given in the table 1.4 below.
Junagadh district has the maximum number of Agricultural produce Market Committees
(14) and has 13 main yards and 2 sub-yards followed by the Sabarkantha district with 13
APMCs, 13 main yards and 15 sub-yards. While in Dang district only one market
committee runs a main market yard at Vaghai. In Porbandar district there are total 3
market committees but only one market yard is operational.
Under the market Act, 101 commodities of agricultural, horticultural produce etc. have
been covered under market regulation. The food grains in 146 market committees, pulses
in 136, cotton in 126, oilseeds in 131, fruits in 7 and vegetables in 76, cattle sheep/goats
in 54 and condiments in 77 market committees are brought under regulation.83
82
Department of Agriculture and Cooperation, Government of Gujarat, op. cit., p. 5. 83
Department of Agriculture and Cooperation, Government of Gujarat, op. cit., p. 7.
32
Table 1.4 District wise details of Market Committees, Principal Market-
Yards and Sub-Yards
Sr. No Name of District No. of APMCs No. of Principal
Market-yards
No. of Sub-yards
1 Ahmedabad 8 8 12
2 Gandhinagar 4 4 8
3 Mehsana 8 8 12
4 Patan 7 7 1
5 Sabarkantha 13 13 15
6 Banaskantha 12 12 12
7 Panchmahal 10 10 17
8 Dahod 6 6 8
9 Kheda 10 10 12
10 Anand 8 7 8
11 Vadodara 12 12 19
12 Bharuch 7 7 14
13 Narmada 4 3 4
14 Surat 8 8 14
15 Valsad 5 3 12
16 Navsari 4 4 7
17 Dang 1 1 0
18 Rajkot 10 9 9
19 Jamnagar 9 9 0
20 Surendranagar 10 10 1
21 Bhavnagar 10 9 5
22 Amareli 11 10 2
23 Junagadh 14 13 2
24 Porbandar 3 1 0
25 Kutch 8 7 1
Total 207 195 206
Source: Regulation of Agricultural Produce Market Committee in Gujarat State, Annual Report,
Agriculture Marketing Board, 2009, P. 6
33
Most of the APMCs in Gujarat enjoy the leading positions in trading of particular
commodity viz. Sidhhpur for Castor, Ahmedabad and Surat for Fruits and Vegetables,
Unjha for Cumin, Isabgul and Fennel. Rajkot and Gondal for Groundnut, Bodeli and
Bavla for Cotton, Dahod for Grains and Pulses and name a few. These market
committees become inter-state market committees where agricultural produce from other
distant districts of neighboring states is also brought for sale. Table 1.5 shows the details
of total market arrivals and its value in all the APMCs of Gujarat state for the last five
years indicates the steady progress of volume in market yards of Gujarat state.
Table 1.5 Total Market Arrivals and its Value in all APMCs of Gujarat
Sr. No. Year Volume (in Lac Kg) Value (In Rs. Crore)
1 2004-05 138665 9376.41
2 2005-06 125897 10770.01
3 2006-07 115253 12411.35
4 2007-08 114485 13270.27
5 2008-09 168752 16989.97
Source: Regulation of Agricultural Produce Market Committee in Gujarat State, Annual Report,
Agriculture Marketing Board, 2009, P. 10
North Gujarat is situated in the northern part of the Gujarat State constituted by four
districts; Banaskantha, Mehsana, Patan and Sabarkantha; having 40 talukas covering total
of 28260.20 sq.kms area. Out of which almost 20100.44 hectors of land is arable. Table
1.6 provides the details of district wise total area, area of arable land and number of
talukas in all the districts.
Table 1.6 District wise details of Total Area, Total Arable Land,
Number of Talukas and Villages.
District
Total Area
(in Sq.kms)
Arable Land
(in hectors)
No. of
Taluka
No. of
Villages
Mehsana 4,393 368,371 9 604
Patan 5667.55 459,488 7 517
Banaskantha 10,400.16 744,087 12 1,250
Sabarkantha 7,390 438,098 13 1,389
Total 27,851 2,010,044 40 3,760
Source: http://www.vibrantgujarat.com/district-profiles/district-profiles.aspx
34
Banaskantha is the largest district in North-Gujarat and second largest in entire Gujarat
state having total area of 10,400.16 sq. kms. The average area covered by each market
yards is 433.34 sq. kms., which is lower than the state average of 486 sq. kms and
average number of villages covered by each market yard is almost 53 which is higher
than state average of 47 villages. Similarly, in Sabarkantha district the average area
covered by each market yard is almost 264 sq. kms and average villages covered in is
almost 50 per market yards.
Mehsana and Patan are relatively small districts compared to other two. In Mehsana
district the average area served by each market-yard is 219.65 sq. kms. and average
number of villages covered is 30 per market yard which is significantly lower than the
state average. Oppositely, in Patan district the average area covered by each market yard
is 708.44 sq.kms and average villages covered is 64.25 per market yards. Both are
significantly higher than the state average.
The major commodities produced in this region are Oilseeds, Castor Seeds, Isabgul,
Cumin, Fennel, Potatoes, Cotton, Groundnut and Tobacco contributing major agriculture
share in the state economy.84
Table 1.7 provides the district wise details of market
committees, market yards and sub-yards under particular market committee, area of
market and major commodities traded.
Table 1.7 District wise details of Market Committees, Market Yards and
Commodity Traded.
District Name of
Market
Committee
and Place
Name of Main and
Sub-yards
Area of
Market
Major Commodities
traded
Mehsana Mehsana
1) Main Market Yard
2) Vegetable Sub-yard
3) Jotana
4) Jornang
Mehsana
Taluka
Grains, Pulses, Cotton,
Oilseeds, Vegetables
etc
84
http://www.vibrantgujarat.com/district-profiles/district-profiles.aspx
35
Unjha
1) Unjha Market Yard Unjha
Taluka
Grains, Pulses,
Oilseeds, Cumin,
Fennel, Isabgul etc.
Kadi
1) Kadi Market Yard
2) Ludasan Kadi Taluka
Grains, Pulses,
Oilseeds, Cotton,
Vegetables, Spices
Vadnagar
1) Vadnagar Market
Yard
2) Kheralu
3) Satlasana
Vadnagar
Taluka
Grains, Pulses,
Oilseeds, Spices,
Paddy,
Rice, Vegetables,
Cotton, Condiments
Vijapur
1) Sardar Patel Market
2) Vegetable Market
3) Ladol
4) Kukarwada
5) Gozaria
Vijapur
Taluka
Grains, Pulses,
Oilseeds, Cotton
Visnagar 1) Visnagar Market
Yard
Visnagar
Taluka
Grains, Pulses,
Vegetables, Cotton,
Unava
1) Unava Market Yard Unjha
Taluka
Cotton, Tobacco,
Oilseeds, Grains,
Pulses, Spices
Becharaji 1) Becharaji Market
Yard
Becharaji
Taluka
Grains, Pulses, Cotton,
Oilseeds, Cattle
Patan
Patan
1) Sardar Gang
2) Vegetable Sub-yard Patan
Taluka
Grains, Pulses,
Oilseeds, Cotton,
Vegetables, Spices
Harij
1) Harij Market Yard
Harij Taluka
Grains, Pulses,
Oilseeds, Cotton,
Spices, Condiments
Radhanpur 1) Radhanpur Market
Yard
Radhanpur
Taluka
Grains, Pulses,
Oilseeds, Cotton
Siddhpur
1) Siddhpur Market
Yard Siddhpur
Taluka
Grains, Pulses,
Oilseeds, Cotton,
Vegetables, Spices
Sami 1) Sami Market Yard
Sami Taluka Grains, Pulses,
Oilseeds, Cattle
Varahi 1) Sanatalpur Market
Yard -
Grains, Pulses,
Oilseeds
Chanasma 1) Chanasma Market
Yard
Chanasam
Taluka
Grains, Pulses,
Oilseeds, Cotton
36
Banaskantha
Palanpur 1) Sardar Vallabhbhai
Patel Market yard
Palanpur
Taluka
Grains, Pulses,
Oilseeds, Spices
Deesa
1) Shantilal Shah
Market Yard
2) Morarji Desai
Marekt
3) Mahatma Gandhi
Market – Lakhani
4) Cattle Market-
Deesa
Deesa
Taluka
Grains, Pulses,
Oilseeds, Vegetables,
Spices, Cattle
Dhanera
1) Dhanera Market
Yard
2) Samarvada
3) Vegetable Market
Dhanera
Taluka
Grains, Pulses,
Oilseeds, Vegetables,
Spices, Cattle
Panthawada 1) Panthawada Market
Yard
Panthawada
Taluka
Grains, Pulses,
Oilseeds, Vegetables,
Bhabhar
1) Bhabhar Market
Yard
2) Pabadi Sub-yard
3) Diyodar Sub-yard
Bhabhar &
Diyodar
Taluka
Grains, Pulses,
Oilseeds, Vegetables,
Cattle
Thara
1) Sardar Krushi Gang
2) Sardar Market-
Sihori
Kankarej
Taluka
Grains, Pulses,
Oilseeds, Vegetables,
Spices, Cattle
Tharad 1) New Market Yard
2) Rah Sub-yard
Tharad
Taluka
Grains, Pulses,
Oilseeds, Spices,
Vadgam - Val Taluka -
Sabarkantha
Himmatnagar
1) Himmatnagar
Market Yard
2) F&V Sub-yard
3) Ganbhoi Sub-yard
Himmatnag
ar Taluka
Grains, Pulses,
Oilseeds, Vegetables,
Cotton, Fruits
Bayad
1) Bayad Market Yard
2) Demoi Sub-yard
3) Sathamba
4) Gabat
5) Tenpur
6) Akrund
7) Amodara
Bayad
Taluka
Grains, Pulses,
Oilseeds, Vegetables,
Cotton, Fruits
Idar
1) Idar Market Yard
2) Jadar
3) Desotar
Idar Taluka
Grains, Pulses,
Oilseeds, Vegetables,
Fruits, Condiments,
37
Vadali
1) Vadali Market
Yard Vadali
Taluka
Grains, Pulses,
Oilseeds, Cattle,
Condiments
Khedbrahma
1) Khedbrahma
Market Yard
2) Posina,
3-Lambadia
Khedbrahma
Taluka
Grains, Pulses,
Oilseeds, Cotton
Malpur 1) Malpur Market
Yard
Malpur
Taluka
Grains, Pulses,
Oilseeds, Cotton
Modasa
1) Modasa Market
Yard, Tintoi Modasa
Taluka
Grains, Pulses,
Oilseeds, Cotton,
Vegetables
Dhansura
1) Dhansura Market
Yard Dhansura
Taluka
Grains, Pulses,
Oilseeds, Cotton,
Paddy
Meghraj
1) Megharaj Market
Yard
2) Rellawad Sub-yard
Megharaj
Taluka
Grains, Pulses,
Oilseeds, Cotton
Bhiloda
1) Bhiloda Market
Yard Bhiloda
Taluka
Grains, Pulses,
Oilseeds, Cattle &
Cattle feed
Talod 1) Talod Market Yard
2) Harsol
Talod
Taluka
Grains, Pulses,
Oilseeds, Vegetables
Prantij
1) Prantij Market
Yard
2) Salal
Prantij
Taluka
Grains, Pulses,
Oilseeds, Vegetables
Vijaynagar - Vijaynagar
Taluka
-
Source: Association of Regulated Markets in Gujarat, Ahmedabad, 58th
Annual Report, 2009
1.4 Outline of the thesis
This thesis contains five chapters and a bibliography. The thesis is divided into two major
sections. First section deals with the literature review on Agriculture Marketing System
through regulated markets and Agriculture Supply Chain Management. The second part
covers the empirical research analysis on Agriculture Supply Chain Management
Practices in selected APMCs in North Gujarat region of Gujarat state.
38
Chapter 1 describes background of the research and need of the study. This chapter also
explains concepts of agricultural marketing and its system, historical development of
agricultural marketing system in India and Gujarat and growth of regulated markets in
last ten five years planning of Indian economy. Chapter also includes the details about the
number of Agricultural Produce Market Committees (APMCs), main market yards and
sub market yards in Gujarat, its functions, constitution of committee and total arrivals and
transaction value of all commodities in all the market yards of the Gujarat. Details of
APMCs in North Gujarat regions are also a part of this chapter.
The aim of Chapter 2 is to develop an understanding of the Supply Chain Management
(SCM) concepts in general and Agriculture Supply Chain (ASCM) in particular.
Therefore, it begins with a discussion of what is supply chain, its historical development,
its importance followed by the literature review and discussion on Agriculture Supply
Chain Management, and challenges of managing supply chain practices in agriculture
sectors. The chapter also includes a presentation of working model of the market-yard
regulated by the Agricultural Produce Market Committee (APMC) which are established
and regulated under the State Agriculture Produce Market Committee Acts (State APMC
Acts), types of chain intermediaries and their role, lacunas of existing system etc. Chapter
is concluded with the explanation of need for the collaboration and integrated supply
chain management practices in Indian agricultural sector.
The methodological approach adopted for this research is presented in Chapter 3. The
initial part of the chapter talks about the main objective of the study followed by specific
objectives of the research. Scope of the chapter is included in the chapter. Exploratory
research design is used. The research design includes explanation about population about
which study is conducted, sampling techniques, sampling unit, sample and sample size,
data collection procedures, different sources of data – primary as well as secondary. The
discussion of data analysis techniques concludes the chapter.
39
Chapter 4 reports the data analysis and interpretation of the research. Different tools-
frequency and percentage analysis, factor analysis, Analysis of Variance (ANOVA) and
T-test is used for data analysis in details to draw the conclusion. The analysis is divided
in major three sections. In the first section, factor analysis is used to extract the Key
Important Variables considered by the intermediaries to sell the commodities, to purchase
the commodities and to select the intermediaries into particular market-yard (APMC) and
to group these variables. The naming of the factor is carried out based on the grouping of
the variables. Analysis of Variance (ANOVA) is applied in the second section. ANOVA
is used to test the hypotheses for the significance. ANOVA is applied to test the mean
difference for the importance given to the Key Important Variables extracted through
factor analysis by the all the six intermediaries of the APMCs of North Gujarat. In the
third part, an attempt is made to learn about the supply chain management practices
pursue by the wholesalers of the selected APMCs of the North Gujarat. In addition, the
effort has been made to know the extent of process integration and functional integration.
The researcher has also included the analysis of the difficulties (barriers) faced for the
integration of supply chain processes. The t-test is applied to test the significance of the
extent of process elements jointly managed, barriers to the supply chain integration and
functional integration. Chapter concludes with analysis on horizontal span length and
span radius of the firms practicing supply chain management.
At the end of the thesis, Chapter 5 concludes the whole work and discusses the overall
findings in response to the research questions. A discussion of opportunities for future
research and limitations of the research work are noted. The chapter concludes with a
discussion on the significance of the findings of the investigation.
40
CHAPTER 2 LITERATURE REVIEW
PART-I: SUPPLY CHAIN MANAGEMENT
2.1 Introduction
2.2 Supply Chain Management: Concepts
2.2.1 Definition
2.2.2 SCM as a Management Philosophy
2.2.3 SCM as a Set of Activities to Implement a Management Philosophy
2.2.4 SCM as a Set of Management Processes
2.2.5 Functional Scope of SCM
2.2.6 Organizational Scope of SCM
2.3 Supply Chain Coordination, Collaboration and Integration
2.3.1 Introduction
2.3.2 Supply Chain Coordination and Integration
2.4 Integration and Management of Business Processes across the Supply Chain
PART-II: AGRICULTURAL SUPPLY CHAIN MANAGEMENT
2.5 Introduction
2.6 Uncertainty in Agricultural Supply Chain
2.6.1 Causes of Demand Uncertainty and Variability
2.6.1.1 Variability in consumer demand
2.6.1.2 Environmental Uncertainty
2.6.1.3 Behavioural Uncertainty
2.6.1.4 Disconnect between agricultural production and consumer
demand
2.6.1.5 Geographical dispersion
2.6.2 Managing Uncertainty
41
PART-III: AGRI SUPPLY CHAIN MANAGEMENT PRACTICES
AT APMCs
2.7 Introduction
2.8 Working Model of APMC
2.8.1 Introduction
2.8.2 Method of Sale
2.8.3 Weight, Sieving and Delivery
2.8.4 Payment System
2.8.5 Market Charges
2.9 Chain Intermediaries
2.9.1 Farmers/Producers
2.9.2 Consolidators/Aggregators
2.9.3 Traders
2.9.4 Commission Agents
2.9.5 Buyer/Wholesaler/Exporters
2.9.6 Processors
2.9.7 Retailers
2.9.8 Support Service Providers
2.10 Lacunas of the existing system
2.11 Need for Collaboration and Integrated Management Practices
42
PART-I: Supply Chain Management
2.1 Introduction
Management on the verge of a major breakthrough in understanding how industrial
company success depends on the interactions between flows of information, materials,
manpower, money, and capital equipment. The way these five flow systems interlock you
amplify one another and to cause change and fluctuation will form the basis for
anticipating the effect of decisions, policies, organizational forms and investment choice
(Forrester, 19581).
More than 40 years ago, Forrester (1958) introduced a theory of management that
recognized the integrated nature of organizational relationships in distribution channels.
He has used simulation exercise to demonstrate the impact of order information flows on
production and distribution performance for each channel member as well as the entire
channel system. Forrester (19582) proposed that after a period of R & D; involving basis
analytic techniques, “there will come generally recognition of advantage enjoyed by
pioneering management, who have been first to improve their understanding of
interrelationships between separate company functions and markets, its industry and its
national economy. Forrester identified key management issues and illustrated the
dynamics of factors associated with what we called today, “Integrated Supply Chain
Management (ISCM)”
The term ‘Supply Chain Management (SCM)’ has risen to prominence over the past 10
years (Cooper et.al, 19973). Supply Chain Management has become such a “Hot Topic”
that it is difficult to pick up a periodical on manufacturing, distribution, marketing,
1 J. W. Forrester, “Industrial dynamics: a major breakthrough for decision makers”, Harvard Business
Review, (July-August, 1958), pp. 37-66. 2 Forrester, J.W. (1958), op.cit p. 52.
3 Martha C. Cooper, Ellram, Lisa M., Gardener, John T., & Hanks, Albert M., “Meshing multiple
alliances”, Journal of Business Logistics, Vol. 18, No. 1, (1997), pp. 67-89.
43
customer management or transportation without seeing an article about Supply Chain
Management and Supply Chain Management related topics (Ross, 19984).
The reason for the popularity of the concept is manifold; however, several specific
drivers can be traced to trends in global sourcing, an emphasis on the time- and quality-
based competition, and their respective contributions to greater environmental
uncertainty.
Corporations have turned increasingly to global sources for their supplies. This
globalisation of supply management has forced companies to look for more
effective ways to coordinate the flow of materials into and out of the company.
Companies and distribution channels complete more today on the basis of time
and quality. Having a defect-free product to the customer faster and more
reliably than the competitor is no longer seen as a competitive advantage but
simply a requirement to be in the market. Customers demand products
consistently delivered faster, exactly on time, and with no damage. Each of
these necessitates closer coordination between all the supply chain partners.
This global orientation and increased performance-based competition
combined with rapidly changing technology and economic conditions all
contribution to marketplace uncertainty. This uncertainty requires greater
flexibility on the part of individual companies and distribution channels, which
in turn demands more flexibility on channel relationships.
The origins and the concept of supply chain are unclear; however its development starts
along the lines of physical distribution and transportation (Croom et al. 20005). Another
antecedent can be found in the works of the total cost approach to distribution and
4 David Frederick Ross, Competing through Supply Chain Management, (New York: Champan & Hall
Publications, 1998). 5 S. Croom, P. Romano, and M. Giannakis, "Supply chain management: an analytical framework for critical
literature review", European Journal of Purchasing & Supply Management, Vol. 6 No.1, (2000), pp.67-83.
44
logistics (Lewis, 19566). Both these approaches show that focusing on a single element in
the chain can’t assure the effectiveness of the whole system (Croom et al. 20007).
Further development in the area of supply chain management is not limited to logistics
activities and planning and control of materials; and the information flows internally
within the company or extending between the companies. Some authors have used it to
describe as an operational terms involving the flow of materials and products (Tyndall,
Gopal, Partsch & Kamauff, 19988); others to view it as a management philosophy
(Ellram & Cooper, 19909) and still others view it in terms of a management process (La
Londe, 199710
). Some other to discuss an alternative organizational form to vertical
integration (Thorelli, 198611
) and still other to identify and describe the relationship a
company develops with its suppliers (Lamming, 199312
); and many of them discuss it as
coordination of traditional business operations within and across the company (CLM,
199813
). Some of the authors has described as a strategic partnership and collaboration
(Zylbersztajn & Filho 200314
); integration and coordination of business activities across
the organizational boundaries (Chandrashekar and Schary, 199915
); coordinated approach
6 H.T. Lewis, J.W. Culliton, and J. D. Steele, The Role of Air Freight in Physical Distribution, (Boston:
Harvard University, Division of Research, Graduate School of Business Administration,1956) 7 S. Croom, P. Romano, and M. Giannakis, "Supply chain management: an analytical framework for critical
literature review", European Journal of Purchasing & Supply Management, Vol. 6 No.1, (2000), pp.67-83 8 Gene Tyndall, Christopher Gopal, W. Partsch, & John Kamauff, Supercharging supply chains: new ways
to increase value through global operational excellence, (New York: John Wiley & Sons, 1998). 9 Lisa M. Ellram, & Martha C. Cooper, “Supply chain management partnerships and the shipper-third party
relation”, International Journal of Logistics Management, Vol. 5, No. 1, (1990), pp. 45-53. 10
Bernard J. La Londe, “Supply Chain Management: Myth or Reality?” Supply Chain Management
Review, Vol. 1(spring, 1997), pp. 6-7. 11
H. B. Thorelli, "Network: Between Markets and Hierarchies", Strategic Management Journal, Vol. 7,
(1986), pp. 37-51. 12
R. Lamming, Beyond partnership strategies for innovation and lean supply, (London: Prentice Hall,
1993). 13
Council of Logistic Management. (1998), available at www.cscmp.org 14
D. Zylbersztajn & C. Filho, “Competitiveness of meat agri-food chain in Brazil”, Supply Chain
Management: An International Journal, Vol. 8, (2003), pp. 155-165. 15
A. Chandrashekar, P. B. Schary, “Towards the virtual supply chain: the convergence of IT and
organization”, International Journal of Logistic Management, Vol. 10, No. 2, (1999), pp. 27-39.
45
for managing the flow of goods (Carter et al., 199516
) and others as an operational
approach to procurement (Johannson, 199417
).
2.2 Supply Chain Management: Concepts
2.2.1 Definition
Within the supply chain literature, there is numerous definitions of a supply chain exist
(Lee and Billington, 199318
), and while they may differ in terminology, they are
reasonably consistent in meaning.
Ellram and Cooper (199019
)
SCM is an integrative philosophy to manage the total flow of distribution channel from
supplier to ultimate user.
Lee & Billington, (199520
)
Supply Chain Management is a network of facilities that produce raw materials,
transform them into intermediate goods and then final products, and deliver the products
to customers through a distribution system. It spans procurement, manufacturing and
distribution
Beamon (1998)21
16
J. R. Carter, B. G. Ferrin, C. R. Carter, “The effect of less-than-truckload rates on the purchase order lot
size decision”, Transportation Journal, Vol. 34, No. 3, (1995), pp. 35-44. 17
L. Johannson, “How can a TQEM approach can add value to your supply chain?”, Total Quality
Environmental Management, Vol. 3, No. 4, (1994), pp. 521-530 18
H. Lee, C. Billington, “Material management in decentralized supply chains”, Operations Research,
Vol.41, No. 5, (1993), pp. 835-852. 19
L. M. Ellram, and M. C. Cooper, ‘‘Supply chain management partnership, and the shipper third party
relationship’’, The International Journal of Logistics Management, Vol. 1 No. 2, (1990), pp. 1-10. 20
H. L. Lee and C. Billington, “The Evolution of Supply Chain -Management Models and Practice at
Hewlett-Packard”, Interfaces, Vol. 25, No. 5, (September-October, 1995), pp. 42-63.
46
SCM is an integrated process wherein suppliers, manufactures, distributors and retailers
work together in an effort to acquire raw materials, convert these materials into specified
final products and deliver these final products.
Council of Logistic Management (CLM, 199822
)
SCM as the systemic, strategic coordination of the traditional business functions and
tactics across these business functions within a particular company and across business
within supply chain for the purpose of improving the long term performance of individual
companies and the supply chain as a whole.
Handfield and Nichols (199923
)
SCM is the integration of these activities (activities associated with flow and
transformation of goods from raw materials stage, through to the end user as well as
associated information flows) through improved supply chain relationships to achieve
sustainable competitive advantage
Zheng et al. (200024
)
SCM is the process of optimizing a company’s internal practices and improving the
interaction with its suppliers and customers.
Russell (200125
)
SCM is the practice of coordinating the flow of goods, services, information, and
finances as they move from raw material to parts supplier to manufacturer to wholesaler
to retailer to consumer.
21
Benita M. Beamon, “Supply Chain Design and Analysis: Models and Methods”, International Journal of
Production Economics, Vol. 55, No. 3, (1998), pp. 281-294. 22
Counsil of Logistics management. (1998), www.cscmp.org 23
R. Handfield, and E. L. Nichols Jr., Introduction to Supply Chain Management, (New Jersey: Prentice
Hall, 1999). 24
S. Zheng, D. C. Yen, and Michael, ‘‘The new spectrum of cross enterprise solutions: the integration of
supply chain management and enterprise resource planning systems’’, Journal of Computer Information
Systems, Vol. 41 No. 2, (2000), pp. 84-93. 25
K. Russell, ‘‘Supply Chain Management’’, Computerworld, Vol. 35 No. 51, (2001), pp. 32.
47
Mentzer et al., (200126
)
Supply Chain is defined as a set of three or more entities (organizations or individuals)
directly involved in the upstream and downstream flows of products, services, finances,
and/or information from a source to a customer.
Quiett (200227
)
SCM is more than a simple tool to evaluate and optimize a supply chain; it is a complex,
structured business relationship model. It takes into consideration all aspects of the events
required to produce your company’s product in the most efficient and cost effective
manner possible.
Mohanty and Deshmukh (200528
)
SCM is a loop. It starts with customer and ends with customer. Through the loop flow all
materials, finished goods, information, and transactions. It requires looking at business as
one continuous, seamless process. This process absorbs distinct functions such as
forecasting, purchasing, manufacturing, distribution, sales, and marketing into a
continuous business transaction.
In other words, it aims to link all the supply chain agents to jointly cooperate within the
firm as a way to maximize productivity in the supply chain and deliver the most benefits
to all related parties (Finch 200629
). Furthermore, Mentzer (200130
) has explained the
significant importance of SCM as “the systematic, strategic coordination of the traditional
business functions within a particular company and across businesses within the supply
chain, for the purposes of improving the long term performance of the individual
companies and the supply chain as a whole”.
26
John T. Mentzer, Supply Chain Management, (New Delhi: SAGE Publications, 2001), p. 5. 27
W. F. Quiett, ‘‘Embracing supply chain management’’, Supply Chain Management Review, (2002), pp.
40-47. 28
R. P. Mohanty and S. G. Deshmukh, “Supply Chain Management: Theories and Practices”, Biztantra,
(2005), Delhi. 29
B J Finch, ‘Operations Now: Profitability, Processes, Performance’, (2nd edn), (McGraw-Hill
Publication, 2006). 30
John T. Mentzer, op. cit., p. 472
48
From the above definition it is clearly revealed that supply chain can be thought of as a
network of entities interacting to transform raw material into finished product for
customers. Each entity provides some activity necessary for this transformation. It also
point out that SCM is an integrated process to acquire and convert raw materials into
final products and deliver them. It is defined as the integration of all the supply chain
partners into one cohesive process. It is the management of upstream and downstream
relationships – both within and beyond their operations – with suppliers and customers to
deliver superior customer value at less cost to the supply chain as a whole (Martin,
199831
; Weber, 200232
). Effective supply chain strategies for creating competitiveness
revolve around the on-time delivery of competitive quality goods and services, at a
reasonable cost, involving the right business partners (Hewitt, 199433
; Hobbs et al.,
199834
; Easton, 200235
). Forrester (1961) has also mentioned that supply chains should be
viewed as an integrated system. Interactions can take the form of material, information or
monetary flow.
In practice, operational SCM continues to reflect managerial functional legacies.
However, academics working in the SCM field have endeavoured to present supply
chains in a way that reflects this integrative ideal. One of the most widely cited academic
definitions comes from Christopher (2005)36
who defined supply chains as, “the network
of organisations that are involved, through upstream and downstream relationships, in the
different processes and activities that produce value in the form of products and services
in the hands of the ultimate customer”. Christopher’s network-based definition of a
supply chain reflects a near universally accepted position within the SCM discipline
31
C. Martin, Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving
Service, Pitman Publishing, London. 32
M. M. Weber, “Measuring supply chain agility in the virtual organisation”, International Journal of
Physical Distribution & Logistics Management, Vol. 32, No. 7, (2002), pp. 577-90. 33
F. Hewitt, “Supply chain redesign”, The International Journal of Logistics Management, Vol. 5, No. 2,
(1994), pp. 1-9. 34
J. E. Hobbs, W. A. Kerr, and K. K. Klein, “Creating international competitiveness through supply chain
management: Danish pork”, Supply Chain Management: An International Journal, Vol. 3 No. 2, (1998),
pp. 68-78. 35
R. Easton, “Seizing the supply chain opportunity in Asia”, Ascet, Vol. 4, (2002). 36
M. Christopher, Logistics and Supply Chain Management: Creating Value-Adding Networks. (Harlow:
Pearson Education, 2005).
49
throughout the business world i.e. that supply chains are a means to a specific end –
customer value creation within the context of a competitive business model. For example,
Mentzer et al. (2001) stated that the ultimate goals of SCM are “lower costs, increased
customer value and satisfaction, and ultimately competitive advantage”. The US-based
Council of Supply Chain Management Professionals (CSCMP, 200537
) stated that SCM’s
purpose is as an integrative function that links other business functions and processes to
create “a cohesive and high-performing business model”.
As illustrated above, the definitions of SCM differ across authors. They can, however, be
classified into three categories: A Management Philosophy, Implementation of
Management Philosophy and A Set of Management Processes (Mentzer et al., 200138
).
2.2.2 SCM as a Management Philosophy
SCM takes a systems approach to viewing the channel as a single entity, rather than as a
set of fragmented parts, each performing its own function (Ellram & Cooper, 199039
;
Houlihan, 198540
). In other words, the philosophy of SCM extends the concept of
partnership into a multiform effort to manage the total flow of goods from supplier to the
ultimate customer (Ellram, 1990; Jones & Riley, 198541
). Thus SCM is a set of beliefs
that each firm in supply chain directly and indirectly affects the performance of all other
37
Council of Supply Chain Management Professionals (CSCMP) (2007), available at:
http://www.cscmp.org/Website/AboutCSCMP/Definitions/Definitions.asp 38
John T. Mentzer, William DeWitt, James S. Keebler, Soonhong Min, Nancy W. Nix, Carlo D. Smith, and
Zach G. Zacharia, "Defining Supply Chain Management," Journal of Business Logistics, Vol. 22, No. 2,
(2001), pp. 1-26. 39
L. M. Ellram and M. C. Cooper, ‘‘Supply chain management partnership, and the shipper third party
relationship’’, The International Journal of Logistics Management, Vol. 1 No. 2, (1990), pp. 1-10. 40
John B. Houlihan, "International Supply Chains: A New Approach," Management Decision, Vol. 26, No.
3, (1998), pp. 13-19. 41
Thomas Jones and Daniel W. Riley, "Using Inventory for Competitive Advantage through Supply Chain
Management," International Journal of Physical Distribution and Materials Management, Vol. 15, No. 5,
(1985), pp.16-26.
50
supply chain members, as well as ultimate, overall channel performance (Cooper, Ellram,
et al., 199742
).
SCM as a management philosophy seeks synchronization and convergence of intra-firm
and inter-firm operational and strategic capabilities into a unified, compelling
marketplace force (Ross 199843
). SCM as an integrative philosophy directs supply chain
members to focus on developing innovative solutions to create unique, individualized
sources of customer value. Langley and Holcomb (199244
) suggest that the objective of
SCM should be the synchronization of all supply chain activities to create customer
value. Thus, SCM philosophy suggests the boundaries of SCM include not only logistics
but also all other functions within a firm and within a supply chain to create customer
value and satisfaction (See Figure 2.2). In this context, understanding customers' values
and requirements is essential (Ellram and Cooper 1990; Tyndall et al. 199845
). In other
words, SCM philosophy drives supply chain members to have a customer orientation.
Based upon the literature review, it is proposed that SCM as a management philosophy
has the following characteristics:
1. A systems approach to viewing the supply chain as a whole, and to managing the total
flow of goods inventory from the supplier to the ultimate customer;
2. A strategic orientation toward cooperative efforts to synchronize and converge intra-
firm and inter-firm operational and strategic capabilities into a unified whole; and
3. A customer centric to create unique and individualized sources of customer value,
leading to customer satisfaction.
42
M. Cooper, L. M. Ellram, John T. Gardner, and Albert M. Hanks, "Meshing Multiple Alliances," Journal
of Business Logistics, Vol. 18, No. 1, (1997), pp. 67-89. 43
David Frederick Ross, Competing Through Supply Chain Management, (New York: Chapman & Hall,
1998). 44
John C. Langley Jr. and Mary C. Holcomb, "Creating Logistics Customer Value," Journal of Business
Logistics, Vol. 13, No. 2, (1992), pp. 1-27. 45
Gene Tyndall, Christopher Gopal, Wolfgang Partsch, and John Kamauff, Supercharging Supply Chains:
New Ways to Increase Value through Global Operational Excellence, (New York: John Wiley &
Sons,1998).
51
2.2.3 SCM as a Set of Activities to Implement a Management Philosophy
In adopting a supply chain management philosophy, firms must establish management
practices that permit them to act or behave consistently with the philosophy. As such,
many authors have focused on the activities that constitute supply chain management.
The previous research has suggested various activities shown in table 2.1 necessary to
successfully implement a SCM philosophy.46
Table 2.1 Supply Chain Management Activities
---------------------------------------------------------------------------------
1. Integrated behaviour
2. Mutually sharing Information
3. Mutually sharing channel Risk and Rewards
4. Cooperation
5. The same goal and the same focus of serving customers
6. Integration of processes
7. Partners to build and maintain long term relationships
--------------------------------------------------------------------------------------
Source: Mentzer, JT et al. (2001), "Defining Supply Chain Management," Journal of Business Logistics,
Vol. 22, No. 2, pp. 1-26.
Bowersox and Closs (199647
) argued that to be fully effective in today's competitive
environment, firms must expand their integrated behavior to incorporate customers and
suppliers. This extension of integrated behaviors, through external integration, is referred
to by Bowersox and Closs as supply chain management. In this context, the philosophy of
SCM turns into the implementation of supply chain management: a set of activities that
carries out the philosophy. This set of activities is a coordinated effort called supply chain
46
John T. Mentzer, William DeWitt, James S. Keebler, Soonhong Min, Nancy W. Nix, Carlo D. Smith, and
Zach G. Zacharia, "Defining Supply Chain Management," Journal of Business Logistics, Vol. 22, No. 2,
(2001), pp. 1-26. 47
Donald J. Bowersox and David C. Closs, Logistical Management: The Integrated Supply Chain Process,
McGraw-Hill Series in Marketing, (New York: The McGraw-Hill Companies, 1996).
52
management between the supply chain partners, such as suppliers, carriers, and
producers, to dynamically respond to the needs of the end customer (Greene 199148
).
Related to integrated behavior, mutually sharing information among supply chain
members is required to implement a SCM philosophy, especially for planning and
monitoring processes (Cooper et al. 199749
; Cooper, Lambert, and Pagh 199750
; Ellram
and Cooper 199051
; Novack, Langley, and Rinehart 199552
; Tyndall et al, 199853
).
Cooper, Lambert, and Pagh (1997) emphasized frequent information updating among the
chain members for effective supply chain management. The Global Logistics Research
Team at Michigan State University (199554
) defines information sharing as the
willingness to make strategic and tactical data available to other members of the supply
chain. Open sharing of information such as inventory levels, forecasts, sales promotion
strategies, and marketing strategies reduces the uncertainty between supply chain partners
and results in enhanced performance (Andel 199755
; Lewis and Talalayevsky 199756
;
Lusch and Brown 199657
; Salcedo and Grackin 200058
).
48
Alice H. Greene, "Supply Chain of Customer Satisfaction," Production and Inventory Management
Review and APICS News, Vol. 11, No. 4, (1991), pp. 24-25. 49
M. Cooper, L. M. Ellram, John T. Gardner, and Albert M. Hanks, "Meshing Multiple Alliances," Journal
of Business Logistics, Vol. 18, No. 1, (1997), pp. 67-89. 50
M. Cooper, Douglas M. Lambert, and Janus D. Pagh, "Supply Chain Management: More Than a New
Name for Logistics," The International Journal of Logistics Management, Vol. 8, No. 1, (1997), pp. 1-14. 51
L. M. Ellram, and Cooper, M. C., ‘‘Supply chain management partnership, and the shipper third party
relationship’’, The International Journal of Logistics Management, Vol. 1, No. 2, (1990), pp. 1-10. 52
Robert A. Novack, John C. Langley, Jr., and Lloyd M. Rinehart, Creating Logistics Value, (Oak Brook,
IL: Council of Logistics Management, 1995). 53
Gene Tyndall, Gopal Christopher, Wolfgang Partsch, and John Kamauff (1998), Supercharging Supply
Chains: New Ways to Increase Value Through Global Operational Excellence, (New York: John Wiley &
Sons, 1998). 54
Global Logistics Research Team at Michigan State University, World Class Logistics: The Challenge of
Managing Continuous Change, (Oak Brook, IL: Council of Logistics Management, 1995). 55
Andel, Tom, "Information Supply Chain: Set and Get Your Goals," Transportation and Distribution,
Vol. 38, No. 2, (1997), pp. 33. 56
I. Lewis and A. Talalayevsky, "Logistics and Information Technology: A Coordination Perspective,"
Journal of Business Logistics, Vol. 18, No. 1, (1997), pp. 141-57. 57
Robert F. Lusch, and James Brown, "Interdependency, Contracting, and Relational Behavior in
Marketing Channels," Journal of Marketing, Vol. 60, (October, 1996), pp. 19-38. 58
Simon Salcedo, and Ann Grackin, "The e-Value Chain”, Supply Chain Management Review, Vol. 3, No.
4, (2000), pp. 63-70.
53
Effective SCM also requires mutually sharing risks and rewards that yield a competitive
advantage (Cooper and Ellram 199359
). Risk and reward sharing should happen over the
long term. Risk and reward sharing is important for long-term focus and cooperation
among the supply chain members (Cooper et al. 199760
; Novack, Langley, and Rinehart
199561
).
Cooperation among the supply chain members is required for effective SCM (Ellram and
Cooper 1990; Tyndall et al. 1998). Cooperation refers to similar or complementary,
coordinated activities performed by firms in a business relationship to produce superior
mutual outcomes or singular outcomes that are mutually expected over time (Anderson
and Narus 199062
). Cooperation is not limited to the needs of the current transaction and
happens at several management levels (e.g., both top and operational managers),
involving cross-functional coordination across the supply chain members (Cooper et al.
199763
).
Joint action in close relationships refers to carrying out the focal activities in a
cooperative or coordinated way (Heide and John 199064
). Cooperation starts with joint
planning and ends with joint control activities to evaluate performance of the supply
chain members, as well as the supply chain as a whole (Cooper et al. 1997; Cooper,
Lambert, and Pagh 1997; Ellram and Cooper 1990; Novack, Langley, and Rinehart 1995;
Spekman 198865
; Tyndall et al. 1998). Joint planning and evaluation involve ongoing
processes over multiple years (Cooper et al. 1997). In addition to planning and control,
59
Martha C. Cooper, and Lisa M. Ellram, "Characteristics of Supply Chain Management and the
Implication for Purchasing and Logistics Strategy," The International Journal of Logistics Management,
Vol. 4, No. 2, (1993), pp. 13-24. 60
Martha C. Cooper, Lisa M. Ellram, John T. Gardner, and Albert M. Hanks, "Meshing Multiple
Alliances," Journal of Business Logistics, Vol. 18, No. 1, (1997), pp. 67-89. 61
Robert A. .Novack, John C. Langley, Jr., and Lloyd M. Rinehart, Creating Logistics Value, (Oak Brook,
IL: Council of Logistics Management, 1995). 62
Erin Anderson and James A. Narus,"A Model of Distributor Finn and Manufacturer Firm Working
Relationships," Journal of Marketing, Vol. 54, (January, 1990), pp. 42-58. 63
Ibid 64
Jan B. Heide and George Johno, "Alliances in Industrial Purchasing: The Determinants of Joint Action in
Buyer - Supplier Relationships," Journal of Marketing Research, Vol. 27, (Winter, 1990), pp. 24-36. 65
Robert E. Spekman, "Strategic Supplier Selection: Understanding Long-Term Buyer Relationships,"
Business Horizons, Vol. 31, (July-August, 1988) pp. 75-81.
54
cooperation is needed to reduce supply chain inventories and pursue supply chain-wide
cost efficiencies (Cooper et al. 1997; Dowst 198866
). Furthermore, supply chain members
should work together on new product development and product portfolio decisions
(Drozdowski 198667
). Finally, design of quality control and delivery systems is also a
joint action (Treleven 198768
).
La Londe and Masters (199469
) proposed that a supply chain succeeds if all the members
of the supply chain have the same goal and the same focus on serving customers.
Establishing the same goal and the same focus among supply chain members is a form of
policy integration. Lassar and Zinn (199570
) suggested that successful relationships aim
to integrate supply chain policy to avoid redundancy and overlap, while seeking a level of
cooperation that allows participants to be more effective at lower cost levels. Policy
integration is possible if there are compatible cultures and management techniques
among the supply chain members.
The implementation of SCM needs the integration of processes from sourcing, to
manufacturing, and to distribution across the supply chain (Cooper et al. 1997; Cooper,
Lambert, and Pagh 1997; Ellram and Cooper 1990; Novack, Langley, and Rinehart 1995;
Tyndall et al. 1998). Integration can be accomplished through cross-functional teams, in-
plant supplier personnel, and third party service providers (Cooper et al. 1997; Cooper,
Lambert, and Pagh 1997; Ellram and Cooper 1990; Manrodt, Holcomb, and Thompson
199771
).
66
Somerby Dowst, "Quality Suppliers: The Search Goes On," Purchasing, (January, 1988), pp. 94A4-12. 67
Ted E. Drozdowski, "At BOC They Start With the Product," Purchasing, (March, 1986), pp. 62B5-11. 68
Mark Treleven, "Single Sourcing: A Management Tool for the Quality Supplier," Journal of Purchasing
and Materials Management, Vol. 23, (Spring, 1987) pp. 19-24. 69
Bernard J. La Londe, and James M. Masters, "Emerging Logistics Strategies: Blueprints for the Next
Century," International Journal of Physical Distribution and Logistics Management, Vol. 24, No. 7, pp.
(1994), 35-47. 70
Walfried Lassar and Walter Zinn, "Informal Channel Relationships in Logistics," Journal of Business
Logistics, Vol. 16, No. 1, (1995), pp. 81-106. 71
Karl B. Manrodt, Mary C. Holcomb, and Richard H. Thompson, "What's missing in Supply Chain
Management?" Supply Chain Management Review, Vol. 1, No. 3, (1997), pp. 80-86.
55
Stevens (198972
) identified four stages of supply chain integration and discussed the
planning and operating implications of each stage:
Stage 1 Represents the base line case. The supply chain is a function of fragmented
operations within the individual company and is characterized by staged inventories,
independent and incompatible control systems and procedures, and functional
segregation.
Stage 2 Begins to focus internal integration, characterized by an emphasis on cost
reduction rather than performance improvement, buffer inventory, initial evaluations of
internal trade-offs, and reactive customer service.
Stage 3 Reaches toward internal corporate integration and characterized by full visibility
of purchasing through distribution, medium-term planning, tactical rather than strategic
focus, emphasis on efficiency, extended use of electronics support for linkages, and a
continued reactive approach to customers.
Stage 4 Achieves supply chain integration by extending the scope of integration outside
the company to embrace suppliers and customers.
Effective SCM is made up of a series of partnerships and, thus, SCM requires partners to
build and maintain long-term relationships (Cooper et al. 1997; Ellram and Cooper 1990;
Tyndall et al. 1998). Cooper et al. (1997) believe the relationship time horizon extends
beyond the life of the contract- perhaps indefinitely-and, at the same time, the number of
partners should be small to facilitate increased cooperation.
Gentry and Vellenga (199673
) argue that it is not usual that all of the primary activities in
a chain - inbound and outbound logistics, operations, marketing, sales, and service - will
72
Graham C. Stevens, "Integrating the Supply Chains," International Journal of Physical Distribution and
Materials Management, Vol. 8, No. 8, (1989), pp. 3-8. 73
Julie J. Gentry and David B. Vellenga, "Using Logistics Alliances to Gain a Strategic Advantage in the
Marketplace", Journal of Marketing Theory and Practice, Vol. 4, No. 2, (1996), pp. 37-43.
56
be performed by any one firm to maximize customer value. Thus, forming strategic
alliances with supply chain partners such as suppliers, customers, or intermediaries (e.g.,
transportation and/or warehousing services) provides competitive advantage through
creating customer value (Langley and Holcomb, 199274
).
2.2.4 SCM as a Set of Management Processes
As opposed to focus on the activities that constitute supply chain management, other
authors have focused on management processes. Davenport (199375
) defines processes as
a structured and measured set of activities designed to produce specific output for a
particular customer or market. La Londe (199776
) proposes that SCM is the process of
managing relationships, information, and materials flow across enterprise borders to
deliver enhanced customer service and economic value through synchronized
management of the flow of physical goods and associated information from sourcing to
consumption. Ross (199877
) defines supply chain process as the actual physical business
functions, institutions, and operations that characterize the way a particular supply chain
moves goods and services to market through the supply pipeline. In other words, a
process is a specific ordering of work activities across time and place, with a beginning,
an end, clearly identified inputs and outputs, and a structure for action.
Lambert, Stock, and Ellram (1998)78
propose that, to successfully implement SCM, all
firms within a supply chain must overcome their own functional silos and adopt a process
approach. Thus, all the functions within a supply chain are reorganized as key processes.
The critical differences between the traditional functions and the process approach are
74
John C. Langley, Jr. and Mary C. Holcomb, "Creating Logistics Customer Value," Journal of Business
Logistics, Vol. 13, No. 2, (1992), pp. 1-27. 75
Thomas H. Davenport, Process Innovation, Reengineering Work through Information Technology,
(Boston: Harvard Business School Press, 1993). 76
Bernard J. La Londe, ‘Supply Chain Management: Myth or Reality?’, Supply Chain Management
Review, Vol. 1, (spring, 1997), pp. 6-7. 77
David Frederick Ross, Competing Through Supply Chain Management, (New York: Chapman & Hall,
1998). 78
Douglas M. Lambert, James R. Stock, and Lisa M. Ellram, Fundamentals of Logistics Management,
(Boston: Irwin/McGraw-Hill, 1998).
57
that the focus of every process is on meeting the customer's requirements and that the
firm is organized around these processes (Cooper et al. 1997; Cooper, Lambert, and Pagh
1997; Ellram and Cooper 1990; Novack, Langley, and Rinehart 1995; Tyndall et al.
1998). Lambert, Stock, and Ellram (199879
) suggest the key processes typically include
customer relationship management, customer service management, demand management,
order fulfillment, manufacturing flow management, supplier relationship management,
product development and commercialization and return management.
Figure 2.1 Supply Chain Management: Integrating and Managing Business
Processes Across the Supply Chain
Source: Adopted from Cooper, Martha C., Douglas M. Lambert, and Janus D. Pagh, “Supply Chain
Management: Implementation Issues and Research Opportunities”, The International Journal of Logistics
Management, Vol. 9, No. 2, (1998), p. 2
These are illustrated in figure 2.1 which depicts a simplified supply chain network
structure, the information and product flows, and the key supply chain management
processes penetrating functional silos within the firm as well as corporate silos across the
79
Martha C. Cooper, Douglas M. Lambert, and Janus D. Pagh, “Supply Chain Management:
Implementation Issues and Research Opportunities”, The International Journal of Logistics Management,
Vol. 9, No. 2, (1998), p. 2.
58
supply chain. Thus, business processes become supply chain processes to manage the
links across intra- and inter-firm boundaries. Table 2.2 below represents the key
processes being integrate across the supply chains and its key concerns.
Table 2.2 Representative processes Being Integrated Across Supply Chains
Process Key Concerns
Customer Relationship
Management
Identifying key customer, target markets, and developing and
implementing programmes with key customers
Customer Service Providing information about the order status, as well as
production and distribution status to customer. This process
also provides product information to the customer.
Demand Management Recognises that the flow of materials and products is
intertwined with customer demand. Forecasting and reducing
variability are key concerns of this process.
Order Fulfillment Provides for timely and accurate delivery of customer orders
with the objective of exceeding customer need dates.
Manufacturing Flow
Management
Concerned with making the products that customer wants.
This is resulting in manufacturing processes that are more
flexible and efforts to have the right mix of products.
Procurement Focuses on managing relationships with strategic suppliers.
The objective is to support the manufacturing flow
management process and new product development.
Product Development
and Commercialisation
Focuses on integrating key customers and suppliers into the
product development process in order to reduce time to
market.
Returns Focuses on recovering the greatest value from reverse
product and materials flows, with emphasis on recycling,
reuse and source reduction.
Source: Adopted from Cooper, Martha C., Douglas M. Lambert, and Janus D. Pagh, “Supply Chain
Management: Implementation Issues and Research Opportunities”, The International Journal of Logistics
Management, Vol. 9, No. 2, (1998), p. 2
59
Mentzer et al. (2001) has noted that the scope of SCM is functional and organizational.
The functional scope of SCM refers to which traditional business functions are included
or excluded in the implementation and the process of SCM. The organizational scope of
SCM concerns what kinds of inter-firm relationships are relevant to the participating
firms in the implementation and the process of SCM.
2.2.5 Functional Scope of SCM
Since process refers to the combination of a particular set of functions to get a specific
output, all of the traditional business functions should be included in the process of SCM.
The supply chain concept originated in the logistics literature, and logistics has continued
to have a significant impact on the SCM concept (Bowersox, Carter, and Monczka
198580
; Dwyer, Schurr, and Oh 198781
; Jones and Riley 198582
; Monczka, Trent, and
Handfield 199883
). In this context, Tyndall et al. (1998) propose that “SCM logistics” is
the art of managing the flow of materials and products from source to user.
The logistics system includes the total flow of materials, from the acquisition of raw
materials to delivery of finished products to the ultimate users, as well as the related
counter-flows of information that both control and record material movement. CLM
(199884
) apparently agreed, since its new definition states, “Logistics is that part of the
supply chain process that plans, implements, and controls the efficient flow and storage
of goods, services, and related information from the point of origin to the point of
consumption in order to meet customers’ requirements”. Thus, CLM has acknowledged
that logistics is one of the functions contained within supply chain management.
80
Donald J. Bowersox, Philip L. Carter, and Robert M. Monczka, "Material Logistics Management,"
Internal Journal of Physical Distribution and Logistical Management, Vol. 15, No. 5, (1985), pp. 27-35. 81
Robert E. Dwyer, Paul H. Schurr, and Sejo Oh, "Developing Buyer-Seller Relationships," Journal of
Marketing, Vol. 51, (April, 1987), pp. 11-27. 82
Thomas Jones and Daniel W. Riley, "Using Inventory for Competitive Advantage through Supply Chain
Management," International Journal of Physical Distribution and Materials Management, Vol. 15, No. 5,
(1985), pp.16-26. 83
Robert Monczka, Robert Trent, and Robert Handheld, Purchasing and Supply Chain Management,
(Cincinnati: South-Western College Publishing, 1998). 84
Council of Logistics Management, (Oak Brook, IL: Council of Logistics Management, 1998).
60
Ross (1998) explains that the role of logistics spans from warehousing and transportation
to integrating the logistics operations of the entire supply chain, whereas SCM merges
marketing and manufacturing with distribution functions to provide the enterprise with
new sources of competitive advantage. Logistics puts more emphasis on efficient
movement and storage to fulfill customer requirements. Customer value and satisfaction
that help a supply chain improve competitive advantage and profitability, however,
require more than logistics (Giunipero and Brand 199685
). Thus, SCM means the
management of multiple business processes, including logistics processes, marketing
research, promotion, sales, information gathering, research and development, product
design, new product development, and total systems/value analysis should also be
included (Bechtel and Jayaram 199786
; Bowersox 199787
; Mentzer 199388
).
2.2.6 Organizational Scope of SCM
According to Christopher (199289
), the real competitions are not company against
company, but rather supply chain against supply chain. Cooper, Lambert, and Pagh
(1997) argue that organizational relationships tie firms to each other and may tie their
success to the supply chain as a whole. In this context, a supply chain as a whole may
have its own identity and function like an independent firm. However, to accomplish this
ultimate supply chain, all companies in the supply chain must have a supply chain
orientation. The result is a fully managed supply chain. Ellram and Cooper (1990)
suggest that effective supply chain management is made up of a series of partnerships
among firms working together and mutually sharing information, risks, and rewards that
yield a competitive advantage. In the same article, Ellram and Cooper also contend the
successful supply chain relies on forming strategic partnerships with long-term
85
Lawrence C. Giunipero and Richard R. Brand, "Purchasing's Role in Supply Chain Management," The
International Journal of Logistics Management, Vol. 7, No. 1, (1996), pp. 29-37. 86
Christian Bechtel and Jayanth Jayaram, "Supply Chain Management: A Strategic Perspective,"
International Journal of Logistics Management, Vol. 8, No. 1 (1997), pp. 15-34. 87
Donald J. Bowersox, "Lessons Learned from the World Class Leaders," Supply Chain Management
Review, Vol. 1, No. 1, (1997), pp. 61-67. 88
John T. Mentzer, "Managing Channel Relations in the 21st Century," Journal of Business Logistics, Vol.
14, No. 1, (1993), pp. 27-42. 89
Martin L. Christopher, Logistics and Supply Chain Management, (London: Pitman Publishing, 1992).
61
orientations. Christopher suggests a network of organizations, through upstream and
downstream linkages, as the organization for SCM.
From this discussion, and given our earlier definition of supply chains, conclude that the
functional scope of SCM encompasses all the traditional intra business functions, while
the organizational scope of SCM emphasized on the inter-firm process integration. The
process of integration must also include the systemic, strategic management of the
activities listed in table 2.1
Figure 2.2 A Model of Supply Chain Management
Source: John T. Mentzer, , William DeWitt, James S. Keebler, Soonhong Min, Nancy W. Nix, Carlo D.
Smith, and Zach G. Zacharia, "Defining Supply Chain Management," Journal of Business Logistics, Vol.
22, No. 2, (2001), pp. 1-26.
Mentzer et al. (2001) have noted supply chain management as the systemic, strategic
coordination of the traditional business functions within a particular company and across
businesses within the supply chain, for the purpose of improving the long-term
performance of the individual companies and the supply chain as a whole. It is also
indicative that the coordination, cooperation and collaboration are the prerequisite of the
building supply chain competitiveness. This is addressed Figure 2.2.
62
From the discussion we can conclude that a supply chain is network consisting of
suppliers, manufacturers, distributors, retailers and customers. The network supports
three types of flows that require careful planning and close coordination:
1. Material flows; represent physical product flows from suppliers to customers as
well as reverse flows for product returns, servicing and recycling;
2. Information flows; represent order transmission and order tracking and which
coordinate physical flows; and
3. Financial flows; credit terms, payment schedules and consignment arrangement.
The network is supported by three pillars:
1. Processes; encompass such value adding activities as logistics, new product
development and knowledge management;
2. Organisational structure; encompass a range of relationships from vertical
integration to networked companies; and
3. Enabling technology; encompass both process and information technologies.
2.3 Supply Chain Coordination, Collaboration and Integration
2.3.1 Introduction
Supply chain coordination concerns with the coordination of the three types of flows
(material, information and financial) over the network. Effective coordination strategies
combine a range of approaches for supply chain transparency through information
sharing and information deployment (Sharing point-of-sales data, vendor-managed
inventories, collaborative planning, forecasting and replenishment) as well as operational
flexibility to react to timely information. These approaches may facilitate new forms of
organisational structure (process orientation) and new forms of inter-organisational
collaboration (outsourcing via third party service provider). Information and
63
communication technologies facilitating closer collaboration and promoting supply chain
transparency are crucial for effective coordination.
2.3.2 Supply Chain Coordination and Integration
Coordination is the management of dependencies between activities (Malone and
Crowston, 199490
). The purpose of coordination is to achieve collectively goals that
individual actors cannot meet. Coordination capability is affected by two main issues:
information sharing and allocating decision rights across channel members (Anand and
Mendelson, 199791
). The coordination theory (Malone and Crowston, 1994) provides a
theoretical basis to consider how companies can jointly manage business processes across
the supply chain.
Dependencies between activities are a prerequisite for coordination; if there are no
dependencies, there is no need to coordinate. These dependencies stem from the lack of
ability to control all the conditions necessary to achieve an action or a desired outcome.
Activities may be organisations, processes, organisational units, or human beings that act
in computational, human, biological, or other systems (Whang, 199592
). Coordination
may take place within operations, across functions (cross-functional coordination) or
between organizations (inter-organisational coordination).
Supply chain coordination offers a means to understand and analyse a supply chain as a
set of dependencies. These dependencies exist both in physical flow, which is the flow
and storage of goods, and informational flow, which deals with the storage and flow of
90
T. W. Malone and K. Crowston, “The interdisciplinary study of coordination”, ACM Computer Surveys,
Vol. 26, No. 1, (1994), pp.87-119. 91
K. S. Anand, and H. Mendelson, “Information and organisation for horizontal multimarket coordination”,
Management Science, Vol. 43, No. 12, (1997), pp. 1609-1627. 92
S. Whang, “Coordination in operations: A taxonomy”, Journal of Operations Management, Vol. 12, No.
3-4, (1995), pp. 413-422.
64
information associated with those goods (Lewis and Talalayevski, 200493
). In the
traditional design of interacting flows, when the physical flow has been the basis for
designing the supply chain, information flow may result in inefficient decision-making
and movement of information. Advances in information technology have made it possible
to separate the design of information flow from the physical flow by, for example,
shortening the information flow. By such changes, the number of decision points can be
reduced and the quality of decisions can be improved.
Lee (200094
) has proffered three dimensions of supply chain integration shown in table
2.3
Table 2.3 Dimensions of Supply Chain Integration
Source: H. L. Lee, “Creating value through supply chain integration”, Supply Chain Management Review,
Vol. 4, No. 4, (2000), pp. 30-36.
1. Information integration; when demand information, inventory status, capacity plans,
production schedules, promotion plans, demand forecasts, and shipment schedules are
shared.
93
I. Lewis and A. Talalayevsky, “Improving the inter-organisational supply through optimization of
information flows”, The Journal of Enterprise Information Management, Vol. 17, No. 3, (2004) pp. 229-
237. 94
H. L. Lee, “Creating value through supply chain integration”, Supply Chain Management Review, Vol. 4,
No. 4, (2000), pp. 30-36.
Dimension Exchanges How
Information
Integration
Information, Knowledge Information sharing; collaborative
planning, forecasting and
replenishment
Coordination and
Resource Sharing
Decisions, work Decision delegation, work
realignment, outsourcing
Organisational
Relationship Linkage
Accountability,
risks/costs/gain
Extended communication and
performance measures, incentive
realignment
65
2. Coordination dimension; in the framework results in redeployment of decision rights,
work, and resources to the optimal-positioned supply chain member.
3. Organisational linkages; includes channels of communication, common performance
measures and incentives.
In this integration framework, information integration is the foundation of broader supply
chain integration. Lee (2000) states that,’ to coordinate material, information, and
financial flows, companies must have access to information reflecting their true supply
chain picture all the times’. In this approach, sharing of information and knowledge
sharing are preconditions for commencing coordination. Only after these are realised, the
coordination can be implemented. Coordination theory, instead, asserts that managing
information flow is one mechanism to realise supply chain coordination.
Figure 2.3: Continuum of integration from cooperation to collaboration
Source: R. E. Spekman, Kamauff, J.W. Jr, Myhr, N, “An empirical investigation into supply chain
management: A perspective on partnerships”, Supply Chain Management, Vol. 3, No. 2, (1998), pp. 53-67.
Supplier and customer relationships are presented as an integration continuum in Figure
2.3 (Spekman et al., 199895
). The model indicates how a supplier may develop into a
partner. In the first stage, the relationship is based on price negotiations and an
95
R. E. Spekman, Kamauff, J.W. Jr, Myhr, N, “An empirical investigation into supply chain management:
A perspective on partnerships”, Supply Chain Management, Vol. 3, No. 2, (1998), pp. 53-67.
66
adversarial relationship. In the ‘cooperation stage’, long-term contracts are established,
and the number of suppliers is actively reduced. In ‘coordination’, (the next stage),
information linkages enable wider and more routine information exchange. In most
supply chains, all key supplier and customer relationships have achieved cooperation or
coordination stages in their integration efforts.
Hines et al. (200096
) present another example of coordination stages. They present a
coordination framework for supplier development consisting of four phases. The first
phase is labelled ‘no-coherent strategy’, when price is the primary buying criterion, and
companies are not cooperating, nor developing a common way of working. ‘Piecemeal
coordination’, the second phase, describes a situation where departments or instances are
functioning with the relevant department in the supplier company. The third phase,
‘systematic coordination’, occurs when companies are working proactively to eliminate
waste. ‘Network coordination’, the fourth phase, is realised if companies are developing
methods and procedures to maximise benefit along the total supply network.
A similar integration model of Bacon et al. (200297
), concentrates on informational
linkages, and addresses levels of customer collaboration. The first level is a ‘transactional
relationship’, which is the traditional way of operating, i.e. exchanging orders and
invoices. In an ‘information-sharing relationship’, additional information, such as
inventory levels or order status is shared. In the ‘joint planning and development of
business plans’, the shared information is used interactively. The CPFR initiative aims to
achieve this last form, whilst VMI can be described as an information-sharing
relationship.
All the theories conclude that integration improves supply chain performance, but
implementing such a relationship is a challenge. Integrative linkages require trust,
commitment, and resources that are not always possible to allocate to a specific supply
96
P. Hines, R. Lamming, Jones, D., Cousins, P., Rich, N., Value Stream Management - Strategy and
Excellence in the Supply Chain, (Harlow: Prentice Hall, 2000), pp. 320-322 97
A. Bacon, L. Lapide, J. Suleski, Supply Chain Collaboration Today: It’s a Tactic, Not a Strategy,
(Boston: AMR Research Inc., 2002).
67
chain relationship. However, not all relationships need to target the highest level of
integration, but rather need to find an appropriate level to ensure an efficient supply
chain. Most contemporary relationships are at the transactional or information-sharing
levels. These observations concur with research results from an empirical study (Edwards
et al., 200198
), that suggests that few companies are externally integrated, and that higher
level of integration requires new sets of skills and capabilities.
The concept of integration has been used to study many different organizational
phenomena (Galbraith, 197799
). It involves various dimensions and varying intensities. In
supply chain management literature, reference is made to structural integration (Bucklin,
1966100
), systems integration (Bask and Juga, 2001101
), process integration (Bowersox et
al., 1999102
), relational integration (Gummesson, 1999103
; Lambert et al., 1998104
) and so-
called soft forms of integration through socialization (Stern et al., 1996105
).
The intensity of integration can range from full vertical integration to discrete market
exchanges, with different co-ordination and integration mechanisms between these two
options (Harland, 1996106
). Actually, the entire concept of supply chain management is
based on integration. Harrigan (1985), in her classic study, argues that a supply chain is
characterized by different patterns that display varying stages and forms of integration.
Stages refer to “the number of steps in the chain of processing which a firm engages in –
from ultra-raw materials to the final consumer”. Forms mean ownership or other
arrangements of control, such as shared ownership, long-term contracts, information
98
P. Edwards, M. Peters, and G. Sharman, “The effectiveness of information systems in supporting the
extended supply chain”, Journal of Business Logistics, Vol. 22, No. 1, (2001), pp. 1-27. 99
J. Galbraith, Organization Design, (Philippines: Addison-Wesley Publishing Company, 1977). 100
L. Bucklin, A theory of distribution channel structure, (Berkley CA: Iber Special Publications, 1966). 101
A. Bask, and J. Juga “Semi-integrated supply chain: towards the new era of supply chain management“,
International Journal of Logistics, Research and Applications, Vol. 4, No. 2, (2001), pp. 137-152. 102
D. Bowersox, D. Closs, and T. Stank, 21st Century Logistics: Managing Supply Chain Integration a
Reality, (Oakwood, IL: Council of Logistics Management, 1999). 103
Gummesson, Total Relationship Marketing, (Butterworth-Heinemann, Oxford Publication, 1999). 104
D. Lambert, Cooper, M. Pagh, J., “Supply chain management, implementation issues and research
opportunities”, International Journal of Logistics Management, Vol. 9, No. 2, (1998), pp. 1-19. 105
L. Stern, El-Ansary, A., A. Coughlan, A., Marketing Channels, (NJ: Prentice-Hall, 1996). 106
C. Harland, “Supply chain management: relationships, chains and networks”, British Journal of
Management, Vol. 7, (1996), pp. 63-80.
68
exchanges, or resource and risk-sharing agreements. The theory behind integration states
that increased integration leads to higher performance (Pagell, 2004107
).
2.4 Integration and Management of Business Processes across the
Supply Chain
The purpose of supply chain management is described by Kaufman (1997108
) as to being
to “remove communication barriers and eliminate redundancies” through coordinating,
monitoring and controlling processes.
The integration of supply chains has been described by Clancy as: attempting to elevate
the linkages within each component of the chain, to facilitate better decision making and
to get all the pieces of the chain to interact in a more efficient way and thus create supply
chain visibility and identify bottlenecks (Clancy, cited in Putzger, 1998109
).
The main drivers of integration are listed by Handfield and Nichols (1999110
) as:
1) The information revolution;
2) Increased levels of global competition creating a more demanding customer and
demand driven markets; and
3) The emergence of new types of inter-organizational relationships.
They describe the three principal elements of an integrated supply chain model as being
information systems (management of information and financial flows), inventory
management (management of product and material flows), and supply chain relationships
(management of relationships between trading partners).
107
M. Pagell, “Understanding the factors that enable and inhibit the integration of operations, purchasing
and logistics”, Journal of Operations Management, Vol. 22, (2004), pp. 459-487. 108
R. Kaufman, “Nobody wins until the consumer says, ‘I’ll take it’”, Apparel Industry Magazine, Vol. 58
No. 3, (1997), pp. 14-16. 109
I. Putzger, “All the ducks in a row”, World Trade, Vol. 11, No. 9, (1998), pp. 54-6. 110
R. B. Handfield and E. L. Nichols, Introduction to Supply Chain Management, (NJ: Prentice-Hall,
1999).
69
The basis of integration can therefore be characterized by cooperation, collaboration,
information sharing, trust, partnerships, shared technology, and a fundamental shift away
from managing individual functional processes, to managing integrated chains of
processes (Akkermans et al., 1999111
).
The extent of integration can begin with product design, and incorporate all steps leading
to the ultimate sale of the item (Transportation and Distribution, 1998112
; Modern
Materials Handling, 1998113
; Ballou et al., 2000114
). Some authors also include all
activities throughout the useful life of the product including service, reverse logistics and
recycling (Carter and Ellram, 1998115
; Thomas and Griffin, 1996116
).
This cooperative theme is further supported by other writers (Fernie, 1995117
; Lawrence,
1997118
; Morton, 1997119
), and is in essence captured by Parnell (1998120
) when he stated
that supply chain integration really occurs when customers and suppliers establish tight
partnerships with the objectives and probable outcomes of reduced inventory, shorter lead
times and better service to the customer. The primary benefits resulting could include
cost and cycle time reductions.
111
H. Akkermans, P. Bogerd, and B. Vos, “Virtuous and vicious cycles on the road towards international
supply chain management”, International Journal of Operations & Production Management, Vol. 19, Nos.
5/6, (1999), pp. 565-81. 112
“Overcoming communication barriers”, Transportation and Distribution, Vol. 39, No. 10, (1998), pp.
91-4. 113
“Survey spotlights need to improve capabilities”, Modern Materials Handling, (April, 1998), pp. 17-19. 114
R. H. Ballou, S. M. Gilbert, and A. Mukherjee, “New managerial challenges from supply chain
opportunities”, Industrial Marketing Management, Vol. 29 No. 1, (2000), pp. 7-18. 115
C. R. Carter and L. M. Ellram, “Reverse logistics: a review of the literature and a framework for future
investigation”, Journal of Business Logistics, Vol. 19 No. 1, (1998), pp. 85-102. 116
D. Thomas and P. M. Griffin, “Coordinated supply chain management [review]”, European Journal of
Operational Research, Vol. 94 No. 1, (1996), pp. 1-15. 117
J. Fernie, “International comparisons of supply chain management in grocery retailing”, Service
Industries Journal, Vol. 15, No. 4, (1995), pp. 134-47. 118
A. Lawrence, “Customer power forces supply chain integration”, Works Management, (April, 1997), pp.
43-7. 119
R. Morton, “Learning from the past to shape the future”, Transportation and Distribution, Vol. 38 No.
1, (1997), pp. 84-5. 120
C. Parnell, “Supply chain management in the soft goods industry”, Apparel Industry Magazine, Vol. 59,
No. 6, (1998), p. 60.
70
Lambert, Cooper and Pagh (1998121
) have developed the comprehensive normative
model of supply chain management for making the decisions concerning supply chain
management. Their three components model (Figure 2.4) is based on the definition that:
“Supply chain management is the integration of key business processes from end user to
original suppliers that provides products, services and information that add value for
customers and other stakeholders”
Figure 2.4: Supply Chain Management Framework: Elements and Key Decisions
Source: Douglas M. Lambert, Martha C. Cooper and Janus Pagh, “Supply Chain management:
Implementation Issues and Research Opportunities”, The International Journal of Logistics Management,
Vol. 9, No. 2, (1998), p. 4.
The figure 2.4 illustrates the interrelated nature of supply chain management. SCM
framework consists of the three of closely inter-related elements:
121
Douglas M. Lambert, , Martha C. Cooper and Janus Pagh, “Supply Chain management: Implementation
Issues and Research Opportunities”, The International Journal of Logistics Management, Vol. 9, No. 2,
(1998), pp. 1-19
71
1. The supply chain network structure; is comprised of the member firms and the link
between these firms.
2. The supply chain management processes; are the activities that produce a specific
output of value to the customer.; and
3. The supply chain management components; are the managerial methods by which the
business processes are integrated and managed across the supply chain.
According Lambert, Cooper and Pagh (1998), the practices of the supply chain
management is comprised of three key decision areas:
1. The number and types business processes to integrate,
2. The supply chain network over which they are integrated; and
3. The aspects of general management to focus the integration upon.
Regarding the first decision area, in line with the principles of business reengineering and
process management, the model is focusing on the decisions about the number and types
of business processes that may be integrated across firms in the supply chain. Lambert,
Cooper and Pagh, have identified internal business processes as opposed to particular
functional activities as the unit of integration in supply chain management. They
proposed that all business processes that focus on meeting end customer requirements are
candidates for supply chain management.
Devenport (1990122
) defines the process as a structured and measured set of activities
designed to produce a specific output for a particular customer to market. It can viewed
122
T.H. Davenport, J. E. Short, "The new industrial engineering: information technology and business
process redesign", Sloan Management Review, Vol. 31 No.4, (1990), pp.11-27.
72
as a structure of activities designed for action with a focus on end-customers and on the
dynamic management of flows involving product, information, cash, knowledge and/or
ideas. Processes exist in all companies. They are cross-functional in nature and can be
broken down hierarchically into process elements, activities and tasks, respectively that
transform materials and information into something of value to customer (Armistead,
Colin and Philip, 1996123
).
The second major decision area deals with establishing the network of firms in supply
chain with which a company will integrate processes. This decision is influenced by a
number of factors, including the complexity of the product, the length of the supply
chain, and the number of suppliers and customers at each level of the chain. As most of
the firms participate in multiple supply chains, it becomes important for a firm to identify
the most critical chains and levels in each chain that will be managed, and pursue the
inter-organisational relationships need to do so.
Lambert, et al. (1998) characterized the network structure of a supply chain in terms of its
horizontal and vertical dimensions. Horizontal structure refers to the number of tiers of
suppliers and customers across the firm’s supply chain. For example, (in figure 2.1) the
immediate suppliers and customers of the focal company reside in the first upstream and
downstream tiers, respectively, of its supply chain. Likewise, its suppliers’ suppliers and
customers’ customers reside in the second upstream and downstream tiers, respectively.
The vertical structure of a firm’s supply chain is characterized by the number of different
suppliers or customers that resides in each tier of its supply chain. For example, (in figure
2.1) if the focal company’s first tier of suppliers consisted of only two companies, its
vertical structure at the point of its supply chain could be characterized as narrow. In
contrast the vertical structure of a company that deals with many first-tier suppliers
would be considered as wide. Building form these definitions, three concepts termed the
horizontal span, vertical span, and horizontal span radius of a firm’s supply chain
management can be defined.
123
Armistead, Colin and Philip Rowland (1996), “Managing by Business Processes”, in Colin, Armistead
and Philip Rowland (Ed.), Managing Business Processes: BPR and Beyond, (Chichester: John Wiley &
Sons), pp. 46-49
73
Horizontal span refers to the number of tiers across which a process is integrated. For
example, a company that integrates its order fulfillment process with a first-tier supplier
and firt-tier customer would have a horizontal span of three tiers when the focal firm’s
tier is counted. Alternatively, a company that only integrates a process with a first-tier
supplier and second-tier supplier would also have a three tier-span.
Similarly, the Vertical span of a company’s supply chain management refers to the
number of firms within a tier with whom it integrates a process or processes.
Horizontal span radius, which derives from the firm’s horizontal span, measures the
longest length of horizontal span from the focal in the either direction, upstream or
downstream. According to this definition, a one-tier radius indicates that the focal firm’s
integration efforts do not reach beyond its first tier of suppliers or customers, while a
two-tier radius indicates that its efforts involve first and second –tier companies and so
on.
The third major decision area under this framework concerns the general management
issues. For example, management attempting to integrate a process across firms needs a
work structure that details how task and activities will be performed across the span of
integration effort. Cooper, et al. (1997124
) identify 10 supply chain management
components that firm must address when trying to integrate business processes: planning
and control, work structure, organisational structure, product flow facility, information
flow facility structure, product structure, measurement methods, power and leadership
structure, risk and reward structure and culture and attitude.
The result of empirical research by Hakansson and Snehota (1995125
), stress that, “the
structure of activities within and between companies is critical cornerstone of creating
unique and superior supply chain performance”. Lambert, Cooper and Pagh (1998), in
124
Martha C. Cooper, Douglas M. Lambert and Janus Pagh, “Supply Chain Management: More than a new
name for Logistics”, The International Journal of Logistics Management, Vol. 8, No. 1, (1997), pp. 1-14 125
Hakan Hakansson and Ivan Snehota, Developing Relationships in Business Networks, (London:
Routledge, 1995)
74
their study mentioned that the executive believed that the competitiveness and
profitability could be increased if internal key activities and business processes are linked
and managed across multiple companies. As stated by Lambart, Giunipero and
Ridenhower (1997126
), “Successful supply chain management requires a change from
managing individual functions to integrating activities into key supply chain business
process”.
Successful supply chain management involves coordination of activities within the firm
and between members of the supply chain. Consequently, supply chain process
integration and reengineering initiatives should be aimed at boosting total process
efficiency and effectiveness across the supply chain.
Although the functional expertise remains in place, implementing supply chain
management requires making the transition form a functional organisation to one focused
on business processes first inside the firm and then across firms in the supply chain.
Figure 2.5 illustrates how each function within the organisation can be mapped with eight
supply chain processes. This figure provides examples of activities that might exist at
each junction of functional area and process.
In the customer relationship management process, sales and marketing provides the
account management expertise, engineering provides the specifications which define the
requirements, logistics provides knowledge of customer service requirements,
manufacturing provides the manufacturing strategy, purchasing provides the sourcing
strategy, and finance provides customer profitability reports. The customer service
requirements must be used as input to manufacturing, sourcing, and logistics strategies.
126
Douglas M. Lambert, Larry C. Giunipero and Gary J. Ridenhower, Supply Chain management: A key to
achieving Business Excellence in the 21st Century, (1997), unpublished manuscript.
75
Figure 2.5 Mapping of Functions within the organisation with
Supply Chain Processes
Source: Douglas M. Lambert, Larry C. Giunipero and Gary J. Ridenhower, Supply Chain Management: A
key to achieving Business Excellence in the 21st Century, unpublished manuscript, (1997).
If the proper coordination mechanisms are not in place across the various functions, the
supply chain process will be neither effective nor efficient. By making a process focus,
all functions that touch the product or provide information must work together. Byron et
al. (2002127
) mentioned that the increasing use of outsourcing has accelerated the need to
coordinate supply chain processes since the organisation becomes more dependent on
suppliers. Consequently, coordination mechanisms must be in place within the
organisation.
127
Auguste, Byron G., Yvonne Hao, Marc Singer and Michael Wiegand, “The other side of Outsourcing”,
The McKinsey Quarterly, No. 1, (2002), pp. 52-63.
76
PART II: AGRICULTURAL SUPPLY CHAIN MANAGEMENT
2.5 Introduction
The number of publications on Agricultural Supply Chain Management (ASCM) has
increased significantly in the last decade (Fischer C. et al., 2008128
; Raynaud E. et al.,
2005129
; Fearne A. et al., 2001130
). This is mainly because of a number of fundamental
changes in the business environment, especially in agri-food chains. Consumers across
the world have become more demanding and place new demands on attributes of
agricultural produces such as quality (guarantees), integrity, safety, diversity and
associated information (services). Demand and supply are no longer restricted to nations
or regions but have become international processes. An increasing concentration in
agribusiness sectors, an enormous increase in cross-border flows of livestock and agri
products and the creation of international forms of cooperation is observed in the recent
time.
Two broad principal explanations can be advanced for the increasing interest in
agricultural SCM: the industrialization of agriculture, and the uncertainty associated with
variations in product quality and safety (Kenneth et al., 1998131
). The trend towards
vertical coordination of agricultural supply chains (ASC), integration of processes from
farm to plates, reduction of government support (subsidies) for agriculture, globalization
and competition among producers, processors and suppliers, explosion in technological
128
C. Fischer, M. Hartmann, N. Reynolds, P. Leat, Revoredo-Giha C., Henchion M.
and Gracia A., “Agri-
food chain relationships in Europe – empirical evidence and implications for sector competitiveness”,
paper read at 12th Congress of the European Association of Agricultural Economists–EAAE, 2008. 129
E. Raynaud, L. Sauvee, and Valceschini E., “Alignment between Quality Enforcement Devices and
Governance Structures in the Agro-food Vertical Chains”, Journal of Management and Governance, Vol.,
9, (2005). 130
A. Fearne, D. Hughes, Duffy R, “Concepts of Collaboration: Supply Chain Management in a Global
Food Industry”. In: Eastham J, Sharples L, Ball S (ed.), Food Supply Chain Management: Issues for the
Hospitality and Retail Sectors, (London: Oxford Publications, 2001), pp. 55-89. 131
J. Kenneth, M. Fulton, Molder, P., and Brookes, H., “Supply chain management: the case of a UK baker
preserving the identity of Canadian milling wheat”, Supply Chain Management, Vol. 3, No. 3, (1998), pp.
157-166.
77
progress applicable to the agri-food industry, changing consumer demand and
consumption patterns, etc, are some of the factors related to the concentration and
industrialization of agriculture.
The agribusiness sector is becoming an interconnected system with a large variety of
complex relationships, reflected in the market place by the formation of Agri Supply
Chain Networks (ASCNs) via alliances, horizontal and vertical cooperation, forward and
backward integration in the supply chain and continuous innovation (Beulens et al.
2004132
). The latter encompass the development and implementation of enhanced quality,
logistics and information systems that enable more efficient execution of business
processes and more frequent exchange of huge amounts of information for coordination
purposes (Van der Vorst et al. 2005133
). All these developments necessitate a
reorientation of all the players in Indian agriculture sector and food industry on their
roles, activities and strategies.
Agriculture is inherently a fragmented industry, involving a diverse range of distinct
enterprises (farmers, processors, wholesalers and distributors), and relies on inputs from
various sources, often at distinct geographical locations. In agriculture marketing chain,
most marketers and processors obtain their supply from diverse sources (farmers,
retailers, brokers) in order to meet marketing and production targets.
In recent years, Supply Chain Management has begun to emerge as a discipline, and the
supply chain framework can yield a deeper understanding of agricultural marketing
132
A.J.M. Beulens, L.W.C.A. Coppens, and Trienekens, J.H., “Traceability requirements in food supply
chain networks”, (Wageningen: Wageningen University, 2004), Working Paper. 133
J.G.A.J. Van der Vorst, A.J.M. Beulens, and Van Beek, P., “Innovations in logistics and ICT in food
supply chain networks”, In: Jongen, W.M.F. and Meulenberg, M.T.G. (ed), Innovation in agri-food
systems: product quality and consumer acceptance, (Wageningen: Wageningen Academic Publishers,
2005), pp. 245-292.
78
issues in developing economies than more traditional approaches to agricultural
marketing (Jagdish and Martin, 2006134
).
Supply chain management encompasses the planning and management of all activities
involved in sourcing and procurement, conversion, and all logistics management
activities. Importantly, it also includes coordination and collaboration with channel
partners, which can be suppliers, intermediaries, third party service providers, and
customers. In essence, supply chain management integrates supply and demand
management within and across companies (CSCMP, 2005135
).
Jagdish and Martin (2006136
), in their study about Agricultural Marketing and
Agribusiness Supply Chain Issues in Developing Economies; envisaged supply chain as a
value-creation process, whereby all firms in a chain link and align with each other to
create value for the chain as a whole. They have argued that value creation occurs
through firm operations, integration of processes, and logistics and quality control
(product maintenance). It is further argued that value creation throughout the chain is
supported by information flows, and achieved through vertical integration and
relationship management
From the above review, it can be noted that value creation occurs primarily through
operations. This is achieved through product transformation (processing) or product
enhancement (cleaning, grading, packaging or presentation). Value is also created
through the integration of processes along the chain; that is, the seamless meshing of
processes as the product moves from one point in the chain to the next. Value is further
created through logistics (where product is transported from one point in the chain to the
next in a cost and time effective manner) and quality control (where the quality of the
134
A. Jagdish and S. Martin, “Agricultural Marketing and Agribusiness Supply Chain Issues in Developing
Economies: The Case of Fresh Produce in Papua New Guinea”, (Paper read at New Zealand Agricultural
and Resource Economics Society, New Zealand, August, 2006), pp. 22 (Photocopy) 135
Council of Supply Chain Management Professionals (2005),
http://cscmp.org/aboutcscmp/definitions.asp. 136
A. Jagdish and S. Martin (2006), op. cit., p. 2.
79
product is maintained through packing, transporting and cool or cold chain procedures).
This value creation is supported by clear information flows up and down the chain. These
information flows link suppliers and intermediate customers with market demands (such
as product form, quality and quantity required), and markets with supply (such as quality
and quantity available).
It is argued that value creation is achieved through vertical integration and relationship
management. Vertical integration often occurs when the key player in the chain – the
chain leader – undertakes a number of processes (for example, production, processing
and distribution) itself and retains ownership of the product while doing so. Value
creation can also be achieved through the management of relationships between various
parties as the product moves down the chain. In most of the cases, but not always, these
relationships will be associated with changes of ownership of the product. Chain
relationships can cover a spectrum, ranging from arms length (open market) to some
involvement (contracts) to extremely close (strategic alliances or even joint ventures).
From the marketing and processing perspectives, SCM is an essential tool for integrating
the activities of the various suppliers within the organisation’s operations in order to
assure the consistent delivery of quality assured products and services to the consumer.
For the consumer and other stakeholders, SCM focuses on improving the performance of
the supply chain through the delivery of guaranteed safe, desirable and good quality
product in a cost effective manner (Viaene and Verbeke, 1998137
). Optimizing the entire
supply chain, therefore, requires a level of information sharing, teamwork, cooperation
and collaboration among the participating enterprises (Horvath, 2001138
).
137
J. Viaene and W. Verbeke, “Traceability as a key instrument towards supply chain and quality
management in Belgian poultry meat chain”, Supply Chain Management, Vol. 3, No. 3, (1998), pp.139-
141. 138
L. Horvath, “Collaboration: the key to value creation in supply chain management”, Supply Chain
Management, Vol. 6, No. 5, (2001), pp. 205-207.
80
2.6 Uncertainty in Agricultural Supply Chain
Characteristics of Supply Chain Networks (SCN) are product and company specific
(Reiner and Trcka, 2004139
) - that implies that each SCN has a specific configuration,
type of processes, resources, market, management strategies, standards, organization etc.
In case of Agri Supply chain Networks (ASCNs), there are some additional
characteristics that make these networks even more specific (Van der Vorst et al,
2005140
):
• Shelf life constraints, quality decay of products, and requirements regarded
product freshness and food safety;
• Long production throughput times, product dependent cleaning and processing
times, production seasonality and (necessity) for quality testing time;
• Variability of product quality and supply quantity of farm-based inputs;
• High volume production systems and capital-intensive machinery;
• Specific requirements for logistic processes;
• Unpredictable consumer demands;
• Legislations concerning production, distribution, trade, quality of products etc.
These specific characteristics of ASCNs lead to a further amplification of uncertainty,
complexity and vulnerability within these networks.
Van Landeghem and Vanmaele, (2002141
) had noted that uncertainty is an inherent
characteristic of SCN and it has a large impact on supply chain performance. Decision
makers experience supply chain uncertainty when they are unable to accurately predict
139
G. Reiner and M. Trcka, “Customized supply chain design: Problems and alternatives for a production
company in the food industry. A simulation based analysis”, International Journal of Production
Economics, Vol. 89, (2004), pp. 217–229. 140
J.G.A.J. Van der Vorst, S. Tromp, D.J. van der Zee, “A simulation environment for the redesign of food
supply chain networks: modeling quality controlled logistics”, M. E. Kuhl, N. M. Steiger, F. B. Armstrong,
and J. A. Joines, (ed)., (Winter Simulation Conference: Conference proceedings, 2005), pp. 1658 – 1667. 141
H. Van Landeghem, and H. Vanmaele “Robust planning: a new paradigm for demand chain planning”,
Journal of Operations Management, Vol.20, (2002), pp. 769 – 783.
81
the impact of control actions on system behavior (Van der Vorst, 2000142
). Uncertainty in
the supply chain can take many forms and one of the key sources of uncertainty in the
supply chain relates to the quantities, timings and specifications of end-customer demand
(Stevenson and Spring, 2007143
). In the other words, uncertainty within the ASCN can be
seen as a characteristic of material, information and financial flow realization and it can
be seen from different aspects, such as:
• Time: in the sense of duration of activity/process, starting or ending moment of
activity realization, how often some activity/demand happen;
• Quantity: in the sense of supply, demand or physical transfer/modification of the
goods;
• Location/place: in the sense where activity starts/finishes;
• Quality: in the sense of quality of service and quality of product;
• Cost: in the sense of transaction cost, but also in the sense where, when and why
some additional cost may be generated.
The high variability in quality and magnitude that is characteristic of the agricultural
environment (both production and handling and processing) and basic raw materials
creates uncertainty in the ability of the industry to assure a consistent supply of good
quality and safe products to the consumer. As a result, the transaction cost (information,
negotiation and monitoring) associated with market-driven (industrialized) agriculture
and the uncertainty of product quality, quantity and safety has increased in recent times
(Williamson, O., 1979144
).
142
J.G.A.J. Van der Vorst, “Effective food supply chains, Generating, modeling and evaluating supply
chain scenarios”, (Wageningen University: doctoral dissertation, 2000). 143
M. Stevenson and M. Spring “Flexibility from a supply chain perspective: definition and review”,
International Journal of Operations & Production Management, Vol. 27, No. 7, (2007), pp. 685 – 713. 144
O. Williamson, “Transaction-cost economics: the governance contractual relations”, Journal of Law and
Economics, Vol. 22, (1979), pp. 233-261.
82
2.6.1 Causes of Demand Uncertainty and Variability
Given the diversity of the agriculture sector there are number of factors leads to
variability and uncertainty in to the supply chain network. These different sources of
uncertainty in the supply chain impact on governance structure and coordination
mechanisms different differently (Sutcliffe and Zaheer, 1998145
).
2.6.1.1 Variability in consumer demand
Variability in demand is a common feature of agri supply chains; however, there are
significant differences in the degree of variability and in the causes of variability. When
end-user demand is highly variable, managers often argued that seasonality or
unpredictable events such as weather changes were the reason (Taylor, 2006146
).
Agricultural products are highly environmental sensitive. Changes in the climate affect
the production and quality of the product. In such events a noticeable demand variation
can be experienced. Even different country has a different quality and safety norms (i.e.
GMO, EUROGAP, ISO etc.) lead to more complexity into the demand prediction and
hence increase the uncertainty across the supply chain.
2.6.1.2 Environmental Uncertainty
According to Folkerts et al., (1998) the agri sector has a high dependency on historical
and cultural aspects. In this sector different distribution systems, legal and regulatory
environments regarding processing, packaging, distribution and food safety requirements
(i.e., AGMARK, ISO or traceability) is found. Again in this sector where cooperatives
are involved there is a need to consider the legal and regulatory environment. The
uncertainty created by the lack of stability in the regulatory environment has discouraged
145
K. M. Sutcliffe and A. Zaheer, “Uncertainty in the Transaction Environment: An Empirical Test”,
Strategic Management Journal, Vol. 19, No. 1, (1998), 1-23. 146
D. H. Taylor “Demand management in agri-food supply chain: An analysis of the characteristics and
problems and a framework for improvement”, The International Journal of Physical Distribution &
Logistics Management, Vol. 17, No. 2, (2006), pp. 163-186.
83
private sector investment in supporting marketing infrastructure, agro-processing and
agro industry that could have expanded demand for primary agricultural products as well
as reduce the vulnerability.
From the logistics point of view, sources of the ASCN vulnerability (Peck, 2006147
;
Dong, 2006148
) are different kinds of deviations (usually regarding customer demands,
but also regarding duration of logistic activities), disruptions and disasters (usually
regarding supply of money, food, water, energy or fuel, system of communication or
regarding climate causes).
2.6.1.3 Behavioural Uncertainty
It has three main components (Vorst and Beulens, 2000149
):
(i) Product quality fluctuations: supply quality, customer demands for product
specification, produced product quality and product quality after storage, and
information accuracy.
(ii) Product quantity fluctuations: supply quantities, customer demand for product
quantity, product yield and scrap, and information availability, and
(iii) Time fluctuations: supplier lead time, customer order distribution lead time,
production throughput time, storing time, and information throughput time.
147
H. Peck, “Resilience in the UK Food & Drink Industry: Research Design and Methodology”, (Oslo: The
Nordic Logistics Research Network Conference Proceedings, June 2006). 148
M. Dong, “Development of supply chain network robustness index”, International Journal of Services
Operations and Informatics, Vol. 1, No. 1, (2006), pp. 54-66. 149
J. Van der Vorst and A. Beulens, “Identifying sources of uncertainty to generate supply chain redesign
strategies”, International Journal of Physical Distribution & Logistics Management, Vol. 32, No. 6, (2002)
pp. 409-30.
84
In the agriculture sector additional factors that increase uncertainty such as agricultural
production seasonality and product perishability. This together with demand uncertainty
makes the agri chain very difficult to predict and control (Bailey, 2001150
).
2.6.1.4 Disconnect between agricultural production and consumer demand
The issues that have been dealt with so far can generally be regarded as operational.
However, there is one particular feature of demand management in agri-food chains that
is more of a structural matter. Generally, agricultural production has long lead times. For
example, cumin, isabgul and fennel have a more than three months production lead time;
planting to harvest cycle. Taylor (2006151
) has found in none of the chains studied out of
six agri-supply chains; there was a systematic attempt to closely link agricultural
production at the time production decision were made, to anticipated consumer demand
at the time the product would be harvested. In none of the chains did the buyer provide
long term forecasts of consumer demand that could help to inform volume decisions at
the start of the farming process. Instead farmers would either make their own judgments
about how much to produce, or at best do this in loose liaison with their immediate
downstream customer. In all of the chains studied, farm production was essentially a
“push” system. In consequence, there was a propensity for imbalance between farm
supply and consumer demand. These imbalances were rectified by the spot market.
However, this had negative consequences for both farmer and retailer. In times of surplus
farmers received lower prices, whilst in times of shortage retailers lost quality assurance
by having to buy from the spot market. The production of a joint long term forecast by
farmers, processors and retailers for a time period determined by the growth cycle of the
particular product would be an important step in helping to link farm production to
consumer demand.
150
W. C. Bailey, “Applying SCOR in a Vertical Industry-Food and Agriculture”, World Supply Chain
Council Annual Meeting, (Palmerston North, New Zeland: Massey University, 2001). 151
D. H. Taylor, “Demand management in agri-food supply chain: An analysis of the characteristics and
problems and a framework for improvement”, The International Journal of Physical Distribution &
Logistics Management, Vol. 17, No. 2, (2006), pp. 163-186.
85
2.6.1.5 Geographical dispersion
The geographical dispersion of processes affects coordination costs in industries where
operations have to be located close to their customer base (Carman and Langeard,
1980152
). A high spatial dispersion of production and commercial processes is considered
an important determinant of supply chain configurations (Combs and Ketchen, 1999153
;
Tan et al., 2002154
; Ziggers and Trienekens, 1999155
). The fact that agricultural
commodities are biological products that can be produced only under certain spatial
characteristics (i.e. soil characteristics, water availability, temperature range, frost-free
areas, insects and diseases presence); this location specific production constraints restrict
the availability of the produces all around the world and does not guarantee all-year-
round supply and hence disturb the smooth flow supply. Isabgul is produced mostly in
North Gujarat, South Rajasthan and in limited area of Madhya Pradesh in the world
because of its suitability of the climate to this commodity. This restricts the availability of
this commodity across the world. Second the commodity is highly vulnerable to climate
changes discourage the production in the limited producing area and hence increase the
supply uncertainty.
2.6.2 Managing Uncertainty
To maximize a competitive advantage, all members within the SC should ‘seamlessly’
work together to serve the end consumer (Towill, 1997156
). It is no longer possible to
cope with uncertainties by building inventories, creating slack in time or by providing
152
J. M. Carman and E. Langeard, "Growth Strategies of Service Firms", Strategic Management Journal,
Vol. 1, (1980), pp. 7-22. 153
J. G. Combs and D. J. Jr. Ketchen, "Explaining Inter-firm Cooperation and Performance: Toward a
Reconciliation of Predictions from the Resource-Based View and Oganizational Economics", Strategic
Management Journal, Vol. 20, (1999), pp. 867-888. 154
K. C. Tan, S. B. Lyman and J. D. Wisner, "Supply Chain Management: A Strategic Perspective."
International Journal of Operations and Production Management, Vol. 22, Nos. 5-6, (2002), pp. 614-631. 155
G. W. Ziggers and J. H. Trienekens, "Quality Assurance in Food and Agribusiness Supply Chains:
Developing Successful Partnerships", Production Economics, Vol. 60-61, (1999), pp. 271-279. 156
D. R. Towill, “The seamless supply chain-the predator’s strategic advantage”, International Journal of
Technology Management, Vol. 13, (1997), pp. 37–56.
86
additional capacity (Newman et al., 1993157
). These anticipations of uncertainties lead to
increased logistic costs and a reduction in the flexibility of the production organisation.
For seamless working of supply chain, integration of supply chain processes is the
prerequisite. The recent developments in Information and Communication Technology
(ICT) facilitate this process. Many companies are re-engineering and rationalising their
logistical networks to take advantage of the reduction in, or elimination of, numerous
artificial barriers that have previously affected all logistical decisions.
SCM provides the opportunity to reduce decision making uncertainty in the SC, which
management has considered as unchangeable ‘givens’ (Silver et al., 1998158
) up to now.
SCM can eliminate ‘broken SCs’, as Davis (1993159
) calls them, which have substantial
stock held at one point to enable another actor in the SC to operate with minimal stock.
Coordination and collaboration with key suppliers and customers will reduce uncertainty
and complexity in an ever-changing global environment and minimise risk while
maintaining flexibility (Handsfield and Nichols, 1995160
). This is confirmed by Sheombar
(1995161
) who concludes that co-ordination in dyadic partnerships leads to a reduction in
task uncertainty, which ultimately results in improved performance. It is also in
accordance with Mason-Jones and Towill (1999162
), who state that ‘those companies
which cope best with uncertainty are most likely to produce internationally competitive
bottom-line performances’.
157
W. R. Newman, M. Hanna and M. J. Maffei, “Dealing with the uncertainties of manufacturing:
flexibility, buffers and integration”, International Journal of Operations & Production Management, Vol.
13, No. 1, (1993), pp. 19-34. 158
E. A. Silver, D. F. Pyke, and R. Peterson, Inventory Management and Production Planning and
Scheduling, (3rd ed), (New York: John Wiley & Sons, 1998). 159
T. Davis, “Effective supply chain management”, Sloan Management Review, (Summer, 1993), pp. 35-
46. 160
R. B. Handfield and E. L. Nichols, Introduction to Supply Chain Management, (NJ: Prentice Hall,
1995). 161
H. S. Sheombar, Understanding logistics co-ordination–a foundation for using EDI in operational
(re)design of dyadical value adding partnerships, (Tilburg: Dissertation KUB, Tutein Bolthenius,`s
Hertogenbosch, Tilburg University, 1995). 162
R. Mason-Jones and D. R. Towill, "Shrinking the supply chain uncertainty circle", Control, (1998),
pp.17-22.
87
PART III: AGRI SUPPLY CHAIN MANAGEMENT PRACTICES
AT APMCS
2.7 Introduction
A supply chain has been described as a system whose constituent parts include material
suppliers, production facilities, distribution services and customers linked together via the
feed-forward flow of materials and the feedback flow of information (Stevens, 1989163
).
Recently there has been a shift of focus in supply chain management towards a more
integrated approach (Towill, 1997164
). Integrated supply chain management is a process-
oriented, integrated approach to procuring, producing, and delivering products and
services to customers. Integrated supply chain management covers the management of
materials, information, and fund flows (Metz, 1998165
).
The agriculture marketing chain is a complex network of enterprises of varying sizes and
activities. It includes the farmers (producers), aggregators at village level, commission
agents (Kutcha Arhatiya), traders, buyers/wholesalers/exporters (Pakka Arhatiya),
processors, marketing organisations including agricultural produce marketing
committees, where commodities’ buying-selling process is carried out through open
auction process, agriculture marketing co-operatives viz. NAFED, GUJCOMASOL etc.
and distributors (wholesalers, retailers etc.)
Some important characteristics and changes in marketing channels witnessed during the
last fifty years are as follows (Acharya, 2005166
):
163
G. C. Stevens, “Integrating the supply chain’’, International Journal of Physical Distribution
&MaterialsManagement, Vol. 19 No. 8, (1989), pp. 3-8. 164
D. R. Towill, “The seamless supply chain-the predator’s strategic advantage”, International Journal of
Technology Management, Vol. 13, (1997), pp. 37–56. 165
P. J. Metz, “Demystifying Supply Chain Management.” Supply Chain Management Review, (Winter
1998), pp. 1-11. 166
S. S. Acharya, Agriculture Marketing and Rural Credit: Status, Issues and Reform Agenda, (New Delhi:
Asian Development Bank Report, 2005), pp. 1-36.
88
(i) The proportion of total marketed surplus going directly from farmers to
consumers continues to be small.
(ii) The role of transporters and processors in the marketing channel has considerably
increased.
(iii) Specialization of traders in agricultural marketing, both in terms of commodity or
marketing functions has shown an increasing tendency.
(iv) The length of marketing channel has tended to increase due to increase in demand
for value-added services and geographic expansion of markets.
(v) The share of private trade in handling marketed surplus has continued to be large.
Taking all agricultural commodities together, the marketed surplus handled by
cooperatives has been estimated as 10 percent, and by public agencies 10 percent.
The private trade handles around 80 percent of the total marketed quantities of
agricultural commodities (Acharya, 1994167
).
(vi) Realizing that the marketing channels for farm products should be as short as
possible, and to eliminate intermediaries, direct marketing by farmers has been
encouraged during the last one decade.
In India wholesalers buy agricultural produces from the Agriculture Produce Market
Committee (APMC or market-yard), which is established in every state or in every major
producing region by the Government. The APMC is meant to consolidate buyers and
sellers in a central place to reduce time, effort and cost. In the APMC there are buyers
and traders who are surrounded by commission agents on all sides. These commission
agents deal with farmers and wholesalers on behalf of farmers as well as traders. These
commission agents deal with consolidators (who represent the small farmer) on the
167
S. S Acharya, “Marketing Environment for Farm Products: Emerging Issues and Challenges”, Indian
Journal of Agricultural Marketing, Volume 8, No. 2, (July-December, 1997), pp. 149-74.
89
farmer’s side and wholesalers on the retailer’s side. These consolidators and commission
agents charge their fees as a percentage of the transaction. This number of people varies
across the markets, and their percentages also vary.
2.8 Working Model of APMC
2.8.1 Introduction
Before the application of the APMC Act, market-yards were unregulated and they were
run on own accord of traders. Facilities of open auction, standard weightment, cash
payment protection from malpractices and amenities were not provided to the sellers.
Farmers (sellers) were exploited by all the intermediaries by imposing one or another
charge and hence major part of the revenue and profit was churned by intermediaries
whilst producers got minor. To protect the interest of the farmers the state government
has established the APMCs in mostly all the taluka centers for different commodities and
standardize the sell-purchase mechanism as under. Although in only few state where
system is well functioning with greater level of transparency into the system.
2.8.2 Method of Sale
The Agricultural Commodities brought by the farmers for the sale in the market-yard, are
arranged in open heaps in the plots allotted to general commission agents. Heaps of
commodities sell through open auction conducted by the paid auctioneers of the
committee. Price has been quoted based on the quality of the agricultural produce and
demand-supply gap. Open observation method is used to judge the quality grade of the
produce. Price has been discovered through open auction outcry. Auction clerks used to
note the details of the sale on the same time, which will be used to solve the disputes. If
expected price is not discovered during open auction, farmer can deny selling his
produce.
90
2.8.3 Weight, Sieving and Delivery
After auction the product is filled into the standard jute bags, weight is noted down and
sieved for minimizing adulteration. According to the serial order, the delivery of the
product is taken by the buyer in the market-yard on the place of auction after standard
weights. The product has been transported from commission agents’ plot or auction
platform to buyer’s place through pull-cart by porters.
2.8.4 Payment System
Once the delivery of the product is handed over, a cash memo is prepared by the general
commission agent or purchaser in triplicate copies. One copy of memo is given to the
seller with cash payment, another to Market Committee office and a copy is kept by
commission agent or purchaser for his own record. It is interesting to observe that the
cash payment method is introduced particularly to the farmers. The full payment is made
on the same day to the farmers, once he agreed to sell his product and the value of sale is
recovered from the purchaser on the fixed day (mostly on the next day) with market fee
(cess) of 0.5% of the total sale value. Commission agent charges commission charges of
1% of the sale value from the buyers (pacca arhatiyas) for providing the services.
2.8.5 Market Charges
The Market Committee collects market charges from the purchasers and sellers. There
isn’t any charge collected from the farmers. On every transaction the buyer has to pay 1%
commission charge to the commission agent and 0.5% market fee (cess) to the APMC. It
is mandatory for all traders to have the license for trading into the notified area of APMC
by paying annual license fee of Rs. 100/-.
The other charges includes loading/unloading charges, weighing charges, charges for
sieving, cleaning and filling the bags, storage and warehousing charges etc. is paid when
and where required.
91
2.9 Chain Intermediaries
Generally, agriculture commodities undergo a change of ownership through time and
space. The intermediaries are involved in passing of the commodities from producer to
ultimate consumer.
In India, agricultural commodities move from the farm gate to consumers through several
channels. These channels for agricultural commodities vary from commodity to
commodity but can be broadly divided into four groups for the commodities under study.
(a) Direct from farmers to consumers;
(b) Farmers ---- Aggregators ---- Commission Agent ---- Pacca Arhatiyas --- Retailers ---
consumers;
(c) Farmers ---- Commission Agent ---- Pacca Arhatiyas --- Exporters; and
(d) Through processors.
Economic Times Intelligence Group (ETIG, 2003168
) has noted that the Indian
Agriculture marketing chains comprise the following major players normally.
(1) Farmers;
(2) Consolidators/Aggregators;
(3) Traders;
(4) Commission agents (Kutcha Arhatiyas);
(5) Wholesalers/Buyers/Exporters (Pacca Arhatiyas);
(6) Processors; and
(7) Retailers;
168
Changing Gears: Retailing in India, (Economic Times Knowledge Series, Mumbai: Economic Times
Intelligence Group, 2003).
92
2.9.1 Farmers/Producers
Farmers or producers perform one or more marketing functions. They sell the produce in
the village or in the market. Some farmers, especially large ones assemble the produce of
small farmers, transport it to the nearby market, sell it and make a profit. They are the
main feeder in to the network. Agrawal and Singh (2003169
) noted that the farmers
behaviour with respect to sale of their surplus produce and the pattern of flow of surplus
produce in the marketing channels is influenced by number of factors as proximity to
market, price of the produce in the market, availability of transport facilities, available
storage facilities, financial position of the farmer, etc.
After harvesting, farmers; mostly small farmers immediately sell their produce to village
aggregators (agents or large farmers) or bring their farm produce to the preferred; nearby
market-yard and dump all the produce at the shop of the known commission agent
(Kutcha Arhatiya). Mostly farmers have an age-old relationship with commission agents
with whom they are in touch round the year for getting the information related to the
prevailing price. The large farmers with a holding capacity mostly store the produce in
their go-down (non-standardise storage place/kutcha house etc.) and wait for preferable
price. They sell their product at any time round the year. They also perform the trading
activities. They are actively involved in the trading activities and get in touch with the
intermediaries i.e. commission agent, buyers etc. to update the information related to the
prevailing prices in the major market yards as well as international markets. Risk
management practices through commodity future markets in commodities like Cumin,
Castor etc. is gaining momentum in the large farmers segments.
Mostly group of small, marginal and medium farmers bring their produce to the preferred
market-yard by hiring common transport services to reduce the transportation cost per
unit. Farmers with small surplus produce preferred to sell the produce to the village
aggregators (agent or trader) or large farmers in his village or in the nearby town.
169
N. L. Agrawal and N.Singh (2003), “Cumin seed marketing in Rajasthan”, Agriculture Marketing, Vol.
46, No. 1, pp. 36-42.
93
Farmers have to accept the price offered by aggregators or large farmers because they do
not have alternate viable choice as quantity is not sufficient to bring to the nearby market-
yard where they can get the better prices through open auction mechanism.
2.9.2 Consolidators/Aggregators
Consolidator/Aggregator is a link between farmers and commission agents; functions as a
consolidator of the small farmers’ produce for his own behalf or for commission agent in
the market-yards. He mostly amasses the small surplus produces of the small farmers and
brings it to the preferred market-yard nearby or to the market-yards where better price
can be realised. Mostly, not always, he works as an agent of the commission agent.
Brings all the produce collected from the small and marginal farmers to the commission
agent’s shop. He advances money to the farmers to cultivate the particular crops and
charges nominal interest with the condition that the produce will be disposed off through
him only. Farmers have to accept the price quoted by him as they don’t have other
alternatives.
If he has amasses the product for his own behalf, he sells off the aggregated product
through commission agent in the preferred market-yard; mostly where better price can be
realised; and earn profit. Sometimes large aggregators sell directly to the pacca arhatiyas
at the preferred price without going through the open auction process and save some
transaction cost (i.e. unloading/loading, commission charges etc.). As an agent of
commission agent, he works as a representative of the commission agent into particular
village or in to particular markets. He consolidates the farm produces of farmers; bring it
to the commission agent’s shop where produces sell off through open auction process. In
this process the transportation cost per unit is reduced and associated other transaction
cost can be minimised. Farmers get the better price as price is discovered through open
auction process. Sometimes commission agent advances money to the farmers through
consolidators. Agent gets the commission from commission agent for services rendered
by him.
94
2.9.3 Traders
Person or entity has holding capability with the objective to earn the profit through
arbitrage opportunity (buying and selling of the commodities) in the same market or
between the markets within particular time period are called the traders in the chain.
Typically, they are known as a Teji-Mandiwala. They amass the commodities by
purchasing from the particular market-yard through commission agent, store it for a
particular period and sell it when they get adequate price of the stored products through
again commission agent or directly to the other intermediaries in the chain. They do not
posses mostly; not always; any infrastructure required adding value to the products. They
consume the services (i.e. storage, arrangement for sale and purchase of products,
packaging etc.) provided by the commission agents or other intermediaries on paying the
charges for the services avail by them.
Even mostly traders accumulate the small quantity produce of the small and marginal
farmers from the far distance markets and bring it to the major markets to sell the same
and earn substantial profits.
2.9.4 Commission Agents
They are mostly known as a Kutcha Arhatiya. They are the license holders in to a
particular APMC. They are an important link between farmers and buyers in the chain.
They assemble the farmers’ produces and make the arrangement for sale of the same. In
few APMCs they also advances the money to the cultivators on the condition that the
produce will be disposed off through him only and hence charges very nominal interest.
Even many times commission agents advance interest free money to the farmers with
whom they have an age old business relationship and encourage them to cultivate the
commodities they trade with the condition of disposing off through him only. They
charges commission for services (related to the sale of the produce) render by them from
buyers not from farmers. It is interesting to note that the commission agents also make
the arrangement of cash payment on the spot to the farmers once the produced has been
95
sold off. If the price discovered in the open auction is lower than the farmers’ expectation
and if farmers do not agreed to sell his produce, in selected market-yards the commission
agent facilitates the famers for storage of the produces till the farmer’s expected price
would not be realised and also advances the partial payment to the farmer on stored
produce, if demanded. They also function as the trader.
2.9.5 Buyer/Wholesaler/Exporters
They are the real buyers of the commodities in the wholesale market on their own behalf
or acting for some businessmen or firms in consuming markets. They are also license
holders in to particular APMC. They are mostly known as Pacca Arhatiyas.
Organisations from food, spices, healthcare etc. industries play as their agent and order
him to purchase certain quantity within a given range of price. When pacca arhatiya
trades on his own, he disposes off his product brought by him through retailers in
different parts of the country as well as abroad.
As a part of forward integration strategies, large firm of pacca arhatiyas have set up their
own processing units for cleaning, grading, de-husking and preparing the quality as
demanded by the customers in the domestic as well as international market. These in turn
reduce their logistic cost i.e. transportation, handling, storage etc. and other commissions
charged by the processors. They also work as indirect as well as direct exporter too. Their
high risk barring capacity makes possible to enjoy the higher profit margin by selling
products in the international market. On other side, they possess the risk of currency
fluctuation, price variability and demand uncertainty. Most of the buyers hedge their risk
through forward/future commodity market.
Only few wholesalers work as direct exporters. Mostly they prefer to sell their goods
through indirect export mode. They prefer to sell their goods to the export merchants or
through importer’s agents working into India to reduce their risks as commodity business
is high risk low margin business.
96
In the recent time a few wholesalers have ventured into retail business too. Retail venture
offers plenty of opportunities in the domestic market as household consumers started
demanding machine cleaned, different quality grades and different forms (i.e. powder
etc.) of the commodities for their daily consumption. These reduce their dependency and
risk of single market outlay as well as reduce the risk of international exposure. They
mostly perform the functions like sales, marketing, branding, storing, grading and
standardization, packaging, logistics and distribution etc and manage the processes like
demand management, new product development, quality management, customer
relationship management, order processing, returns, supplier relationship management
etc.
Pacca Arhatiyas are the major source of information related to the demand of the
particular commodities into domestic as well as international market for the most of the
intermediaries as well as farmers. As such there isn’t any scientific method being used by
them to estimate the demand. Based on the export inquiry, domestic consumption,
domestic production estimates, stock available in the market and production output
estimation in other producing countries etc. the demand and the price of the commodities
are estimated. Mostly information about all these would be disseminated through rumors
than any authentic agencies. Hence a huge volatility into the price and variability into the
demand can be observed in to the system.
2.9.6 Processors
They are independent entities or business concerns specialized in performing various
functions viz; washing, cleaning, cutting, de-husking, milling, grading, packaging,
quality management etc. Mostly pacca arhatiya, wholesaler and retailers, who do not
have processing facilities, make the contract for processing their products in prescribed
quality grade. Processors are also facilitating in packaging, quality certification etc. as
and when demanded by the customers.
97
Processing units are normally located nearest to the market-yard of the particular place.
Though Rajasthan occupies a premium position in production of Isabgul (60%), Cumin
(58%) and significant share of Fennel of the total production of the country, Gujarat
emerged as a processing hub for all three commodities. In Gujarat, majority of the
processing units are located near Unjha, on Siddhpur-Mehsana Highway. Development of
the processing facilities nearby Rajkot city is also observed.
Processors are also directly purchasing the agriculture produces from farmers or from the
market-yard through open auction process. They do the processing activities, prepare
different quality grade fulfilling all the international and domestic food standards. They
also act as indirect as well as direct exporter. In domestic market they mostly sell their
goods across the country through wholesale and retail networks.
2.9.7 Retailers
Retailer can be an independent entity or forward integrated venture of any entity (i.e.
buyers, processors etc.) into the agriculture marketing channels. They are selling their
products directly to the final consumers. Mostly retailers from the place where trading
activities are performed; purchase the raw agricultural produces from the market yard
directly and send it to the processing units where cleaning, cutting, grading,
standardizing, packaging activities can be performed. While retailer from the far distance
places mostly purchase the products either directly from processors or from the pacca
arhatiyas with specified quality and packaging size specification. In villages farmers with
little marketable surplus, sell their produce directly to the retailers.
2.9.8 Support Service Providers
The other supported intermediaries include transporters, storage service providers, banks
etc. They are the value added service providers. Most of the goods transported through
roadways in the domestic market. Railway is another; but rarely used mode of the
transportation. While for export markets mostly sea route is preferred as it is cheapest.
98
Air way is also preferred. For domestic distribution, marketer prefers local transporter
while for international distribution specialized logistic services providers’ services like
export documents preparation, custom clearance, cargo booking etc. is obtained.
Storage is a critical and important value adding activities in agriculture marketing.
Requirement and the type of the storage facilities is commodity specific. This is based on
physical characteristic of the product (i.e. perishability). In the agriculture sector, quality
(i.e. smell, colour, fiber, etc. in case of fennel, cumin and isabgul) of the product gets
affected if the proper storage mechanism is not used as most of the products are sensitive
to climate change. Cold storage as well as normal storage can be used for storing.
Particularly, in normal storage facilities a scientific design and construction mechanism is
required to maintain the temperature, moisture etc and to protect it from germ and pest.
This helps in maintaining quality while storing the products for long duration. In cold
storage the quality of the produce can be maintained.
Banks are the major source of finance. Most of the intermediaries get the finance from
the bank on their stored goods. Based on the valuation of the stored goods in the go-
down, banks; mostly cooperative banks finance 70 % of the value of the stored goods to
the owner of the goods with the normal interest charges of 12 % per annum. Owner
mortgage all the goods to the banks as a guarantee against the finance. Bank sealed the
go-down. On the request to sell the produce in the market bank open the seal, allows the
owner of the goods to sell the goods and recover all the advances from the transaction.
One more important link is the future commodity market. Future market is providing the
platform for managing the risk of price volatility to all the intermediaries. By using this
platform one can hedge the risk by selling or purchasing a required quantity lot into or
from the future commodity market. Commodity wise contract lot size is standard and
specified.
Mostly all produces are brought into the market-yard, stored and packed into the jute
bags as a raw produces. To protect from the moisture contains, a plastic bag is also used
99
inside the jute bag. Processed commodities are packed into different sizes varies from 60
kgs bags to 5 grams sachet on demand. The different sizes i.e. 60 kgs, 40 kgs and 10 kgs
plastic bag as well as corrugated box packaging and 1 kg, 500 gms, 200 gms, 100 gms,
50 gms, and 5 gms plastic bag packets are prepared. For 1 kg to 5 gms packet, the packets
are packed into the corrugated box and then transported to the different part of the
country and abroad. The packaging materials provider facilitates into label printing and
other details requested by the client.
2.10 Lacunas of the existing system
As compared to developed countries, the Indian agricultural supply chain is more
complex and difficult to manage because of an unorganised market, price escalation and
large number of intermediaries. Table 2.4 represents the Indian intermediaries and
comparable American intermediaries along with the margins and value additions made by
them. Some of the reasons for the existence of these intermediaries in the supply chain
are:
• Age-old historical loyalty of farmers to their agents, because these agents provide
debt to the farmer;
• Local understanding and relationships with transporters;
• Lethargy on the part of government and NGOs to educate farmers regarding other
options;
• Lack of guidelines and rules in the development and supply of produce staples;
• Organised cartels between commission agents, wholesalers and transporters;
• Lack of scale in terms of what each farmer produces, sheer numbers of small
farmers drive down bargaining power; and
• Lack of effort in development from front-end players (retailers) and institutions.
100
Table 2.4 Intermediaries in the agricultural supply chain and their margins and
value additions
Intermediary in Indian
Agricultural chain
Margin
added (in %)
Principal value added Comparable
American
intermediary
Small farmer
Consolidator
Commission agent
Trader
Wholesaler/Buyer/ Exporters
Retailer
Number of
Intermediaries
N/A
10-15
10-15
10-15
25-30
25-30
7-8
Production
Aggregation at village
level
Negotiating and demand
supply matching
Consolidation at AMPC
level
Consolidation and
reselling transaction to
retailers/Export
Sells to consumers
Large farmer,
cooperatives
Wholesaler
Wholesaler
Wholesaler
Wholesaler
Retailer
3-4
Source: Economic Times Intelligence Group (2003)
In order to manage these supply chain, the Indian Government has established regulated
market-yards managed by Agricultural Produce Market Committee. Market-yard acts as a
marketing exchange where purchase and sell activities of agricultural produce are carried
out. But due to poor connectivity of villages, small land holding size and low education
levels of farmers, commission agents (intermediaries) appear in the chain. With time
these intermediaries have become powerful and have formed cartels. These cartels
become counter-productive for the farmers who are left with no choice but have to sell
their agri produce through these commission agents. Similarly, wholesalers or retailers
101
have to purchase through these commission agents. This disruption in the selling and
buying process leads to price escalation and high transaction costs (three to four times the
actual price). Sharma et al. (2007170
) in his study about public private partnership for agri
value chain of APMCs in Gujarat state found the following problems in agri supply chain
managed through APMCs.
• There is a great concern about the scale of operation because farms are small and
getting smaller with passing of time. Hence it is difficult and costly to provide
knowledge of modern agriculture practices.
• Agri marketing and agro processing industries facing a problem of the availability
of material of right quality, in right quantity, at right time and at right price.
• The wide dispersal of produces with small quantity, results in costly
transportation and handling efforts to ensure the final markets.
• Long marketing chains and clumsy transfers, resulting in substantial leakage of
value and actual physical loss.
• Numerous intermediaries widely spread between farm gate and consumer increase
the cost of consumer but not the value received by the producer.
• Limited value addition because of poor linkages to agro processing industries.
Even, inefficiencies at post harvest operations like; cleaning, grading, storage,
handling and packaging. The losses occur due to excessive moisture, infestation
by insects, pests and rodents etc.
• Lack of modern warehousing and storage facilities.
170
Sharma M. Patel A. and Pandya M., “Public Public Private Partnership (PPP) Approach –for sustainable
development of APMCs in Gujarat”, (Conference Proceedings: International Conference on Global
Competition and Competitiveness of Indian Corporates, IIM-Kozhikode, May, 2007, pp.6-7), (Photocopy).
102
• Local brokers are often in collusion with arhatiyas and therefore the price which
is settled is generally to the advantage of the arhatiyas and not to the farmer.
• Farmer does not ordinarily get the information about the ruling prices in the big
markets. As a result the farmer have to accept whatever price quoted to them and
have to believe whatever the traders tell them.
• Awareness and exploiting the benefits of trading with future market is restricted
to big farmers, traders, and other intermediaries; except producers.
• The standard lot contract size of agricultural commodities under future trading is
considerably large for individual farmer; mostly above 3Tonnes.
• Poor Backward and Forward linkages of market-yards with producers as well as
agro processing industries.
• Lack of linkage and orientation with R & D institutions, as well as poor market
intelligence.
• Lack of knowledge of quality parameters and standards.
• Lack of vision, leadership, professional competence etc restricts the development
of market-yards and increases the corruption and mal practices.
• Role of APMCs for building integrated channel to connect farm gate to food plate
is negligible.
• There is hardly any facility/infrastructure for handling, assembling, sorting,
grading, packing, transportation, quality certification, palatisation, labeling, pre -
cooling, cold storage, ripening chambers etc.
103
Poor infrastructure can have two key impacts on supply chains. Firstly, it can reduce the
effectiveness of a chain; that is, its ability to meet the needs of its customers through the
provision of product of the required quality and quantity at a specified time. Secondly, it
can increase costs in the chain, thereby reducing efficiency and returns to all participants
along the chain.
Coase (1937171
) observed that under this condition, the cost of conducting economic
exchange in a market may exceed the cost of organising the exchange within a firm. In
order to manage the high transaction cost, a great deal of cooperation and collaboration is
required in the supply chain.
2.11 Need for Collaboration and Integrated Management Practices
The wisdom of developing collaborative agri supply chains is widely acknowledged in
the recent time in many developing countries, where agricultural policy is meant to
reinforce “the link between primary production, the processing industry and other
economic activities around agriculture” as a part of a strategy to pursue growth and jobs,
not only on farms “but in the industries and companies that depend on primary
production” (Fischer Boel, 2006172
). However, such linkage needs an understanding of
the attitudes and circumstances of the various supply chain participants.
The Agriculture Marketing Reforms initiatives by Department of Agriculture,
Government of India, in 2003, sought to reform the agriculture marketing system in ways
that would enable farmers and all participants of agriculture marketing chains to become
more market orientated, competitive and sustainable, both economically and
environmentally. The introduction of the Model APMC (Development & Regulations)
Act has made possible for producers, processor, traders, buyers/wholesaler/exporters,
retailers and the food and healthcare service sector to work together to identify, inform
171
R. H. Coase, “The nature of the firm”, Economica, Vol. 4, (1937), pp. 386-405. 172
Boel M. Fischer, “European model of agriculture”, (National Parliaments Conference-European Model
of Agriculture, Helsinki, October, 2006), http://europa.eu/rapid/pressReleasesAction.do?reference =
SPEECH/06/589
104
and meet market demand, drawing on business advice and sharing resources and
experience to control costs and increase incomes173
. The salient features of the Model Act
are summarised in table 2.5.
Table 2.5 Silent features of Model Act
• Legal persons, growers and local authorities permitted to establish new markets in
any area.
• No compulsion on growers to sell their produce through existing regulated
markets.
• Establishment of direct purchase centers, Consumers / Farmers Markets for direct
sell.
• Promotion of public private partnership in the management and development of
agricultural markets.
• Separate constitution for special markets for commodities like onions, fruits,
vegetables and flowers.
• A separate chapter to regulate and promote contract-farming arrangements in the
country.
• Prohibition of commission agency in any transaction of agricultural commodities
with the producers.
• Market committee to promote alternative marketing system, contract farming,
direct marketing and Farmers/ Consumers markets.
• State marketing boards to promote standardization, grading, quality certification,
market led extension and training of farmers and market functionaries in
marketing related areas.
• Constitution of State Marketing Standards Bureau for promotion of grading,
standardization and quality certification of agricultural produce.
Source: Patel et al. (2008)
173
Patel A., Sharma K. and Pandya M., Twisting Tale of APMC, (A case is developed and presented in the
3rd
National Level Case Writing Workshop, V M Patel Institute of Management, Ganpat University,
Kherva, April, 2008).
105
Furthermore, these strategic initiatives seek to “strengthen the links between primary
producers and other food industry sectors” and to “promote wider use of the principles of
integrated and collaborative supply chains, with producers, processors and retailers
working together to develop markets, share information and achieve sustainable
contracts”. These prescribed actions reflect the recognized need for Indian agriculture to
adopt a greater market orientation in response to the upcoming challenges of integrating
all these and bring them on a common platform to build and maintain competitive
advantage.
106
CHAPTER 3 RESEARCH METHODOLOGY
3.1 Introduction
3.2 Objectives of the Study
3.2.1 Specific objectives of the research
3.3 Scope of the Study
3.4 Research Methodology
3.4.1 Research Design
3.4.2 Population
3.4.3 Sampling Technique
3.4.4 Sampling Unit
3.4.5 Sample
3.4.6 Sample size
3.4.7 Method of Data Collection
3.4.8 Tool of Data Collection
3.4.9 Sources of Data
3.4.10 Data Analysis
107
“Research is a careful investigation or inquiry specifically
through search for new facts in any branch of knowledge”.1
“Research is a systematized effort to gain new knowledge”.2
3.1 Introduction
Research is a scientific and systematic search for pertinent information on a specific
topic. In fact, it is an art of scientific investigation.3 This chapter provides the detailed
view of how the researcher has carried out this research.
3.2 Objectives of the Study
The broad objective of this research study is “An in-depth comparative study of supply
chain management practices at selected agriculture produce marketing committees
(market yard) of North Gujarat”.
3.2.1 Specific objectives of the research are:
(1) To understand the emergence, development and growth of the APMCs.
(2) To develop an Agri Supply Chain Management perspective.
(3) To learn about the different intermediaries of the agriculture supply chain.
(4) To know the important variables considered by the intermediaries to select the
particular intermediaries to sell the products.
(5) To know the important variables considered by the intermediaries to select the
particular intermediaries to purchase the products.
(6) To know the important variables considered by the intermediaries to select the
intermediaries into particular market-yard.
1 The Advance Learner’s Dictionary of Current English, (Oxford, 1952), p. 1069.
2 L. V. Redman and A. V. H. Mory, The Romance of Research, (1923), p. 10.
3 C. R. Kothari, Research Methodology: Method and Techniques, (New Delhi: New Age International
Publication, 1999), p.1
108
(7) To extract the factors which are important for selecting the intermediaries to sell
the commodities.
(8) To extract the factors which are important for selecting the intermediaries to
purchase the commodities.
(9) To extract the factors which are important for selecting the intermediaries into
particular market-yard.
(10) To compare the factors’ importance given by the different intermediaries to sell
and to purchase the commodities as well as to select the intermediaries into
particular APMC of all the APMCs in North Gujarat.
(11) To understand the extent of integrated supply chain management practices
adopted by the wholesalers (Pacca Arhatiya) of the selected APMCs of North
Gujarat.
3.3 Scope of the Study
The scope of this research study is limited to 1) the extraction of the factors which are
important for selecting the intermediaries to sell or purchase the commodities and
selecting the intermediaries into particular APMC only 2) comparison of importance
given to the particular factor for selecting the intermediaries to sell and purchase the
commodities as well as importance given to the particular factor for selecting the
intermediaries into the particular APMC by all the intermediaries of all selected APMCs
of North Gujarat, and 3) to know the extent of integrated supply chain management
practices pursued by the wholesalers (Pacca Arhatiya) of selected APMCs of North
Gujarat. In addition to these;
• The geographical scope of the study is limited to the North Gujarat region only.
• The researcher has restricted the study to the APMCs where trading of cumin,
fennel and isabgul takes place.
• Researcher has surveyed only important entities involved into the trading and
processing of the three commodities viz; cumin, fennel and isabgul.
109
3.4 Research Methodology
A system of models, procedures and techniques used to find the result of a research
problem is called Research Methodology.4 It gives a framework and direction to the
study. The technique of data collection and the methodology of their analysis has a great
bearing on the reliability of the result arrived at.5 Well-planned research methodology
explains the logic behind the methods the researcher has used in the context of his
research study. It also explains why researcher has used a particular method or technique
and why he has not used others so that research results are capable of being evaluated
either by the researcher himself or by others.6
3.4.1 Research Design
A research design is a specification of methods and procedures for acquiring the needed
information. It is the overall operational pattern or framework, of the project that
stipulates what type of information is to be collected from which sources and by what
procedures.7 Means, it is the conceptual structure within which research is conducted; it
constitutes the blue print for the collection, measurement and analysis of data.
To develop hypothesis, to isolate key variables and relationship, to provide insights into,
and understanding of the problem, exploratory research design has been used. To identify
the problem, develop an approach to the problem and to formulate an appropriate
research design secondary data have been used. To understand the supply chain
management practices of selected APMCs of North Gujarat, from six different
intermediaries named; Farmer, Commission Agent, Stockiest, Wholesaler, Exporter and
Processor, the primary information was collected for the study.
4 R. Panneerselvem, Research Methodology, (New Delhi: Prentice-Hall India Ltd, 2005), p. 2.
5 D. N Elhance and B. M Aggarwal, Fundamentals of Statistics, (Allshabad: Kitab Mahal, 2005), p. 261
6 C. R. Kothari, op. cit, p.11.
7 C. R. Kothari, op. cit, p.31.
110
3.4.2. Population
A population is the set of all elements of interest in a study. Means it is the total
collection of elements about which researcher wish to make some inferences.8 The
population elements for the study were the six intermediaries; farmers, commission
agents, stockiest, wholesalers, exporters and processors dealing in cumin, fennel and
isabgul at the APMCs of North Gujarat.
3.4.3 Sampling Technique
Sampling techniques may be broadly classified as non-probability and probability. Non-
probability sampling relies on the personal judgment of the researcher rather than chance
to select sample elements. The researcher can arbitrarily or consciously decide what
elements to include in the sample. It may yield good estimates of the population
characteristics.
In probability sampling, sampling units are selected by chance. Therefore Non-
probability Sampling Method is appropriate sampling method for the study. Commonly
used non-probability sampling techniques includes convenience sampling, judgmental
sampling, quota sampling and snowball sampling.
Convenience sampling attempts to obtain a sample of convenient elements. Researcher
has selected any readily available individuals at APMCs in North Gujarat region as
participants.9 Response was collected from the six intermediaries of APMCs of North-
Gujarat.
8 Donald Cooper and Pamela Schindler, Business Researcher Methods, (New Delhi: TMH Publication,
2008), p. 402. 9 Naresh Malhotra, Marketing Research: An Applied orientation, (Fifth edn.), (New Delhi: Pearson
Education, 2008), p. 371.
111
3.4.4 Sampling Unit
There are total Thirty Eight APMCs in four district; Mehsana, Patan, Banaskantha and
Sabarkantha of North Gujarat region. Researcher has restricted his scope of research to
study the supply chain management practices at the APMCs where trading of Cumin,
fennel and isabgul takes place. There are total twelve APMCs in entire North Gujarat
region where trading of atleast any one of the three commodities takes place. Table 3.1
shows details of district wise APMCs where trading of any one commodity viz; cumin,
fennel and isabgul takes place. The table explains about particular commodity/ies trade
and cumulative value of all three commodities for the last three consecutive years. Total
value of last three years is also given.
Table 3.1 Value of Three Commodities of Various APMCs of North Gujarat
Source: District Registrar Office of each district and Annual Report of each APMC;
Read C = Cumin, F = Fennel, I = Isabgul
District Name of
APMC
Commodity
Trade
Cumulative value of three
commodities in Rs. (Lac/year)
Total in Rs.
(Lac)
2006-07 2007-08 2008-09
Mehsana Unjha C F I 50936.71 67444.23 96940.75 215321.69
Becharaji C F I 703.92 414.05 2911.29 4029.26
Patan Patan C F I 4507.09 2812.64 4075.41 11395.14
Siddhpur C F I 810.02 375.83 595.23 1781.08
Banaskantha Palanpur C F I 583.73 544.48 885.7 2013.91
Thara C F I 1474.63 916.19 2099.73 4490.55
Deesa C F I 437.18 371.78 947.23 1756.19
Panthawada C F I 128.59 160.44 326.45 615.48
Dhanera C - I
Tharad C - I
Vav C - I
Ikabalgadh - F I
112
The criteria for selecting the APMCs in North Gujarat are; 1) Type of the commodity
trading takes place. 2) Total value of trading for all three commodities for last three years
of each district.
Table 3.1 shows that there are only two APMCs each in Mehsana as well as Patan district
where trading of all three commodities take place. In Banaskantha district there are total
four APMCs in which trading of all three commodities take place. Hence researcher has
selected first two out of four APMCs. These two APMCs comprise more than 60 percent
of cumulative value of all APMCs in Banaskantha district for three commodities. There
isn’t any APMC in Sabarkantha district where trading of these three commodities take
place.
3.4.5 Sample
Sample is the group of respondents consisting of a portion of the target population,
carefully selected to represent the population. Means it is a subset of the population
drawn to collect data. Researcher has selected anyone who performs the role as a farmer,
commission agent, stockiest, wholesaler, exporter and processor into the supply chain as
a sample for study.
3.4.6 Sample size
There were total 600 respondents surveyed. After removing questionnaire with error and
half filled, the effective sample size taken for the study was 546. The respondent wise
(entity wise) sample size is shown in the table 3.2
113
Table 3.2 Respondent wise Sample Size
Entity (Role played by the Respondent)
in Supply Chain) Frequency Percent
Cumulative
Percent
Farmer 205 37.5 37.5
Commission Agent 131 24.0 61.5
Stockiest 124 22.7 84.2
Wholesaler 43 7.9 92.1
Exporter 22 4.0 96.2
Processor 21 3.8 100.0
Total 546 100.0
3.4.7 Method of Data Collection
There are various methods available for the collection of the data. It includes personal
contact, telephonic survey, mail survey and electronic survey. Researcher has preferred
the personal contact method to collect the primary data as it is more effective compared
to other.
3.4.8 Tool of Data Collection
Various tools viz; interview of respondent or group of respondents, questionnaire and
observation methods; are available for data collection. Researcher has developed the
structured questionnaire comprise close-ended as well as open-ended questions for the
purpose of data collection.
The questionnaire was pre-tested among seventy five selected sample respondents to
check its workability for the purpose of the study and time required to fill up the
questionnaire.
114
3.4.9 Sources of Data
Two types of data are collected. Major source of information was primary data. The
primary data was collected from all six intermediaries i.e. farmers, commission agents,
stockiest, wholesalers, exporters and processors working at various APMCs of North
Gujarat.
To understand and explore the research problem, to build the theoretical frame work,
various secondary data sources were used. This includes research journals, magazines,
books, websites, research report, news papers etc.
3.4.10 Data Analysis
Data analysis begins with preliminary check of all questionnaires for its completeness.
Examination of the filled up questionnaire is required to detect the error, omission of
half-filled and unqualified questionnaires and to correct the errors wherever possible.
This ensures accuracy, consistency and uniformity of data. Then numerical codes have
been assigned to represent a specific response to a specific question. After this, the data
were tabulated, i.e. arranged in a logical manner in columns and rows for further analysis.
Various statistical tools have been used for the analysis of the data. Appropriate statistical
tools were applied according to the objectives and hypothesis of the study. This includes
Frequency Distribution, Cross-Tabulation, Mean, Standard Deviation, Factor Analysis,
Analysis of Variance and T-Test etc.
The data was analysed using Statistical Package for Social Sciences version 12.0 and
MS-Excel.
115
CHAPTER 4 DATA ANALYSIS AND INTERPRETATION
4.1 Introduction
4.2 Respondents Profile
4.2.1 Entity-Wise Profile of Respondents
4.2.2 City (APMC)-Wise Profile of Respondents
4.2.3 Commodity Trade-Wise Profile of Respondents
4.3 Questionnaire structure and Variables included in the Questionnaire for
study
4.3.1 Channel Structure
4.3.2 Selection of Supply Chain Intermediaries
4.3.3 Selection of Intermediaries into particular Market-Yard (APMC)
4.3.4 Integrated Supply Chain Management Practices
4.4 Factor Analysis
4.4.1 Reliability of Measurement
4.4.2. Factor Analysis for section – II for sell related variables
4.4.2.1. KMO and Bartlett’s Test
4.4.2.2 Measure of Sampling Adequacy (MSA)
4.4.2.3 Anti-Image Correlation Matrix
4.4.2.4 Communalities
4.4.2.5 Eigenvalues and Total Variance Explained
4.4.2.6 Factor Extraction
4.4.2.7 Factor Loading
4.4.3. Interpretation of Factors extracted from section-II for Sales related
variables
4.4.4 Factor Analysis for section-II for Purchase related variables
4.4.4.1. KMO and Bartlett’s Test
4.4.4.2 Measure of Sampling Adequacy (MSA)
4.4.4.3 Anti-Image Correlation Matrix
4.4.4.4 Communalities
4.4.4.5 Eigenvalues and Total Variance Explained
116
4.4.4.6 Rotated Component Matrix
4.4.5. Interpretation of Factors extracted from section-II for Purchase
related variables
4.4.6 Factor Analysis for section-III for Selection of Intermediaries into
particular Market-Yard (APMC)
4.4.6.1. KMO and Bartlett’s Test
4.4.6.2 Measure of Sampling Adequacy (MSA)
4.4.6.3 Anti-Image Correlation Matrix
4.4.6.4 Communalities
4.4.6.5 Eigenvalues and Total Variance Explained
4.4.6.6 Rotated Component Matrix
4.4.7. Interpretation of Factors extracted from section-III for selection of
intermediaries into particular Market-Yard (APMC) related
variables
4.5 Analysis of Variance (ANOVA)
4.6 Hypothesis of the study
4.6.1 Hypothesis for Key Important Variables for selecting the
intermediaries to sell the products.
4.6.2 Hypothesis testing of Key Important Variables for selecting the
intermediaries to sell the products
4.6.3 Hypothesis for Key Important Variables for selecting the
intermediaries to purchase the products.
4.6.4 Hypothesis testing of Key Important Variables for selecting the
intermediaries to purchase the products
4.6.5 Hypothesis for importance given to the Key Important Variables by
the intermediaries to select the intermediaries into particular APMC
4.6.6 Hypothesis testing for importance given to the Key Important
Variables by the intermediaries to select the intermediaries in
particular APMC.
4.7 Integrated Supply Chain Management Practices
117
4.1 Introduction
Collecting and analyzing data are important steps in the scientific process. Researcher
needs to analyze data to see how the data supports (or does not support) the hypotheses.
Analysis and interpretation of the data, collected through survey using questionnaire is
presented in this chapter. Various relevant statistical tools and techniques were employed
for the purpose of analysis and interpretation of the data and results were drawn.
The term analysis refers to the computation of certain measures along with searching for
the patterns of relationship that exist among data groups. Thus, “in the process of
analysis, relationships or differences supporting or conflicting with original or new
hypotheses should be subjected to statistical tests of significance to determine with what
validity, data can be said to indicate any conclusions”1.
As defined above, the collected primary data was processed and analysed in accordance
with the outline laid down at the time of developing the research plan.
4.2 Respondents Profile
Supply chain of APMC consists of number of intermediaries (i.e. Entity) which perform
different roles. For this study the researcher has considered the six most important
intermediaries; the Farmer, Commission Agent (Kutcha Arhatiya), Wholesaler (Pacca
Arhatiya), Stockiest (Arbitrager), Processor and Exporter. Total 546 respondents from
six APMCs of North Gujarat region were surveyed for the study. The detailed analysis of
the respondents is as follows:
1 C. R. Kothari, Research Methodology: Method and Techniques, (New Delhi: New Age International
Publication, 1999), p.151.
118
4.2.1 Entity-Wise Profile of Respondents
The table 4.1 represents information about the roles of intermediaries surveyed.
Table 4.1: Entity-Wise Profile of Respondents
The table reveals that 37.5 percent respondents were farmers (n = 205), 24 percent
Commission agents (n = 131) and 22.7 percentage of Stockists (n = 124). While
Wholesalers (n=43) of 7.9 percent, Exporters (n = 22) of 4.0 percent and Processors (n =
21) of 3.8 percentage.
The reason for lower number of respondents in the category of Wholesaler, Exporter and
Processor was; because of lower population size of those particular categories. This could
be supported by the table 4.2.
Table 4.2: Place * Entity Cross tabulation
Entity (Role Played by Respondent) in Supply Chain Total
Place
Farmer
Commission
Agent Trader Wholesaler Exporter Processor
Unjha 70 50 50 20 10 10 210
Patan 50 40 30 13 3 2 138
Siddhpur 20 8 10 5 9 9 61
Palanpur 20 8 8 5 0 0 41
Thara 30 18 16 0 0 0 64
Becharaji 15 7 10 0 0 0 32
Total 205 131 124 43 22 21 546
Entity (Role played by the
Respondent) in supply chain Frequency Percent
Cumulative
Percent
Farmer 205 37.5 37.5
Commission Agent 131 24.0 61.5
Stockist 124 22.7 84.2
Wholesaler 43 7.9 92.1
Exporter 22 4.0 96.2
Processor 21 3.8 100.0
Total 546 100.0
119
From the table 4.2 we can see that there was no existence of entity named Exporter and
Processor at APMCs of Palanpur, Thara and Becharaji. While only 5 wholesalers were at
Siddhpur APMC and Palanpur APMC and no wholesaler was present at APMC Thara
and Becharaji. Unjha was well developed APMC and hence all entities were in good
numbers there. While because of proximity to Unjha APMC, Siddhpur – Unjha highway
region (Approx. 12 Kms.) was developed as a hub for processing. And hence the large
numbers of processors (For Unjha, n = 10 and Siddhpur, n = 9) and exporters (For Unjha,
n = 10 and Siddhpur, n = 9) in the surroundings of both the cities were observed.
4.2.2 City (APMC)-Wise Profile of Respondents
The researcher had surveyed following six APMCs for getting the response from the
respondents.
Table 4.3: City (APMC)-Wise Profile of Respondents
As per the table 4.3, Thirty-nine percent respondents were from Unjha APMC, Twenty-
five, Eleven, Eight, Twelve and Six percent respondents were from APMCs of Patan,
Siddhpur, Palanpur, Thara and Becharaji respectively. Unjha APMC and Patan APMC
were well developed compared to other APMCs for the trading of the commodities;
Cumin, Fennel and Isabgul. Hence large numbers of well developed different entities
were observed performing the commercial activities and so large numbers of respondents
were taken from these two places.
Place Frequency Percent
Cumulative
Percent
Unjha 210 38.5 38.5
Patan 138 25.3 63.7
Siddhpur 61 11.2 74.9
Palanpur 41 7.5 82.4
Thara 64 11.7 94.1
Becharaji 32 5.9 100.0
Total 546 100.0
120
4.2.3 Commodity Trade-Wise Profile of Respondents
Table 4.4 below shows that most of the respondents; Seventy-three percent (n = 401)
were trading in all the commodities. Eighteen percent respondents were in trading
activities of cumin and fennel only while Five percent respondents were trading in
Isabgul and Two percent each of Cumin and Fennel respectively.
Table 4.4: Commodity Trade-Wise Profile of Respondents
Table 4.5: Commodity Trade * Entity Name Cross-tabulation
Entity name Total
Commodity
Trade Farmer
Commission
Agent Trader wholesaler Exporter Processor
Cumin 12 0 0 0 0 0 12
Fennel 9 0 0 0 0 0 9
Isabgul 17 0 0 0 0 9 26
All 75 131 124 43 22 6 401
Cumin-
Fennel 92 0 0 0 0 6 98
Total 205 131 124 43 22 21 546
In the Table 4.5, it is observed that Forty-five percent of the farmers (n= 92) produced
cumin and fennel both while Thirty-seven percent farmers (n=75) produced all the
commodities. Only few farmers (6% cumin, 4% fennel and 8% isabgul) produced single
commodity. It was also observed that all the commission agents, stockists, wholesalers
and exporters traded in all the commodities. Forty-three percent processors processed
Commodity Frequency Percent
Cumulative
Percent
Cumin 12 2.2 2.2
Fennel 9 1.6 3.8
Isabgul 26 4.8 8.6
All 401 73.4 82.1
Cumin-Fennel 98 17.9 100.0
Total 546 100.0
121
only isabgul, Twenty-nine percent processor processed all commodities (n = 6) and also
same number of processors processed cumin and fennel (n = 6).
4.3 Questionnaire structure and Variables included in the
Questionnaire for study
For collecting the information to fulfill the research study objectives the researcher has
divided the questionnaire into following four sections:
1) Channel Structure
2) Factors considered for selection of Supply Chain Intermediaries
3) Selection of intermediaries into particular Market-Yard (APMC)
4) Integrated Supply Chain Management Practices (for wholesaler only)
List of different variables were added in the questionnaire for each section and responses
were collected from the different respondents from six APMCs of North Gujarat where
trading of cumin, fennel and isabgul took place.
The factor analysis was employed to extract the key important variables and to group the
variables of section 2 and 3 of the questionnaire followed by ANOVA to compare the
mean values of key important variables extracted through factor analysis of these two
sections. T-test was applied to the variables of last section to check the significance.
4.3.1 Channel Structure
The researcher has kept the questions related to understanding the structure of the
channel in APMCs. This section helped the researcher to understand the structure of the
channel, number of different intermediaries involved in the supply chain of APMCs, role
of intermediaries as a buyer, supplier, stockist and/or commission agent, processor etc. It
also revealed who would play what kind role for whom in the channel structure.
122
4.3.2 Selection of Supply Chain Intermediaries
The important variables considered by intermediaries to purchase and/or sell the
commodities from/to the particular intermediaries of supply/demand side of the channel
were included in this section. The variables were kept for rating. The five scale rating
was used, where rating 5 means most important and 1 means unimportant.
The respondents were requested to rate the importance given by them to particular
variable for selecting the intermediaries to sell or to purchase the commodities to/from
the other intermediaries in the chain. There were total 22 variables; each for purchasing
the commodities and related operation as well for selling the commodities and related
operation; included for study purpose and responses were collected.
Table 4.6 Variables under Section-2 for Selecting the Intermediaries to Sell the
Commodities with their Coded Name
Coded Name
Variables for Selection of Intermediaries to
Sell the Commodities
Sa Age-old business relationship
Sb Friend/family member
Sc Trust
Sd Pays best/helps to get best price
Se Spot/Cash Payment
Sf Credit Finance
Sg Financial Assistance
Sh Comes first to purchase
Si Assurance to purchase
Sj Cleaning Assistance
Sk Grading Assistance
Sl Packaging Assistance
Sm Storage & Warehousing services
123
Sn Quality Testing & Certificate Assistant
So Quantity to be sold/process
Sp Makes transport arrangements/comes to collect
Sq Customer’s location
Sr Provides Demand Information
Ss Updating the price information
St Provides Weather forecast information
Su Provides Production estimation information
Sv Well known in the market
There were total 22 variables; listed in table 4.6 as above included in this section to
understand the importance given by the intermediaries to sell the commodities to other
intermediaries of the chain.
The variables Age-old Business Relationship and Friend/Family member explains the
importance of developing and maintaining the relationship in the business transaction for
longer period of time while the trust among the channel partners is most important to
maintain the business relations and hence for the growth of the organisation.
In the commodity business price is very important factor. Hence, who pays best,
intermediaries prefers to sell their goods to them.
Fifth, sixth and seventh variables; Spot/Cash Payment, Credit Finance and Financial
Assistance are the value adding finance related support services provided by the selected
intermediaries to the others in the downward or upward channel. Some of the
intermediaries at few selected APMCs pay on the spot once the sale-purchase deed gets
settled and hence sellers prefer to sell their goods to them. Intermediaries, on demand
based on the creditworthiness, business relation etc. land or borrow the money on
nominal interest to/from the other intermediaries to manage the business transactions as
well as sometimes to fulfill social obligations also. Even in selected APMCs, proactive
intermediaries provide the financial assistance without interest charges to upward or
124
downward; mostly downward channel partners. Mostly commission agents provide the
financial assistance to farmers to purchase agriculture inputs and encouraging them to
sow cumin, fennel or isabgul with the condition that farmers have to sell their agriculture
produce through them only. And this way, commission agents increase their volume of
business.
Eighth variable is comes first to purchase. Intermediaries can sell the goods to buyer who
comes first to purchase, considering the factors like prevailing price, demand supply
scenario, price prediction etc. While ninth variable; assurance to purchase reduces the
risk of supplier as buyer has assured him to buy from him or promised him to sell his
goods.
The variables cleaning and grading assistance improve the quality of the product by
sorting into different quality grades and de-husking through machines while packaging
assistance is provided by the buyer, reduces the packaging related issues and cost for
seller.
Storage and Warehousing services reduce the issues related to storing of the goods at the
appropriate place if unsold. Scientific storage methods increase the shelf life as well as
reduce the issues related to the deterioration of the quality. This in turn reduces the risk of
quality and value. It is common sense that if seller does not get the appropriate price of
his goods then he requires the facilities of storage to prevent his goods from rain,
moisture, rodents, excessive heat, till he will realise the expected price of his goods,
otherwise he has to transport back his goods to his place which in turn increases the cost.
Exporter and sometimes wholesaler who is the supplier to large organised retailers
demand the Quality Testing Certificate as a statutory requirement or on demand by their
customer. Quality testing laboratories established by the Spices Board of India is an
approved agency to issues the quality certificate to any intermediaries on demand after
testing the sample of the commodities.
125
To sell large quantity seller prefers the large buyer/processor and vice versa. The
sixteenth variable is makes transport arrangement/comes to collect. Depending on the
contract term negotiated, anyone party is responsible for making the arrangement of
transport or sometimes intermediaries make the transport arrangement; particularly for
farmers. If buyer makes the transport arrangement then it’s a risk reduction as well as
cost reduction activities for the seller and vice-versa.
Customer location is an important variable because sellers prefer to sell commodities to
the customers of known places where trading activities take place since last so many
years as well as in a good volume. Dealing with the known place customer reduces the
counter party risk as seller can check the creditworthiness and trustworthiness of the
buyer through his references. As well as they prefer to sell the goods to the well known
customer in the same market (twenty second variable).
The variables eighteenth to twenty first are the information support services provided by
the buyer to the seller. They are providing information of current and future demand in
the domestic as well international market, providing prevailing price in the major markets
as well as future price predictions. Weather forecast information helps the intermediaries
to take the advantage of impact of changes in the climate on the changes into price of the
commodities. Production estimation is helpful to estimate the level of supply in the
market and hence its impact on the price of the commodities.
Table 4.7 Variables under Section-2 for purchasing/processing the commodities with
their coded name
Coded Name Variables for Purchase/Process activities
Pa Age-old business relationship
Pb Friend/family member
Pc Trust
Pd Offers best price
Pe Spot/Cash Payment
126
Pf Credit
Pg Financial Assistance
Ph Comes first to sale/process
Pi Assurance to offers required quantity
Pj Cleaning Assistance
Pk Grading Assistance
Pl Packaging Assistance
Pm Storage & Warehousing services
Pn Quality Testing & Certificate Assistance
Po Quantity to be purchased/processed
Pp Makes the delivery arrangement
Pq Seller’s location
Pr Provides Demand Information
Ps Updating the price information
Pt Provides Weather forecast information
Pu Provides Production estimation information
Pv Well known in the market
Similarly, there are total 22 variables; listed in table 4.7 as above included in this section
to understand the importance given by the intermediaries to purchase/process the
commodities from/of other intermediaries of the chain.
The explanation for the above listed variable is same as variables listed in the table 4.6
except the following few variables.
The fourth variable is offers best price. It is obvious that the buyer/processor always
prefers the seller who quotes the best price among all suppliers for the similar kind of
quality.
Buyer mostly prefers to purchase on credit instead of paying on the spot. Processor
prefers to get the processing charges on the spot.
127
Eighth variable is comes first to sell/process. Intermediaries can purchase/process the
goods from/of first come supplier considering the factors like prevailing prices, demand
supply scenario, price prediction etc. While ninth variable; assurance to offers required
quantity is reducing the supply risk of buyer/processor because supplier has assured him
to supply the required quantity. It ensures the processing capacity utilization of processor
and hence reduces the overall per unit processing cost.
Cleaning and Grading Assistance helps the buyer to get the value added products of
different quality and grades, which in turn increase the range of market offering to
different segments.
The seventeenth variable, Location defines the quality of the commodities, i.e. Cumin
from Unjha etc.
4.3.3 Selection of Intermediaries into particular Market-Yard (APMC)
This section is divided into two parts. First part covers the variables related to the
services or facilities available within the premises of the APMC as well as in the city or
town of particular APMC. Respondents were asked to rate the availability of the same
variables on dichotomous scale ‘Yes’ or ‘No’. There are total 14 variables included for
getting the response from the respondents.
Table 4.8 (a) Variables related to Services / Facilities available within the city/town
of the APMC
Coded
Name Availability of Services / Facilities
Fa Open Auction System
Fc Storage facilities
Fd Quality Testing Laboratory
Fe Availability of processing facilities
Ff Availability of buyers all the time
128
Fg Spot Payment System
Fh Financial Assistance by/to the channel intermediaries
Fi Banks facility
Fj Warehouse receipt finance
Fu Availability of information about demand in domestic as well international
markets
Fv Availability of information about Prevailing prices in the major markets
Fw Availability of Demand Forecasting Information
Fx Availability of Weather forecasting information
Fy Availability of Production forecast information
Above mentioned facilities or services are value-adding activities or facilities for all the
intermediaries of the chain. Explanation of all variables is mentioned below with the
explanation of variables mentioned in the table 4.8 (b).
In second part there are total 23 variables listed in table 4.8 (b) included for study. The
respondents were asked to rate the importance of all the variables considered by them to
select the intermediaries into particular APMC of North Gujarat region to sell or purchase
the commodities like cumin, fennel and isabgul.
The first variable is open auction system. Open auction system is used to discover the
price of the commodities through open outcry. It is considered as a one of the best
systems to discover the price.
Availability of cold storage, warehouses open shades are necessary storage infrastructure
requirements for storing the commodities with scientific methods. Quality Testing
Laboratory is the Quality certificate issuing authority, issues the quality certificate after
checking and testing the samples. This ensures the quality standards. Processing facility
ensures the cleaning and grading of the commodities and prepares different quality
products.
129
Table 4.8 (b) Variable; under Section-III, affecting the selection of intermediaries
in particular Market-Yard
Coded
Name Variables affecting the selection of intermediaries in particular Market-Yard
Ma Open Auction System
Mc Storage facilities
Md Quality Testing Laboratory
Me Availability of processing facilities
Mf Availability of buyers all the time
Mg Spot Payment System
Mh Financial Assistance by/to the channel intermediaries
Mi Banks facility
Mj Warehouse receipt finance
Mk Demand at the marketplace compared to other markets
Ml Number of Buyers at the market place
Mm Types of the Buyers
Mo Connectivity with and Distance from major Roads, Railway, Ports and Airports
Mq Well known for particular commodity
Mr Transparency in the governing system
Mt Involvement of Governing Body for disputes settlement
Mu Availability of information about demand in domestic as well international markets
Mv Availability of information about Prevailing prices in the major markets
Mw Availability of Demand Forecasting Information
Mx Availability of Weather forecasting information
My Availability of Production forecast information
Mz Involvement of Governing body into development of APMC
Maa Quantity to be purchased/sold
Availability of the buyer all the time ensures the seller can hold the commodities until he
receives better price instead of selling at a distress price. Spot payment reduces the risk of
default and other transaction cost. Financial assistance by/to the channel intermediaries
130
encourages the counter party to prefer the assistance provider than others. Bank facility is
necessary for managing the financial transaction. It also useful in getting the working
capital funds, investment funds as well as to get the funds on stored commodities into the
warehouses, go-downs etc through warehouse receipt finance.
Higher demand and large number of buyers increase the chances of getting the better
price. Presence of different types of buyers; i.e. exporter, wholesaler etc. increase the
volume of business, increase the demand which increase chances of the development
trading activities in a more organised way.
Variable thirteen is related to the transportation infrastructure and related cost. The
connectivity with the major roads, railways and airport as well as lower distance from
major roads, railway station and airport encourage the trading activities and reduces total
transportation related cost.
Well known APMC for particular commodities increase the trust of the chain partner to
do the trading activities with the intermediaries of that APMC.
Next two are the management of market yard related variables. Transparency in to the
governing system of the market yard and involvement of governing body into dispute
settlement as well as twenty second variable, involvement of governing body into the
development of APMC increase the trustworthiness and ensure the fair and transparent
trading practices at the market place.
Variables from seventeenth to twenty first are information support services useful for all
the intermediaries. Availability of information about demand in domestic and
international market educates the channel intermediaries about when to sell and where to
sell. Availability of information about prevailing prices in major markets provides the
arbitrage opportunity to the channel members, if available. Demand forecasting
information helps to plan the production activities. Weather forecast information helps
the intermediaries to take the advantage of the likely changes in the price of the
131
commodities because of the changes in the climate. Production estimation is helpful to
estimate the level of supply in the market and hence its impact on the price of the
commodities.
Larger the quantity to be purchased/sold, the intermediaries can evaluate different options
to get a better price and required quantity. As well as it can reduce the per unit
transportation cost also. Otherwise the nearest Market-yard would be preferred by the
intermediaries keeping the transportation cost in mind.
4.3.4 Integrated Supply Chain Management Practices
The variables included in this section are used to get the response from only wholesaler
(Pacca Arhatiya) to understand the integrated supply chain management practices
adopted by them. The variables under study help the researcher to understand the extent
of integration made by the wholesaler with particular chain intermediaries, extent of
process integration between the supply chain partner and functional integration within the
organisation. It also reveals barrier to supply chain integration. The five scale rating was
used, where rating 5 means ‘great extent’ and 1 means ‘not at all’.
4.4 Factor Analysis
Factor analysis is a procedure that tries to reduce a large number of correlated variables
into small number of independent factors which account for commonality of the
variables.
According to Nargundkar R. (20042) it is a set of techniques which, by analyzing
correlations between variables, reduces their number into fewer factors which explain
much of the original data, more economically.
2 R. Nargundkar, Marketing Research: Text and Cases, (New Delhi: TMH Publication, 2004), p. 312.
132
4.4.1 Reliability of Measurement
The factor analysis is applied for variables of the section II (Factors considered for
selection of Supply Chain Intermediaries) and section III (Selection of intermediaries into
particular Market-Yard (APMC)) of the questionnaire. It is essential to measure the
Reliability statistics for both of the sections of the questionnaire.
Reliability is an assessment of the degree of consistency between multiple measurements
of a variable.3 It has to do with the accuracy and precision of a measurement procedure.
4
Reliability is concerned with estimates of the degree to which a measurement is free of
random or unstable error. Reliable instruments can be used with confidence that transient
and situational factors are not interfering. They are robust; they work well at different
times under different conditions.5
The assessment of the consistency of the entire scale can be measured through reliability
coefficient. The most widely used reliability measure is Cronbach’s alpha. The generally
agreed upon lower limit for Cronbach’s alpha is 0.70, although it may decrease to 0.60 in
exploratory research.6
The Cronbach’s alpha values for section – II variables; factors considered for selecting
intermediaries to Sell and to Purchase is 0.828 and 0.892 respectively. While the same for
the factors considered for selecting the intermediaries into Particular Market-yard is
0.642. This reveals that the tools developed for all sections are reliable and hence
researcher can proceed further.
3 Joseph Hair, et al. Multivariate Data Analysis, (Sixth edn.) (New Delhi: Pearson Education Publication,
2009), p. 161. 4 Donald Cooper and Pamela Schindler, Business Researcher Methods, (New Delhi: TMH Publication,
2008), p. 318. 5 Donald Cooper and Pamela Schindler, op. cit, p.321.
6 ibid, p. 161
133
Table 4.9 Reliability Statistics For factors considered for selecting the
intermediaries to Sell the products
Cronbach's Alpha Number of Items
0.828 22
Table 4.10 Reliability Statistics for factors considered for selecting the
intermediaries to Purchase the products
Cronbach's Alpha Number of Items
0.892 22
Table 4.11 Reliability Statistics For factors considered for selecting the
intermediaries into Particular Market-yard
Cronbach's Alpha Number of Items
.642 23
4.4.2. Factor Analysis for section – II for Sell related variables
4.4.2.1. KMO and Bartlett’s Test
The method of determining the appropriateness of factor analysis examines the entire
correlation matrix. The Bartlett’s Test of Sphericity is a statistical test for measuring the
presence of correlations among the variables.7 It is used to test the null hypothesis that the
variables are uncorrelated in the population.8 Means it provides the statistical significance
that the correlation matrix has significant correlations among at least some of the
variables.
7 Joseph Hair, et al. op cit., p. 138.
8 Naresh Malhotra, Marketing Research: An Applied orientation, (Fifth edn.) (New Delhi: Pearson
Education), 2008, p. 644.
134
Table 4.12 KMO and Bartlett's Test for Sales related variables
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.829
Bartlett's Test of Sphericity 9861.941
Sig. 0.000
Bartlett’s Test of Sphericity value 9861.941 with significance level of 0.000 in the table
4.12 above satisfies the necessary condition. This indicates the statistical significance that
the correlation matrix has significant correlation among the variables.
4.4.2.2 Measure of Sampling Adequacy (MSA)
It is an index to quantify the degree of inter-correlations among the variables. This
examines the appropriateness of factor analysis. The index ranges from 0 to 1, reaching 1
when each variable is perfectly predicted without error by the other variable. MSA value
should be above 0.5 for the applicability of the factor analysis.9
From the table 4.12, it can be observed that the MSA value is 0.829 which is greater than
necessary condition value 0.5. Hence, factor analysis can be applied on it.
4.4.2.3 Anti-Image Correlation Matrix
It is the matrix of the partial correlations among variables after factor analysis,
representing the degree to which the factors explain each other in the results.10
The
diagonal values in the Anti-Image Correlation Matrix represent MSA value. The
variable(s) with the value less than 0.5 should be omitted from the factor analysis one by
one, with the smallest being omitted first. The values which are not on the diagonal
represent the partial correlations among the variables.
9 Joseph Hair, et al. op cit., p. 138.
10 Joseph Hair, et al. op cit., p. 125
135
Sa Sb Sc Sd Se Sf Sg Sh Si Sj Sk Sl Sm Sn So Sp Sq Sr Ss St Su Sv
Sa 0.674 -0.316 -0.381 -0.008 -0.045 0.044 -0.102 -0.061 0.002 0.017 -0.005 -0.047 0.139 0.085 -0.026 -0.095 0.028 0.064 -0.017 0.094 -0.064 0.063
Sb -0.316 0.879 -0.090 -0.077 0.051 0.035 -0.046 0.133 0.110 0.030 0.008 -0.076 -0.011 0.116 0.011 0.026 -0.059 0.138 -0.066 -0.100 0.087 0.008
Sc -0.381 -0.090 0.745 -0.111 -0.009 -0.155 0.098 -0.058 0.022 -0.004 -0.043 0.033 0.119 0.072 -0.002 0.085 -0.138 -0.064 -0.006 -0.019 0.057 -0.007
Sd -0.008 -0.077 -0.111 0.668 -0.482 -0.017 0.073 0.158 0.024 0.054 -0.027 0.033 -0.127 -0.149 -0.045 -0.042 -0.065 0.088 0.059 0.037 -0.055 -0.025
Se -0.045 0.051 -0.009 -0.482 0.610 -0.074 -0.062 -0.057 -0.038 -0.002 -0.052 0.059 0.090 0.125 -0.084 0.129 -0.056 -0.082 0.007 -0.034 0.024 0.048
Sf 0.044 0.035 -0.155 -0.017 -0.074 0.822 -0.598 -0.024 0.033 -0.050 0.020 -0.002 -0.067 -0.114 0.198 0.059 -0.171 0.033 0.048 -0.078 0.002 0.072
Sg -0.102 -0.046 0.098 0.073 -0.062 -0.598 0.832 -0.042 0.061 0.054 -0.033 0.028 -0.045 -0.085 0.127 0.041 -0.113 -0.098 -0.015 0.086 0.026 0.047
Sh -0.061 0.133 -0.058 0.158 -0.057 -0.024 -0.042 0.895 -0.127 0.198 -0.147 -0.056 -0.058 -0.309 -0.179 -0.188 0.102 0.018 -0.075 0.025 -0.009 -0.123
Si 0.002 0.110 0.022 0.024 -0.038 0.033 0.061 -0.127 0.930 -0.095 0.033 0.060 -0.003 -0.001 -0.129 -0.207 -0.092 0.040 -0.041 0.139 0.023 -0.239
Sj 0.017 0.030 -0.004 0.054 -0.002 -0.050 0.054 0.198 -0.095 0.763 -0.803 -0.095 -0.035 0.014 0.049 -0.015 0.030 -0.058 -0.030 -0.067 0.045 -0.075
Sk -0.005 0.008 -0.043 -0.027 -0.052 0.020 -0.033 -0.147 0.033 -0.803 0.728 -0.457 0.124 -0.126 0.029 0.091 0.007 0.039 0.010 0.044 -0.043 0.005
Sl -0.047 -0.076 0.033 0.033 0.059 -0.002 0.028 -0.056 0.060 -0.095 -0.457 0.873 -0.209 0.028 -0.152 -0.064 -0.038 -0.012 0.061 0.041 -0.023 0.074
Sm 0.139 -0.011 0.119 -0.127 0.090 -0.067 -0.045 -0.058 -0.003 -0.035 0.124 -0.209 0.848 0.331 0.105 0.198 -0.042 0.239 -0.092 0.096 -0.123 -0.026
Sn 0.085 0.116 0.072 -0.149 0.125 -0.114 -0.085 -0.309 -0.001 0.014 -0.126 0.028 0.331 0.819 -0.111 -0.044 -0.247 0.302 0.104 -0.170 -0.001 -0.086
So -0.026 0.011 -0.002 -0.045 -0.084 0.198 0.127 -0.179 -0.129 0.049 0.029 -0.152 0.105 -0.111 0.923 -0.187 0.123 -0.063 0.056 -0.148 -0.021 -0.190
Sp -0.095 0.026 0.085 -0.042 0.129 0.059 0.041 -0.188 -0.207 -0.015 0.091 -0.064 0.198 -0.044 -0.187 0.858 -0.328 -0.352 0.110 -0.045 0.009 0.053
Sq 0.028 -0.059 -0.138 -0.065 -0.056 -0.171 -0.113 0.102 -0.092 0.030 0.007 -0.038 -0.042 -0.247 0.123 -0.328 0.829 -0.147 -0.449 0.090 -0.100 0.110
Sr 0.064 0.138 -0.064 0.088 -0.082 0.033 -0.098 0.018 0.040 -0.058 0.039 -0.012 0.239 0.302 -0.063 -0.352 -0.147 0.807 0.023 -0.187 -0.022 -0.090
Ss -0.017 -0.066 -0.006 0.059 0.007 0.048 -0.015 -0.075 -0.041 -0.030 0.010 0.061 -0.092 0.104 0.056 0.110 -0.449 0.023 0.771 -0.026 -0.705 -0.058
St 0.094 -0.100 -0.019 0.037 -0.034 -0.078 0.086 0.025 0.139 -0.067 0.044 0.041 0.096 -0.170 -0.148 -0.045 0.090 -0.187 -0.026 0.879 -0.095 -0.042
Su -0.064 0.087 0.057 -0.055 0.024 0.002 0.026 -0.009 0.023 0.045 -0.043 -0.023 -0.123 -0.001 -0.021 0.009 -0.100 -0.022 -0.705 -0.095 0.801 -0.273
Sv 0.063 0.008 -0.007 -0.025 0.048 0.072 0.047 -0.123 -0.239 -0.075 0.005 0.074 -0.026 -0.086 -0.190 0.053 0.110 -0.090 -0.058 -0.042 -0.273 0.918
Table 4.12 indicates that all the variables have MSA value more than 0.5. Hence
researcher can proceed further.
Table 4.13 Anti Image Correlation Matrix
4.4.2.4 Communalities
Total amount of variance an original variable shares with all other variables included in
the analysis. Means, a variable’s communality is the estimate of its shared, or common,
variance among the variables as represented by the derived factors.11
The size of the
communality is a useful index for assessing how much variance in a particular variable is
accounted for by the factor solution. Higher communality values indicate that a large
amount of the variance in a variable has been extracted by the factor solution. Small
communalities show that a substantial portion of the variable’s variance is not accounted
for by the factors. Although no statistical guidelines indicate exactly what is “large” or
“small”, practical considerations dictate a lower level of 0.5 for communalities.12
11
Joseph Hair, et al. op cit., p. 141. 12
Joseph Hair, et al. op cit., p. 173
136
Table 4.14 Communalities
Variables Initial Extraction
Sa 1.000 .743
Sb 1.000 .625
Sc 1.000 .647
Sd 1.000 .771
Se 1.000 .754
Sf 1.000 .874
Sg 1.000 .851
Sh 1.000 .638
Si 1.000 .644
Sj 1.000 .931
Sk 1.000 .961
Sl 1.000 .905
Sm 1.000 .799
Sn 1.000 .677
So 1.000 .810
Sp 1.000 .765
Sq 1.000 .879
Sr 1.000 .567
Ss 1.000 .927
St 1.000 .353
Su 1.000 .916
Sv 1.000 .769
Extraction Method: Principal Component Analysis.
The communalities values are kept in the table 4.14. The communality value for variable
St is 0.353 which is less than lower limit value 0.5. Hence the variable St is omitted from
the list and the Anti-Image Matrix is to be developed once again, new MSA values are
observed (Table 4.15) and revised communalities is extracted (Table 4.16).
137
Sa Sb Sc Sd Se Sf Sg Sh Si Sj Sk Sl Sm Sn So Sp Sq Sr Ss Su Sv
Sa 0.676 -0.310 -0.381 -0.011 -0.042 0.052 -0.111 -0.064 -0.011 0.024 -0.009 -0.051 0.131 0.103 -0.013 -0.091 0.020 0.083 -0.015 -0.056 0.067
Sb -0.310 0.887 -0.093 -0.074 0.048 0.027 -0.037 0.137 0.125 0.024 0.012 -0.072 -0.001 0.101 -0.004 0.022 -0.051 0.123 -0.069 0.078 0.004
Sc -0.381 -0.093 0.744 -0.110 -0.010 -0.157 0.100 -0.057 0.025 -0.005 -0.042 0.034 0.121 0.070 -0.005 0.084 -0.137 -0.068 -0.006 0.056 -0.008
Sd -0.011 -0.074 -0.110 0.667 -0.482 -0.015 0.070 0.157 0.019 0.057 -0.029 0.031 -0.131 -0.145 -0.040 -0.041 -0.069 0.096 0.060 -0.052 -0.023
Se -0.042 0.048 -0.010 -0.482 0.611 -0.077 -0.059 -0.056 -0.034 -0.004 -0.051 0.061 0.093 0.121 -0.091 0.128 -0.053 -0.090 0.006 0.021 0.047
Sf 0.052 0.027 -0.157 -0.015 -0.077 0.823 -0.596 -0.022 0.044 -0.055 0.023 0.001 -0.060 -0.129 0.190 0.056 -0.165 0.019 0.046 -0.005 0.069
Sg -0.111 -0.037 0.100 0.070 -0.059 -0.596 0.830 -0.045 0.050 0.060 -0.037 0.025 -0.054 -0.072 0.142 0.045 -0.121 -0.083 -0.013 0.034 0.051
Sh -0.064 0.137 -0.057 0.157 -0.056 -0.022 -0.045 0.890 -0.132 0.200 -0.148 -0.057 -0.061 -0.309 -0.178 -0.187 0.100 0.023 -0.074 -0.006 -0.122
Si -0.011 0.125 0.025 0.019 -0.034 0.044 0.050 -0.132 0.934 -0.086 0.027 0.055 -0.017 0.024 -0.111 -0.203 -0.106 0.068 -0.038 0.036 -0.236
Sj 0.024 0.024 -0.005 0.057 -0.004 -0.055 0.060 0.200 -0.086 0.762 -0.803 -0.092 -0.028 0.003 0.040 -0.018 0.036 -0.072 -0.032 0.038 -0.078
Sk -0.009 0.012 -0.042 -0.029 -0.051 0.023 -0.037 -0.148 0.027 -0.803 0.726 -0.460 0.120 -0.120 0.036 0.093 0.003 0.049 0.011 -0.039 0.007
Sl -0.051 -0.072 0.034 0.031 0.061 0.001 0.025 -0.057 0.055 -0.092 -0.460 0.872 -0.214 0.036 -0.148 -0.062 -0.042 -0.005 0.062 -0.019 0.076
Sm 0.131 -0.001 0.121 -0.131 0.093 -0.060 -0.054 -0.061 -0.017 -0.028 0.120 -0.214 0.832 0.354 0.121 0.203 -0.051 0.263 -0.090 -0.115 -0.023
Sn 0.103 0.101 0.070 -0.145 0.121 -0.129 -0.072 -0.309 0.024 0.003 -0.120 0.036 0.354 0.817 -0.139 -0.053 -0.236 0.279 0.101 -0.018 -0.095
So -0.013 -0.004 -0.005 -0.040 -0.091 0.190 0.142 -0.178 -0.111 0.040 0.036 -0.148 0.121 -0.139 0.919 -0.196 0.139 -0.093 0.053 -0.035 -0.199
Sp -0.091 0.022 0.084 -0.041 0.128 0.056 0.045 -0.187 -0.203 -0.018 0.093 -0.062 0.203 -0.053 -0.196 0.848 -0.326 -0.367 0.109 0.005 0.052
Sq 0.020 -0.051 -0.137 -0.069 -0.053 -0.165 -0.121 0.100 -0.106 0.036 0.003 -0.042 -0.051 -0.236 0.139 -0.326 0.832 -0.133 -0.449 -0.092 0.115
Sr 0.083 0.123 -0.068 0.096 -0.090 0.019 -0.083 0.023 0.068 -0.072 0.049 -0.005 0.263 0.279 -0.093 -0.367 -0.133 0.799 0.018 -0.040 -0.100
Ss -0.015 -0.069 -0.006 0.060 0.006 0.046 -0.013 -0.074 -0.038 -0.032 0.011 0.062 -0.090 0.101 0.053 0.109 -0.449 0.018 0.769 -0.711 -0.059
Su -0.056 0.078 0.056 -0.052 0.021 -0.005 0.034 -0.006 0.036 0.038 -0.039 -0.019 -0.115 -0.018 -0.035 0.005 -0.092 -0.040 -0.711 0.800 -0.278
Sv 0.067 0.004 -0.008 -0.023 0.047 0.069 0.051 -0.122 -0.236 -0.078 0.007 0.076 -0.023 -0.095 -0.199 0.052 0.115 -0.100 -0.059 -0.278 0.912
Table 4.15 Revised Anti-Image Matrix
Table 4.15 shows that all variables in the tables have MSA value more than 0.5. So,
researcher can proceed further.
138
Table 4.16 Revised Communalities
Variables Initial Extraction
Sa 1.000 .744
Sb 1.000 .642
Sc 1.000 .649
Sd 1.000 .771
Se 1.000 .755
Sf 1.000 .875
Sg 1.000 .853
Sh 1.000 .655
Si 1.000 .659
Sj 1.000 .932
Sk 1.000 .961
Sl 1.000 .905
Sm 1.000 .799
Sn 1.000 .677
So 1.000 .808
Sp 1.000 .782
Sq 1.000 .880
Sr 1.000 .554
Ss 1.000 .929
Su 1.000 .918
Sv 1.000 .768 Extraction Method: Principal Component Analysis.
Table 4.16 shows all the variables share more than one-half of their variance with the
factors to be extracted. All the communalities are sufficiently high; values are more than
0.5 to proceed with the rotation of the factor matrix.
4.4.2.5 Eigenvalues and Total Variance Explained
The most commonly used method to answer the key question: How many factors to
extract or retain?
The rational for the latent roots or eigenvalues criterion is that any individual factor
should account for the variance of at least a single variable if it is to be retained for
interpretation13
. Eigenvalues are the sum of the variances of the factor values. It
13
Joseph Hair, et al. op cit., p. 144
139
represents the amount of variance associated with the factor. Hence, only factors with a
variance greater than 1.0 are considered significant; all factors with eigenvalues less than
1 are considered insignificant and are disregarded.14
Using the eigenvalue for establishing
a cutoff is most reliable when number of variables is between 20 and 50.15
Table 4.17 Eigenvalues and Total Variance Explained
Component Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 5.648 26.894 26.894 5.648 26.894 26.894 4.048 19.278 19.278
2 4.155 19.786 46.681 4.155 19.786 46.681 3.374 16.067 35.345
3 2.571 12.242 58.923 2.571 12.242 58.923 3.262 15.535 50.879
4 1.767 8.414 67.337 1.767 8.414 67.337 2.420 11.522 62.402
5 1.232 5.865 73.201 1.232 5.865 73.201 1.877 8.938 71.340
6 1.143 5.444 78.645 1.143 5.444 78.645 1.534 7.305 78.645
7 .778 3.707 82.351
8 .566 2.697 85.049
9 .524 2.494 87.542
10 .428 2.038 89.580
11 .407 1.937 91.517
12 .367 1.747 93.264
13 .333 1.587 94.852
14 .235 1.121 95.973
15 .206 .983 96.955
16 .200 .952 97.907
17 .161 .766 98.674
18 .107 .509 99.182
19 .094 .447 99.629
20 .050 .240 99.869
21 .027 .131 100.000
Extraction Method: Principal Component Analysis.
The percentage of variance criteria is an approach based on achieving a specified
cumulative percentage of total variance extracted by successive factors. The purpose is to
ensure practical significance for the derived factors by ensuring that they explain at least
14
Naresh Malhotra, Marketing Research: An Applied orientation, (Fifth edn.) (New Delhi: Pearson
Education), 2008, p. 647. 15
Joseph Hair, et al. op cit., p. 144
140
a specified amount of variance. In the social science, it is common to consider a solution
that accounts for 60 percent of total variance as satisfactory.16
Table 4.17 contains the information regarding the 21 possible factors and their relative
explanatory power as expressed by their eigenvalues. There are total six factors having
Eigenvalues more than 1.0. Hence, researcher has retained these six factors for study.
Total variance explained by the six factors is 78.645. Percent of variance explained by
each factor is computed by dividing Eigenvalues by the number of variables i.e.
eigenvalue of first factor is 5.648 by dividing it by the total variables 21 we find 0.26894
which is 26.894 percent.
4.4.2.6 Factor Extraction
There are two types of extraction methods.
1. Principal Component Analysis
2. Common Factor Analysis
Principal component analysis considers the total variance in the data and derives the
factors that contain small proportions of unique variance and in some instances, error
variance.
This method is most appropriate when data reduction is primary concern, focusing on the
minimum number of factors needed to account for the maximum portion of the total
variance represented in the original set of the variables.17
In Common factor analysis, the factors are estimated based only on the common or
shared variance, assuming that both the unique and error variance are not of interest in
defining the structure of the variables. Communalities are inserted in the diagonal of the
correlation matrix to employ common variance in the estimation of the factors. This
16
ibid 17
Joseph Hair, et al. op cit., p. 142
141
method is most appropriate when the primary objective is to identify the underlying
dimensions and the common variance is of interest.18
The objective of this study is to extract the factors and so the data reduction becomes the
primary concern. Hence, the principal component analysis method is used to extract the
unrotated component matrix. Table 4.18 below indicates the unrotated factor matrix.
Table 4.18 Component Matrix (a)
Component
1 2 3 4 5 6
So .796 -.314 -.117 .185 .057 .153
Sh .777 .162 -.091 .049 -.007 -.122
Si .742 .180 -.248 -.014 .045 .114
Sp .727 .052 -.360 .291 -.105 -.158
Sv .711 .319 -.232 -.166 .037 .279
Sn .678 .199 .193 .077 .128 -.343
Sb -.561 .171 .190 .266 -.296 .321
Sr .543 .106 -.353 .277 -.116 -.182
Sm -.538 .434 -.004 -.491 .059 .278
Sq .100 .897 -.237 -.017 -.049 -.080
Ss .097 .872 -.281 -.217 -.063 .170
Su .220 .833 -.276 -.237 -.032 .206
Sf -.450 .652 .179 .005 .022 -.463
Sg -.475 .625 .080 .002 -.028 -.479
Sk .549 .206 .778 -.105 .013 .034
Sj .532 .191 .771 -.129 .005 .050
Sl .520 .173 .762 -.129 -.030 .084
Sa -.150 .319 .158 .607 -.417 .230
Sc -.107 .402 .209 .596 -.262 .088
Se -.145 .277 .093 .457 .661 .047
Sd -.247 .317 .012 .378 .635 .251
Extraction Method: Principal Component Analysis.
a. 6 components extracted.
18
ibid
142
4.4.2.7 Factor Loading
A factor loading represents the correlations between the factors and variables. It shows
the strength of the variables that compose the factor. The larger the absolute value of the
factor loading, the factor and the variable are more closely related. Means the more
important role the variable plays in interpreting the factor matrix19
. With the objective of
achieving the power level of 80 percent, significance level of 0.05 and standard errors
assumed to be twice those of conventional correlation coefficients of factor loading,
following table guiding for identifying significant factor loadings based on sample size.
Table 4.19 Guideline for identifying significant factor loadings based on sample size.
Factor Loading Sample size needed for significance
0.30 350
0.35 250
0.40 200
0.45 150
0.50 120
0.55 100
0.60 85
0.65 70
0.70 60
0.75 50
The sample size taken by the researcher is 546 for the study. Hence factor loading 0.30 is
sufficient. But the factor loading value greater than 0.5 is generally considered necessary
for practical significance.20
Hence researcher has considered factor loading 0.5 for study.
19
Naresh Malhotra, Marketing Research: An Applied orientation, (Fifth edn.) (New Delhi: Pearson
Education), 2008, pp. 648 20
Joseph Hair, et al. op cit., p. 152
143
4.4.2.8 Rotation of factors
An unrotated factor matrix (Table 4.18) indicates the relationship between the factors and
individual variables. It seldom results in factors that can be interpreted, because the
factors are correlated with many variables. In such a complex matrix it is difficult to
interpret the factors. Therefore through rotation, the factor matrix is transformed into a
simpler one that is easier to interpret.21
Rotation of the factors improves the interpretation
by reducing some of the ambiguities that often accompany initial unrotated factor
solutions. Therefore, researcher must employ a rotational method to achieve simpler and
theoretically more meaningful factor solutions.
The reference axes of the factors are turned about the origin until some other position has
been reached. The unrotated factor solutions extract factors in order of their variance
extracted. The first factor solutions extract factors in the order of their variance extracted.
The first factor tends to be a general factor with almost every variable loading
significantly and it accounts for the largest amount of variance. The second and
subsequent factors are then based on the residual amount of variance. Each accounts for
successively smaller portions of variance. The ultimate effect of rotating the factor matrix
is to redistribute the variance from earlier factors to later ones to achieve a simpler,
theoretically more meaningful factor pattern.22
The two types of factor rotation methods are:
1. Orthogonal factor rotation
2. Oblique factor rotation
In orthogonal factor rotation, the axes are maintained at 90 degrees. It is also possible
to rotate the axes and not retain the 90-degree angle between the reference axes. This is a
more widely used approach for factor rotation.
21
Naresh Malhotra, Marketing Research: An Applied orientation, (Fifth edn.) (New Delhi: Pearson
Education), 2008, p. 647. 22
Joseph Hair, et al. op cit., p. 147
144
When 90-degree is not maintained it is called oblique factor rotation. This method is
not widely used because the analytical procedures for performing the oblique rotations
are not as well developed and are still subject to some controversy. Therefore researcher
has employed the orthogonal factor rotation method.
Out of the three types of orthogonal rotation method; Quartimax, Varimax, Equimax; the
VARIMAX method has proved successful as an analytical approach to obtain an
orthogonal rotation of factors. Furthermore, the Kaiser’s experiment indicates that the
factor pattern obtained by the VARIMAX rotation tends to be more invariant than the
pattern obtained by the Quartimax method when different subsets of variables are
analysed. While the Equimax approach is a compromise between the Quartimax and
Varimax. Rather than concentrating on simplification of either rows or columns, it tries to
accomplish some of each.23
Therefore researcher has adopted VARIMAX rotation
method.
Given the sample size of 546, factor loadings of 0.5 and higher will be considered
significant for interpretative purposes. Using this threshold for the factor loadings, we can
see that the unrotated matrix (Table 4.18) does little to identify any form of simple
structure. Three variables have cross-loadings, and for many of the other variables, the
significant loadings are fairly low. Rotated matrix (Table 4.20) may improve the
understanding of the relationship among the variables.
23
Joseph Hair, et al. op cit., pp. 147-150
145
Table 4.20 Rotated Component Matrix (a)
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
As shown in the table 4.20, the VARIMAX rotation improves the structure considerably
in two ways. First, the loadings are improved for almost every variable, with loadings
more closely aligned to the objectives of having a high loading on only a single factor.
Second, now only two variables (Sm & So) have a cross-loading on two factors. And
hence they are omitted from the list and Revised Rotated Factor Loading Matrix is
prepared.
Component
1 2 3 4 5 6
Sp .857 .133 -.016 -.152 .033 -.071
Sr .719 .140 -.085 -.040 .070 -.058
Sm -.699 .516 -.081 .172 -.084 .029
Sh .696 .229 .300 -.114 -.108 -.059
So .654 -.091 .192 -.569 -.102 -.005
Sn .619 .055 .493 .149 -.147 .064
Si .607 .389 .176 -.303 -.130 -.004
Su .117 .940 .064 .117 .044 .045
Ss .047 .934 .004 .209 .095 .044
Sq .210 .784 .005 .424 .168 .110
Sv .460 .598 .231 -.355 -.139 -.017
Sk .115 .037 .972 -.021 .029 -.005
Sj .088 .039 .960 -.035 .019 -.021
Sl .070 .039 .943 -.067 .044 -.048
Sf -.207 .219 .042 .861 .143 .145
Sg -.188 .224 -.061 .857 .142 .090
Sa .013 .060 .018 .059 .856 .056
Sc .077 .064 .098 .198 .748 .173
Sb -.492 .008 -.109 .061 .619 .034
Se .015 -.009 .016 .146 .095 .851
Sd -.142 .131 -.074 .029 .127 .843
146
Table 4.21 Revised Rotated Factor Loading Matrix
Component
1 2 3 4 5 6
Sp .872 -.009 .086 -.153 .059 -.068
Sr .736 -.080 .100 -.055 .096 -.048
Sh .727 .302 .175 -.088 -.086 -.063
Si .642 .180 .350 -.299 -.113 .001
Sn .595 .499 .049 .156 -.144 .055
Sk .107 .973 .041 -.017 .031 -.005
Sj .083 .961 .044 -.033 .021 -.020
Sl .074 .943 .032 -.051 .049 -.049
Su .146 .059 .944 .119 .036 .045
Ss .077 -.003 .941 .208 .087 .044
Sq .235 -.001 .780 .420 .170 .112
Sv .475 .237 .590 -.350 -.141 -.018
Sf -.200 .030 .222 .869 .144 .140
Sg -.182 -.074 .227 .867 .142 .084
Sa .001 .018 .058 .057 .859 .056
Sc .073 .096 .054 .195 .757 .176
Sb -.533 -.111 .056 .055 .594 .028
Se .015 .016 -.015 .148 .098 .852
Sd -.140 -.073 .138 .030 .121 .843 Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
Each of the variables in the above table 4.21 has a significant factor loading on only one
factor except the variable Sb which cross-loads on two factors. Therefore it is omitted
and entire process is revised. Total five factors have been extracted in the revised rotated
factor loading matrix (Table 4.22).
147
Table 4.22 Revised Rotated Factor Loading Matrix
Component
1 2 3 4 5
Sp .858 -.018 .007 .113 -.073
Si .772 .133 .185 -.148 .002
Sh .720 .089 .319 -.029 -.070
Sv .704 .313 .234 -.252 -.009
Sr .699 .048 -.066 .173 -.062
Ss .225 .912 -.001 -.004 .052
Sq .244 .882 .013 .175 .111
Su .319 .867 .060 -.062 .052
Sg -.442 .647 -.047 .298 .069
Sf -.462 .642 .056 .306 .123
Sk .107 .011 .973 .041 -.008
Sj .092 .005 .959 .024 -.023
Sl .090 -.014 .940 .039 -.049
Sn .476 .106 .526 -.027 .045
Sa .004 .085 -.001 .816 .061
Sc .005 .148 .087 .796 .164
Sd -.095 .137 -.076 .074 .851
Se -.046 .064 .023 .147 .845 Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
148
Table 4.23 Eigenvalues and Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared
Loadings Rotation Sums of Squared Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 4.807 26.703 26.703 4.807 26.703 26.703 3.721 20.674 20.674
2 3.612 20.069 46.772 3.612 20.069 46.772 3.382 18.789 39.463
3 2.507 13.930 60.702 2.507 13.930 60.702 3.243 18.019 57.482
4 1.458 8.101 68.802 1.458 8.101 68.802 1.677 9.318 66.801
5 1.166 6.477 75.279 1.166 6.477 75.279 1.526 8.478 75.279
6 .979 5.440 80.719
7 .771 4.282 85.002
8 .520 2.890 87.891
9 .484 2.687 90.578
10 .403 2.241 92.819
11 .332 1.842 94.660
12 .296 1.646 96.306
13 .215 1.193 97.499
14 .162 .900 98.399
15 .109 .605 99.005
16 .101 .560 99.564
17 .051 .281 99.846
18 .028 .154 100.000
Table 4.23 contains the information regarding the eigenvalues and total variance
explained. There are five factors out of a total of eighteen, having Eigenvalue more than
1 with total variance explained is 75 percent. Hence five factors can be extracted and
explained further.
The required variance explained by all the factors should be at least 60 percent. In the
table 4.23, we can observe that the first four factors very well explain the total variance of
more than 68 percent, which is more than minimum requirement of 60 percent. On other
hand total variance explained by fifth factor is only 6.4 percent which is very less
compared to other four factors. Hence, the entire process of factor analysis is repeated
with the objective to extract four factors instead of five.
149
Table 4.24 Revised Communalities
Extraction Method: Principal Component Analysis
.
Communalities value of all the variables is more than 0.5 except Variables Sa (0.440), Sd
(0.413), Se (0.445) and Sr (0.495). Therefore, all the variables starting from lowest value
variable Sd is omitted one by one from the list and new revised communalities are
observed. The process is repeated until communalities for all variables become more than
0.5.
Communalities values in the table 4.25 are above 0.5 for all variables; hence process is
carried out further.
Variables Initial Extraction
Sa 1.000 0.440
Sc 1.000 0.539
Sd 1.000 0.413
Se 1.000 0.445
Sf 1.000 0.730
Sg 1.000 0.692
Sh 1.000 0.633
Si 1.000 0.662
Sj 1.000 0.928
Sk 1.000 0.959
Sl 1.000 0.896
Sn 1.000 0.512
Sp 1.000 0.730
Sq 1.000 0.879
Sr 1.000 0.495
Ss 1.000 0.884
Su 1.000 0.858
Sv 1.000 0.689
150
Table 4.25 Revised Communalities
Variables Initial Extraction
Sa 1.000 .680
Sc 1.000 .703
Sf 1.000 .738
Sg 1.000 .706
Sh 1.000 .632
Si 1.000 .666
Sj 1.000 .928
Sk 1.000 .960
Sl 1.000 .895
Sn 1.000 .513
Sp 1.000 .751
Sq 1.000 .880
Sr 1.000 .520
Ss 1.000 .883
Su 1.000 .861
Sv 1.000 .709 Extraction Method: Principal Component Analysis.
Table 4.26 Eigenvalues and Total Variance Explained
Extraction Method: Principal Component Analysis.
Component Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 4.801 30.009 30.009 4.801 30.009 30.009 3.704 23.150 23.150
2 3.391 21.193 51.202 3.391 21.193 51.202 3.392 21.200 44.350
3 2.484 15.527 66.729 2.484 15.527 66.729 3.250 20.313 64.663
4 1.349 8.428 75.157 1.349 8.428 75.157 1.679 10.494 75.157
5 0.971 6.067 81.224
6 0.753 4.704 85.928
7 0.502 3.139 89.067
8 0.409 2.555 91.622
9 0.361 2.257 93.879
10 0.309 1.932 95.810
11 0.218 1.363 97.174
12 0.162 1.015 98.189
13 0.109 0.683 98.872
14 0.102 0.635 99.507
15 0.051 0.319 99.825
16 0.028 0.175 100.000
151
Table 4.27 Final Rotated Component Matrix (a)
Component
1 2 3 4
Sp: Makes transport arrangements/comes to collect .860 -.021 .013 .102
Si: Assurance to purchase .769 .138 .189 -.141
Sh: Comes first to purchase .720 .087 .323 -.035
Sv: Well known in the market .702 .316 .238 -.244
Sr: Provides Demand Information .701 .047 -.063 .151
Ss: Updating the price information .222 .913 .000 .002
Sq: Customer’s location .238 .888 .014 .183
Su: Provides Production estimation information .316 .869 .062 -.054
Sf: Credit Finance -.469 .649 .051 .309
Sg: Financial Assistance -.445 .648 -.051 .292
Sk: Grading Assistance .101 .011 .973 .041
Sj: Cleaning Assistance .087 .004 .959 .023
Sl: Packaging Assistance .088 -.019 .941 .035
Sn: Quality Testing & Certificate Assistance .469 .113 .529 -.013
Sa: Age-old business relationship .004 .082 -.003 .820
Sc: Trust -.002 .157 .084 .819
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 5 iterations.
Table 4.27 represents the rotated component matrix. Loadings of all the variables are
more than 0.5 with four factors extracted. The eigenvalues of first four factors is more
than 1 and total variance explained by these four factors is more than 75 percent.
As shown in Table 4.27, the factor structure for the remaining sixteen variables is now
very well defined, representing four distinct groups of variables.
152
The cutoff point of loadings for interpretation purpose is kept at 0.5. Factors 1 and 2 have
five variables each with significant loadings. Factor 3 has four variables with significant
loadings and factor 4 has two variables each with significant loadings. Naming of the
factors is carried out based on this table.
4.4.3. Interpretation of Factors extracted from section – II for Sales
related variables
First factor is constructed with five variables named; Makes transport
arrangements/comes to collect, Assurance to purchase, Comes first to purchase, Well-
known in the market, Provides Demand Information. The first variable; Makes transport
arrangements/comes to collect (Sp) having the highest loadings (0.858) on the factor 1,
second variable (Si) having loadings (0.772) while remaining three (Sh, Sv and Sr)
express almost equal loadings (approx. 0.7). Variable Sp, Sh, Si and Sr are related to
providing the cooperation by the buyer to the seller by making the arrangement of
transport services, promising to purchase the material, providing demand information on
regular basis and approaching and coming first to purchase the material. Considering all
these, the factor is labeled as “Buyer’s Cooperation”. This factor explains 26.70 percent
of variance, which is highest among all the factors.
There are also five variables; explained factor 2. The total variance explained by this
factor is 20 percent. The variables identified in this factor are; Updating the price
information (Ss, 0.912), Customer’s location (Sq, 0.882), Provides Production estimation
information (Su, 0.867), Financial Assistance (Sg, 0.647) and Credit Finance (Sf, 0.642).
Variable Ss, Su, Sg and Sf are the value added support services provided by the buyer to
the supplier. Hence, the name of this factor is given as “Support Services”. First two
factors cumulatively explain 48 percent of variance.
Total four variables are identified under factor 3. These are; Grading Assistance (Sk,
0.973), Cleaning Assistance (Sj, 0.959), Packaging Assistance (Sl, 0.940) and Quality
153
Testing & Certificate Assistance (Sn, 0.526). All these variables are related to the quality
management and value addition to the products. Hence this factor is named as “Quality
Management”. This factor explains almost 14 percent of the variance.
The fourth factor is defined by two variables; Age-old business relationship (Sa, 0.816)
and Trust (Sc, 0.796). This factor explains 8 percent of the variance. Both variables
explain the building and maintaining the long term relationship and building the mutual
trust in the relationship with buyer. This ensures the long term mutual benefits to both the
parties. Hence this factor is labeled as “Relationship”.
4.4.4 Factor Analysis for section – II for Purchase related variables
4.4.4.1. KMO and Bartlett’s Test
The Bartlett’s Test of Sphericity for measuring the presence of correlations among the
variables for the section – II for Purchase related variables is shown in Table 4.28
Table 4.28 KMO and Bartlett's Test for Purchase related variables
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.906
Bartlett's Test of Sphericity 8582.864
Sig. 0.000
Bartlett’s Test of Sphericity value 8582.864 with significance level of 0.000 in the table
4.28 above satisfies the necessary condition. This indicates the statistical significance that
the correlation matrix has significant correlation among the variables.
4.4.4.2 Measure of Sampling Adequacy (MSA)
From the table 4.28, it can be observed that the MSA value is 0.906 which is greater than
necessary condition value 0.5. Hence, factor analysis can be applied to it.
154
Pa Pb Pc Pd Pe Pf Pg Ph Pi Pj Pk Pl Pm Pn Po Pp Pq Pr Ps Pt Pu Pv
Pa 0.865 -0.311 -0.210 0.111 -0.118 -0.090 0.118 0.095 0.001 0.074 -0.063 -0.054 0.035 0.028 -0.136 0.110 -0.167 0.069 -0.015 0.012 -0.061 0.185
Pb -0.311 0.729 -0.257 -0.216 0.221 0.057 0.109 0.074 -0.086 -0.003 -0.015 0.058 -0.198 0.000 -0.029 0.096 -0.092 0.038 0.084 0.042 0.026 -0.067
Pc -0.210 -0.257 0.686 -0.123 -0.035 0.062 -0.116 -0.049 -0.069 0.057 0.030 -0.186 -0.053 0.014 0.010 0.133 -0.025 -0.018 0.068 0.005 0.084 -0.091
Pd 0.111 -0.216 -0.123 0.963 0.093 -0.048 -0.101 0.099 -0.049 0.017 -0.011 0.007 -0.089 -0.030 -0.133 -0.066 0.120 -0.058 -0.199 -0.195 -0.082 -0.086
Pe -0.118 0.221 -0.035 0.093 0.940 0.198 -0.114 -0.045 0.189 0.101 -0.095 0.045 -0.058 0.076 -0.058 -0.007 -0.254 0.021 0.037 -0.038 0.122 0.003
Pf -0.090 0.057 0.062 -0.048 0.198 0.933 0.057 -0.135 -0.151 0.030 0.054 -0.144 -0.115 -0.129 -0.088 0.236 -0.236 0.127 -0.151 0.055 -0.078 -0.077
Pg 0.118 0.109 -0.116 -0.101 -0.114 0.057 0.774 -0.174 0.082 0.011 -0.171 0.332 0.068 -0.062 0.027 -0.424 -0.027 -0.107 0.309 -0.127 -0.219 0.035
Ph 0.095 0.074 -0.049 0.099 -0.045 -0.135 -0.174 0.771 -0.201 0.026 0.031 -0.164 -0.028 0.089 -0.097 0.082 0.099 -0.012 -0.007 -0.044 0.035 -0.021
Pi 0.001 -0.086 -0.069 -0.049 0.189 -0.151 0.082 -0.201 0.933 0.038 -0.122 0.135 0.294 0.050 -0.063 -0.203 -0.117 -0.133 0.140 -0.142 -0.136 -0.048
Pj 0.074 -0.003 0.057 0.017 0.101 0.030 0.011 0.026 0.038 0.840 -0.869 -0.274 0.097 -0.041 0.056 0.130 -0.101 -0.032 0.034 0.128 -0.049 -0.088
Pk -0.063 -0.015 0.030 -0.011 -0.095 0.054 -0.171 0.031 -0.122 -0.869 0.843 -0.170 -0.142 -0.122 -0.073 -0.001 0.061 0.057 -0.070 -0.098 0.078 0.058
Pl -0.054 0.058 -0.186 0.007 0.045 -0.144 0.332 -0.164 0.135 -0.274 -0.170 0.917 -0.026 -0.055 -0.008 -0.267 0.035 0.000 0.018 -0.084 0.032 0.056
Pm 0.035 -0.198 -0.053 -0.089 -0.058 -0.115 0.068 -0.028 0.294 0.097 -0.142 -0.026 0.854 0.283 0.305 -0.099 0.098 -0.180 0.015 0.012 -0.262 -0.023
Pn 0.028 0.000 0.014 -0.030 0.076 -0.129 -0.062 0.089 0.050 -0.041 -0.122 -0.055 0.283 0.950 -0.098 -0.171 -0.038 -0.139 0.174 0.023 -0.168 0.029
Po -0.136 -0.029 0.010 -0.133 -0.058 -0.088 0.027 -0.097 -0.063 0.056 -0.073 -0.008 0.305 -0.098 0.911 -0.139 0.046 -0.033 0.031 0.151 -0.005 -0.226
Pp 0.110 0.096 0.133 -0.066 -0.007 0.236 -0.424 0.082 -0.203 0.130 -0.001 -0.267 -0.099 -0.171 -0.139 0.866 -0.371 0.128 -0.065 -0.065 0.010 -0.002
Pq -0.167 -0.092 -0.025 0.120 -0.254 -0.236 -0.027 0.099 -0.117 -0.101 0.061 0.035 0.098 -0.038 0.046 -0.371 0.892 -0.085 -0.169 -0.001 0.134 -0.096
Pr 0.069 0.038 -0.018 -0.058 0.021 0.127 -0.107 -0.012 -0.133 -0.032 0.057 0.000 -0.180 -0.139 -0.033 0.128 -0.085 0.920 -0.624 -0.103 -0.035 0.125
Ps -0.015 0.084 0.068 -0.199 0.037 -0.151 0.309 -0.007 0.140 0.034 -0.070 0.018 0.015 0.174 0.031 -0.065 -0.169 -0.624 0.890 -0.005 -0.344 -0.191
Pt 0.012 0.042 0.005 -0.195 -0.038 0.055 -0.127 -0.044 -0.142 0.128 -0.098 -0.084 0.012 0.023 0.151 -0.065 -0.001 -0.103 -0.005 0.967 -0.176 -0.155
Pu -0.061 0.026 0.084 -0.082 0.122 -0.078 -0.219 0.035 -0.136 -0.049 0.078 0.032 -0.262 -0.168 -0.005 0.010 0.134 -0.035 -0.344 -0.176 0.938 -0.264
Pv 0.185 -0.067 -0.091 -0.086 0.003 -0.077 0.035 -0.021 -0.048 -0.088 0.058 0.056 -0.023 0.029 -0.226 -0.002 -0.096 0.125 -0.191 -0.155 -0.264 0.958
Measures of Sampling Adequacy(MSA)
4.4.4.3 Anti-Image Correlation Matrix
Table 4.29 indicates that all the variables have MSA value more than 0.5. Hence
researcher can proceed further.
Table 4.29 Anti Image Correlation Matrix
155
4.4.4.4 Communalities
Table 4.30 Communalities
Variables Initial Extraction
Pa 1.000 .670
Pb 1.000 .683
Pc 1.000 .651
Pd 1.000 .862
Pe 1.000 .656
Pf 1.000 .655
Pg 1.000 .694
Ph 1.000 .378
Pi 1.000 .712
Pj 1.000 .937
Pk 1.000 .935
Pl 1.000 .901
Pm 1.000 .687
Pn 1.000 .863
Po 1.000 .540
Pp 1.000 .742
Pq 1.000 .505
Pr 1.000 .878
Ps 1.000 .921
Pt 1.000 .802
Pu 1.000 .922
Pv 1.000 .851 Extraction Method: Principal Component Analysis.
The communality value for variable Ph is 0.378 (Table 4.30) which is less than the
required value 0.5. Hence the variable Ph is omitted from the list and the process is
revised to extract the revised value of communalities (Table 4.31).
156
Table 4.31 Revised Communalities
Variables Initial Extraction
Pa 1.000 .655
Pb 1.000 .664
Pc 1.000 .426
Pd 1.000 .861
Pe 1.000 .630
Pf 1.000 .655
Pg 1.000 .579
Pi 1.000 .649
Pj 1.000 .875
Pk 1.000 .884
Pl 1.000 .844
Pm 1.000 .665
Pn 1.000 .853
Po 1.000 .483
Pp 1.000 .715
Pq 1.000 .490
Pr 1.000 .878
Ps 1.000 .917
Pt 1.000 .791
Pu 1.000 .923
Pv 1.000 .848
Extraction Method: Principal Component Analysis.
Revised communalities values in Table 4.31 shows all the variables’ communalities
values are more than 0.5 except Pc (0.426), Po (0.483) and Pq (0.490). Therefore they are
omitted one by one starting from lowest value variable Pc and revised values are
extracted. The revised communalities table is shown below (Table 4.32)
157
Table 4.32 Revised Communalities
Extraction Method: Principal Component Analysis.
The communalities for all the variables in the above table are more than 0.5. Therefore
researcher can proceed further.
Variables Initial Extraction
Pa 1.000 .627
Pb 1.000 .601
Pd 1.000 .864
Pe 1.000 .661
Pf 1.000 .669
Pg 1.000 .690
Pi 1.000 .634
Pj 1.000 .937
Pk 1.000 .941
Pl 1.000 .908
Pm 1.000 .644
Pn 1.000 .867
Pp 1.000 .732
Pr 1.000 .875
Ps 1.000 .913
Pt 1.000 .807
Pu 1.000 .924
Pv 1.000 .846
158
4.4.4.5 Eigenvalues and Total Variance Explained
From the table 4.33, it is observed that total variance explained by the three factors is
almost 78 percent. These factors can be studied further.
Table 4.33 Eigenvalues and Total Variance Explained
Extraction Method: Principal Component Analysis.
Component Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 8.796 48.866 48.866 8.796 48.866 48.866 7.334 40.742 40.742
2 3.664 20.353 69.219 3.664 20.353 69.219 4.596 25.531 66.273
3 1.682 9.346 78.566 1.682 9.346 78.566 2.213 12.292 78.566
4 .778 4.320 82.886
5 .635 3.526 86.412
6 .509 2.828 89.240
7 .323 1.797 91.037
8 .291 1.617 92.654
9 .267 1.482 94.136
10 .249 1.384 95.521
11 .192 1.068 96.588
12 .170 .943 97.532
13 .144 .801 98.333
14 .133 .741 99.074
15 .074 .414 99.488
16 .047 .261 99.749
17 .037 .206 99.956
18 .008 .044 100.000
159
4.4.4.6 Rotated Component Matrix
As shown in the table 4.34 below, through VARIMAX rotation total three factors have
been extracted. But the variables Pi and Pp reveal the cross-loading. Hence they are
omitted and entire process is revised. The revised rotated component matrix is shown in
table 4.35
Table 4.34 Rotated Component Matrix (a)
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
Component
1 2 3
Ps .948 .114 .040
Pu .935 .173 .143
Pr .919 .135 .112
Pd .905 .194 .090
Pv .879 .237 .127
Pt .819 .226 .292
Pm .754 -.237 -.141
Pf .753 .256 -.190
Pe -.745 -.277 .172
Pi .585 .506 .190
Pj .153 .952 .088
Pk .154 .951 .110
Pl .156 .940 .022
Pn .171 .885 .231
Pg .018 .380 .738
Pb .323 -.007 -.705
Pa -.398 -.003 -.685
Pp .241 .558 .602
160
Table 4.35 Final Rotated Component Matrix(a)
Component
Communalities 1 2 3
Ps .951 .111 .021 .918
Pu .939 .169 .118 .925
Pr .923 .133 .094 .878
Pd .908 .188 .056 .863
Pv .883 .231 .099 .843
Pt .825 .222 .258 .797
Pm .758 -.230 -.146 .649
Pf .748 .245 -.200 .660
Pe -.741 -.268 .179 .653
Pj .164 .961 .088 .958
Pk .166 .960 .106 .960
Pl .167 .946 .016 .924
Pn .183 .888 .210 .867
Pb .304 -.031 -.756 .665
Pa -.416 -.023 -.716 .687
Pg .036 .386 .695 .634 Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 5 iterations.
Table 4.36 Eigenvalues and Total Variance Explained
Component Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 7.915 49.471 49.471 7.915 49.471 49.471 6.990 43.686 43.686
2 3.379 21.118 70.589 3.379 21.118 70.589 4.061 25.381 69.067
3 1.584 9.901 80.490 1.584 9.901 80.490 1.828 11.423 80.490
4 .679 4.245 84.735
5 .514 3.212 87.947
6 .502 3.140 91.088
7 .313 1.958 93.045
8 .289 1.808 94.853
9 .198 1.238 96.091
10 .170 1.062 97.153
11 .148 .923 98.076
12 .135 .845 98.921
13 .075 .466 99.387
14 .050 .311 99.698
15 .040 .251 99.949
16 .008 .051 100.000
161
Table 4.35 explains the significant loading; above 0.5 of each of the variables on only
one factor. Moreover all of the loadings are above 0.6, meaning that more than one-half
of the variance is accounted for by the loading on a single factor.
Table 4.36 explains the eigenvalues and total variance explained. There are total three
factors having eigenvalue of more than 1 which explains almost 80 percent of variance.
Table 4.37 Final Rotated Factor Loading Matrix
As shown in Table 4.37, the factor structure for the sixteen variables is now very well
defined, representing three distinct groups of variables. Naming of the factors is carried
out based on this table.
Component
1 2 3
Ps: Updating the price information .951
Pu: Provides Production estimation information .939
Pr: Provides Demand Information .923
Pd: Offers best price//helps to get best price .908
Pv: Well known in the market .883
Pt: Provides Weather forecast information .825
Pm: Storage & Warehousing services .758
Pf: Credit .748
Pe: Spot/Cash Payment -.741
Pj: Cleaning Assistance .961
Pk: Grading Assistance .960
Pl: Packaging Assistance .946
Pn: Quality Testing & Certificate Assistance .888
Pb: Friend/family member -.756
Pa: Age-old business relationship -.716
Pg: Financial Assistance .695
162
4.4.5. Interpretation of Factors extracted from section – II for Purchase
related variables
There are totally nine variables which explain the first factor. The variables are; Updating
the price information (Ps, 0.951), Provides Production estimation information (Pu,
0.939), Provides Demand Information (Pr, 0.923), Offers best price//helps to get best
price (Pd, 0.908), Well known in the market (Pv, 0.883), Provides Weather forecast
information (Pt, 0.825), Storage & Warehousing services (Pm, 0.758), Credit (Pf, 0.748),
Spot/Cash Payment (Pe, -0.741). The four variables; Updating the price information
(0.951), Provides Production estimation information (0.939), Provides Demand
Information (0.923) and Offers best price//helps to get best price (0.908) having
comparatively more loadings on the factor 1, representing the factor. Amongst the four,
the first three are information services provided by the seller to the buyer. And fourth is,
the best deal offers by the seller or the intermediaries helps to get the best price. The sixth
variable, Provides Weather forecast information, is also information services. These all
information services help the buyer to take the decision related to, when to purchase and
how much to purchase to minimize the purchase cost and hence maximize the profit. The
Seventh variable is Storage & Warehousing services reduce the efforts of the buyer to
make arrangements for storing the material till they dispose off their material and hence
reduce the cost of managing storage services. Buyer prefers the seller who facilitates him
through “credit” for purchasing the material; on other hand he doesn’t prefer to make on
the spot payment. And hence the loading of this variable (-0.741) is of opposite sign.
Hence, it varies together with all other variables but moves in opposite direction of
others. Considering all these support services provided by the seller to buyer, the factor is
labeled as “Support Services”. This factor explains 49.47 percent of variance, which is
highest among all the factors.
Total four variables are identified under factor 2. These are; Cleaning Assistance (Sj,
0.961), Grading Assistance (Sk, 0.960), Packaging Assistance (Sl, 0.941) and Quality
Testing & Certificate Assistance (Sn, 0.888). Buyer prefers to purchase the products from
the intermediaries who provide all these value added quality related services. All these
163
variables are related to quality management and value addition to the products. Hence
this factor is named as “Quality Management”. This factor explains almost 21.11
percent of the variance.
The Third factor is defined by three variables; Friend/family members (Sb, -0.756), Age-
old business relationship (Sa, -0.716) and Financial Assistance (Sg, 0.6). This factor
explains almost 10 percent of the variance. Friend/Family members as a suppliers ensure
right quality of the products with timely supply of required quantity. This in turn reduces
supply risk. The same can be experienced, if buyer has an age long relationship with the
supplier. Normally financial assistance is provided to the creditworthy/reliable vendor.
But the financial risk of the buyer increases if he provides the financial assistance to the
vendor. Hence, this factor is labeled as “Buyer’s Risk”. First two variables (Pb and Pa)
and third variable (Pg) have opposite signs. It means that all three variables vary together
but move in directions opposite to each other. Risk is reduced if the material is sourced
from reliable vendor (friend/family member as a vendor or vendor with whom buyer has
a long term relationship) but it increases with increasing amount of financial assistance
provided to the supplier
4.4.6 Factor Analysis for section – III: Selection of Intermediaries into
particular Market-Yard (APMC)
4.4.6.1. KMO and Bartlett’s Test
The Bartlett’s Test of Sphericity for measuring the presence of correlations among the
variables for the section – III for selection of intermediaries into particular market yard
(APMC) related variables is shown in Table 4.38
Table 4.38 KMO and Bartlett's Test for Purchase related variables
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.866
Bartlett's Test of Sphericity 5842.378
Sig. 0.000
164
Bartlett’s Test of Sphericity value 5842.378 with significance level of 0.000 in the table
4.38 above satisfies the necessary condition. This indicates the statistical significance that
the correlation matrix has significant correlation among the variables.
4.4.6.2 Measure of Sampling Adequacy (MSA)
From the table 4.38, it can be observed that the MSA value is 0.866 which is greater than
necessary condition value 0.5. Hence, factor analysis can be applied to it.
4.4.6.3 Anti-Image Correlation Matrix
Table 4.39 indicates that all the variables have MSA value more than 0.5. Hence
researcher can proceed further.
Table 4.39 Anti Image Correlation Matrix
165
4.4.6.4 Communalities
In the table 4.40, the communality values for all variables are more than 0.5 except Mc
(0.465), Mi (0.457), and Mx (0.471). Hence these variables should be omitted from the
list, starting from the lowest value variable, Mi. The Anti-Image Matrix is to be
developed once again, new MSA values (Table 4.41) and communalities (Table 4.42)
are observed.
Table 4.40 Communalities
Variables Initial Extraction
Ma 1 0.643
Mc 1 0.465
Md 1 0.810
Me 1 0.717
Mf 1 0.668
Mg 1 0.739
Mh 1 0.623
Mi 1 0.457
Mj 1 0.659
Mk 1 0.719
Ml 1 0.596
Mm 1 0.754
Mo 1 0.606
Mq 1 0.637
Mr 1 0.583
Mt 1 0.524
Mu 1 0.539
Mv 1 0.571
Mw 1 0.519
Mx 1 0.471
My 1 0.644
Mz 1 0.500
Maa 1 0.778
Extraction Method: Principal Component Analysis.
166
Table 4.41 Revised Anti Image Correlation Matrix
Table 4.42 Revised Communalities
Variables Initial Extraction
Ma 1 0.642
Mc 1 0.476
Md 1 0.814
Me 1 0.710
Mf 1 0.667
Mg 1 0.743
Mh 1 0.626
Mj 1 0.655
Mk 1 0.729
Ml 1 0.604
Mm 1 0.754
Mo 1 0.616
Mq 1 0.631
Mr 1 0.590
Mt 1 0.525
Mu 1 0.547
Mv 1 0.570
Mw 1 0.520
Mx 1 0.470
My 1 0.646
Mz 1 0.497
Maa 1 0.773 Extraction Method: Principal Component Analysis.
167
Revised communalities values in Table 4.42 shows the communalities values for
variables Mc (0.476), Mx (0.470) and Mz (0.497) is less than 0.5. Hence, the lowest
three; communality value variable Mx is omitted from the list and factor analysis process
is revised. Revised Anti-Image Correlation Matrix and Communalities is shown below in
table 4.43 and 4.44 respectively.
Table 4.43 Revised Anti Image Correlation Matrix
168
Table 4.44 Revised Communalities
Extraction Method: Principal Component Analysis.
Revised communalities values in Table 4.44 shows the communalities values for
variables Mc (0.476) only is less than 0.5. Hence, it is omitted from the list and factor
analysis process is revised. Revised Anti-Image Correlation Matrix and Communalities is
shown below in table 4.45 and 4.46 respectively. In both the tables all the values are
more than the required value of 0.5. Therefore researcher can proceed further.
Variables Initial Extraction
Ma 1 0.633
Mc 1 0.476
Md 1 0.820
Me 1 0.710
Mf 1 0.666
Mg 1 0.742
Mh 1 0.647
Mj 1 0.667
Mk 1 0.731
Ml 1 0.580
Mm 1 0.741
Mo 1 0.630
Mq 1 0.634
Mr 1 0.593
Mt 1 0.563
Mu 1 0.566
Mv 1 0.582
Mw 1 0.518
My 1 0.641
Mz 1 0.546
Maa 1 0.773
169
Table 4.45 Revised Anti Image Correlation Matrix
Table 4.46 Revised Communalities
Extraction Method: Principal Component Analysis
Variables Initial Extraction
Ma 1 0.631
Md 1 0.818
Me 1 0.714
Mf 1 0.673
Mg 1 0.740
Mh 1 0.649
Mj 1 0.672
Mk 1 0.743
Ml 1 0.561
Mm 1 0.744
Mo 1 0.654
Mq 1 0.655
Mr 1 0.597
Mt 1 0.571
Mu 1 0.572
Mv 1 0.581
Mw 1 0.511
My 1 0.643
Mz 1 0.561
Maa 1 0.784
170
4.4.6.5 Eigenvalues and Total Variance Explained
From the table 4.47, it is observed that total variance explained is almost 65 percent by
the five factors. These factors can be studied further.
Table 4.47 Revised Eigenvalues and Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 6.225 31.126 31.126 6.225 31.126 31.126 4.605 23.024 23.024
2 2.291 11.453 42.579 2.291 11.453 42.579 3.033 15.163 38.187
3 2.093 10.467 53.047 2.093 10.467 53.047 2.159 10.796 48.983
4 1.365 6.823 59.870 1.365 6.823 59.870 1.769 8.843 57.826
5 1.100 5.499 65.369 1.100 5.499 65.369 1.509 7.543 65.369
6 .825 4.126 69.495
7 .749 3.744 73.239
8 .702 3.509 76.748
9 .662 3.312 80.060
10 .594 2.971 83.031
11 .500 2.502 85.533
12 .471 2.356 87.889
13 .424 2.119 90.007
14 .384 1.919 91.926
15 .344 1.720 93.646
16 .330 1.649 95.295
17 .293 1.467 96.762
18 .278 1.391 98.153
19 .230 1.148 99.300
20 .140 .700 100.000 Extraction Method: Principal Component Analysis.
171
4.4.6.6 Rotated Component Matrix
As shown in the table 4.48 below, through VARIMAX rotation, total five factors have
been extracted. The required factor loading for all variables is 0.5. Factor loadings of 0.5
and higher will be considered significant for interpretative purposes. But for the variables
Mw (0.498), loading is lower than the required 0.5. Hence it is omitted and entire factor
analysis process is carried out once again and revised rotated component matrix is
prepared as shown in table 4.49
Table 4.48 Rotated Component Matrix (a)
Component
1 2 3 4 5
Mr .762 -.112 .001 .029 .053
Maa .751 .176 -.297 -.153 -.277
Mq -.706 .052 .305 .111 .220
Mg .688 -.467 -.109 -.092 -.165
Mh .680 -.165 .280 -.173 -.227
Mt .638 -.156 -.231 .163 .243
Mj -.638 .337 .198 .176 .285
Ma .591 -.511 -.100 -.107 .003
Mm .575 .296 .568 .043 -.021
Md -.116 .875 .076 .009 .184
Me .073 .791 .097 .073 .263
Mu -.188 .727 -.037 -.076 -.007
Mw -.374 .498 -.021 .334 -.106
Mk -.242 .049 .814 .040 .135
Mf -.129 -.001 .771 .241 .062
Mv -.051 .091 .090 .735 -.147
Mz .061 -.061 .063 .726 .149
My -.426 .106 .208 .609 .189
Mo -.058 .234 .002 .020 .772
Ml -.262 .087 .365 .045 .591
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 23 iterations.
172
As shown in the table 4.49, one variable Mm cross-loads on two factors. And hence it is
omitted from the list and Revised Rotated Factor Loading Matrix is prepared.
Table 4.49 Revised Rotated component Matrix
Component
1 2 3 4 5
Mr .772 -.094 .007 .014 .082
Maa .743 .196 -.304 -.143 -.278
Mg .707 -.436 -.109 -.080 -.170
Mq -.707 .030 .309 .101 .220
Mh .687 -.131 .276 -.152 -.252
Mt .655 -.161 -.215 .118 .308
Mj -.651 .310 .202 .160 .294
Ma .611 -.472 -.098 -.086 -.022
Mm .560 .334 .558 .069 -.052
Md -.156 .880 .064 .027 .155
Me .033 .816 .083 .107 .216
Mu -.221 .721 -.049 -.067 -.019
Mk -.233 .029 .826 .009 .158
Mf -.132 .009 .767 .252 .045
Mv -.074 .092 .074 .761 -.168
Mz .051 -.056 .058 .736 .145
My -.439 .087 .206 ```.605 .196
Mo -.065 .260 .008 .032 .737
Ml -.255 .082 .380 .024 .598
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 6 iterations.
173
Table 4.50 Revised Rotated component Matrix and communalities
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 5 iterations.
Table 4.50 reveals that the factor loadings as well as communalities for all variables are
more than required value 0.5. Hence, the analysis can be extended further.
Eigenvalues are the sum of the variances of the factor values. It represents the amount of
variance associated with the factor. Only factors with a variance greater than 1.0 are
considered for the study.
In the table 4.51, there are total five factors having Eigenvalues more than 1.0. Total
variance explained by the five factors is 67.155.
Component
Communalities 1 2 3 4 5
Mr .784 -.067 -.008 .021 .086 0.627
Mg .727 -.409 -.106 -.075 -.176 0.744
Mh .723 -.085 .273 -.142 -.257 0.692
Maa .722 .201 -.355 -.136 -.261 0.774
Mq -.696 .023 .343 .095 .207 0.655
Mj -.670 .281 .195 .154 .303 0.681
Mt .644 -.158 -.235 .120 .318 0.610
Ma .620 -.459 -.108 -.083 -.018 0.615
Md -.166 .886 .058 .030 .157 0.841
Me .014 .814 .051 .111 .234 0.733
Mu -.212 .743 -.014 -.065 -.040 0.604
Mk -.193 .057 .845 .011 .142 0.775
Mf -.081 .050 .801 .256 .024 0.717
Mv -.096 .071 .034 .762 -.141 0.616
Mz .062 -.041 .087 .735 .127 0.569
My -.427 .091 .243 .602 .183 0.644
Mo -.077 .252 .016 .028 .736 0.612
Ml -.254 .075 .382 .020 .601 0.578
174
Table 4.51 Revised Eigenvalues and Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 5.931 32.949 32.949 5.931 32.949 32.949 4.303 23.904 23.904
2 2.140 11.888 44.837 2.140 11.888 44.837 2.624 14.580 38.484
3 1.709 9.497 54.334 1.709 9.497 54.334 2.012 11.179 49.663
4 1.274 7.078 61.412 1.274 7.078 61.412 1.666 9.257 58.920
5 1.034 5.744 67.155 1.034 5.744 67.155 1.482 8.235 67.155
6 .741 4.118 71.274
7 .720 4.001 75.274
8 .700 3.891 79.165
9 .646 3.589 82.754
10 .504 2.799 85.553
11 .456 2.535 88.088
12 .433 2.405 90.494
13 .387 2.150 92.644
14 .340 1.886 94.531
15 .312 1.734 96.264
16 .279 1.552 97.816
17 .251 1.396 99.212
18 .142 .788 100.000
Extraction Method: Principal Component Analysis.
The required variance explained by all the factors should be at least 60 percent. In the
table 4.15, we can observe that the first four factors very well explain the total variance of
more than 61 percent, which is more than minimum requirement of 60 percent. At the
same time total variance explained by fifth factor is only 5.7 percent which is very less
compared to other four factors. Hence, the entire process of factor analysis is repeated
with the objective to extract four factors instead of five.
175
Table 4.52 Revised Communalities
Extraction Method: Principal Component Analysis.
The communalities values in the table 4.52 are more than 0.5 except three variables; Ml
(0.438), Mo (0.279) and Mt (0.460). The lowest value variable Mo is omitted first and
entire process is revised until all the communalities values are 0.5 or more.
The communalities values in table 4.53 are more than required value 0.5. Hence
researcher can extend the analysis further.
The eigenvalue table 4.54 represents the four factors explaining the total variance of
almost 68 percent which is sufficient for factor analysis.
Variables Initial Extraction
Ma 1.000 .593
Md 1.000 .827
Me 1.000 .731
Mf 1.000 .620
Mg 1.000 .744
Mh 1.000 .604
Mj 1.000 .668
Mk 1.000 .737
Ml 1.000 .438
Mo 1.000 .279
Mq 1.000 .653
Mr 1.000 .620
Mt 1.000 .460
Mu 1.000 .556
Mv 1.000 .558
My 1.000 .644
Mz 1.000 .569
Maa 1.000 .752
176
Table 4.53 Revised Communalities
Variables Initial Extraction
Ma 1.000 .612
Md 1.000 .840
Me 1.000 .738
Mf 1.000 .728
Mg 1.000 .755
Mh 1.000 .675
Mj 1.000 .681
Mk 1.000 .784
Mq 1.000 .665
Mr 1.000 .571
Mu 1.000 .579
Mv 1.000 .578
My 1.000 .643
Mz 1.000 .586
Maa 1.000 .763
Extraction Method: Principal Component Analysis.
Table 4.54 Eigenvalues and Total Variance Explained
Extraction Method: Principal Component Analysis.
Component Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 5.313 35.420 35.420 5.313 35.420 35.420 3.976 26.504 26.504
2 2.098 13.987 49.407 2.098 13.987 49.407 2.624 17.490 43.995
3 1.566 10.438 59.846 1.566 10.438 59.846 1.972 13.144 57.139
4 1.221 8.140 67.985 1.221 8.140 67.985 1.627 10.846 67.985
5 .744 4.958 72.943
6 .686 4.575 77.518
7 .603 4.017 81.535
8 .499 3.326 84.861
9 .458 3.050 87.911
10 .432 2.877 90.788
11 .358 2.385 93.173
12 .314 2.093 95.267
13 .309 2.058 97.325
14 .259 1.724 99.049
15 .143 .951 100.000
177
Table 4.55 Revised Rotated Component Matrix(a)
Component
1 2 3 4
Mh .775 -.123 .213 -.116
Mr .753 -.047 .017 .032
Maa .750 .164 -.396 -.126
Mg .736 -.437 -.140 -.056
Mq -.715 .045 .383 .074
Mj -.701 .322 .259 .138
Ma .618 -.456 -.147 -.026
Md -.166 .898 .062 .034
Me .002 .846 .061 .137
Mu -.198 .728 -.035 -.090
Mk -.182 .069 .864 .001
Mf -.073 .049 .820 .221
Mz .042 -.022 .113 .756
Mv -.091 .041 -.007 .754
My -.451 .115 .271 .594 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 5 iterations.
Table 4.55 represents the rotated component matrix. All the variables express the
loadings more than 0.5.
Table 4.56 Final Rotated Component Matrix
Component
1 2 3 4
Mh: Financial Assistance by/to the channel intermediaries .775
Mr: Transparency in the governing system .753
Maa: Quantity to be purchased / sold .750
Mg: Spot Payment System .736
Mq: Well known for particular commodity -.715
Mj: Warehouse receipt finance -.701
Ma: Open Auction System .618
Md: Quality Testing Laboratory .898
Me: Availability of processing facilities .846
Mu: Availability of information about demand in domestic as
well as international market .728
Mk: Demand at the marketplace compared to other markets .864
Mf: Availability of buyers all the time .820
Mz: Involvement of Governing body in development of APMC .756
Mv: Availability of information about Prevailing prices in the
major markets .754
My: Availability of Production estimation information .594
178
As shown in Table 4.56, the factor structure for fifteen variables is now very well
defined, representing four distinct groups of variables. Naming of the factors is carried
out based on this table.
4.4.7. Interpretation of Factors extracted from section – III for selection
of intermediaries into particular APMC related variables
There are seven variables with significant loadings identified under factor 1. These are;
financial assistance by/to the channel intermediaries (Mh, 0.775), transparency in the
governing system (Mr, 0.753), quantity to be purchased/sold (Maa, 0.750), spot payment
system (Mg, 0.736), well-known for particular commodity (Mq, -0.715), warehouse
receipt finance (Mj, -0.701) and open auction system (Ma, 0.618).
The first variable financial assistance by/to the channel intermediaries (Mh, 0.775)
encourages the counter party to prefer the assistance provider than others. This in turn
increases the volume of the business, turnover and hence increases the profitability.
Transparency in the governing system of the market yard (Mr, 0.753) increase the
trustworthiness, ensure the fair and transparent trading practices at the market place.
Based on the quantity to be purchased /sold (Maa, 0.750), the intermediaries can evaluate
different options to get a better price and required quantity. Spot payment system (Mg,
0.736) reduces the risk of default and other costs of transaction. And hence increase the
returns.
The variable-well known for particular commodities (Mq, -0.715) increases the trust of
the chain partner to do the trading activities with the intermediaries of that APMC. This
reduces the search cost of the intermediaries. To get the funds on stored commodities into
the warehouses, go-downs etc. through warehouse receipt finance (Mj, -0.701) increases
the cost, particularly interest cost and bank charges to get the warehouse receipt finance.
As explained in the earlier section, these two variables moves in opposite directions to
remaining variables of defining factor 1.
179
The last variable is open auction system (Ma, 0.618). Open auction system is used to
discover the price of the commodities through open outcry. It is considered as a one of
the best systems to discover the price of the commodity.
All these variables are related to financial matters and particularly risk reduction, cost
reduction and increasing the profitability and ultimately return on investment, hence
factor 1 is termed as “Return” factor. This factor explains almost 35 percent of the
variance.
Factor 2 is extracted by three variables, collectively explain almost 14 percent of
variance. These variables are; Quality Testing Laboratory (Md, 0.898), Availability of
processing facilities (Me, 0.846) and Availability of information about demand in
domestic as well as international market (Mu, 0.728). All these are value added
infrastructure facilities required for quality management. Hence second factor is named
as “Value Adding Infrastructure Facility”
The third factor is defined by two variables; Demand at the marketplace compared to
other markets (Mk, 0.864) and Availability of buyers all the time (Mf, 0.820). Both the
variables are related to the demand at the market yard. Higher the demand more the
preferable market-yard by the intermediaries and availability of buyer all the time ensure,
round the year trading activities and higher demand at a particular market-yard compared
to others. Hence this variable is labeled as “Demand” factor.
There are total three factors identified under factor 4. These are; Involvement of
governing body in development of APMC (Mz, 0.756), Availability of information about
prevailing prices in the major markets (Mv, 0.754) and Availability of production
estimation information (My, 0.594). All these are related to the developmental and
information support services. It is the responsibility of the governing body to ensure the
continuous development of the market-yard to make the trading activities fair and
transparent. Second they need to develop value added support services network which
helps the intermediaries to get the real time needed information. Availability of
180
information about prices in major markets provides the arbitrage opportunity to the
channel members, if available. Production estimation is helpful to estimate the level of
supply in the market and hence its impact on the price of the commodities. This helps the
intermediaries to take the appropriate the decision related to the trading activities. Thus
this factor is named as “Information Support Services”. This factor explains 8 percent
of variance.
181
4.5 Analysis of Variance (ANOVA)
Analysis of variance (ANOVA) is a statistical technique for examining the differences
among means for two or more populations24
. ANOVA is a procedure for testing the
difference among different groups of data for homogeneity. “The essence of ANOVA is
that the total amount of variation in a set of data is broken down into two types, that
amount which can be attributed to chance and that amount which can be attributed to
specific causes.”25
ANOVA consists in splitting the variance for analytical purposes.
Hence, it is a method of analyzing the variance to which a response is subject to its
various components corresponding to various sources of variation.
ANOVA is used for examining the differences in the mean values of the dependent
variable associated with the effect of controlled independent variables. In ANOVA
dependent variable is metric while independent variables must be all categorical (non-
metric). Categorical independent variables are also called factors.
In an ANOVA model, each group has its own mean and values that deviate from that
mean. Similarly all the data points from all of the groups produce an overall grand mean.
The total deviation is the sum of the squared differences between each data point and the
overall grand mean.
The total deviation of any particular data point may be partitioned into between-groups
variance and within-groups variance. The between groups variance represents the effect
of the treatment or factors. The differences between groups mean simply that each group
was treated differently and the treatment will appear as deviations of the sample means
from the grand mean. The within group variance describe the deviations of the data
points within each group from the sample mean. This results from variability among
subjects and from random variation. It is often called error.
24
Naresh Malhotra, Marketing Research: An Applied orientation, (Fifth edn.), (New Delhi: Pearson
Education, 2008), p. 535. 25
C. R. Kothari, Research Methodology: Method and Techniques, (New Delhi: New Age International
Publication, 1999), p.301.
182
The null hypothesis of ANOVA, typically, is that “all means are equal”. If the null
hypothesis of equal category means is not rejected, then the independent variable does
not have a significant effect on the dependent variable. On the other hand, if the null
hypothesis is rejected, then the effect of the independent variable is significant In other
words, the mean value of the dependent variable will be different for different categories
of the independent variable.26
The statistic for ANOVA is the F ratio, which is the ratio of between-group variance to
within-group variance. If the null hypothesis is true, there should be no difference
between the population means and the ratio should be close to 1. If the population means
are not equal, the numerator should manifest this difference, and the F ratio should be
greater than 1.27
If researcher involves only one categorical variable/factor and investigates the differences
amongst its various categories having numerous possible values, it is said to be a one-
way analysis of variance. If two or more factors are involved, the analysis is termed as n-
way analysis of variance. Researcher has used one way analysis of variance for this
study as only one factor is involved.
Researcher has applied the ANOVA with the objective to compare the importance given
by the different intermediaries of all the APMCs in North Gujarat to Key Important
Variables extracted from the factor analysis to select the intermediaries to sell and to
purchase the commodities. ANOVA is also applied to compare the factor’s importance
given by the different intermediaries of all the APMCs in North Gujarat to select the
intermediaries in a particular market-yard.
Through factor analysis, researcher has extracted the key important factors considered by
the different intermediaries to sell and to purchase the products. Important factors are also
extracted for selecting the intermediaries into particular market-yard. ANOVA is applied
26
Naresh Malhotra, op. cit., p. 540. 27
C. R. Kothari, op.cit., p.517
183
on these extracted factors only. The following hypotheses are developed and tested for
significance.
4.6 Hypothesis of The Study
All hypotheses are divided into three sections in accordance of factors extracted through
factor analysis.
1. Hypothesis for Key Important Variables for selecting the intermediaries to sell the
products.
2. Hypothesis for Key Important Variables for selecting the intermediaries to purchase
the products.
3. Hypothesis for Key Important Variables for selecting the intermediaries in a particular
APMC.
4.6.1 Hypothesis for Key Important Variables for selecting the intermediaries to
Sell the products.
Hos There is no statistical significant difference between the rated importance of the
variable ‘_____*_______’ by the ‘ # ’ of all the APMCs for selecting
particular intermediaries to sell the products.
H1s There is statistical significant difference between the rated importance of the
variable ‘______*______’ by the ‘ # ’ of all the APMCs for selecting
particular intermediaries to sell the products
Note: * Indicates the variable listed in the following table 4.57
# Indicates the type of the intermediary (i.e. Farmer, Commission Agent, Stockist,
Wholesaler, Exporter and Processor)
184
4.6.2 Hypothesis testing of Key Important Variables for selecting the
intermediaries to Sell the products
The researcher has an objective to know and compare the importance given to the Key
Important Variables by the particular intermediary to select the particular intermediaries
to sell the commodities. Hence following hypothesis is developed and tested in line with
the above stated objective.
4.6.2.1 ANOVA for importance given to the Key Important Variables by the
Farmer to sell the product
Table 4.57 ANOVA for importance given to the key important variables by the
farmer to sell the product related variable
Name of Variable Variance
Sum of
Squares Df Mean
Square F Sig.
Sp: Makes transport
arrangements/comes to
collect Between Groups 0.102 5 0.020 0.116 0.989
Within Groups 35.020 199 0.176
Total 35.122 204
Si: Assurance to purchase Between Groups 1.058 5 0.212 0.843 0.521
Within Groups 49.986 199 0.251
Total 51.044 204
Sh: Comes first to
purchase Between Groups 0.085 5 0.017 0.588 0.709
Within Groups 5.740 199 0.029
Total 5.824 204
Sv: Well known in the
market Between Groups 3.793 5 0.759 1.114 0.354
Within Groups 135.505 199 0.681
Total 139.298 204
Sr: Provides Demand
Information Between Groups 0.071 5 0.014 0.067 0.997
Within Groups 42.173 199 0.212
Total 42.244 204
185
Ss: Updating the price
information Between Groups 1.189 5 0.238 1.040 0.395
Within Groups 45.523 199 0.229
Total 46.712 204
Sq: Customer’s location Between Groups 0.982 5 0.196 0.746 0.590
Within Groups 52.413 199 0.263
Total 53.395 204
Su: Provides Production
estimation information Between Groups 2.724 5 0.545 1.607 0.160
Within Groups 67.451 199 0.339
Total 70.176 204
Sg: Financial Assistance Between Groups 148.989 5 29.798 82.800 0.000
Within Groups 71.616 199 0.360
Total 220.605 204
Sf: Credit Finance Between Groups 40.812 5 8.162 24.204 0.000
Within Groups 67.110 199 0.337
Total 107.922 204
Sk: Grading Assistance Between Groups 0.135 5 0.027 0.480 0.791
Within Groups 11.163 199 0.056
Total 11.298 204
Sj: Cleaning Assistance Between Groups 0.135 5 0.027 0.480 0.791
Within Groups 11.163 199 0.056
Total 11.298 204
Sl: Packaging Assistance Between Groups 0.135 5 0.027 0.480 0.791
Within Groups 11.163 199 0.056
Total 11.298 204
Sn: Quality Testing &
Certificate Assistance Between Groups 0.097 5 0.019 0.374 0.866
Within Groups 10.313 199 0.052
Total 10.410 204
Sa: Age-old business
relationship Between Groups 0.389 5 0.078 0.308 0.908
Within Groups 50.323 199 0.253
Total 50.712 204
Sc: Trust Between Groups 0.905 5 0.181 0.772 0.571
Within Groups 46.656 199 0.234
Total 47.561 204
186
The significant value for the variables Comes to collect/Makes transport arrangement,
Assurance to Purchase, Comes first to Purchase, Well known in the market, Provides
demand information, Updating the price information, Customer’s location, Provides
production estimation information, Grading Assistance, Cleaning Assistance, Packaging
Assistance, Quality testing & certificate assistance, Age-old business relationship, Trust,
is more than significant value (α=0.05). This means that for these variables the null
hypothesis: There is no statistical significant difference between the rated importance of
the variable by the farmers of all the APMCs for selecting particular intermediaries to sell
the products; is accepted.
While for two variables Financial assistance (0.000) and Credit finance (0.000), the
significant value is less than 0.05. Hence we can conclude that the there is statistically
significant difference between rated importance of the variables financial assistance and
credit finance by the farmers of all the APMCs for selecting particular intermediaries to
sell the products. Thus alternate hypothesis for these variables is not rejected. The mean
and standard deviation is given in the table 4.63.
This is because the farmers at Unjha and Patan give more importance to financial
assistance as well as credit finance. As can be seen from the table 4.63, the sample means
of variables Sg (Financial Assistance) and Sf (Credit finance), with values of 4.49, 3.92,
2.60, 2.65, 2.50 and 2.60 and 3.77, 3.80, 3.20, 2.90, 2.77 and 2.80 respectively are quite
different. For variable financial assistance, farmers at Unjha APMCs give highest
importance (4.49) followed by farmers at Patan APMC (3.92). At the same time, farmers
at remaining centers give less importance to this variable to select the intermediaries to
sell their produces. Similarly, farmers at Unjha (3.77) and Patan (3.80) APMCs give
almost equal importance to the variable credit finance. These findings seem plausible.
The probable reason behind this is that the intermediaries of these two APMCs and
particularly Unjha APMC are encouraging the farmers to sow the crop of cumin, fennel
and isabgul by providing financial assistance to the farmers to purchase the inputs. Credit
finance is provided for this as well as sometimes to fulfill social obligations too.
187
4.6.2.2 ANOVA for importance given to the Key Important Variables by the
Commission Agent to sell the products
Table 4.58 ANOVA for importance given to the key important variables by the
commission agent to sell the products
Name of Variable
Sum of
Squares Df
Mean
Square F Sig.
Sp: Makes transport
arrangements/comes to
collect Between Groups 1.210 5 0.242 0.966 0.441
Within Groups 31.309 125 0.250
Total 32.519 130
Si: Assurance to purchase Between Groups 0.249 5 0.050 0.191 0.965
Within Groups 32.454 125 0.260
Total 32.702 130
Sh: Comes first to purchase Between Groups 0.084 5 0.017 0.125 0.987
Within Groups 16.862 125 0.135
Total 16.947 130
Sv: Well known in the
market Between Groups 0.269 5 0.054 0.200 0.962
Within Groups 33.579 125 0.269
Total 33.847 130
Sr: Provides Demand
Information Between Groups 0.263 5 0.053 0.232 0.948
Within Groups 28.424 125 0.227
Total 28.687 130
Ss: Updating the price
information Between Groups 0.641 5 0.128 0.653 0.660
Within Groups 24.535 125 0.196
Total 25.176 130
Sq: Customer’s location Between Groups 0.179 5 0.036 0.696 0.628
Within Groups 6.447 125 0.052
Total 6.626 130
Su: Provides Production
estimation information Between Groups 0.105 5 0.021 0.134 0.984
Within Groups 19.498 125 0.156
Total 19.603 130
Sg: Financial Assistance Between Groups 1.149 5 0.230 0.982 0.432
188
Within Groups 29.263 125 0.234
Total 30.412 130
Sf: Credit Finance Between Groups 0.699 5 0.140 0.552 0.736
Within Groups 31.622 125 0.253
Total 32.321 130
Sk: Grading Assistance Between Groups 0.488 5 0.098 1.170 0.327
Within Groups 10.413 125 0.083
Total 10.901 130
Sj: Cleaning Assistance Between Groups 0.488 5 0.098 1.170 0.327
Within Groups 10.413 125 0.083
Total 10.901 130
Sl: Packaging Assistance Between Groups 0.331 5 0.066 0.679 0.640
Within Groups 12.173 125 0.097
Total 12.504 130
Sn: Quality Testing &
Certificate Assistance Between Groups 0.488 5 0.098 1.170 0.327
Within Groups 10.413 125 0.083
Total 10.901 130
Sa: Age-old business
relationship Between Groups 2.227 5 0.445 1.572 0.173
Within Groups 35.422 125 0.283
Total 37.649 130
Sc: Trust Between Groups 0.183 5 0.037 0.164 0.975
Within Groups 27.985 125 0.224
Total 28.168 130
Variables Comes to collect/Makes transport arrangement, Assurance to Purchase, Comes
first to Purchase, Well known in the market, Provides demand information, Updating the
price information, Customer’s location, Provides production estimation information,
Credit finance, financial assistance, Grading Assistance, Cleaning Assistance, Packaging
Assistance, Quality testing & certificate assistance, Age-old business relationship and
Trust have significant value more than 0.05. This means that for these variables the null
hypothesis: There is no statistical significant difference between the rated importance of
these variables by the commission agents of all the APMCs for selecting particular
intermediaries to sell the products; is accepted. The mean and standard deviation for all
the variables is given in below table 4.63.
189
4.6.2.3 ANOVA for importance given to the Key Important Variables by the
Stockist to sell the products
Table 4.59 ANOVA for importance given to the key important variables by the
stockist to sell the products
Name of Variable
Sum of
Squares df
Mean
Square F Sig.
Sp: Makes transport
arrangements/comes to
collect Between Groups 1.563 5 0.313 1.254 0.288
Within Groups 29.405 118 0.249
Total 30.968 123
Si: Assurance to purchase Between Groups 2.939 5 0.588 2.151 0.064
Within Groups 32.247 118 0.273
Total 35.185 123
Sh: Comes first to purchase Between Groups 0.477 5 0.095 0.383 0.859
Within Groups 29.362 118 0.249
Total 29.839 123
Sv: Well known in the
market Between Groups 3.649 5 0.730 2.073 0.074
Within Groups 41.537 118 0.352
Total 45.185 123
Sr: Provides Demand
Information Between Groups 0.556 5 0.111 0.474 0.795
Within Groups 27.662 118 0.234
Total 28.218 123
Ss: Updating the price
information Between Groups 1.318 5 0.264 1.618 0.161
Within Groups 19.230 118 0.163
Total 20.548 123
Sq: Customer’s location Between Groups 16.056 5 3.211 9.496 0.000
Within Groups 39.904 118 0.338
Total 55.960 123
Su: Provides Production
estimation information Between Groups 1.146 5 0.229 0.943 0.456
Within Groups 28.693 118 0.243
Total 29.839 123
Sg: Financial Assistance Between Groups 1.321 5 0.264 1.064 0.384
190
Within Groups 29.284 118 0.248
Total 30.605 123
Sf: Credit Finance Between Groups 0.785 5 0.157 0.611 0.691
Within Groups 30.312 118 0.257
Total 31.097 123
Sk: Grading Assistance Between Groups 0.238 5 0.048 0.294 0.915
Within Groups 19.117 118 0.162
Total 19.355 123
Sj: Cleaning Assistance Between Groups 0.238 5 0.048 0.294 0.915
Within Groups 19.117 118 0.162
Total 19.355 123
Sl: Packaging Assistance Between Groups 0.444 5 0.089 0.521 0.760
Within Groups 20.104 118 0.170
Total 20.548 123
Sn: Quality Testing &
Certificate Assistance Between Groups 0.272 5 0.054 0.719 0.610
Within Groups 8.922 118 0.076
Total 9.194 123
Sa: Age-old business
relationship Between Groups 2.344 5 0.469 1.778 0.123
Within Groups 31.100 118 0.264
Total 33.444 123
Sc: Trust Between Groups 1.618 5 0.324 1.569 0.174
Within Groups 24.342 118 0.206
Total 25.960 123
Sd: Pays best/helps to get
best price Between Groups 0.497 5 0.099 0.622 0.683
Within Groups 18.858 118 0.160
Total 19.355 123
Se: Spot/Cash Payment Between Groups 0.089 5 0.018 0.083 0.995
Within Groups 25.459 118 0.216
Total 25.548 123
All the variables in the above table are not significant at α=0.05 except variable
Customer’s location. This means that for all the variables except Customer’s location, the
null hypothesis: There is no statistical significant difference between the rated importance
of these variables by the stockists of all the APMCs for selecting particular intermediaries
to sell the products; is accepted. While for variable Customer location null hypothesis is
191
rejected and hence alternate hypothesis: There is statistical significant difference between
the rated importance of the variable customer location by the stockists of all the APMCs
for selecting particular intermediaries to sell the products; is not rejected. This is because
the importance given to variable customer’s location by the stockists at Unjha APMC
(2.80) is comparatively low. The mean values for this variable for stockists at Patan,
Siddhpur, Palanpur, Thara and Becharaji are 3.53, 3.30, 3.50, 3.69 and 3.10 respectively.
The mean and standard deviation values are given in the table 4.63 below.
4.6.2.4 ANOVA for importance given to the Key Important Variables by the
Wholesaler to sell the products
Table 4.60 ANOVA for importance given to the key important variables by the
wholesaler to sell the products
Name of Variable
Sum of
Squares df
Mean
Square F Sig.
Sp: Makes transport
arrangements/comes to
collect Between Groups 0.424 3 0.141 0.824 0.489
Within Groups 6.692 39 0.172
Total 7.116 42
Si: Assurance to
purchase Between Groups 1.562 3 0.521 1.071 0.372
Within Groups 18.950 39 0.486
Total 20.512 42
Sh: Comes first to
purchase Between Groups 0.517 3 0.172 0.370 0.775
Within Groups 18.181 39 0.466
Total 18.698 42
Sv: Well known in the
market Between Groups 2.399 3 0.800 1.450 0.243
Within Groups 21.508 39 0.551
Total 23.907 42
Sr: Provides Demand
Information Between Groups 0.498 3 0.166 0.662 0.580
Within Groups 9.781 39 0.251
Total 10.279 42
192
Ss: Updating the price
information Between Groups 0.082 3 0.027 0.101 0.959
Within Groups 10.569 39 0.271
Total 10.651 42
Sq: Customer’s
location Between Groups 1.376 3 0.459 1.979 0.133
Within Groups 9.042 39 0.232
Total 10.419 42
Su: Provides
Production estimation
information Between Groups 0.136 3 0.045 0.235 0.871
Within Groups 7.492 39 0.192
Total 7.628 42
Sg: Financial
Assistance Between Groups 0.388 3 0.129 0.358 0.783
Within Groups 14.077 39 0.361
Total 14.465 42
Sf: Credit Finance Between Groups 0.132 3 0.044 0.137 0.937
Within Groups 12.519 39 0.321
Total 12.651 42
Sk: Grading Assistance Between Groups 0.932 3 0.311 1.034 0.388
Within Groups 11.719 39 0.300
Total 12.651 42
Sj: Cleaning Assistance Between Groups 0.252 3 0.084 0.234 0.872
Within Groups 14.027 39 0.360
Total 14.279 42
Sl: Packaging
Assistance Between Groups 0.838 3 0.279 0.800 0.502
Within Groups 13.627 39 0.349
Total 14.465 42
Sn: Quality Testing &
Certificate Assistance Between Groups 1.247 3 0.416 0.723 0.544
Within Groups 22.427 39 0.575
Total 23.674 42
Sa: Age-old business
relationship Between Groups 1.151 3 0.384 1.422 0.251
Within Groups 10.523 39 0.270
Total 11.674 42
Sc: Trust Between Groups 1.084 3 0.361 1.239 0.309
Within Groups 11.381 39 0.292
Total 12.465 42
193
Variables Comes to collect/Makes transport arrangement, Assurance to Purchase, Comes
first to Purchase, Well known in the market, Provides demand information, Updating the
price information, Customer’s location, Provides production estimation information,
Credit finance, financial assistance, Grading Assistance, Cleaning Assistance, Packaging
Assistance, Quality testing & certificate assistance, Age-old business relationship and
Trust have significant value more than 0.05. This means that for these variables the null
hypothesis: There is no statistical significant difference between the rated importance of
the variable by the wholesaler of all the APMCs for selecting particular intermediaries to
sell the products; is accepted. The mean and standard deviation for all the variables is
given in below table 4.63. Out of six APMCs, only in four APMCs viz; Unjha, Patan,
Siddhpur and Palanpur the wholesaler type entity exists. Therefore, the ANOVA as well
as mean and standard deviation values are calculated for only four APMCs.
4.6.2.5 ANOVA for importance given to the Key Important Variables by the
Exporter to sell the product
Table 4.61 ANOVA for importance given to the key important variables by the
exporter to sell the products
Name of Variable
Sum of
Squares df
Mean
Square F Sig.
Sp: Makes transport
arrangements/comes to
collect Between Groups 0.733 2 0.367 1.462 0.257
Within Groups 4.767 19 0.251
Total 5.500 21
Si: Assurance to
purchase Between Groups 0.388 2 0.194 0.727 0.496
Within Groups 5.067 19 0.267
Total 5.455 21
Sh: Comes first to
purchase Between Groups 0.297 2 0.148 0.507 0.610
Within Groups 5.567 19 0.293
Total 5.864 21
Sv: Well known in the
market Between Groups 0.029 2 0.015 0.053 0.949
194
Within Groups 5.289 19 0.278
Total 5.318 21
Sr: Provides Demand
Information Between Groups 1.188 2 0.594 1.801 0.192
Within Groups 6.267 19 0.330
Total 7.455 21
Ss: Updating the price
information Between Groups 0.717 2 0.359 1.680 0.213
Within Groups 4.056 19 0.213
Total 4.773 21
Sq: Customer’s location Between Groups 0.451 2 0.225 0.990 0.390
Within Groups 4.322 19 0.227
Total 4.773 21
Su: Provides
Production estimation
information Between Groups 0.288 2 0.144 0.529 0.597
Within Groups 5.167 19 0.272
Total 5.455 21
Sg: Financial Assistance Between Groups 0.102 2 0.051 0.194 0.825
Within Groups 4.989 19 0.263
Total 5.091 21
Sf: Credit Finance Between Groups 0.006 2 0.003 0.012 0.988
Within Groups 4.767 19 0.251
Total 4.773 21
Sk: Grading Assistance Between Groups 0.041 2 0.021 0.103 0.903
Within Groups 3.822 19 0.201
Total 3.864 21
Sj: Cleaning Assistance Between Groups 0.041 2 0.021 0.103 0.903
Within Groups 3.822 19 0.201
Total 3.864 21
Sl: Packaging
Assistance Between Groups 0.041 2 0.021 0.103 0.903
Within Groups 3.822 19 0.201
Total 3.864 21
Sn: Quality Testing &
Certificate Assistance Between Groups 0.151 2 0.075 0.309 0.738
Within Groups 4.622 19 0.243
Total 4.773 21
Sa: Age-old business
relationship Between Groups 0.102 2 0.051 0.194 0.825
195
Within Groups 4.989 19 0.263
Total 5.091 21
Sc: Trust Between Groups 0.329 2 0.165 0.627 0.545
Within Groups 4.989 19 0.263
Total 5.318 21
The significant value for variables Comes to collect/Makes transport arrangement,
Assurance to Purchase, Comes first to Purchase, Well known in the market, Provides
demand information, Updating the price information, Customer’s location, Provides
production estimation information, Credit finance, financial assistance, Grading
Assistance, Cleaning Assistance, Packaging Assistance, Quality testing & certificate
assistance, Age-old business relationship and Trust is more than 0.05. This means that for
these variables the null hypothesis: There is no statistical significant difference between
the rated importance of the variable by the exporter of all the APMCs for selecting
particular intermediaries to sell the products; is accepted. The mean and standard
deviation for all the variables is given in below table 4.63. Out of six APMCs, only in
three APMCs viz; Unjha, Patan and Siddhpur the exporter type entity exists. Therefore,
the ANOVA as well as mean and standard deviation values are calculated for only three
APMCs.
4.6.2.6 ANOVA for importance given to the Key Important Variables by the
Processor to sell the products
Table 4.62 ANOVA for importance given to the key important variables by the
processor to sell the products
Name of Variable
Sum of
Squares df
Mean
Square F Sig.
Sp: Makes transport
arrangements/comes to
collect Between Groups 0.211 2 0.106 0.426 0.659
Within Groups 4.456 18 0.248
Total 4.667 20
Si: Assurance to purchase Between Groups 0.186 2 0.093 0.408 0.671
Within Groups 4.100 18 0.228
196
Total 4.286 20
Sh: Comes first to
purchase Between Groups 0.116 2 0.058 0.234 0.794
Within Groups 4.456 18 0.248
Total 4.571 20
Sv: Well known in the
market Between Groups 0.063 2 0.032 0.117 0.890
Within Groups 4.889 18 0.272
Total 4.952 20
Sr: Provides Demand
Information Between Groups 0.021 2 0.010 0.036 0.964
Within Groups 5.122 18 0.285
Total 5.143 20
Ss: Updating the price
information Between Groups 0.154 2 0.077 0.379 0.690
Within Groups 3.656 18 0.203
Total 3.810 20
Sq: Customer’s location Between Groups 0.838 2 0.419 1.714 0.208
Within Groups 4.400 18 0.244
Total 5.238 20
Su: Provides Production
estimation information Between Groups 0.186 2 0.093 0.408 0.671
Within Groups 4.100 18 0.228
Total 4.286 20
Sg: Financial Assistance Between Groups 0.186 2 0.093 0.408 0.671
Within Groups 4.100 18 0.228
Total 4.286 20
Sf: Credit Finance Between Groups 0.663 2 0.332 0.783 0.472
Within Groups 7.622 18 0.423
Total 8.286 20
Sk: Grading Assistance Between Groups 0.021 2 0.010 0.104 0.902
Within Groups 1.789 18 0.099
Total 1.810 20
Sj: Cleaning Assistance Between Groups 0.021 2 0.010 0.104 0.902
Within Groups 1.789 18 0.099
Total 1.810 20
Sl: Packaging Assistance Between Groups 0.021 2 0.010 0.104 0.902
Within Groups 1.789 18 0.099
Total 1.810 20
197
Sn: Quality Testing &
Certificate Assistance Between Groups 3.838 2 1.919 4.668 0.023
Within Groups 7.400 18 0.411
Total 11.238 20
Sa: Age-old business
relationship Between Groups 0.743 2 0.371 1.519 0.246
Within Groups 4.400 18 0.244
Total 5.143 20
Sc: Trust Between Groups 0.067 2 0.033 0.130 0.879
Within Groups 4.600 18 0.256
Total 4.667 20
Sd: Pays best/helps to get
best price Between Groups 0.186 2 0.093 0.408 0.671
Within Groups 4.100 18 0.228
Total 4.286 20
Se: Spot/Cash Payment Between Groups 0.052 2 0.026 0.096 0.909
Within Groups 4.900 18 0.272
Total 4.952 20
The significant value for all the variables in the above table is above 0.05 except variable
Quality Testing & Certificate Assistance (0.023). This means that the null hypothesis for
the variables Comes to collect/Makes transport arrangement, Assurance to Purchase,
Comes first to Purchase, Well known in the market, Provides demand information,
Updating the price information, Customer’s location, Provides production estimation
information, Credit finance, financial assistance, Grading Assistance, Cleaning
Assistance, Packaging Assistance, Age-old business relationship and Trust is accepted.
Means there is no statistical significant difference between the rated importances of these
variables by the exporters of all the APMCs for selecting particular intermediaries to sell
the products.
But for the variable Quality testing & certificate assistance the null is rejected. Hence, the
alternative hypothesis, there is statistical significant difference between the rated
importance of the variable Quality Testing & Certificate Assistance by the processors of
all the APMCs for selecting particular intermediaries to sell the products. This is because
198
the importance given to this variable by the processor at Unjha and Siddhpur is low; 3.10
and 3.00 respectively. But, the same is high for processor at Patan (4.50).
The mean and standard deviation for all the variables is given in below table 4.63. Out of
six APMCs, only in three APMCs viz; Unjha, Patan and Siddhpur the processor type
entity exists. Therefore, the ANOVA as well as mean and standard deviation values are
calculated for only three APMCs.
Table 4.63 below shows the Mean and Standard Deviation values for sell related Key
Important Variables for all six intermediaries of all APMCs
Table 4.63 Mean and Standard Deviation for importance given to the key
important variables by the different intermediaries to sell the product
Farmer Commission Agent Stockist Wholesaler Exporter Processor
Name of
Variable N Mean S.D. N Mean S.D. N Mean S.D N Mean S.D N Mean S.D N Mean S.D.
Sp Unjha 70 1.20 0.40 50 1.56 0.50 50 1.52 0.50 20 2.30 0.47 10 4.70 0.48 10 4.40 0.52
Patan 50 1.24 0.43 40 1.45 0.50 30 1.40 0.50 13 2.15 0.38 3 4.33 0.58 2 4.50 0.71
Siddhpur 20 1.20 0.41 8 1.38 0.52 10 1.50 0.53 5 2.20 0.45 9 4.33 0.50 9 4.22 0.44
Palanpur 20 1.20 0.41 8 1.25 0.46 8 1.63 0.52 5 2.00 0.00 0 0.00 0.00 0 0.00 0.00
Thara 30 1.23 0.43 18 1.33 0.49 16 1.63 0.50 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 1.27 0.46 7 1.43 0.53 10 1.20 0.42 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 1.22 0.41 131 1.46 0.50 124 1.48 0.50 43 2.21 0.41 22 4.50 0.51 21 4.33 0.48
Si Unjha 70 2.51 0.50 50 2.54 0.50 50 3.38 0.49 20 3.85 0.67 10 4.60 0.52 10 4.30 0.48
Patan 50 2.50 0.51 40 2.50 0.51 30 3.53 0.51 13 4.00 0.82 3 4.33 0.58 2 4.00 0.00
Siddhpur 20 2.50 0.51 8 2.63 0.52 10 3.30 0.67 5 3.40 0.55 9 4.33 0.50 9 4.33 0.50
Palanpur 20 2.70 0.47 8 2.50 0.53 8 3.00 0.53 5 3.60 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 2.47 0.51 18 2.44 0.51 16 3.50 0.52 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 2.67 0.49 7 2.57 0.53 10 3.10 0.57 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 2.53 0.50 131 2.52 0.50 124 3.38 0.53 43 3.81 0.70 22 4.45 0.51 21 4.29 0.46
Sh Unjha 70 1.03 0.17 50 1.18 0.39 50 1.56 0.50 20 2.45 0.83 10 2.10 0.57 10 2.90 0.57
Patan 50 1.02 0.14 40 1.13 0.33 30 1.67 0.48 13 2.54 0.52 3 2.33 0.58 2 3.00 0.00
Siddhpur 20 1.05 0.22 8 1.13 0.35 10 1.60 0.52 5 2.80 0.45 9 2.33 0.50 9 2.78 0.44
Palanpur 20 1.00 0.00 8 1.13 0.35 8 1.63 0.52 5 2.60 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 1.07 0.25 18 1.17 0.38 16 1.50 0.52 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 1.00 0.00 7 1.14 0.38 10 1.70 0.48 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 1.03 0.17 131 1.15 0.36 124 1.60 0.49 43 2.53 0.67 22 2.23 0.53 21 2.86 0.48
199
Sv Unjha 70 1.74 0.76 50 1.40 0.57 50 3.76 0.72 20 4.20 0.77 10 4.40 0.52 10 4.00 0.47
Patan 50 1.70 0.86 40 1.30 0.46 30 3.33 0.48 13 3.77 0.73 3 4.33 0.58 2 4.00 0.00
Siddhpur 20 1.80 0.83 8 1.38 0.52 10 3.60 0.52 5 3.80 0.84 9 4.44 0.53 9 4.11 0.60
Palanpur 20 2.00 0.92 8 1.38 0.52 8 3.75 0.46 5 3.60 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 1.50 0.78 18 1.33 0.49 16 3.63 0.50 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 1.93 0.96 7 1.29 0.49 10 3.70 0.48 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 1.74 0.83 131 1.35 0.51 124 3.62 0.61 43 3.95 0.75 22 4.41 0.50 21 4.05 0.50
Sr Unjha 70 1.27 0.48 50 1.26 0.53 50 1.24 0.52 20 1.35 0.49 10 4.20 0.63 10 2.60 0.52
Patan 50 1.28 0.45 40 1.25 0.44 30 1.27 0.52 13 1.54 0.52 3 4.67 0.58 2 2.50 0.71
Siddhpur 20 1.30 0.47 8 1.38 0.52 10 1.10 0.32 5 1.40 0.55 9 4.67 0.50 9 2.56 0.53
Palanpur 20 1.25 0.44 8 1.25 0.46 8 1.38 0.52 5 1.20 0.45 0 0.00 0.00 0 0.00 0.00
Thara 30 1.23 0.43 18 1.17 0.38 16 1.25 0.45 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 1.27 0.46 7 1.29 0.49 10 1.10 0.32 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 1.27 0.46 131 1.25 0.47 124 1.23 0.48 43 1.40 0.49 22 4.45 0.60 21 2.57 0.51
Ss Unjha 70 4.57 0.50 50 1.34 0.48 50 4.68 0.47 20 4.30 0.57 10 4.50 0.53 10 4.80 0.42
Patan 50 4.72 0.45 40 1.18 0.38 30 4.80 0.41 13 4.31 0.48 3 5.00 0.00 2 4.50 0.71
Siddhpur 20 4.60 0.50 8 1.25 0.46 10 4.90 0.32 5 4.20 0.45 9 4.78 0.44 9 4.78 0.44
Palanpur 20 4.60 0.50 8 1.25 0.46 8 5.00 0.00 5 4.20 0.45 0 0.00 0.00 0 0.00 0.00
Thara 30 4.77 0.43 18 1.22 0.43 16 4.88 0.34 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 4.67 0.49 7 1.29 0.49 10 4.90 0.32 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 4.65 0.48 131 1.26 0.44 124 4.79 0.41 43 4.28 0.50 22 4.68 0.48 21 4.76 0.44
Sq Unjha 70 4.13 0.61 50 1.04 0.20 50 2.80 0.64 20 4.25 0.44 10 4.70 0.48 10 4.60 0.52
Patan 50 4.24 0.43 40 1.03 0.16 30 3.53 0.51 13 4.15 0.55 3 5.00 0.00 2 5.00 0.00
Siddhpur 20 4.15 0.49 8 1.13 0.35 10 3.30 0.48 5 3.80 0.45 9 4.56 0.53 9 4.33 0.50
Palanpur 20 4.30 0.47 8 1.13 0.35 8 3.50 0.53 5 3.80 0.45 0 0.00 0.00 0 0.00 0.00
Thara 30 4.23 0.43 18 1.06 0.24 16 3.69 0.48 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 4.33 0.49 7 1.14 0.38 10 3.10 0.74 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 4.20 0.51 131 1.05 0.23 124 3.20 0.67 43 4.12 0.50 22 4.68 0.48 21 4.52 0.51
Su Unjha 70 3.96 0.58 50 1.18 0.39 50 4.52 0.50 20 4.10 0.55 10 4.50 0.53 10 4.80 0.42
Patan 50 4.22 0.55 40 1.18 0.38 30 4.60 0.50 13 4.15 0.38 3 4.33 0.58 2 4.50 0.71
Siddhpur 20 4.00 0.65 8 1.25 0.46 10 4.70 0.48 5 4.00 0.00 9 4.67 0.50 9 4.67 0.50
Palanpur 20 4.20 0.62 8 1.13 0.35 8 4.88 0.35 5 4.00 0.00 0 0.00 0.00 0 0.00 0.00
Thara 30 4.07 0.52 18 1.22 0.43 16 4.56 0.51 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 3.93 0.70 7 1.14 0.38 10 4.70 0.48 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 4.06 0.59 131 1.18 0.39 124 4.60 0.49 43 4.09 0.43 22 4.55 0.51 21 4.71 0.46
Sg Unjha 70 4.49 0.50 50 1.44 0.50 50 1.48 0.50 20 2.50 0.69 10 1.30 0.48 10 1.80 0.42
Patan 50 3.92 0.78 40 1.35 0.48 30 1.73 0.45 13 2.38 0.51 3 1.33 0.58 2 1.50 0.71
Siddhpur 20 2.60 0.60 8 1.50 0.53 10 1.50 0.53 5 2.20 0.45 9 1.44 0.53 9 1.67 0.50
Palanpur 20 2.65 0.59 8 1.38 0.52 8 1.50 0.53 5 2.40 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 2.50 0.51 18 1.22 0.43 16 1.56 0.51 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 2.60 0.51 7 1.14 0.38 10 1.50 0.53 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 3.56 1.04 131 1.37 0.48 124 1.56 0.50 43 2.42 0.59 22 1.36 0.49 21 1.71 0.46
200
Sf Unjha 70 3.77 0.62 50 1.48 0.50 50 1.48 0.50 20 2.75 0.64 10 1.30 0.48 10 1.90 0.74
Patan 50 3.80 0.40 40 1.58 0.50 40 1.58 0.50 13 2.69 0.48 3 1.33 0.58 2 1.50 0.71
Siddhpur 20 3.20 0.77 8 1.63 0.52 8 1.63 0.52 5 2.60 0.55 9 1.33 0.50 9 1.56 0.53
Palanpur 20 2.90 0.55 8 1.75 0.46 8 1.75 0.46 5 2.80 0.45 0 0.00 0.00 0 0.00 0.00
Thara 30 2.77 0.57 18 1.61 0.50 18 1.61 0.50 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 2.80 0.68 7 1.57 0.53 7 1.57 0.53 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 3.42 0.73 131 1.56 0.50 131 1.56 0.50 43 2.72 0.55 22 1.32 0.48 21 1.71 0.64
Sk Unjha 70 1.03 0.17 50 1.04 0.20 50 1.04 0.20 20 1.85 0.59 10 1.20 0.42 10 1.10 0.32
Patan 50 1.06 0.24 40 1.08 0.27 40 1.08 0.27 13 1.69 0.48 3 1.33 0.58 2 1.00 0.00
Siddhpur 20 1.10 0.31 8 1.13 0.35 8 1.13 0.35 5 1.40 0.55 9 1.22 0.44 9 1.11 0.33
Palanpur 20 1.10 0.31 8 1.13 0.35 8 1.13 0.35 5 1.60 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 1.07 0.25 18 1.22 0.43 18 1.22 0.43 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 1.07 0.26 7 1.14 0.38 7 1.14 0.38 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 1.06 0.24 131 1.09 0.29 131 1.09 0.29 43 1.72 0.55 22 1.23 0.43 21 1.10 0.30
Sj Unjha 70 1.03 0.17 50 1.04 0.20 50 1.04 0.20 20 1.65 0.67 10 1.20 0.42 10 1.10 0.32
Patan 50 1.06 0.24 40 1.08 0.27 40 1.08 0.27 13 1.62 0.51 3 1.33 0.58 2 1.00 0.00
Siddhpur 20 1.10 0.31 8 1.13 0.35 8 1.13 0.35 5 1.40 0.55 9 1.22 0.44 9 1.11 0.33
Palanpur 20 1.10 0.31 8 1.13 0.35 8 1.13 0.35 5 1.60 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 1.07 0.25 18 1.22 0.43 18 1.22 0.43 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 1.07 0.26 7 1.14 0.38 7 1.14 0.38 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 1.06 0.24 131 1.09 0.29 131 1.09 0.29 43 1.60 0.58 22 1.23 0.43 21 1.10 0.30
Sl Unjha 70 1.03 0.17 50 1.08 0.27 50 1.08 0.27 20 1.65 0.67 10 1.20 0.42 10 1.10 0.32
Patan 50 1.06 0.24 40 1.08 0.27 40 1.08 0.27 13 1.62 0.51 3 1.33 0.58 2 1.00 0.00
Siddhpur 20 1.10 0.31 8 1.13 0.35 8 1.13 0.35 5 1.60 0.55 9 1.22 0.44 9 1.11 0.33
Palanpur 20 1.10 0.31 8 1.13 0.35 8 1.13 0.35 5 1.20 0.45 0 0.00 0.00 0 0.00 0.00
Thara 30 1.07 0.25 18 1.22 0.43 18 1.22 0.43 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 1.07 0.26 7 1.14 0.38 7 1.14 0.38 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 1.06 0.24 131 1.11 0.31 131 1.11 0.31 43 1.58 0.59 22 1.23 0.43 21 1.10 0.30
Sn Unjha 70 1.03 0.17 50 1.04 0.20 50 1.04 0.20 20 3.25 0.72 10 1.40 0.52 10 3.10 0.74
Patan 50 1.06 0.24 40 1.08 0.27 40 1.08 0.27 13 3.38 0.87 3 1.33 0.58 2 4.50 0.71
Siddhpur 20 1.05 0.22 8 1.13 0.35 8 1.13 0.35 5 3.20 0.84 9 1.22 0.44 9 3.00 0.50
Palanpur 20 1.10 0.31 8 1.13 0.35 8 1.13 0.35 5 2.80 0.45 0 0.00 0.00 0 0.00 0.00
Thara 30 1.07 0.25 18 1.22 0.43 18 1.22 0.43 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 1.07 0.26 7 1.14 0.38 7 1.14 0.38 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 1.05 0.23 131 1.09 0.29 131 1.09 0.29 43 3.23 0.75 22 1.32 0.48 21 3.19 0.75
Sa Unjha 70 4.57 0.50 50 4.34 0.56 50 4.34 0.56 20 4.40 0.50 10 4.30 0.48 10 4.60 0.52
Patan 50 4.58 0.50 40 4.30 0.52 40 4.30 0.52 13 4.08 0.49 3 4.33 0.58 2 4.00 0.00
Siddhpur 20 4.50 0.51 8 4.50 0.53 8 4.50 0.53 5 4.00 0.71 9 4.44 0.53 9 4.67 0.50
Palanpur 20 4.60 0.50 8 4.00 0.53 8 4.00 0.53 5 4.20 0.45 0 0.00 0.00 0 0.00 0.00
Thara 30 4.47 0.51 18 4.06 0.54 18 4.06 0.54 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 4.53 0.52 7 4.14 0.38 7 4.14 0.38 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 4.55 0.50 131 4.27 0.54 131 4.27 0.54 43 4.23 0.53 22 4.36 0.49 21 4.57 0.51
201
4.6.3 Hypothesis for Key Important Variables for selecting the intermediaries to
Purchase the products.
Hop There is no statistical significant difference between the rated importance of the
variable ‘_____*_______’ by the ‘ # ’ of all the APMCs for selecting
particular intermediaries to purchase the products.
H1p There is statistical significant difference between the rated importance of the
variable ‘______*______’ by the ‘ # ’ of all the APMCs for selecting
particular intermediaries to purchase the products
Note: * Indicates the variables listed in the following table 4.64
# Indicates the type of the intermediaries (i.e Farmer, Commission Agent, Stockist,
Wholesaler, Exporter and Processor)
4.6.4 Hypothesis testing of Key Important Variables for selecting the
intermediaries to Purchase the products
The researcher has another objective to know and compare the importance given by the
particular intermediary of all the APMCs to the key important variables to purchase the
commodities. Farmers are the main feeder of the agriculture produce commodity chain.
As researcher’s main objectives of studying the APMC’s supply chain and hence he has
excluded farmer’s input purchase related activities. Hence in this section ANOVA is
applied to compare the importance given by the only five different intermediaries to the
Sc Unjha 70 4.63 0.49 50 4.34 0.48 50 4.34 0.48 20 4.75 0.55 10 4.70 0.48 10 4.30 0.48
Patan 50 4.68 0.47 40 4.28 0.45 40 4.28 0.45 13 4.46 0.52 3 4.67 0.58 2 4.50 0.71
Siddhpur 20 4.70 0.47 8 4.38 0.52 8 4.38 0.52 5 4.40 0.55 9 4.44 0.53 9 4.33 0.50
Palanpur 20 4.60 0.50 8 4.38 0.52 8 4.38 0.52 5 4.40 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 4.50 0.51 18 4.28 0.46 18 4.28 0.46 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 4.73 0.46 7 4.29 0.49 7 4.29 0.49 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 4.63 0.48 131 4.31 0.47 131 4.31 0.47 43 4.58 0.54 22 4.59 0.50 21 4.33 0.48
202
key factors to purchase the commodities. Hence, above hypothesis is developed and
tested in line with the above stated objective.
4.6.4.1 ANOVA for importance given to the Key Important Variable by the
Commission Agent to purchase the products
Table 4.64 ANOVA for importance given to the key variables by the commission
agent to purchase the product
Name of variable Sum of
Squares Df
Mean
Square F Sig.
Pa: Age-old Business
Relationship Between Groups 0.48 5.00 0.10 0.589 0.708
Within Groups 20.36 125.00 0.16
Total 20.84 130.00
Pb: Friend / Family
Member Between Groups 0.53 5.00 0.11 0.263 0.932
Within Groups 50.75 125.00 0.41
Total 51.28 130.00
Pd: offers best
price/helps to get best
price Between Groups 0.52 5.00 0.10 0.320 0.900
Within Groups 40.84 125.00 0.33
Total 41.36 130.00
Pe: Spot/Cash
payment Between Groups 1.06 5.00 0.21 0.463 0.803
Within Groups 57.24 125.00 0.46
Total 58.31 130.00
Pf: Credit Between Groups 0.39 5.00 0.08 0.306 0.908
Within Groups 31.81 125.00 0.25
Total 32.20 130.00
Pg: Financial
Assistance Between Groups 120.29 5.00 24.06 32.719 0.000
Within Groups 91.91 125.00 0.74
Total 212.20 130.00
Pj: Cleaning
Assistance Between Groups 0.42 5.00 0.08 0.819 0.539
Within Groups 12.86 125.00 0.10
Total 13.28 130.00
Pk: Grading Between Groups 0.39 5.00 0.08 0.677 0.641
203
Assistance
Within Groups 14.40 125.00 0.12
Total 14.79 130.00
Pl: Packaging
Assistance Between Groups 0.27 5.00 0.05 0.557 0.733
Within Groups 12.23 125.00 0.10
Total 12.50 130.00
Pm: Storage &
Warehouse Services Between Groups 0.31 5.00 0.06 0.684 0.636
Within Groups 11.40 125.00 0.09
Total 11.71 130.00
Pn: Quality Testing &
Certificate Assistance Between Groups 0.44 5.00 0.09 1.381 0.236
Within Groups 7.94 125.00 0.06
Total 8.38 130.00
Pr: Provides Demand
Information Between Groups 0.35 5.00 0.07 0.292 0.917
Within Groups 29.79 125.00 0.24
Total 30.14 130.00
Ps: Updating the price
information Between Groups 0.35 5.00 0.07 0.292 0.917
Within Groups 29.79 125.00 0.24
Total 30.14 130.00
Pt: Provides Weather
forecast information Between Groups 0.18 5.00 0.04 0.696 0.628
Within Groups 6.45 125.00 0.05
Total 6.63 130.00
Pu: Provides
Production estimation
information Between Groups 1.44 5.00 0.29 1.282 0.276
Within Groups 28.10 125.00 0.22
Total 29.54 130.00
Pv: Well known in the
market Between Groups 0.53 5.00 0.11 1.689 0.142
Within Groups 7.85 125.00 0.06
Total 8.38 130.00
All the variables in the above table are not significant at α=0.05 except variable financial
assistance. This means that for all the variables except financial assistance, the null
hypothesis: There is no statistical significant difference between the rated importance of
the variable by the commission agents of all the APMCs for selecting particular
204
intermediaries to purchase the commodities; is accepted. While for variable financial
assistance the null hypothesis is rejected and hence alternate hypothesis: There is
statistical significant difference between the rated importance of the variable by the
commission agents of all the APMCs for selecting particular intermediaries to purchase
the commodities; is not rejected. This is because the importance given to variable
financial assistance by the commission agents at Unjha APMC (3.64) is comparatively
high. Also the importance given by the commission agents at Patan APMCs (2.48) is
moderate while at other places it is very low. The mean values for this variable for
commission agents at Siddhpur, Palanpur, Thara and Becharaji are 1.25, 1.63, 1.28 and
1.29 respectively. The mean and standard deviation values are given in the table 4.69
below.
4.6.4.2 ANOVA for importance given to the Key Important Variable by the Stockist
to purchase the products
Table 4.65 ANOVA for importance given to the key important variables by the
stockist to purchase the product
Name of variable
Sum of
Squares Df
Mean
Square F Sig.
Pa: Age-old Business
Relationship Between Groups 1.49 5 0.30 1.353 0.247
Within Groups 25.96 118 0.22
Total 27.44 123
Pb: Friend / Family
Member Between Groups 1.13 5 0.23 0.971 0.439
Within Groups 27.54 118 0.23
Total 28.67 123
Pd: offers best
price/helps to get best
price Between Groups 0.76 5 0.15 0.738 0.596
Within Groups 24.36 118 0.21
Total 25.12 123
Pe: Spot/Cash payment Between Groups 0.40 5 0.08 0.329 0.895
Within Groups 28.78 118 0.24
Total 29.19 123
Pf: Credit Between Groups 0.70 5 0.14 0.576 0.718
205
Within Groups 28.74 118 0.24
Total 29.44 123
Pg: Financial
Assistance Between Groups 0.47 5 0.09 0.712 0.616
Within Groups 15.62 118 0.13
Total 16.09 123
Pj: Cleaning Assistance Between Groups 0.24 5 0.05 0.294 0.915
Within Groups 19.12 118 0.16
Total 19.35 123
Pk: Grading Assistance Between Groups 0.24 5 0.05 0.294 0.915
Within Groups 19.12 118 0.16
Total 19.35 123
Pl: Packaging
Assistance Between Groups 0.44 5 0.09 0.521 0.760
Within Groups 20.10 118 0.17
Total 20.55 123
Pm: Storage &
Warehouse Services Between Groups 2.98 5 0.60 1.855 0.107
Within Groups 37.95 118 0.32
Total 40.93 123
Pn: Quality Testing &
Certificate Assistance Between Groups 0.48 5 0.10 1.087 0.371
Within Groups 10.36 118 0.09
Total 10.84 123
Pr: Provides Demand
Information Between Groups 0.36 5 0.07 0.310 0.906
Within Groups 27.72 118 0.23
Total 28.09 123
Ps: Updating the price
information Between Groups 0.13 5 0.03 0.142 0.982
Within Groups 21.00 118 0.18
Total 21.12 123
Pt: Provides Weather
forecast information Between Groups 0.44 5 0.09 0.410 0.841
Within Groups 25.11 118 0.21
Total 25.55 123
Pu: Provides
Production estimation
information Between Groups 1.72 5 0.34 1.518 0.189
Within Groups 26.67 118 0.23
Total 28.39 123
Pv: Well known in the Between Groups 1.16 5 0.23 0.733 0.600
206
market
Within Groups 37.33 118 0.32
Total 38.48 123
All the variables in the above table are not significant at α=0.05. This means that for all
the variables the null hypothesis: There is no statistical significant difference between the
rated importance of the variable by the commission agent of all the APMCs for selecting
particular intermediaries to purchase the commodities; is accepted.
4.6.4.3 ANOVA for importance given to the Key Important Variable by the
Wholesaler to purchase the products
Table 4.66 ANOVA for importance given to the key important variables by the
wholesaler to purchase the product
Name of variable
Sum of
Squares Df
Mean
Square F Sig.
Pa: Age-old Business
Relationship Between Groups 2.18 3 0.73 1.380 0.263
Within Groups 20.52 39 0.53
Total 22.70 42
Pb: Friend / Family
Member Between Groups 2.55 3 0.85 1.651 0.193
Within Groups 20.06 39 0.51
Total 22.60 42
Pd: offers best
price/helps to get best
price Between Groups 0.15 3 0.05 0.348 0.791
Within Groups 5.71 39 0.15
Total 5.86 42
Pe: Spot/Cash
payment Between Groups 0.70 3 0.23 0.736 0.537
Within Groups 12.37 39 0.32
Total 13.07 42
Pf: Credit Between Groups 0.02 3 0.01 0.015 0.997
Within Groups 16.49 39 0.42
Total 16.51 42
Pg: Financial
Assistance Between Groups 3.13 3 1.04 2.054 0.122
207
Within Groups 19.84 39 0.51
Total 22.98 42
Pj: Cleaning
Assistance Between Groups 0.47 3 0.16 0.279 0.840
Within Groups 21.72 39 0.56
Total 22.19 42
Pk: Grading
Assistance Between Groups 0.47 3 0.16 0.279 0.840
Within Groups 21.72 39 0.56
Total 22.19 42
Pl: Packaging
Assistance Between Groups 0.28 3 0.09 0.180 0.909
Within Groups 20.37 39 0.52
Total 20.65 42
Pm: Storage &
Warehouse Services Between Groups 3.03 3 1.01 2.369 0.085
Within Groups 16.64 39 0.43
Total 19.67 42
Pn: Quality Testing &
Certificate Assistance Between Groups 5.72 3 1.91 1.986 0.132
Within Groups 37.44 39 0.96
Total 43.16 42
Pr: Provides Demand
Information Between Groups 0.50 3 0.17 0.662 0.580
Within Groups 9.78 39 0.25
Total 10.28 42
Ps: Updating the price
information Between Groups 0.71 3 0.24 0.674 0.573
Within Groups 13.71 39 0.35
Total 14.42 42
Pt: Provides Weather
forecast information Between Groups 0.11 3 0.04 0.139 0.936
Within Groups 10.63 39 0.27
Total 10.74 42
Pu: Provides
Production estimation
information Between Groups 0.07 3 0.02 0.107 0.956
Within Groups 8.12 39 0.21
Total 8.19 42
Pv: Well known in the
market Between Groups 0.16 3 0.05 0.201 0.895
Within Groups 10.58 39 0.27
208
Total 10.74 42
The significant value for all the variables in the above table is above 0.05. This means
that the null hypothesis for the variables Age-old business relationship, friend/family
member, offers best price or helps to get best price, Spot/cash payment, Credit, Financial
Assistance, Cleaning Assistance, Grading Assistance, packaging Assistance, Storage &
Warehouse Services, Quality testing & certificate assistance, provides demand
information, Updating price information, Provides weather forecast information, Provides
production estimation information and Well-known in the market is accepted. Means
there is no statistical significant difference between the rated importances of these
variables by the wholesalers of all the APMCs for selecting particular intermediaries to
purchase the commodities.
4.6.4.4 ANOVA for importance given to the Key Important Variable by the
Exporter to purchase the products
Table 4.67 ANOVA for importance given to the key important variables by the
exporter to purchase the product
Name of variable
Sum of
Squares Df
Mean
Square F Sig.
Pa: Age-old Business
Relationship Between Groups 0.21 2 0.11 0.379 0.689
Within Groups 5.29 19 0.28
Total 5.50 21
Pb: Friend / Family
Member Between Groups 1.02 2 0.51 1.748 0.201
Within Groups 5.57 19 0.29
Total 6.59 21
Pd: offers best
price/helps to get best
price Between Groups 0.70 2 0.35 1.430 0.264
Within Groups 4.62 19 0.24
Total 5.32 21
Pe: Spot/Cash payment Between Groups 1.19 2 0.59 1.365 0.279
Within Groups 8.27 19 0.44
Total 9.45 21
209
Pf: Credit Between Groups 0.25 2 0.13 0.472 0.631
Within Groups 5.07 19 0.27
Total 5.32 21
Pg: Financial
Assistance Between Groups 0.01 2 0.00 0.012 0.988
Within Groups 4.77 19 0.25
Total 4.77 21
Pj: Cleaning Assistance Between Groups 3.80 2 1.90 1.305 0.294
Within Groups 27.66 19 1.46
Total 31.45 21
Pk: Grading Assistance Between Groups 3.80 2 1.90 1.305 0.294
Within Groups 27.66 19 1.46
Total 31.45 21
Pl: Packaging
Assistance Between Groups 4.71 2 2.35 2.607 0.100
Within Groups 17.16 19 0.90
Total 21.86 21
Pm: Storage &
Warehouse Services Between Groups 1.13 2 0.57 1.701 0.209
Within Groups 6.32 19 0.33
Total 7.45 21
Pn: Quality Testing &
Certificate Assistance Between Groups 3.37 2 1.69 4.941 0.019
Within Groups 6.49 19 0.34
Total 9.86 21
Pr: Provides Demand
Information Between Groups 0.82 2 0.41 1.304 0.295
Within Groups 5.96 19 0.31
Total 6.77 21
Ps: Updating the price
information Between Groups 0.54 2 0.27 1.346 0.284
Within Groups 3.82 19 0.20
Total 4.36 21
Pt: Provides Weather
forecast information Between Groups 0.97 2 0.49 1.344 0.284
Within Groups 6.89 19 0.36
Total 7.86 21
Pu: Provides
Production estimation
information Between Groups 0.17 2 0.08 0.298 0.746
Within Groups 5.29 19 0.28
Total 5.45 21
210
Pv: Well known in the
market Between Groups 0.41 2 0.20 0.651 0.533
Within Groups 5.96 19 0.31
Total 6.36 21
The significant value for all the variables in the above table is above 0.05 except Quality
Testing & Certificate Assistance (0.019). This means that the null hypothesis for the
variables Age-old business relationship, friend/family member, offers best price or helps
to get best price, Spot/cash payment, Credit, Financial Assistance, Cleaning Assistance,
Grading Assistance, packaging Assistance, Storage & Warehouse Services, Provides
demand information, Updating price information, Provides weather forecast information,
Provides production estimation information and Well-known in the market is accepted.
Means there is no statistical significant difference between the rated importances of these
variables by the exporters of all the APMCs for selecting particular intermediaries to
purchase the commodities. The null hypothesis for the variable Quality Testing &
Certificate Assistance is rejected. This means that the alternative hypothesis: There is
statistical significant difference between the rated importance of the variable by the
exporters of all APMCs for selecting particular intermediaries to purchase the
commodities; is not rejected.
The mean value for exporter of Patan is 4.69. While the same is for exporter of Unjha and
Siddhpur is 3.8 and 3.44 respectively.
4.6.4.5 ANOVA for importance given to the Key Important Variable by the
Processor to purchase the products
Table 4.68 ANOVA for importance given to the key important variables by the
processor to purchase the product
Name of variable
Sum of
Squares Df
Mean
Square F Sig.
Pa: Age-old Business
Relationship Between Groups 0.74 2 0.37 1.476 0.255
Within Groups 4.50 18 0.25
Total 5.24 20
211
Pb: Friend / Family
Member Between Groups 0.34 2 0.17 0.717 0.502
Within Groups 4.32 18 0.24
Total 4.67 20
Pd: offers best
price/helps to get best
price Between Groups 0.28 2 0.14 0.860 0.440
Within Groups 2.96 18 0.16
Total 3.24 20
Pe: Spot/Cash payment Between Groups 0.69 2 0.34 1.208 0.322
Within Groups 5.12 18 0.28
Total 5.81 20
Pf: Credit Between Groups 0.12 2 0.06 0.162 0.852
Within Groups 6.46 18 0.36
Total 6.57 20
Pg: Financial
Assistance Between Groups 0.13 2 0.07 0.282 0.758
Within Groups 4.16 18 0.23
Total 4.29 20
Pj: Cleaning Assistance Between Groups 0.02 2 0.01 0.104 0.902
Within Groups 1.79 18 0.10
Total 1.81 20
Pk: Grading Assistance Between Groups 0.02 2 0.01 0.104 0.902
Within Groups 1.79 18 0.10
Total 1.81 20
Pl: Packaging
Assistance Between Groups 0.02 2 0.01 0.104 0.902
Within Groups 1.79 18 0.10
Total 1.81 20
Pm: Storage &
Warehouse Services Between Groups 0.02 2 0.01 0.104 0.902
Within Groups 1.79 18 0.10
Total 1.81 20
Pn: Quality Testing &
Certificate Assistance
Between Groups 0.64 2 0.32 0.870 0.436
Within Groups 6.60 18 0.37
Total 7.24 20
Pr: Provides Demand
Information Between Groups 0.33 2 0.17 0.643 0.537
Within Groups 4.62 18 0.26
Total 4.95 20
212
Ps: Updating the price
information Between Groups 0.13 2 0.07 0.243 0.787
Within Groups 4.82 18 0.27
Total 4.95 20
Pt: Provides Weather
forecast information Between Groups 0.34 2 0.17 0.621 0.549
Within Groups 4.90 18 0.27
Total 5.24 20
Pu: Provides
Production estimation
information Between Groups 0.32 2 0.16 0.598 0.560
Within Groups 4.82 18 0.27
Total 5.14 20
Pv: Well known in the
market Between Groups 0.25 2 0.13 0.411 0.669
Within Groups 5.56 18 0.31
Total 5.81 20
The significant value for all the variables in the above table is above 0.05. This means
that the null hypothesis for the variables Age-old business relationship, friend/family
member, offers best price or helps to get best price, Spot/cash payment, Credit, Financial
Assistance, Cleaning Assistance, Grading Assistance, packaging Assistance, Storage &
Warehouse Services, Quality testing & certificate assistance, provides demand
information, Updating price information, Provides weather forecast information, Provides
production estimation information and Well-known in the market is accepted. Means
there is no statistical significant difference between the rated importance of these
variables by the processors of all the APMCs for selecting particular intermediaries to
purchase the commodities.
213
Table 4.69 Mean and Standard Deviation for Importance given to the key
variables by the different intermediaries to purchase the products
For Commission
Agent For Stockist For Wholesaler For Exporter For Processor
Name of
Variable N Mean S.D. N Mean S.D. N Mean S.D. N Mean S.D. N Mean S.D.
Pa Unjha 50 4.80 0.40 50 4.38 0.49 20 3.65 0.75 10 4.60 0.52 10 4.50 0.53
Patan 40 4.78 0.42 30 4.20 0.41 13 3.31 0.48 3 4.33 0.58 2 4.00 0.00
Siddhpur 8 4.88 0.35 10 4.60 0.52 5 3.60 0.89 9 4.44 0.53 9 4.67 0.50
Palanpur 8 4.63 0.52 8 4.38 0.52 5 3.00 1.00 0 0.00 0.00 0 0.00 0.00
Thara 18 4.89 0.32 16 4.25 0.45 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 4.86 0.38 10 4.30 0.48 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 4.80 0.40 124 4.33 0.47 43 3.47 0.74 22 4.50 0.51 21 4.52 0.51
Pb Unjha 50 3.92 0.63 50 4.36 0.48 20 3.65 0.67 10 3.90 0.57 10 4.30 0.48
Patan 40 3.90 0.59 30 4.47 0.51 13 3.23 0.73 3 4.33 0.58 2 4.00 0.00
Siddhpur 8 3.75 0.71 10 4.40 0.52 5 3.60 0.55 9 4.33 0.50 9 4.44 0.53
Palanpur 8 4.00 0.76 8 4.25 0.46 5 3.00 1.00 0 0.00 0.00 0 0.00 0.00
Thara 18 3.78 0.55 16 4.38 0.50 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 3.86 0.90 10 4.10 0.32 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 3.89 0.63 124 4.36 0.48 43 3.44 0.73 22 4.14 0.56 21 4.33 0.48
Pd Unjha 50 2.38 0.64 50 4.72 0.45 20 4.90 0.31 10 4.40 0.52 10 4.90 0.32
Patan 40 2.48 0.55 30 4.73 0.45 13 4.77 0.44 3 4.67 0.58 2 4.50 0.71
Siddhpur 8 2.38 0.52 10 4.80 0.42 5 4.80 0.45 9 4.78 0.44 9 4.78 0.44
Palanpur 8 2.25 0.46 8 4.63 0.52 5 4.80 0.45 0 0.00 0.00 0 0.00 0.00
Thara 18 2.39 0.50 16 4.81 0.40 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 2.29 0.49 10 4.50 0.53 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 2.40 0.56 124 4.72 0.45 43 4.84 0.37 22 4.59 0.50 21 4.81 0.40
Pe Unjha 50 4.12 0.69 50 2.58 0.50 20 3.60 0.50 10 1.80 0.79 10 2.90 0.57
Patan 40 4.13 0.65 30 2.67 0.48 13 3.69 0.63 3 1.33 0.58 2 3.00 0.00
Siddhpur 8 4.38 0.74 10 2.70 0.48 5 3.80 0.45 9 1.33 0.50 9 2.56 0.53
Palanpur 8 4.25 0.71 8 2.50 0.53 5 4.00 0.71 0 0.00 0.00 0 0.00 0.00
Thara 18 4.17 0.62 16 2.69 0.48 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 4.43 0.79 10 2.60 0.52 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 4.17 0.67 124 2.62 0.49 43 3.70 0.56 22 1.55 0.67 21 2.76 0.54
Pf Unjha 50 1.60 0.49 50 3.10 0.58 20 1.80 0.52 10 4.40 0.52 10 3.90 0.57
Patan 40 1.55 0.50 30 3.27 0.45 13 1.85 0.69 3 4.67 0.58 2 4.00 0.00
Siddhpur 8 1.63 0.52 10 3.20 0.42 5 1.80 0.84 9 4.33 0.50 9 3.78 0.67
Palanpur 8 1.63 0.52 8 3.13 0.35 5 1.80 0.84 0 0.00 0.00 0 0.00 0.00
Thara 18 1.44 0.51 16 3.25 0.45 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 1.57 0.53 10 3.10 0.32 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 1.56 0.50 124 3.17 0.49 43 1.81 0.63 22 4.41 0.50 21 1.29 0.46
Pg Unjha 50 3.64 0.80 50 1.12 0.33 20 4.25 0.91 10 1.30 0.48 10 1.30 0.48
214
Patan 40 2.48 1.15 30 1.20 0.41 13 3.85 0.38 3 1.33 0.58 2 1.50 0.71
Siddhpur 8 1.25 0.46 10 1.20 0.42 5 3.60 0.55 9 1.33 0.50 9 1.22 0.44
Palanpur 8 1.63 0.52 8 1.25 0.46 5 3.60 0.55 0 0.00 0.00 0 0.00 0.00
Thara 18 1.28 0.46 16 1.19 0.40 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 1.29 0.49 10 1.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 2.56 1.28 124 1.15 0.36 43 3.98 0.74 22 1.32 0.48 21 1.29 0.46
Pj Unjha 50 1.08 0.27 50 1.20 0.40 20 1.85 0.88 10 3.70 0.95 10 1.10 0.32
Patan 40 1.10 0.30 30 1.17 0.38 13 1.69 0.63 3 4.33 1.15 2 1.00 0.00
Siddhpur 8 1.25 0.46 10 1.30 0.48 5 1.60 0.55 9 3.11 1.45 9 1.11 0.33
Palanpur 8 1.13 0.35 8 1.25 0.46 5 1.60 0.55 0 0.00 0.00 0 0.00 0.00
Thara 18 1.11 0.32 16 1.13 0.34 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 1.29 0.49 10 1.20 0.42 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 1.11 0.32 124 1.19 0.40 43 1.74 0.73 22 3.55 1.22 21 1.10 0.30
Pk Unjha 50 1.10 0.30 50 1.20 0.40 20 1.85 0.88 10 3.70 0.95 10 1.10 0.32
Patan 40 1.10 0.30 30 1.17 0.38 13 1.69 0.63 3 4.33 1.15 2 1.00 0.00
Siddhpur 8 1.25 0.46 10 1.30 0.48 5 1.60 0.55 9 3.11 1.45 9 1.11 0.33
Palanpur 8 1.13 0.35 8 1.25 0.46 5 1.60 0.55 0 0.00 0.00 0 0.00 0.00
Thara 18 1.17 0.38 16 1.13 0.34 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 1.29 0.49 10 1.20 0.42 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 1.13 0.34 124 1.19 0.40 43 1.74 0.73 22 3.55 1.22 21 1.10 0.30
Pl Unjha 50 1.10 0.30 50 1.20 0.40 20 1.80 0.83 10 3.20 0.63 10 1.10 0.32
Patan 40 1.08 0.28 30 1.17 0.38 13 1.69 0.63 3 4.33 1.15 2 1.00 0.00
Siddhpur 8 1.13 0.35 10 1.40 0.52 5 1.60 0.55 9 2.89 1.17 9 1.11 0.33
Palanpur 8 1.13 0.35 8 1.25 0.46 5 1.60 0.55 0 0.00 0.00 0 0.00 0.00
Thara 18 1.11 0.32 16 1.19 0.40 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 1.29 0.49 10 1.20 0.42 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 1.11 0.31 124 1.21 0.41 43 1.72 0.70 22 3.23 1.02 21 1.10 0.30
Pm Unjha 50 1.10 0.30 50 3.36 0.53 20 1.95 0.69 10 1.70 0.67 10 1.10 0.32
Patan 40 1.08 0.28 30 3.40 0.62 13 1.85 0.69 3 1.33 0.58 2 1.00 0.00
Siddhpur 8 1.13 0.35 10 3.70 0.67 5 1.40 0.55 9 1.22 0.44 9 1.11 0.33
Palanpur 8 1.13 0.35 8 3.88 0.64 5 1.20 0.45 0 0.00 0.00 0 0.00 0.00
Thara 18 1.06 0.24 16 3.63 0.50 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 1.29 0.49 10 3.50 0.53 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 1.10 0.30 124 3.48 0.58 43 1.77 0.68 22 1.45 0.60 21 1.10 0.30
Pn Unjha 50 1.04 0.20 50 1.16 0.37 20 3.15 0.88 10 3.80 0.63 10 1.70 0.67
Patan 40 1.05 0.22 30 1.07 0.25 13 2.85 1.14 3 4.67 0.58 2 1.50 0.71
Siddhpur 8 1.13 0.35 10 1.10 0.32 5 2.60 0.55 9 3.44 0.53 9 1.33 0.50
Palanpur 8 1.13 0.35 8 1.13 0.35 5 2.00 1.22 0 0.00 0.00 0 0.00 0.00
Thara 18 1.06 0.24 16 1.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 1.29 0.49 10 1.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 1.07 0.25 124 1.10 0.30 43 2.86 1.01 22 3.77 0.69 21 1.52 0.60
Pr Unjha 50 1.60 0.49 50 1.34 0.48 20 1.35 0.49 10 4.40 0.52 10 2.40 0.52
215
Pu Unjha 50 1.46 0.50 50 4.52 0.50 20 4.75 0.44 10 4.60 0.52 10 4.30 0.48
Patan 40 1.23 0.42 30 4.73 0.45 13 4.69 0.48 3 4.67 0.58 2 4.50 0.71
Siddhpur 8 1.25 0.46 10 4.80 0.42 5 4.80 0.45 9 4.44 0.53 9 4.56 0.53
Palanpur 8 1.25 0.46 8 4.88 0.35 5 4.80 0.45 0 0.00 0.00 0 0.00 0.00
Thara 18 1.39 0.50 16 4.63 0.50 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 1.29 0.49 10 4.70 0.48 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 1.34 0.48 124 4.65 0.48 43 4.74 0.44 22 4.55 0.51 21 4.43 0.51
Pv Unjha 50 1.04 0.20 50 3.40 0.64 20 3.55 0.51 10 3.60 0.52 10 4.00 0.67
Patan 40 1.03 0.16 30 3.60 0.50 13 3.54 0.52 3 3.67 0.58 2 4.00 0.00
Siddhpur 8 1.13 0.35 10 3.40 0.52 5 3.40 0.55 9 3.89 0.60 9 3.78 0.44
Palanpur 8 1.13 0.35 8 3.38 0.52 5 3.40 0.55 0 0.00 0.00 0 0.00 0.00
Thara 18 1.11 0.32 16 3.38 0.50 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 1.29 0.49 10 3.30 0.48 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 1.07 0.25 124 3.44 0.56 43 3.51 0.51 22 3.73 0.55 21 3.90 0.54
Patan 40 1.63 0.49 30 1.37 0.49 13 1.54 0.52 3 4.67 0.58 2 2.00 0.00
Siddhpur 8 1.75 0.46 10 1.50 0.53 5 1.40 0.55 9 4.11 0.60 9 2.44 0.53
Palanpur 8 1.63 0.52 8 1.25 0.46 5 1.20 0.45 0 0.00 0.00 0 0.00 0.00
Thara 18 1.72 0.46 16 1.31 0.48 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 1.71 0.49 10 1.30 0.48 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 1.64 0.48 124 1.35 0.48 43 1.40 0.49 22 4.32 0.57 21 2.38 0.50
Ps Unjha 50 1.60 0.49 50 4.76 0.43 20 4.10 0.45 10 4.80 0.42 10 4.70 0.48
Patan 40 1.63 0.49 30 4.80 0.41 13 4.23 0.73 3 5.00 0.00 2 4.50 0.71
Siddhpur 8 1.75 0.46 10 4.80 0.42 5 4.20 0.84 9 4.56 0.53 9 4.56 0.53
Palanpur 8 1.63 0.52 8 4.88 0.35 5 3.80 0.45 0 0.00 0.00 0 0.00 0.00
Thara 18 1.72 0.46 16 4.75 0.45 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 1.71 0.49 10 4.80 0.42 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 1.64 0.48 124 4.78 0.41 43 4.12 0.59 22 4.73 0.46 21 4.62 0.50
Pt Unjha 50 1.04 0.20 50 1.26 0.44 20 1.50 0.51 10 3.00 0.67 10 2.10 0.57
Patan 40 1.03 0.16 30 1.33 0.48 13 1.54 0.52 3 2.67 0.58 2 2.00 0.00
Siddhpur 8 1.13 0.35 10 1.30 0.48 5 1.60 0.55 9 2.56 0.53 9 2.33 0.50
Palanpur 8 1.13 0.35 8 1.13 0.35 5 1.40 0.55 0 0.00 0.00 0 0.00 0.00
Thara 18 1.28 0.46 16 1.19 0.40 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 7 1.29 0.49 10 1.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 131 2.56 1.28 124 1.15 0.36 43 3.98 0.74 22 1.32 0.48 21 1.29 0.46
216
4.6.5 Hypothesis for importance given to the Key Important Variables by the
intermediaries to select the intermediaries into particular APMC
Hoa There is no statistical significant difference between the rated importance of the
variable ‘_____*_______’ by the ‘ # ’ of all the APMCs for selecting
intermediaries in particular APMC.
H1a There is statistical significant difference between the rated importance of the
variable ‘______*______’ by the ‘ # ’ of all the APMCs for selecting
intermediaries in particular APMC.
Note: * Indicates the variable listed in the following table 4.70
# Indicates the type of the intermediary (i.e. Farmer, Commission Agent, Stockist,
Wholesaler, Exporter and Processor)
4.6.6 Hypothesis testing for importance given to the Key Important Variables by
the intermediaries to select the intermediaries in particular APMC.
The researcher also has the objective to know and compare the importance given to the
key important variables considered by the particular intermediary to select the
intermediaries in particular APMC to purchase/sell the commodities. Hence, the above
hypothesis is developed for the fifteen key variables, identified through factor analysis
and tested in line with the above stated objective.
4.6.6.1 ANOVA for importance given to the Key Important Variable by the Farmer
to select the intermediaries into particular APMC
Table 4.70 ANOVA for importance given to the key variables by the farmers to
select the intermediaries in particular APMC
Name of variable
Sum of
Squares
Df Mean
Squares
F Sig.
Ma: Open Auction
System Between Groups 1.12 5 0.22 0.913 0.474
217
Within Groups 48.80 199 0.25
Total 49.92 204
Md: Quality Testing
Laboratory Between Groups 0.35 5 0.07 2.161 0.060
Within Groups 6.41 199 0.03
Total 6.76 204
Me: Availability of
processing facility Between Groups 0.35 5 0.07 2.161 0.060
Within Groups 6.41 199 0.03
Total 6.76 204
Mf: Availability of
Buyer all the time Between Groups 179.89 5 35.98 116.603 0.000
Within Groups 61.40 199 0.31
Total 241.30 204
Mg: Spot payment
System Between Groups 0.91 5 0.18 2.009 0.079
Within Groups 17.94 199 0.09
Total 18.85 204
Mh: Financial
Assistance by/ to the
channel
intermediaries Between Groups 154.14 5 30.83 105.318 0.000
Within Groups 58.25 199 0.29
Total 212.39 204
Mj: Warehouse
Receipt Finance Between Groups 0.83 5 0.17 0.391 0.855
Within Groups 84.39 199 0.42
Total 85.22 204
Mk: Demand at the
marketplace
compared to other
markets Between Groups 93.36 5 18.67 49.792 0.000
Within Groups 74.62 199 0.37
Total 167.98 204
Mq: Well-known for
particular
commodities Between Groups 4.21 5 0.84 2.121 0.064
Within Groups 78.92 199 0.40
Total 83.12 204
Mr: Transparency in
the governing system Between Groups 12.00 5 2.40 10.839 0.000
Within Groups 44.05 199 0.22
Total 56.05 204
218
Mu: Availability of
information about
demand in domestic
and international
markets Between Groups 0.07 5 0.01 0.067 0.997
Within Groups 42.17 199 0.21
Total 42.24 204
Mv: Availability of
information about
prevailing prices in
major markets Between Groups 0.28 5 0.06 0.223 0.952
Within Groups 50.32 199 0.25
Total 50.60 204
My: Availability of
Production
estimation
information Between Groups 3.12 5 0.62 1.911 0.094
Within Groups 65.07 199 0.33
Total 68.20 204
Mz: Involvement of
Governing body in
development of
APMC Between Groups 0.68 5 0.14 0.635 0.674
Within Groups 42.57 199 0.21
Total 43.25 204
Maa: Quantity to be
Purchased / Sold Between Groups 0.50 5 0.10 0.240 0.944
Within Groups 83.14 199 0.42
Total 83.64 204
The significant value for the variables; Open Auction System, Quality Testing
Laboratory, Availability of Processing Facility, Spot Payment System, Warehouse
Receipt Finance, Well-known for particular commodities, Availability of information
about demand in domestic as well as international markets, Availability of information
about prevailing prices in major markets, Availability of Production estimation
information, Involvement of Governing body in the development of APMC and Quantity
to be purchased/sold in the above table is above 0.05. This means that the null
hypothesis: There is no statistical significant difference between the rated importance of
these variables by the farmers of all the APMCs for selecting intermediaries into
particular APMC, is accepted. While the significant value for the variables Availability of
219
buyers all the time, Financial Assistance by / to the channel intermediaries, Demand at
the market place compared to other markets and Transparency into the governing system
is less than 0.05. The null hypothesis for these variables is rejected. This means that the
alternative hypothesis: There is statistical significant difference between the rated
importance of these variables by the farmers of all APMCs for selecting intermediaries
into particular APMC; is not rejected.
The mean values for importance given by the farmers to the variable Availability of
buyers all the time for farmers of Unjha (4.70) and Patan (4.26) is very high compared to
Siddhpur (2.75), Palanpur (2.60), Thara (2.60) and Becharaji (2.67).
The mean values for the variables Demand at market place compared to other markets are
4.63, 3.82, 3.00, 3.10, 3.23 and 2.87 respectively.
Similarly, the mean values for the variable Financial Assistance by/to the channel
intermediaries for the farmers of Unjha, Patan, Siddhpur, Palanpur, Thara and Becharaji
is 4.80, 3.30, 2.65, 2.40, 3.53 and 3.13 respectively.
And, the mean values for the variable Transparency into the governing system for the
farmers of Unjha, Patan, Siddhpur, Palanpur, Thara and Becharaji are 4.04, 4.18, 3.7, 3.6,
3.6 and 3.6 respectively.
4.6.6.2 ANOVA for importance given to the key variables by the Commission
Agent to select the intermediaries in particular APMC
Table 4.71 ANOVA for importance given to the key variables by the commission
agents to select the intermediaries in particular APMC
Name of variable
Sum of
Squares
df Mean
Squares
F Sig.
Ma: Open Auction
System Between Groups 0.33 5 0.07 0.339 0.889
Within Groups 24.36 125 0.19
220
Total 24.69 130
Md: Quality Testing
Laboratory Between Groups 0.11 5 0.02 0.333 0.892
Within Groups 8.27 125 0.07
Total 8.38 130
Me: Availability of
processing facility Between Groups 0.11 5 0.02 0.333 0.892
Within Groups 8.27 125 0.07
Total 8.38 130
Mf: Availability of
Buyer all the time Between Groups 0.54 5 0.11 0.517 0.763
Within Groups 26.01 125 0.21
Total 26.55 130
Mg: Spot payment
System Between Groups 0.64 5 0.13 0.434 0.824
Within Groups 37.08 125 0.30
Total 37.73 130
Mh: Financial
Assistance by/ to the
channel
intermediaries Between Groups 123.68 5 24.74 39.406 0.000
Within Groups 78.46 125 0.63
Total 202.14 130
Mj: Warehouse
Receipt Finance Between Groups 1.58 5 0.32 0.788 0.560
Within Groups 50.06 125 0.40
Total 51.63 130
Mk: Demand at the
marketplace
compared to other
markets Between Groups 0.23 5 0.05 0.196 0.964
Within Groups 29.31 125 0.23
Total 29.54 130
Mq: Well-known for
particular
commodities Between Groups 0.36 5 0.07 0.480 0.791
Within Groups 18.60 125 0.15
Total 18.96 130
Mr: Transparency in
the governing system Between Groups 1.06 5 0.21 0.822 0.536
Within Groups 32.22 125 0.26
Total 33.28 130
221
Mu: Availability of
information about
demand in domestic
and international
markets Between Groups 0.10 5 0.02 0.134 0.984
Within Groups 19.50 125 0.16
Total 19.60 130
Mv: Availability of
information about
prices in major
markets Between Groups 0.65 5 0.13 0.708 0.618
Within Groups 23.01 125 0.18
Total 23.66 130
My: Availability of
Production
estimation
information Between Groups 0.94 5 0.19 0.774 0.570
Within Groups 30.21 125 0.24
Total 31.15 130
Mz: Involvement of
Governing body into
development of
APMC Between Groups 1.20 5 0.24 0.978 0.434
Within Groups 30.71 125 0.25
Total 31.91 130
Maa: Quantity to be
Purchased / Sold Between Groups 0.24 5 0.05 0.185 0.968
Within Groups 32.19 125 0.26
Total 32.43 130
The significant value for the variables; Open Auction System, Quality Testing
Laboratory, Availability of Processing Facility, Availability of Buyers all the time, Spot
Payment System, Warehouse Receipt Finance, Demand at the market place compared to
other markets ,Well-known for particular commodities, Transparency into the governing
system, Availability of information about demand in domestic as well as international
markets, Availability of information about prevailing prices in major markets,
Availability of Production estimation information, Involvement of Governing body in the
development of APMC and Quantity to be purchased/sold in the above table is above
0.05. This means that the null hypothesis: There is no statistical significant difference
222
between the rated importance of these variables by the commission agents of all the
APMCs for selecting intermediaries in particular APMC, is accepted. While the
significant value for the variables Financial Assistance by/to the channel intermediaries,
is less than 0.05. The null hypothesis for this variable is rejected. This means that the
alternative hypothesis: There is statistical significant difference between the rated
importance of this variable by the commission agents of all the APMCs for selecting
intermediaries into particular APMC; is not rejected.
The mean values for importance given to the variable Financial Assistance by/to the
channel intermediaries by the Commission Agents of Unjha, Patan, Siddhpur, Palanpur,
Thara and Becharaji is 3.76, 2.48, 1.25, 1.63, 1.39 and 1.57 respectively.
4.6.6.3 ANOVA for importance given to the key variables by the Stockist to select
the intermediaries into particular APMC
Table 4.72 ANOVA for importance given to the key variables by the stockiest to
select the intermediaries into particular APMC
Name of variable
Sum of
Squares
df Mean
Squares
F Sig.
Ma: Open Auction
System Between Groups 2.85 5 0.57 1.231 0.299
Within Groups 54.60 118 0.46
Total 57.44 123
Md: Quality Testing
Laboratory Between Groups 0.52 5 0.10 0.748 0.589
Within Groups 16.26 118 0.14
Total 16.77 123
Me: Availability of
processing facility Between Groups 0.55 5 0.11 0.600 0.700
Within Groups 21.67 118 0.18
Total 22.22 123
Mf: Availability of
Buyer all the time Between Groups 1.65 5 0.33 1.141 0.343
Within Groups 34.12 118 0.29
Total 35.77 123
Mg: Spot payment Between Groups 2.59 5 0.52 2.274 0.052
223
System
Within Groups 26.89 118 0.23
Total 29.48 123
Mh: Financial
Assistance by/ to the
channel
intermediaries Between Groups 1.31 5 0.26 0.835 0.528
Within Groups 36.91 118 0.31
Total 38.22 123
Mj: Warehouse
Receipt Finance Between Groups 2.69 5 0.54 1.496 0.196
Within Groups 42.49 118 0.36
Total 45.19 123
Mk: Demand at the
marketplace
compared to other
markets Between Groups 0.73 5 0.15 0.570 0.723
Within Groups 30.26 118 0.26
Total 30.99 123
Mq: Well-known for
particular
commodities Between Groups 8.35 5 1.67 6.403 0.000
Within Groups 30.77 118 0.26
Total 39.12 123
Mr: Transparency
in the governing
system Between Groups 2.07 5 0.41 1.187 0.320
Within Groups 41.13 118 0.35
Total 43.19 123
Mu: Availability of
information about
demand in domestic
and international
markets Between Groups 0.42 5 0.08 0.340 0.888
Within Groups 29.22 118 0.25
Total 29.64 123
Mv: Availability of
information about
prices in major
markets Between Groups 1.58 5 0.32 1.458 0.209
Within Groups 25.52 118 0.22
Total 27.10 123
My: Availability of Between Groups 0.23 5 0.05 0.183 0.968
224
Production
estimation
information
Within Groups 29.19 118 0.25
Total 29.42 123
Mz: Involvement of
Governing body in
development of
APMC Between Groups 2.41 5 0.48 1.445 0.213
Within Groups 39.42 118 0.33
Total 41.84 123
Maa: Quantity to be
Purchased / Sold Between Groups 0.63 5 0.13 0.339 0.888
Within Groups 43.92 118 0.37
Total 44.55 123
The significant value for all the variables in the above table is above 0.05 except the
variable well-known for particular commodities. This means that the null hypothesis:
there is no statistical significant difference between the rated importance for the variables
Open Auction System, Quality Testing Laboratory, Availability of Processing Facility,
Availability of Buyers all the time, Spot Payment System, Financial Assistance by/to the
channel intermediaries, Warehouse receipt finance, Demand at the market place
compared to other markets, Transparency in the governing system, Availability of
information about demand in domestic as well as international markets, Availability of
information about prevailing prices in major markets, Availability of Production
estimation information, Involvement of Governing body in the development of APMC
and Quantity to be purchased/sold by the stockist of all the APMCs for selecting
intermediaries in particular APMC is accepted.
While the significant value for the variables Well-known for particular commodities is
less than 0.05. Therefore the null hypothesis for this variable is rejected. This means that
the alternative hypothesis: There is statistical significant difference between the rated
importance of the variable well-known for particular commodities by the stockists of all
APMCs for selecting intermediaries in particular APMC; is not rejected.
225
The mean values for importance given to the variable well-known for particular
commodities by the stockists of Unjha (3.92), Patan (4.53), Siddhpur (4.40), Palanpur
(4.38), Thara (4.38) and Becharaji (4.20).
4.6.6.4 ANOVA for importance given to the key variables by the Wholesaler to
select the intermediaries in particular APMC
Table 4.73 ANOVA for importance given to the key variables by the wholesaler
to select the intermediaries into particular APMC
Name of variable
Sum of
Squares
df Mean
Squares
F Sig.
Ma: Open Auction
System Between Groups 0.51 3 0.17 0.670 0.575
Within Groups 9.91 39 0.25
Total 10.42 42
Md: Quality Testing
Laboratory Between Groups 0.28 3 0.09 0.318 0.812
Within Groups 11.35 39 0.29
Total 11.63 42
Me: Availability of
processing facility Between Groups 0.29 3 0.10 0.484 0.695
Within Groups 7.89 39 0.20
Total 8.19 42
Mf: Availability of
Buyer all the time Between Groups 1.19 3 0.40 1.370 0.266
Within Groups 11.28 39 0.29
Total 12.47 42
Mg: Spot payment
System Between Groups 2.31 3 0.77 2.533 0.071
Within Groups 11.87 39 0.30
Total 14.19 42
Mh: Financial
Assistance by/ to the
channel
intermediaries Between Groups 0.65 3 0.22 0.363 0.780
Within Groups 23.26 39 0.60
Total 23.91 42
Mj: Warehouse Between Groups 0.07 3 0.02 0.053 0.984
226
Receipt Finance
Within Groups 16.72 39 0.43
Total 16.79 42
Mk: Demand at the
marketplace
compared to other
markets Between Groups 0.16 3 0.05 0.190 0.902
Within Groups 11.28 39 0.29
Total 11.44 42
Mq: Well-known for
particular
commodities Between Groups 0.45 3 0.15 0.593 0.623
Within Groups 9.83 39 0.25
Total 10.28 42
Mr: Transparency in
the governing system Between Groups 1.41 3 0.47 1.901 0.145
Within Groups 9.66 39 0.25
Total 11.07 42
Mu: Availability of
information about
demand in domestic
and international
markets Between Groups 0.50 3 0.17 0.662 0.580
Within Groups 9.78 39 0.25
Total 10.28 42
Mv: Availability of
information about
prices in major
markets Between Groups 0.07 3 0.02 0.180 0.909
Within Groups 5.09 39 0.13
Total 5.16 42
My: Availability of
Production
estimation
information Between Groups 0.16 3 0.05 0.201 0.895
Within Groups 10.58 39 0.27
Total 10.74 42
Mz: Involvement of
Governing body in
development of
APMC Between Groups 0.24 3 0.08 0.358 0.784
Within Groups 8.83 39 0.23
Total 9.07 42
227
Maa: Quantity to be
Purchased / Sold Between Groups 2.50 3 0.83 1.408 0.255
Within Groups 23.12 39 0.59
Total 25.63 42
The significant value for all the variables in the above table is above 0.05. This means
that the null hypothesis: there is no statistical significant difference between the rated
importance for the variables Open Auction System, Quality Testing Laboratory,
Availability of Processing Facility, Availability of Buyers all the time, Spot Payment
System, Financial Assistance by/to the channel intermediaries, Warehouse receipt
finance, Demand at the market place compared to other markets, Well-known for
particular commodities, Transparency into the governing system, Availability of
information about demand in domestic as well as international markets, Availability of
information about prevailing prices in major markets, Availability of Production
estimation information, Involvement of Governing body in the development of APMC
and Quantity to be purchased/sold by the wholesalers of all the APMCs for selecting
intermediaries in particular APMC; is accepted.
4.6.6.5 ANOVA for importance given to the key variables by the Exporter to select
the intermediaries in particular APMC
Table 4.74 ANOVA for importance given to the key variables by the exporter to
select the intermediaries into particular APMC
Name of variable
Sum of
Squares
df Mean
Squares
F Sig.
Ma: Open Auction
System Between Groups 0.17 2 0.08 0.298 0.746
Within Groups 5.29 19 0.28
Total 5.45 21
Md: Quality Testing
Laboratory Between Groups 0.33 2 0.16 0.627 0.545
Within Groups 4.99 19 0.26
Total 5.32 21
Me: Availability of
processing facility Between Groups 1.36 2 0.68 1.524 0.243
228
Within Groups 8.50 19 0.45
Total 9.86 21
Mf: Availability of
Buyer all the time Between Groups 0.97 2 0.49 1.718 0.206
Within Groups 5.39 19 0.28
Total 6.36 21
Mg: Spot payment
System Between Groups 0.57 2 0.29 0.943 0.407
Within Groups 5.79 19 0.30
Total 6.36 21
Mh: Financial
Assistance by/ to the
channel intermediaries Between Groups 0.28 2 0.14 0.601 0.559
Within Groups 4.49 19 0.24
Total 4.77 21
Mj: Warehouse
Receipt Finance Between Groups 0.03 2 0.01 0.053 0.949
Within Groups 5.29 19 0.28
Total 5.32 21
Mk: Demand at the
marketplace compared
to other markets Between Groups 0.73 2 0.37 1.462 0.257
Within Groups 4.77 19 0.25
Total 5.50 21
Mq: Well-known for
particular commodities Between Groups 0.17 2 0.08 0.298 0.746
Within Groups 5.29 19 0.28
Total 5.45 21
Mr: Transparency into
the governing system Between Groups 0.47 2 0.23 0.887 0.428
Within Groups 4.99 19 0.26
Total 5.45 21
Mu: Availability of
information about
demand in domestic
and international
markets Between Groups 0.72 2 0.36 1.680 0.213
Within Groups 4.06 19 0.21
Total 4.77 21
Mv: Availability of
information about
prices in major
markets Between Groups 0.10 2 0.05 0.194 0.825
229
Within Groups 4.99 19 0.26
Total 5.09 21
My: Availability of
Production estimation
information Between Groups 0.28 2 0.14 0.601 0.559
Within Groups 4.49 19 0.24
Total 4.77 21
Mz: Involvement of
Governing body in
development of APMC Between Groups 1.20 2 0.60 1.587 0.231
Within Groups 7.17 19 0.38
Total 8.36 21
Maa: Quantity to be
Purchased / Sold Between Groups 0.61 2 0.30 0.664 0.526
Within Groups 8.67 19 0.46
Total 9.27 21
The significant value for all the variables in the above table is above 0.05. This means
that the null hypothesis: there is no statistical significant difference between the rated
importance for all these variables by the exporters of all the APMCs for selecting
intermediaries into particular APMC; is accepted.
4.6.6.6 ANOVA for importance given to the key variables by the Processor to select
the intermediaries in particular APMC
Table 4.75 ANOVA for importance given to the key variables by the processor to
select the intermediaries in particular APMC
Name of variable
Sum of
Squares
df Mean
Squares
F Sig.
Ma: Open Auction
System Between Groups 0.02 2 0.01 0.036 0.964
Within Groups 5.12 18 0.28
Total 5.14 20
Md: Quality Testing
Laboratory Between Groups 0.13 2 0.07 0.243 0.787
Within Groups 4.82 18 0.27
Total 4.95 20
230
Me: Availability of
processing facility Between Groups 0.08 2 0.04 0.298 0.746
Within Groups 2.49 18 0.14
Total 2.57 20
Mf: Availability of
Buyer all the time Between Groups 0.63 2 0.32 0.897 0.425
Within Groups 6.32 18 0.35
Total 6.95 20
Mg: Spot payment
System Between Groups 0.91 2 0.45 1.186 0.328
Within Groups 6.90 18 0.38
Total 7.81 20
Mh: Financial
Assistance by/ to the
channel
intermediaries Between Groups 0.02 2 0.01 0.027 0.973
Within Groups 5.22 18 0.29
Total 5.24 20
Mj: Warehouse
Receipt Finance Between Groups 0.84 2 0.42 1.179 0.330
Within Groups 6.40 18 0.36
Total 7.24 20
Mk: Demand at the
marketplace
compared to other
markets Between Groups 0.46 2 0.23 1.091 0.357
Within Groups 3.82 18 0.21
Total 4.29 20
Mq: Well-known for
particular
commodities Between Groups 0.07 2 0.03 0.130 0.879
Within Groups 4.60 18 0.26
Total 4.67 20
Mr: Transparency
into the governing
system Between Groups 0.50 2 0.25 0.529 0.598
Within Groups 8.46 18 0.47
Total 8.95 20
Mu: Availability of
information about
demand in domestic
and international
markets Between Groups 0.12 2 0.06 0.204 0.818
231
Within Groups 5.12 18 0.28
Total 5.24 20
Mv: Availability of
information about
prices in major
markets Between Groups 0.21 2 0.11 0.426 0.659
Within Groups 4.46 18 0.25
Total 4.67 20
My: Availability of
Production
estimation
information Between Groups 0.45 2 0.23 0.905 0.422
Within Groups 4.50 18 0.25
Total 4.95 20
Mz: Involvement of
Governing body in
development of
APMC Between Groups 0.32 2 0.16 0.598 0.560
Within Groups 4.82 18 0.27
Total 5.14 20
Maa: Quantity to be
Purchased / Sold Between Groups 0.66 2 0.33 0.720 0.500
Within Groups 8.29 18 0.46
Total 8.95 20
The significant value for all the variables in the above table is above 0.05. This means
that the null hypothesis: there is no statistical significant difference between the rated
importance for all these variables by the processors of all the APMCs for selecting
intermediaries in particular APMC; is accepted.
232
Table 4.76 Mean and Standard Deviation for importance given to the Key
Important Variables by the different intermediaries to select the
intermediaries into particular APMC.
Farmer Commission Agent Stockist Wholesaler Exporter Processor
Name of
Variable N Mean S.D. N Mean S.D. N Mean S.D. N Mean S.D. N Mean S.D. N Mean S.D.
Ma Unjha 70 4.61 0.49 50 4.30 0.46 50 3.04 0.67 20 4.00 0.56 10 2.40 0.52 10 2.60 0.52
Patan 50 4.50 0.51 40 4.25 0.44 30 3.20 0.76 13 3.77 0.44 3 2.33 0.58 2 2.50 0.71
Siddhpur 20 4.45 0.51 8 4.25 0.46 10 3.20 0.63 5 3.80 0.45 9 2.56 0.53 9 2.56 0.53
Palanpur 20 4.70 0.47 8 4.25 0.46 8 3.13 0.64 5 3.80 0.45 0 0.00 0.00 0 0.00 0.00
Thara 30 4.63 0.49 18 4.17 0.38 16 3.25 0.68 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 4.60 0.51 7 4.14 0.38 10 3.60 0.52 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 4.58 0.49 131 4.25 0.44 124 3.17 0.68 43 0.00 0.50 22 2.45 0.51 21 2.57 0.51
Md Unjha 70 1.03 0.17 50 1.06 0.24 50 1.22 0.42 20 3.15 0.67 10 4.70 0.48 10 4.30 0.48
Patan 50 1.04 0.20 40 1.05 0.22 30 1.13 0.35 13 3.00 0.00 3 4.33 0.58 2 4.50 0.71
Siddhpur 20 1.15 0.37 8 1.13 0.35 10 1.10 0.32 5 3.00 0.71 9 4.56 0.53 9 4.44 0.53
Palanpur 20 1.00 0.00 8 1.13 0.35 8 1.13 0.35 5 3.20 0.45 0 0.00 0.00 0 0.00 0.00
Thara 30 1.00 0.00 18 1.06 0.24 16 1.19 0.40 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 1.00 0.00 7 1.14 0.38 10 1.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 1.03 0.18 131 1.07 0.25 124 1.16 0.37 43 3.09 0.53 22 4.59 0.50 21 4.38 0.50
Me Unjha 70 1.03 0.17 50 1.06 0.24 50 1.30 0.46 20 4.70 0.47 10 4.50 0.53 10 1.20 0.42
Patan 50 1.04 0.20 40 1.05 0.22 30 1.13 0.35 13 4.85 0.38 3 4.00 1.00 2 1.00 0.00
Siddhpur 20 1.15 0.37 8 1.13 0.35 10 1.20 0.42 5 4.80 0.45 9 4.00 0.71 9 1.11 0.33
Palanpur 20 1.00 0.00 8 1.13 0.35 8 1.25 0.46 5 4.60 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 1.00 0.00 18 1.06 0.24 16 1.25 0.45 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 1.00 0.00 7 1.14 0.38 10 1.20 0.42 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 1.03 0.18 131 1.07 0.25 124 1.23 0.43 43 4.74 0.44 22 4.23 0.69 21 1.14 0.36
Mf Unjha 70 4.70 0.46 50 4.68 0.47 50 4.44 0.50 20 4.70 0.47 10 4.50 0.53 10 4.20 0.63
Patan 50 4.26 0.72 40 4.70 0.46 30 4.27 0.58 13 4.62 0.51 3 4.00 0.00 2 4.50 0.71
Siddhpur 20 2.75 0.55 8 4.63 0.52 10 4.30 0.48 5 4.40 0.89 9 4.11 0.60 9 4.56 0.53
Palanpur 20 2.60 0.50 8 4.75 0.46 8 4.00 0.00 5 4.20 0.45 0 0.00 0.00 0 0.00 0.00
Thara 30 2.60 0.50 18 4.83 0.38 16 4.31 0.70 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 2.67 0.49 7 4.86 0.38 10 4.40 0.52 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 3.74 1.09 131 4.72 0.45 124 4.34 0.54 43 4.58 0.54 22 4.27 0.55 21 4.38 0.59
Mg Unjha 70 4.97 0.17 50 3.92 0.57 50 2.78 0.42 20 3.45 0.51 10 1.90 0.57 10 4.10 0.74
Patan 50 4.90 0.30 40 3.90 0.55 30 3.00 0.53 13 3.08 0.64 3 1.67 0.58 2 4.00 0.00
Siddhpur 20 4.80 0.41 8 4.00 0.53 10 3.10 0.57 5 2.80 0.45 9 1.56 0.53 9 3.67 0.50
Palanpur 20 4.80 0.41 8 4.00 0.53 8 3.13 0.64 5 3.40 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 4.83 0.38 18 4.06 0.54 16 2.94 0.44 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 4.93 0.26 7 4.14 0.38 10 3.20 0.42 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 4.90 0.30 131 3.95 0.54 124 2.94 0.49 43 3.26 0.58 22 1.73 0.55 21 3.90 0.62
233
Mh Unjha 70 4.80 0.40 50 3.76 0.66 50 1.70 0.61 20 3.05 0.83 10 1.80 0.42 10 1.50 0.53
Patan 50 3.30 0.65 40 2.48 1.09 30 1.80 0.55 13 2.77 0.73 3 1.67 0.58 2 1.50 0.71
Siddhpur 20 2.65 0.49 8 1.25 0.46 10 1.60 0.52 5 3.00 0.71 9 1.56 0.53 9 1.44 0.53
Palanpur 20 2.40 0.50 8 1.63 0.74 8 1.63 0.52 5 3.00 0.71 0 0.00 0.00 0 0.00 0.00
Thara 30 3.53 0.63 18 1.39 0.50 16 1.94 0.44 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 3.13 0.64 7 1.57 0.53 10 1.60 0.52 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 3.68 1.02 131 2.64 1.25 124 1.73 0.56 43 2.95 0.75 22 1.68 0.48 21 1.48 0.51
Mj Unjha 70 1.44 0.56 50 2.88 0.69 50 4.22 0.51 20 4.10 0.64 10 3.60 0.52 10 4.60 0.52
Patan 50 1.46 0.68 40 2.85 0.66 30 4.20 0.71 13 4.08 0.64 3 3.67 0.58 2 5.00 0.00
Siddhpur 20 1.50 0.61 8 2.63 0.52 10 4.10 0.57 5 4.00 0.71 9 3.56 0.53 9 4.33 0.71
Palanpur 20 1.50 0.69 8 2.50 0.76 8 4.13 0.64 5 4.00 0.71 0 0.00 0.00 0 0.00 0.00
Thara 30 1.63 0.76 18 2.94 0.42 16 3.81 0.66 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 1.47 0.74 7 2.86 0.38 10 3.90 0.57 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 1.49 0.65 131 2.84 0.63 124 4.12 0.61 43 4.07 0.63 22 3.59 0.50 21 4.52 0.60
Mk Unjha 70 4.63 0.49 50 4.66 0.48 50 4.54 0.50 20 4.30 0.47 10 4.70 0.48 10 4.80 0.42
Patan 50 3.82 0.60 40 4.65 0.48 30 4.47 0.51 13 4.38 0.51 3 4.33 0.58 2 5.00 0.00
Siddhpur 20 3.00 0.73 8 4.75 0.46 10 4.30 0.48 5 4.20 0.84 9 4.33 0.50 9 4.56 0.53
Palanpur 20 3.10 0.72 8 4.75 0.46 8 4.38 0.52 5 4.40 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 3.23 0.73 18 4.61 0.50 16 4.50 0.52 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 2.87 0.64 7 4.57 0.53 10 4.60 0.52 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 3.79 0.91 131 4.66 0.48 124 4.49 0.50 43 4.33 0.52 22 4.50 0.51 21 4.71 0.46
Mq Unjha 70 2.89 0.71 50 4.24 0.43 50 3.92 0.53 20 3.30 0.47 10 4.60 0.52 10 4.30 0.48
Patan 50 2.92 0.70 40 4.15 0.36 30 4.53 0.51 13 3.46 0.52 3 4.67 0.58 2 4.50 0.71
Siddhpur 20 2.65 0.49 8 4.13 0.35 10 4.40 0.52 5 3.60 0.55 9 4.44 0.53 9 4.33 0.50
Palanpur 20 2.60 0.50 8 4.13 0.35 8 4.38 0.52 5 3.40 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 2.60 0.50 18 4.11 0.32 16 4.38 0.50 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 2.60 0.51 7 4.14 0.38 10 4.20 0.42 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 2.78 0.64 131 4.18 0.38 124 4.22 0.56 43 3.40 0.49 22 4.55 0.51 21 4.33 0.48
Mr Unjha 70 4.04 0.49 50 3.02 0.59 50 2.80 0.61 20 3.75 0.55 10 2.70 0.48 10 3.10 0.57
Patan 50 4.18 0.39 40 3.20 0.46 30 3.00 0.53 13 3.77 0.44 3 2.33 0.58 2 3.00 0.00
Siddhpur 20 3.70 0.47 8 3.00 0.53 10 2.90 0.57 5 3.20 0.45 9 2.44 0.53 9 2.78 0.83
Palanpur 20 3.60 0.50 8 3.13 0.35 8 3.13 0.64 5 3.80 0.45 0 0.00 0.00 0 0.00 0.00
Thara 30 3.60 0.50 18 3.22 0.43 16 2.88 0.62 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 3.60 0.51 7 3.14 0.38 10 3.20 0.63 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 3.90 0.52 131 3.11 0.51 124 2.92 0.59 43 3.70 0.51 22 2.55 0.51 21 2.95 0.67
Mu Unjha 70 1.27 0.48 50 1.18 0.39 50 1.40 0.49 20 1.35 0.49 10 4.50 0.53 10 2.40 0.52
Patan 50 1.28 0.45 40 1.18 0.38 30 1.43 0.50 13 1.54 0.52 3 4.00 0.00 2 2.50 0.71
Siddhpur 20 1.30 0.47 8 1.25 0.46 10 1.50 0.53 5 1.40 0.55 9 4.22 0.44 9 2.56 0.53
Palanpur 20 1.25 0.44 8 1.13 0.35 8 1.25 0.46 5 1.20 0.45 0 0.00 0.00 0 0.00 0.00
Thara 30 1.23 0.43 18 1.22 0.43 16 1.38 0.50 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 1.27 0.46 7 1.14 0.38 10 1.30 0.48 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 1.27 0.46 131 1.18 0.39 124 1.40 0.49 43 1.40 0.49 22 4.32 0.48 21 2.48 0.51
234
Mv Unjha 70 4.57 0.50 50 4.68 0.47 50 4.66 0.48 20 4.90 0.31 10 4.70 0.48 10 4.60 0.52
Patan 50 4.52 0.50 40 4.80 0.41 30 4.80 0.41 13 4.85 0.38 3 4.67 0.58 2 4.50 0.71
Siddhpur 20 4.60 0.50 8 4.88 0.35 10 4.70 0.48 5 4.80 0.45 9 4.56 0.53 9 4.78 0.44
Palanpur 20 4.60 0.50 8 4.75 0.46 8 4.50 0.53 5 4.80 0.45 0 0.00 0.00 0 0.00 0.00
Thara 30 4.57 0.50 18 4.83 0.38 16 4.75 0.45 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 4.47 0.52 7 4.86 0.38 10 4.40 0.52 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 4.56 0.50 131 4.76 0.43 124 4.68 0.47 43 4.86 0.35 22 4.64 0.49 21 4.67 0.48
My Unjha 70 3.96 0.58 50 4.54 0.50 50 4.58 0.50 20 4.45 0.51 10 4.80 0.42 10 4.50 0.53
Patan 50 3.80 0.64 40 4.63 0.49 30 4.60 0.50 13 4.54 0.52 3 4.67 0.58 2 5.00 0.00
Siddhpur 20 3.60 0.50 8 4.50 0.53 10 4.70 0.48 5 4.60 0.55 9 4.56 0.53 9 4.67 0.50
Palanpur 20 3.70 0.66 8 4.63 0.52 8 4.63 0.52 5 4.60 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 3.73 0.45 18 4.78 0.43 16 4.69 0.48 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 3.67 0.49 7 4.71 0.49 10 4.60 0.52 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 3.80 0.58 131 4.61 0.49 124 4.61 0.49 43 4.51 0.51 22 4.68 0.48 21 4.62 0.50
Mz Unjha 70 3.30 0.46 50 3.60 0.49 50 3.24 0.66 20 3.75 0.44 10 3.50 0.53 10 3.30 0.48
Patan 50 3.36 0.48 40 3.48 0.51 30 3.53 0.51 13 3.62 0.51 3 3.33 0.58 2 3.50 0.71
Siddhpur 20 3.25 0.44 8 3.50 0.53 10 3.60 0.52 5 3.80 0.45 9 3.00 0.71 9 3.56 0.53
Palanpur 20 3.30 0.47 8 3.63 0.52 8 3.50 0.53 5 3.60 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 3.33 0.48 18 3.67 0.49 16 3.44 0.51 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 3.13 0.35 7 3.86 0.38 10 3.50 0.53 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 3.30 0.46 131 3.58 0.50 124 3.40 0.58 43 3.70 0.46 22 3.27 0.63 21 3.43 0.51
Maa Unjha 70 3.84 0.63 50 1.60 0.49 50 1.78 0.62 20 3.10 0.79 10 3.00 0.67 10 1.90 0.74
Patan 50 3.90 0.65 40 1.53 0.51 30 1.90 0.61 13 2.92 0.76 3 2.67 0.58 2 1.50 0.71
Siddhpur 20 3.95 0.60 8 1.50 0.53 10 1.80 0.63 5 2.60 0.89 9 2.67 0.71 9 2.11 0.60
Palanpur 20 3.95 0.60 8 1.50 0.53 8 1.75 0.46 5 2.40 0.55 0 0.00 0.00 0 0.00 0.00
Thara 30 3.87 0.68 18 1.50 0.51 16 1.69 0.60 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Becharaji 15 4.00 0.76 7 1.57 0.53 10 1.70 0.67 0 0.00 0.00 0 0.00 0.00 0 0.00 0.00
Total 205 3.89 0.64 131 1.55 0.50 124 1.79 0.60 43 2.91 0.78 22 2.82 0.66 21 1.95 0.67
235
4.7 Integrated Supply Chain Management Practices
In India, Agriculture Marketing processes through APMCs, involves number of
intermediaries depending upon the commodities to be traded. But the intermediaries
commonly observed are Farmers, Commission Agents, Stockists, Wholesalers (Pacca
Arhatiya), Processors, Exporters and Retailers. Pacca Arhatiya is the well developed and
powerful intermediaries in the chain and can play a chain leader role in the Indian
agriculture supply chain. Hence, it can initiate the supply chain integration with other
channel members.
In this section researcher has furnished the analysis about the supply chain integration
practices pursued by the wholesalers. The purpose of this study is to answer the following
important issues:
1. The number and types of business processes to integrate
2. The degree to which business processes are being integrated across the supply chains
3. To identify the firms that integrates any processes with other firms in the supply chain
and barriers to the process integration
4. The supply chain network over which they are integrated, and
5. To know the extent of functional integration pursued by the firms.
To answer these questions, the first question was asked to the respondents on the survey
instruments that defined supply chain management as “…the integration of one or more
logistical, marketing, purchasing , or other business processes from end user to original
suppliers that provides products, services, and information that add value for customers.”
236
Respondents were asked whether or not their company currently jointly manage any one
or more business processes. Those who indicated “No” were directed to skip more
probing questions and simply provide the demographic information. The primary purpose
of this question was to identify whether or not a company was actively integrating major
business processes with other members of their supply chains. An affirmative response to
this question did not necessarily mean that the respondent’s company integrated more
than one processes, but simply that some degree of external integration is occurring.
Wholesalers 15 out of the 43, almost 34 percent, integrated one or more business
processes across their supply chain (Table 4.77).
Table 4.77 Proportion of Respondents Jointly Manage one or more business
processes
Frequency Valid Percent
Yes*
15 34.88
No**
28 65.12
Total 43 100
* Respondent indicated that their firm did integrate one or more processes
** Respondent indicated that their firm did not integrate one or more processes
The questions related to rating the extent to which wholesaler jointly managed each of the
9 listed major business activities support the finding further to answer the above stated
questions. Wholesalers rated each process element on a scale that ranged from one to
five, where “one” indicated that a process element was not at all jointly managed and
“five” indicated that it was jointly managed to a great extent.
The mean ratings assigned to each of the 9 process elements are listed in the table 4.78. If
a respondent answered “Yes” to the first question, it was expected that they would rate at
least one process element higher than one.
237
Table 4.78 Degree to Which Process Elements are Jointly Managed
Firms rating “Not at
All”
Supply Chain Process Element N Mean S.D. Number Percent
ISCM8A: Delivery of produce in timely fashion
15 3.20 0.56 0 0
ISCM8B: Improving product quality 15 2.67 0.62 0 0
ISCM8C: Providing information about the
customer order status
15 1.13 0.35 13 86.67
ISCM8D: Demand Forecasting 15 1.20 0.56 13 86.67
ISCM8E: Implementing marketing programmes
with customers
15 1.07 0.26 14 93.33
ISCM8F: Supporting new product development 15 2.80 0.68 0 0
ISCM8G: New Product Development 15 1.20 0.56 13 86.67
ISCM8H: Identifying key markets 15 1.07 0.26 14 93.33
ISCM8I: Reducing fluctuation in customer
demand
15 1.07 0.26 14 93.33
Among the 15 wholesalers who integrated one or more supply chain processes, the
proportions of those that did not jointly manage a given process with other firms ranged
from 0 to 93 percent. The firm’s rating “Not at all” indicates that most of the wholesalers
were integrating only few processes; Delivery of produce in timely fashion, Improving
Quality and Supporting New Product Development. On other hand, almost all of them
were not at all integrating the process elements like, implementing marketing
programmes with customers, Identifying key markets and reducing fluctuation in
customer demand. This suggests that only few wholesalers have integrated two or more
process elements with those of other companies in their supply chain.
238
The mean values reveal the information about the extent of wholesalers jointly managed
various process elements. The mean and standard deviation rating in the table 4.78
reveals each process element was jointly managed to some extent, although no process
was managed to great extent.
Furthermore, to know how many process elements and types of elements the wholesaler
was integrating, the number of process elements that each wholesaler did not jointly
manage at all was counted and a frequency distribution of the count values analysed. The
frequency distribution is shown in table 4.79.
Table 4.79 Distribution of Number of Process Elements Not Jointly Managed At All
Number of process elements
Not jointly managed.
Number of
wholesaler firms
Percent Cumulative
percent
0
1
2
3
4
5
6
7
8
0
0
0
2
0
3
10
0
0
0
0
0
13.33
0
20.00
66.67
0
0
0
0
0
13.33
13.33
33.33
100
100
100
15 100
A count value of zero indicates that a firm jointly managed to some degree each of the 9
process elements, while a value of 8 indicates that it jointly managed only one of the
process elements. The result in table 4.79 shows not a single firm jointly managed all the
processes while only 2 or 13.33 percent wholesalers of the 15 wholesalers sample
identified as integrating business processes across their supply chains jointly managed a
degree of 6 out of 9 process elements with other firms. Moreover 66.67 percent or 10,
239
wholesalers jointly managed to some extent at least three out of nine process elements
suggesting small proportions of wholesalers are practicing supply chain management,
integrate small number of supply chain process elements across their supply chains.
Table 4.80 T-test to know Process Elements are jointly managed
Confidence Level = 95%
Supply Chain Process Element N Mean S.D. Df tcal ttab
ISCM8A: Delivery of produce in timely fashion
15 3.20 0.56 14 15.188 1.7613
ISCM8B: Improving product quality 15 2.67 0.62 14 10.483
ISCM8C: Providing information about the
customer order status
15 1.13 0.35 14 1.430
ISCM8D: Demand Forecasting 15 1.20 0.56 14 1.381
ISCM8E: Implementing marketing programmes
with customers
15 1.07 0.26 14 1.043
ISCM8F: Supporting new product development 15 2.80 0.68 14 10.313
ISCM8G: New Product Development 15 1.20 0.56 14 1.381
ISCM8H: Identifying key markets 15 1.07 0.26 14 1.043
ISCM8I: Reducing fluctuation in customer
demand
15 1.07 0.26 14 1.043
The result of t-test in table 4.80 evaluates whether each process element was jointly
managed with other firms or not. Mean rating equal to 1 reveals that wholesaler was not
attempting to externally integrate the process elements. The analysis revealed that for
only three process elements namely; Delivery of produce in timely fashion, Improving
240
product quality and Supporting new product development, the t-test of mean found value
to be significantly higher than 1. This does not mean that each of these three process
elements was jointly managed to a great degree but simply that the wholesaler who
practiced supply chain management, put in some effort to coordinate each of these
process elements with other firms.
Table 4.81 T-test for Barriers to Supply Chain Integration
Confidence Level = 95%
Barriers to Supply Chain Integration N Mean S.D. df tcal ttab
ISCM10A: Difficult to set well defined
relationship in the process of sharing risks and
rewards.
15 4.67 0.49 14 13.200 1.7613
ISCM10B: Unwilling and uncommunicative
channel members
15 4.73 0.46 14 14.566
ISCM10C: Difficulty in establishing supply
chain wide Information Network
15 4.67 0.26 14 24.876
ISCM10D: Strategic Goals are not
homogeneous
15 4.73 0.46 14 14.566
ISCM10E: Difficulty in measuring the role
and contribution of individual members of
supply chain
15 4.87 0.35 14 20.693
ISCM10F: Operational goals are not
homogeneous
15 4.60 0.51 14 12.151
ISCM10G: Actual and perceived boundaries
of organisation render integration difficult
15 4.73 0.46 14 14.629
ISCM10H: Difficulty in defining clear
guidelines for managing supply chain
alliances.
15 4.73 0.46 14 14.566
ISCM10I: Difficult to set common standard 15 4.13 0.35 14 12.438
241
Further, probing on the barriers wholesaler faced while integrating the processes revealed
that most of the wholesalers experienced a higher level of barriers in the integration
which discouraged them to initiate for integration of supply chain processes. Values in
the table 4.81 show that all the t-test of mean found values to be significantly higher than
3.0.
Table 4.82 T-test for Functional Integration
Confidence Level = 95%
Functional Integration within organisation N Mean S.D t-cal
ISCM9A: Purchase and Production 15 3.93 0.26 29.000
ISCM9B: Purchase and Logistics 15 3.07 0.59 6.977
ISCM9C: Purchase and Marketing 15 4.67 0.49 21.166
ISCM9D: Purchase and Quality Control 15 4.47 0.52 18.500
ISCM9E: Purchase and Finance 15 3.53 0.52 11.500
ISCM9F: Production and Logistics 15 2.87 0.35 9.539
ISCM9G: Production and Marketing 15 3.07 0.59 6.977
ISCM9H: Production and Quality Control 15 4.40 0.51 18.330
ISCM9I: Production and Finance 15 1.87 0.35 -1.468
ISCM9J: Marketing and Quality Control 15 2.73 0.70 4.016
ISCM9K: Marketing and Finance 15 2.93 0.46 7.897
ISCM9L: Marketing and Logistics 15 3.13 0.74 5.890
ISCM9M: Logistics and Quality Control 15 3.33 0.49 10.583
ISCM9N: Logistics and Finance 15 3.40 1.06 5.135
ISCM9O: Quality Control and Finance 15 1.47 0.52 -4.000
Interesting to note that the wholesaler faced higher level of difficulties (barriers) in
process integration but on other hand, they pursued the functional integration at least up
to some extent. The mean values and t-test for mean values in table 4.82 indicates that t-
test for mean values for all variables of functional integration is significantly higher than
2 at 95% confidence level, except variables Production and Finance and Quality Control
and Finance. This does not mean that each of these functional element was jointly
242
managed to a great degree but simply that the wholesaler who practiced supply chain
management, there was some effort to integrate each of these functional elements within
the firm except two.
To answer the fourth question about the network structure over which wholesalers were
practicing supply chain management, the horizontal spans and span radii of wholesalers’
firm’s supply chain was determined. To gain insight regarding this matter, respondents
were asked to indicate with whom and extent to which supply chain management
practices managed by them. The list of members with whom wholesalers managed supply
chain activities is shown in table 4.83
Table 4.83 Intermediaries with whom sample firm manages supply chain activities
Response Category
Intermediaries with whom wholesaler
manages supply chain activities
Yes No Percent Responding
“Yes”
Farmer 02 13 13.33
Commission Agent 10 05 66.67
Stockist 10 05 66.67
Exporter 08 07 53.33
Retailer 07 08 46.67
Processor 15 0 100
Transport Service provider 04 11 26.67
Storage & Warehouse Service Provider 14 01 93.33
243
The majority of the wholesalers reported that their companies practiced supply chain
management with first tier suppliers (Commission agents & Stockists) and customers
(Exporters & Retailers). Nearly 67 percent coordinate with first-tier suppliers, while
almost about 50 percent worked together with first-tier customers. In contrast, the
proportions of wholesalers practicing with second-tier suppliers (Farmers) were much
lower at 13.33 percent.
The data also reflected the wholesaler efforts to integrate the facilitating agencies into
their supply chain. A strong majority, 100 percent and 93 percent of the wholesalers that
practice supply chain management involved Processors and Storage & warehousing
service providers respectively in the external integration of their business processes. Also
26 percent of the wholesalers have integrated with transport service providers.
The data also permitted the estimation of the proportions of firms that pursued supply
chain initiatives of varying horizontal span lengths and span radii. Table 4.84 reports the
horizontal configuration of supply chain relationships reported by the wholesalers, the
span length and span radius associated with each configuration.
Table 4.84 Horizontal Span Length and Span Radius of Sample firms Practicing
Supply Chain Management
Horizontal Configuration Span
Length
Span
Radius
Number
of firms
Valid
Percent
1st-tier supplier and wholesaler
Wholesaler and 1st –tier customer
1st-tier supplier, wholesaler and 1
st –tier customer
2nd
-tier supplier, 1st-tier supplier and wholesaler
Wholesaler, 1st–tier customer and 2
nd-tier customer
2nd
-tier supplier, 1st-tier supplier, wholesaler and
1st-tier customer
Two-tier
Two-tier
Three-tier
Three-tier
Three-tier
Four-tier
One-tier
One-tier
One-tier
Two-tier
Two-tier
Two-tier
0
0
13
0
0
2
0
0
86.67
0
0
13.33
Total 15 100
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Examining the horizontal span length Two-tier indicates that the wholesaler coordinated
with either a 1st-tier supplier or 1
st-tier customer, but not both. No wholesaler was
practicing supply chain management fell into this category.
A three-tier span length indicates that a firm coordinated processes with a 1st-tier supplier
and customer, a 1st-tier and 2
nd-tier supplier, or a 1
st-tier and 2
nd-tier customer. Table 4.84
shows that 13 or about 86 percent of the wholesalers have these horizontal
configurations. This means that the wholesalers’ efforts were not focusing on
coordinating only either inbound process flow-managing inputs from suppliers or
outbound process flow-outputs to customers, but its efforts were focused on coordinating
both the process flows. Similarly, only two or 13 percent of wholesalers have configured
four tiers span length, coordinated processes with 2nd
-tier supplier and customer. This
means wholesalers were not still involving farmers into the channel to a great extent.
When responding wholesalers were grouped according to horizontal span radius, their
proportions suggest that most of the wholesalers that attempted to integrate supply chain
processes pursue initiatives that reach into the first tier of their supply chains. Table 4.86
shows that 87 percent of wholesalers practiced supply chain management have one-tier
span radius and rest have two-tier span radius.
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CHAPTER 5 FINDINGS AND CONCLUSION
5.1 Introduction
5.2 Summary of the Study
5.3 Objectives of the Study
5.4 Limitations of the Study
5.5 Research Methodology
5.6 Reliability Test for the Questionnaire
5.7 Factor Analysis for section-II for sell related variables
5.7.1 The Bartlett’s Test of Sphericity and Measure of Sampling Adequacy
5.7.2 Variables disqualified for the factor analysis for section-II for sell
related variables.
5.7.3 Extracted Factors for section-II for sell related variables
5.8 Factor Analysis for section-II for purchase related variables.
5.8.1 The Bartlett’s Test of Sphericity and Measure of Sampling Adequacy
5.8.2 Variables disqualified for the factor analysis for section-II for
purchase related variables.
5.8.3 Extracted Factors for section-II for purchase related variables
5.9 Factor Analysis for section-III for selection of intermediaries into particular
market-yard (APMC).
5.9.1 The Bartlett’s Test of Sphericity and Measure of Sampling Adequacy
5.9.2 Variables disqualified for the factor analysis for section-III for
selection of intermediaries into particular market-yard (APMC).
5.9.3 Extracted Factors for section-III for selection of intermediaries into
particular market-yard (APMC).
5.10 Hypothesis Testing
5.11 Integrated Supply Chain Management Practice
5.12 Discussion and Major Findings of the Study
5.12.1 Factor Analysis
5.12.2 Hypothesis and ANOVA
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5.12.3 Integrated Supply Chain Management Practices
5.13 Significance of the Study
5.14 Future Scope of the Research
247
5.1 Introduction
The purpose of this chapter is to present the conclusions in light of the research
objectives (Section 3.2). The chapter begins with the summary of the study followed by
objectives and limitations of the study. A brief summary of research methodology
adopted for the study is presented. The chapter also includes the concise outline of the
data analysis and interpretation. Future scope of the research is also discussed. The in
detail discussion and major findings are the thrust of this chapter. The significance of the
research is also simultaneously presented. .
5.2 Summary of the Study
The chapter 1 has begun with background of research of the study. Researcher has
explained the rational for selecting the topic for the study. The detail about the
weaknesses of the Indian agricultural supply chain was furnished. Researcher has
explained the importance of the supply chain management practices for building
competitive advantage of Indian agricultural sector, in the wake of liberalization of
Indian economy and its efforts to integrate with global economy. This chapter also
explained concepts of agricultural marketing and its system, historical development of
agricultural marketing system in India and Gujarat and growth of regulated markets in
last ten, five years planning of Indian economy. Chapter also included the details about
the number of Agricultural Produce Market Committees (APMC), main market yards and
sub market yards in Gujarat, its functions, constitution of committee and total arrivals and
transaction value of all commodities in all the market yards of Gujarat. Growth and
development of APMCs in Gujarat and particularly in North Gujarat was noticeable as
well as most of the market-yards functioning very well. Market yards of North Gujarat
were very well known for the commodities they were trading in. Moreover there was no
previous study conducted about the supply chain management practices of APMCs in
North Gujarat in particular and Gujarat in general.
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Chapter 2 was aimed at developing theoretical framework for the study. The detailed
literature review was presented in this chapter and theory building task was carried out.
The chapter was divided into three parts. i) Supply Chain Management, ii) Agricultural
Supply Chain Management, and (iii) Agricultural supply chain management practices at
APMCs. An understanding of the Supply Chain Management (SCM) concepts in general
and Agriculture Supply Chain Management (ASCM) in particular was developed.
Therefore, it began with a discussion of what is supply chain, its historical development,
its importance, followed by the literature review and discussion on Agriculture Supply
Chain Management, and challenges of managing supply chain practices in agriculture
sectors. Through literature review of international research study as well as national level
research study, researcher has found that the study of integrated supply chain
management practices is at nascent stage. The chapter also has included a presentation of
working model of the market-yard, all functionaries and their role, lacunas of existing
system etc. Chapter was concluded with the explanation of need for collaboration and
integrated supply chain management practices in Indian agricultural sector.
Chapter 3 provides a brief outline of the present research study. It gave detailed picture of
Research Methodology. Scope of the research was included in the chapter. Exploratory
research design was used. The research design included explanation about population
about which study was conducted, sampling techniques, sampling unit, sample and
sample size, data collection procedures, different sources of data – primary as well as
secondary and software used for data analysis. The discussion of data analysis techniques
concluded the chapter.
Data analysis and interpretation of the primary data collected was presented in Chapter 4.
Different tools-frequency and percentage analysis, factor analysis, Analysis of Variance
(ANOVA) and T-test were used for data analysis in detail to draw the conclusion. The
analysis was divided into three major sections. In the first section, factor analysis was
used to extract the Key Important Variables considered by the intermediaries to sell the
commodities, to purchase the commodities and to select the intermediaries into particular
market-yard (APMC) and to group these variables. The naming of the factor was carried
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out based on the grouping of the variables. Analysis of Variance (ANOVA) was applied
in the second section. ANOVA was used to test the hypotheses for the significance.
ANOVA was applied to test the mean difference for the importance given to the Key
Important Variables extracted through factor analysis by all the six intermediaries of the
APMCs of North Gujarat. In the third part, an attempt was made to learn about the supply
chain management practices carried out by the wholesalers of the selected APMCs of
North Gujarat. In addition, the effort has been made to know the extent of process
integration and functional integration. The researcher has also included the analysis of the
difficulties (barriers) faced for the integration of supply chain processes. The t-test was
employed to test the significance of the extent of process elements jointly managed,
barriers to the supply chain process integration and function integration. Chapter
concluded with analysis on horizontal span length and span radius of the firms practicing
supply chain management.
The present chapter talks on the major findings and its implication in the research world.
It highlights the philosophy behind the major findings. The concluding part of the chapter
talks on the applicability of the research to academicians, all the intermediaries and other
functionaries of the market-yards and researchers.
5.3 Objectives of the Study
The objective of this research study is “An in-depth comparative study of supply chain
management practices at selected agriculture produce market committees in North
Gujarat region”
The specific objectives of the research are:
(1) To understand the emergence, development and growth of the APMCs.
(2) To develop an Agri Supply Chain Management perspective.
(3) To learn about the different intermediaries of the agriculture supply chain.
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(4) To know the important variables considered by the intermediaries to select the
particular intermediaries to sell the products.
(5) To know the important variables considered by the intermediaries to select the
particular intermediaries to purchase the products.
(6) To know the important variables considered by the intermediaries to select the
intermediaries into particular market-yard.
(7) To extract the factors which are important for selecting the intermediaries to sell
the commodities.
(8) To extract the factors which are important for selecting the intermediaries to
purchase the commodities.
(9) To extract the factors which are important for selecting the intermediaries into
particular market-yard.
(10) To compare the factors’ importance given by the different intermediaries to sell
and to purchase the commodities as well as to select the intermediaries into
particular APMC of all the APMCs in North Gujarat.
(11) To understand the extent of integrated supply chain management practices
adopted by the wholesalers (Pacca Arhatiya) of the selected APMCs of North
Gujarat.
5.4 Limitations of the Study
Care and attention has been taken to ensure that the research was designed and conducted
to optimise the ability to achieve the objectives of the research. However, researcher
sometimes was unable to conduct study with zero defects due to personal resource
constraints in terms of time, manpower and money, which results in error in data
collection and analysis. Some other limitations of the study are:
• The method adopted for the data collection was non-probability convenience
sampling, hence, the limitations of the convenience sampling automatically
applies to the study.
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• Accuracy of the analysis and interpretation is depending upon the accuracy and
reliability of the sources of the data collected; both primary as well as secondary.
• Policy regulation related to agriculture sector and practices are continuously
changing. Hence, findings are also subject to change over a period of time.
• The fourth section about the integrated supply chain management practices of the
questionnaire has collected the information related to the integrated supply chain
management practices pursued by the wholesalers. The findings of this study are
based on respondents’ perceptions regarding the level of integration their firms
pursue and the supply chain members with whom they coordinate. It was assumed
that respondents had enough knowledge about the integrated practices.
5.5 Research Methodology
The study was conducted in the selected agricultural market-yards (APMC) of four
districts of North Gujarat region of Gujarat State in the context of supply chain
management practices of Agricultural Produce Market Committee (APMC). The
objective of the study was to identify the key important variables which were considered
by the supply chain intermediaries to take the decisions pertaining to sell and/or to
purchase the commodities and to select the intermediaries into particular market-yard.
Also research was focused to compare the importance given to the key important
variables by the different intermediaries. A noble effort was made to know the integration
of supply chain activities carried out by the wholesalers of the market-yards.
Through literature review and personal interviews with the different intermediaries,
officials of the APMCs and Gujarat Agriculture marketing Board, Gandhinagar; large
numbers of different variables have been identified. Through factor analysis less
important or unimportant variables were reduced and group of important variables in the
form of factors were identified. The meaningful naming of these factors was carried out.
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Factors analysis was carried out to get the manageable factors which could be easily
manageable, measurable and controllable compared to large number of variables.
5.6 Reliability Test for the Questionnaire
The factor analysis was applied for variables of the section II (Factors considered for
selection of Supply Chain Intermediaries) and section III (Selection of intermediaries into
particular Market-Yard (APMC)) of the questionnaire. It was essential to measure the
reliability statistics for both the sections of the questionnaire to assess the degree of
consistency between multiple measurements of a variable.
Section II of the questionnaire was further divided into two separate sections of which
first section consists of variables related to selection of intermediaries to sell the products
and second was related to the selection of intermediaries to purchase the products.
Therefore, reliability of variables of all the three parts of the questionnaire was assessed
separately. Researcher has used Cronbach’s Alpha as a measure of reliability for all the
three parts of variables. If the value of Cronbach’s Alpha is greater than or equal to 0.6,
the tool is considered as reliable. The Cronbach’s Alpha value for the variables related to
the selection of intermediaries to sell the products was 0.828 which was greater than
required value. Means the tool developed for this part of the questionnaire was reliable. It
can be further used for statistical analysis to draw the empirical conclusion. The
Cronbach’s Alpha value for the second part of the first section; variables related to the
selection of intermediaries for purchasing the products was 0.892.
Similarly, the value for Cronbach’s Alpha for the third section of the questionnaire;
variables related to the selection of intermediaries into particular market-yard (APMC)
was 0.642. Therefore, the tool designed for variables related to the selection of
intermediaries into particular market-yard was reliable.
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5.7 Factor Analysis for section-II for sell related variables
5.7.1 The Bartlett’s Test of Sphericity and Measure of Sampling Adequacy
The Bartlett’s Test of Sphericity is a statistical test for measuring the presence of
correlations among the variables. It is used to test the null hypothesis that the variables
are uncorrelated in the population. The value of the test for sell related part was 9861.941
with significance level of 0.000. This indicates the statistical significance that, the
correlation matrix has significant correlation among the variables.
Measure of Sampling Adequacy is an index to quantify the degree of inter-correlations
among the variables. This examines the appropriateness of factor analysis. The index
ranges from 0 to 1, reaching 1 when each variable is perfectly predicted without error by
the other variable. MSA value should be above 0.5 for the applicability of the factor
analysis. The MSA value was observed as 0.829 which was greater than necessary
condition value 0.5. Hence, factor analysis could be applied on it.
Anti-image Correlation Matrix for the sell related variables is studied after observing the
overall MSA value is greater than required. Individual variable is studied for the MSA
value. The variable is to be removed from the analysis, if MSA value is below 0.5. If
more than such variables observed, the variable with lowest MSA value is removed first
and the process is revised once again.
5.7.2 Variables disqualified for the factor analysis for section II for sell related
variables.
The variable St: Provides Weather Forecast Information, was removed first from the
study because it posed the lower communalities than required 0.5.
The variables had been grouped based on Varimax Rotation Method. The variables were
summarised into rotated component matrix. Given the sample size of 546, factor loadings
254
of 0.5 and higher would be considered significant for interpretative purposes. Two
variables Sm (Storage & Warehousing services) and So (Quantity to be sold) had a cross-
loading on two factors. And hence they were omitted from the list and Revised Rotated
Factor Loading Matrix was prepared.
Each of the variables in the revised rotated factor loading matrix had a significant factor
loading on only one factor except the variable Sb (Friend/family member) which cross-
loaded on two factors. Therefore it was omitted. Total five factors have been extracted in
the revised rotated factor loading matrix.
The first four factors explained the total variance more than required 60 percent. Hence
researcher had decided to fix four factors from five. Sa (Age-old business relationship),
Sd (Pays best/helps to get best price), Se (Spot/Cash Payment) and Sr (Provides Demand
Information) were omitted from the revised analysis as they shared lower communalities
than required one.
The extracted variables were summarised into the final rotated component matrix. All
variables have loadings more than 0.5. The total variance explained by four factors was
almost 75 percent which was more than required value 60 percent. Based on the grouping
of variables, the four factors were identified for the study.
5.7.3 Extracted Factors for section II for sell related variables
There were four factors identified for section II for sale related variables. They were
Buyer’s Cooperation, Support Services, Quality Management and Relationship.
The first factor was Buyer’s Cooperation. Cooperation among the supply chain members
was required for effective SCM. Cooperation was necessary to develop the trust and
transparency among the chain partners and to create cohesive team of all the
intermediaries rather than working individually. This would reduce the number of
suppliers or buyers and hence reduce transaction cost as well as overall cost of managing
255
the large number of suppliers or buyers. Cooperation would increase the information
sharing among the chain intermediaries, reduce the uncertainty between the supply chain
partners resulting in enhanced performance. It is also needed to reduce the supply chain
inventories and pursue supply chain wide cost efficiencies. This in turn yields a
competitive advantage. Cooperation, collaboration and coordination are the prerequisites
of building the supply chain competitiveness. Optimizing the entire supply chain requires
a level of information sharing, teamwork, cooperation and collaboration among the
participating enterprises.
The second factor, Support Services was made up of variables, Updating price
information, Customer’s location, Provides production estimation information, Financial
assistance and Credit finance.
The total variance explained by this factor was 20 percent. This indicated that
intermediaries consider the importance of support services by 20 percent to sell the
products to particular intermediaries. Support services were value added services,
necessary to enhance the competitiveness of the supply chain. Updating information into
the system enhances the decision making process. The assistance provided by the
intermediaries to other chain intermediaries strengthens the weak link of the chain and
hence ultimately increases the overall productivity of the chain.
The third factor was Quality Management. The quality management is very important for
enhancing the competitiveness of the supply chain. Quality products improve the image
of the company. It is necessary to manage the quality standard as it affects the human
health and life.
And the Fourth factor was Relationship. Relationship management was an integral part of
the supply chain practices. Building and managing the long term relationship with
suppliers as well as customers is very crucial for the long term survival of the business.
Relationship between the chain partners is a prerequisite for the joint actions between the
firms. The perishability of agricultural produce and the increasing globalisation of
256
production and consumption require tightly coordinated chains. This requires that the
suppliers and buyers are working, not with the concept of a single relationship, but
managing sets of relationships as portfolios.
5.8 Factor Analysis for section II for purchase related variables.
5.8.1 The Bartlett’s Test of Sphericity and Measure of Sampling Adequacy
The second part included the purchase related variables. The value of the Bartlett’s test of
sphericity for purchase related part was 8582.864 with significance level of 0.000. This
indicated the statistical significance that the correlation matrix has significant correlation
among the variables.
Measure of Sampling Adequacy value should be above 0.5 for the applicability of the
factor analysis. The MSA value was 0.906 which was greater than required value 0.5.
Hence, factor analysis could be applied on it. The overall MSA value in the Anti-image
Correlation Matrix for all the purchase related variables were greater than required.
5.8.2 Variables disqualified for the factor analysis for section II for purchase
related variables.
Variables Ph (Come first to sell/process), Pc (Trust), Po (Quantity to be
purchased/processed) and Pq (Seller’s location) were taken out of the analysis as it shared
communalities was lesser than required 0.5. Rotation of factor was performed once again
to get the required factor loadings. All the variables shared the required loadings on the
single factors. There were total three factors extracted which together explain more than
80 percent of variance.
5.8.3 Extracted Factors for section II for purchase related variables
There are total three factors extracted from the analysis of the variables related to this
part. These factors were; Support Services, Quality Management and Buyer’s risk.
257
The first factor, Support Services explained almost 50 percent of variance alone. It meant
while selecting the intermediaries to purchase the products, buyers should consider this
factor as most important to select the right one. Support services are needed for the value
creation across the chain. The value creation is supported by clear information flows up
and down the chain. These information flows link suppliers and intermediate customers
with market demands (such as product form, quality and quantity required), and markets
with supply (such as quality and quantity available). Storage and warehouse services are
necessary for the improving the shelf life, maintaining the quality of the products etc.
Credit and Cash/Spot payment reduces the financial risk of the buyers. Therefore, this
factor got the highest attention of the buyers to select the intermediaries to purchase the
products.
Second factor, Quality management is very crucial in value creation. Value creation
occurs through the quality management. This is very vital for building the competitive
advantage.
The third factor was Buyer’s Risk. Buyers experience mostly quality, quantity and default
risks. All can be minimised, if it built and maintained the strong relationship with
suppliers. It can also be minimised by dealing with creditworthy and reliable suppliers.
5.9 Factor Analysis for section III for selection of intermediaries into
particular market-yard (APMC).
5.9.1 The Bartlett’s Test of Sphericity and Measure of Sampling Adequacy
The third section was related to the selection of intermediaries into particular market-
yard. The value of the Bartlett’s test of sphericity for variables of this section was
5842.378 with significance level of 0.000. This indicated the statistical significance that
the correlation matrix has significant correlation among the variables.
258
It was observed that the Measure of Sampling Adequacy value 0.906 is greater than
required value 0.5. Hence, factor analysis could be applied on it. The overall MSA value
in the Anti-image Correlation Matrix for all the purchase related variables were greater
than required.
5.9.2 Variables disqualified for the factor analysis for section III for selection of
intermediaries into particular market-yard (APMC).
Variables Mc (Storage facilities), Mi (Bank facility), Mx (Availability of weather
forecasting information) and Mz (Involvement of Governing body into development of
APMC) were eliminated from the analysis as the communalities values for these
variables were lesser than required 0.5. After this, rotation of factor was performed once
again to get the required factor loadings. The variable Mw (Availability of Demand
Forecasting Information) was removed as its loading is less than 0.5. The revised rotated
component matrix revealed that variable Mm (Types of the Buyers) cross loads on two
factors. Hence, it was also removed from the analysis. There were total five factors
extracted. But the fifth factor had explained only 5.7 percent variance which was very
less and on other hand first four factors together explained the total variance of 61
percent which was more than required 60 percent. Hence, researcher has fixed four
factors instead of five and entire process was revised to get the required loading on four
factors.
The variables Ml (Number of Buyers at the market place), Mo (Connectivity with and
Distance from major Roads, Railway, Ports and Airports) and Mt (Involvement of
Governing Body for disputes settlement) were omitted from the analysis and final rotated
component matrix was developed. All the variables shared the factor loadings more than
0.5 on single factor. All the four factors together explained almost 68 percent of variance.
5.9.3 Extracted Factors for section III for selection of intermediaries into
particular market-yard (APMC).
259
There were total four factors extracted in this section. These were; Return, Value adding
Infrastructure Facility, Demand and Information Support services.
The first factor, Return was very important for any business organisation or individual.
Higher realisation of value of goods and lower the risk of managing the business
activities are crucial for long term survival. Searching for better ways of managing the
financial matters and controlling the risk associated with the same is extremely vital.
Value adding Infrastructure Facility was the second important factor. The necessary
infrastructure facility is required for the value creation. Processing facilities and quality
testing laboratory are important for creating the value for the products. On the other hand
making available the demand information services ensure the smooth flows of material in
the market and reduces uncertainty associated with demand and supply.
Third was the Demand factor. Demand is very important as it determines the preference
of the market yard because of higher price realisation. If the demand at the market yard is
higher than others as well as number of buyers are higher than the others, the
intermediaries prefer mostly the other intermediaries in that particular market-yard. This
is because large numbers of buyers lead to the higher demand which in turn increases
higher realization as well as chances of increased development of trading activities.
The fourth factor was Information Support Services. Apart from operations, quality and
logistics, value creation throughout the chain is supported by the information flows up
and down the chain. This information flow links suppliers and intermediate customers
with market demands and markets with supply. This approach helps the intermediaries to
take the appropriate decision related to the trading activities.
5.10 Hypothesis Testing
The researcher has an objective to know and compare the importance given by all the
intermediaries to the Key Important Variables extracted through factor analysis from the
260
variables of section II (both the parts) and section III of the questionnaire. The hypothesis
related to all these variables were developed and tested in line with the above stated
objectives.
Researcher has selected sample respondents from six selected APMCs of North Gujarat.
Analysis of Variance (ANOVA) was applied to test the significance of the hypothesis.
Researcher has applied the ANOVA with the objective to compare the importance given
by the different intermediaries of all the APMCs in North Gujarat to Key Important
Variables extracted from the factor analysis to select the intermediaries to sell and to
purchase the commodities. ANOVA was also applied to compare the factor’s importance
given by the different intermediaries of all the APMCs in North Gujarat to select the
intermediaries into particular market-yard.
It has been observed that most of the hypothesis for selecting the intermediaries to sell
the products related variables were not significant at 95% confidence level except
financial assistance (F=82.800) and credit finance (F=24.204), customer location
(F=9.496) and quality testing & certificate assistance (F=4.668) for the entity farmer,
stockist and processor respectively.
It has been found that the farmers at Unjha (mean=4.49) gave highest importance to
financial assistance to select the intermediaries to sell the products followed by farmers
at Patan (mean=3.92). Farmers at Siddhpur (2.60), Palanpur (2.65), Thara (2.50) and
Becharaji (2.60) gave modest important to financial assistance for selecting the
intermediaries to sell the products. Further, farmers at Patan (mean=3.80) and Unjha
(3.77) considered almost equal importance of credit finance to select the intermediaries
to sell the products. But, the farmers at Siddhpur (3.20), Palanpur (2.90), Thara (2.77)
and Becharaji (2.80) gave modest importance to credit finance for selecting the
intermediaries to sell the products.
Similarly, stockist of Thara (mean=3.69), Patan (3.53) and Palanpur (3.50) gave more
importance to customer location while selecting the customer to sell the products. This
261
was followed by Siddhpur (3.30) and Becharaji (3.10). Stockist at Unjha (mean=2.80)
gave not much of importance to the customer location to select the customer to sell the
products.
And, processors of Patan (mean=5.00) considered the Quality testing & certificate
assistance as a most important variable for selecting the intermediaries to sell the
products. Similarly, processors of Unjha (4.60) and Siddhpur (4.33) also considered the
significant importance of the variable quality testing & certificate assistance to sell the
products. There were only three locations where processors existed.
These variables can be studied further separately and relevant conclusion can be drawn.
Hypothesis testing for almost all the variables related to selecting the intermediaries to
purchase the products showed no significant results. Only one variable each viz; financial
assistance (F=32.719) for commission agent and quality testing & certificate assistance
(F=4.941) for exporters showed the significant study.
The importance given to the variable financial assistance by the commission agents at
Unjha APMC (3.64) was comparatively high for selecting the intermediaries to purchase
the products. Also the importance given by the commission agents at Patan APMCs
(2.48) was below average while at other places it was very low. The mean values for this
variable for commission agents at Siddhpur, Palanpur, Thara and Becharaji were 1.25,
1.63, 1.28 and 1.29 respectively.
Likewise, the mean rating of the exporters for quality testing & certificate assistance of
Patan (4.69) was considerably higher. This was followed by Unjha (3.80) and Siddhpur
(3.44) which was moderate. There were only three places in North Gujarat where
exporter kind of entity existed for commodities like cumin, fennel and isabgul.
These can be studied separately and insight can be developed by identifying reasons of
significance for these variables.
262
Similarly, the hypotheses were developed and tested for the Key Important Variables of
section III of the questionnaire. It was found that most of the hypotheses for selecting the
intermediaries into particular market-yard (APMC); were not significant. But four
variables viz; Availability of buyers all the time (F=116.603), Financial Assistance by/to
the channel intermediaries (F=105.318), Demand at the market place compared to other
markets (F=49.792) and Transparency into the governing system (F=10.839) for farmers,
one variable Financial Assistance by/to the channel intermediaries (F=39.406) for the
commission agents and a variable Well-known for particular commodities (F=6.403) for
the stockist showed the significant results.
Furthermore, it was found that the mean values for importance given by the farmers to
the variables Availability of buyers all the time; for farmers of Unjha (4.70) and Patan
(4.26) were very high compared to Siddhpur (2.75), Palanpur (2.60), Thara (2.60) and
Becharaji (2.67). Similarly, famers at Unjha (mean=4.63) and Patan (mean=3.82)
considered the higher importance of Demand at market place compared to other markets
Siddhpur (3.00), Palanpur (3.10), Thara (3.23) and Becharaji (2.87) to select the
intermediaries into particular APMC.
Likewise, farmers at Unjha (mean=4.80) considered the significant importance of the
variable Financial Assistance by/to the channel intermediaries to select the
intermediaries into particular APMC. Farmers at Thara (3.53), Patan (3.30) and
Becharaji (3.13) considered moderate importance. And farmers at Siddhpur (2.65) and
Palanpur (2.40) considered less importance of the financial assistance to select the
intermediaries into particular APMC.
And, the farmers at Unjha (mean=4.04) and Patan (4.18) APMCs considered more
importance for the variable Transparency into the governing system followed by
Siddhpur (3.7), Palanpur (3.6), Thara (3.6) and Becharaji (3.6) for selecting the
intermediaries into particular APMC.
263
The commission agents at Unjha (mean=3.76) APMC gave more weightage to the
variable Financial Assistance by/to the channel intermediaries to select the
intermediaries into particular APMC. Commission agents at Patan (2.48) considered
moderate importance but the commission agents at Siddhpur (1.25), Palanpur (1.63),
Thara (1.39) and Becharaji (1.57) considered very less importance for the variable
financial assistance by/to the channel intermediaries to select the intermediaries into
particular APMC.
Again, the mean values for importance given to the variable, well-known, for particular
commodities to select the intermediaries into particular APMC by the stockist of Patan
(4.53), Siddhpur (4.40), Palanpur (4.38), Thara (4.38) and Becharaji (4.20) were
considerably higher, followed by the stockist of Unjha (3.92).
Reasons for these can be studied separately and conclusion can be drawn for decision
making purpose.
5.11 Integrated Supply Chain Management Practice
Researcher has furnished the analysis about the supply chain integration practices
adopted by the wholesalers of the different market-yards to know the number and types
of business processes to integrate, the degree to which business processes were being
integrated across the supply chains, to identify the firms that integrate any processes with
other firms in the supply chain and barriers to the integration, the supply chain network
over which they were integrated, and to know the extent of functional integration within
the organisation.
The study revealed that the only few wholesalers; 34 percent of the sample surveyed;
have initiated to integrate the processes. The result of t-test showed that for only three
processes out of nine processes; Delivery of produce in timely fashion, Improving
product quality and Supporting new product development the t-test of mean found; value
to be significantly higher than 1. This did not mean that each of these three process
264
elements was jointly managed to a great degree but simply that the wholesaler who
practiced supply chain management, there was some effort to coordinate each of these
process elements with other firms.
Further, the analysis about the barriers to supply chain integration has divulged that all
the t-test of mean found values to be significantly higher than 3.0. Opposite to lower level
of external integration, the wholesalers have initiated internal integration i.e. functional
integration within the organisation. T-test for mean values for all variables of functional
integration was significantly higher than 2, at 95% confidence level, except variables
Production and Finance and Quality Control and Finance. But, this did not mean that
each of these functional elements was jointly managed to a great degree but simply that
the wholesaler who practiced supply chain management, there was some effort to
integrate each of these functional elements within the firm except two.
To know the network structure over which wholesalers were practicing supply chain
management, the horizontal spans and span radii of wholesalers’ firm’s supply chain was
determined.
The majority of the wholesalers reported that their companies practiced supply chain
management with first tier suppliers (Commission agent & Stockist) and customers
(Exporters & Retailers). Nearly 67 percent indicated coordinating with first-tier suppliers,
while almost about 50 percent worked together with first-tier customers. In contrast, the
proportion of wholesalers practicing with second-tier suppliers (Farmers) was much
lower at 13.33 percent. A strong majority, 100 percent and 93 percent of the sample
wholesalers that practiced supply chain management involved Processors and Storage &
warehousing service providers respectively in the external integration of their business
processes. Also 26 percent of the wholesalers have integrated with transport service
providers.
Examining the horizontal span length Two-tier indicated that the wholesaler coordinated
with either a 1st-tier supplier or 1
st-tier customer, but not both. No wholesaler practicing
265
supply chain management fell into this category. There were 13 or about 86 percent of
the wholesalers who have three-tier span length. This means that the wholesalers’ efforts
were not focusing on coordinating only either inbound process flow - managing inputs
from suppliers or outbound process flow - outputs to customers, but their efforts were
focused on coordinating both the process flows. Similarly, only two or 13 percent of
wholesalers have configured four tier coordinated processes with 2nd
-tier supplier and
customer. Similarly, 87 percent of wholesalers who practiced supply chain management
had one-tier span radius and rest were having two-tier span radius.
5.12 Discussion and Major Findings of the Study
5.12.1 Factor Analysis
Researcher has extracted the following factors important for the supply chain
management practices of APMCs in North Gujarat.
Buyer’s cooperation, Support services, Quality Management and Relationship were the
important factors, seller should consider while selecting the buyer. Mentzer et al. (2001)
have explained that cooperation between the chain partners, service flows across the
chain, quality management and relationship management are the prerequisites of effective
supply chain management.
Similarly, Support services, Quality Management and Buyer’s risk were the important
factors to be considered by the buyers to select the seller. Support services and Quality
management are crucial for value addition and hence creating competitive advantage.
Risk can be mitigated through the inter-organisational coordination and cooperation.
Mentzer et al. (2001) has mentioned all these factors in their model of supply chain.
Likewise, Return, Value adding Infrastructure Facilities, Demand and Information
Support Services were considerably important factors for selecting the intermediaries into
266
particular market-yard (APMC). Mentzer et al. (2001) has noted, finance as integral part
of the supply chain practices. Return, is part of the finance.
Value adding Infrastructure facility was the new factor extracted from the study. This is
not included in the model of Mentzer et al. (2001). Demand and the information services
are the crucial factors for creating the competitive advantage of the supply chain.
Information sharing increases the transparency into the system and reduces the
uncertainty across the chain.
5.12.2 Hypothesis and ANOVA
The factor analysis helped the researcher to know the Key Important Variables affecting
the supply chain practices. Analysis of Variance presents following results about these
key important variables.
i) Findings for the variables related to the selection of intermediaries to sell the
products
Farmers of all the APMCs gave equal importance to the all the variables except
financial assistance and credit finance to select the intermediaries to sell the products.
Entity Commission agents, Wholesalers and Exporters, each one of all the APMCs
considered an equal importance of Variables, Comes to collect/Makes transport
arrangement, Assurance to purchase, Comes first to purchase, Well known in the
market, Provides demand information, Updating the price information, Customer’s
location, Provides production estimation information, Credit finance, financial
assistance, Grading assistance, Cleaning assistance, Packaging assistance, Quality
testing & certificate assistance, Age-old business relationship and Trust to select the
intermediaries to sell the products.
267
Stockists of all the APMCs considered equal importance of all the variables except
customer location while processors of all the APMCs gave equal importance to all the
variables except quality testing & certificate assistance.
ii) Findings for the variables related to the selection of intermediaries to purchase
the products
Hypothesis testing for the variables - Age-old business relationship, friend/family
member, offers best price or helps to get best price, Spot/cash payment, Credit,
Financial assistance, Cleaning assistance, Grading assistance, Packaging assistance,
Storage & warehouse services, Quality testing & certificate assistance, Provides
demand information, Updating price information, Provides weather forecast
information, Provides production estimation information and Well-known in the
market, for selecting the intermediaries to purchase the products by each of the entity
stockist, wholesaler and processor, have not shown significant results.
Only one variable each viz; financial assistance for commission agent and quality
testing & certificate assistance for exporters showed significant results in the study.
iii) Findings for the variables related to the selection of intermediaries into
particular market-yard (APMC)
All the farmers at six APMCs considered equal importance of variables - Open
Auction System, Quality Testing Laboratory, Availability of Processing Facility, Spot
Payment System, Warehouse Receipt Finance, Well-known for particular
commodities, Availability of information about demand in domestic as well as
international markets, Availability of information about prevailing prices in major
markets, Availability of Production estimation information, Involvement of
Governing body into the development of APMC and Quantity to be purchased/sold.
On other hand, they did not give equal importance to the variables - Availability of
buyers all the time, Financial Assistance by / to the channel intermediaries, Demand
268
at the market place compared to other markets and Transparency into the governing
system for selecting the intermediaries into particular market-yard.
But the wholesalers, exporters and processors each of all the APMCs gave equal
importance to all the key important variables to select the intermediaries into
particular market-yard.
Commission agents and stockists of all the APMCs considered equal importance of
all the key important variables except Financial Assistance by/to the channel
intermediaries and well-known for particular commodities respectively to select the
intermediaries into particular market-yard.
5.12.3 Integrated Supply Chain Management Practices
The research analyses divulged that only 34 percent of the sample wholesalers have
initiated to integrate the processes. Only three processes; Delivery of produce in timely
fashion, Improving product quality and Supporting new product development; where
some effort has been made to integrate the processes by the wholesalers. At the same
time remaining processes; Providing information about the customer order status,
Demand Forecasting, Implementing marketing programmes with customers, New
Product Development, Identifying key markets and Reducing fluctuation in customer
demand; where no efforts have been made to integrate or jointly manage.
It was found that not a single firm jointly managed all the processes. Only 2 or 13.33
percent wholesalers of the 15 wholesalers sample identified; were integrating business
processes across their supply chains; jointly managed to some degree of 6 out of 9
process elements with other firms. Moreover 66.67 percent or 10, wholesalers jointly
managed to some extent at least three out of nine process elements, suggesting that a
small proportions of wholesalers were practicing supply chain management, integrate
small number of supply chain process elements across their supply chains.
269
There were 13 or about 86 percent of the wholesalers have three-tier span length. This
means that the wholesalers’ efforts were not focusing on coordinating only either inbound
process flow-managing inputs from suppliers or outbound process flow-outputs to
customers, but their efforts were focused on coordinating both the sides of the flows i.e
upward as well as downward channel flows. This means that wholesalers have integrated
the supply chain activities with commission agent and stockist (1st tier suppliers) and
exporters and retailers (1st tier customers).
Oppositely, only 2 or 13 percent of wholesalers have configured four tier coordinated
processes with 2nd
-tier supplier and customer. This indicates that the farmers were not
considered integral part of the chain because only 2 or 13.33 percent wholesalers have
pursued the integration with farmers.
Similarly, 87 percent of wholesalers who practiced supply chain management have one-
tier span radius and rest have two-tier span radius.
Study of Mejza and Wisner (2001)1 about the scope and span of the supply chain based
on business process integration and management across the supply chain model indicated
that companies that pursue supply chain initiatives involving broad scope of processes
and long span length might enjoy greater competitive advantage than the firms that tackle
less ambitious initiatives. Initiatives that involve the integration of a single process or
process element or that do not span beyond the first tier of suppliers and customers might
not be enough to ensure the cost or value advantage over supply chains that pursue
broader initiatives.
This study concludes that the wholesaler has attempted to integrate three processes but
the span length of the majority of the wholesalers was not extended beyond the three
tiers. Means integration was made with the first tier of suppliers and customers only. This
narrow scope might not be enough to ensure the customization of the supply chain
1 Michael C. Mejza and Joel D. Wisner, “The Scope and Span of Supply Chain Management”, The
International Journal of Logistics Management, Vol. 12, No. 2, (2001), pp. 37-55.
270
according to market demand, delivery channels, production requirements and market
segments.
The higher level of barriers to supply chain integration discouraged them to initiate for
integration of supply chain processes. But the analysis revealed that the opposite of
conservative approach to process integration, the wholesalers have pursued the broad
scope of functional integration. All the functional elements except Production and
Finance and Quality Control and Finance were integrated by the wholesalers. This did not
mean that each of these functional elements was jointly managed to a great degree but
simply that the wholesaler who practiced supply chain management, there was some
effort to integrate each of these functional elements except two.
5.13 Significance of the Study
Researcher has made a noble attempt to contribute to the body of knowledge in the area
of supply chain management. Through this attempt researcher has made an important
contribution to fill the gap of literature and develop the new proposition in the area of
Agricultural Supply Chain Management as a parent discipline. This is an initiative to
develop new insights for supply chain management practices at Agricultural Produce
Market Committees. Micro level understanding has been developed and attempt has been
made to identify the important factors affecting the supply chain management practices
through APMCs.
Most of the previous research has studied the macro level problem and having economic
overtones and emphasis. This is the first attempt of this kind of study which has been
made by the researcher to extract the important factors which can be easily employable,
controllable and measurable compared to large number of factors.
The result of this study helps the intermediaries to understand the important factors
affecting their business practices and hence, they can delineate the strategies accordingly.
It also facilitates the management of the APMCs (governing body) to understand the
271
important factors considered by the intermediaries to select the APMC. It also helps the
management of APMCs to understand the significance of value adding infrastructure and
support services for the long term growth of the market-yards.
5.14 Future Scope of the Research
In spite of enough care and attention being taken to ensure that the research covers the
comprehensive details, researcher sometimes was unable to cover all affecting areas to
the research area in detail. Therefore, the research raised several questions requiring
further investigation.
The research is restricted to the APMCs of North Gujarat region only and limited to
trading practices of three selected commodities i.e. cumin, fennel and isabgul only.
Significant opportunities exist in examining the supply chain management practices of
APMCs of Gujarat state and different states of India. Applicability of the research can be
checked to individual APMCs only, as well as all the APMCs of the state as well as
country and separate conclusions can be drawn. Research can also be extended to study
the supply chain practices of all individual commodities.
Researcher has left the study area of impact of cooperatives on supply chain practices.
Interesting opportunities exist for studying the structures and development of
cooperatives and role in development of APMCs, its role in strengthening the supply
chain competitiveness etc.
The present research has gone some way to uncovering the complexity of supply chain
dynamics in the agricultural sector. It proposes integrated supply chain practices of
wholesalers. Further research can extend the framework and refine the findings.
The study is restricted to understand the factors affecting the selection of intermediaries
to sell and to purchase the commodities. Significant avenues exist for studying the buyer-
supplier dyadic relationship, power, conflicts etc affecting the supply chain management
272
practices. Research has covered the factors affecting the selection of intermediaries into
particular market-yards. Further research can be extended to understand the development
of infrastructure facilities and supply chain competitiveness.
Researcher has covered six major intermediaries of the supply chain for the study.
Research can be extended covering all the primary intermediaries as well as secondary
and support services providers too.
273
Bibliography
Abbott, J. C. “Marketing an Accelerator of Economic Growth” in proceedings,
Agriculture Marketing Conference (MFA). Nepal: Ministry of Food and
Agriculture, Government of Nepal, June, 1972. 15-28.
Acharya, S. S. “Regulation of Agricultural Produce Markets: Some Observations on its
Impact”, Development Policy and Administration Review, Vol. 11, No. 2, (July-
December,1985).
________. Agriculture Production, Marketing and Price Policy in India: A study of
Pulses. New Delhi: Mittal Publishers, 1988. 506 pp.
________. “Marketing Environment for Farm Products: Emerging Issues and
Challenges”, Indian Journal of Agricultural Marketing, Volume 8, No. 2, (July-
December, 1997), pp. 149-74.
________. Agricultural Marketing in India: Millennium Study of Indian Farmers.
Volume 17. New Delhi: Government of India, Academic Foundation, 2004. 259
pp.
________. Agriculture Marketing and Rural Credit: Status, Issues and Reform Agenda.
New Delhi: Asian Development Bank Report, 2005. 36 pp.
________. Agriculture Marketing and Rural Credit for Strengthening Indian Agriculture.
India Resident Mission Policy Brief Series. New Delhi: Asian Development
Bank, 2006. 14 pp.
Acharya, S. S. and Agarwal, N. L. Agricultural Marketing in India. Fourth Edn. New
Delhi: Oxford and IBH.
Agarwal, N. L. and Meena, B. L., “Agricultural Marketing in India: Performance of
Cumin Marketing in Rajasthan”, Bihar Journal of Agricultural Marketing, Vol. 5,
No. 3, (September-December, 1997), 319-28.
Agrawal, N. L. and Singh, N. “Cumin seed marketing in Rajasthan”, Agriculture
Marketing, Vol. 46, No. 1, (2003), 36-42.
Akkermans, H.; Bogerd,P. and Vos, B. “Virtuous and vicious cycles on the road towards
international supply chain management”, International Journal of Operations &
Production Management, Vol. 19, Nos. 5/6, (1999), 565-581.
274
Anand, K. S. and Mendelson, H. “Information and organisation for horizontal
multimarket coordination”, Management Science, Vol. 43, No. 12, (1997), 1609-
1627.
Anderson, E. and Narus, J. A. "A Model of Distributor Finn and Manufacturer Firm
Working Relationships," Journal of Marketing, Vol. 54, (January, 1990), 42-58.
Arya, Anita. Agriculture Marketing in Gujarat. New Delhi: Concept Publishing, 1993.
156 pp.
Asturker, B. M. and Deole C. D., “Producers’ Share in Consumers Rupee”, Indian
Journal of Agriculture Economics, Vol. 40, No. 3, (1985).
Bacon, A.; Lapide,L. and Suleski, J. Supply Chain Collaboration Today: It’s a Tactic,
Not a Strategy. Boston: AMR Research Inc., 2002.
Bailey, W. C. “Applying SCOR in a Vertical Industry-Food and Agriculture”, World
Supply Chain Council Annual Meeting. Palmerston North, New Zealand: Massey
University, 2001.
Balakrishnan, P.; Golait R.; and Kumar P. Agricultural Growth in India since 1991.
Mumbai: Reserve Bank of India, 2008.
Ballou, R. H.; Gilbert, S. M. and Mukherjee, A. “New managerial challenges from supply
chain opportunities”, Industrial Marketing Management, Vol. 29 No. 1, (2000), 7-
18.
Bapna, S. L. and Rao K. R. Supply and Price outlook of Crops: A study based on Pre-
harvest Market Information in Gujarat. New Delhi: Oxford and IBH, 1987.
Bask, A. and Juga, J. “Semi-integrated supply chain: towards the new era of supply chain
management“, International Journal of Logistics, Research and Applications,
Vol. 4, No. 2, (2001), 137-152.
Beamon, B. M. “Supply Chain Design and Analysis: Models and Methods”,
International Journal of Production Economics, Vol. 55, No. 3, (1998), 281-294.
Bechtel, Christian and Jayaram, Jayanth "Supply Chain Management: A Strategic
Perspective," International Journal of Logistics Management, Vol. 8, No. 1,
(1997), 15-34.
275
Beulens, A. J. M.; Coppens, L.W.C.A. and Trienekens, J. H., “Traceability requirements
in food supply chain networks”, Wageningen: Wageningen University, 2004.
Working Paper.
Bhatt, B. D.; Antaniasd K. L. and Shiyani R. L. “An Analysis of Arrivals and Prices of
Important Vegetable Crops in Ahmedabad Regulated Market in Gujarat State”,
Indian Journal of Agricultural Marketing, Vol. 1, No. 1, (June 1988).
Bhinde, S., et al., “Structural Changes in an Agricultural Assembling Market: A Case
Study of Arecanut Market in Mangalore, Karnataka State”, Indian Journal of
Agriculture Economics, Vol. 36, No. 2, (1981), 25-34.
Bowersox, D. J., "Lessons Learned from the World Class Leaders," Supply Chain
Management Review, Vol. 1, No. 1, (1997), 61-67.
Bowersox, D. J. and Closs D. C., Logistical Management: The Integrated Supply Chain
Process,McGraw-Hill Series in Marketing. New York: The McGraw-Hill
Companies, 1996.
Bowersox, D. J.; Closs, D. C. and Stank, T. 21st Century Logistics: Managing Supply
Chain Integration a Reality .Oakwood, IL: Council of Logistics Management,
1999.
Bowersox, D. J.; Carter P. L., and Monczka R. M., "Material Logistics Management,"
Internal Journal of Physical Distribution and Logistical Management, Vol. 15,
No. 5, (1985), 27-35.
Bucklin, L. A theory of distribution channel structure. Berkley CA: Iber Special
Publications, 1966.
Byron, A. G. et. al., “The other side of Outsourcing”, The McKinsey Quarterly, No. 1,
(2002), 52-63.
Carman, J. M. and Langeard, E. "Growth Strategies of Service Firms", Strategic
Management Journal, Vol. 1, (1980), 7-22.
Carter, C. R. and Ellram, L. M. “Reverse logistics: a review of the literature and a
framework for future investigation”, Journal of Business Logistics, Vol. 19 No. 1,
(1998), 85-102.
276
Carter, J. R.; Ferrin B. G. and Carter C. R. “The effect of less-than-truckload rates on the
purchase order lot size decision”, Transportation Journal, Vol. 34, No. 3, (1995),
35-44.
Chakravarthy, Kalyan, et al., Agribusiness in Gujarat: Unleashing the potential.
Mumbai: Confederation of Indian Industry-Yes Bank Knowledge Initiative, 2007.
128 pp.
Chandrashekar, A. and Schary P. B. “Towards the virtual supply chain: the convergence
of IT and organization”, International Journal of Logistic Management, Vol. 10,
No. 2, (1999), 27-39.
Changing Gears: Retailing in India. Economic Times Knowledge Series, Mumbai:
Economic Times Intelligence Group, 2003.
Charan, A. S.; Seetharaman S. P. and Bapna S. L. “Agriculture Marketing System in
Gujarat: A perspective.” A paper read at Fifteenth Gujarat Economic Conference,
Surat, October-November 1983. (Photocopy).
Chatterjee, D. R. and Bhattacharya K. “A note on Marketing of Rice in Burwada District
of Best Bengal: An enquiry of its Spatial and Seasonal Pricing Efficiency”, Indian
Journal of Economics, Vol. 46, No. 2, (1986), 125-135.
Chhabilendra, Roul. Bitter to Better Harvest: Post Green Revolution Agriculture and
marketing Strategy for India. New Delhi: Northern Bloc Centre, 2001, 409 pp.
Christopher, M. Logistics and Supply Chain Management. London: Pitman Publishing,
1992.
________. Logistics and Supply Chain Management: Strategies for Reducing Cost and
Improving Service, London: Financial Times, 1998. 294 pp.
________. Logistics and Supply Chain Management: Creating Value-Adding Networks.
Harlow: Pearson Education, 2005. 305 pp.
Coase, R. H. “The nature of the firm”, Economica, Vol. 4, (1937), 386-405.
Cohen R. The Economics of Agriculture. Cambridge Economic Handbook, Cambridge
University Press, 1965.
Colin, Armistead and Philip Rowland. “Managing by Business Processes”, in Colin,
Armistead and Philip Rowland (Ed.), Managing Business Processes: BPR and
Beyond. Chichester: John Wiley & Sons, 1996, 46-49.
277
Combs, J. G. and Jr. Ketchen, D. J. "Explaining Inter-firm Cooperation and Performance:
Toward a Reconciliation of Predictions from the Resource-Based View and
Organizational Economics", Strategic Management Journal, Vol. 20, (1999), 867-
888.
Cooper, D. and Schindler, P. Business Research Methods. New Delhi: TMH Publication,
2008, 746 pp.
Cooper, M. C. et. al., “Meshing Multiple Alliances”, Journal of Business Logistics, Vol.
18, No. 1, (1997), 67-89.
Cooper, M. C; Lambert, D. M., and Pagh, J. D., "Supply Chain Management: More Than
a New Name for Logistics," The International Journal of Logistics Management,
Vol. 8, No. 1, (1997), 1-14.
________. “Supply Chain Management: Implementation Issues and Research
Opportunities”, The International Journal of Logistics Management, Vol. 9, No.
2, (1998), 1-19.
Cotton and Grain markets Act of Hyderabad assigned District, 1897 or so called Berar
Law.
Council of Logistic Management (1998), available at www.cscmp.org
Council of Supply Chain Management Professionals (CSCMP) (2007), available at:
http://www.cscmp.org/Website/AboutCSCMP/Definitions/Definitions.asp
Croom, S.; Romano, P. and Giannakis, M. "Supply chain management: an analytical
framework for critical literature review", European Journal of Purchasing &
Supply Management, Vol. 6 No.1, (2000), 67-83.
Datt, Ruddar and Sundharam, K. P. M. Indian Economy. Revised 54th edn. New Delhi:
S. Chand & Co Ltd, 2006, 796 pp.
Davis, T. “Effective Supply Chain Management”, Sloan Management Review, (Summer,
1993), 35-46.
Department of Agriculture and Cooperation, Government of Gujarat. Gujarat Agro vision
2010:Action Plan. Gandhinagar.
Department of Agriculture, Ministry of Agriculture and Irrigation, Government of India.
National Commission on Agriculture-Abridge Report. New Delhi, 1976. 748 pp.
278
Department of Agriculture & Cooperation, Ministry of Agriculture, Government of India,
Shankarlal Guru Report on Agricultural Marketing Reforms. New Delhi, June
2002. Available on www.indiabudget.nic.in.
Department of Agriculture and Cooperation, Ministry of Agriculture, Government of
India. Annual Report, 2006-07[Agricultural Marketing]. New Delhi: The
Manager of Publications, 2007. Available on
http://agricoop.nic.in/AnnualReport06-07/AGRICULTURAL%20MARKETING.pdf
Department of Agriculture and Cooperation, Government of Gujarat. Regulation of
Agricultural Produce Market and Market Committee in Gujarat State: Annual
Report. Gandhinagar: Agriculture Marketing Board, 2009.
Davenport, T. H. Process Innovation, Reengineering Work through Information
Technology, Boston: Harvard Business School Press, 1993.
Davenport, T. H. and Short, J. E. "The new industrial engineering: information
technology and business process redesign", Sloan Management Review, Vol. 31
No.4, (1990), 11-27.
Dong, M. “Development of supply chain network robustness index”, International
Journal of Services Operations and Informatics, Vol. 1, No. 1, (2006), 54-66.
Dowst, Somerby, "Quality Suppliers: The Search Goes On," Purchasing, (January,
1988), 94A4- 12.
Drozdowski, Ted E., "At BOC They Start With the Product," Purchasing, (March, 1986),
62B5- 11.
Dwyer, R. E.; Schurr P. H., and Oh Sejo, "Developing Buyer-Seller Relationships,"
Journal of Marketing, Vol. 51, (April, 1987), 11-27.
Easton, R. “Seizing the Supply Chain Opportunity in Asia”, Ascet, Vol. 4, (2002).
Edwards, P.; Peters, M. and Sharman, G. “The effectiveness of information systems in
supporting the extended supply chain”, Journal of Business Logistics, Vol. 22,
No. 1, (2001), 1-27.
Ellram, L. M. and Cooper, M. C. “Supply chain management partnerships and the
shipper-third party relation”, International Journal of Logistics Management, Vol.
5, No. 1, (1990), 45-53.
279
________. "Characteristics of Supply Chain Management and the Implication for
Purchasing and Logistics Strategy," The International Journal of Logistics
Management, Vol. 4, No. 2, (1993), 13-24.
Elhance, D. N. and Aggarwal, B. M. Fundamentals of Statistics. Allshabad: Kitab Mahal,
2005.
Fearne, A.; Hughes, D. and Duffy, R. “Concepts of Collaboration: Supply Chain
Management in a Global Food Industry”. In: Eastham J, Sharples L, Ball S (ed.).
Food Supply Chain Management: Issues for the Hospitality and Retail Sectors.
London: Oxford Publications, 2001. 55-89.
Fernie, J. “International comparisons of supply chain management in grocery retailing”,
Service Industries Journal, Vol. 15, No. 4, (1995), 134-47.
Finch, B. J. Operations Now: Profitability, Processes, Performance. 2nd edn. Irwin:
McGraw-Hill Publication, 2006. 650 pp.
Fischer, B. M. “European model of agriculture.” National Parliaments Conference-
European Model of Agriculture, Helsinki, October, 2006. Available on
http://europa.eu/rapid/pressReleasesAction.do?reference = SPEECH/06/589
Fischer, C. et. al. “Agri-food chain relationships in Europe – empirical evidence and
implications for sector competitiveness”, paper read at 12th Congress of the
European Association of Agricultural Economists–EAAE, 2008.
Forrester, J. W. “Industrial dynamics: a major breakthrough for decision makers”,
Harvard Business Review, (July-August, 1958), 37-66.
Galbraith, J. Organization Design. Philippines: Addison-Wesley Publishing Company,
1977, 426 pp.
Gentry, Julie J. and Vellenga, David B. "Using Logistics Alliances to Gain a Strategic
Advantage in the Marketplace", Journal of Marketing Theory and Practice, Vol.
4, No. 2, (1996), 37-43.
Giunipero, Lawrence C. and Brand, Richard R., "Purchasing's Role in Supply Chain
Management," The International Journal of Logistics Management, Vol. 7, No. 1,
(1996), 29-37.
280
Global Logistics Research Team at Michigan State University. World Class Logistics:
The Challenge of Managing Continuous Change. Oak Brook, IL: Council of
Logistics Management 1995.
Government of India, Planning Commission, “First Five Year Plan”, 243-244.
Government of India, Planning Commission, “Second Five Year Plan”, 276-281.
Government of India, Planning Commission, “Third Five Year Plan”, 321 p.
Government of India, Planning Commission, “Fourth Five Year Plan”, 142-143.
Government of India, Planning Commission, “Fifth Five Year Plan, part II”, 83-91.
Government of India, Planning Commission, “Sixth Five Year Plan”, 112 p.
Government of India, Planning Commission, “Seventh Five Year Plan, Part II”, 20 p.
Government of India, Planning Commission, “Eighth Five Year Plan, Part II”, 11-12.
Government of India, Planning Commission, “Ninth Five Year Plan, Part II”, 450 p.
Government of India, Planning Commission, “Tenth Five Year Plan, Part II”, 550-551.
Greene, Alice H., "Supply Chain of Customer Satisfaction," Production and Inventory
Management Review and APICS News, Vol. 11, No. 4, (1991), 24-25.
Gujarat Agriculture-A Synoptic View, Reading material for “Training Program on Agri-
clinic and Agri-Business Centers”. EDI Gandhinagar/MANAGE, August 2004.
Gummesson. Total Relationship Marketing. Butterworth-Heinemann, Oxford
Publication, 1999.
Hair, Joseph et al. Multivariate Data Analysis. 6th edn. New Delhi: Pearson Education
Publication, 2009. 785 pp.
Hakansson, Hakan and Snehota, Ivan. Developing Relationships in Business Networks,
London: Routledge, 1995. 418 pp.
Handfield, R. B. and Nichols E. L. Jr. Introduction to Supply Chain Management. New
Jersey: Prentice Hall, 1999. 183 pp.
Harland, C. “Supply chain management: relationships, chains and networks”, British
Journal of Management, Vol. 7, (1996), 63-80.
Heide, J. B. and Johno George. "Alliances in Industrial Purchasing: The Determinants of
Joint Action in Buyer-Supplier Relationships," Journal of Marketing Research,
Vol. 27, (Winter, 1990), 24-36.
281
Hewitt, F. “Supply Chain Redesign”, The International Journal of Logistics
Management, Vol. 5, No. 2, (1994), 1-9.
Hines, P. et. al., Value Stream Management - Strategy and Excellence in the Supply
Chain. Harlow: Prentice Hall, 2000.474 pp.
Hobbs, J. E.; Kerr, W. A. and Klein, K. K. “Creating International Competitiveness
through Supply Chain Management: Danish Pork”, Supply Chain Management:
An International Journal, Vol. 3 No. 2, (1998), 68-78.
Hoda, M. Agriculture Marketing in Backward Regions. New Delhi: Rajat Publication,
2006.
Horvath, L. “Collaboration: the key to value creation in supply chain management”,
Supply Chain Management, Vol. 6, No. 5, (2001), 205-207.
Houlihan, John B., "International Supply Chains: A New Approach," Management
Decision, Vol. 26, No. 3, (1998), 13-19.
Indiresan, P. V. Vision 2020: What India can be, and How to make it happen, 1st edn.
Hyderabad: ICFAI Press, 2003. 125 pp.
Jagdish, A. and Martin, S. “Agricultural Marketing and Agribusiness Supply Chain
Issues in Developing Economies: The Case of Fresh Produce in Papua New
Guinea.” Paper read at New Zealand Agricultural and Resource Economics
Society, New Zealand, August, 2006. 22 pp. (Photocopy)
Jasdanwalla, Z. Y. Market Efficiency in Indian Agriculture. New Delhi: Allied Publishers
Pvt. Ltd, 1985. 132 pp.
Johannson, L. “How can a TQEM approach can add value to your supply chain?” Total
QualityEnvironmental Management, Vol. 3, No. 4, (1994), 521-530.
Jones, T. and Riley, D. W., "Using Inventory for Competitive Advantage through Supply
Chain Management," International Journal of Physical Distribution and
Materials Management, Vol. 15, No. 5, (1985), 16-26.
Joshi, V.R. Regulated Markets in Gujarat. Vallabh Vidyanagar: Sardar patel University,
Gujarat, 1971.
________. Regulated Markets in Gujarat. Nadiad Kaira District Cooperative Union.
1971, 364 pp.
282
Kaufman, R. “Nobody wins until the consumer says, ‘I’ll take it’”, Apparel Industry
Magazine, Vol. 58 No. 3, (1997), 14-16.
Kenneth, J. et. al., “Supply chain management: the case of a UK baker preserving the
identity of Canadian milling wheat”, Supply Chain Management, Vol. 3, No. 3,
(1998), 157-166.
Khols, R. L. Marketing of Agriculture Products. 3rd
edn. New York: The MacMillan
Company, 1967.
Kohl, R. L. and Joseph, N. U. Marketing of Agriculture Products. 6th
edn. New York:
Macmillan, 1985. 544 pp.
Kothari, C. R. Research Methodology: Method and Techniques. New Delhi: New Age
International Publication, 1999, 418 pp.
Kulkarni, K. R. Agriculture Marketing in India. Volume. 1. 1956. 563 pp.
La Londe, Bernard J. “Supply Chain Management: Myth or Reality?” Supply Chain
Management Review, Vol. 1, (Spring, 1997), 6-7.
La Londe, Bernard J. and Masters James M., "Emerging Logistics Strategies: Blueprints
for the Next Century," International Journal of Physical Distribution and
Logistics Management, Vol. 24, No. 7, (1994), 35-47.
Lambert, D. M.; Stock J. R. and Ellram L. M. Fundamentals of Logistics Management.
Boston: Irwin/McGraw-Hill, 1998.
Lambert, D. M.; Giunipero L. C. and Ridenhower, G. J. Supply Chain management: A
key to achieving Business Excellence in the 21st Century, (1997), unpublished
manuscript.
Lamming, R. Beyond partnership strategies for innovation and lean supply. London:
Prentice Hall, 1993. 293 pp.
Langley, John C. Jr. and Holcomb Mary C., "Creating Logistics Customer Value,"
Journal of Business Logistics, Vol. 13, No. 2, (1992), 1-27.
Lassar, W. and Zinn, W. "Informal Channel Relationships in Logistics," Journal of
Business Logistics, Vol. 16, No. 1, (1995), 81-106.
Lawrence, A. “Customer power forces supply chain integration”, Works Management,
(April, 1997), 43-47.
283
Lee, H. and Billington, C. “Material management in decentralized supply chains”,
Operations Research, Vol.41, No. 5, (1993), 835-852.
________.“The Evolution of Supply Chain -Management Models and Practice at
Hewlett-Packard”, Interfaces, Vol. 25, No. 5, (September-October, 1995), 42-63.
________.“Creating value through supply chain integration”, Supply Chain Management
Review, Vol. 4, No. 4, (2000), 30-36.
Lele, U. J. Working of Grain Markets in Selected States of India 1955-56 to 1965-66.
Ithaca: Cornell University, Department of Agriculture Economics, 1968.
Occasional Paper No. 12.
Lewis, H. T.; Culliton, J. W. and Steele, J. D. The Role of Air Freight in Physical
Distribution. Boston: Harvard University, Division of Research, Graduate School
of Business Administration,1956.
Lewis, I. and Talalayevsky, A. "Logistics and Information Technology: A Coordination
Perspective," Journal of Business Logistics, Vol. 18, No. 1, (1997), 141-57.
________. “Improving the inter-organisational supply through optimization of
information flows”, The Journal of Enterprise Information Management, Vol. 17,
No. 3, (2004), 229-237.
Lusch, Robert F. and Brown, James, "Interdependency, Contracting, and Relational
Behavior in Marketing Channels," Journal of Marketing, Vol. 60, (October,
1996), 19-38.
Madaliya, V. K. “Functioning of Regulated Markets at Surat and Its Impact”, Indian
Journal of Agricultural Marketing, Vol 2, No. 1, (June 1988).
Malhotra, N. Marketing Research: An Applied orientation. 5th edn. New Delhi: Pearson
Education, 2008. 960 pp.
Malone, T. W. and Crowston, K., “The interdisciplinary study of coordination”, ACM
Computer Surveys, Vol. 26, No. 1, (1994), 87-119.
Manrodt, Karl B.; Holcomb, Mary C., and Thompson, Richard H. "What's missing in
Supply Chain Management?" Supply Chain Management Review, Vol. 1, No. 3,
(1997), 80-86.
Mason-Jones, R. and Towill, D. R. "Shrinking the supply chain uncertainty circle",
Control, (1998), 17-22.
284
Mejza, M. C. and Wisner, J. D. “The Scope and Span of Supply Chain Management”,
The International Journal of Logistics Management, Vol. 12, No. 2, (2001), 37-
55.
Metz, P. J. “Demystifying Supply Chain Management.” Supply Chain Management
Review, (Winter 1998), 1-11.
Mentzer, J. T., "Managing Channel Relations in the 21st Century," Journal of Business
Logistics, Vol. 14, No. 1, (1993), 27-42.
________. Supply Chain Management. New Delhi: SAGE Publications, 2001, 512 pp.
Mentzer, J. T., et. al., "Defining Supply Chain Management," Journal of Business
Logistics, Vol. 22, No. 2, (2001),1-26.
Ministry of Agriculture and Rural Development, Government of India. Activities of
Directorate of Marketing and Inspection, 1985, 2-3.
Ministry of Agriculture, Government of India. National Agriculture Policy. New Delhi,
2000. Available on
http://www.unescap.org/rural/doc/pai/Vol%2010%20No%204%20%20October%
20December%202000/art7.pdf
Ministry of Agriculture, Government of India. Report of Task Force on Agriculture
Marketing Reforms, New Delhi, 2002. Available on
http://agmarknet.nic.in/taskrep.htm
Mohanty, R. P. and Deshmukh, S. G., “Supply Chain Management: Theories and
Practices”, Biztantra, (2005), Delhi.
Monczka, R; Trent, R., and Handheld, R. Purchasing and Supply Chain Management.
Cincinnati: South-Western College Publishing, 1998.
Morton, R. “Learning from the past to shape the future”, Transportation and
Distribution, Vol. 38 No. 1, (1997), 84-85.
Nargundkar, R. Marketing Research: Text and Cases. New Delhi: TMH Publication,
2004. 494 pp.
Newman, W. R.; Hanna, M. and Maffei, M. J. “Dealing with the uncertainties of
manufacturing: flexibility, buffers and integration”, International Journal of
Operations & Production Management, Vol. 13, No. 1, (1993), 19-34.
285
Novack, Robert A.; Langley, John C. Jr. and Rinehart, L M. Creating Logistics Value.
Oak Brook, IL: Council of Logistics Management. 1995.O
“Overcoming Communication Barriers”, Transportation and Distribution, Vol. 39, No.
10, (1998), 91-94.
Pagell, M. “Understanding the factors that enable and inhibit the integration of
operations, purchasing and logistics”, Journal of Operations Management, Vol.
22, (2004), 459-487.
Panneerselvem, R. Research Methodology. New Delhi: Prentice-Hall India Ltd, 2005,
528 pp.
Parnell, C. “Supply chain management in the soft goods industry”, Apparel Industry
Magazine, Vol. 59, No. 6, (1998).
Patel, A.; Sharma, K. and Pandya, M. Twisting Tale of APMC. A case is developed and
presented in the 3rd
National Level Case Writing Workshop, V M Patel Institute
of Management, Ganpat University, Kherva, April, 2008.
Patel, S. K. “Problems of Tobacco Marketing in Kheda District.” A paper read at the
Fifteenth Gujarat Economic Conference, Surat, October-November 1983.
(Photocopy).
Patnaik, K. and Shankar, U. “Economioc Performance of Groundnut Marketing
Channels: A Case Study of Rayalaseema Region of Andhra Pradesh”, Indian
Journal of AgricultureEconomics; Vol. 40, No. 1, (1985), 26-35.
Peck, H. “Resilience in the UK Food & Drink Industry: Research Design and
Methodology.” Oslo: The Nordic Logistics Research Network Conference
Proceedings, June 2006.
Putzger, I. “All the ducks in a row”, World Trade, Vol. 11, No. 9, (1998), 54-56.
Quiett, W. F. ‘‘Embracing Supply Chain Management’’, Supply Chain Management
Review, (2002), 40-47.
Quoted in Kahlon, A.S. and George, M.V. Agriculture and Price Policies. New Delhi,
1985, Table 4.1, p. 39.
Rajagopal. “Economics of Linseed Marketing in Madhya Pradesh: A Case Study”,
Agriculture Situation in India, Vol. 20, (1985), 50-60.
286
________. State and Agriculture Trade. Delhi: Renaissance Publishing House, 1989, 156
pp.
________. “Development of Agricultural Marketing in India.” In Jagdish Prasad (Ed.),
Encyclopedia of Agricultural Marketing: Concept, Issue, Problems & Prospects.
New Delhi: Mittal Publications, 1999, Volume I, 338 pp.
Ranade, C. G.; Rao, K. H. and Shah, D. C. Groundnut Marketing: A study of Cooperative
and Private Trade. CMA Monograph No. 92. Ahmedabad: Indian Institute of
Management, 1982.
Raynaud, E.; Sauvee, L. and Valceschini, E., “Alignment between Quality Enforcement
Devices and Governance Structures in the Agro-food Vertical Chains”, Journal of
Management and Governance, Vol., 9, (2005).
Redman, L. V. and Mory, A. V. H. The Romance of Research. 1923.
Reiner, G. and Trcka, M. “Customized supply chain design: Problems and alternatives for
a production company in the food industry. A simulation based analysis”,
International Journal of Production Economics, Vol. 89, (2004), 217–229.
Report on FMCG, New Delhi: Investment Information and Credit Rating Agency, March
2001.
Ross, D. F. Competing through Supply Chain Management. New York: Champan & Hall
Publications, 1998, 365 pp.
Russell, K. ‘‘Supply Chain Management’’, Computerworld, Vol. 35, No. 51, (2001).
Sakthivel, N. and Selvaraj, A. “Farmers’ Perception towards Regulated Markets: A Case
Analysis”, Financing Agriculture-Journal of Agriculture & Rural Development,
Vol. 41, 2009, p.7
Salcedo, Simon and Grackin, Ann, "The e-Value Chain”, Supply Chain Management
Review, Vol. 3, No. 4, (2000), 63-70.
Saxena, P. Marketing and Sustainable Development. New Delhi: Rawat Publication,
2003. 223 pp.
Sharma, M.; Patel, A. and Pandya, M., “Public Public Private Partnership (PPP)
Approach-for sustainable development of APMCs in Gujarat.” Conference
Proceedings: International Conference on Global Competition and
287
Competitiveness of Indian Corporates, IIM-Kozhikode, May, 2007. 6-7.
(Photocopy).
Sheombar, H. S. Understanding logistics co-ordination–a foundation for using EDI in
operational (re)design of dyadical value adding partnerships. Tilburg:
Dissertation KUB, Tutein Bolthenius,`s Hertogenbosch, Tilburg University, 1995.
Silver, E. A.; Pyke, D. F. and Peterson, R. Inventory Management and Production
Planning and Scheduling. 3rd edn. New York: John Wiley & Sons, 1998.
Spekman, R. E. "Strategic Supplier Selection: Understanding Long-Term Buyer
Relationships," Business Horizons, Vol. 31, (July-August, 1988), 75-81.
Spekman, R. E.; Kamauff, J.W. Jr. and Myhr, N, “An empirical investigation into supply
chain management: A perspective on partnerships”, Supply Chain Management,
Vol. 3, No. 2, (1998), 53-67.
Stern, L.; El-Ansary, A. and Coughlan, A., Marketing Channels, NJ: Prentice-Hall, 1996.
Stevens, G. C. "Integrating the Supply Chains," International Journal of Physical
Distribution and Materials Management, Vol. 8, No. 8, (1989), 3-8.
Stevenson, M. and Spring, M. “Flexibility from a supply chain perspective: definition and
review”, International Journal of Operations & Production Management, Vol.
27, No. 7, (2007), 685 – 713.
Subbanarasaiah N. Marketing of Horticulture Crops in India. Delhi: Anmol Publishing
Co., 1991. 246 pp.
Subbarao, K. Agriculture Marketing and Credit. Monograph 2. Research in Economics,
Secondary Survey. New Delhi: Indian Council of Social Research, 1989. 59 pp.
“Survey spotlights need to improve capabilities”, Modern Materials Handling, (April,
1998), pp. 17-19.
Suryawanshi, R. R.; Pawar, B. N. and Deshmukh, P. D. “Marketable Surplus and
Marketing Cost of Oilseeds and Pulses in Western Maharashtra”, Bihar Journal of
Agricultural Marketing, Vol. 3, No. 2, (April-June, 1995), 201-4.
Sutcliffe, K. M. and Zaheer, A. “Uncertainty in the Transaction Environment: An
Empirical Test”, Strategic Management Journal, Vol. 19, No. 1, (1998), 1-23.
288
Tan, K. C.; Lyman, S. B. and Wisner, J. D. "Supply Chain Management: A Strategic
Perspective." International Journal of Operations and Production Management,
Vol. 22, Nos. 5-6, (2002), 614-631.
Taylor, D. H. “Demand management in agri-food supply chain: An analysis of the
characteristics and problems and a framework for improvement”, The
International Journal of Physical Distribution & Logistics Management, Vol. 17,
No. 2, (2006), 163-186.
Thakur, D. S. “Foodgrain Marketing Efficiency: A Case Study of Gujarat”, Indian
Journal of Agriculture Economics, Vol. 29, No. 4, (Ocober-December, 1974).
The Advance Learner’s Dictionary of Current English. Oxford, 1952..
Thomas, D. and Griffin, P. M. “Coordinated supply chain management [review]”,
European Journal of Operational Research, Vol. 94 No. 1, (1996), 1-15.
Thorelli, H. B. "Network: Between Markets and Hierarchies", Strategic Management
Journal, Vol. 7, (1986), 37-51.
Tom, Andel. "Information Supply Chain: Set and Get Your Goals," Transportation and
Distribution, Vol. 38, No. 2, (1997). 33 pp.
Towill, D. R. “The seamless supply chain-the predator’s strategic advantage”,
International Journal of Technology Management, Vol. 13, (1997), 37–56.
Treleven, Mark. "Single Sourcing: A Management Tool for the Quality Supplier,"
Journal of Purchasing and Materials Management, Vol. 23, (Spring, 1987), 19-
24.
Tyndall, G. et. al., Supercharging supply chains: new ways to increase value through
global operational excellence. New York: John Wiley & Sons, 1998, 269 pp.
Van der Vorst, J.G.A.J. “Effective food supply chains, Generating, modeling and
evaluating supply chain scenarios.” Wageningen University: doctoral dissertation,
2000.
Van der Vorst, J.G.A.J and Beulens, A. “Identifying sources of uncertainty to generate
supply chain redesign strategies”, International Journal of Physical Distribution
& Logistics Management, Vol. 32, No. 6, (2002) 409-430.
Van der Vorst, J.G.A.J.; Beulens, A. and Van Beek, P., “Innovations in logistics and
ICT in food supply chain networks”, In: Jongen, W.M.F. and Meulenberg, M.T.G.
289
(ed). Innovation in agri-food systems: product quality and consumer acceptance,
Wageningen: Wageningen Academic Publishers, 2005. 245-292.
Van der Vorst, J.G.A.J.; Tromp, S. and Van der Zee, D.J. “A simulation environment for
the redesign of food supply chain networks: modeling quality controlled
logistics”, In: Kuhl, M. E.; Steiger, N. M.; Armstrong, F. B. and Joines, J. A.
(ed.). Winter Simulation Conference: Conference proceedings, 2005. 1658 –
1667.
Van Landeghem, H. and Vanmaele, H. “Robust planning: a new paradigm for demand
chain planning”, Journal of Operations Management, Vol.20, (2002), 769 – 783.
Viaene, J. and Verbeke, W. “Traceability as a key instrument towards supply chain and
quality management in Belgian poultry meat chain”, Supply Chain Management,
Vol. 3, No. 3, (1998), 139-141.
Whang, S. “Coordination in operations: A taxonomy”, Journal of Operations
Management, Vol. 12, No. 3-4, (1995), 413-422.
Weber, M. M. “Measuring supply chain agility in the virtual organisation.” International
Journal of Physical Distribution & Logistics Management, Vol. 32, No. 7, (2002),
577-90.
Williamson, O. “Transaction-cost economics: the governance contractual relations”,
Journal of Law and Economics, Vol. 22, (1979), 233-261.
Ziggers, G. W. and Trienekens, J. H. "Quality Assurance in Food and Agribusiness
Supply Chains: Developing Successful Partnerships", Production Economics,
Vol. 60-61, (1999), 271-279.
Zheng, S.; Yen, D. C., and Michael. ‘‘The new spectrum of cross enterprise solutions: the
integration of supply chain management and enterprise resource planning
systems’’, Journal of Computer Information Systems, Vol. 41, No. 2, (2000), 84-
93.
Zylbersztajn, D. and Filho, C. “Competitiveness of meat agri-food chain in Brazil”,
Supply Chain Management: An International Journal, Vol. 8, (2003), 155-165.
290
Weblinks
http://agmarknet.nic.in
http://agmarknet.nic.in/amrscheme/markdevechap12.htm
http://agricoop.nic.in/AnnualReport06-07/AGRICULTURAL%20MARKETING.pdf
http://www.agrifood-forum.net
http://assamagribusiness.nic.in/
http://www.crnindia.com/index.asp
www.cscmp.org
http://www.cscmp.org/Website/AboutCSCMP/Definitions/Definitions.asp
http://www.fao.org
http://www.gujagro.org/
http://www.igovernment.in/site/poor-infrastructure-costs-india-rs-50000-crore-agri-loss-
every-year/Default.aspx
http://www.iari.res.in/index.php
www.indiabudget.nic.in.
http://www.indiainbusiness.nic.in
http://www.indianspices.com/index.php
http://www.itcibd.com/
http://www.krishiworld.com
http://logisticsmanagementandsupplychainmanagement.wordpress.com/2007/05/23/impa
ct-of-transportation-infrastructure-on-logistics-in-india/
http://www.nmce.com/
http://www.scmr.com/
http://scrc.ncsu.edu/public/a1perspective.html
http://www.supplychainasia.com/
http://www.unescap.org/rural/doc/pai/Vol%2010%20No%204%20-
%20October%20December%202000/art7.pdf
http://www.vibrantgujarat.com/district-profiles/district-profiles.aspx
http://www.world-agriculture.com/agricultural_marketing/agricultural-marketing.php
ANNEXURE Place: __________ Dear Respondent, Season’s Greetings to You!!! I am Amit Patel, doing my Ph.D research on the topic of “An in-depth comparative supply chain management practices at selected APMCs of North Gujarat”. Your valuable comments will help me to accomplish my work successfully. I am grateful to you if you will provide the information relevant to your business practices. I assure you the information will be used for academic purpose only and it will not be disclosed to anyone in any circumstances. Warm regards, Amit Patel
======================
1) Kindly mention the role you or your organisation is performing?
(a) Farmer/Producer (b) Stockist/Trader (c) Commission Agent (Kutcha Arhatiya) (d) Buyer/Wholesaler (Pacca Arhatiya) (e) Processor (f) Exporter (g) Manufacturer (h) Grinder (i) Retailer (j) Any other; please specify
2) In which commodity do you/your organisation deal? (a) Cumin (b) Fennel (c) Isabgol (d) Any Other; Please Specify: __________
Channel Structure
3) From whom/whose products do you purchase/process?
_____Product/s_______
____________________
(a) Farmer/Producer
(b) Stockist/Traders
(c) Commission Agent
(d) Wholesaler/Buyer
(e) Processor
(f) Exporter
(g) Manufacturer
(h) Grinder
(i) Retailer
(j) Any other: please specify: _________
4) To whom/through whom do you sell of your products?
Product/s______
_____________________
(a) Commission Agent
(b) Other Buyers/Wholesalers across the country
(c) Exporters
(d) Processor
(e) Manufacturer
(f) Grinder
(g) Retailer
(h) Traders
(i) Direct Consumer
(j) Any other: please specify: _________
Selection of Supply chain Intermediaries
5) Rate the importance of the following factors while choosing particular
intermediaries to sell/process the products?
(5 = Most Important; 1= Unimportant)
(a) Age-old business relationship 5 4 3 2 1
(b) Friend/family member 5 4 3 2 1
(c) Trust 5 4 3 2 1
(d) Pays best/helps to get best price 5 4 3 2 1
(e) Spot/Cash Payment 5 4 3 2 1
(f) Credit Finance 5 4 3 2 1
(g) Financial Assistance 5 4 3 2 1
(h) Comes first to purchase 5 4 3 2 1
(i) Assurance to purchase 5 4 3 2 1
(j) Cleaning Assistance 5 4 3 2 1
(k) Grading Assistance 5 4 3 2 1
(l) Packaging Assistance 5 4 3 2 1
(m) Storage & Warehousing services 5 4 3 2 1
(n) Quality Testing & Certificate Assistance 5 4 3 2 1
(o) Quantity to be sold 5 4 3 2 1
(p) Makes transport arrangements/comes 5 4 3 2 1
to collect
(q) Customer’s location 5 4 3 2 1
(r) Provides Demand Information 5 4 3 2 1
(s) Updating the price information 5 4 3 2 1
(t) Provides Weather forecast information 5 4 3 2 1
(u) Provides Production estimation information 5 4 3 2 1
(v) Well known in the market 5 4 3 2 1
(w) Any other; Please Specify: ___________ 5 4 3 2 1
6) Rate the importance of the following factors while choosing particular
intermediaries to purchase/process the products?
(5 = Most Important; 1= Unimportant)
(a) Age-old business relationship 5 4 3 2 1
(b) Friend/family member 5 4 3 2 1
(c) Trust 5 4 3 2 1
(d) Offers best price/helps to get best price 5 4 3 2 1
(e) Spot/Cash Payment 5 4 3 2 1
(f) Credit 5 4 3 2 1
(g) Financial Assistance 5 4 3 2 1
(h) Comes first to sell/process 5 4 3 2 1
(i) Assurance to offers required quantity 5 4 3 2 1
(j) Cleaning Assistance 5 4 3 2 1
(k) Grading Assistance 5 4 3 2 1
(l) Packaging Assistance 5 4 3 2 1
(m) Storage & Warehousing services 5 4 3 2 1
(n) Quality Testing & Certificate Assistance 5 4 3 2 1
(o) Quantity to be purchased/processed 5 4 3 2 1
(p) Makes the delivery arrangement 5 4 3 2 1
(q) Seller’s location 5 4 3 2 1
(r) Provides Demand Information 5 4 3 2 1
(s) Updating the price information 5 4 3 2 1
(t) Provides Weather forecast information 5 4 3 2 1
(u) Provides Production estimation information 5 4 3 2 1
(v) Well known in the market 5 4 3 2 1
(w) Any other; Please Specify : _________ 5 4 3 2 1
Selection of intermediaries in particular Market-Yard
7) Factors affecting the supply chain management practices at APMC
Mention the services offered/facility available and Rate the following factors for
selecting channel partners into the city/town/village of particular APMC?
Services Practiced/ Facility Available
Yes No
Importance
(5=Most Important,1=Unimportant)
(a) Open Auction System 5 4 3 2 1
(c) Storage facilities 5 4 3 2 1
(Warehouses, Cold storage, Shade)
(d) Quality Testing Laboratory 5 4 3 2 1
(e) Availability of processing facilities 5 4 3 2 1
(Sufficient / Not Sufficient)
(f) Availability of buyers all the time 5 4 3 2 1
(g) Spot Payment System 5 4 3 2 1
(h) Financial Assistance by/to 5 4 3 2 1
the channel intermediaries
(i) Banks facility 5 4 3 2 1
(j) Warehouse receipt finance 5 4 3 2 1
(k) Demand at the marketplace 5 4 3 2 1
compared to other markets
(Very High / High / Average / Low / Very Low)
(l) Number of Buyers at the market place 5 4 3 2 1
(Very large / Large / Average / Small / Very small in number)
(m) Types of the Buyers 5 4 3 2 1
(o) Connectivity with and Distance from major 5 4 3 2 1
Roads, Railway, Ports and Airports
(q) Well known for particular commodity 5 4 3 2 1
(r) Transparency in the governing system 5 4 3 2 1
(t) Involvement of Governing Body 5 4 3 2 1
for disputes settlement
(Very High / High / Average / Low / Very Low)
(u) Availability of information 5 4 3 2 1
about demand in domestic as well international markets
(v) Availability of information 5 4 3 2 1
about Prevailing prices in the major markets
(w) Availability of Demand 5 4 3 2 1
Forecasting Information
(x) Availability of Weather 5 4 3 2 1
forecasting information
(y) Availability of Production 5 4 3 2 1
estimation information
(z) Involvement of Governing body 5 4 3 2 1
in development of APMC
(aa) Quantity to be purchased / sold 5 4 3 2 1
(bb) Other please specify: _________ 5 4 3 2 1
Integrated Supply Chain Management Practices (For Wholesaler only)
Supply chain Management is “…the integration of one or more logistical, marketing,
purchasing , or other business processes from end user to original suppliers that provides
products, services, and information that add value for customers.”
6) Do you jointly manage (integrate) one or more supply chain processes with
the channel partners?
Yes No
7) From the following with whom and extend to which the supply chain
management practices are managed by you?
(5 = Great extent; 1= Not at all)
(a) Farmers 5 4 3 2 1
(b) Stockiest 5 4 3 2 1
(c) Commission Agent 5 4 3 2 1
(d) Processors 5 4 3 2 1
(e) Retailers 5 4 3 2 1
(f) Exporters 5 4 3 2 1
(g) Transport Service Provider 5 4 3 2 1
(h) Storage & Warehousing Suppliers 5 4 3 2 1
8) Degree to which the following process elements are jointly managed with the
supply chain partners? (5 = Great extent; 1= Not at all)
(a) Delivery of produce in timely fashion 5 4 3 2 1
(Order Fulfillment)
(b) Improving product quality 5 4 3 2 1
(Production flow management)
(c) Providing information about the 5 4 3 2 1
customer order status
(Customer Service)
(d) Demand Forecasting 5 4 3 2 1
(Demand Management)
(e) Implementing marketing programmes 5 4 3 2 1
with customers
(Customer Relationship Management)
(f) Supporting new product development 5 4 3 2 1
(Procurement)
(g) New Product Development 5 4 3 2 1
(New Product Development)
(h) Identifying key markets 5 4 3 2 1
(Customer Relationship Management)
(i) Reducing fluctuation in customer 5 4 3 2 1
demand (Demand Management)
9) Rate the extent of the functional integration within your organisation?
(5 = Great extent; 1= Not at all)
(a) Purchase and Production 5 4 3 2 1
(b) Purchase and Logistics 5 4 3 2 1
(c) Purchase and Marketing 5 4 3 2 1
(d) Purchase and Quality Control 5 4 3 2 1
(e) Purchase and Finance 5 4 3 2 1
(f) Production and Logistics 5 4 3 2 1
(g) Production and Marketing 5 4 3 2 1
(h) Production and Quality Control 5 4 3 2 1
(i) Production and Finance 5 4 3 2 1
(j) Marketing and Quality Control 5 4 3 2 1
(k) Marketing and Finance 5 4 3 2 1
(l) Marketing and Logistics 5 4 3 2 1
(m) Logistics and Quality Control 5 4 3 2 1
(n) Logistics and Finance 5 4 3 2 1
(o) Quality Control and Finance 5 4 3 2 1
10) Rate the degree of barriers to supply chain integration.
(5 = Very High; 1= Very Low)
(a) Difficult to set well defined relationship 5 4 3 2 1
in the process of sharing risks and rewards.
(b) Unwilling and uncommunicative 5 4 3 2 1
channel members
(c) Difficulty in establishing supply chain 5 4 3 2 1
wide Information network
(d) Strategic Goals are not homogeneous 5 4 3 2 1
(e) Difficulty in measuring the role and 5 4 3 2 1
contribution of individual members of Supply Chain
(f) Operational goals are not homogeneous 5 4 3 2 1
(g) Actual and perceived boundaries of 5 4 3 2 1
organisation render integration difficult
(h) Difficulty in defining clear guidelines for5 4 3 2 1
managing supply chain alliances
(i) Difficult to set common standards 5 4 3 2 1
(j) Any other please specify: ___________ 5 4 3 2 1
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Name: __________________________________________________
Age: __________
Address: _______________________________________________
Turnover of the organisation/Income (Rs.): _____________________
No. of Employee: ________ Establishment Year: ________
Processing Capacity (if available): __________
Land (for farmer only): ______________
==========0=====Thank You Very Much====0=============