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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


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




xiii


Table 4.27 Final Rotated Component Matrix (a) 151

Table 4.28 KMO and Bartlett's Test for Purchase related variables 153







Table 4.35 Final Rotated Component Matrix(a) 160


Table 4.37 Final Rotated Factor Loading Matrix 161

Table 4.38 KMO and Bartlett's Test for Purchase related variables 163



Table 4.41 Revised Anti Image Correlation Matrix 166






Table 4.47 Revised Eigenvalues and Total Variance Explained 170


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 4.55 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


by the wholesaler to sell the products 191


by the exporter to sell the products 193


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


by the stockiest to purchase the product 204


by the wholesaler to purchase the product 206


by the exporter to purchase the product 208


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


commission agent to select the intermediaries into

particular APMC 219


stockiest to select the intermediaries into particular APMC 222


wholesalers to select the intermediaries into particular APMC 225


exporter to select the intermediaries into particular APMC 227


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,


Spices, Cattle

Panthawada 1) Panthawada Market

Yard

Panthawada

Taluka

Grains, Pulses,


Bhabhar

1) Bhabhar Market

Yard

2) Pabadi Sub-yard

3) Diyodar Sub-yard

Bhabhar &

Diyodar

Taluka

Grains, Pulses,


Cattle

Thara

1) Sardar Krushi Gang

2) Sardar Market-

Sihori

Kankarej

Taluka

Grains, Pulses,


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,


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,


Cotton, Fruits

Idar

1) Idar Market Yard

2) Jadar

3) Desotar

Idar Taluka

Grains, Pulses,


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,


Prantij

1) Prantij Market

Yard

2) Salal

Prantij

Taluka

Grains, Pulses,


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


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


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.


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.


particular intermediaries to purchase the products.


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


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






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)







4.4.7. 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.


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.


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


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


0.892 22


intermediaries into Particular Market-yard


.642 23

4.4.2. Factor Analysis for section – II for Sell related variables


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.


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.


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


Table 4.13 Anti Image Correlation Matrix


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,


138

Table 4.16 Revised Communalities


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.


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


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


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


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


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


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


Education), 2008, pp. 648 20


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


Education), 2008, p. 647. 22


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)


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.



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.



148


Component

Initial Eigenvalues


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


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.


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



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.





Loadings


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



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


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



Sig. 0.000






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)





155




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



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


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



The communalities for all the variables in the above table are more than 0.5. Therefore



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


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.





Loadings


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


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





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






Loadings


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)


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



Sig. 0.000

164






necessary condition value 0.5. Hence, factor analysis can be applied to it.





165


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.



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


166

Table 4.41 Revised Anti Image Correlation Matrix



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.


168



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.


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



Extraction Method: Principal Component Analysis


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


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


Loadings


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


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


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




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




173

Table 4.50 Revised Rotated component Matrix and communalities




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


Loadings


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


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



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.


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



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






Loadings


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.


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


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


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


Total 51.044 204

Sh: Comes first to

purchase Between Groups 0.085 5 0.017 0.588 0.709


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


Total 42.244 204

185

Ss: Updating the price

information Between Groups 1.189 5 0.238 1.040 0.395


Total 46.712 204

Sq: Customer’s location Between Groups 0.982 5 0.196 0.746 0.590


Total 53.395 204

Su: Provides Production

estimation information Between Groups 2.724 5 0.545 1.607 0.160


Total 70.176 204

Sg: Financial Assistance Between Groups 148.989 5 29.798 82.800 0.000


Total 220.605 204

Sf: Credit Finance Between Groups 40.812 5 8.162 24.204 0.000


Total 107.922 204

Sk: Grading Assistance Between Groups 0.135 5 0.027 0.480 0.791


Total 11.298 204

Sj: Cleaning Assistance Between Groups 0.135 5 0.027 0.480 0.791


Total 11.298 204

Sl: Packaging Assistance Between Groups 0.135 5 0.027 0.480 0.791


Total 11.298 204

Sn: Quality Testing &

Certificate Assistance Between Groups 0.097 5 0.019 0.374 0.866


Total 10.410 204

Sa: Age-old business

relationship Between Groups 0.389 5 0.078 0.308 0.908


Total 50.712 204

Sc: Trust Between Groups 0.905 5 0.181 0.772 0.571


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


Commission Agent to sell the products


commission agent to sell the products

Name of Variable

Sum of

Squares Df

Mean

Square F Sig.

Sp: Makes transport




Total 32.519 130



Total 32.702 130

Sh: Comes first to purchase Between Groups 0.084 5 0.017 0.125 0.987


Total 16.947 130




Total 33.847 130

Sr: Provides Demand



Total 28.687 130




Total 25.176 130



Total 6.626 130




Total 19.603 130


188


Total 30.412 130



Total 32.321 130



Total 10.901 130



Total 10.901 130



Total 12.504 130




Total 10.901 130




Total 37.649 130



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


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


Stockist to sell the products


stockist to sell the products

Name of Variable

Sum of

Squares df

Mean

Square F Sig.

Sp: Makes transport




Total 30.968 123



Total 35.185 123

Sh: Comes first to purchase Between Groups 0.477 5 0.095 0.383 0.859


Total 29.839 123




Total 45.185 123

Sr: Provides Demand



Total 28.218 123




Total 20.548 123



Total 55.960 123




Total 29.839 123


190


Total 30.605 123



Total 31.097 123



Total 19.355 123



Total 19.355 123



Total 20.548 123




Total 9.194 123




Total 33.444 123



Total 25.960 123

Sd: Pays best/helps to get

best price Between Groups 0.497 5 0.099 0.622 0.683


Total 19.355 123

Se: Spot/Cash Payment Between Groups 0.089 5 0.018 0.083 0.995


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.


Wholesaler to sell the products


wholesaler to sell the products

Name of Variable

Sum of

Squares df

Mean

Square F Sig.

Sp: Makes transport




Total 7.116 42

Si: Assurance to



Total 20.512 42

Sh: Comes first to



Total 18.698 42




Total 23.907 42

Sr: Provides Demand



Total 10.279 42

192




Total 10.651 42

Sq: Customer’s

location Between Groups 1.376 3 0.459 1.979 0.133


Total 10.419 42

Su: Provides

Production estimation



Total 7.628 42

Sg: Financial

Assistance Between Groups 0.388 3 0.129 0.358 0.783


Total 14.465 42



Total 12.651 42



Total 12.651 42



Total 14.279 42

Sl: Packaging



Total 14.465 42




Total 23.674 42




Total 11.674 42



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


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.


Exporter to sell the product


exporter to sell the products

Name of Variable

Sum of

Squares df

Mean

Square F Sig.

Sp: Makes transport




Total 5.500 21

Si: Assurance to



Total 5.455 21

Sh: Comes first to



Total 5.864 21



194


Total 5.318 21

Sr: Provides Demand



Total 7.455 21




Total 4.773 21



Total 4.773 21

Su: Provides




Total 5.455 21



Total 5.091 21



Total 4.773 21



Total 3.864 21



Total 3.864 21

Sl: Packaging



Total 3.864 21




Total 4.773 21



195


Total 5.091 21



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.


Processor to sell the products


processor to sell the products

Name of Variable

Sum of

Squares df

Mean

Square F Sig.

Sp: Makes transport




Total 4.667 20



196

Total 4.286 20

Sh: Comes first to



Total 4.571 20




Total 4.952 20

Sr: Provides Demand



Total 5.143 20




Total 3.810 20



Total 5.238 20




Total 4.286 20



Total 4.286 20



Total 8.286 20



Total 1.810 20



Total 1.810 20



Total 1.810 20

197




Total 11.238 20




Total 5.143 20



Total 4.667 20

Sd: Pays best/helps to get

best price Between Groups 0.186 2 0.093 0.408 0.671


Total 4.286 20

Se: Spot/Cash Payment Between Groups 0.052 2 0.026 0.096 0.909


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



H1p There is statistical significant difference between the rated importance of the


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,



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


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


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


Total 6.63 130.00

Pu: Provides


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


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


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


stockist to purchase the product

Name of variable

Sum of

Squares Df

Mean

Square F Sig.


Relationship Between Groups 1.49 5 0.30 1.353 0.247


Total 27.44 123

Pb: Friend / Family

Member Between Groups 1.13 5 0.23 0.971 0.439


Total 28.67 123

Pd: offers best


price Between Groups 0.76 5 0.15 0.738 0.596


Total 25.12 123

Pe: Spot/Cash payment Between Groups 0.40 5 0.08 0.329 0.895


Total 29.19 123

Pf: Credit Between Groups 0.70 5 0.14 0.576 0.718

205


Total 29.44 123

Pg: Financial



Total 16.09 123

Pj: Cleaning Assistance Between Groups 0.24 5 0.05 0.294 0.915


Total 19.35 123

Pk: Grading Assistance Between Groups 0.24 5 0.05 0.294 0.915


Total 19.35 123

Pl: Packaging



Total 20.55 123

Pm: Storage &

Warehouse Services Between Groups 2.98 5 0.60 1.855 0.107


Total 40.93 123




Total 10.84 123

Pr: Provides Demand



Total 28.09 123




Total 21.12 123


forecast information Between Groups 0.44 5 0.09 0.410 0.841


Total 25.55 123

Pu: Provides




Total 28.39 123

Pv: Well known in the Between Groups 1.16 5 0.23 0.733 0.600

206

market


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.


Wholesaler to purchase the products


wholesaler to purchase the product

Name of variable

Sum of

Squares Df

Mean

Square F Sig.




Total 22.70 42

Pb: Friend / Family



Total 22.60 42

Pd: offers best




Total 5.86 42

Pe: Spot/Cash

payment Between Groups 0.70 3 0.23 0.736 0.537


Total 13.07 42



Total 16.51 42

Pg: Financial


207


Total 22.98 42

Pj: Cleaning



Total 22.19 42

Pk: Grading



Total 22.19 42

Pl: Packaging



Total 20.65 42

Pm: Storage &



Total 19.67 42




Total 43.16 42

Pr: Provides Demand



Total 10.28 42




Total 14.42 42




Total 10.74 42

Pu: Provides




Total 8.19 42




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



Exporter to purchase the products


exporter to purchase the product

Name of variable

Sum of

Squares Df

Mean

Square F Sig.




Total 5.50 21

Pb: Friend / Family



Total 6.59 21

Pd: offers best




Total 5.32 21



Total 9.45 21

209



Total 5.32 21

Pg: Financial



Total 4.77 21



Total 31.45 21



Total 31.45 21

Pl: Packaging



Total 21.86 21

Pm: Storage &



Total 7.45 21




Total 9.86 21

Pr: Provides Demand



Total 6.77 21




Total 4.36 21




Total 7.86 21

Pu: Provides




Total 5.45 21

210




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.


Processor to purchase the products


processor to purchase the product

Name of variable

Sum of

Squares Df

Mean

Square F Sig.




Total 5.24 20

211

Pb: Friend / Family



Total 4.67 20

Pd: offers best




Total 3.24 20



Total 5.81 20



Total 6.57 20

Pg: Financial



Total 4.29 20



Total 1.81 20



Total 1.81 20

Pl: Packaging



Total 1.81 20

Pm: Storage &



Total 1.81 20


Certificate Assistance

Between Groups 0.64 2 0.32 0.870 0.436


Total 7.24 20

Pr: Provides Demand



Total 4.95 20

212




Total 4.95 20




Total 5.24 20

Pu: Provides




Total 5.14 20




Total 5.81 20


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


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


intermediaries in particular APMC.

H1a There is statistical significant difference between the rated importance of the


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,


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


Total 49.92 204

Md: Quality Testing

Laboratory Between Groups 0.35 5 0.07 2.161 0.060


Total 6.76 204

Me: Availability of

processing facility Between Groups 0.35 5 0.07 2.161 0.060


Total 6.76 204

Mf: Availability of

Buyer all the time Between Groups 179.89 5 35.98 116.603 0.000


Total 241.30 204

Mg: Spot payment



Total 18.85 204

Mh: Financial

Assistance by/ to the

channel

intermediaries Between Groups 154.14 5 30.83 105.318 0.000


Total 212.39 204

Mj: Warehouse

Receipt Finance Between Groups 0.83 5 0.17 0.391 0.855


Total 85.22 204

Mk: Demand at the

marketplace

compared to other

markets Between Groups 93.36 5 18.67 49.792 0.000


Total 167.98 204

Mq: Well-known for

particular

commodities Between Groups 4.21 5 0.84 2.121 0.064


Total 83.12 204

Mr: Transparency in

the governing system Between Groups 12.00 5 2.40 10.839 0.000


Total 56.05 204

218

Mu: Availability of

information about

demand in domestic

and international



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


Total 50.60 204

My: Availability of

Production

estimation



Total 68.20 204

Mz: Involvement of

Governing body in

development of

APMC Between Groups 0.68 5 0.14 0.635 0.674


Total 43.25 204

Maa: Quantity to be

Purchased / Sold Between Groups 0.50 5 0.10 0.240 0.944


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


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



220

Total 24.69 130

Md: Quality Testing



Total 8.38 130

Me: Availability of



Total 8.38 130

Mf: Availability of



Total 26.55 130

Mg: Spot payment



Total 37.73 130

Mh: Financial


channel



Total 202.14 130

Mj: Warehouse



Total 51.63 130

Mk: Demand at the

marketplace

compared to other



Total 29.54 130

Mq: Well-known for

particular



Total 18.96 130

Mr: Transparency in



Total 33.28 130

221

Mu: Availability of

information about

demand in domestic

and international



Total 19.60 130

Mv: Availability of

information about

prices in major



Total 23.66 130

My: Availability of

Production

estimation



Total 31.15 130

Mz: Involvement of

Governing body into

development of



Total 31.91 130

Maa: Quantity to be



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



Total 57.44 123

Md: Quality Testing



Total 16.77 123

Me: Availability of



Total 22.22 123

Mf: Availability of



Total 35.77 123

Mg: Spot payment Between Groups 2.59 5 0.52 2.274 0.052

223

System


Total 29.48 123

Mh: Financial


channel



Total 38.22 123

Mj: Warehouse



Total 45.19 123

Mk: Demand at the

marketplace

compared to other



Total 30.99 123

Mq: Well-known for

particular



Total 39.12 123

Mr: Transparency

in the governing

system Between Groups 2.07 5 0.41 1.187 0.320


Total 43.19 123

Mu: Availability of

information about

demand in domestic

and international



Total 29.64 123

Mv: Availability of

information about

prices in major



Total 27.10 123

My: Availability of Between Groups 0.23 5 0.05 0.183 0.968

224

Production

estimation

information


Total 29.42 123

Mz: Involvement of

Governing body in

development of



Total 41.84 123

Maa: Quantity to be



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


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



Total 10.42 42

Md: Quality Testing



Total 11.63 42

Me: Availability of



Total 8.19 42

Mf: Availability of



Total 12.47 42

Mg: Spot payment



Total 14.19 42

Mh: Financial


channel



Total 23.91 42

Mj: Warehouse Between Groups 0.07 3 0.02 0.053 0.984

226

Receipt Finance


Total 16.79 42

Mk: Demand at the

marketplace

compared to other



Total 11.44 42

Mq: Well-known for

particular



Total 10.28 42

Mr: Transparency in



Total 11.07 42

Mu: Availability of

information about

demand in domestic

and international



Total 10.28 42

Mv: Availability of

information about

prices in major



Total 5.16 42

My: Availability of

Production

estimation



Total 10.74 42

Mz: Involvement of

Governing body in

development of



Total 9.07 42

227

Maa: Quantity to be



Total 25.63 42


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



Total 5.45 21

Md: Quality Testing



Total 5.32 21

Me: Availability of


228


Total 9.86 21

Mf: Availability of



Total 6.36 21

Mg: Spot payment



Total 6.36 21

Mh: Financial


channel intermediaries Between Groups 0.28 2 0.14 0.601 0.559


Total 4.77 21

Mj: Warehouse



Total 5.32 21

Mk: Demand at the

marketplace compared

to other markets Between Groups 0.73 2 0.37 1.462 0.257


Total 5.50 21

Mq: Well-known for

particular commodities Between Groups 0.17 2 0.08 0.298 0.746


Total 5.45 21

Mr: Transparency into



Total 5.45 21

Mu: Availability of

information about

demand in domestic

and international



Total 4.77 21

Mv: Availability of

information about

prices in major


229


Total 5.09 21

My: Availability of




Total 4.77 21

Mz: Involvement of

Governing body in

development of APMC Between Groups 1.20 2 0.60 1.587 0.231


Total 8.36 21

Maa: Quantity to be



Total 9.27 21



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


Name of variable

Sum of

Squares

df Mean

Squares

F Sig.

Ma: Open Auction



Total 5.14 20

Md: Quality Testing



Total 4.95 20

230

Me: Availability of



Total 2.57 20

Mf: Availability of



Total 6.95 20

Mg: Spot payment



Total 7.81 20

Mh: Financial


channel



Total 5.24 20

Mj: Warehouse



Total 7.24 20

Mk: Demand at the

marketplace

compared to other



Total 4.29 20

Mq: Well-known for

particular



Total 4.67 20

Mr: Transparency

into the governing

system Between Groups 0.50 2 0.25 0.529 0.598


Total 8.95 20

Mu: Availability of

information about

demand in domestic

and international


231


Total 5.24 20

Mv: Availability of

information about

prices in major



Total 4.67 20

My: Availability of

Production

estimation



Total 4.95 20

Mz: Involvement of

Governing body in

development of



Total 5.14 20

Maa: Quantity to be



Total 8.95 20



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


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


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


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

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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

244

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.

245

CHAPTER 5 FINDINGS AND CONCLUSION

5.1 Introduction

5.2 Summary of the Study


5.4 Limitations of the Study


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.


5.8.2 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).


5.9.2 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

246


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.

248

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

249

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.


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.

250







(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


(9) To extract the factors which are important for selecting the intermediaries into


(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.


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


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

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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

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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

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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.


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.

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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



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


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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


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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

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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


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


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

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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.

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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


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


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


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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

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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

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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

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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


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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

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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


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



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.

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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

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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

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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 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

==========================================================

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=============

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